Gene ontology-guided analyses of pan-primate regulatory LTR elements contributing to embryogenesis, development of physiological traits and pathological phenotypes of Modern Humans

Gene ontology-guided analyses of pan-primate regulatory LTR elements contributing to embryogenesis, development of physiological traits and pathological phenotypes of Modern Humans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gene ontology-guided analyses of pan-primate regulatory LTR elements contributing to embryogenesis, development of physiological traits and pathological phenotypes of Modern Humans Gennadi V Glinsky This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4020067/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Two distinct families of pan-primate endogenous retroviruses, namely HERVL and HERVH, infected primates germline, colonized host genomes, and evolved into the global genomic regulatory dominion (GRD) operating during human embryogenesis (HE). Analyzed in this contribution GRD derived from HERV sequences constitutes 8839 highly conserved fixed LTR elements linked to 5444 down-stream target genes forged by evolution into a functionally-consonant constellation of 26 genome-wide multimodular genomic regulatory networks (GRNs), each of which is defined by significant enrichment of numerous single gene ontology (GO)-specific traits. Locations of GRNs appear scattered across chromosomes to occupy from 5.5%-15.09% of human genome. Each GRN harbors from 529–1486 regulatory LTRs derived from LTR7, MLT2A1, and MLT2A2 sequences that appear quantitatively balanced according to their genome-wide abundance. GRNs integrate activities from 199–805 down-stream target genes, including transcription factors, chromatin-state remodelers, signal-sensing and signal-transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. GRNs compositions consist of several hundred to thousands smaller GO enrichment-defined genomic regulatory modules (GRMs) combining from a dozen to hundreds LTRs and down-stream target genes, which appear to operate on individuals life-span timescale along specific phenotypic avenues to exert profound effects on patterns of transcription, protein-protein interactions, developmental phenotypes, physiological traits, and pathological conditions of Modern Humans. Overall, this study identifies 69,573 statistically significant regulatory LTR-linked GRMs (Binominal FDR q-value threshold of 0.001), including 27,601 GRMs validated by the single GO-specific directed acyclic graph (DAG) analyses across six GO annotations. Collectively, reported herein observations support the hypothesis that pan-primate ERVs have made significant impacts on evolution of GRNs affecting physiological traits and pathological conditions of Modern Humans, including phenotypes manifestation of which considered human-specific. human endogenous retrovirus type H (HERVH) human endogenous retrovirus type L (HERVL) LTR7 MLT2A1 MLT2A2 retrotransposition primate evolution mammalian offspring survival genes human embryogenesis reduced fertility infertility synaptic transmission protein-protein interactions at synapses human endogenous complexomes viral-host protein-protein interactions neoplasm metastasis neurodevelopmental disorders neurodegenerative diseases human-specific phenotypic traits human-specific regulatory sequences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In the genome of Modern Humans, there are thousands of genomic loci origin of which could be mapped with high confidence to hundreds of distinct families and subfamilies of retroviruses collectively defined as Human Endogenous Retroviruses, HERV (Bao et al., 2015 ; Kojima, 2018 ; Vargiu et al., 2016 ). It has been suggested that colonization of human genome by HERVs is the result of long-term reinfections during primate evolution, specifically, germ-line reinfections, rather than retrotransposition in cis or complementation in trans (Belshaw et al. 2004 ), indicating that an infectious pool of endogenous retroviruses has persisted within the primate lineage throughout the past 30–40 million years. The full-length retroviruses typically consist of two or three structural genes encoding viral proteins flanked by two identical regulatory sequences termed Long Terminal Repeats (LTRs). Unique structures of regulatory LTRs might provide the evolutionary advantage for hosts for the exaptation of these sequences to function as powerful genomic regulatory elements (Thompson et al. 2016 ). Homologous recombination mediated by LTRs frequently results in the elimination of internal open reading frames (Mager and Goodchild 1989 ; Belshaw et al. 2007 ) and yields solitary regulatory LTRs without adjacent viral structural genes. These regulatory LTRs represent endogenous genomic regulatory elements, which function as alternative promoters, transcription factor binding sites, proximal and distal long-range enhancers, and splice sites. Selection of specific integration sites of retroviruses in the human genome may reflect an evolutionary compromise of stochastic and deterministic forces because it favors active genes and integrated retroviruses appear preferentially detected in the open chromatin regions representing hallmarks of transcriptionally active genomic loci (Schröder et al, 2002 ; Cereseto and Giacca, 2004 ; Bushman et al., 2005 ; Wang et al., 2007 ; Albanese et al., 2008 ), however, within this genomic context precise insertion sites of ERVs may have elements of a random and probabilistic nature. Conservative estimates indicate that there are more than 3,000 DNA sequences derived from insertions of the human endogenous retrovirus type H (HERVH), including highly conserved sequences of its promoter LTR7, that are scattered across human genome at fixed non-polymorphic locations (Thomas et al., 2018 ). Sequences derived from LTR7/HERVH retroviruses are among the most abundant and extensively investigated genomic regulatory elements that were originated from various HERV families. Regulatory LTR7/HERVH insertions originated from a gamma retrovirus that presumably infected primates approximately 40 million years ago (mya) and colonized the genome of the common ancestor of Apes, Old World Monkeys, and New World Monkeys (Goodchild et al., 1993 ; Mager and Freeman, 1995 ). The long terminal repeats designated LTR7 harbor the promoter sequence of the HERVH retroviruses and the LTR7/HERVH family has been extensively investigated in the context of its regulatory functions and locus-specific differential expression in human preimplantation embryogenesis, human embryonic and pluripotent stem cells (Fort et al., 2014 ; Gemmell et al., 2015 ; 2019 ; Glinsky et al., 2018 ; Göke et al.,2015; Izsvák et al., 2016 ; Kelley and Rinn, 2012 ; Loewer et al., 2010 ; Lu et al., 2014 ; Ohnuki et al., 2014 ; Pontis et al., 2019 ; Römer et al., 2017 ; Santoni et al., 2012 ; Takahashi et al., 2021 Theunissen et al., 2016 ; Wang et al., 2014 ; Zhang et al., 2019 ). It has been reported that LTR7/HERVH sequences harbor binding sites for master pluripotency transcription factors OCT4, NANOG, SP1, and SOX2 which bind HERVH/LTRs and activate their expression (Glinsky, 2015 ; Göke et al., 2015 ; Ito et al., 2017 ; Kelley and Rinn, 2012 ; Kunarso et al., 2010 ; Ohnuki et al., 2014 ; Pontis et al., 2019 ; Santoni et al., 2012 ). Human endogenous retrovirus type K [HERVK (HTML-2)] is the primate-specific retrovirus that most recently colonized primate genomes and all human-specific and human-polymorphic HERVK insertions are associated with a specific LTR subtype designated LTR5_Hs (Hanke et al., 2016 ; Subramanian et al., 2011 ). There are at least 36 non-reference polymorphic HERVK proviruses with insertion frequencies ranging from 0.75 that varied by distinct human populations (Wildschutte et al., 2016 ). In contrast to HERVH insertions manifesting no insertional polymorphism in human population (Thomas et al., 2018 ), insertional polymorphism of HERVK retroviruses has been documented by the analyses of individual human genomes, including genomes from the 1000 Genomes Project and the Human Genome Diversity Project (Autio et al., 2021 ; Hughes, Coffin, 2004 ; Barbulescu et al., 1999 ; Belshaw et al., 2005 ; Shin et al., 2013 ; Turner et al., 2001 ; Wildschutte et al., 2016 ). It has been reported that LTR5_Hs/HERVK manifests transcriptional and biological activities in human preimplantation embryos and in naïve hESCs (Grow et al., 2015 ). Notably, LTR5_Hs elements acquire enhancer-like chromatin state signatures concomitantly with transcriptional reactivation of HERVK (Grow et al., 2015 ). Genome-wide CRISPR-guided activation and interference experiments targeting LTR5_Hs elements demonstrated global long-range effects on expression of human genes consistent with postulated functions of LTR5_Hs as distal enhancers (Fuentes et al., 2018 ). Recently reported several lines of observations highlight putative mechanisms by which regulatory LTRs of distinct retroviral families may have affected phenotypic traits contributing to species segregation during primate evolution and development of human-specific phenotypic features (Glinsky, 2022 ). Notable findings of this category are discoveries of potential regulatory impacts of regulatory LTRs residing in the human genome on expression of mammalian offspring survival (MOS) genes as well as regulatory effects of human-specific LTR7 and LTR5_Hs loci on expression of genes encoding markers of 12 distinct cells’ populations of fetal gonads and genes implicated in pathophysiology of human spermatogenesis, including Y-linked spermatogenic failure, oligo- and azoospermia. Most recently, direct experimental evidence linking specific regulatory LTR loci (LTR7Y and LTR5_Hs) to gene regulatory networks shared between human primordial germ cells and naïve pluripotent cells has been reported (Ito et al., 2022 ). On a population scale, to achieve the successful colonization of host genomes and heritable transmission of retroviral sequences, retroviruses must infect the germline during the early embryogenesis prior to or at the stage of the germ cells’ biogenesis. The germline infection, propagation, and stable integration of multiple copies of viral genomes into host chromosomes are required to ensure the passage of integrated viral sequences to offspring. These could be accomplished only within a relatively small window of the developmental timeline which represents an essential temporal target for viral infections to enable successful transitions of exogenous retroviruses to the state of endogenous retroviruses (ERVs) integrated into host genomes (Glinsky, 2022 ). Based on these considerations, it has been concluded that HERVs colonizing the human genome during the relatively narrow embryonic development window may target functionally closely related panels of genes and developmental pathways, thus potentially affecting common sets of phenotypic traits. Recently reported analyses of human embryo regulatory LTR elements (LTR7 and LTR5_Hs) support the model of cooperative phenotypic impacts on human pathophysiology exerted by genes representing down-stream regulatory targets of different families of regulatory LTR elements, such as LTR7 and LTR5_Hs, despite their markedly distinct evolutionary histories of the human genome colonization spanning millions of years (Glinsky, 2022 ). During embryonic genome activation (EGA), DNA sequences derived from HERVL retroviruses are transcriptionally activated by the homeobox transcription factor DUX4 (Hendrickson et al. 2017 ). HERVL retroviruses harbor regulatory LTRs designated MLT2A elements, which are subdivided into two distinct subfamilies designated MLT2A1 and MLT2A2. Chromatin at MLT2A loci becomes accessible in two-cell human embryos, MLT2A elements are transcribed in four-cell human embryos, and specific MLT2A elements function as promoters and splicing sites in primate embryos (Hashimoto et al., 2021). These observations indicate that MLT2A1 and MLT2A2 elements that are derived from primate-specific HERVL retroviruses could be classified as human embryo regulatory LTRs. To understand when HERVH and HERVL retroviruses have infiltrated the primates’ germline, detailed analyses of evolutionary conservation patterns of regulatory LTR7, MLT2A1, and MLT2A2 elements were carried out in 17 species of non-human primates (NHP) vis-à-vis reference genome of present day humans. According to these analyses, HERVH and HERVL represent pan-primate endogenous retroviruses that appear to infect and colonize primate lineage at different evolutionary time points and achieved in distinct primate species quantitatively different levels of genomic amplification during evolution. To delineate connectivity patterns between 8839 loci encoded by the LTR7 and MLT2A elements and the phenotypic pleiotropy of putative regulatory functions of distinct families of human embryo regulatory LTRs, the GREAT algorithm was employed to identify and characterize LTR-linked genes. Comprehensive Gene Set Enrichment Analyses (GSEA) of 5444 LTR-linked genes were performed to infer potential phenotypic impacts of genomic regulatory networks governed by human embryo regulatory LTRs. Observations reported in this contribution support the hypothesis that evolution of human embryo regulatory LTR elements created the global genomic regulatory dominion (GRD) consisting of 26 genome-wide genomic regulatory networks (GRNs) defined by the significant enrichment of specific gene ontology terms. Regulatory LTR-associated GRD and GRNs could be viewed as an intrinsically integrated developmental compendium of 69,573 genomic regulatory modules (GRMs) defined by significant enrichment of specific genotype-phenotypic trait associations. Many GRMs may represent the evolutionary selection units driven by inherent genotype-phenotype associations affecting mammalian species’ fitness and survival with the focal selection point on nervous system development and functions. Collectively, these findings demonstrate that despite markedly distinct evolutionary histories of different families of human embryo regulatory LTRs, genes representing down-stream regulatory targets of LTR7 and MLT2A elements exert the broad cooperative phenotypic impacts on development, physiology, and pathology of Modern Humans, apparently encompassing diverse cellular compositions and structural-functional architectures of all tissues, major organs, and organ systems of human body. Results Evolutionary patterns of origin, expansion, conservation, and divergence of pan-primate endogenous retroviruses encoding human embryo regulatory LTR elements. Evolutionary timelines of transitions of retroviruses from the state of exogenous infection agents to endogenous retroviral sequences integrated into host genomes could be inferred from the comparative quantitative analyses of highly conserved orthologous retrovirus-derived Long Terminal Repeat (LTR) loci in genomes of multiple distinct primate species which diverged from the last extinct common ancestor (ECA) at distinct time points during primates’ evolution (Glinsky, 2022 ). To accomplish this task, a total of 8839 fixed non-polymorphic sequences of human embryo regulatory LTRs residing in genomes of Modern Humans (hg38 human reference genome database) were retrieved and the number of highly conserved orthologous loci in genomes of sixteen non-human primates (NHP) were determined (Fig. 1 ). Present analyses were focused on human embryo regulatory LTR loci (see Introduction) the evolutionary origins of which could be mapped to genomes of Old World Monkeys (OWM), indicating that these genomic loci could be defined as highly-conserved pan-primate regulatory sequences because they have been preserved in primates’ genomes for ~ 30 MYA. Three distinct LTR families meet these criteria, namely MLT2A1 (2416 loci), MLT2A2 (3069 loci), and LTR7 (3354 loci). It has been observed that MLT2A1, MLTA2, and LTR7 appear to have similar evolutionary histories defined by evolutionary patterns of expansion, conservation, and divergence during primate evolution (Fig. 1 A). However, they have clearly distinguishable histories of timelines of evolutionary origins, which are defined herein as the estimated timelines when retroviruses infect primates’ germlines and transition from the state of exogenous infection agents to endogenous retroviral sequences integrated into host genomes (Fig. 1 A). The consistent earliest presence of highly-conserved pan-primate MLT2A1 and MLT2A2 loci could be mapped to genomes of both New World Monkeys (NWM) and OWM, indicating that both MLT2A1/HERVL and MLT2A2/HERVL retroviruses have infected primates’ germlines before the segregation of OWM and NWM lineages (Fig. 1 A). In contrast, the consistent earliest presence in primates’ genomes of LTR7 loci could be mapped to genomes of OWM (Fig. 1 A), suggesting that LTR7/HERVH retroviruses have entered germlines of the primate lineage after the separation of the NWM lineage (Glinsky, 2022 ). Pan-primate endogenous MLT2A1/HERVL, MLT2A2/HERVL, and LTR7/HERVH retroviruses appear to achieve a significant expansion in genomes of Great Apes, ranging from ~ 2-fold expansion for MLT2A2 loci to ~ 5-fold expansion for LTR7 loci (Fig. 1 A). In contrast to highly-conserved pan-primate human embryo regulatory LTRs, the consistent earliest presence of highly-conserved LTR5_Hs loci has been observed in the Gibbon’s genome (Glinsky, 2022 ), in accord with the hypothesis that LTR5_Hs/HERVK retroviruses successfully colonized germlines of the primate lineage after the segregation of Gibbons’ species and subsequently underwent a marked expansion in genomes of Great Apes. In agreement with the previously reported findings for highly-conserved LTR7 and LTR5_Hs loci (Glinsky, 2022 ), timeline sequences of quantitative expansion of human embryo pan-primate regulatory LTRs in different primate species appear to replicate the consensus evolutionary sequence of increasing cognitive and behavioral complexities during primate evolution, which seems particularly striking for LTR7 loci (Fig. 1 A). One of notable findings of LTR loci sequence conservation analyses is the observation that hundreds of highly-conserved pan-primate human embryo regulatory LTRs have DNA sequences divergent by more than 5% from orthologous sequences in genomes of Modern Humans in genomes of our closest evolutionary relatives, Chimpanzee and Bonobo. It was of interest to determine how many of fixed non-polymorphic human embryo regulatory LTR loci could be defined as candidate human-specific (HS) genomic regulatory elements compared to both Chimpanzee and Bonobo genomes. Setting a selection threshold at < 10% of sequence identity, there are 45; 35; and 175 sequences that could be classified as human-specific MLT2A2, MLT2A1, and LTR7 loci, respectively (Fig. 1 B). A majority of candidate HS regulatory LTR loci of human embryo could be classified as bona fide human-specific insertions because they did not intersect any chains in genomes of both Chimpanzee and Bonobo. Results of previously reported evolutionary conservation analyses indicated that nearly half (84/175; 48%) of candidate HS-LTR7 loci have been identified as highly-conserved orthologous sequences in genomes of OWM, Gibbon, Orangutan, and Gorilla (Glinsky, 2022 ), consistent with the hypothesis that these HS-LTR7 loci were not retained in genomes of Chimpanzee and Bonobo but preserved in genomes of Modern Humans. Similar to HS-LTR7 loci, the patterns of failure to retain human-specific DNA sequences have been observed in genomes of both Chimpanzee and Bonobo for candidate HS regulatory LTR elements encoded by MLT2A1 and MLT2A2 loci (Fig. 1 B). Collectively, these observations strongly argue that species-specific loss of highly-conserved pan-primate regulatory DNA sequences encoded by regulatory LTRs may have contributed to the emergence during primate evolution of human-specific features of genomic regulatory networks (GRN). Identification of regulatory LTR’s down-stream target genes and analysis of potential phenotypic impacts of human embryo regulatory LTR elements on pathophysiology of Modern Humans. One of the main mechanisms defining biological functions of regulatory LTRs is their activity as distal genomic regulatory elements (GRE) affecting transcription of down-stream target genes. Therefore, the inference of potential biological functions of human embryo regulatory LTR elements could be made based on evaluations of experimentally documented biological activities of their down-stream target genes. To this end, the Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm was employed (Methods) to identify putative down-stream target genes of human embryo regulatory LTRs. Concurrently with the identification of putative regulatory target genes of GREs, the GREAT algorithm performs stringent statistical enrichment analyses of functional annotations of identified down-stream target genes, thus enabling the inference of potential biological significance of interrogated GRNs. Importantly, the assignment of phenotypic traits as putative statistically valid components of GRN actions entails the assessments of statistical significance of the enrichment of both GREs and down-stream target genes. The GREAT algorithm identified 5444 genes as potential down-stream regulatory targets of 8839 human embryo regulatory LTR elements (Tables 1 – 3 ; Supplementary Table S1 ). It has been observed that distinct families of human embryo regulatory LTR elements share overlapping sets of down-stream target genes, including 709 genes that were identified as the consensus set of candidate down-stream regulatory targets for MLT2A1; MLT2A2; and LTR7 elements (Table 1 ; Supplemental Table S1 ). At the statistical significance threshold of the binominal FDR Q value < 0.002 (Table 2 ) , a total of 54 significantly enriched records of biological processes appear preferentially affected by the regulatory actions of 8839 human embryo regulatory LTRs (Table 2 ; Supplementary Table S1 ). Cell-cell adhesion via plasma-membrane adhesion molecules (GO:0098742; 677 LTRs linked with 95 genes; binominal FDR Q value = 2.65E-15), cell-cell adhesion (GO:0098609; 1089 LTRs linked with 238 genes; binominal FDR Q value = 3.18E-05), and homophilic cell adhesion (GO:0007156; 481 LTRs linked with 62 genes; binominal FDR Q value = 1,15E-12) via plasma membrane adhesion molecules have been identified among top-ranked biological processes associated with regulatory effects of human embryo regulatory LTR elements (Table 2 ; Supplementary Table S1 ). Table 1 Numbers of human embryo regulatory LTR loci and their putative down-stream regulatory target genes in human genome. Human embryo regulatory LTR families Number of loci in human genome Number of putative regulatory target genes MLT2A1 2416 2450 MLT2A2 3069 2701 LTR7 3354 2955 MLT2A1 & MLTA2 (all loci) 5485 3971 LTR7 & MLT2A1 & MLT2A2 (all loci) 8839 5444 Human embryo regulatory LTR families Number of loci in human genome Number of common regulatory target genes MLT2A1 & MLTA2 loci common regulatory target genes 5485 1180 MLTA1 & LTR7 loci common regulatory target genes 5770 1044 MLTA2 & LTR7 loci common regulatory target genes 6423 1149 LTR7 & MLT2A1 & MLT2A2 loci common regulatory target genes 8839 709 Table 2 Top-ranked biological processes preferentially affected by 8,839 human embryo regulatory LTRs identified by proximity placement-guided Genomic Regulatory Network (GRN) analysis* of Gene Ontology (GO) Biological Process database. GO ID Description Binominal Rank Binominal P value Binominal Bonferroni P value Binominal FDR Q value LTR Fold Enrichment Observed LTRs Gene Fold Enrichment Observed Genes GO:0098742 cell-cell adhesion via plasma-membrane adhesion molecules 1 2.05E-19 2.65E-15 2.65E-15 1.42 677 1.46 95 GO:0007156 homophilic cell adhesion via plasma membrane adhesion molecules 2 1.79E-16 2.31E-12 1.15E-12 1.47 481 1.38 62 GO:0051965 positive regulation of synapse assembly 3 1.12E-12 1.45E-08 4.83E-09 1.48 355 2.12 40 GO:0006408 snRNA export from nucleus 4 1.03E-10 1.33E-06 3.33E-07 14.46 12 1.72 1 GO:0051963 regulation of synapse assembly 5 4.35E-10 5.62E-06 1.12E-06 1.38 392 2.05 51 GO:0051030 snRNA transport 6 8.87E-10 1.15E-05 1.91E-06 11.92 12 1.15 1 GO:0044691 tooth eruption 7 3.88E-09 5.02E-05 7.17E-06 3.74 29 2.30 2 GO:0050803 regulation of synapse structure or activity 8 1.21E-08 1.56E-04 1.95E-05 1.31 448 1.80 68 GO:0098609 cell-cell adhesion 9 2.21E-08 2.86E-04 3.18E-05 1.17 1089 1.29 238 GO:0050807 regulation of synapse organization 10 2.87E-08 3.71E-04 3.71E-05 1.30 440 1.82 66 GO:0098904 regulation of AV node cell action potential 11 8.99E-08 1.16E-03 1.06E-04 3.22 29 3.45 3 GO:0086072 AV node cell-bundle of His cell adhesion involved in cell communication 12 1.47E-07 1.90E-03 1.58E-04 3.89 22 3.45 1 GO:0007210 serotonin receptor signaling pathway 13 1.60E-07 2.07E-03 1.59E-04 2.21 54 3.45 7 GO:1904292 regulation of ERAD pathway 14 1.78E-07 2.30E-03 1.64E-04 2.13 58 0.89 7 GO:0046068 cGMP metabolic process 15 3.43E-07 4.43E-03 2.95E-04 1.78 90 1.56 14 GO:0051096 positive regulation of helicase activity 16 3.66E-07 4.72E-03 2.95E-04 3.16 27 1.15 2 GO:0007416 synapse assembly 17 3.77E-07 4.86E-03 2.86E-04 1.36 285 1.57 35 GO:0000710 meiotic mismatch repair 18 3.77E-07 4.87E-03 2.70E-04 8.73 10 1.72 1 GO:0034497 protein localization to pre-autophagosomal structure 19 4.80E-07 6.20E-03 3.27E-04 2.56 37 1.15 4 GO:0072378 blood coagulation, fibrin clot formation 20 4.90E-07 6.34E-03 3.17E-04 2.29 46 1.46 11 GO:0097104 postsynaptic membrane assembly 21 5.18E-07 6.69E-03 3.19E-04 1.79 86 2.07 6 GO:0099560 synaptic membrane adhesion 22 5.29E-07 6.83E-03 3.10E-04 2.05 59 3.45 3 GO:0006182 cGMP biosynthetic process 23 5.44E-07 7.03E-03 3.06E-04 1.99 63 1.29 9 GO:0007215 glutamate receptor signaling pathway 24 7.16E-07 9.25E-03 3.85E-04 1.48 173 2.12 27 GO:0098792 xenophagy 25 7.41E-07 9.57E-03 3.83E-04 5.79 13 0.86 1 GO:0046629 gamma-delta T cell activation 26 7.53E-07 9.72E-03 3.74E-04 3.30 24 1.15 2 GO:1904293 negative regulation of ERAD pathway 27 8.32E-07 1.07E-02 3.98E-04 2.43 39 0.80 3 GO:0052652 cyclic purine nucleotide metabolic process 28 1.19E-06 1.53E-02 5.47E-04 1.89 68 1.08 10 GO:1900371 regulation of purine nucleotide biosynthetic process 29 1.19E-06 1.54E-02 5.30E-04 1.36 260 1.47 67 GO:0006538 glutamate catabolic process 30 1.22E-06 1.58E-02 5.26E-04 2.28 43 1.72 4 GO:0009190 cyclic nucleotide biosynthetic process 31 1.41E-06 1.82E-02 5.86E-04 1.88 68 1.05 10 GO:0051962 positive regulation of nervous system development 32 1.47E-06 1.90E-02 5.95E-04 1.16 983 1.50 204 GO:0030808 regulation of nucleotide biosynthetic process 33 1.48E-06 1.91E-02 5.78E-04 1.35 260 1.46 67 GO:0097105 presynaptic membrane assembly 34 1.95E-06 2.52E-02 7.42E-04 1.79 77 1.92 5 GO:0051095 regulation of helicase activity 35 2.02E-06 2.61E-02 7.47E-04 2.88 27 0.77 2 GO:0034332 adherens junction organization 36 2.45E-06 3.17E-02 8.81E-04 1.39 216 1.39 29 GO:0007158 neuron cell-cell adhesion 37 2.60E-06 3.36E-02 9.09E-04 1.61 106 2.30 10 GO:0045409 negative regulation of interleukin-6 biosynthetic process 38 2.87E-06 3.70E-02 9.75E-04 4.43 15 2.07 3 GO:0010838 positive regulation of keratinocyte proliferation 39 3.44E-06 4.44E-02 1.14E-03 2.11 47 2.30 6 GO:0097090 presynaptic membrane organization 40 3.60E-06 4.65E-02 1.16E-03 1.76 77 1.72 5 GO:0007157 heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules 41 4.17E-06 5.38E-02 1.31E-03 1.38 208 1.93 28 GO:0061739 protein lipidation involved in autophagosome assembly 42 4.22E-06 5.45E-02 1.30E-03 4.58 14 1.72 2 GO:0070295 renal water absorption 43 4.45E-06 5.75E-02 1.34E-03 3.25 21 0.86 1 GO:0045656 negative regulation of monocyte differentiation 44 4.62E-06 5.97E-02 1.36E-03 2.64 29 2.46 5 GO:0009065 glutamine family amino acid catabolic process 45 5.01E-06 6.47E-02 1.44E-03 1.92 57 1.38 10 GO:0045659 negative regulation of neutrophil differentiation 46 5.89E-06 7.61E-02 1.65E-03 3.57 18 3.45 2 GO:0003097 renal water transport 47 6.18E-06 7.99E-02 1.70E-03 3.18 21 0.69 1 GO:0016074 snoRNA metabolic process 48 6.30E-06 8.14E-02 1.69E-03 2.98 23 1.72 7 GO:1902567 negative regulation of eosinophil activation 49 6.38E-06 8.25E-02 1.68E-03 10.53 7 3.45 2 GO:0045658 regulation of neutrophil differentiation 50 6.72E-06 8.69E-02 1.74E-03 3.54 18 2.30 2 GO:0006536 glutamate metabolic process 51 7.24E-06 9.35E-02 1.83E-03 1.83 63 1.19 10 GO:0030853 negative regulation of granulocyte differentiation 52 7.30E-06 9.43E-02 1.81E-03 3.14 21 1.29 3 GO:0071362 cellular response to ether 53 7.34E-06 9.48E-02 1.79E-03 2.80 25 2.07 3 GO:1904637 cellular response to ionomycin 54 7.82E-06 1.01E-01 1.87E-03 2.94 23 1.72 2 Legend: *Proximity-placement-guided Genomic Regulatory Network (GRN) analyses were performed employing the GREAT algorithm software as described in Methods. Table 3 Quantitative profiles of regulatory LTR elements, down-stream target genes, and genomic regulatory modules (GRMs) * within 26 gene ontology enrichment analysis-defined genomic regulatory networks (GRNs) ** of the genomic dominion of human embryo regulatory LTRs. Gene ontology ID Genomic regulatory networks Number of LTRs Number of genes GO Biological Process GO Cellular Component GO Molecular Function Human Phenotype Mouse Phenotype Single KO Mouse Phenotype Number of GRNs & GRMs NA Genomic dominion of human embryo regulatory LTRs 8839 5444 38 20 15 7 6 9 95 GO:0051960 Regulation of nervous system development 1424 716 1676 110 132 261 1028 1413 4620 GO:0051962 Positive regulation of nervous system development 983 443 1390 103 115 178 887 1121 3794 GO:0098609 Cell-cell adhesion 1089 492 787 77 51 135 491 707 2231 GO:0098742 Cell-cell adhesion via plasma-membrane adhesion molecules 677 199 375 46 44 52 299 326 1142 GO:0099537 Trans-synaptic signaling 616 344 919 112 146 142 599 781 2699 GO:0007268 Chemical synaptic transmission 604 332 888 110 145 142 564 725 2574 GO:0050804 Modulation of synaptic transmission 529 298 1253 118 161 154 625 768 3079 GO:0044708 Single organism behavior 674 372 1336 91 111 196 718 843 3295 GO:0042391 Regulation of membrane potential 592 340 1315 145 203 387 732 822 3604 GO:1901137 Carbohydrate derivative biosynthetic process 678 448 526 41 148 70 218 249 1252 GO:0043005 Neuron projection 1486 805 1526 188 200 163 868 981 3926 GO:0098590 Plasma membrane region 1310 788 1016 193 247 75 642 833 3006 GO:0044456 Synapse part 1089 604 951 167 196 136 611 732 2793 GO:0009986 Cell surface 921 560 1965 142 172 137 1048 1164 4628 GO:0098794 Postsynapse 826 406 842 159 170 148 588 727 2634 GO:0044297 Cell body 737 409 1644 204 198 163 881 977 4067 GO:0097060 Synaptic membrane 679 305 664 133 169 138 572 680 2356 GO:0045211 Postsynaptic membrane 577 240 515 116 148 136 543 646 2104 GO:0036477 Somatodendritic compartment 995 582 1640 180 208 246 898 999 4171 GO:0043025 Neuronal cell body 645 359 1480 174 176 191 855 874 3750 GO:0030424 Axon 651 335 1029 152 130 143 465 562 2481 GO:0031012 Extracellular matrix 674 395 259 57 97 309 402 405 1529 GO:0005578 Proteinaceous extracellular matrix 554 311 356 40 87 232 229 245 1189 GO:0005509 Calcium ion binding 979 462 397 26 98 43 192 212 968 GO:0008233 Peptidase activity 574 400 303 30 83 71 175 204 849 GO:0070011 Peptidase activity, acting on L-amino acid peptides 547 384 306 29 83 47 162 197 824 NA Cumulative, within 26 genomic regulatory networks 5237 3054 1105 121 190 71 424 486 2397 NA Cumulative, excluding 26 genomic regulatory networks 3602 2685 27 12 23 17 26 24 129 Legend: *For each GRN, reported numbers of LTRs, genes, and GRMs were determined at the statistical significance threshold of the Binominal FDR Q-Value < 0.001 applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (Supplementary Materials); **, A total of 26 gene ontology enrichment analyses-defined GRNs associated with human embryo regulatory LTRs were identified at the statistical significance threshold of the Binominal FDR Q-value < 0.05 (Supplementary Table S1 ). GRNs contain from 529 to 1486 regulatory LTR elements and from 199 to 805 down-stream target genes. Interestingly, neuron cell-cell adhesion (GO:0007158) has been scored as one of significantly enriched cell type-specific GO categories (106 LTRs linked to 10 genes; binominal FDR Q-value = 9.09E-04). Multiple distinct GO categories related to biogenesis, structure, and functions of synapses appear preferential regulatory targets of human embryo regulatory LTRs (Table 2 ; Supplementary Table S1 ). These include regulation of synapse assembly (GO:0051963); regulation of synapse structure or activity (GO:0050803); synapse organization (GO:0050807); synapse assembly (GO:0007416); and synapse membrane adhesion (GO:0099560). Several findings suggest that putative regulatory actions of human embryo regulatory LTRs may affect fine-tuned biological structures and activities of synapses, including the presynaptic membrane assembly (GO:0097105; binominal FDR Q value = 7.42E-04) and organization (GO:0097090; 77 LTRs linked to 5 genes; binominal FDR Q value = 1.16E-03); postsynaptic membrane assembly (GO:0097104; 86 LTRs linked to 6 genes; binominal FDR Q value = 3.19E-04) as well as positive regulation of synapse assembly (GO:0051965; 355 LTRs linked to 40 genes; binominal FDR Q value = 4.83E-09). Notably, regulatory LTR elements and genes associated with the GO category of positive regulation of synapse assembly (GO:0051965) constitute a marked majority of regulatory LTRs and genes assigned to more general GO category of regulation of synapse assembly (GO:0051963): 91% (355/392) of LTRs and 78% (40/51) of genes, respectively (Table 2 ; Supplementary Table S1 ). Overall, the GO category of regulation of nervous system development (GO:0051960; binominal FDR Q value = 4,38E-02) has been linked with 1424 regulatory LTRs and 716 target genes, 333 of which have been implicated in development of nervous system ( Supplementary Table S1 ) . Collectively, these findings suggest that biological processes of nervous system development and functions, in particular, biogenesis and activity of synapses, may represent one of important evolutionary targets of regulatory actions of human embryo regulatory LTR elements. Consistent with this hypothesis, significantly enriched GO category of positive regulation of nervous system development (GO:0051962; binominal FDR Q value = 5.95E-04) has been linked with 983 regulatory LTRs and 204 target genes (Table 2 ; Supplementary Table S1 ), while significantly enriched GO category of glutamate receptor signaling pathway (GO:0007215; binominal FDR Q value = 3.85E-04) has been associated with 173 regulatory LTRs and 27 target genes (Table 2 ; Supplementary Table S1 ). In contrast, GO category of negative regulation of nervous system development (GO:0051962) has been linked with 483 regulatory LTRs and 119 target genes known to exert negative effects on development of nervous system ( Supplementary Table S1 ) . Furthermore, the validity of the concept of human embryo regulatory LTRs’ regulatory impacts on nervous system development and functions is supported by the identification of numerous significantly enriched phenotypic traits of biological relevance assigned by GO Cellular Component and by GO Molecular Function annotations as well as Mouse Phenotype Single KO and Mouse Phenotype databases ( Supplementary Table S1 ). Notable observations of potential biological relevance include identification of serotonin receptor signaling pathway (GO:0007210; 54 LTRs linked to 7 genes; binominal FDR Q-value = 0.00016) and Y-linked inheritance (HP:0001450; 80 LTRs linked to 14 genes; binominal FDR Q-value = 5.60E-07) that were scored by the GREAT algorithm as the significantly enriched records of GO Biological Process and Human Phenotype databases-defined phenotypic traits, respectively ( Supplementary Table S1 ). Among multiple significantly enriched phenotypes’ classification categories, GO Cellular Component annotations identified neuron projection (GO:0043005; 1486 LTRs linked to 386 genes) and synaptic membrane (GO:0097060; 679 LTRs linked to 132 genes), while GO Molecular Function analysis scored glutamate receptor activity (GO:0008066; 152 LTRs linked to 19 genes); neurotransmitter receptor activity (GO:0030594; 220 LTRs linked to 43 genes); neurexin family protein binding (GO:0042043; 58 LTRs linked to 7 genes); and calcium ion binding (GO:0005509; 979 LTRs linked to 243 genes) ( Supplementary Table S1 ). Interestingly, structural elements of the postsynaptic membrane defined by the GO Cellular Component category (GO:0045211; 577 LTRs linked to 102 genes; binominal FDR Q-value = 1.98E-04) appears preferentially targeted by regulatory actions of human embryo regulatory LTRs compared to the presynaptic membrane features (GO:0042734; 186 LTRs linked to 36 genes; binominal FDR Q-value = 0.042) ( Supplementary Table S1 ). The extended summary illustrating quantitative and qualitative distinctions of the regulatory LTR elements’ associations with GO analyses-defined features of the postsynaptic membrane versus the presynaptic membrane is reported in the Supplementary Summary S1 . Identification and characterization of 26 gene ontology-defined genomic regulatory networks (GRNs) facilitating functions of a genomic regulatory dominion (GRD) of human embryo regulatory LTR elements. Results of Gene Ontology terms’ enrichment analyses indicate that a set of 5444 genes constituting candidate down-stream targets of human embryo regulatory LTRs appears segregated into multiple genomic regulatory networks (GRNs) defined by a single ontology-specific enrichment analysis (Fig. 1 ; Tables 2 – 5 ; Supplementary Table S1 ). Identification of GRNs is based on analyses of regulatory LTRs and down-stream target genes distribution patterns associated with each of significantly-enriched terms (Binominal FDR Q value < 0.05) identified by the GREAT algorithm during analyses of GO Biological Process (227 significant terms), GO Cellular Component (47 significant terms), and GO Molecular Function (80 significant terms) databases (Fig. 1 ). It has been observed that for a set of single ontology-defined significantly-enriched terms there is a significant positive correlation ( R 2 > 0.99 ) between genome fractions occupied by genomic features assigned to a set of significantly-enriched terms (referred to as the genomic modicums) and proportions of LTR elements residing within the genomic modicums (referred to as the LTR set coverage). A compendium of regulatory LTRs and down-stream target genes assigned to the individual significantly-enriched GO terms, genomic modicums of which were occupying more than 5% of human genome and harbor at least 6% of human embryo regulatory LTRs, were designated as GRNs (Fig. 1 ; Tables 2 – 5 ; Supplementary Table S1 ). Table 5 Quantitative profiles of a directed acyclic graph (DAG) * test-validated genomic regulatory modules (GRMs) ** within 26 gene ontology enrichment analysis-defined genomic regulatory networks (GRNs) *** of the genomic dominion of human embryo regulatory LTRs. Gene ontology ID Genomic regulatory networks Number of LTRs GRN linked genes GO Biological Process GO Cellular Component GO Molecular Function Human Phenotype Mouse Phenotype Single KO Mouse Phenotype Number of GRNs & GRMs NA Genomic dominion of human embryo regulatory LTRs 8839 NA 1 0 0 1 0 0 2 GO:0051960 Regulation of nervous system development 1424 333 972 46 31 50 521 838 2458 GO:0051962 Positive regulation of nervous system development 983 204 800 39 19 27 401 598 1884 GO:0098609 Cell-cell adhesion 1089 238 470 64 14 52 230 369 1199 GO:0098742 Cell-cell adhesion via plasma-membrane adhesion molecules 677 95 69 34 2 0 1 1 107 GO:0099537 Trans-synaptic signaling 616 151 482 64 102 6 322 435 1411 GO:0007268 Chemical synaptic transmission 604 146 440 60 96 7 253 356 1212 GO:0050804 Modulation of synaptic transmission 529 130 746 71 42 18 279 357 1513 GO:0044708 Single organism behavior 674 165 638 59 19 10 344 467 1537 GO:0042391 Regulation of membrane potential 592 153 620 103 90 82 217 268 1380 GO:1901137 Carbohydrate derivative biosynthetic process 678 209 215 26 52 0 0 0 293 GO:0043005 Neuron projection 1486 386 606 107 58 28 335 419 1553 GO:0098590 Plasma membrane region 1310 371 433 153 110 0 266 345 1307 GO:0044456 Synapse part 1089 270 468 118 83 4 238 320 1231 GO:0009986 Cell surface 921 267 854 96 43 3 394 523 1913 GO:0098794 Postsynapse 826 179 361 104 68 0 229 279 1041 GO:0044297 Cell body 737 188 536 81 50 32 190 250 1139 GO:0097060 Synaptic membrane 679 132 209 83 64 0 162 220 738 GO:0045211 Postsynaptic membrane 577 102 154 72 62 0 146 167 601 GO:0036477 Somatodendritic compartment 995 286 599 96 64 62 245 361 1427 GO:0043025 Neuronal cell body 645 165 464 71 45 51 170 204 1005 GO:0030424 Axon 651 158 516 93 50 0 161 208 1028 GO:0031012 Extracellular matrix 674 187 211 31 43 178 146 163 772 GO:0005578 Proteinaceous extracellular matrix 554 145 136 29 41 119 86 96 507 GO:0005509 Calcium ion binding 979 236 101 7 36 0 9 5 158 GO:0008233 Peptidase activity 574 199 50 6 38 0 0 0 94 GO:0070011 Peptidase activity, acting on L-amino acid peptides 547 191 49 5 39 0 0 0 93 NA Cumulative, within 26 genomic regulatory networks 5237 ND 173 21 37 8 68 68 375 NA Cumulative, excluding 26 genomic regulatory networks 3602 0 0 0 1 1 0 0 2 Legend: *, A directed acyclic graph (DAG) test was carried out on the enriched terms from a single ontology-specific table generated by the GREAT algorithm; **, For each GRN, reported numbers of GRMs were determined using a directed acyclic graph (DAG) test applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (Supplementary Materials); ***, A total of 26 gene ontology enrichment analyses-defined GRNs associated with human embryo regulatory LTRs were identified at the statistical significance threshold of the Binominal FDR Q-value < 0.05 (Supplementary Table S1 ). GRNs contain from 529 to 1486 regulatory LTR elements and from 199 to 805 down-stream target genes. In total, there are 26 gene ontology-defined GRNs associated with human embryo regulatory LTRs each of which contain more than 500 regulatory LTR elements and more than 199 down-stream target genes (Fig. 1 ; Tables 3 – 5 ; Supplementary Table S1 ). Genomic locations of GRNs appear scattered across chromosomes to occupy from 5.5–15.09% of the human genome. GRNs integrate activities from 529 to 1486 human embryo regulatory LTR elements and from 199 to 805 down-stream target genes, including transcription factors, chromatin-state remodelers, sensing and signal transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. One of notable common structural features of GRNs is that numbers of regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences residing within the boundaries of GRN’s genomic modicums are quantitatively balanced in strict accordance to their genome-wide abundance levels (Table 4 ). Table 4 Quantitative profiles of regulatory LTR elements and down-stream target genes within 26 gene ontology enrichment analysis-defined genomic regulatory networks (GRNs) of the genomic dominion of human embryo regulatory LTRs. Gene ontology ID Genomic regulatory networks Number, LTR7 Number, MLT2A1 Number, MLT2A2 Number of linked genes Percent, LTR7 Percent, MLT2A1 Percent, MLT2A2 NA Genomic dominion of human embryo regulatory LTRs 3354 2416 3069 5444 100.0 100.0 100.0 GO:0051960 Regulation of nervous system development 500 409 515 716 14.9 16.9 16.8 GO:0051962 Positive regulation of nervous system development 333 290 360 443 9.9 12.0 11.7 GO:0098609 Cell-cell adhesion 385 301 403 492 11.5 12.5 13.1 GO:0098742 Cell-cell adhesion via plasma-membrane adhesion molecules 235 186 256 199 7.0 7.7 8.3 GO:0099537 Trans-synaptic signaling 215 161 240 344 6.4 6.7 7.8 GO:0007268 Chemical synaptic transmission 211 158 235 332 6.3 6.5 7.7 GO:0050804 Modulation of synaptic transmission 174 133 222 298 5.2 5.5 7.2 GO:0044708 Single organism behavior 230 185 259 372 6.9 7.7 8.4 GO:0042391 Regulation of membrane potential 217 151 224 340 6.5 6.3 7.3 GO:1901137 Carbohydrate derivative biosynthetic process 261 175 242 448 7.8 7.2 7.9 GO:0043005 Neuron projection 542 411 533 805 16.2 17.0 17.4 GO:0098590 Plasma membrane region 519 337 454 788 15.5 13.9 14.8 GO:0044456 Synapse part 373 293 423 604 11.1 12.1 13.8 GO:0009986 Cell surface 370 239 312 560 11.0 9.9 10.2 GO:0098794 Postsynapse 276 229 321 406 8.2 9.5 10.5 GO:0044297 Cell body 275 204 258 409 8.2 8.4 8.4 GO:0097060 Synaptic membrane 232 182 265 305 6.9 7.5 8.6 GO:0045211 Postsynaptic membrane 195 156 226 240 5.8 6.5 7.4 GO:0036477 Somatodendritic compartment 346 281 368 582 10.3 11.6 12.0 GO:0043025 Neuronal cell body 225 188 232 359 6.7 7.8 7.6 GO:0030424 Axon 246 174 231 335 7.3 7.2 7.5 GO:0031012 Extracellular matrix 244 199 231 395 7.3 8.2 7.5 GO:0005578 Proteinaceous extracellular matrix 191 176 187 311 5.7 7.3 6.1 GO:0005509 Calcium ion binding 354 228 397 462 10.6 9.4 12.9 GO:0008233 Peptidase activity 240 158 176 400 7.2 6.5 5.7 GO:0070011 Peptidase activity, acting on L-amino acid peptides 226 152 169 384 6.7 6.3 5.5 NA Cumulative, within 26 genomic regulatory networks 1952 1393 1892 3054 58.2 57.7 61.6 NA Cumulative, excluding 26 genomic regulatory networks 1402 1023 1177 2685 41.8 42.3 38.4 Legend: *For each GRN, reported numbers of LTRs and genes were determined at the statistical significance threshold of the Binominal FDR Q-Value < 0.001 applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (Supplementary Materials); **, A total of 26 gene ontology enrichment analyses-defined GRNs associated with human embryo regulatory LTRs were identified at the statistical significance threshold of the Binominal FDR Q-value < 0.05 (Supplementary Table S1 ). GRNs contain from 529 to 1486 regulatory LTR elements and from 199 to 805 down-stream target genes. Follow-up gene ontology enrichment analyses of 26 GRNs revealed further details of their genotype-phenotype association-defined architectural complexity: GRNs consist from several hundred to thousands quantitatively smaller gene ontology enrichment analysis-defined genomic regulatory modules (GRMs) (Table 3 ; Supplementary Table S1 ; Supplementary Data Set S1: GRN1-GRN26 ). Importantly, to increase the stringency of statistical definitions of candidate regulatory LTR-associated GRMs, the cut-off threshold of the Binominal FDR Q-value < 0.001 was applied in these series of GO terms enrichment analyses. One of the implications of this approach is that the false discovery rates (FDR) in these analyses could be estimated at less than 0.1%, thus indicating that the reported observations have more than 99.9% likelihood of being true findings. Based on the results of these analyses, it has been concluded that genomic constituents of each GRN comprise of several hundred to thousands smaller gene ontology enrichment analysis-defined GRMs, each of which contains from a dozen to hundreds LTRs and down-stream target genes. Overall, these analyses identified 69,573 statistically significant regulatory LTR-linked GRMs (Tables 3 – 4 ; Supplementary Table S1 ; Supplementary Data Set S1: GRN1-GRN26 ), including 27,601 GRMs validated by the single ontology-specific directed acyclic graph (DAG) analyses across 6 gene ontology annotations databases (Table 5 ; Supplementary Table S1 ; Supplementary Data Set S1: GRN1-GRN26 ). It has been determined that within each GRN there is a significant positive correlation between the human genome fractions occupied by the GRMs and LTR set coverage for the GRMs ( Supplementary Summary S2 ). These observations suggest that human embryo regulatory LTRs may exert regulatory effects on thousands distinct GRMs containing functionally and/or structurally-related sub-sets of genes affecting multiple facets of Modern Humans’ developmental processes as well as physiological and pathological traits, in particular, a constellation of biological processes, cellular components, and molecular activities affecting development and functions of the nervous system. Selected examples of GRMs and associated phenotypic traits identified by GO analyses of specific regulatory LTR-governed GRNs. Potential phenotypic impacts on human pathophysiology of human embryo regulatory LTR-governed GRNs could be inferred by analyses of significantly enriched genotype-phenotype associations annotated to their composite GRMs. The GRN “Regulation of Membrane Potential” have the largest number of significantly-enriched GRMs and associated phenotypic traits identified by GO analyses of the Human Phenotype database (Tables 3 – 5 ; Supplementary Data Set S1 ). A set of 79 regulatory LTRs linked to 10 genes have been identified that were annotated to GRM X-linked inheritance (HP:0001417) and GRM Gonosomal inheritance (HP:0010985) at Binominal FDR Q-values of 2.82E-29 and 1.86E-25, respectively. Multiple significantly-enriched GRMs are associated with human phenotypes of major clinical importance. These include: Autism (HP:0000717; 57 LTRs linked to 7 genes; Binominal FDR Q-value = 3.39E-33); and Autistic behavior (HP:0000729; 65 LTRs linked to 11 genes; Binominal FDR Q-value = 2.10E-26); Parkinsonism (HP:0001300; 26 LTRs linked to 8 genes; Binominal FDR Q value = 1.71E-11); Dementia (HP:0000726; 26 LTRs linked to 7 genes; Binominal FDR Q-value = 1.20E-09); Intellectual disability (HP:0001249; 179 LTRs linked to 43 genes; Binominal FDR Q value = 4.28E-31) and a severe form of Intellectual disability (HP:0010864; 52 LTRs linked to 8 genes; Binominal FDR Q value = 2.13E-24); Syncope (HP:0001279; 35 LTRs linked to 11 genes; Binominal FDR Q value = 5.46E-24); Abnormality of cardiovascular system electrophysiology (HP:0030956; 64 LTRs linked to 22 genes; Binominal FDR Q value = 1.96E-26); and Seizures (HP:0001250; 160 LTRs linked to 36 genes; Binominal FDR Q value = 5.19E-33). The extended summary illustrating selected findings derived from GO analyses of the Human Phenotype database related to human disorders is reported in the Supplementary Summary S3 . GO Biological Process analyses identified multiple GRMs representing common significantly-enriched phenotypic traits associated with numerous GRNs. Several GRMs common for the GRN “Synapse Part” and the GRN “Postsynapse” include regulation of amyloid precursor protein catabolic process; regulation of beta-amyloid formation; positive regulation of beta-amyloid formation; response to beta-amyloid; and cellular response to beta-amyloid. GRNs “Synaptic Membrane” and “Postsynaptic Membrane” have several common GRMs, namely regulation of amyloid precursor protein catabolic process; regulation of beta-amyloid formation; and positive regulation of beta-amyloid formation. Similarly, common GRMs for the GRN “Trans-synaptic Signaling” and the GRN “Chemical Synaptic Transmission” are beta-amyloid metabolic process; response to beta-amyloid; and cellular response to beta-amyloid; while the GRM “Regulation of Membrane Potential” harbors a set of functionally-related GRMs, namely regulation of beta-amyloid formation; positive regulation of beta-amyloid formation; response to beta-amyloid; and cellular response to beta-amyloid. GO Molecular Function analyses identified several GRMs representing common significantly-enriched phenotypic traits associated with seven GRNs, namely “ Synapse Part”; “Postsynapse”; “Synaptic Membrane”; “Postsynaptic Membrane”; “Trans-synaptic Signaling”; “Chemical Synaptic Transmission”; “Regulation of Membrane Potential”. A set of common GRMs shared by these GRNs include beta-amyloid binding; ionotropic glutamate receptor activity; kainate selective glutamate receptor; NMDA glutamate receptor; AMPA glutamate receptor. Interestingly, the GO Molecular Function terms ionotropic glutamate receptor activity; kainate selective glutamate receptor; NMDA glutamate receptor; AMPA glutamate receptor represent the exactly same selection of phenotypic traits that were identified as down-stream regulatory targets of human-specific LTR7 and LTR5_Hs loci (Glinsky, 2022 ). Overall, genes linked to phenotypic traits reflecting various aspects of physiological and pathological processes associated with the biogenesis, metabolism, and functions of the amyloid precursor protein and beta-amyloid peptides appear to represent one of the down-stream targets of preference during the evolution of human embryo regulatory LTR elements. Validation of GRNs and GRMs governed by human embryo regulatory LTR elements employing the directed acyclic graph (DAG) analysis. It was important to confirm the validity of statistical definitions of GRNs and GRMs based on the binominal and hypergeometrc FDR Q values (Methods) employing independent analytical approaches. To this end, a directed acyclic graph (DAG) test based on the enriched terms from a single ontology-specific table generated by a GREAT algorithm has been employed (Figs. 2 – 3 ; Table 3 ; Supplementary Data Sets S1: GRN1-GRN26 ). Patterns and directions of connections between significantly enriched GO modules are guided by the experimentally-documented temporal logic of developmental processes and structural/functional relationships between gene ontology enrichment analysis-defined statistically significant terms. Therefore, only statistically significant GRMs from a single gene ontology-specific table generated by the GREAT algorithm that manifest connectivity patterns defined by experimentally documented developmental and/or structure/function/activity relationships are deemed valid observations and visualized as a consensus hierarchy network of the ontology-specific DAGs (Figs. 2 – 3 ; Tables 3 – 5 ; Supplementary Data Sets S1: GRN1-GRN26 ). Based on these considerations, the DAG algorithm draw the developmental and structure/function/activity relationships-guided hierarchy of connectivity between statistically significant gene ontology enriched GRMs. Visualization snapshots of representative examples illustrating the individual ontology-specific DAGs are reported in the Figs. 2 – 3 and comprehensive reports of quantitative and qualitative components of these analyses are presented in Tables 3 – 5 ; Supplementary Data Sets S1: GRN1-GRN26 . GO analyses of Mouse phenotype databases and GSEA of the MGI Mammalian Phenotype Level 4 2021 database revealed that genes comprising down-stream regulatory targets of human embryo regulatory LTRs affect development and functions of essentially all major tissues, organs, and organ systems. These include central and peripheral nervous systems, cardio-vascular (circulatory) and lymphatic systems, gastrointestinal (digestive) and urinary (excretory) systems, musculoskeletal and respiratory systems, endocrine and immune systems, as well as integumentary and reproductive systems. Analyses of single gene KO models revealed profound developmental defects and numerous examples of organogenesis failures, indicating that activities of regulatory LTR down-stream target genes are essential for development and functions of major organs and organ systems. Of potentially major interest are observations that all 26 GRNs governed by human embryo regulatory LTR elements harbor significantly enriched records of GRMs annotated to multiple distinct GO terms of Mammalian Offspring’s Survival (MOS) phenotypes (Table 6 ; Supplementary Data Set S1: GRN1-GRN26 ). For example, GRN2 “Regulation of nervous system development” (GO:0051960) harbors numerous significantly enriched GRMs annotated to distinct MOS phenotypes, including: neonatal lethality, complete penetrance and neonatal lethality, incomplete penetrance; lethality at weaning, complete penetrance; perinatal lethality, complete penetrance; postnatal lethality, complete penetrance and postnatal lethality, incomplete penetrance; embryonic lethality during organogenesis and embryonic lethality during organogenesis, complete penetrance; lethality throughout fetal growth and development and lethality throughout fetal growth and development, incomplete penetrance; lethality at weaning; perinatal, neonatal, and postnatal lethality phenotypes (Table 6 ; Supplementary Data Set S1: GRN2 ). Other notable GRMs of regulatory LTR-linked GRNs that were annotated to significantly enriched MOS phenotypes include: embryonic lethality between implantation and placentation, complete penetrance; embryonic lethality between implantation and somite formation, incomplete penetrance; embryonic lethality between somite formation and embryo turning, incomplete penetrance; prenatal lethality prior to heart atrial septation; embryonic lethality, complete penetrance (Table 6 ; Supplementary Data Set S1: GRN1-GRN26 ). Table 6 Genes regulating Mammalian Offspring’s Survival (MOS) phenotypes are intrinsic components of twenty six Genomic Regulatory Networks (GRNs) governed by human embryo regulatory LTR elements. GRN ID GO Category GO ID GRN GO Name Number of MOS phenotypes* Top scoring MOS phenotype GO ID Binominal Rank Binominal FDR Q-value GRN1 GO Cellular Component GO:0043005 neuron projection 5 postnatal lethality, complete penetrance MP:0011085 167 5.56E-16 GRN2 GO Biological Process GO:0051960 regulation of nervous system development 12 neonatal lethality, complete penetrance MP:0011087 33 1.35E-33 GRN3 GO Cellular Component GO:0044456 synapse part 6 postnatal lethality, incomplete penetrance MP:0011086 245 1.18E-10 GRN4 GO Biological Process GO:0098609 cell-cell adhesion 4 lethality at weaning, complete penetrance MP:0011083 41 4.63E-13 GRN5 GO Cellular Component GO:0036477 somatodendritic compartment 5 lethality at weaning MP:0008569 180 3.16E-13 GRN6 GO Biological Process GO:0051962 positive regulation of nervous system development 9 neonatal lethality, complete penetrance MP:0011087 110 7.28E-20 GRN7 GO Molecular Function GO:0005509 calcium ion binding 3 embryonic lethality between implantation and placentation, complete penetrance MP:0011095 329 2.01E-02 GRN8 GO Cellular Component GO:0098794 postsynapse 4 lethality at weaning, complete penetrance MP:0011083 150 9.09E-13 GRN9 GO Cellular Component GO:0097060 synaptic membrane 4 lethality at weaning, complete penetrance MP:0011083 183 2.98E-10 GRN10 GO Biological Process GO:1901137 carbohydrate derivative biosynthetic process 6 embryonic lethality between implantation and somite formation, incomplete penetrance MP:0011106 40 1.44E-11 GRN11 GO Biological Process GO:0098742 cell-cell adhesion via plasma-membrane adhesion molecules 5 neonatal lethality, complete penetrance MP:0011087 106 1.45E-08 GRN12 GO Biological Process GO:0044708 single-organism behavior 6 postnatal lethality, incomplete penetrance MP:0011086 65 9.98E-25 GRN13 GO Cellular Component GO:0031012 extracellular matrix 5 lethality throughout fetal growth and development, incomplete penetrance MP:0011109 14 2.45E-14 GRN14 GO Cellular Component GO:0030424 axon 5 postnatal lethality, complete penetrance MP:0011085 178 4.09E-10 GRN15 GO Cellular Component GO:0043025 neuronal cell body 3 embryonic lethality between implantation and placentation, complete penetrance MP:0011095 341 3.66E-07 GRN16 GO Biological Process GO:0099537 trans-synaptic signaling 8 lethality at weaning MP:0008569 104 9.72E-24 GRN17 GO Biological Process GO:0007268 chemical synaptic transmission 8 lethality at weaning MP:0008569 94 3.79E-24 GRN18 GO Biological Process GO:0042391 regulation of membrane potential 7 postnatal lethality, incomplete penetrance MP:0011086 129 3.50E-16 GRN19 GO Cellular Component GO:0045211 postsynaptic membrane 3 lethality at weaning, complete penetrance MP:0011083 143 6.43E-12 GRN20 GO Molecular Function GO:0008233 peptidase activity 2 embryonic lethality during organogenesis, complete penetrance MP:0011098 304 2.26E-02 GRN21 GO Cellular Component GO:0005578 proteinaceous extracellular matrix 5 lethality throughout fetal growth and development, incomplete penetrance MP:0011109 6 1.90E-17 GRN22 GO Molecular Function GO:0070011 peptidase activity, acting on L-amino acid peptides 2 postnatal lethality, complete penetrance MP:0011085 236 1.06E-02 GRN23 GO Biological Process GO:0050804 modulation of synaptic transmission 6 preweaning lethality, incomplete penetrance MP:0011110 194 6.09E-11 GRN24 GO Cellular Component GO:0044297 cell body 2 embryonic lethality between implantation and placentation, complete penetrance MP:0011095 72 5.69E-16 GRN25 GO Cellular Component GO:0009986 cell surface 7 neonatal lethality, complete penetrance MP:0011087 22 1.97E-21 GRN26 GO Cellular Component GO:0098590 plasma membrane region 3 postnatal lethality, incomplete penetrance MP:0011086 352 2.31E-07 Legend: *, number of MOS phenotypes among top 500 enriched GO terms. Collectively, these observations support the conclusion that GRNs comprising the GRD of human embryo regulatory LTR elements could be viewed as an intrinsically integrated compendium of thousands genotype-phenotypic trait associations-defined GRMs, many of which may represent the evolutionary selection units defined by inherent genotype-phenotype associations affecting species’ fitness and survival. Gene Set Enrichment Analyses (GSEA) of down-stream target genes of human embryo regulatory LTR elements revealed global multifaceted impacts on physiological traits, developmental phenotypes, and pathological conditions of Modern Humans. To gain further insights into architectural frameworks and functional features of the GRD associated with human embryo regulatory LTR elements, the Enrichr bioinformatics platform was utilized to carry out the comprehensive series of GSEA employing more than 70 genomics and proteomics databases (Methods). GSEA were executed by imputing 5444 candidate down-stream regulatory target genes of human embryo regulatory LTR elements and retrieving significantly enriched records capturing phenotypic traits affected by subsets of candidate LTR-regulated genes (Tables 7 – 8 ; Supplementary Figure S1 ; Supplementary Tables S1-S2 ). These analyses documented global multifaceted impacts of genes representing putative down-stream regulatory targets of human embryo regulatory LTR elements on physiological traits, developmental phenotypes, and pathological conditions of Modern Humans (Table 7 ; Supplementary Figure S1 ; Supplementary Table S1 ). Table 7 Gene Set Enrichment Analyses (GSEA) of 5444 genes comprising putative down-stream targets of human embryo regulatory LTRs revealed their global multifaceted impacts on physiological traits, developmental phenotypes, and pathological conditions of Modern Humans. Classification category Number of significantly enriched records* Common & rare human diseases DisGeNET 477 Orphanet Augmented 2021 1095 Rare Diseases AutoRIF ARCHS4 Predictions 140 Rare Diseases GeneRIF ARCHS4 Predictions 97 Rare Diseases GeneRIF Gene Lists 96 Rare Diseases AutoRIF Gene Lists 610 Disease Perturbations from GEO down 128 Disease Perturbations from GEO up 116 Diabetes Perturbations GEO 2022 217 MAGMA Drugs and Diseases 82 HDSigDB Human 2021 (Huntington disease) 1004 HDSigDB Mouse 2021 (Huntington disease) 677 MSigDB Oncogenic Signatures 49 Jensen database of human diseases 79 RNA-Seq Disease Gene and Drug Signatures from GEO 323 Developmental & Regulatory Pathways WikiPathway 2021 Human 33 KEGG 2021 Human 17 BioPlanet 2019 48 Panther 2016 1 Reactome 2022 26 Transcription Factor PPIs 1 ARCHS4 TF Co-expression 284 ARCHS4 Kinases Co-expression 106 TF Perturbations Followed by Expression 532 Gene Perturbations from GEO up 342 Gene Perturbations from GEO down 252 Endogenous Ligand Perturbations from GEO up 43 Endogenous Ligand Perturbations from GEO down 46 lncHUB lncRNA Co-Expression 220 Kinase Perturbations from GEO down 17 Kinase Perturbations from GEO up 27 Gene Ontology GO Cellular Component 2021 17 GO Molecular Function 2021 12 GO Biological Process 2021 177 MGI Mammalian Phenotype Level 4 2021 332 Human Phenotype Ontology 27 Single cell genomics & epigenetics PanglaoDB Augmented 2021 87 CellMarker Augmented 2021 255 Azimuth Cell Types 2021 101 HuBMAP ASCTplusB Augmented 2022 191 Human tissues GTEx Tissue Expression Up 513 GTEx Tissue Expression Down 163 Human Gene Atlas 15 ARCHS4 Tissues 48 Allen Brain Atlas up 787 Allen Brain Atlas down 245 RNAseq and Proteomics RNAseq Automatic GEO Signatures Human Up 1494 RNAseq Automatic GEO Signatures Human Down 1571 ProteomicsDB 2020 6 CCLE Proteomics 2020 49 Aging phenotype signatures GTEx Aging Signatures 2021 13 Aging Perturbations from GEO up 10 Aging Perturbations from GEO down 30 Genotype-phenotype associations GWAS Catalog 2019 271 OMIM Expanded 175 ClinVar 2019 1 PhenGenI Association 2021 150 PheWeb 2019 862 dbGaP 144 Host-pathogen interactions COVID-19 Related Gene Sets 2021 138 Virus Perturbations from GEO up 55 Virus Perturbations from GEO down 55 Microbe Perturbations from GEO up 18 Microbe Perturbations from GEO down 13 Legend: * Numbers of significantly enriched records identified by GSEA during analyses of corresponding genomics and proteomics databases to define entries at the statistical significance threshold of adjusted P value (FDR q value) < 0.05. Table 8 Quantitative profiles of a directed acyclic graph (DAG)* test-validated genomic regulatory modules (GRMs)** within GSEA-defined genomic regulatory panels (GRPs)*** of the genomic dominion of human embryo regulatory LTRs. Genomic regulatory panels (GRPs) Number of LTRs Number of linked genes GO Biological Process GO Cellular Component GO Molecular Function Human Phenotype Mouse Phenotype Single KO Mouse Phenotype Number of GRMs DAG Visualization Report Neurotrophins (neurotrophic factors) 8 12 12 0 2 30 70 77 191 Figure S6 Neurotrophins (expanded) 29 147 4 3 8 0 70 59 144 Supplementary Data Sets S2 Neurexins and Neuroligins R-HSA-6794361 120 30 61 19 4 0 0 0 84 Figure S5 Neurotransmitter receptor activity 137 30 34 29 38 0 20 32 153 Figure S7 Signaling by Receptor Tyrosine Kinases R-HSA-9006934 241 81 172 8 12 0 24 69 285 Figure S9 RET-GDNF pathway 115 28 181 0 1 8 107 191 488 Figure S4 Neuroactive ligand-receptor interactions 305 84 85 30 43 0 80 85 323 Figure S8 GSEA-defined GRN of regulatory LTRs 966 255 287 38 20 2 136 264 747 Figure S10 GSEA-defined GRN of regulatory LTRs (all significant GRMs)# 966 255 1359 96 132 178 955 1165 3885 Supplementary Data Sets S2 Legend: *, A directed acyclic graph (DAG) test was carried out on the enriched terms from a single ontology-specific table generated by the GREAT algorithm; **, For each GRP, reported numbers of GRMs were determined using a directed acyclic graph (DAG) test applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (see Supplementary Data Sets S2 for complete reports); ***, GSEA-defined GRPs associated with human embryo regulatory LTRs were identified using the Enrichr Bioinformatics Platform at the statistical significance threshold of the adjusted P-value < 0.05 (Supplementary Data Sets S2); #, reported numbers of GRMs were identified by the GREAT algorithm at the statistical significance threshold of the Binominal FDR Q-value < 0.001 (Supplementary Data Sets S2). Broadly, identified in this study regulatory LTR target genes have been implicated in pathogenesis and manifestation of a large spectrum of human common, orphan, and rare disorders; they are engaged in numerous developmental and regulatory pathways contributing to differentiation and specialization processes across the hierarchy of human organs, tissues, cells, and sub-cellular structures; have been shown to affect phenotypic traits at the multiple levels of a cellular molecular composition and regulatory infrastructure, including signal transduction triggered by numerous endogenous ligands and multiple families of transmembrane receptors; structural/functional organization of mitochondria, endoplasmic reticulum, and nucleus; activities of transcription factors and chromatin remodeling effectors. Analyzed herein regulatory LTR target genes have been linked with the human aging phenotype in gene expression profiling experiments and with a multitude of both physiological and pathological human phenotypes in genotype-phenotype association studies across various analytical platforms and databases, in particular, human disease associations’ databases (Table 7 ; Supplementary Figure S1 ; Supplementary Table S1 ). Protein products of regulatory LTR down-stream target genes have been documented to participate in protein-protein interactions of endogenous multiprotein complexes as well as host-pathogen interactions during viral and microbial infections. GSEA of regulatory LTR down-stream target genes employing the OMIM Expanded database of protein-protein interactions (PPI) of human disease-causing genes identified 34 regulatory LTR target genes, protein products of which appear engaged in PPIs with protein products of disease-causing genes implicated in development of a wide spectrum of human disorders ( Supplementary Figure S1 ; Supplementary Table S1 ). Genes comprising the 34-gene signature of regulatory LTR targets are significantly enriched in 1387 records of human diseases (GSEA of the DisGeNET database; adjusted p-value < 0.05). These observations prompted in depth follow-up investigations of a regulatory network comprising 88 regulatory LTR elements (27LTR7; 35 MLT2A1; and 26 MLT2A2 sequences), which were mapped as putative up-stream regulator elements of the 34 genes. Characterization of 88 human embryo regulatory LTRs comprising putative up-stream regulatory elements of PPI networks of human disease-causing genes. Results of the DAG analyses of 88 human embryo regulatory LTR elements revealed highly complex hierarchical networks of significantly enriched phenotypic traits identified by analyses of GO Biological Process and Mouse Phenotype databases, while analyses of GO Cellular Component and GO Molecular Function documented relatively simple architectures of significantly enriched phenotypic traits (Fig. 4 ; Supplementary Table S1 ; Supplementary Data Set S2 ). Overall, 1533 significantly enriched records linked to down-stream target genes of 88 regulatory LTR elements have been identified at the statistical threshold of the Binominal FDR Q-value < 0.001. These include 432; 36; 42; 58; 243; and 722 significantly enriched phenotypic traits identified by Gene Ontology analyses of GO Biological Process; GO Cellular Component; GO Molecular Function; Human Phenotype; Mouse Phenotype Single KO; and Mouse Phenotype databases, respectively ( Supplementary Data Set S2 ). Of note, GO enrichment analyses of 88 regulatory LTR elements employing the Mouse Phenotype Single KO database identified numerous significantly records of genes annotated to distinct mammalian offspring’s survival (MOS) phenotypes. The list of enriched GO terms of this category includes several prenatal and perinatal lethality phenotypes [perinatal lethality (MP:0002081); perinatal lethality, complete penetrance (MP:0011089); perinatal lethality, incomplete penetrance (MP:0011090); prenatal lethality, complete penetrance (MP:0011091); prenatal lethality, incomplete penetrance (MP:0011101)]; distinct embryonic lethality phenotypes [embryonic lethality, complete penetrance (MP:0011092); embryonic lethality between implantation and somite formation (MP:0006205); embryonic lethality during organogenesis MP:0006207); embryonic lethality between implantation and placentation (MP:0009850); and the premature death phenotype (MP:0002083). GSEA of 34 genes identified hundreds of significantly enriched phenotypic traits across numerous genomics and proteomics databases, representative examples of which are reported in the Fig. 5 ; Supplementary Figure S1 ; Supplementary Table S1 . In multiple instances numbers of significantly enriched phenotypic records linked with 34 genes are markedly higher compared to the parent set of 5444 genes (Fig. 5 B; Supplementary Figure S1 ; Supplementary Table S1 ) . For example, there are 121 and 124 significantly enriched records of 34 genes identified by GSEA of the Transcription Factors PPI database and the Hub Proteins PPI database, respectively, while there is only one significantly enriched record in each database identified by GSEA of 5444 genes (Fig. 5 B). Similarly, there are 1387 and 477 significantly enriched records of human diseases identified by GSEA of 34 genes and 5444 genes, respectively, employing the DisGeNET database ( Supplementary Figure S1 ; Supplementary Table S1 ) . Apparent qualitative distinctions were noted between top 10 significantly enriched records of human diseases: all top 10 GSEA records of 34 genes’ enrichment were various records of malignant neoplasms, while top 10 records linked with GSEA of 5444 genes were neurodevelopmental and substance abuse disorders ( Supplementary Figure S1 ; Supplementary Table S1 ) . In contrast to common human diseases, 78 and 1095 significantly enriched records of human orphan disorders have been identified by GSEA of 34 genes and 5444 genes, respectively, employing the Orphanet Augmented 2021 database ( Supplementary Figure S1 ; Supplementary Table S1 ) . Follow-up analyses revealed that overall there are 206 human genes encoding proteins manifesting significantly enriched records of protein-protein interactions (PPIs) with protein products encoded by 34 genes (Figs. 5 – 6 ; Supplementary Table S1 ; Supplementary Summaries S4-S5 ). Because protein products of 34 genes manifest significant enrichment of PPIs with human disease-causing genes (Fig. 5 ; Supplementary Figure S1 ; Supplementary Table S1 ; Supplementary Summary S4 ), these observations suggest that a network of proteins encoded by 240 genes may be engaged in PPIs with human disease-causing genes as well. This hypothesis was tested by performing GSEA of 240 genes comprising the putative PPI network of human disease-causing genes (Figs. 5 – 6 ; Supplementary Table S1 ; Supplementary Summaries S4-S5 ). GSEA of 240 genes consistently demonstrated that numbers of significantly enriched phenotypic traits linked with 240 genes are markedly higher compared to either 34 genes or the parent set of 5444 genes (Figs. 5 – 6 ; Supplementary Table S1 ; Supplementary Summaries S4-S5) . GSEA employing the DisGeNET database identified 3298; 1387; and 477 significantly enriched records of human diseases linked with 240 genes, 34 genes, and 5444 genes, respectively (Fig. 5 ; Supplementary Figure S1 ; Supplementary Table S1 ; Supplementary Summary S4) . Qualitatively distinct patterns noted between top 10 significantly enriched records of human diseases associated with 34 genes versus 5444 genes ( Supplementary Figure S1 ; Supplementary Table S1 ) were recapitulated by GSEA of 240 genes: all top 10 GSEA records linked with 240 genes were multiple records of malignant neoplasms, while top 10 records linked with GSEA of 5444 genes were neurodevelopmental and substance abuse disorders (Fig. 5 ; Supplemental Table S1 ; Supplementary Summary S4 ). In contrast to common human diseases, GSEA employing the Orphanet Augmented 2021 database identified 199 and 1095 significantly enriched records of human orphan disorders associated with 240 genes and 5444 genes, respectively (Fig. 5 ; Supplementary Table S1 ; Supplementary Summary S4) . Particularly intriguing observations were recorded during GSEA of proteomics databases focused on the molecular architecture of a large compendium of endogenous PPI networks. There are 268 and 228 significantly enriched records of subsets of 240 genes identified by GSEA of the Transcription Factors PPI database and the Hub Proteins PPI database, respectively, while there are 121 and 124 significantly enriched record in corresponding databases identified by GSEA of 34 genes (Figs. 5 – 6 ; Supplementary Table S1 ; Supplementary Summary S4-S5 ). Similarly, there are 606 and 62 significantly enriched records of 240 genes identified by GSEA of the CORUM Protein Complexes database and the SILAC Phosphoproteomics database, respectively, while there are 143 and 22 significantly enriched record in corresponding databases identified by GSEA of 34 genes (Fig. 6 ; Supplementary Table S1 ; Supplementary Summary S5 ). Strikingly, GSEA employing the NURSA Human Endogenous Complexome database identified 1783 significantly enriched records linked with 240 genes, while no significantly enriched traits were recorded for GSEA of either 34 genes or 5444 genes (Fig. 6 ; Supplementary Table S1 ; Supplementary Summary S5 ). These observations indicate that protein products selected from a panel of 240 genes are significantly enriched in PPIs of a marked majority of endogenous human multimolecular complexes recorded in the NURSA Human Endogenous Complexome database. It follows, that a set of 88 human embryo regulatory LTR elements may extend its regulatory impacts on vast networks of human endogenous multimolecular complexes via protein products of 34 down-stream target genes engaged in PPIs with 268 transcription factors and 228 hub proteins. Strikingly, these networks appear impacting PPIs that are significantly enriched in 6584 records of virus-host PPIs documented in Virus-Host PPI P-HIPSTer 2020 database and 2071 records of human rare disorders catalogued in the Rare Diseases AutoRIF Gene Lists database ( Supplementary Table S1 ) . Notably, GSEA of 240 genes employing the OMIM Expanded database of PPIs of human disease-causing genes further validated and expanded this concept by identification of 51 genes, protein products of which appear engaged in PPIs with protein products of disease-causing genes implicated in development of numerous human disorders of an extremely broad spectrum of clinical phenotypes (Fig. 6 ; Supplementary Table S1 ; Supplementary Summary S5 ). Collectively, present analyses suggest that a common molecular mechanism driving development of human pathologies may be associated with altered expression of proteins encoded by regulatory LTR target genes and down-stream effects on PPI networks engaging protein products of disease-causing genes. Malfunctions of endogenous multimolecular complexes tagged by PPIs of proteins encoded by regulatory LTR target genes may contribute to molecular pathogenesis of an exceedingly large number of human diseases, which exhibit highly diverse spectrum of clinical manifestations including malignancies of different solid organs and hematopoietic system; blood, lymphatic and endocrine system disorders; immunoinflammatory and autoimmune maladies; neurodevelopmental, neuropsychiatric, and neurodegenerative diseases. Genes representing common regulatory targets of human embryo regulatory LTR elements are segregated into sub-sets of genetic loci coalesced within genomic regulatory pathways of common biological functions. Notable common features of all identified herein GRNs governed by human embryo regulatory LTR elements are the quantitatively balanced numbers of residing within GRN’s boundaries regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences (Table 4 ). These observations indicate that LTR elements of distinct families may affect genes (including sub-sets of common down-stream target genes) comprising constituents of corresponding GRNs, which then likely to exert biologically discernable effects on corresponding phenotypic traits. If the model is correct, this consonant regulatory framework should be observable within previously defined signal transduction pathways known to affect specific phenotypes. The validity of this hypothesis was tested by performing GSEA of 709 genes comprising a set of common down-stream regulatory targets of LTR7, MLT2A1, and MLT2A2 elements (Table 1 ). In these analytical experiments, GSEA-defined significantly enriched sets of genes operating within specific pathways were mapped to sub-sets of their putative up-stream regulatory elements among human embryo regulatory LTRs ( Supplementary Table S2 ; Supplementary Data Set S2 ). Then each of these sub-sets of regulatory LTRs associated with specific pathways were subjected to GO analyses using the GREAT algorithm. Results of these analyses demonstrate that genetic loci representing common regulatory targets of human embryo regulatory LTRs appear segregated into relatively small sets of LTR/gene panels coalesced within genomic regulatory pathways of common biological functions (Tables 8 – 10 ; Supplementary Figures S2 -S10; Supplementary Table S2 ; Supplementary Data Set S2 ). These GSEA/GREAT analyses-defined sets of genes and associated regulatory LTR elements were designated Genomic Regulatory Pathways (GRPs) because they were identified by GSEA as significantly enriched genetic loci (adjusted p value < 0.05) assigne to corresponding previously defined regulatory pathways. Table 9 Enrichment of regulatory LTR elements and genes associated with Mammalian Offspring Survival phenotypes within the regulatory LTR-governed GRM of reduced fertility phenotype. Phenotype Binominal Rank Binominal Raw P-Value Binominal FDR Q-Value Binominal Fold Enrichment Binominal Observed LTR Hits Binominal LTR Set Coverage Hypergeometric Rank Hypergeometric FDR Q-Value Hypergeometric Fold Enrichment Hypergeometric Observed Gene Hits Hypergeometric Total Genes Hypergeometric Gene Set Coverage reduced fertility 1 0 0 11.34 311 1.00 1 1.08E-62 13.04 77 652 0.45 postnatal lethality 14 8.35E-84 5.71E-81 3.62 218 0.70 20 2.46E-15 4.01 50 1378 0.29 lethality during fetal growth through weaning 29 7.68E-68 2.53E-65 2.51 248 0.80 21 3.00E-15 3.09 65 2325 0.38 postnatal lethality, incomplete penetrance 48 5.59E-49 1.11E-46 3.83 141 0.45 29 5.90E-14 5.07 37 806 0.22 infertility 104 7.29E-34 6.70E-32 3.61 109 0.35 115 1.65E-07 3.59 29 893 0.17 male infertility 113 2.04E-32 1.73E-30 4.29 89 0.29 154 5.62E-06 3.77 22 644 0.13 premature death 116 8.52E-32 7.02E-30 2.61 147 0.47 74 1.05E-08 3.04 41 1491 0.24 perinatal lethality 141 3.62E-30 2.46E-28 2.41 155 0.50 39 9.61E-12 3.60 44 1349 0.26 postnatal lethality, complete penetrance 157 1.63E-28 9.94E-27 3.30 103 0.33 128 6.13E-07 3.97 24 667 0.14 preweaning lethality, incomplete penetrance 282 5.62E-20 1.91E-18 3.60 67 0.22 268 0.000239 3.73 16 474 0.09 neonatal lethality, incomplete penetrance 288 1.06E-19 3.53E-18 3.51 68 0.22 137 2.03E-06 4.92 18 404 0.11 lethality at weaning, complete penetrance 310 6.32E-19 1.95E-17 7.88 32 0.10 833 0.038637 6.50 4 68 0.02 neonatal lethality 319 1.67E-18 5.01E-17 2.30 112 0.36 58 5.92E-10 3.87 35 998 0.21 perinatal lethality, incomplete penetrance 356 6.07E-17 1.63E-15 3.49 59 0.19 185 2.14E-05 4.95 15 335 0.09 neonatal lethality, complete penetrance 359 7.04E-17 1.88E-15 2.52 90 0.29 67 3.47E-09 4.49 28 689 0.16 lethality at weaning 399 1.62E-15 3.87E-14 5.73 33 0.11 812 0.035547 5.11 5 108 0.03 abnormal cell death 436 2.42E-14 5.31E-13 2.07 107 0.34 291 0.000436 2.27 31 1510 0.18 prenatal lethality, incomplete penetrance 492 6.78E-13 1.32E-11 2.99 54 0.17 572 0.011505 3.01 12 440 0.07 increased cell death 565 2.24E-11 3.79E-10 2.25 74 0.24 375 0.001637 2.58 21 898 0.12 prenatal lethality, complete penetrance 926 2.07E-07 2.14E-06 2.38 42 0.14 837 0.038723 2.78 10 397 0.06 female infertility 1090 2.70E-06 2.37E-05 2.49 32 0.10 531 0.008139 3.37 11 360 0.06 Table 10 High-confidence LTR-regulated genes comprise a significant majority of 5444 genes and 709 genes defined as cumulative and common sets of putative down-stream regulatory targets for LTR7 & MLT2A1 & MLT2A2 human embryo regulatory LTR loci. Classification category Number of genes Percent Common set of LTR7 & MLT2A1 & MLT2A2 target genes 709 100.00 LTR5_Hs regulated genes 195 27.50 LTR7Y/B regulated genes 202 28.49 HERVH lncRNA regulated genes 253 35.68 SVA regulated genes 96 13.54 LTR5_Hs/SVA regulated genes 158 22.28 All LTR-regulated common target genes 473 66.71 Cumulative set of LTR7 & MLT2A1 & MLT2A2 target genes 5444 100.00 LTR5_Hs regulated genes 1344 24.69 LTR7Y/B regulated genes 1873 34.40 HERVH lncRNA regulated genes 1876 34.46 SVA regulated genes 841 15.45 LTR5_Hs/SVA regulated genes 937 17.21 All LTR-regulated target genes 3659 67.21 Similar to GRNs, all identified by GSEA/GREAT analyses GRPs contain numerous regulatory LTRs of LTR7, MLT2A1, and MLT2A2 families and harbor hundreds of GO annotated GRMs. However, GRPs appear to represent quantitatively smaller genomic regulatory units compared to 26 GRNs based on the assessment of numbers of significantly enriched constituent GRMs (Tables 3 – 5 ; Table 8 ; Supplementary Table S2 ; Supplementary Data Sets S1; S2 ) as well as relatively scarce representations of constituent GRMs within DAG analyses-defined GO-specific hierarchical networks (Figs. 2 – 3 ; Supplementary Figures S3-S10; Supplementary Data Sets S1; S2 ). Various regulatory aspects of development and functions of peripheral and central nervous systems at cell type-specific levels and distinct morphological regions appear to represent a dominant common theme shared by GRPs ( Supplementary Figures S3-S10; Supplementary Data Set S2 ). These observations suggest that GSEA-defined GRPs, similar to GO annotations-defined GRMs and GRNs, may constitute a constellation of genomic regulatory units each of which is aligned to specialized biological pathways united by a functional synergy required to preserve the developmental and operational integrity of nervous system. It has been noted that a compendium of GSEA/GREAT analyses-defined GRPs comprising 966 human embryo regulatory LTR elements and 255 down-stream target genes ( Supplementary Figure S10; Table 8 ; Supplementary Data Set S2 ) manifests genomic features quantitatively and qualitatively resembling GRNs, including overlapping subsets of regulatory LTRs and down-stream target genes assigned by GO annotations to 26 GRNs and their constituent GRMs. Intriguing examples of these common GRMs of potential evolutionary significance include sets of regulatory LTRs/down-stream target genes annotated to significantly enriched records of mammalian offspring survival (MOS) phenotypes. GO annotations employing the Mouse Phenotype Single KO database identified the following significantly-enriched records of relevance to MOS phenotypes: 1. MP:0002082; postnatal lethality; 247 LTRs; 45 genes; Binominal FDR Q-value = 1.59E-17; 2. MP:0002081; perinatal lethality; 251 LTRs; 41 genes; Binominal FDR Q-value = 7.30E-15; 3. MP:0011089; perinatal lethality, complete penetrance; 91 LTRs; 14 genes; Binominal FDR Q-value = 6.84E-12; 4. MP:0011090; perinatal lethality, incomplete penetrance; 88 LTRs; 13 genes; Binominal FDR Q-value = 5.84E-11; 5. MP:0011086; postnatal lethality, incomplete penetrance; 149 LTRs; 30 genes; Binominal FDR Q-value = 7.92E-10; 6. MP:0011083; lethality at weaning, complete penetrance; 33 LTRs; 5 genes; Binominal FDR Q-value = 1.43E-08; 7. MP:0011085; postnatal lethality, complete penetrance; 106 LTRs; 20 genes; Binominal FDR Q-value = 2.25E-07; 8. MP:0011110; preweaning lethality, incomplete penetrance; 94 LTRs; 14 genes; Binominal FDR Q-value = 1.16E-06; 9. MP:0001926; female infertility; 65 LTRs; 11 genes; Binominal FDR Q-value = 1.36E-06; Collectively, these observations indicate that two critically important classes of genetic loci appear co-segregated within identified herein human embryo regulatory LTR-governed genomic regulatory units of various architecture: a). Genes essential for the integrity of nervous system development and functions; b). Genes essential for mammalian offspring survival. It seems likely that this striking co-segregation may reflect co-regulation of these two classes of genes by common sets of regulatory LTR elements. Since knockout of many genes within these regulatory LTR-governed GRNs causes offspring lethality, it is logical to expect that their sustained activity would be essential for offspring survival. Sustained activity of MOS genes within regulatory LTR-governed GRNs could be achieved either by relieving the repressor actions of regulatory LTRs or maintaining the activator effects of regulatory LTRs. Alignments of this genomic regulatory architecture of human embryo regulatory LTR-governed GRNs with control of genes essential for development and functions of nervous system may have contributed to acceleration of central nervous system’s dramatic changes during primate evolution. Enrichment patterns of genomic loci annotated to mammalian phenotypes of reduced fertility and infertility within GRNs governed by human embryo regulatory LTR elements. One of the potential biological consequences of essential MOS genes being under control of human embryo regulatory LTR elements is potential effects of this genomic regulatory architecture on species’ fertility phenotypes. In agreement with this hypothesis, it has been observed that GO-defined GRMs annotated to reduced fertility and/or infertility phenotypes are consistently enriched within 18 of 26 GRNs governed by human embryo regulatory LTR elements ( Supplementary Table S3 ). Notably, all GRNs annotated to phenotypic traits closely related to development and functions of nervous system harbor significantly enriched GRMs of reduced fertility and/or infertility phenotypes ( Supplementary Table S3 ). In contrast, GRNs that are more distantly related to or not directly associated with nervous system development and functions do not harbor GRMs of reduced fertility and/or infertility phenotypes among significantly enriched GRMs. These findings suggest that during primate evolution functionally essential fertility genes (that is, genes knockout of which causes reduced fertility and/or infertility phenotypes) and genes essential for development and functions of nervous system were placed under regulatory control of human embryo regulatory LTR elements. It follows that biologically meaningful repressor effects on both classes of genes during ontogenesis would cause defects in development and functions of nervous system as well as would result in reduced fertility and/or infertility phenotypes, thus directly affecting the survivability of the offspring lineage and evolutionary fitness of species. It was of interest to determine whether GO analyses of human embryo regulatory LTR elements would reveal the potential genomic regulatory connectivity between functionally essential fertility genes and genes essential for mammalian offspring survival. To this end, GO annotations records of the regulatory LTR-governed GRM of the reduced fertility phenotype (MP:0001921) were analyzed to identify the significantly enriched records of regulatory LTR elements and genes associated with Mammalian Offspring Survival (MOS) phenotypes (Table 9 ). It has been observed that one of the important features of the regulatory LTR-governed GRM of the reduced fertility phenotype is the significant enrichment of regulatory LTR elements and genes annotated by GO analyses to multiple records of MOS phenotypes (Table 9 ). These observations indicate that abrogated functions of genes essential for mammalian offspring survival (that is, genes knockout of which causes the offspring lethality phenotypes) may represent one of key genetic elements contributing to reduced fertility and/or infertility phenotypes. Based on reported herein observations, it has been concluded that during primate evolution functionally essential fertility genes, genes essential for offspring survival, and genes essential for development and functions of nervous system may have been placed under regulatory control of human embryo regulatory LTR elements. This implied genomic regulatory connectivity may constitute a critical element of evolutionary concordance linking the requirement of balanced and coherent development and functions of nervous system to the survivability of the offspring lineage and evolutionary fitness of primate species. High-confidence down-stream target genes of HERVs constitute a significant majority of candidate regulatory targets of human embryo regulatory LTR elements. Reported herein inferences of phenotypic impacts of HERVs was made based on the analyses of potential regulatory impacts of human embryo regulatory LTR elements on down-stream target genes without taking into account direct experimental evidence of gene expression changes in human cells following targeted genetic manipulations of HERVs. To address this limitation, genes expression of which is significantly altered in human cells subjected to either shRNA-mediated interference or CRISPR-guided epigenetic silencing directed at defined genetic loci of specific HERV families were identified among candidate down-stream regulatory targets of human embryo regulatory LTR elements. Then proportions of these high-confidence down-stream regulatory targets of regulatory LTRs with experimentally documented gene expression changes were calculated (Table 10 ). Results of these analyses demonstrate that selective targeting in human cells of defined regulatory sequences encoded by HERVs caused significant expression changes of sub-sets of genes among candidate down-stream regulatory targets of human embryo regulatory LTRs (Table 10 ; Supplementary Table S2 ). These significant changes of gene expression were documented in multiple independent experiments employing various human cell lines, which were genetically engineered using either shRNA interference or CRISPR-guided epigenetic silencing tools tailored for selective targeting of distinct regulatory elements of HERVs (including regulatory LTRs and long non-coding RNAs, lncRNAs). While identified proportions of genes comprising high-confidence down-stream regulatory targets varied from 14–36% in different experimental settings, cumulative sets of identified herein high-confidence down-stream regulatory targets constitute 67% of candidate regulatory LTR-regulated genes (Table 10 ; Supplementary Table S2 ). Based on these observations, it has been concluded that high-confidence regulatory LTR-regulated genes defined by expression profiling experiments comprise a significant majority of 5444 genes and 709 genes, which represent, respectively, cumulative and common sets of identified herein putative down-stream regulatory targets of LTR7; MLT2A1; and MLT2A2 human embryo regulatory LTR loci. It was of interest to determine whether the inferences of potential global phenotypic impacts of human embryo regulatory LTR elements on human development and pathophysiology could be reproduced by GSEA of high-confidence down-stream regulatory target genes of regulatory LTRs. To address this question, significantly enriched records identified by GSEA of 709 genes and 473 genes during analyses of corresponding genomics and proteomics databases were retrieved and catalogued at the statistical significance threshold of adjusted P value (FDR q value) < 0.05 (Table 11 ; Supplementary Figure S2 ; Supplementary Table S2 ). Side by side comparisons of the results demonstrate that GSEA of 473 high-confidence down-stream regulatory target genes of regulatory LTRs essentially recapitulated the quantitative and qualitative GSEA patterns of 703 genes representing a common set of candidate down-stream regulatory targets of regulatory LTRs (Table 11 ; Supplementary Figure S2 ; Supplementary Table S2 ). Therefore, the inferences of potential global phenotypic impacts of human embryo regulatory LTR elements on human development and pathophysiology that have been made based on GO-guided annotations and GSEA of candidate down-stream target genes are recapitulated by GSEA of gene expression profiling experiments-validated high-confidence down-stream regulatory target genes of HERVs. Table 11 GSEA of phenotypic impacts of common sets of LTR7, MLT2A1, and MLT2A2 down-stream target genes defined by proximity placement analyses (709 genes) and by genetic/epigenetic silencing of regulatory LTR loci (473 genes). Classification category Proximity placement defined genes* Genetic/epigenetic silencing defined genes* Genomics & proteomics databases 709 genes 473 genes DisGeNET database of human diseases 52 72 Orphanet Augmented 2021 697 732 Rare Diseases AutoRIF ARCHS4 Predictions 63 65 Rare Diseases GeneRIF ARCHS4 Predictions 41 43 Rare Diseases GeneRIF Gene Lists 8 13 Rare Diseases AutoRIF Gene Lists 16 25 WikiPathway 2021 Human 5 11 KEGG 2021 Human 2 1 BioPlanet 2019 6 6 GO Cellular Component 2021 17 18 GO Molecular Function 2021 17 13 GO Biological Process 2021 67 55 MGI Mammalian Phenotype Level 4 2021 28 57 PanglaoDB Augmented 2021 66 72 CellMarker Augmented 2021 159 187 Azimuth Cell Types 2021 75 89 GTEx Tissue Expression Up 431 555 GTEx Tissue Expression Down 147 221 Human Gene Atlas 5 5 ARCHS4 Human Tissues 43 52 Allen Brain Atlas up 336 312 Allen Brain Atlas down 165 159 Disease Perturbations from GEO down 55 85 Disease Perturbations from GEO up 22 31 RNAseq Automatic GEO Signatures Human Up 507 821 RNAseq Automatic GEO Signatures Human Down 541 856 Transcription Factor PPIs 1 1 Reactome 2022 13 9 ARCHS4 Kinases Co-expression 61 68 Panther 2016 2 2 NCI-Nature 2016 1 4 HuBMAP ASCTplusB augmented 2022 94 91 ARCHS4 TF Co-expression 204 190 ProteomicsDB 2020 0 0 Legend: * Numbers of significantly enriched records identified by GSEA of 709 genes and 473 genes during analyses of corresponding genomics and proteomics databases to define entries at the statistical significance threshold of adjusted P value (FDR q value) < 0.05. Human embryo regulatory LTRs derived from distinct families of pan-primate endogenous retroviruses and human-specific genomic regulatory sequences (HSRS) share common sets of candidate down-stream target genes. One of important conclusions that could be derived from reported herein observations is that human embryo regulatory LTR elements could have contributed to primate evolution by driving the emergence of human-specific phenotypic traits. To test this hypothesis and gauge potential impacts of regulatory LTRs on emergence of human-specific phenotypic traits, identified and functionally characterized in this study candidate down-stream regulatory targets of human embryo regulatory LTR elements were cross-referenced with previously reported down-stream target genes of 110,339 human-specific genomic regulatory sequences (HSRS), which are fixed in human population and creation of which was attributed to several distinct mechanisms (Glinsky, 2015 –2022; Methods). For each binary category of distinct regulatory loci and corresponding down-stream regulatory targets, common sets of down-stream target genes were identified and evaluated based on two quantitative metrics: a) enrichment of observed numbers of common target genes compared to expected numbers based on the random target distribution model; b) evaluations of relative abundance profiles of common sets of down-stream target genes calculated as the percentage of all down-stream target genes. It has been surmised that identification of significantly enriched common sets of genes independently defined as putative down-stream regulatory targets of HSRS and human embryo regulatory LTRs may reveal common phenotypic traits affected by different classes of genomic regulatory elements. Consistent with the idea of overlapping sets of down-stream targets for different classes of genomic regulatory elements, genes identified as down-stream regulatory targets of MLT2A2 and MLT2A1 loci are significantly enriched among LTR7 target genes, while genes identified as down-stream regulatory targets of LTR7 loci are significantly enriched among MLT2A2 and MLT2A1 target genes (Fig. 7 ; Supplementary Table S4 ). Furthermore, genome-wide connectivity profiles of human embryo regulatory LTRs and putative target genes are significantly recapitulated within genomic regulatory networks presumably governed by 72,349 human-specific mutations of regulatory DNA sequences, including 17,777 fixed human-specific insertions and deletions; 35,074 fixed human-specific neuro-regulatory single nucleotide mutations shared with archaic humans; and 7,236 fixed human-specific regulatory regions devoided of archaic humans’ DNA (Fig. 7 ; Supplementary Table S4 ). In several instances it was possible to dissect common patterns of down-stream regulatory targets’ enrichment of genes linked with different families of human embryo regulatory LTRs vis-a-vis defined sets of human-specific sequences previously identified as components of different GRNs (Fig. 7 ; Table 12 ; Supplementary Table S4 ). These set of analyses included the pairwise comparisons of the relative enrichment profiles of genes comprising down-stream targets of Naïve & Primed hESC functional enhancers and genes linked with 1619 human-specific Naïve & Primed hESC functional enhancers (Barakat et al., 2018 ; Glinsky and Barakat, 2019 ); genes comprising down-stream targets of transposable elements (TE) loci expressed in dorsolateral prefrontal cortex (DLPFC) of the adult human brain and genes linked with 4690 human-specific TE loci expressed in human adult DLPFC (Guffanti et al., 2018 ); genes identified as down-stream targets of a highly-diverse set of 59,732 HSRS and genes associated with 12,262 created de novo HSRS (Glinsky, 2020). Notably, in all instances genes comprising down-stream targets of bona fide human-specific components of genomic regulatory networks appear significantly enriched among genes defined as down-stream targets of different families of human embryo regulatory LTR elements (Fig. 7 ; Table 12 ; Supplementary Table S4 ). Table 12 Enrichment of genes linked to distinct families of Human-Specific Regulatory Sequences (HSRS) among putative down-stream target genes of distinct families of human embryo regulatory LTR elements. Classification category Observed Expected Enrichment/Depletion P value* Genes associated with LTR7 regulatory elements 2957 2957 1.00 NA Genes linked with Naïve & Primed hESC functional enhancers 1703 1822 0.93 0.001763 Genes linked with 1619 human-specific Naïve & Primed hESC functional enhancers 522 203 2.57 1.67E-37 Genes linked with TE loci expressed in human adult DLPFC 2262 2094 1.08 8.085E-07 Genes linked with 4690 human-specific TE loci expressed in human adult DLPFC 1328 661 2.01 4.04E-76 Genes associated with 59,732 HSRS 2615 2080 1.26 7.732E-68 Genes associated with 12,262 created de novo HSRS 1390 685 2.03 3.274E-83 Genes associated with HSRS (except genes linked with de novo HSRS) 1225 1395 0.88 9.634E-06 Genes linked with MLT2A1 & MLT2A2 3971 3971 1.00 NA Genes linked with Naïve & Primed hESC functional enhancers 2121 2447 0.87 1.54E-13 Genes linked with 1619 human-specific Naïve & Primed hESC functional enhancers 655 272 2.40 8.30E-41 Genes linked with TE loci expressed in human adult DLPFC 2059 2811 0.73 1.30E-67 Genes linked with 4690 human-specific TE loci expressed in human adult DLPFC 1903 887 2.15 5.15E-117 Genes associated with 59,732 HSRS 3525 2794 1.26 1.66E-94 Genes associated with 12,262 created de novo HSRS 1910 920 2.08 1.12E-120 Genes associated with HSRS (except genes linked with de novo HSRS) 1615 1874 0.86 5.35E-09 Genes linked with LTR7 & MLT2A1 & MLT2A2 5444 5444 1.00 NA Genes linked with Naïve & Primed hESC functional enhancers 3040 3354 0.91 1.10E-09 Genes linked with 1619 human-specific Naïve & Primed hESC functional enhancers 814 373 2.18 3.26E-43 Genes linked with TE loci expressed in human adult DLPFC 4147 3854 1.08 2.24E-10 Genes linked with 4690 human-specific TE loci expressed in human adult DLPFC 2306 1216 1.90 7.80E-119 Genes associated with 59,732 HSRS 4782 3830 1.25 5.12E-114 Genes associated with 12,262 created de novo HSRS 2361 1261 1.87 5.66E-112 Genes associated with HSRS (except genes linked with de novo HSRS) 2421 2569 0.94 0.0046903 Legend: *, 2-tail p values were estimated using the Fisher's exact test. Identified herein consistent patterns of significant enrichment of common sets of genes comprising down-stream regulatory targets of HSRS and human embryo regulatory LTRs seem related to the 3D topological domains’ architecture of linear chromatin fibers as well as chromatin state signatures. This notion is supported by observations of significant enrichments of genes associated with rapidly evolving in humans topologically associating domains (TADs) among genes comprising down-stream regulatory targets of human embryo regulatory LTRs (Fig. 7 ; Supplementary Table S4 ). In a similar fashion, evaluation of genes associated with dynamic TADs, which are TADs undergoing marked changes of their genomic boundaries during differentiation of human cells, documented their significant enrichment among down-stream target genes of human embryo regulatory LTR elements (Fig. 7 ; Supplementary Table S4 ). It has been observed that enrichment score values of these categories of common down-stream target genes appeared placed consistently among top-scoring significantly-enriched gene sets within panels of down-stream regulatory targets of distinct families of human embryo regulatory LTRs. Genes identified herein as candidate down-stream regulatory targets of distinct families of human embryo regulatory LTR elements appear related to a significant degree to down-stream target genes of DNA sequences defined as genomic regulatory elements based on their chromatin state’s signatures, namely 8,297 fixed human-specific regulatory regions (Marnetto et al., 2014 ) and 7,598 hESC developmental enhancers (Rada-Iglesias et al., 2011). Results of these analyses documented significant enrichment of genes comprising down-stream targets of chromatin state-defined regulatory DNA sequences, revealing genome-wide connectivity components of human embryo regulatory LTR elements and their putative target genes that are recapitulated, in part, within genomic regulatory networks governed by 8,297 fixed human-specific regulatory regions and 7,598 hESC developmental enhancers defined by chromatin state signatures. These findings imply that subsets of genes comprising down-stream targets of regulatory LTRs represent down-stream target genes regulated by hESC developmental enhancers and/or fixed human-specific regulatory regions implicated in control of differentiation phenotypes of human cells. One of biological processes affecting the integrity of genomic DNA sequences during evolution is colonization and functional/structural remodeling of primates’ genomes by mobile genetic elements (MGE), including various families of TE and HERVs, most of which lost their mobility and have been co-opted to perform a variety of genomic regulatory functions. L1, Alu and SVA elements are the only transposable elements that have unequivocally been shown to be currently active in humans, as shown by de novo insertions that are responsible for genetic disorders (Cordaux and Batzer, 2009 ). Inevitable mutational losses of essential regulatory features of transposon insertions dictate the necessity of continuous retrotransposition during evolution. Latest examples of structural/functional exaptation of MGEs likely reflecting relatively recent events of genomic evolution of present day Modern Humans have been mapped based on identification and characterization of fixed insertions of MGEs that were most recently active in human genome (Autio et al., 2021 ). It has been observed that down-stream target genes of 1937 genomic regulatory elements constituting fixed DNA insertions of most recently active MGEs are significantly enriched among down-stream target genes of human embryo regulatory LTRs of distinct HERV families. Overall, a majority of genes (536 of 926 genes; 58%) comprising down-stream regulatory targets of fixed MGE loci most recently active in human genome appears selected from genes identified in this study as putative down-stream targets of distinct families of human embryo regulatory LTR elements. These findings indicate that GRNs governed by different families of human embryo regulatory LTRs and insertions of MGEs that were most recently active in human genome share a common set of down-stream regulatory targets comprising 536 genes. Comparative analyses of genome-wide connectivity maps of genomic coordinates of human embryo regulatory LTRs, human-specific regulatory elements, and their candidate down-stream target genes imply concordant patterns of their inferred phenotypic impacts and suggest a genomic regulatory mechanism contributing to emergence of human-specific traits during primate evolution. Considering evolutionary timelines of introduction and expansion of distinct HERVs’ families within the primate lineage vis-à-vis human-specific genomic regulatory sequences, these observations are consistent with the idea that human embryo regulatory LTR elements have made important contribution to creation of genomic fingerprints guiding the evolution of regulatory DNA features facilitating the emergence of human-specific phenotypic traits. Down-stream target genes of human embryo regulatory LTR elements are significantly enriched among genes manifesting species-specific expression mapping bias in Human-Chimpanzee hybrids of induced pluripotent stem cells and brain organoids. Dissection of genomic and molecular anatomy of regulatory mechanisms defining development and manifestations of human-specific phenotypic traits remains a highly significant challenge despite recent significant progress in explorations of species-specific developmental features using primate-derived induced pluripotent stem cells (iPSC) and brain organoids, which were particularly useful for identification of species-specific differences of gene expression (Camp et al., 2015 ; Gallego Romero. et al., 2015; Glinsky, 2020; Kanton, et al., 2019 ; Kronenberg et al., 2017; Lancaster et al., 2013 ; Mora-Bermudez et al., 2016 ; Muchnik et al., 2019 ; Otani et al., 2016 ; Pasca et al., 2015 ; Pasca, 2018 ; Pollen et al., 2019 ; Prescott et al., 2015 ; Qian et al., 2019 ; Sloan et al., 2017 ). Most recently, human-chimpanzee hybrid iPSC (hyiPSC) has been developed and utilized to study species-specific gene expression differences in hyiPSC and hyiPS cells differentiated into region-specific brain organoids termed hybrid cortical spheroids, hyCS (Agoglia et al., 2021 ; Gokhman et al., 2021). Stable tetraploid hybrids of human and chimpanzee cells enabled to control for non-genetic confounding factors such as different timing of developmental dynamics and differentiation processes, micro-environmental fluctuations, differences in cellular compositions, experimental batch effects, which are limited the utility of other experimental approaches aiming to disentangle species-specific divergence of gene expression. Significantly, cross-species tetraploid hybrid cells allowed to unambiguously identify species-specific gene expression differences caused specifically by genomic cis -regulatory divergence between species, which include promoters and enhancers affecting only down-stream target genes residing on the same species-specific DNA molecules (Agoglia et al., 2021 ; Gokhman et al., 2021). Therefore, it was logical to test the hypothesis that human embryo regulatory LTR elements may contribute to emergence of human-specific phenotypic traits by evaluating their potential regulatory effects on genes manifesting species-specific expression differences in human-chimpanzee hybrid cells. To this end, individual binary categories of 5444 down-stream regulatory targets of human embryo regulatory LTRs with three different sets of genes manifesting species-specific gene expression biases in human-chimpanzee hybrid cells were selected, then common (overlapping) sets of genes were identified and significance of enrichment was estimated of observed numbers of common genes compared to corresponding expected numbers based on the random distribution model (Fig. 8 ; Table 13 ). Notably, consistent patterns of significant enrichment of human embryo regulatory LTR target genes among three different categories of genes manifesting species-specific biases of gene expression in human-chimpanzee hybrid cells (Fig. 8 ; Table 13 ). There are 95 genes putatively regulated by human embryo LTRs among 374 genes with species-specific expression mapping bias in hyiPSC of human-chimpanzee hybrids (Table 13 ; Fig. 8 ), which constitute 2.2-fold enrichment compared to expected numbers of common genes (p = 9.58E-14). Similarly, there was observed the 1.9-fold (p = 5.06E-07) enrichment of human embryo LTRs’ down-stream target genes among genes exhibiting species-specific expression mapping bias in human-chimpanzee hybrids of Cortical Spheroid (Brain Organoids), hyCS (Table 13 ; Fig. 8 ). Finally, a total of 56 down-stream target genes of human embryo regulatory LTRs constitute 30.6% of genes manifesting species allele-specific expression profiles in human-chimpanzee hybrid cells (Table 13 ), which represents a 2.7-fold enrichment (p = 4.36E-12) compared to expected by chance alone numbers. Examination of expression profiles of genes with species-specific expression bias in human-chimpanzee hybrids revealed the apparently discordant patterns of their expression in both hyiPSC (Fig. 8 ; panels A and B) and hyCS brain organoids’ settings (Fig. 8 ; panels C and D) supporting the idea that species-specific differences in gene expression may contribute to phenotypic divergence of Humans and Chimpanzee in regulation and manifestation of stemness phenotype and cortical differentiation. Table 13 Enrichment of human embryo regulatory LTR down-stream target genes among genes with species-specific expression mapping bias in hybrids of Human and Chimpanzee cells. Classification category Number of genes Percent Genes with expression mapping bias in hybrid induced Pluripotent Stem (iPS) Cells of Human and Chimpanzee 374 100 Genes regulated by human embryo regulatory LTR elements 95 25.40 Expected by chance 44 Enrichment 2.2 P value* 9.58E-14 Genes with expression mapping bias in hybrid Cortical Spheroids (Brain Organoids) of Human and Chimpanzee 291 100 Genes regulated by human embryo regulatory LTR elements 63 21.65 Expected by chance 34 Enrichment 1.9 P value* 5.06E-07 Genes manifesting allele-specific expression in hybrids of Human and Chimpanzee cells 183 100 Genes regulated by human embryo regulatory LTR elements 56 30.60 Expected by chance 21 Enrichment 2.7 P value* 4.36E-12 Legend: * p values were estimated using the hypergeometric distribution test. Overall, these analyses revealed that a significant fraction of genes with species-specific expression bias in human-chimpanzee hybrid cells represents down-stream target genes of human embryo regulatory LTRs (177 of 676 genes; 26%), indicating that regulatory effects of regulatory LTR elements may have affected species segregation during the evolution of Great Apes by contributing to phenotypic divergence of Modern Humans. It was of interest to determine what morphological structures and phenotypic traits were preferred targets of regulatory LTR elements during Modern Human divergence during primate evolution. This question was addressed by performing GSEA of identified herein 177 genes ( Supplementary Table S5 ) across multiple genomic database (Methods). GSEA of 177 genes employing the ARCHS4 Human Tissues database identified 23 records with significant enrichment of human embryo regulatory LTRs’ target genes (adjusted p value < 0.05), among which the Dentate Granule Cells were the top-scoring term (adjusted p value = 3.62E-09). A total of 53 genes linked with 223 human embryo regulatory LTRs were identified among genes constituting genetic markers of the Dentate Granule Cells (Fig. 8 E). In addition to the Dentate Granule Cells, prefrontal cortex (52 genes), cingulate gyrus (51 genes), cerebellum (48 genes), and cerebral cortex (48 genes) were identified among the top 5 scoring records (Fig. 8 E). To gain insights into the broader developmental context of biological functions of genes associated with 223 human embryo regulatory LTR elements, the DAG analyses were carried out employing the GREAT algorithm (Methods). Notably, GO annotations of the Mouse Single KO database generated local DAG which identified abnormal synaptic transmission (MP:0003635; linked with 66 LTRs and 17 genes; Binominal FDR Q-value = 1.49E-15); abnormal learning/memory/conditioning (MP:0002063) and abnormal cognition (MP:0014114; both terms linked with 49 LTRs and 14 genes; Binominal FDR Q-value = 7.98E-08); abnormal excitatory postsynaptic currents (MP:0002910; 19 LTRs linked with 6 genes; Binominal FDR Q-value = 2.08E-06); abnormal synaptic vesicle morphology (MP:0004769; 11 LTRs linked with 5 genes; Binominal FDR Q-value = 5.37E-06); and abnormal CNS synaptic transmission (MP:0002206; linked with 65 LTRs and 16 genes; Binominal FDR Q-value = 3.07E-17) among 250 significantly enriched functional and developmental anomalies caused by knockout of individual genes associated with 223 human embryo regulatory LTRs (Fig. 8 ; panels F and G). Taken together, reported observations indicate that genes manifesting species-specific expression bias in human-chimpanzee hybrid cells and linked by putative regulatory associations with human embryo regulatory LTR elements are implicated in higher cognitive functions of learning and memory; biological processes of CNS synaptic transmission, excitatory postsynaptic currents, and neuroactive ligands-receptors interactions; gene expression markers of numerous morphological components of human brain, including the Dentate Granule Cells, prefrontal cortex, cingulate gyrus, cerebellum, cerebral cortex, superior frontal gyrus, dorsal striatum, motor and sensory neurons, midbrain, oligodendrocytes, and neuronal epithelium (Table 14 ; Fig. 8 ; Supplementary Table S5 ). Additionally, this family of genes represents gene expression markers of adult human somatic tissues (small intestine; testis; alpha cells; retina) and morphological elements of human embryogenesis namely fetal brain cortex, oocyte, and zygote (Table 14 ; Supplementary Table S5 ). Table 14 Summary of significantly enriched phenotypic traits and morphological elements associated with 177 down-stream target genes of human embryo regulatory LTRs manifesting species-specific expression bias in human-chimpanzee hybrids. KEGG 2021 Human Database Human embryo regulatory LTR target genes Adjusted P-value Genes manifesting species-specific expression bias Adjusted P-value Percent, LTR regulated Relative enrichment* Neuroactive ligand-receptor interactions 14 4.50E-04 24 0.116711 58 2.2 HuBMAP ASCTplusB augmented 2022 Database Human embryo regulatory LTR target genes Adjusted P-value Genes manifesting species-specific expression bias Adjusted P-value Percent, LTR regulated Relative enrichment* Inh L1 SST DEFB108B - Brain 8 8.29E-04 8 0.387543 100 3.8 Inh L1-2 VIP PTGER3 - Brain 8 8.29E-04 9 0.207386 89 3.4 Inh L1-3 SST FAM20A - Brain 7 0.004064 11 0.129625 64 2.4 Inh L1-2 PVALB CDK20 - Brain 7 0.004064 13 0.028948 54 2.1 Inh L1-2 VIP SCML4 - Brain 6 0.02756 10 0.129625 60 2.3 ARCHS4 Human Tissues Database Human embryo regulatory LTR target genes Adjusted P-value Genes manifesting species-specific expression bias Adjusted P-value Percent, LTR regulated Relative enrichment* DENTATE GRANULE CELLS 53 3.62E-09 119 6.25E-05 45 1.7 PREFRONTAL CORTEX 52 6.03E-09 118 7.90E-05 44 1.7 CINGULATE GYRUS 51 1.30E-08 109 0.001818 47 1.8 CEREBELLUM 48 1.40E-07 98 0.062407 49 1.9 CEREBRAL CORTEX 48 1.40E-07 116 1.74E-04 41 1.6 SPINAL CORD 48 1.40E-07 115 2.04E-04 42 1.6 SPINAL CORD (BULK) 48 1.40E-07 115 2.04E-04 42 1.6 SUPERIOR FRONTAL GYRUS 48 1.40E-07 136 5.62E-09 35 1.4 BRAIN (BULK) 47 3.62E-07 125 3.29E-06 38 1.4 DORSAL STRIATUM 45 2.51E-06 112 7.31E-04 40 1.5 MOTOR NEURON 41 9.55E-05 111 9.16E-04 37 1.4 MIDBRAIN 38 0.001047 97 0.076015 39 1.5 SMALL INTESTINE (BULK TISSUE) 37 0.002074 76 0.999994 49 1.9 FETAL BRAIN 36 0.003502 109 0.001818 33 1.3 OLIGODENDROCYTE 36 0.003502 100 0.039555 36 1.4 TESTIS (BULK TISSUE) 36 0.003502 111 9.16E-04 32 1.2 ALPHA CELLS 35 0.005636 101 0.032484 35 1.3 FETAL BRAIN CORTEX 35 0.005636 96 0.092408 36 1.4 RETINA 35 0.005636 99 0.05124 35 1.4 SENSORY NEURON 35 0.005636 92 0.229934 38 1.5 NEURONAL EPITHELIUM 33 0.018769 79 0.999994 42 1.6 OOCYTE 33 0.018769 100 0.039555 33 1.3 HUMAN ZYGOTE 32 0.032657 91 0.268966 35 1.4 SPERM 25 0.514091 102 0.024863 25 0.9 Legend: *Relative enrichment values were estimated by dividing observed values by expected values in corresponding category based on 26% representation of all regulatory LTR down-stream target genes among all genes manifesting species-specific expression bias in human-chimpanzee hybrids. The hypothesis that down-stream target genes of human embryo regulatory LTRs may affect human-specific gene expression networks operating in adult human brain was further validated by observations that human embryo regulatory LTRs’ target genes are significantly enriched among genes expression of which demarcates human-specific features of spatial gene expression profiles delineated by comparative analyses of eight brain regions of humans, chimpanzees, gorillas, a gibbon, and macaques (Xu et al., 2018 ). Overall, a significant fraction of genes comprising down-stream target of human embryo regulatory LTRs represents genes implicated in differential transcriptomic patterns of eight regions of primates’ brains (1968 of 5444 genes; 36.2%; p = 1.03604E-20; hypergeometric distribution test). It has been observed that human embryo regulatory LTRs’ target genes constitute 35.2% of genes manifesting human-specific expression differences in examined eight brain regions in contrast to 25.4% genes exhibiting chimpanzee-specific expression differences (p = 0.026; 2-tail Fisher’s exact test). Furthermore, detailed analyses of expression profiles of genes assigned to 26 distinct gene expression modules of primates’ brain identified 3 modules (modules 22; 16; and 15; Fig. 8 H) that manifest most significant relative enrichment of human embryo regulatory LTR’s target genes in contrast to 7 modules (modules 4; 7–11; 18) exhibiting most significant relative depletion (Fig. 8 H). Notably, two of three modules with most significant relative enrichment (human-specific module 22 and module 16) of human embryo regulatory LTRs’ down-stream target genes are known to contain the disproportionally high numbers of genes with hippocampus-specific expression (Xu et al., 2018 ). These findings indicate that hippocampus-specific genes may constitute one of preferred regulatory targets of human embryo regulatory LTR elements. Collectively, reported herein observations support the hypothesis that genes comprising putative down-stream regulatory targets of human embryo regulatory LTRs contributed to phenotypic divergence of Modern Humans during the primate evolution and may have impacted the emergence of uniquely humans’ phenotypic traits. Identification of hippocampus and more specifically Dentate Granule Cells among significant morphological targets of preference of regulatory effects of human embryo regulatory LTR elements may assist in precise experimental definition of important morphological structures of human brain having evolutionary shaped critical impacts on human-specific features of higher cognitive functions. Discussion Transitions of exogenous retroviral infections to the state of endogenous retroviruses have persisted within the primate lineage throughout the past 30–40 million years. Concurrently with transitions of exogenous retroviral infections to the state of endogenous retroviruses (ERVs), sophisticated and highly complex molecular mechanisms are co-evolved in mammalian cells to identify and control endogenous retroviral sequences (Dopkins et al., 2022 ). One of the outcomes of these processes was a large-scale colonization of human genome by HERVs, which expanded during evolution to represent ~ 8% of DNA sequences in human cells. Colonization of human genome by HERVs is likely to represent the end result of long-term germ-line reinfections during primate evolution, rather than retrotransposition in cis or complementation in trans following a single infection cycle (Belshaw et al. 2004 ). Resulting large-scale expansion of highly homologous sequences derived from individual retroviruses and high diversity of numerous retroviral families created formidable scientific challenges of both analytical and conceptual dimensions. In recent years, several bioinformatics pipelines have been developed and implemented which are dedicated to highly accurate identification and quantification at the level of individual genetic loci resolution of DNA and RNA sequences originated from endogenous retroviruses in various experimental settings, including DNA and RNA extracted from human tissues and cells (Guffanti et al., 2018 ), ChIP-seq and HiC sequencing outputs (Lerat, 2022 ), as well as from single-cell RNA sequencing experiments (Berrens et al., 2022 ; He et al., 2021 ). These advances facilitated significant progress in our understanding of biological roles of DNA sequences derived from individual families of HERVs and revealed novel epigenetic, transcriptional, and posttranscriptional mechanisms of their locus-specific regulation. However, advances in development and implementation of analytical and conceptual approaches aiming to dissect the potential cross-talks of distinct families of HERVs that colonized human genomes during primate evolution and their contribution to emergence of human-specific phenotypic traits remain limited. Present analyses were focused on human embryo regulatory LTR loci defined herein as LTR loci with experimentally documented genomic regulatory functions affecting phenotypes deemed essential for human embryonic development, in particular, preimplantation stages of human embryogenesis. To this end, patterns of evolutionary origins and global effects of 8839 human embryo regulatory LTR elements derived from distinct families of pan-primate endogenous retroviruses on genome-wide transcriptional regulatory networks were evaluated with emphasis on the assessment of their potential impacts on developmental processes and pathological conditions of Modern Humans. Phenotypic patterns of regulatory effects on pathophysiology of Modern Humans were validated by differential GSEA of high confidence down-stream target genes expression of which is altered following genetic and/or epigenetic targeting of human embryo regulatory LTR elements. Collectively, present analyses demonstrate a global scale of potential phenotypic impacts of human embryo regulatory LTR elements on pathophysiology of Modern Humans. Sequential patterns of evolutionary origins and quantitative expansion during primate evolution of distinct families of human embryo regulatory LTR elements. Overall, analyzed in this contribution three distinct LTR families, namely MLT2A1 (2416 loci), MLT2A2 (3069 loci), and LTR7 (3354 loci), appear to have similar evolutionary histories defined by evolutionary patterns of expansion, conservation, and divergence during primate evolution. However, they manifest clearly discernable distinct timelines of evolutionary origins, which were defined as the estimated timelines when retroviruses infect primates’ germlines and transition from the state of exogenous infection agents to endogenous retroviral sequences integrated into host genomes of primate lineage. Present analyses indicate that both MLT2A1/HERVL and MLT2A2/HERVL retroviruses have infected primates’ germlines before the segregation of Old World Monkeys (OWM) and New World Monkeys (NWM) lineages. This conclusion is based on the evidence that the consistent earliest presence of highly-conserved pan-primate MLT2A1 and MLT2A2 loci has been mapped to genomes of both NWM and OWM. In contrast, the consistent earliest presence in primates’ genomes of LTR7 loci could be mapped to genomes of OWM but not to genomes of NWM. These observations suggest that LTR7/HERVH retroviruses have entered germlines of the primate lineage after the separation of the OWM and NWM lineages. Pan-primate endogenous MLT2A1/HERVL, MLT2A2/HERVL, and LTR7/HERVH retroviruses appear to achieve a significant expansion in genomes of Great Apes, which is ranging from ~ 2-fold expansion for MLT2A2 loci to ~ 5-fold expansion for LTR7 loci. Interestingly, the consistent earliest presence of highly-conserved LTR5_Hs loci has been mapped to the Gibbon’s genome (Glinsky, 2022 ), consistent with the primate evolution model stating that LTR5_Hs/HERVK retroviruses successfully colonized germlines of the primate lineage after the segregation of Gibbons’ species. Subsequently, MLT2A1/HERVL; MLT2A2/HERVL; LTR7/HERVH; and LTR5_Hs/HERVK retroviruses appear to undergo a marked expansion in genomes of Great Apes. Interestingly, the consistent patterns of increasing cognitive and behavioral complexities during primate evolution have been observed on the OWM evolutionary branch, which continually evolved in Africa, but not on geographically segregated to South America the NWM branch. Consistent with the previously reported findings for highly-conserved LTR7 and LTR5_Hs loci (Glinsky, 2022 ), patterns of quantitative expansion in different primate species of human embryo regulatory LTR elements appear to replicate the consensus evolutionary sequence of increasing cognitive and behavioral complexities during primate evolution. The apparent concordance of increasing cognitive and behavioral complexities during the evolution of primate species with clearly discernable evidence of sequential introduction and expansion of distinct families of regulatory LTR elements in genomes of the primate lineage suggest that human embryo regulatory LTRs originated from distinct retroviruses may have contributed to the guidance of primate evolution toward the emergence of human-specific phenotypic traits. This hypothesis seems particularly appealing when considered together with the evidence of global impacts on exceedingly broad spectrum of developmental phenotypes of genes constituting down-stream regulatory targets of human embryo regulatory LTR elements. Emergence during primate evolution of the global Genomic Regulatory Dominion (GRD) governed by human embryo regulatory LTR elements. Observations reported in this contribution provide the foundation for the hypothesis that evolution of human embryo regulatory LTR elements created the global retroviral Genomic Regulatory Dominion (GRD) consisting of 26 gene ontology enrichment-defined genome-wide Genomic Regulatory Networks (GRNs). Decoded in this study the hierarchical super-structure of regulatory LTR-associated GRD and GRNs represents an intrinsically integrated developmental compendium of 69,573 Genomic Regulatory Modules (GRMs) each of which is congregated along specific experimentally-defined genotype-phenotypic trait associations. On the levels of individual genomic loci, described in this contribution retroviral GRD integrates 8839 highly conserved LTR elements linked to 5444 down-stream target genes. These scattered across human genome individual genomic loci were forged by evolution into a functionally-consonant constellation of 26 genome-wide multimodular GRNs, each of which harbors hundreds significantly enriched single gene ontology-specific traits. Genomic coordinates of GRNs are scattered across chromosomes to occupy from 5.5–15.09% of the human genome. Each GRN acquired during evolution similar regulatory architecture: it harbors from 529 to 1486 human embryo regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences that are quantitatively balanced corresponding to their genome-wide abundance. GRNs integrate activities of functionally and structurally highly diverse panels of down-stream regulatory targets comprising from 199 to 805 genes. These include genes encoding transcription factors, chromatin-state remodelers, signal sensing and signal transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. Compositions of individual GRNs consist of several hundred to thousands smaller gene ontology enrichment analysis-defined genomic regulatory modules (GRMs), each of which combines from a dozen to hundreds regulatory LTRs and down-stream target genes. A total of 69,573 statistically significant regulatory LTR-linked GRMs (Binominal FDR q-value < 0.001) have been identified in this contribution, including 27,601 GRMs that have been validated by the single ontology-specific directed acyclic graph (DAG) analyses across 6 gene ontology annotations databases. Insights into putative concurrent mechanisms of biological functions and global organism-level impacts of human embryo regulatory LTR elements. To corroborate and extend findings documented based on GO-guided analytical approaches, comprehensive series of Gene Set Enrichment Analyses (GSEA) of regulatory LTRs down-stream target genes were carried out employing more than 70 genomics and proteomics databases, including a large panel of databases developed from single-cell resolution studies of healthy and diseased human’s organs and tissues. It has been observed that distinct GRNs and GRMs appear to operate on individuals’ life-span timescale along specific phenotypic venues selected from an exceedingly broad spectrum of down-stream gene ontology-defined and signaling pathways-guided frameworks. GRNs and GRMs governed by human embryo regulatory LTRs seem functionally designed to exert profound effects on patterns of transcription, protein-protein interactions, developmental phenotypes, physiological traits, and pathological conditions of Modern Humans. GO analyses of Mouse phenotype databases and GSEA of the MGI Mammalian Phenotype Level 4 2021 database revealed that down-stream regulatory targets of human embryo regulatory LTRs are enriched for genes that are essential for faithful execution of developmental processes and functionally-adequate states of all major tissues, organs, and organ systems. These conclusions are supported by observations documenting profound developmental defects in corresponding single gene KO mouse models. Furthermore, genes comprising candidate down-stream regulatory targets of human embryo regulatory LTRs are engaged in PPI networks that have been implicated in pathogenesis of human common and rare disorders (3298 and 2071 significantly enriched records, respectively), in part, by impacting PPIs that are significantly enriched in 1783 multiprotein complexes recorded in the NURSA Human Endogenous Complexome database and 6584 records of virus-host PPIs documented in Virus-Host PPI P-HIPSTer 2020 database. Collectively, results of multiple analytical experiments reported in this contribution consistently demonstrate that genes comprising candidate down-stream regulatory targets of human embryo regulatory LTR elements affect development and functions of essentially all major tissues, organs, and organ systems, including central and peripheral nervous systems, cardio-vascular (circulatory) and lymphatic systems, gastrointestinal (digestive) and urinary (excretory) systems, musculoskeletal and respiratory systems, endocrine and immune systems, as well as integumentary and reproductive systems. In-depth follow-up analyses of single gene knockout models of defined regulatory LTR down-stream targets revealed a multitude of profound developmental defects and numerous examples of organogenesis failures, indicating that activities of human embryo regulatory LTR down-stream target genes are essential for development and functions of major organs and organ systems. Analytical inference of a cell-type-level preferred morphological targets of regulatory effects of human embryo regulatory LTR elements. Applications of GO annotations and GSEA approaches revealed exceedingly broad impacts of genes comprising putative regulatory targets of human embryo regulatory LTRs on the levels of tissues, organs, and organ systems of the human body. Similarly, GSEA of single-cell genomic databases (for example, PanglaoDB Augmented 2021 database; CellMarker Augmented 2021 database; Azimuth Cell Types 2021 database) documented a very large number of distinct cell types among significantly enriched records, including a multitude of specific types of neurons, radial glia and Bergmann glia cells, alpha and beta cells, oligodendrocytes, astrocytes, multiple types of stem cells and precursor cells, and anterior pituitary gland cells among others. However, targets of preference interrogations among 177 LTR target genes manifesting species-specific biases in human-chimpanzee hybrids employing the ARCHS4 Human Tissues database (Table 14 ) enabled identification of some specific cell types that appear to represent the preferred regulatory targets of human embryo regulatory LTR elements, specifically Dentate Granule Cells and neuronal epithelium. Identification of Dentate Granule Cells is of particular interest because Dentate Granule Cells have been implicated in the parallel emergence of both dynamic and stable memory engrams in the hippocampus (Hainmueller and Bartos, 2018 ) and the contribution of adult-born Dentate Granule Cells of Dentate Gyrus to the downstream firing dynamics of neocortical information flow to the hippocampus (McHugh et al., 2022 ). These latest findings are highly consistent with the critical role of the adult hippocampal neurogenesis, which occurs precisely only in the Dentate Gyrus, in higher cognitive functions, especially learning, memory, and emotions (Kempermann et al., 2015 ; 2023 ; Kuhn et al., 2018 ). These cell types remain consistently among top-scoring significantly enriched records throughout the layered multistage process of GSEA-guided interrogations of different sets of regulatory LTR down-stream target genes, including 5444 genes comprising the cumulative set of regulatory LTR regulatory targets ( Supplementary Table S1 ); 709 genes representing the common set of regulatory LTR regulatory targets ( Supplementary Table S2 ); 473 genes comprising high-confidence down-stream regulatory targets of regulatory LTRs ( Supplementary Table S2 ); and 177 regulatory LTR down-stream target genes manifesting species-specific expression bias in human-chimpanzee hybrid cells (Table 14 ; Supplementary Table S5 ). For example, Radial Glia Cells (78 regulatory LTR target genes; adjusted p value = 8.80E-18; PanglaoDB Augmented 2021 database); Neuronal epithelium (937 regulatory LTR target genes; adjusted p value = 2.24E-47; ARCHS4 Human Tissues database); and Dentate Granule Cells (863 regulatory LTR target genes; adjusted p value = 2.45E-28; ARCHS4 Human Tissues database), have been identified among top-scoring significantly enriched terms during GSEA of 5444 genes comprising a cumulative set of putative down-stream regulatory targets of human embryo regulatory LTR elements ( Supplementary Table S1 ). It has been observed that overall human embryo regulatory LTRs’ down-stream target genes constitute large fractions of all gene expression markers assigned in the respective genomic databases to corresponding cell types: they constitute 68% of genes for Radial Glia Cells; 40% of genes for Neuronal epithelium; and 37% of genes for Dentate Granule Cells. Collectively, these observations strongly argue that specific cell types, namely neuronal epithelium, Radial Glia Cells, and Dentate Granule Cells, may represent the cellular targets of preference of genomic regulatory networks governed by human embryo regulatory LTR elements and suggest that embryonic and adult neurogenesis could have been among the principal biological processes contributing to striking phenotypic divergence of Modern Humans during the evolution of Great Apes. In agreement with this notion, GSEA of human embryo regulatory LTRs down-stream target genes comprising of sets of 709 genes; 473 genes; or 177 genes employing the GTEx Human Tissue Expression database of up-regulated genes consistently demonstrated that all top 100 significantly enriched records constitute the brain tissues of male or female individuals ( Supplementary Tables S2; S5 ). In contrast, there were no significantly enriched records of human brain tissue samples identified by GSEA of human embryo regulatory LTRs down-stream target genes employing the GTEx Human Tissue Expression database of down-regulated genes. Insights into molecular mechanisms of global genome-wide and proteome-wide actions of human embryo regulatory LTR elements. Human embryo regulatory LTR elements undergo programmed epigenetic activation and silencing during human embryonic development starting from the earliest stages of preimplantation embryogenesis (Introduction). Mechanistically, activation of human endogenous regulatory LTR elements may function as epigenetic switch facilitating targeted reversible transitions of the 3D regulatory chromatin architecture within topologically associating domains from dominant single enhancer modes of active regulatory LTRs to the state of agile host super-enhancer loci of the regulatory chromatin microenvironment. Epigenetic silencing of regulatory LTR loci could create distribution patterns of silenced chromatin domains of H3K9me3 repressive histone marks. Conversely, active regulatory LTR elements may contribute to creation of transcriptionally active chromatin state by impacting deposition of H3K4me3 histone marks and expansion of broad H3K4me3 domains. Deposition of H3K9me3 histone marks is significantly enriched on LTR retrotransposons throughout human preimplantation embryogenesis (Yu et al., 2022 ; Xu et al., 2022 ), indicating that human embryo regulatory LTR elements facilitate genome-wide heterochromatin patterning. Genes that are marked by or positioned near H3K9me3 peaks throughout the human development are related to cell fate specification, which is consistent with the idea that one of important functions of H3K9me3 chromatin modifications is to block the premature expression of genes essential for regulation of developmental stages (Yu et al., 2022 ; Xu et al., 2022 ). Embryonic stage-specific establishment of H3K9me3 histone modifications silences targeted LTR loci of different families during human embryogenesis and 8-cell-specific H3K9me3 domains demarcate enhancer-like genomic regions marked by hallmarks of enhancers H3K4me1 and H3K27Ac at the later developmental stages and in adult human somatic tissues (Xu et al., 2022 ). In human blastocysts, H3K4me3/H3K9me3 and H3K4me3/H3K27me3 bivalent chromatin domains have been identified that contributed to heterochromatin patterning primed for lineage segregation and likely involved in lineage differentiation (Xu et al., 2022 ). It has been reported that H3K4me3/H3K9me3 bivalent histone methylation signature denotes poised differentiation master regulatory genes in trophoblast stem cells, extraembryonic endoderm stem cells, and preadipocytes (Matsumura et al., 2015 ; Rugg-Gunn et al., 2010 ). Genes occupied by H3K4me3/H3K9me3 bivalent chromatin domains were implicated in various metabolic processes and are not significantly expressed during early embryonic development (Xu et al., 2022 ), while nearly 30% of LTRs were occupied by H3K4me3/H3K9me3 bivalent chromatin modifications indicating that some human embryo regulatory LTR loci may be poised for activation at the later developmental stages. These observations suggest that human embryo regulatory LTR elements may contribute to creation of bivalent H3K4me3/H3K9me3 chromatin domains, thus affecting the genome-wide patterns of both H3K9me3 repressive chromatin marks and H3K4me3 active chromatin modifications. I Interestingly, CRISPRi-mediated silencing of the LTR7Y/B loci in hESC documented significant effects on expression of eight chromatin writers (methyltransferases) and erasers (demethylases) regulating methylation status of the H3K4 histone, suggesting a mechanism of selective activation of the KMT2D methyltransferase gene expression by LTR7Y/B (Glinsky, data not shown and manuscript in preparation). These findings imply that human embryo regulatory LTRs, in particular, LTR7Y/B loci, may facilitate the establishment of the broad H3K4me3 chromatin domains. Consistent with this hypothesis, the KMT2D methyltransferase plays the principal role in establishing the broad H3K4me3 chromatin domains and constitutes an intrinsic components of enhancers-forming multimolecular liquid-liquid phase-separated condensates, which are particularly prominent at super-enhancers (reviewed in Kent et al., 2023 ). Consequently, activation/repression cycles of regulatory LTR actions may function as an epigenetic state transitions' shuttle between on/off epigenetic control of super-enhancer activities at the selected topological domains and nanodomains of the interphase chromatin. Aberrations of these epigenetic reprogramming patterns may contribute to developmental defects and pathogenesis of human disorders. Observations reported in this contribution may have important implications for our understanding of the molecular pathophysiology of human diseases linked with malfunctions of endogenous multimolecular complexes tagged by PPI networks of protein products encoded by regulatory LTR-target genes. Present analyses suggest that one of widely shared mechanisms underlying development of a broad spectrum of human pathologies of common, rare, or orphan classifications is associated with altered engagements of protein products of disease-causing genes within PPI networks of proteins encoded by down-stream target genes of regulatory LTR elements. Multiple malfunctions’ focal points of these human pathology-driving PPI networks may operate by impacting protein-protein interactions that are significantly enriched in identified herein 1783 multiprotein complexes recorded in the NURSA Human Endogenous Complexome database and 6594 records of virus-host protein-protein interactions reported in the Virus-Host PPI P-HIPSTer 2020 database. Insights into mechanisms of potential impacts of human embryo regulatory LTR elements during primate evolution. This contribution highlights potential multidimensional impacts of retroviral GRD on emergence of human-specific traits during primate evolution. Genome-wide candidate regulatory targets and high-confidence down-stream target genes of human embryo regulatory LTR elements identified and functionally characterized in this study were cross-referenced with putative down-stream target genes of more than 100 thousands human-specific genomic regulatory elements. These analyses revealed consistent patterns of significant enrichment of common sets of genes comprising down-stream regulatory targets of HSRS and human embryo regulatory LTRs that are related to the 3D topological domains’ architecture of linear chromatin fibers as well as regulatory chromatin state signatures. Overall, identification of significantly enriched sets of intersecting genes independently defined as putative down-stream regulatory targets of different families of HSRS and human embryo regulatory LTR elements depicts common phenotypic traits potentially affected by evolutionary and structurally distinct classes of genomic regulatory elements. Future follow-up studies would be required to dissect genetic and molecular mechanisms of connectivity, coopetition, and discordancy effects on down-stream target genes of human embryo regulatory LTRs of distinct retroviral families and human-specific genomic regulatory loci. Collectively, observations reported in this contribution indicate that two critically important classes of genetic loci appear co-segregated within identified herein human embryo regulatory LTR-governed genomic regulatory units of various architecture: a). Genes essential for the integrity of nervous system development and functions; b). Genes essential for mammalian offspring survival. It seems likely that this striking co-segregation within genomic regulatory units of the GRD governed by regulatory LTRs may reflect co-regulation of these two classes of genes by common sets of regulatory LTR elements. Since knockout of many genes within these regulatory LTR-governed GRNs causes offspring lethality, it is logical to expect that their sustained activity would be essential for offspring survival. Sustained activity of MOS genes within regulatory LTR-governed GRNs could be achieved either by relieving the repressor actions of regulatory LTRs or maintaining the activator effects of regulatory LTRs. Alignments of this genomic regulatory architecture of human embryo regulatory LTR-governed GRNs with control of genes essential for development and functions of nervous system may have contributed to acceleration of central nervous system’s dramatic changes during primate evolution. These findings suggest that during primate evolution functionally essential fertility genes (that is, genes knockout of which causes reduced fertility and/or infertility phenotypes) and genes essential for development and functions of nervous system were placed under regulatory control of human embryo regulatory LTR elements. It follows that biologically meaningful repressor effects on both classes of genes during ontogenesis would cause defects in development and functions of nervous system as well as would result in reduced fertility and/or infertility phenotypes, thus directly affecting the survivability of the offspring lineage and evolutionary fitness of species. Furthermore, present observations indicate that altered functions of genes essential for mammalian offspring survival (that is, genes knockout of which causes the offspring lethality phenotypes) may represent one of critically important genetic elements contributing to reduced fertility and/or infertility phenotypes. Reported herein observations suggest that during primate evolution functionally essential fertility genes, genes essential for offspring survival, and genes essential for development and functions of nervous system may have been placed under regulatory control of human embryo regulatory LTR elements. This implied genomic regulatory connectivity may constitute a key element of evolutionary concordance linking the requirement of balanced and coherent development and functions of nervous system to the survivability of the offspring lineage and evolutionary fitness of primate species. Conclusion: Human embryo regulatory LTR elements function as a pan-primate epigenetic architect contributing to evolution, development, physiological traits, and pathological phenotypes of Modern Humans. During millions years of primate evolution, two distinct families of pan-primate endogenous retroviruses, namely HERVL and HERVH, infected primates’ germline, colonized host genomes and evolved to contribute to creation of the global retroviral genomic regulatory dominion (GRD) performing vital regulatory and morphogenetic functions during human embryogenesis. Retroviral GRD constitutes of 8839 highly conserved LTR elements linked to 5444 down-stream target genes forged by evolution into a functionally-consonant constellation of 26 genome-wide multimodular genomic regulatory networks (GRNs) each of which is defined by significant enrichment of numerous single gene ontology-specific traits. Locations of GRNs appear scattered across chromosomes to occupy from 5.5–15.09% of the human genome. Each GRN harbors from 529 to 1486 human embryo regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences that are quantitatively balanced according to their genome-wide abundance. GRNs integrate activities from 199 to 805 down-stream target genes, including transcription factors, chromatin-state remodelers, signal sensing and signal transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. GRN’s compositions consist of several hundred to thousands smaller gene ontology enrichment analysis-defined genomic regulatory modules (GRMs), each of which combines from a dozen to hundreds LTRs and down-stream target genes. Overall, this study identifies 69,573 statistically significant regulatory LTR-linked GRMs (Binominal FDR q-value < 0.001), including 27,601 GRMs validated by the single ontology-specific directed acyclic graph (DAG) analyses across 6 gene ontology annotations databases. These observations were corroborated and extended by execution of a comprehensive series of Gene Set Enrichment Analyses (GSEA) of regulatory LTRs down-stream target genes employing more than 70 genomics and proteomics databases, including a large panel of databases developed from single-cell resolution studies of healthy and diseased human’s organs and tissues. Genes assigned to distinct GRNs and GRMs appear to operate on individuals’ life-span timescale along specific phenotypic avenues selected from a multitude of down-stream gene ontology-defined and signaling pathways-guided frameworks to exert profound effects on patterns of transcription, protein-protein interactions, developmental phenotypes, physiological traits, and pathological conditions of Modern Humans. GO analyses of Mouse phenotype databases and GSEA of the MGI Mammalian Phenotype Level 4 2021 database revealed that down-stream regulatory targets of human embryo regulatory LTRs are enriched for genes making essential contributions to development and functions of all major tissues, organs, and organ systems, including central and peripheral nervous systems, cardio-vascular (circulatory) and lymphatic systems, gastrointestinal (digestive) and urinary (excretory) systems, musculoskeletal and respiratory systems, endocrine and immune systems, as well as integumentary and reproductive systems. These observations were corroborated by numerous developmental defects documented in a single gene KO mouse models. Genes comprising candidate down-stream regulatory targets of human embryo regulatory LTRs are engaged in protein-protein interaction (PPI) networks that have been implicated in pathogenesis of human common and rare disorders (3298 and 2071 significantly enriched records, respectively), in part, by impacting PPIs that are significantly enriched in 1783 multiprotein complexes recorded in the NURSA Human Endogenous Complexome database and 6584 records of virus-host PPIs documented in Virus-Host PPI P-HIPSTer 2020 database. GSEA-guided analytical inference of the preferred cellular targets of human embryo regulatory LTR elements supported by analyses of genes with species-specific expression mapping bias in Human-Chimpanzee hybrids identified Neuronal epithelium, Radial Glia, and Dentate Granule Cells as cell-type-specific marks within a Holy Grail sequence of embryonic and adult neurogenesis. Observations reported in this contribution support the hypothesis that evolution of human embryo regulatory LTR elements created the global GRD consisting of 26 gene ontology enrichment-defined genome-wide GRNs. Decoded herein the hierarchical super-structure of regulatory LTR-associated GRD and GRNs represents an intrinsically integrated developmental compendium of thousands GRMs congregated on specific genotype-phenotypic trait associations. Many highlighted in this contribution GRMs may represent the evolutionary selection units driven by inherent genotype-phenotype associations affecting primate species’ fitness and survival by exerting control over mammalian offspring survival genes implicated in reduced fertility and infertility phenotypes. Mechanistically, programmed activation during embryogenesis and ontogenesis of genomic constituents of human embryo retroviral GRD coupled with targeted epigenetic silencing may guide genome-wide heterochromatin patterning within nanodomains and topologically-associated domains during differentiation, thus affecting 3D folding dynamics of linear chromatin fibers and active transcription compartmentalization within interphase chromatin of human cells. Methods Data source and analytical protocols Present analyses were focused on human embryo regulatory LTR loci (see Introduction) the evolutionary origins of which were mapped to genomes of Old World Monkeys (OWM), indicating that these genomic loci could be defined as highly-conserved pan-primate regulatory sequences because they have been present in genomes of primate species for ~ 30–40 MYA. Three distinct LTR families meet these criteria, namely MLT2A1 (2416 loci), MLT2A2 (3069 loci), and LTR7 (3354 loci). A total of 8839 fixed non-polymorphic sequences of human embryo regulatory LTR elements residing in genomes of Modern Humans (hg38 human reference genome database) were retrieved as described in recent studies (Hashimoto et al., 2021; Carter et al., 2022 ; Glinsky, 2022 ) and the number of highly conserved orthologous loci in genomes of sixteen non-human primates (NHP) were determined exactly as previously reported (Glinsky, 2022 ). Briefly, fixed non-polymorphic regulatory LTR loci residing in the human genome (hg38 human reference genome database) has been considered highly conserved in the genome of non-human primates (NHP) only if the following two requirements are met: (1) During the direct LiftOver test ( https://genome.ucsc.edu/cgi-bin/hgLiftOver ), the human LTR sequence has been mapped in the NHP genome to the single orthologous locus with a threshold of at least 95% sequence identity; (2) During the reciprocal LiftOver test, the NHP sequence identified in the direct LiftOver test has been remapped with at least 95% sequence identity threshold to the exactly same human orthologous sequence which was queried during the direct LiftOver test. To ascertain the potential regulatory cross-talks of human embryo regulatory LTRs and human-specific regulatory sequences (HSRS), analyses of a total of 110,339 candidate HSRS were carried-out, including 59,732 HSRS (Glinsky, 2020a ); 35,074 neuro-regulatory human-specific SNCs (Kanton et al., 2019 ; Glinsky, 2020b ); 8,297 fixed human-specific regulatory regions defined by chromatin state signatures (Marnetto et al., 2014 ); 7,236 fixed human-specific regulatory regions devoided of archaic humans’ DNA (Schaefer et al., 2021 ). Detailed catalogues of candidate HSRS including descriptions of diverse origins and regulatory features of different families of HSRS and corresponding references of original contributions are reported in the Supplementary Materials and earlier publications [Barakat et al. 2018 ; Fuentes et al. 2018 ; Glinsky 2015 ; 2016a , b ; 2018; 2019; 2020a , b , c , 2021 ; 2022 ; Guffanti et al. 2018 ; Glinsky and Barakat, 2019 ; McLean et al. 2010 ; 2011 ; Pontis et al. 2019 ; Wang et al. 2014 ]. To evaluate the potential impacts of human embryo regulatory LTRs on genomic regulatory networks implicated in pluripotency and stemness phenotypes as well as developmental processes, analyses have been performed of 7,598 hESC developmental enhancers defined by chromatin state signatures (Rada-Iglesias et al., 2011); 32,353 and 36,417 functional enhancers operating in Primed and Naïve hESC, respectively (Barakat et al., 2018 ), as well as 1,619 human-specific hESC functional enhancers (Glinsky and Barakat, 2019 ). Solely publicly available datasets and resources were used in this contribution. The significance of the differences in the expected and observed numbers of events was calculated using two-tailed Fisher’s exact test. Multiple proximity placement enrichment tests were performed for individual families and sub-sets of LTRs and HSRS taking into account the size in bp of corresponding genomic regions, size distributions in human cells of topologically associating domains, distances to putative regulatory targets, bona fide regulatory targets identified in targeted genetic interference and/or epigenetic silencing experiments. Additional details of methodological and analytical approaches are provided in the Supplementary Materials and previously reported contributions [Barakat et al. 2018 ; Fuentes et al. 2018 ; Glinsky 2015 ; 2016a , b ; 2018; 2019; 2020a , b , c , 2021 ; 2022 ; Guffanti et al. 2018 ; Glinsky and Barakat, 2019 ; McLean et al. 2010 ; 2011 ; Pontis et al. 2019 ; Wang et al. 2014 ]. Gene set enrichment and genome-wide proximity placement analyses Gene set enrichment analyses were carried-out using the Enrichr bioinformatics platform, which enables the interrogation of nearly 200,000 gene sets from more than 100 gene set libraries. The Enrichr API (January 2018 through January 2023 releases) [Chen et al. 2013 ; Kuleshov et al. 2016 ; Xie et al. 2021 ] was used to test genes linked to regulatory LTR elements, HSRS, or other regulatory loci of interest for significant enrichment in numerous functional categories. When technically feasible, larger sets of genes comprising several thousand entries were analyzed. Regulatory connectivity maps between HSRS, regulatory LTRs and coding genes and additional functional enrichment analyses were performed with the Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm [McLean et al. 2010 ; 2011 ] at default settings. The reproducibility of the results was validated by implementing two releases of the GREAT algorithm: GREAT version 3.0.0 (02/15/2015 to 08/18/2019) and GREAT version 4.0.4 (08/19/2019) applying default settings at differing maximum extension thresholds as previously reported (Glinsky 2020a , b , c ; 2021 ; 2022 ). The GREAT algorithm allows investigators to identify and annotate the genome-wide connectivity networks of user-defined distal regulatory loci and their putative target genes. Concurrently, the GREAT algorithm performs functional Gene Ontology (GO) annotations and analyses of statistical enrichment of GO annotations of identified genomic regulatory elements (GREs) and target genes, thus enabling the inference of potential biological impacts of interrogated genomic regulatory networks. The Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm was employed to identify putative down-stream target genes of human embryo regulatory LTRs. Concurrently with the identification of putative regulatory target genes of GREs, the GREAT algorithm performs stringent statistical enrichment analyses of functional annotations of identified down-stream target genes, thus enabling the inference of potential significance of phenotypic impacts of interrogated GRNs. Importantly, the assignment of phenotypic traits as putative statistically valid components of GRN actions entails the assessments of statistical significance of the enrichment of both GREs and down-stream target genes by applying independent statistical tests. The validity of statistical definitions of genomic regulatory networks (GRNs) and genomic regulatory modules (GRMs) based on the binominal (regulatory elements) and hypergeometrc (target genes) FDR Q values was evaluated using a directed acyclic graph (DAG) test based on the enriched terms from a single ontology-specific table generated by the GREAT algorithm (Figs. 2 – 3 ; Table 3 ; Supplementary Data Sets S1: GRN1-GRN26 ). DAG test draws patterns and directions of connections between significantly enriched GO modules based on the experimentally-documented temporal logic of developmental processes and structural/functional relationships between gene ontology enrichment analysis-defined statistically significant terms. A specific DAG test utilizes only a sub-set of statistically significant GRMs from a single gene ontology-specific table generated by the GREAT algorithm by extracting GRMs manifesting connectivity patterns defined by experimentally documented developmental and/or structure/function/activity relationships. These GRMs are deemed valid observations and visualized as a consensus hierarchy network of the ontology-specific DAGs (Figs. 2 – 3 ; Tables 3 – 5 ; Supplementary Data Sets S1: GRN1-GRN26 ). Based on these considerations, the DAG algorithm draws the developmental and structure/function/activity relationships-guided hierarchy of connectivity between statistically significant gene ontology enriched GRMs. Genome-wide Proximity Placement Analysis (GPPA) of down-stream target genes and distinct genomic features co-localizing with regulatory LTRs and HSRS was carried out as described previously and originally implemented for human-specific transcription factor binding sites [Glinsky et al. 2018 ; Glinsky, 2015 , 2016a , 2016b , 2017 , 2018, 2019 , 2020a , 2020b , 2020c , 2021 ; Guffanti et al. 2018 ]. Differential GSEA to infer the relative contributions of distinct subsets of regulatory LTR elements and down-stream target genes on phenotypes of interest. When technically and analytically feasible, different sets of regulatory LTRs and candidate down-stream target genes defined at several significance levels of statistical metrics and comprising from dozens to several thousand individual genetic loci were analyzed using differential GSEA. This approach was utilized to gain insights into biological effects of regulatory LTRs and down-stream target genes and infer potential mechanisms of phenotype affecting activities. Previously, this approach was successfully implemented for identification and characterization of human-specific regulatory networks governed by human-specific transcription factor-binding sites [Glinsky et al. 2018 ; Glinsky, 2015 , 2016a , 2016b , 2017 , 2018, 2019 , 2020a , 2020b , 2020c , 2021 ; Guffanti et al. 2018 ] and functional enhancer elements [Barakat et al. 2018 ; Glinsky et al. 2018 ; Glinsky and Barakat 2019 ; Glinsky 2015 , 2016a , 2016b , 2017 , 2018, 2019 , 2020a , 2020b , 2020c , 2021 ]. Differential GSEA approach has been utilized for characterization of phenotypic impacts of 13,824 genes associated with 59,732 human-specific regulatory sequences [Glinsky, 2020a ], 8,405 genes associated with 35,074 human-specific neuroregulatory single-nucleotide changes [Glinsky, 2020b ], 8,384 genes regulated by stem cell-associated retroviral sequences (SCARS) [Glinsky 2021 ], as well as human genes and medicinal molecules affecting the susceptibility to SARS-CoV-2 coronavirus [Glinsky, 2020c ]. Initial GSEA entail interrogations of each specific set of candidate down-stream target genes using ~ 70 distinct genomic databases, including comprehensive pathway enrichment Gene Ontology (GO) analyses. Upon completion, these analyses were followed by in-depth interrogations of the identified significantly-enriched gene sets employing selected genomic databases deemed most statistically informative at the initial GSEA. In all reported tables and plots (unless stated otherwise), in addition to the nominal p values and adjusted p values, the Enrichr software calculate the “combined score”, which is a product of the significance estimate and the magnitude of enrichment (combined score c = log(p) * z, where p is the Fisher’s exact test p-value and z is the z-score deviation from the expected rank). Statistical Analyses of the Publicly Available Datasets All statistical analyses of the publicly available genomic datasets, including error rate estimates, background and technical noise measurements and filtering, feature peak calling, feature selection, assignments of genomic coordinates to the corresponding builds of the reference human genome, and data visualization, were performed exactly as reported in the original publications and associated references linked to the corresponding data visualization tracks ( http://genome.ucsc.edu/ ). Additional elements or modifications of statistical analyses are described in the corresponding sections of the Results. Statistical significance of the Pearson correlation coefficients was determined using GraphPad Prism version 6.00 software. Both nominal and Bonferroni adjusted p values were estimated and considered as reported in corresponding sections of the Results. The significance of the differences in the numbers of events between the groups was calculated using two-sided Fisher’s exact and Chi-square test, and the significance of the overlap between the events was determined using the hypergeometric distribution test [Tavazoie et al. 1999 ]. Declarations Acknowledgements. This work was made possible by the open public access policies of major grant funding agencies and international genomic databases and the willingness of many investigators worldwide to share their primary research. Author would like to thank you Victoria Glinskii for invaluable expert assistance with graphical presentation of the results of this study. Author Contributions This is a single author contribution. All elements of this work, including the conception of ideas, formulation, and development of concepts, execution of experiments, analysis of data, and writing of the paper, were performed by the author. Funding In part, this work was supported by OncoScar, LLC. Declarations Conflict of interest statement Dr. Glinsky is co-founder of the OncoScar, LLC, an early-stage biotechnology company dedicated to exploration of the potential translational utility of stem cell-associated retroviral sequences. Data availability statement All data supporting the reported observations and required to reproduce the findings are provided in the main body of the paper and Supplementary materials. 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Zhang Y, Li T, Preissl S, Amaral ML, Grinstein JD, Farah EN, Destici E, Qiu Y, Hu R, Lee AY, Chee S, Ma K, Ye Z, Zhu Q, Huang H, Fang R, Yu L, Izpisua Belmonte JC, Wu J, Evans SM, Chi NC, Ren B. 2019. Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells. Nature Genetics 51 :1380–1388. doi:10.1038/s41588-019-0479-7 Additional Declarations No competing interests reported. Supplementary Files 02.27.24SupplementaryDataGlinsky.zip 02.27.24SupplementaryInformation.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4020067","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277164108,"identity":"c4100714-2671-4988-b9b8-d194fdd3b78b","order_by":0,"name":"Gennadi V Glinsky","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFAC5oYDDAwJPGzsDUAOGxAfIKiFEaqF5wAJWoBEAgODRAKRWuT9FzYeLviTJsMn+cbw448yBjm+Gwn4tRjeeNhweGZbDg+bdI6xNM85BmNJglpmHGw4zNtQAdJixszYxpC4gSgtPH+AWiTPmDH+bGOoJ6hFnr8RqIUN6DAJHjMG3jaGBANCWgwkGIEOa0sDBnJaMdAvEoYzzzwgYEv/4cOfef4k28u3H94IDDEbeb7jhGxBc4YEfuUQWw4QVjQKRsEoGAUjHAAA2ldEf5+7rs4AAAAASUVORK5CYII=","orcid":"","institution":"University of California, San Diego","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Gennadi","middleName":"V","lastName":"Glinsky","suffix":""}],"badges":[],"createdAt":"2024-03-06 08:45:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4020067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4020067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52533684,"identity":"a69fc163-fdda-4071-b805-bf5d0a8332d9","added_by":"auto","created_at":"2024-03-12 16:27:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88668,"visible":true,"origin":"","legend":"\u003cp\u003eEvolutionary conservation analysis of human embryo regulatory LTR elements (A; B) and graphical summaries of genomic features defining 26 Genomic Regulatory Networks (GRNs) governed by human embryo regulatory LTRs (C - F).\u003c/p\u003e\n\u003cp\u003eConservation profiles reported as percentiles of highly conserved orthologous LTR loci identified in genomes 16 species of non-human primates (NHP) based on sequences identity analyses of 2416 MLT2A1, 3069 MLT2A2, and 3354 LTR7 fixed non-polymorphic LTR sequences in human genome (A). Conservation profiles reported as percentiles of highly conserved orthologous loci of human-specific LTR sequences identified in genomes 10 species of non-human primates (NHP) based on sequences identity analyses of 35 MLT2A1, 45 MLT2A2, and 175 LTR7 human-specific fixed non-polymorphic LTR sequences in human genome (B). Correlation plots (C; D; F) illustrate direct correlations between genome fractions and LTR set coverage values observed for 354 significantly enriched records (Binominal FDR Q value \u0026lt; 0.05) of Gene Ontology annotations. Reported numerical values were documented by the GREAT analyses of 5444 genes representing putative down-stream regulatory targets of 8839 human embryo regulatory LTRs. Correlation plots shown for 227 significantly enriched terms of GO Biological Process database (C), 47 significantly-enriched terms of GO Cellular Component database (D), and 80 significantly enriched terms of GO Molecular Function database (F). Panel (E) shows distribution of numbers of human embryo regulatory LTRs assigned by the GREAT algorithm to each of the 47 significantly enriched terms identified by GO annotations of GO Cellular Component database. Note a sharp drop of the numbers of LTR loci assigned to 13 top-scoring significantly enriched records compared to remaining 34 significant terms. These 13 top-scoring significantly enriched records were defined thereafter as Genomic Regulatory Networks (GRNs) identified by GO annotations of GO Cellular Component database (D). Shaded areas within correlation plots shown in (C; D; F) highlight 26 GRNs representing top-scoring significantly enriched terms identified during the GO annotations of corresponding GO databases. Additional information pertinent to statistical, genomic, molecular, and phenotypic features of 26 GRNs associated with human embryo regulatory LTRs is reported in the text; Tables 2 – 5; and Supplementary Materials (Supplementary Data Set S1: GRN1 – GRN26).\u003c/p\u003e","description":"","filename":"Binder21.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/37dbbcd395d8c224a157365f.png"},{"id":52533733,"identity":"8e57d36a-baed-4281-b0b8-4e66217945bd","added_by":"auto","created_at":"2024-03-12 16:27:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":707471,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical summaries of genomic features (A; B) and Directed Acyclic Graph (DAG) analyses (C – H) of the Genomic Regulatory Network 18 (GRN18: Regulation of Membrane Potential) consisting of 592 human embryo regulatory LTRs and 340 down-stream target genes.\u003c/p\u003e\n\u003cp\u003ePanel (A; top) reports numerical values of main genomic features assigned to the GRN18: Regulation of Membrane Potential and panel (A; bottom) presents a graphical summary of distribution of numbers of human embryo regulatory LTRs among the significantly enriched terms documented by GO annotations of GO Biological Process database (top-scoring 500 of 1315 significantly-enriched terms are shown, which were identified by the GREAT algorithm at the Binominal FDR Q value \u0026lt; 0.001). Panel (B) shows the correlation plot illustrating direct correlation between genome fractions and LTR set coverage values observed for 1315 significantly enriched records (Binominal FDR Q value \u0026lt; 0.001) of GO annotations of GO Biological Process database. Corresponding numerical values were obtained from the GREAT algorithm-generated tables using as an input 592 human embryo regulatory LTR loci assigned to the GRN18.\u003c/p\u003e\n\u003cp\u003ePanels (C – H) reports graphical summaries of DAG analyses of the GRN18: Regulation of Membrane Potential. Hierarchical networks were generated by the GREAT algorithm using as an input 592 human embryo regulatory LTR loci assigned to the GRN18 by the GO annotation analyses. Hierarchical networks of local DAG are shown for significantly enriched terms in GO Biological Process annotations (C; 620 significantly-enriched terms); GO Cellular Component annotations (D; 103 significantly-enriched terms); Human phenotype annotations (E; 82 significantly-enriched terms); GO Molecular Function annotations (F; 90 significantly-enriched terms); Mouse Phenotype annotations (G; 268 significantly-enriched terms); Mouse Phenotype Single KO annotations (H; 217 significantly-enriched terms). Complete tabular data and graphical summary reports pertinent to statistical, genomic, molecular, and phenotypic attributes of 26 GRNs and 69,573 regulatory LTR-linked Genomic Regulatory Modules (GRMs) associated with human embryo regulatory LTR elements are presented in the text; Tables 2 – 5; and Supplementary Materials (Supplementary Data Set S1: GRN1 – GRN26).\u003c/p\u003e","description":"","filename":"Binder22.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/6aa8d3597d76710fd9d5f216.png"},{"id":52533737,"identity":"5b3f1c7a-868a-4946-a694-4a78d594d8d4","added_by":"auto","created_at":"2024-03-12 16:27:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":722697,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical summaries of Directed Acyclic Graph (DAG) analyses of the Genomic Regulatory Network 5 (GRN5: Somatodendritic Compartment) consisting of 995 human embryo regulatory LTRs and 582 down-stream target genes.\u003c/p\u003e\n\u003cp\u003ePanels (A – H) reports graphical summaries of DAG analyses of the GRN5: Somatodendritic Compartment. Hierarchical networks were generated by the GREAT algorithm using as an input 995 human embryo regulatory LTR loci assigned by the GO annotation analyses to the GRN5. Hierarchical networks of local DAG are shown for significantly enriched terms in GO Biological Process annotations [A; B; 599 significantly-enriched terms shown in (A) as an array of bars sorted in descending order of the log10(Binominal p values) and reported in (B) as local DAG of a developmental hierarchical network]; Human phenotype annotations [C; D; 62 significantly-enriched terms shown in (C) as an array of bars sorted in descending order of the log10(Binominal p values) and reported in (D) as local DAG of a developmental hierarchical network]; GO Cellular Component annotations (E; 96 significantly-enriched terms reported as local DAG of a developmental hierarchical network); GO Molecular Function annotations (F; 64 significantly-enriched terms reported as local DAG of a developmental hierarchical network); Mouse Phenotype annotations (G; 361 significantly-enriched terms reported as local DAG of a developmental hierarchical network); Mouse Phenotype Single KO annotations (H; 245 significantly-enriched terms reported as local DAG of a developmental hierarchical network). Complete tabular data and graphical summary reports pertinent to statistical, genomic, molecular, and phenotypic attributes of 26 GRNs and 69,573 regulatory LTR-linked Genomic Regulatory Modules (GRMs) associated with human embryo regulatory LTR elements are presented in the text; Tables 2 – 5; and Supplementary Materials (Supplementary Data Set S1: GRN1 – GRN26).\u003c/p\u003e","description":"","filename":"Binder23.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/8a887565f94b6c19ddebccfc.png"},{"id":52533799,"identity":"2556dd08-cccb-408e-8a27-5a876f6f41a3","added_by":"auto","created_at":"2024-03-12 16:27:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":577620,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical summaries of Directed Acyclic Graph (DAG) analyses of the GSEA-defined networks of protein-protein interactions (PPI) consisting of 88 human embryo regulatory LTRs and 34 down-stream target genes.\u003c/p\u003e\n\u003cp\u003ePanels (A – I) reports graphical summaries of DAG analyses of the GSEA-defined PPI networks of 34 genes linked with 88 regulatory LTRs. Hierarchical networks were generated by the GREAT algorithm using as an input genomic coordinates of 88 human embryo regulatory LTR loci assigned by the GREAT to the core set of 34 genes of the PPI networks engaged in PPI with 121 transcription factors (Transcription Factors’ PPI database) and 124 PPI Hub proteins (PPI Hub Proteins database). Hierarchical networks of local DAG are shown for significantly enriched terms in GO Biological Process annotations [A; B; 142 significantly-enriched terms shown in (A) as an array of bars sorted in descending order of the log10(Binominal p values) and reported in (B) as local DAG of a developmental hierarchical network]; GO Cellular Component annotations [C; D; 6 significantly-enriched terms shown in (C) as an array of bars sorted in descending order of the log10(Binominal p values) and reported in (D) as local DAG of a developmental hierarchical network]; GO Molecular Function annotations [E; F; 4 significantly-enriched terms shown in (E) as an array of bars sorted in descending order of the log10(Binominal p values) and reported in (F) as local DAG of a developmental hierarchical network]; Mouse Phenotype annotations [G; H; 327 significantly-enriched terms shown in (G) as an array of bars sorted in descending order of the log10(Binominal p values) and reported in (H) as local DAG of a developmental hierarchical network]; Mouse Phenotype Single KO annotations [I; J; 69 significantly-enriched terms shown in (I) as an array of bars sorted in descending order of the log10(Binominal p values) and reported in (J) as local DAG of a developmental hierarchical network]. Complete tabular data and graphical summary reports pertinent to statistical, genomic, molecular, and phenotypic attributes of regulatory LTR-linked GSEA-defined Genomic Regulatory Pathways (GRPs)and their constituent Genomic Regulatory Modules (GRMs) associated with human embryo regulatory LTR elements are presented in the text; Tables 5 – 6; and Supplementary Materials (Supplementary Table S1; Supplementary Figure S1; Supplementary Summaries S4-S5; Supplementary Data Set S2).\u003c/p\u003e","description":"","filename":"Binder24.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/3d61e4b49b0234db4cdb7c7f.png"},{"id":52533802,"identity":"4f804e45-83b0-42bb-98bd-481a1ff8b201","added_by":"auto","created_at":"2024-03-12 16:27:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":780612,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization patterns of GSEA of regulatory LTRs down-stream targets comprising 5444 genes (A; B; E – H) and 34 genes (B; C) vis-a-vis of GSEA of 240 genes (D – H) encoding proteins engaged in PPI networks associated with 34 LTR target genes.\u003c/p\u003e\n\u003cp\u003ePanel (A) shows a clustergram visualization of GSEA of 5444 genes comprising a cumulative set of down-stream regulatory targets of human embryo regulatory LTRs employing the OMIM Expanded PPI database of human disease-causing genes (Enrichr). Note the uniform pattern of engagement of 34 genes in PPI with protein products of disease-causing genes implicated in thousands common human disorders. Top-scoring 30 of 175 significantly enriched records are shown. Panel (B) reports a scatter plot visualization of GSEA of 5444 genes (left scatter plots) and 34 genes (right scatter plots) employing the Transcription Factors PPI database (top scatter plots) and the Hub Proteins PPI database (bottom scatter plots). Panel (C) reports a clustergram visualization of GSEA of 34 genes employing the Transcription Factors PPI database (left clustergram) and the Hub Proteins PPI database (right clustergram). Top 30 of 121 (Transcription Factors PPI database) and top 30 of 124 (Hub Proteins PPI database) significantly enriched records are shown. Panel (D) reports a clustergram visualization of GSEA of 240 genes employing the Transcription Factors PPI database (left clustergram) and the Hub Proteins PPI database (right clustergram). Top 30 of 268 (Transcription Factors PPI database) and top 30 of 228 (Hub Proteins PPI database) significantly enriched records are shown. Panels (E – G) reports results of GSEA of 5444 genes (left figures) and 240 genes (right figures) employing the DisGeNET database of common human disorders. Panel (E) reports top 10 of 477 (left set of bars; GSEA of 5444 genes) and top 10 of 3298 (right set of bars; GSEA of 240 genes) significantly enriched records. Panel (F) shows a clustergram visualization of top 30 of 477 (left clustergram; GSEA of 5444 genes) and top 30 of 3298 (right clustergram; GSEA of 240 genes) significantly enriched records. Panel (G) reports a scatter plot visualization of 477 (left set of bars; GSEA of 5444 genes) and of 3298 (right set of bars; GSEA of 240 genes) significantly enriched records. Panel (H) a clustergram visualization of top 30 of 1095 (left clustergram; GSEA of 5444 genes) and top 30 of 199 (right clustergram; GSEA of 240 genes) significantly enriched records identified employing the Orphanet Augmented 2021 database of human orphan disorders (Enrichr). Significance threshold levels were set at adjusted p value \u0026lt; 0.05 (Methods).\u003c/p\u003e\n\u003cp\u003eComplete tabular data and graphical summary reports pertinent to statistical, genomic, molecular, and phenotypic attributes of regulatory LTR-linked GSEA-defined Genomic Regulatory Pathways (GRPs) and their constituent Genomic Regulatory Modules (GRMs) associated with human embryo regulatory LTR elements are presented in the text; Tables 5 – 6; and Supplementary Materials (Supplementary Table S1; Supplementary Figure S1; Supplementary Summaries S4-S5; Supplementary Data Set S2).\u003c/p\u003e","description":"","filename":"Binder25.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/0ee9edc3bc5078ec14d953be.png"},{"id":52533765,"identity":"bbaef0f4-0833-4d47-91a8-32da831068ec","added_by":"auto","created_at":"2024-03-12 16:27:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":435094,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization patterns of GSEA of retroviral 34 genes comprising down-stream regulatory targets of 88 regulatory LTRs vis-a-vis of GSEA of 240 genes encoding proteins that are engaged in PPI networks associated with 34 regulatory LTR target genes. Panel (A) reports a scatter plot visualization of GSEA of 240 genes (268 significantly enriched records; left scatter plots) and 34 genes (121 significantly enriched records; right scatter plots) employing the Transcription Factors PPI database. Panel (B) reports a scatter plot visualization of GSEA of 240 genes (228 significantly enriched records; left scatter plots) and 34 genes (124 significantly enriched records; right scatter plots) employing the Hub Proteins PPI database. Panel (C) shows graphical representations of protein-protein interaction networks identified by the Enrichr software (Methods) from GSEA reported in (A) and (B). Panel (D) reports a scatter plot visualization of GSEA of 240 genes (606 significantly enriched records; left scatter plots) and 34 genes (143 significantly enriched records; right scatter plots) employing the CORUM Protein Complexes database. Panel (E) reports a scatter plot visualization of GSEA of 240 genes (62 significantly enriched records; left scatter plots) and 34 genes (22 significantly enriched records; right scatter plots) employing the SILAC Phosphoproteomics database. Panel (F) reports a scatter plot visualization of GSEA of 240 genes (1783 significantly enriched records; left scatter plots) and 34 genes (no significantly enriched records at adjusted p value \u0026lt; 0.05; right scatter plots) employing the NURSA Human Endogenous Complexome database. Panel (G) shows graphical representations of protein-protein interaction networks identified by the Enrichr software (Methods) from GSEA reported in (D - F). Panel (H) shows a clustergram visualization of GSEA of 240 genes that are engaged in PPI networks associated with 34 regulatory LTR target genes. Reported GSEA employed the OMIM Expanded PPI database of human disease-causing genes (Enrichr). Note the uniform pattern of engagement of 51 genes in PPI with protein products of disease-causing genes implicated in pathogenesis of 3298 common human disorders [Figure 5F (right panel); GSEA of the DisGeNET database]. Top-scoring 30 of 181 significantly enriched records are shown. Significance threshold levels were set at adjusted p value \u0026lt; 0.05 (Methods).\u003c/p\u003e\n\u003cp\u003eComplete tabular data and graphical summary reports pertinent to statistical, genomic, molecular, and phenotypic attributes of regulatory LTR-linked GSEA-defined Genomic Regulatory Pathways (GRPs) and their constituent Genomic Regulatory Modules (GRMs) associated with human embryo regulatory LTR elements are presented in the text; Tables 5 – 6; and Supplementary Materials (Supplementary Table S1; Supplementary Figure S1; Supplementary Summaries S4-S5; Supplementary Data Set S2).\u003c/p\u003e","description":"","filename":"Binder26.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/8c891794fbf18f474aaee62f.png"},{"id":52533725,"identity":"ed55dbe5-07ef-4a32-8b68-a478bc423ca3","added_by":"auto","created_at":"2024-03-12 16:27:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":304377,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory cross-talks of 8,839 human embryo regulatory LTR elements and 110,339 human specific regulatory sequences (HSRS) representing different families of HSRS.\u003c/p\u003e\n\u003cp\u003eComparative analyses of enrichment and abundance patterns of genes comprising candidate down-stream regulatory targets of human embryo regulatory LTR elements. Genes identified in this contribution as candidate down-stream regulatory targets of human embryo regulatory LTR elements were compared with previously reported gene sets expression of which is governed by different categories of genomic regulatory loci, including various families of primate-specific and human-specific regulatory sequences (HSRS). Panels A; C; E; G report graphical summaries of the enrichment analyses, while Panels B; D; F; H report graphical summaries of the analyses of abundance profiles. Results of the analyses of 3971 down-stream target genes associated with MLT2A1 \u0026amp; MLT2A2 loci and 2955 genes associated with LTR7 elements are shown in the panels (A; B) and (C; D), respectively. Panels (E; F) report results of the analyses of a cumulative set of 5444 genes comprising a cumulative set of candidate down-stream regulatory targets of LTR7, MLT2A1, and MLT2A2 human embryo regulatory LTRs. Panels (G; H) show results of the analyses of a common set of 709 down-stream regulatory target genes shared by LTR7, MLT2A1, and MLT2A2 human embryo regulatory LTR elements. Please refer to text for additional information and interpretations of reported observations. Detailed reports of the quantitative metrics pertinent to these analyses are presented in the Table 12; Supplementary Table S4 (Excel spreadsheets st1 – st5).\u003c/p\u003e","description":"","filename":"Binder27.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/7141b8d5df23e53d840c9e4b.png"},{"id":52533648,"identity":"cfb01573-fe69-41da-a8a6-055644b7c182","added_by":"auto","created_at":"2024-03-12 16:27:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":282929,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and characterization of down-stream target genes of human embryo regulatory LTR elements that are significantly enriched among genes with species-specific expression mapping bias in Human-Chimpanzee hybrids of induced pluripotent stem cells (iPSC) and brain organoids (cortical spheroids, CS).\u003c/p\u003e\n\u003cp\u003eGenes manifesting species-specific expression mapping bias in Human-Chimpanzee hybrids were analyzed to identify down-stream target genes of human embryo regulatory LTR elements with species-specific expression bias in Human-Chimpanzee hybrids (Table 13; Supplementary Summary S6). Significance of the enrichment of human embryo regulatory LTR targets among genes with species-specific expression bias in Human-Chimpanzee hybrids were ascertained for three different categories of genes, including 374 genes with species-specific expression bias in hybrid iPSC (Panels A – B; Table 13), 291 genes with species-specific expression bias in hybrid CS (Panels C –D; Table 13), and 183 genes manifesting species allele-specific expression profiles in human-chimpanzee hybrid cells (Table 13). Panels (A – D) show species-specific expression profiles in hybrid iPSC (A; B) and hybrid CS (C; D) of 374 genes (A) and 95 regulatory LTR target genes (B) as well as 291 genes (C) and 63 regulatory LTR target genes (D). In panels (A – D) genes were sorted in descending orders of human alleles’ expression values (blue bars) to highlight species-specific differences of gene expression profiles in Human-Chimpanzee hybrids. Panel (E) reports the visualization summary of GSEA-guided identification of 53 regulatory LTR target genes constituting genetic markers of Dentate Granule Cells (Table 14; Supplementary Table S5; Supplementary Summary S6). A total of 177 human embryo regulatory LTR target genes with species-specific expression mapping bias in Human-Chimpanzee hybrids were identified and subjected to GSEA employing a panel of genomics and proteomics database. GSEA of 177 genes employing the ARCHS4 Human Tissues database identified the Dentate Granule Cells as the top-scoring significantly enriched record (Panel E; Table 14; Supplementary Table S5; Supplementary Summary S6). Panels (F – G) reports significantly enriched terms of the Mouse Phenotype Single KO database annotations of the 223 regulatory LTRs linked to 53 down-stream target genes comprising genetic markers of the Dentate Granule Cells. Panels (F; G) report 6 significantly-enriched terms shown in (F) as an array of bars sorted in descending order of the log10 (Binominal p values) and reported in (G) as local DAG of a developmental hierarchical network (see text for details). Panel (H) reports enrichment/depletion patterns of human embryo regulatory LTR regulatory targets among genes comprising 24 differential gene expression modules of primates’ brain regions delineated by comparative analyses of eight brain regions of humans, chimpanzees, gorillas, a gibbon, and macaques (see text for details). Red bars marked with stars denote 3 modules manifesting most significant enrichment (modules 22; 16; 15), while yellow bars denote 7 modules, including module 7 with chimpanzee-specific expression, exhibiting most apparent relative depletion of human embryo regulatory LTR target genes.\u003c/p\u003e","description":"","filename":"Binder28.png","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/3d78a955fe24e6d5d41f30f6.png"},{"id":54595102,"identity":"2163b5de-7222-409c-ae32-962ee56f8361","added_by":"auto","created_at":"2024-04-12 18:38:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6363658,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/c99ef478-2811-4e44-9acb-0e3bbc5f7384.pdf"},{"id":52533741,"identity":"f240bdf0-921a-4054-9379-02ceb574daf6","added_by":"auto","created_at":"2024-03-12 16:27:10","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":80192222,"visible":true,"origin":"","legend":"","description":"","filename":"02.27.24SupplementaryDataGlinsky.zip","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/6d3d456666b49423d93bd080.zip"},{"id":52533766,"identity":"203db21d-3c7f-4393-858d-85033afe430d","added_by":"auto","created_at":"2024-03-12 16:27:14","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30267390,"visible":true,"origin":"","legend":"","description":"","filename":"02.27.24SupplementaryInformation.zip","url":"https://assets-eu.researchsquare.com/files/rs-4020067/v1/7c1c6873f719edae57a18c6d.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gene ontology-guided analyses of pan-primate regulatory LTR elements contributing to embryogenesis, development of physiological traits and pathological phenotypes of Modern Humans","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the genome of Modern Humans, there are thousands of genomic loci origin of which could be mapped with high confidence to hundreds of distinct families and subfamilies of retroviruses collectively defined as Human Endogenous Retroviruses, HERV (Bao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kojima, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vargiu et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It has been suggested that colonization of human genome by HERVs is the result of long-term reinfections during primate evolution, specifically, germ-line reinfections, rather than retrotransposition in cis or complementation in trans (Belshaw et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), indicating that an infectious pool of endogenous retroviruses has persisted within the primate lineage throughout the past 30\u0026ndash;40\u0026nbsp;million years. The full-length retroviruses typically consist of two or three structural genes encoding viral proteins flanked by two identical regulatory sequences termed Long Terminal Repeats (LTRs). Unique structures of regulatory LTRs might provide the evolutionary advantage for hosts for the exaptation of these sequences to function as powerful genomic regulatory elements (Thompson et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Homologous recombination mediated by LTRs frequently results in the elimination of internal open reading frames (Mager and Goodchild \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Belshaw et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and yields solitary regulatory LTRs without adjacent viral structural genes. These regulatory LTRs represent endogenous genomic regulatory elements, which function as alternative promoters, transcription factor binding sites, proximal and distal long-range enhancers, and splice sites.\u003c/p\u003e \u003cp\u003eSelection of specific integration sites of retroviruses in the human genome may reflect an evolutionary compromise of stochastic and deterministic forces because it favors active genes and integrated retroviruses appear preferentially detected in the open chromatin regions representing hallmarks of transcriptionally active genomic loci (Schr\u0026ouml;der et al, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Cereseto and Giacca, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bushman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Albanese et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), however, within this genomic context precise insertion sites of ERVs may have elements of a random and probabilistic nature. Conservative estimates indicate that there are more than 3,000 DNA sequences derived from insertions of the human endogenous retrovirus type H (HERVH), including highly conserved sequences of its promoter LTR7, that are scattered across human genome at fixed non-polymorphic locations (Thomas et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Sequences derived from LTR7/HERVH retroviruses are among the most abundant and extensively investigated genomic regulatory elements that were originated from various HERV families. Regulatory LTR7/HERVH insertions originated from a gamma retrovirus that presumably infected primates approximately 40\u0026nbsp;million years ago (mya) and colonized the genome of the common ancestor of Apes, Old World Monkeys, and New World Monkeys (Goodchild et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Mager and Freeman, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe long terminal repeats designated LTR7 harbor the promoter sequence of the HERVH retroviruses and the LTR7/HERVH family has been extensively investigated in the context of its regulatory functions and locus-specific differential expression in human preimplantation embryogenesis, human embryonic and pluripotent stem cells (Fort et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gemmell et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Glinsky et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; G\u0026ouml;ke et al.,2015; Izsv\u0026aacute;k et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kelley and Rinn, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Loewer et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ohnuki et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pontis et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; R\u0026ouml;mer et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Santoni et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Takahashi et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e Theunissen et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It has been reported that LTR7/HERVH sequences harbor binding sites for master pluripotency transcription factors OCT4, NANOG, SP1, and SOX2 which bind HERVH/LTRs and activate their expression (Glinsky, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; G\u0026ouml;ke et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ito et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kelley and Rinn, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kunarso et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ohnuki et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pontis et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Santoni et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHuman endogenous retrovirus type K [HERVK (HTML-2)] is the primate-specific retrovirus that most recently colonized primate genomes and all human-specific and human-polymorphic HERVK insertions are associated with a specific LTR subtype designated LTR5_Hs (Hanke et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Subramanian et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). There are at least 36 non-reference polymorphic HERVK proviruses with insertion frequencies ranging from \u0026lt;\u0026thinsp;0.0005 to \u0026gt;\u0026thinsp;0.75 that varied by distinct human populations (Wildschutte et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast to HERVH insertions manifesting no insertional polymorphism in human population (Thomas et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), insertional polymorphism of HERVK retroviruses has been documented by the analyses of individual human genomes, including genomes from the 1000 Genomes Project and the Human Genome Diversity Project (Autio et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hughes, Coffin, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Barbulescu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Belshaw et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Shin et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Turner et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wildschutte et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt has been reported that LTR5_Hs/HERVK manifests transcriptional and biological activities in human preimplantation embryos and in na\u0026iuml;ve hESCs (Grow et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, LTR5_Hs elements acquire enhancer-like chromatin state signatures concomitantly with transcriptional reactivation of HERVK (Grow et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Genome-wide CRISPR-guided activation and interference experiments targeting LTR5_Hs elements demonstrated global long-range effects on expression of human genes consistent with postulated functions of LTR5_Hs as distal enhancers (Fuentes et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recently reported several lines of observations highlight putative mechanisms by which regulatory LTRs of distinct retroviral families may have affected phenotypic traits contributing to species segregation during primate evolution and development of human-specific phenotypic features (Glinsky, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notable findings of this category are discoveries of potential regulatory impacts of regulatory LTRs residing in the human genome on expression of mammalian offspring survival (MOS) genes as well as regulatory effects of human-specific LTR7 and LTR5_Hs loci on expression of genes encoding markers of 12 distinct cells\u0026rsquo; populations of fetal gonads and genes implicated in pathophysiology of human spermatogenesis, including Y-linked spermatogenic failure, oligo- and azoospermia. Most recently, direct experimental evidence linking specific regulatory LTR loci (LTR7Y and LTR5_Hs) to gene regulatory networks shared between human primordial germ cells and na\u0026iuml;ve pluripotent cells has been reported (Ito et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn a population scale, to achieve the successful colonization of host genomes and heritable transmission of retroviral sequences, retroviruses must infect the germline during the early embryogenesis prior to or at the stage of the germ cells\u0026rsquo; biogenesis. The germline infection, propagation, and stable integration of multiple copies of viral genomes into host chromosomes are required to ensure the passage of integrated viral sequences to offspring. These could be accomplished only within a relatively small window of the developmental timeline which represents an essential temporal target for viral infections to enable successful transitions of exogenous retroviruses to the state of endogenous retroviruses (ERVs) integrated into host genomes (Glinsky, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on these considerations, it has been concluded that HERVs colonizing the human genome during the relatively narrow embryonic development window may target functionally closely related panels of genes and developmental pathways, thus potentially affecting common sets of phenotypic traits. Recently reported analyses of human embryo regulatory LTR elements (LTR7 and LTR5_Hs) support the model of cooperative phenotypic impacts on human pathophysiology exerted by genes representing down-stream regulatory targets of different families of regulatory LTR elements, such as LTR7 and LTR5_Hs, despite their markedly distinct evolutionary histories of the human genome colonization spanning millions of years (Glinsky, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring embryonic genome activation (EGA), DNA sequences derived from HERVL retroviruses are transcriptionally activated by the homeobox transcription factor DUX4 (Hendrickson et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). HERVL retroviruses harbor regulatory LTRs designated MLT2A elements, which are subdivided into two distinct subfamilies designated MLT2A1 and MLT2A2. Chromatin at MLT2A loci becomes accessible in two-cell human embryos, MLT2A elements are transcribed in four-cell human embryos, and specific MLT2A elements function as promoters and splicing sites in primate embryos (Hashimoto et al., 2021). These observations indicate that MLT2A1 and MLT2A2 elements that are derived from primate-specific HERVL retroviruses could be classified as human embryo regulatory LTRs.\u003c/p\u003e \u003cp\u003eTo understand when HERVH and HERVL retroviruses have infiltrated the primates\u0026rsquo; germline, detailed analyses of evolutionary conservation patterns of regulatory LTR7, MLT2A1, and MLT2A2 elements were carried out in 17 species of non-human primates (NHP) vis-\u0026agrave;-vis reference genome of present day humans. According to these analyses, HERVH and HERVL represent pan-primate endogenous retroviruses that appear to infect and colonize primate lineage at different evolutionary time points and achieved in distinct primate species quantitatively different levels of genomic amplification during evolution. To delineate connectivity patterns between 8839 loci encoded by the LTR7 and MLT2A elements and the phenotypic pleiotropy of putative regulatory functions of distinct families of human embryo regulatory LTRs, the GREAT algorithm was employed to identify and characterize LTR-linked genes. Comprehensive Gene Set Enrichment Analyses (GSEA) of 5444 LTR-linked genes were performed to infer potential phenotypic impacts of genomic regulatory networks governed by human embryo regulatory LTRs. Observations reported in this contribution support the hypothesis that evolution of human embryo regulatory LTR elements created the global genomic regulatory dominion (GRD) consisting of 26 genome-wide genomic regulatory networks (GRNs) defined by the significant enrichment of specific gene ontology terms. Regulatory LTR-associated GRD and GRNs could be viewed as an intrinsically integrated developmental compendium of 69,573 genomic regulatory modules (GRMs) defined by significant enrichment of specific genotype-phenotypic trait associations. Many GRMs may represent the evolutionary selection units driven by inherent genotype-phenotype associations affecting mammalian species\u0026rsquo; fitness and survival with the focal selection point on nervous system development and functions. Collectively, these findings demonstrate that despite markedly distinct evolutionary histories of different families of human embryo regulatory LTRs, genes representing down-stream regulatory targets of LTR7 and MLT2A elements exert the broad cooperative phenotypic impacts on development, physiology, and pathology of Modern Humans, apparently encompassing diverse cellular compositions and structural-functional architectures of all tissues, major organs, and organ systems of human body.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEvolutionary patterns of origin, expansion, conservation, and divergence of pan-primate endogenous retroviruses encoding human embryo regulatory LTR elements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvolutionary timelines of transitions of retroviruses from the state of exogenous infection agents to endogenous retroviral sequences integrated into host genomes could be inferred from the comparative quantitative analyses of highly conserved orthologous retrovirus-derived Long Terminal Repeat (LTR) loci in genomes of multiple distinct primate species which diverged from the last extinct common ancestor (ECA) at distinct time points during primates\u0026rsquo; evolution (Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). To accomplish this task, a total of 8839 fixed non-polymorphic sequences of human embryo regulatory LTRs residing in genomes of Modern Humans (hg38 human reference genome database) were retrieved and the number of highly conserved orthologous loci in genomes of sixteen non-human primates (NHP) were determined (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Present analyses were focused on human embryo regulatory LTR loci (see Introduction) the evolutionary origins of which could be mapped to genomes of Old World Monkeys (OWM), indicating that these genomic loci could be defined as highly-conserved pan-primate regulatory sequences because they have been preserved in primates\u0026rsquo; genomes for ~\u0026thinsp;30 MYA. Three distinct LTR families meet these criteria, namely MLT2A1 (2416 loci), MLT2A2 (3069 loci), and LTR7 (3354 loci). It has been observed that MLT2A1, MLTA2, and LTR7 appear to have similar evolutionary histories defined by evolutionary patterns of expansion, conservation, and divergence during primate evolution (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, they have clearly distinguishable histories of timelines of evolutionary origins, which are defined herein as the estimated timelines when retroviruses infect primates\u0026rsquo; germlines and transition from the state of exogenous infection agents to endogenous retroviral sequences integrated into host genomes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The consistent earliest presence of highly-conserved pan-primate MLT2A1 and MLT2A2 loci could be mapped to genomes of both New World Monkeys (NWM) and OWM, indicating that both MLT2A1/HERVL and MLT2A2/HERVL retroviruses have infected primates\u0026rsquo; germlines before the segregation of OWM and NWM lineages (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). In contrast, the consistent earliest presence in primates\u0026rsquo; genomes of LTR7 loci could be mapped to genomes of OWM (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA), suggesting that LTR7/HERVH retroviruses have entered germlines of the primate lineage after the separation of the NWM lineage (Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Pan-primate endogenous MLT2A1/HERVL, MLT2A2/HERVL, and LTR7/HERVH retroviruses appear to achieve a significant expansion in genomes of Great Apes, ranging from ~\u0026thinsp;2-fold expansion for MLT2A2 loci to ~\u0026thinsp;5-fold expansion for LTR7 loci (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). In contrast to highly-conserved pan-primate human embryo regulatory LTRs, the consistent earliest presence of highly-conserved LTR5_Hs loci has been observed in the Gibbon\u0026rsquo;s genome (Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), in accord with the hypothesis that LTR5_Hs/HERVK retroviruses successfully colonized germlines of the primate lineage after the segregation of Gibbons\u0026rsquo; species and subsequently underwent a marked expansion in genomes of Great Apes.\u003c/p\u003e\n\u003cp\u003eIn agreement with the previously reported findings for highly-conserved LTR7 and LTR5_Hs loci (Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), timeline sequences of quantitative expansion of human embryo pan-primate regulatory LTRs in different primate species appear to replicate the consensus evolutionary sequence of increasing cognitive and behavioral complexities during primate evolution, which seems particularly striking for LTR7 loci (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eOne of notable findings of LTR loci sequence conservation analyses is the observation that hundreds of highly-conserved pan-primate human embryo regulatory LTRs have DNA sequences divergent by more than 5% from orthologous sequences in genomes of Modern Humans in genomes of our closest evolutionary relatives, Chimpanzee and Bonobo. It was of interest to determine how many of fixed non-polymorphic human embryo regulatory LTR loci could be defined as candidate human-specific (HS) genomic regulatory elements compared to both Chimpanzee and Bonobo genomes. Setting a selection threshold at \u0026lt;\u0026thinsp;10% of sequence identity, there are 45; 35; and 175 sequences that could be classified as human-specific MLT2A2, MLT2A1, and LTR7 loci, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). A majority of candidate HS regulatory LTR loci of human embryo could be classified as bona fide human-specific insertions because they did not intersect any chains in genomes of both Chimpanzee and Bonobo.\u003c/p\u003e\n\u003cp\u003eResults of previously reported evolutionary conservation analyses indicated that nearly half (84/175; 48%) of candidate HS-LTR7 loci have been identified as highly-conserved orthologous sequences in genomes of OWM, Gibbon, Orangutan, and Gorilla (Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), consistent with the hypothesis that these HS-LTR7 loci were not retained in genomes of Chimpanzee and Bonobo but preserved in genomes of Modern Humans. Similar to HS-LTR7 loci, the patterns of failure to retain human-specific DNA sequences have been observed in genomes of both Chimpanzee and Bonobo for candidate HS regulatory LTR elements encoded by MLT2A1 and MLT2A2 loci (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Collectively, these observations strongly argue that species-specific loss of highly-conserved pan-primate regulatory DNA sequences encoded by regulatory LTRs may have contributed to the emergence during primate evolution of human-specific features of genomic regulatory networks (GRN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of regulatory LTR\u0026rsquo;s down-stream target genes and analysis of potential phenotypic impacts of human embryo regulatory LTR elements on pathophysiology of Modern Humans.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the main mechanisms defining biological functions of regulatory LTRs is their activity as distal genomic regulatory elements (GRE) affecting transcription of down-stream target genes. Therefore, the inference of potential biological functions of human embryo regulatory LTR elements could be made based on evaluations of experimentally documented biological activities of their down-stream target genes. To this end, the Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm was employed (Methods) to identify putative down-stream target genes of human embryo regulatory LTRs. Concurrently with the identification of putative regulatory target genes of GREs, the GREAT algorithm performs stringent statistical enrichment analyses of functional annotations of identified down-stream target genes, thus enabling the inference of potential biological significance of interrogated GRNs. Importantly, the assignment of phenotypic traits as putative statistically valid components of GRN actions entails the assessments of statistical significance of the enrichment of both GREs and down-stream target genes.\u003c/p\u003e\n\u003cp\u003eThe GREAT algorithm identified 5444 genes as potential down-stream regulatory targets of 8839 human embryo regulatory LTR elements (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). It has been observed that distinct families of human embryo regulatory LTR elements share overlapping sets of down-stream target genes, including 709 genes that were identified as the consensus set of candidate down-stream regulatory targets for MLT2A1; MLT2A2; and LTR7 elements (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; \u003cstrong\u003eSupplemental Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). At the statistical significance threshold of the binominal FDR Q value\u0026thinsp;\u0026lt;\u0026thinsp;0.002 (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e, a total of 54 significantly enriched records of biological processes appear preferentially affected by the regulatory actions of 8839 human embryo regulatory LTRs (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Cell-cell adhesion via plasma-membrane adhesion molecules (GO:0098742; 677 LTRs linked with 95 genes; binominal FDR Q value\u0026thinsp;=\u0026thinsp;2.65E-15), cell-cell adhesion (GO:0098609; 1089 LTRs linked with 238 genes; binominal FDR Q value\u0026thinsp;=\u0026thinsp;3.18E-05), and homophilic cell adhesion (GO:0007156; 481 LTRs linked with 62 genes; binominal FDR Q value\u0026thinsp;=\u0026thinsp;1,15E-12) via plasma membrane adhesion molecules have been identified among top-ranked biological processes associated with regulatory effects of human embryo regulatory LTR elements (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumbers of human embryo regulatory LTR loci and their putative down-stream regulatory target genes in human genome.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHuman embryo regulatory LTR families\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of loci in human genome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of putative regulatory target genes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLT2A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLT2A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLT2A1 \u0026amp; MLTA2 (all loci)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR7 \u0026amp; MLT2A1 \u0026amp; MLT2A2 (all loci)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman embryo regulatory LTR families\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of loci in human genome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of common regulatory target genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLT2A1 \u0026amp; MLTA2 loci common regulatory target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLTA1 \u0026amp; LTR7 loci common regulatory target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLTA2 \u0026amp; LTR7 loci common regulatory target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR7 \u0026amp; MLT2A1 \u0026amp; MLT2A2 loci common regulatory target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop-ranked biological processes preferentially affected by 8,839 human embryo regulatory LTRs identified by proximity placement-guided Genomic Regulatory Network (GRN) analysis* of Gene Ontology (GO) Biological Process database.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal Rank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal P value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal Bonferroni P value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal FDR Q value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLTR Fold Enrichment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObserved LTRs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene Fold Enrichment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObserved Genes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecell-cell adhesion via plasma-membrane adhesion molecules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehomophilic cell adhesion via plasma membrane adhesion molecules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epositive regulation of synapse assembly\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.83E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esnRNA export from nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.33E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eregulation of synapse assembly\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.35E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esnRNA transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.87E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etooth eruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.02E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.17E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0050803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eregulation of synapse structure or activity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecell-cell adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.18E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0050807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eregulation of synapse organization\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.71E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.71E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulation of AV node cell action potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.99E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0086072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAV node cell-bundle of His cell adhesion involved in cell communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eserotonin receptor signaling pathway\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1904292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulation of ERAD pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0046068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecGMP metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive regulation of helicase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003esynapse assembly\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0000710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeiotic mismatch repair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.87E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0034497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprotein localization to pre-autophagosomal structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.80E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.20E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.27E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0072378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eblood coagulation, fibrin clot formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.34E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epostsynaptic membrane assembly\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.18E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.69E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0099560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003esynaptic membrane adhesion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.29E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.83E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecGMP biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.44E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eglutamate receptor signaling pathway\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.16E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.25E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003exenophagy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.57E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0046629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egamma-delta T cell activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.53E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.72E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1904293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative regulation of ERAD pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.32E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0052652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecyclic purine nucleotide metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.47E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1900371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulation of purine nucleotide biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.30E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglutamate catabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.26E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0009190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecyclic nucleotide biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.86E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epositive regulation of nervous system development\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.95E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0030808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulation of nucleotide biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epresynaptic membrane assembly\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.42E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulation of helicase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.61E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.47E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0034332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadherens junction organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.81E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eneuron cell-cell adhesion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.09E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative regulation of interleukin-6 biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.70E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.75E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0010838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive regulation of keratinocyte proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epresynaptic membrane organization\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eheterophilic cell-cell adhesion via plasma membrane cell adhesion molecules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.38E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0061739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprotein lipidation involved in autophagosome assembly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.22E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.45E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0070295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erenal water absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.45E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.75E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative regulation of monocyte differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.97E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0009065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglutamine family amino acid catabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.01E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.47E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative regulation of neutrophil differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.89E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.61E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0003097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erenal water transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.18E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.99E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0016074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esnoRNA metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.14E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1902567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative regulation of eosinophil activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.38E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.25E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulation of neutrophil differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.72E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.69E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglutamate metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.24E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.35E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0030853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative regulation of granulocyte differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.30E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.43E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0071362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecellular response to ether\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.34E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.48E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1904637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecellular response to ionomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.82E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eLegend: *Proximity-placement-guided Genomic Regulatory Network (GRN) analyses were performed employing the GREAT algorithm software as described in Methods.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQuantitative profiles of regulatory LTR elements, down-stream target genes, and genomic regulatory modules (GRMs) * within 26 gene ontology enrichment analysis-defined genomic regulatory networks (GRNs) ** of the genomic dominion of human embryo regulatory LTRs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene ontology ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic regulatory networks\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of LTRs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHuman Phenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMouse Phenotype Single KO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMouse Phenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of GRNs \u0026amp; GRMs\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenomic dominion of human embryo regulatory LTRs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive regulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-cell adhesion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-cell adhesion via plasma-membrane adhesion molecules\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0099537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrans-synaptic signaling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemical synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0050804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModulation of synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle organism behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0042391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation of membrane potential\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1901137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbohydrate derivative biosynthetic process\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuron projection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma membrane region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynapse part\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0009986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell surface\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostsynapse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostsynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0036477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSomatodendritic compartment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuronal cell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0030424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0031012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteinaceous extracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium ion binding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0008233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptidase activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0070011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptidase activity, acting on L-amino acid peptides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative, within\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e26 genomic regulatory networks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative, excluding 26 genomic regulatory networks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eLegend: *For each GRN, reported numbers of LTRs, genes, and GRMs were determined at the statistical significance threshold of the Binominal FDR Q-Value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (Supplementary Materials); **, A total of 26 gene ontology enrichment analyses-defined GRNs associated with human embryo regulatory LTRs were identified at the statistical significance threshold of the Binominal FDR Q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). GRNs contain from 529 to 1486 regulatory LTR elements and from 199 to 805 down-stream target genes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eInterestingly, neuron cell-cell adhesion (GO:0007158) has been scored as one of significantly enriched cell type-specific GO categories (106 LTRs linked to 10 genes; binominal FDR Q-value\u0026thinsp;=\u0026thinsp;9.09E-04). Multiple distinct GO categories related to biogenesis, structure, and functions of synapses appear preferential regulatory targets of human embryo regulatory LTRs (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). These include regulation of synapse assembly (GO:0051963); regulation of synapse structure or activity (GO:0050803); synapse organization (GO:0050807); synapse assembly (GO:0007416); and synapse membrane adhesion (GO:0099560). Several findings suggest that putative regulatory actions of human embryo regulatory LTRs may affect fine-tuned biological structures and activities of synapses, including the presynaptic membrane assembly (GO:0097105; binominal FDR Q value\u0026thinsp;=\u0026thinsp;7.42E-04) and organization (GO:0097090; 77 LTRs linked to 5 genes; binominal FDR Q value\u0026thinsp;=\u0026thinsp;1.16E-03); postsynaptic membrane assembly (GO:0097104; 86 LTRs linked to 6 genes; binominal FDR Q value\u0026thinsp;=\u0026thinsp;3.19E-04) as well as positive regulation of synapse assembly (GO:0051965; 355 LTRs linked to 40 genes; binominal FDR Q value\u0026thinsp;=\u0026thinsp;4.83E-09). Notably, regulatory LTR elements and genes associated with the GO category of positive regulation of synapse assembly (GO:0051965) constitute a marked majority of regulatory LTRs and genes assigned to more general GO category of regulation of synapse assembly (GO:0051963): 91% (355/392) of LTRs and 78% (40/51) of genes, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Overall, the GO category of regulation of nervous system development (GO:0051960; binominal FDR Q value\u0026thinsp;=\u0026thinsp;4,38E-02) has been linked with 1424 regulatory LTRs and 716 target genes, 333 of which have been implicated in development of nervous system (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings suggest that biological processes of nervous system development and functions, in particular, biogenesis and activity of synapses, may represent one of important evolutionary targets of regulatory actions of human embryo regulatory LTR elements. Consistent with this hypothesis, significantly enriched GO category of positive regulation of nervous system development (GO:0051962; binominal FDR Q value\u0026thinsp;=\u0026thinsp;5.95E-04) has been linked with 983 regulatory LTRs and 204 target genes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e), while significantly enriched GO category of glutamate receptor signaling pathway (GO:0007215; binominal FDR Q value\u0026thinsp;=\u0026thinsp;3.85E-04) has been associated with 173 regulatory LTRs and 27 target genes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). In contrast, GO category of negative regulation of nervous system development (GO:0051962) has been linked with 483 regulatory LTRs and 119 target genes known to exert negative effects on development of nervous system (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, the validity of the concept of human embryo regulatory LTRs\u0026rsquo; regulatory impacts on nervous system development and functions is supported by the identification of numerous significantly enriched phenotypic traits of biological relevance assigned by GO Cellular Component and by GO Molecular Function annotations as well as Mouse Phenotype Single KO and Mouse Phenotype databases (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Notable observations of potential biological relevance include identification of serotonin receptor signaling pathway (GO:0007210; 54 LTRs linked to 7 genes; binominal FDR Q-value\u0026thinsp;=\u0026thinsp;0.00016) and Y-linked inheritance (HP:0001450; 80 LTRs linked to 14 genes; binominal FDR Q-value\u0026thinsp;=\u0026thinsp;5.60E-07) that were scored by the GREAT algorithm as the significantly enriched records of GO Biological Process and Human Phenotype databases-defined phenotypic traits, respectively (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Among multiple significantly enriched phenotypes\u0026rsquo; classification categories, GO Cellular Component annotations identified neuron projection (GO:0043005; 1486 LTRs linked to 386 genes) and synaptic membrane (GO:0097060; 679 LTRs linked to 132 genes), while GO Molecular Function analysis scored glutamate receptor activity (GO:0008066; 152 LTRs linked to 19 genes); neurotransmitter receptor activity (GO:0030594; 220 LTRs linked to 43 genes); neurexin family protein binding (GO:0042043; 58 LTRs linked to 7 genes); and calcium ion binding (GO:0005509; 979 LTRs linked to 243 genes) (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Interestingly, structural elements of the postsynaptic membrane defined by the GO Cellular Component category (GO:0045211; 577 LTRs linked to 102 genes; binominal FDR Q-value\u0026thinsp;=\u0026thinsp;1.98E-04) appears preferentially targeted by regulatory actions of human embryo regulatory LTRs compared to the presynaptic membrane features (GO:0042734; 186 LTRs linked to 36 genes; binominal FDR Q-value\u0026thinsp;=\u0026thinsp;0.042) (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). The extended summary illustrating quantitative and qualitative distinctions of the regulatory LTR elements\u0026rsquo; associations with GO analyses-defined features of the postsynaptic membrane versus the presynaptic membrane is reported in the \u003cstrong\u003eSupplementary Summary S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification and characterization of 26 gene ontology-defined genomic regulatory networks (GRNs) facilitating functions of a genomic regulatory dominion (GRD) of human embryo regulatory LTR elements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of Gene Ontology terms\u0026rsquo; enrichment analyses indicate that a set of 5444 genes constituting candidate down-stream targets of human embryo regulatory LTRs appears segregated into multiple genomic regulatory networks (GRNs) defined by a single ontology-specific enrichment analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Identification of GRNs is based on analyses of regulatory LTRs and down-stream target genes distribution patterns associated with each of significantly-enriched terms (Binominal FDR Q value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified by the GREAT algorithm during analyses of GO Biological Process (227 significant terms), GO Cellular Component (47 significant terms), and GO Molecular Function (80 significant terms) databases (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). It has been observed that for a set of single ontology-defined significantly-enriched terms there is a significant positive correlation (\u003cstrong\u003eR\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e\u0026gt;\u0026thinsp;0.99\u003c/strong\u003e) between genome fractions occupied by genomic features assigned to a set of significantly-enriched terms (referred to as the genomic modicums) and proportions of LTR elements residing within the genomic modicums (referred to as the LTR set coverage). A compendium of regulatory LTRs and down-stream target genes assigned to the individual significantly-enriched GO terms, genomic modicums of which were occupying more than 5% of human genome and harbor at least 6% of human embryo regulatory LTRs, were designated as GRNs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQuantitative profiles of a directed acyclic graph (DAG) * test-validated genomic regulatory modules (GRMs) ** within 26 gene ontology enrichment analysis-defined genomic regulatory networks (GRNs) *** of the genomic dominion of human embryo regulatory LTRs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene ontology ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic regulatory networks\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of LTRs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRN linked genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHuman Phenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMouse Phenotype Single KO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMouse Phenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of GRNs \u0026amp; GRMs\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenomic dominion of human embryo regulatory LTRs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive regulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-cell adhesion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-cell adhesion via plasma-membrane adhesion molecules\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0099537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrans-synaptic signaling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemical synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0050804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModulation of synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle organism behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0042391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation of membrane potential\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1901137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbohydrate derivative biosynthetic process\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuron projection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma membrane region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynapse part\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0009986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell surface\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostsynapse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostsynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0036477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSomatodendritic compartment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuronal cell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0030424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0031012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteinaceous extracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium ion binding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0008233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptidase activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0070011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptidase activity, acting on L-amino acid peptides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative, within\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e26 genomic regulatory networks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative, excluding 26 genomic regulatory networks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eLegend: *, A directed acyclic graph (DAG) test was carried out on the enriched terms from a single ontology-specific table generated by the GREAT algorithm;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e**, For each GRN, reported numbers of GRMs were determined using a directed acyclic graph (DAG) test applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (Supplementary Materials); ***, A total of 26 gene ontology enrichment analyses-defined GRNs associated with human embryo regulatory LTRs were identified at the statistical significance threshold of the Binominal FDR Q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). GRNs contain from 529 to 1486 regulatory LTR elements and from 199 to 805 down-stream target genes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn total, there are 26 gene ontology-defined GRNs associated with human embryo regulatory LTRs each of which contain more than 500 regulatory LTR elements and more than 199 down-stream target genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Genomic locations of GRNs appear scattered across chromosomes to occupy from 5.5\u0026ndash;15.09% of the human genome. GRNs integrate activities from 529 to 1486 human embryo regulatory LTR elements and from 199 to 805 down-stream target genes, including transcription factors, chromatin-state remodelers, sensing and signal transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. One of notable common structural features of GRNs is that numbers of regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences residing within the boundaries of GRN\u0026rsquo;s genomic modicums are quantitatively balanced in strict accordance to their genome-wide abundance levels (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQuantitative profiles of regulatory LTR elements and down-stream target genes within 26 gene ontology enrichment analysis-defined genomic regulatory networks (GRNs) of the genomic dominion of human embryo regulatory LTRs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene ontology ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic regulatory networks\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber, LTR7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber, MLT2A1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber, MLT2A2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of linked genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent, LTR7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent, MLT2A1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent, MLT2A2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenomic dominion of human embryo regulatory LTRs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive regulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-cell adhesion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell-cell adhesion via plasma-membrane adhesion molecules\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0099537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrans-synaptic signaling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemical synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0050804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModulation of synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle organism behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0042391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation of membrane potential\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1901137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbohydrate derivative biosynthetic process\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuron projection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma membrane region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynapse part\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0009986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell surface\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostsynapse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostsynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0036477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSomatodendritic compartment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuronal cell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0030424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0031012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteinaceous extracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium ion binding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0008233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptidase activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0070011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptidase activity, acting on L-amino acid peptides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative, within\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e26 genomic regulatory networks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative, excluding\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e26 genomic regulatory networks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eLegend: *For each GRN, reported numbers of LTRs and genes were determined at the statistical significance threshold of the Binominal FDR Q-Value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (Supplementary Materials); **, A total of 26 gene ontology enrichment analyses-defined GRNs associated with human embryo regulatory LTRs were identified at the statistical significance threshold of the Binominal FDR Q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). GRNs contain from 529 to 1486 regulatory LTR elements and from 199 to 805 down-stream target genes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFollow-up gene ontology enrichment analyses of 26 GRNs revealed further details of their genotype-phenotype association-defined architectural complexity: GRNs consist from several hundred to thousands quantitatively smaller gene ontology enrichment analysis-defined genomic regulatory modules (GRMs) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Data Set S1: GRN1-GRN26\u003c/strong\u003e). Importantly, to increase the stringency of statistical definitions of candidate regulatory LTR-associated GRMs, the cut-off threshold of the Binominal FDR Q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 was applied in these series of GO terms enrichment analyses. One of the implications of this approach is that the false discovery rates (FDR) in these analyses could be estimated at less than 0.1%, thus indicating that the reported observations have more than 99.9% likelihood of being true findings. Based on the results of these analyses, it has been concluded that genomic constituents of each GRN comprise of several hundred to thousands smaller gene ontology enrichment analysis-defined GRMs, each of which contains from a dozen to hundreds LTRs and down-stream target genes. Overall, these analyses identified 69,573 statistically significant regulatory LTR-linked GRMs (Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Data Set S1: GRN1-GRN26\u003c/strong\u003e), including 27,601 GRMs validated by the single ontology-specific directed acyclic graph (DAG) analyses across 6 gene ontology annotations databases (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Data Set S1: GRN1-GRN26\u003c/strong\u003e). It has been determined that within each GRN there is a significant positive correlation between the human genome fractions occupied by the GRMs and LTR set coverage for the GRMs (\u003cstrong\u003eSupplementary Summary S2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThese observations suggest that human embryo regulatory LTRs may exert regulatory effects on thousands distinct GRMs containing functionally and/or structurally-related sub-sets of genes affecting multiple facets of Modern Humans\u0026rsquo; developmental processes as well as physiological and pathological traits, in particular, a constellation of biological processes, cellular components, and molecular activities affecting development and functions of the nervous system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelected examples of GRMs and associated phenotypic traits identified by GO analyses of specific regulatory LTR-governed GRNs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential phenotypic impacts on human pathophysiology of human embryo regulatory LTR-governed GRNs could be inferred by analyses of significantly enriched genotype-phenotype associations annotated to their composite GRMs. The GRN \u0026ldquo;Regulation of Membrane Potential\u0026rdquo; have the largest number of significantly-enriched GRMs and associated phenotypic traits identified by GO analyses of the Human Phenotype database (Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Set S1\u003c/strong\u003e). A set of 79 regulatory LTRs linked to 10 genes have been identified that were annotated to GRM X-linked inheritance (HP:0001417) and GRM Gonosomal inheritance (HP:0010985) at Binominal FDR Q-values of 2.82E-29 and 1.86E-25, respectively. Multiple significantly-enriched GRMs are associated with human phenotypes of major clinical importance. These include: Autism (HP:0000717; 57 LTRs linked to 7 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;3.39E-33); and Autistic behavior (HP:0000729; 65 LTRs linked to 11 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;2.10E-26); Parkinsonism (HP:0001300; 26 LTRs linked to 8 genes; Binominal FDR Q value\u0026thinsp;=\u0026thinsp;1.71E-11); Dementia (HP:0000726; 26 LTRs linked to 7 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;1.20E-09); Intellectual disability (HP:0001249; 179 LTRs linked to 43 genes; Binominal FDR Q value\u0026thinsp;=\u0026thinsp;4.28E-31) and a severe form of Intellectual disability (HP:0010864; 52 LTRs linked to 8 genes; Binominal FDR Q value\u0026thinsp;=\u0026thinsp;2.13E-24); Syncope (HP:0001279; 35 LTRs linked to 11 genes; Binominal FDR Q value\u0026thinsp;=\u0026thinsp;5.46E-24); Abnormality of cardiovascular system electrophysiology (HP:0030956; 64 LTRs linked to 22 genes; Binominal FDR Q value\u0026thinsp;=\u0026thinsp;1.96E-26); and Seizures (HP:0001250; 160 LTRs linked to 36 genes; Binominal FDR Q value\u0026thinsp;=\u0026thinsp;5.19E-33). The extended summary illustrating selected findings derived from GO analyses of the Human Phenotype database related to human disorders is reported in the \u003cstrong\u003eSupplementary Summary S3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eGO Biological Process analyses identified multiple GRMs representing common significantly-enriched phenotypic traits associated with numerous GRNs. Several GRMs common for the GRN \u0026ldquo;Synapse Part\u0026rdquo; and the GRN \u0026ldquo;Postsynapse\u0026rdquo; include regulation of amyloid precursor protein catabolic process; regulation of beta-amyloid formation; positive regulation of beta-amyloid formation; response to beta-amyloid; and cellular response to beta-amyloid.\u003c/p\u003e\n\u003cp\u003eGRNs \u0026ldquo;Synaptic Membrane\u0026rdquo; and \u0026ldquo;Postsynaptic Membrane\u0026rdquo; have several common GRMs, namely regulation of amyloid precursor protein catabolic process; regulation of beta-amyloid formation; and positive regulation of beta-amyloid formation. Similarly, common GRMs for the GRN \u0026ldquo;Trans-synaptic Signaling\u0026rdquo; and the GRN \u0026ldquo;Chemical Synaptic Transmission\u0026rdquo; are beta-amyloid metabolic process; response to beta-amyloid; and cellular response to beta-amyloid; while the GRM \u0026ldquo;Regulation of Membrane Potential\u0026rdquo; harbors a set of functionally-related GRMs, namely regulation of beta-amyloid formation; positive regulation of beta-amyloid formation; response to beta-amyloid; and cellular response to beta-amyloid.\u003c/p\u003e\n\u003cp\u003eGO Molecular Function analyses identified several GRMs representing common significantly-enriched phenotypic traits associated with seven GRNs, namely \u003cstrong\u003e\u0026ldquo;\u003c/strong\u003eSynapse Part\u0026rdquo;; \u0026ldquo;Postsynapse\u0026rdquo;; \u0026ldquo;Synaptic Membrane\u0026rdquo;; \u0026ldquo;Postsynaptic Membrane\u0026rdquo;; \u0026ldquo;Trans-synaptic Signaling\u0026rdquo;; \u0026ldquo;Chemical Synaptic Transmission\u0026rdquo;; \u0026ldquo;Regulation of Membrane Potential\u0026rdquo;. A set of common GRMs shared by these GRNs include beta-amyloid binding; ionotropic glutamate receptor activity; kainate selective glutamate receptor; NMDA glutamate receptor; AMPA glutamate receptor. Interestingly, the GO Molecular Function terms ionotropic glutamate receptor activity; kainate selective glutamate receptor; NMDA glutamate receptor; AMPA glutamate receptor represent the exactly same selection of phenotypic traits that were identified as down-stream regulatory targets of human-specific LTR7 and LTR5_Hs loci (Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOverall, genes linked to phenotypic traits reflecting various aspects of physiological and pathological processes associated with the biogenesis, metabolism, and functions of the amyloid precursor protein and beta-amyloid peptides appear to represent one of the down-stream targets of preference during the evolution of human embryo regulatory LTR elements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of GRNs and GRMs governed by human embryo regulatory LTR elements employing the directed acyclic graph (DAG) analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was important to confirm the validity of statistical definitions of GRNs and GRMs based on the binominal and hypergeometrc FDR Q values (Methods) employing independent analytical approaches. To this end, a directed acyclic graph (DAG) test based on the enriched terms from a single ontology-specific table generated by a GREAT algorithm has been employed (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Sets S1: GRN1-GRN26\u003c/strong\u003e). Patterns and directions of connections between significantly enriched GO modules are guided by the experimentally-documented temporal logic of developmental processes and structural/functional relationships between gene ontology enrichment analysis-defined statistically significant terms. Therefore, only statistically significant GRMs from a single gene ontology-specific table generated by the GREAT algorithm that manifest connectivity patterns defined by experimentally documented developmental and/or structure/function/activity relationships are deemed valid observations and visualized as a consensus hierarchy network of the ontology-specific DAGs (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Sets S1: GRN1-GRN26\u003c/strong\u003e). Based on these considerations, the DAG algorithm draw the developmental and structure/function/activity relationships-guided hierarchy of connectivity between statistically significant gene ontology enriched GRMs. Visualization snapshots of representative examples illustrating the individual ontology-specific DAGs are reported in the Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and comprehensive reports of quantitative and qualitative components of these analyses are presented in Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Sets S1: GRN1-GRN26\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eGO analyses of Mouse phenotype databases and GSEA of the MGI Mammalian Phenotype Level 4 2021 database revealed that genes comprising down-stream regulatory targets of human embryo regulatory LTRs affect development and functions of essentially all major tissues, organs, and organ systems. These include central and peripheral nervous systems, cardio-vascular (circulatory) and lymphatic systems, gastrointestinal (digestive) and urinary (excretory) systems, musculoskeletal and respiratory systems, endocrine and immune systems, as well as integumentary and reproductive systems. Analyses of single gene KO models revealed profound developmental defects and numerous examples of organogenesis failures, indicating that activities of regulatory LTR down-stream target genes are essential for development and functions of major organs and organ systems.\u003c/p\u003e\n\u003cp\u003eOf potentially major interest are observations that all 26 GRNs governed by human embryo regulatory LTR elements harbor significantly enriched records of GRMs annotated to multiple distinct GO terms of Mammalian Offspring\u0026rsquo;s Survival (MOS) phenotypes (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Set S1: GRN1-GRN26\u003c/strong\u003e). For example, GRN2 \u0026ldquo;Regulation of nervous system development\u0026rdquo; (GO:0051960) harbors numerous significantly enriched GRMs annotated to distinct MOS phenotypes, including: neonatal lethality, complete penetrance and neonatal lethality, incomplete penetrance; lethality at weaning, complete penetrance; perinatal lethality, complete penetrance; postnatal lethality, complete penetrance and postnatal lethality, incomplete penetrance; embryonic lethality during organogenesis and embryonic lethality during organogenesis, complete penetrance; lethality throughout fetal growth and development and lethality throughout fetal growth and development, incomplete penetrance; lethality at weaning; perinatal, neonatal, and postnatal lethality phenotypes (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Set S1: GRN2\u003c/strong\u003e). Other notable GRMs of regulatory LTR-linked GRNs that were annotated to significantly enriched MOS phenotypes include: embryonic lethality between implantation and placentation, complete penetrance; embryonic lethality between implantation and somite formation, incomplete penetrance; embryonic lethality between somite formation and embryo turning, incomplete penetrance; prenatal lethality prior to heart atrial septation; embryonic lethality, complete penetrance (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Set S1: GRN1-GRN26\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenes regulating Mammalian Offspring\u0026rsquo;s Survival (MOS) phenotypes are intrinsic components of twenty six Genomic Regulatory Networks (GRNs) governed by human embryo regulatory LTR elements.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRN ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRN GO Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of MOS phenotypes*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop scoring MOS phenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal Rank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal FDR Q-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eneuron projection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostnatal lethality, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eregulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eneonatal lethality, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35E-33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003esynapse part\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostnatal lethality, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ecell-cell adhesion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality at weaning, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0036477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003esomatodendritic compartment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality at weaning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0008569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epositive regulation of nervous system development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eneonatal lethality, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.28E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ecalcium ion binding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eembryonic lethality between implantation and placentation, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostsynapse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality at weaning, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.09E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003esynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality at weaning, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1901137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ecarbohydrate derivative biosynthetic process\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eembryonic lethality between implantation and somite formation, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ecell-cell adhesion via plasma-membrane adhesion molecules\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eneonatal lethality, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003esingle-organism behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostnatal lethality, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.98E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0031012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eextracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality throughout fetal growth and development, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0030424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eaxon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostnatal lethality, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.09E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0043025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eneuronal cell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eembryonic lethality between implantation and placentation, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0099537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003etrans-synaptic signaling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality at weaning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0008569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.72E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003echemical synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality at weaning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0008569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0042391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eregulation of membrane potential\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostnatal lethality, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostsynaptic membrane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality at weaning, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.43E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0008233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epeptidase activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eembryonic lethality during organogenesis, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eproteinaceous extracellular matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elethality throughout fetal growth and development, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0070011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epeptidase activity, acting on L-amino acid peptides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostnatal lethality, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0050804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003emodulation of synaptic transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epreweaning lethality, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.09E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ecell body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eembryonic lethality between implantation and placentation, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.69E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0009986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ecell surface\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eneonatal lethality, complete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRN26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eplasma membrane region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epostnatal lethality, incomplete penetrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMP:0011086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eLegend: *, number of MOS phenotypes among top 500 enriched GO terms.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eCollectively, these observations support the conclusion that GRNs comprising the GRD of human embryo regulatory LTR elements could be viewed as an intrinsically integrated compendium of thousands genotype-phenotypic trait associations-defined GRMs, many of which may represent the evolutionary selection units defined by inherent genotype-phenotype associations affecting species\u0026rsquo; fitness and survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analyses (GSEA) of down-stream target genes of human embryo regulatory LTR elements revealed global multifaceted impacts on physiological traits, developmental phenotypes, and pathological conditions of Modern Humans.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo gain further insights into architectural frameworks and functional features of the GRD associated with human embryo regulatory LTR elements, the Enrichr bioinformatics platform was utilized to carry out the comprehensive series of GSEA employing more than 70 genomics and proteomics databases (Methods). GSEA were executed by imputing 5444 candidate down-stream regulatory target genes of human embryo regulatory LTR elements and retrieving significantly enriched records capturing phenotypic traits affected by subsets of candidate LTR-regulated genes (Tables \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Tables S1-S2\u003c/strong\u003e). These analyses documented global multifaceted impacts of genes representing putative down-stream regulatory targets of human embryo regulatory LTR elements on physiological traits, developmental phenotypes, and pathological conditions of Modern Humans (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGene Set Enrichment Analyses (GSEA) of 5444 genes comprising putative down-stream targets of human embryo regulatory LTRs revealed their global multifaceted impacts on physiological traits, developmental phenotypes, and pathological conditions of Modern Humans.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of significantly enriched records*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eCommon \u0026amp; rare human diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisGeNET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrphanet Augmented 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases AutoRIF ARCHS4 Predictions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases GeneRIF ARCHS4 Predictions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases GeneRIF Gene Lists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases AutoRIF Gene Lists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes Perturbations GEO 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAGMA Drugs and Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDSigDB Human 2021 (Huntington disease)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDSigDB Mouse 2021 (Huntington disease)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSigDB Oncogenic Signatures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJensen database of human diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNA-Seq Disease Gene and Drug Signatures from GEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eDevelopmental \u0026amp; Regulatory Pathways\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWikiPathway 2021 Human\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKEGG 2021 Human\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioPlanet 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanther 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReactome 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTranscription Factor PPIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARCHS4 TF Co-expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARCHS4 Kinases Co-expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTF Perturbations Followed by Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGene Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGene Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndogenous Ligand Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndogenous Ligand Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elncHUB lncRNA Co-Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKinase Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKinase Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eGene Ontology\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMGI Mammalian Phenotype Level 4 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Phenotype Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSingle cell genomics \u0026amp; epigenetics\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanglaoDB Augmented 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCellMarker Augmented 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAzimuth Cell Types 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuBMAP ASCTplusB Augmented 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHuman tissues\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTEx Tissue Expression Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTEx Tissue Expression Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Gene Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARCHS4 Tissues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAllen Brain Atlas up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAllen Brain Atlas down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eRNAseq and Proteomics\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNAseq Automatic GEO Signatures Human Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNAseq Automatic GEO Signatures Human Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteomicsDB 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCLE Proteomics 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAging phenotype signatures\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTEx Aging Signatures 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAging Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAging Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eGenotype-phenotype associations\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGWAS Catalog 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOMIM Expanded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinVar 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenGenI Association 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePheWeb 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbGaP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHost-pathogen interactions\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOVID-19 Related Gene Sets 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirus Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirus Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicrobe Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicrobe Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eLegend: * Numbers of significantly enriched records identified by GSEA during analyses of corresponding genomics and proteomics databases to define entries at the statistical significance threshold of adjusted P value (FDR q value)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQuantitative profiles of a directed acyclic graph (DAG)* test-validated genomic regulatory modules (GRMs)** within GSEA-defined genomic regulatory panels (GRPs)*** of the genomic dominion of human embryo regulatory LTRs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic regulatory panels (GRPs)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of LTRs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of linked genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Biological Process\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003cp\u003eCellular Component\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHuman Phenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMouse Phenotype Single KO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMouse Phenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of GRMs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDAG Visualization Report\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurotrophins (neurotrophic factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure S6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurotrophins (expanded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupplementary Data Sets S2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurexins and Neuroligins R-HSA-6794361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure S5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurotransmitter receptor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure S7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignaling by Receptor Tyrosine Kinases R-HSA-9006934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure S9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRET-GDNF pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure S4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroactive ligand-receptor interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure S8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEA-defined GRN of regulatory LTRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure S10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEA-defined GRN of regulatory LTRs (all significant GRMs)#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupplementary Data Sets S2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eLegend: *, A directed acyclic graph (DAG) test was carried out on the enriched terms from a single ontology-specific table generated by the GREAT algorithm;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e**, For each GRP, reported numbers of GRMs were determined using a directed acyclic graph (DAG) test applied to 6 independent gene ontology-specific enrichment analyses employing the GREAT algorithm (see Supplementary Data Sets S2 for complete reports);\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e***, GSEA-defined GRPs associated with human embryo regulatory LTRs were identified using the Enrichr Bioinformatics Platform at the statistical significance threshold of the adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Supplementary Data Sets S2);\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e#, reported numbers of GRMs were identified by the GREAT algorithm at the statistical significance threshold of the Binominal FDR Q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Supplementary Data Sets S2).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBroadly, identified in this study regulatory LTR target genes have been implicated in pathogenesis and manifestation of a large spectrum of human common, orphan, and rare disorders; they are engaged in numerous developmental and regulatory pathways contributing to differentiation and specialization processes across the hierarchy of human organs, tissues, cells, and sub-cellular structures; have been shown to affect phenotypic traits at the multiple levels of a cellular molecular composition and regulatory infrastructure, including signal transduction triggered by numerous endogenous ligands and multiple families of transmembrane receptors; structural/functional organization of mitochondria, endoplasmic reticulum, and nucleus; activities of transcription factors and chromatin remodeling effectors. Analyzed herein regulatory LTR target genes have been linked with the human aging phenotype in gene expression profiling experiments and with a multitude of both physiological and pathological human phenotypes in genotype-phenotype association studies across various analytical platforms and databases, in particular, human disease associations\u0026rsquo; databases (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eProtein products of regulatory LTR down-stream target genes have been documented to participate in protein-protein interactions of endogenous multiprotein complexes as well as host-pathogen interactions during viral and microbial infections. GSEA of regulatory LTR down-stream target genes employing the OMIM Expanded database of protein-protein interactions (PPI) of human disease-causing genes identified 34 regulatory LTR target genes, protein products of which appear engaged in PPIs with protein products of disease-causing genes implicated in development of a wide spectrum of human disorders (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Genes comprising the 34-gene signature of regulatory LTR targets are significantly enriched in 1387 records of human diseases (GSEA of the DisGeNET database; adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These observations prompted in depth follow-up investigations of a regulatory network comprising 88 regulatory LTR elements (27LTR7; 35 MLT2A1; and 26 MLT2A2 sequences), which were mapped as putative up-stream regulator elements of the 34 genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of 88 human embryo regulatory LTRs comprising putative up-stream regulatory elements of PPI networks of human disease-causing genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of the DAG analyses of 88 human embryo regulatory LTR elements revealed highly complex hierarchical networks of significantly enriched phenotypic traits identified by analyses of GO Biological Process and Mouse Phenotype databases, while analyses of GO Cellular Component and GO Molecular Function documented relatively simple architectures of significantly enriched phenotypic traits (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Data Set S2\u003c/strong\u003e). Overall, 1533 significantly enriched records linked to down-stream target genes of 88 regulatory LTR elements have been identified at the statistical threshold of the Binominal FDR Q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. These include 432; 36; 42; 58; 243; and 722 significantly enriched phenotypic traits identified by Gene Ontology analyses of GO Biological Process; GO Cellular Component; GO Molecular Function; Human Phenotype; Mouse Phenotype Single KO; and Mouse Phenotype databases, respectively (\u003cstrong\u003eSupplementary Data Set S2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eOf note, GO enrichment analyses of 88 regulatory LTR elements employing the Mouse Phenotype Single KO database identified numerous significantly records of genes annotated to distinct mammalian offspring\u0026rsquo;s survival (MOS) phenotypes. The list of enriched GO terms of this category includes several prenatal and perinatal lethality phenotypes [perinatal lethality (MP:0002081); perinatal lethality, complete penetrance (MP:0011089); perinatal lethality, incomplete penetrance (MP:0011090); prenatal lethality, complete penetrance (MP:0011091); prenatal lethality, incomplete penetrance (MP:0011101)]; distinct embryonic lethality phenotypes [embryonic lethality, complete penetrance (MP:0011092); embryonic lethality between implantation and somite formation (MP:0006205); embryonic lethality during organogenesis MP:0006207); embryonic lethality between implantation and placentation (MP:0009850); and the premature death phenotype (MP:0002083).\u003c/p\u003e\n\u003cp\u003eGSEA of 34 genes identified hundreds of significantly enriched phenotypic traits across numerous genomics and proteomics databases, representative examples of which are reported in the Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e. In multiple instances numbers of significantly enriched phenotypic records linked with 34 genes are markedly higher compared to the parent set of 5444 genes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e. For example, there are 121 and 124 significantly enriched records of 34 genes identified by GSEA of the Transcription Factors PPI database and the Hub Proteins PPI database, respectively, while there is only one significantly enriched record in each database identified by GSEA of 5444 genes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). Similarly, there are 1387 and 477 significantly enriched records of human diseases identified by GSEA of 34 genes and 5444 genes, respectively, employing the DisGeNET database (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e. Apparent qualitative distinctions were noted between top 10 significantly enriched records of human diseases: all top 10 GSEA records of 34 genes\u0026rsquo; enrichment were various records of malignant neoplasms, while top 10 records linked with GSEA of 5444 genes were neurodevelopmental and substance abuse disorders (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e. In contrast to common human diseases, 78 and 1095 significantly enriched records of human orphan disorders have been identified by GSEA of 34 genes and 5444 genes, respectively, employing the Orphanet Augmented 2021 database (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFollow-up analyses revealed that overall there are 206 human genes encoding proteins manifesting significantly enriched records of protein-protein interactions (PPIs) with protein products encoded by 34 genes (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summaries S4-S5\u003c/strong\u003e). Because protein products of 34 genes manifest significant enrichment of PPIs with human disease-causing genes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S4\u003c/strong\u003e), these observations suggest that a network of proteins encoded by 240 genes may be engaged in PPIs with human disease-causing genes as well. This hypothesis was tested by performing GSEA of 240 genes comprising the putative PPI network of human disease-causing genes (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summaries S4-S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eGSEA of 240 genes consistently demonstrated that numbers of significantly enriched phenotypic traits linked with 240 genes are markedly higher compared to either 34 genes or the parent set of 5444 genes (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summaries S4-S5)\u003c/strong\u003e. GSEA employing the DisGeNET database identified 3298; 1387; and 477 significantly enriched records of human diseases linked with 240 genes, 34 genes, and 5444 genes, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S4)\u003c/strong\u003e. Qualitatively distinct patterns noted between top 10 significantly enriched records of human diseases associated with 34 genes versus 5444 genes (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e were recapitulated by GSEA of 240 genes: all top 10 GSEA records linked with 240 genes were multiple records of malignant neoplasms, while top 10 records linked with GSEA of 5444 genes were neurodevelopmental and substance abuse disorders (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplemental Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S4\u003c/strong\u003e). In contrast to common human diseases, GSEA employing the Orphanet Augmented 2021 database identified 199 and 1095 significantly enriched records of human orphan disorders associated with 240 genes and 5444 genes, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eParticularly intriguing observations were recorded during GSEA of proteomics databases focused on the molecular architecture of a large compendium of endogenous PPI networks. There are 268 and 228 significantly enriched records of subsets of 240 genes identified by GSEA of the Transcription Factors PPI database and the Hub Proteins PPI database, respectively, while there are 121 and 124 significantly enriched record in corresponding databases identified by GSEA of 34 genes (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S4-S5\u003c/strong\u003e). Similarly, there are 606 and 62 significantly enriched records of 240 genes identified by GSEA of the CORUM Protein Complexes database and the SILAC Phosphoproteomics database, respectively, while there are 143 and 22 significantly enriched record in corresponding databases identified by GSEA of 34 genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S5\u003c/strong\u003e). Strikingly, GSEA employing the NURSA Human Endogenous Complexome database identified 1783 significantly enriched records linked with 240 genes, while no significantly enriched traits were recorded for GSEA of either 34 genes or 5444 genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S5\u003c/strong\u003e). These observations indicate that protein products selected from a panel of 240 genes are significantly enriched in PPIs of a marked majority of endogenous human multimolecular complexes recorded in the NURSA Human Endogenous Complexome database. It follows, that a set of 88 human embryo regulatory LTR elements may extend its regulatory impacts on vast networks of human endogenous multimolecular complexes via protein products of 34 down-stream target genes engaged in PPIs with 268 transcription factors and 228 hub proteins. Strikingly, these networks appear impacting PPIs that are significantly enriched in 6584 records of virus-host PPIs documented in Virus-Host PPI P-HIPSTer 2020 database and 2071 records of human rare disorders catalogued in the Rare Diseases AutoRIF Gene Lists database (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eNotably, GSEA of 240 genes employing the OMIM Expanded database of PPIs of human disease-causing genes further validated and expanded this concept by identification of 51 genes, protein products of which appear engaged in PPIs with protein products of disease-causing genes implicated in development of numerous human disorders of an extremely broad spectrum of clinical phenotypes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Summary S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eCollectively, present analyses suggest that a common molecular mechanism driving development of human pathologies may be associated with altered expression of proteins encoded by regulatory LTR target genes and down-stream effects on PPI networks engaging protein products of disease-causing genes. Malfunctions of endogenous multimolecular complexes tagged by PPIs of proteins encoded by regulatory LTR target genes may contribute to molecular pathogenesis of an exceedingly large number of human diseases, which exhibit highly diverse spectrum of clinical manifestations including malignancies of different solid organs and hematopoietic system; blood, lymphatic and endocrine system disorders; immunoinflammatory and autoimmune maladies; neurodevelopmental, neuropsychiatric, and neurodegenerative diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenes representing common regulatory targets of human embryo regulatory LTR elements are segregated into sub-sets of genetic loci coalesced within genomic regulatory pathways of common biological functions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotable common features of all identified herein GRNs governed by human embryo regulatory LTR elements are the quantitatively balanced numbers of residing within GRN\u0026rsquo;s boundaries regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). These observations indicate that LTR elements of distinct families may affect genes (including sub-sets of common down-stream target genes) comprising constituents of corresponding GRNs, which then likely to exert biologically discernable effects on corresponding phenotypic traits. If the model is correct, this consonant regulatory framework should be observable within previously defined signal transduction pathways known to affect specific phenotypes.\u003c/p\u003e\n\u003cp\u003eThe validity of this hypothesis was tested by performing GSEA of 709 genes comprising a set of common down-stream regulatory targets of LTR7, MLT2A1, and MLT2A2 elements (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In these analytical experiments, GSEA-defined significantly enriched sets of genes operating within specific pathways were mapped to sub-sets of their putative up-stream regulatory elements among human embryo regulatory LTRs (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e; Supplementary Data Set S2\u003c/strong\u003e). Then each of these sub-sets of regulatory LTRs associated with specific pathways were subjected to GO analyses using the GREAT algorithm. Results of these analyses demonstrate that genetic loci representing common regulatory targets of human embryo regulatory LTRs appear segregated into relatively small sets of LTR/gene panels coalesced within genomic regulatory pathways of common biological functions (Tables \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e; \u003cstrong\u003eSupplementary Figures \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e-S10; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e; Supplementary Data Set S2\u003c/strong\u003e). These GSEA/GREAT analyses-defined sets of genes and associated regulatory LTR elements were designated Genomic Regulatory Pathways (GRPs) because they were identified by GSEA as significantly enriched genetic loci (adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) assigne to corresponding previously defined regulatory pathways.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEnrichment of regulatory LTR elements and genes associated with Mammalian Offspring Survival phenotypes within the regulatory LTR-governed GRM of reduced fertility phenotype.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal Rank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal Raw P-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal FDR Q-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal Fold Enrichment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal Observed LTR Hits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinominal LTR Set Coverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypergeometric Rank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypergeometric FDR Q-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypergeometric Fold Enrichment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003eHypergeometric Observed Gene Hits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003eHypergeometric Total Genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypergeometric Gene Set Coverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereduced fertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08E-62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epostnatal lethality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.35E-84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.71E-81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e1378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elethality during fetal growth through weaning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.68E-68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.53E-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e2325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epostnatal lethality, incomplete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.59E-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11E-46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einfertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29E-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.70E-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04E-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epremature death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.52E-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.02E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e1491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eperinatal lethality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.62E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.61E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e1349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epostnatal lethality, complete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.94E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.13E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epreweaning lethality, incomplete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eneonatal lethality, incomplete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.53E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elethality at weaning, complete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.32E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eneonatal lethality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.01E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.92E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eperinatal lethality, incomplete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.07E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eneonatal lethality, complete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.04E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.47E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elethality at weaning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eabnormal cell death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.31E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e1510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprenatal lethality, incomplete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eincreased cell death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprenatal lethality, complete penetrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.37E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.6602%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 12.0437%;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHigh-confidence LTR-regulated genes comprise a significant majority of 5444 genes and 709 genes defined as cumulative and common sets of putative down-stream regulatory targets for LTR7 \u0026amp; MLT2A1 \u0026amp; MLT2A2 human embryo regulatory LTR loci.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommon set of LTR7 \u0026amp; MLT2A1 \u0026amp; MLT2A2 target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR5_Hs regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR7Y/B regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHERVH lncRNA regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVA regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR5_Hs/SVA regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll LTR-regulated common target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative set of LTR7 \u0026amp; MLT2A1 \u0026amp; MLT2A2 target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR5_Hs regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR7Y/B regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHERVH lncRNA regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVA regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTR5_Hs/SVA regulated genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll LTR-regulated target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSimilar to GRNs, all identified by GSEA/GREAT analyses GRPs contain numerous regulatory LTRs of LTR7, MLT2A1, and MLT2A2 families and harbor hundreds of GO annotated GRMs. However, GRPs appear to represent quantitatively smaller genomic regulatory units compared to 26 GRNs based on the assessment of numbers of significantly enriched constituent GRMs (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e; Supplementary Data Sets S1; S2\u003c/strong\u003e) as well as relatively scarce representations of constituent GRMs within DAG analyses-defined GO-specific hierarchical networks (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cstrong\u003eSupplementary Figures S3-S10; Supplementary Data Sets S1; S2\u003c/strong\u003e). Various regulatory aspects of development and functions of peripheral and central nervous systems at cell type-specific levels and distinct morphological regions appear to represent a dominant common theme shared by GRPs (\u003cstrong\u003eSupplementary Figures S3-S10; Supplementary Data Set S2\u003c/strong\u003e). These observations suggest that GSEA-defined GRPs, similar to GO annotations-defined GRMs and GRNs, may constitute a constellation of genomic regulatory units each of which is aligned to specialized biological pathways united by a functional synergy required to preserve the developmental and operational integrity of nervous system.\u003c/p\u003e\n\u003cp\u003eIt has been noted that a compendium of GSEA/GREAT analyses-defined GRPs comprising 966 human embryo regulatory LTR elements and 255 down-stream target genes (\u003cstrong\u003eSupplementary Figure S10;\u003c/strong\u003e Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Set S2\u003c/strong\u003e) manifests genomic features quantitatively and qualitatively resembling GRNs, including overlapping subsets of regulatory LTRs and down-stream target genes assigned by GO annotations to 26 GRNs and their constituent GRMs. Intriguing examples of these common GRMs of potential evolutionary significance include sets of regulatory LTRs/down-stream target genes annotated to significantly enriched records of mammalian offspring survival (MOS) phenotypes. GO annotations employing the Mouse Phenotype Single KO database identified the following significantly-enriched records of relevance to MOS phenotypes:\u003c/p\u003e\n\u003cp\u003e1. MP:0002082; postnatal lethality; 247 LTRs; 45 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;1.59E-17;\u003c/p\u003e\n\u003cp\u003e2. MP:0002081; perinatal lethality; 251 LTRs; 41 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;7.30E-15;\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e3. MP:0011089; perinatal lethality, complete penetrance; 91 LTRs; 14 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;6.84E-12;\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e4. MP:0011090; perinatal lethality, incomplete penetrance; 88 LTRs; 13 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;5.84E-11;\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e5. MP:0011086; postnatal lethality, incomplete penetrance; 149 LTRs; 30 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;7.92E-10;\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e6. MP:0011083; lethality at weaning, complete penetrance; 33 LTRs; 5 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;1.43E-08;\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e7. MP:0011085; postnatal lethality, complete penetrance; 106 LTRs; 20 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;2.25E-07;\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e8. MP:0011110; preweaning lethality, incomplete penetrance; 94 LTRs; 14 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;1.16E-06;\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e9. MP:0001926; female infertility; 65 LTRs; 11 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;1.36E-06;\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eCollectively, these observations indicate that two critically important classes of genetic loci appear co-segregated within identified herein human embryo regulatory LTR-governed genomic regulatory units of various architecture: a). Genes essential for the integrity of nervous system development and functions; b). Genes essential for mammalian offspring survival. It seems likely that this striking co-segregation may reflect co-regulation of these two classes of genes by common sets of regulatory LTR elements. Since knockout of many genes within these regulatory LTR-governed GRNs causes offspring lethality, it is logical to expect that their sustained activity would be essential for offspring survival. Sustained activity of MOS genes within regulatory LTR-governed GRNs could be achieved either by relieving the repressor actions of regulatory LTRs or maintaining the activator effects of regulatory LTRs. Alignments of this genomic regulatory architecture of human embryo regulatory LTR-governed GRNs with control of genes essential for development and functions of nervous system may have contributed to acceleration of central nervous system\u0026rsquo;s dramatic changes during primate evolution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment patterns of genomic loci annotated to mammalian phenotypes of reduced fertility and infertility within GRNs governed by human embryo regulatory LTR elements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the potential biological consequences of essential MOS genes being under control of human embryo regulatory LTR elements is potential effects of this genomic regulatory architecture on species\u0026rsquo; fertility phenotypes. In agreement with this hypothesis, it has been observed that GO-defined GRMs annotated to reduced fertility and/or infertility phenotypes are consistently enriched within 18 of 26 GRNs governed by human embryo regulatory LTR elements (\u003cstrong\u003eSupplementary Table S3\u003c/strong\u003e). Notably, all GRNs annotated to phenotypic traits closely related to development and functions of nervous system harbor significantly enriched GRMs of reduced fertility and/or infertility phenotypes (\u003cstrong\u003eSupplementary Table S3\u003c/strong\u003e). In contrast, GRNs that are more distantly related to or not directly associated with nervous system development and functions do not harbor GRMs of reduced fertility and/or infertility phenotypes among significantly enriched GRMs. These findings suggest that during primate evolution functionally essential fertility genes (that is, genes knockout of which causes reduced fertility and/or infertility phenotypes) and genes essential for development and functions of nervous system were placed under regulatory control of human embryo regulatory LTR elements. It follows that biologically meaningful repressor effects on both classes of genes during ontogenesis would cause defects in development and functions of nervous system as well as would result in reduced fertility and/or infertility phenotypes, thus directly affecting the survivability of the offspring lineage and evolutionary fitness of species.\u003c/p\u003e\n\u003cp\u003eIt was of interest to determine whether GO analyses of human embryo regulatory LTR elements would reveal the potential genomic regulatory connectivity between functionally essential fertility genes and genes essential for mammalian offspring survival. To this end, GO annotations records of the regulatory LTR-governed GRM of the reduced fertility phenotype (MP:0001921) were analyzed to identify the significantly enriched records of regulatory LTR elements and genes associated with Mammalian Offspring Survival (MOS) phenotypes (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). It has been observed that one of the important features of the regulatory LTR-governed GRM of the reduced fertility phenotype is the significant enrichment of regulatory LTR elements and genes annotated by GO analyses to multiple records of MOS phenotypes (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). These observations indicate that abrogated functions of genes essential for mammalian offspring survival (that is, genes knockout of which causes the offspring lethality phenotypes) may represent one of key genetic elements contributing to reduced fertility and/or infertility phenotypes. Based on reported herein observations, it has been concluded that during primate evolution functionally essential fertility genes, genes essential for offspring survival, and genes essential for development and functions of nervous system may have been placed under regulatory control of human embryo regulatory LTR elements. This implied genomic regulatory connectivity may constitute a critical element of evolutionary concordance linking the requirement of balanced and coherent development and functions of nervous system to the survivability of the offspring lineage and evolutionary fitness of primate species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-confidence down-stream target genes of HERVs constitute a significant majority of candidate regulatory targets of human embryo regulatory LTR elements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReported herein inferences of phenotypic impacts of HERVs was made based on the analyses of potential regulatory impacts of human embryo regulatory LTR elements on down-stream target genes without taking into account direct experimental evidence of gene expression changes in human cells following targeted genetic manipulations of HERVs. To address this limitation, genes expression of which is significantly altered in human cells subjected to either shRNA-mediated interference or CRISPR-guided epigenetic silencing directed at defined genetic loci of specific HERV families were identified among candidate down-stream regulatory targets of human embryo regulatory LTR elements. Then proportions of these high-confidence down-stream regulatory targets of regulatory LTRs with experimentally documented gene expression changes were calculated (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eResults of these analyses demonstrate that selective targeting in human cells of defined regulatory sequences encoded by HERVs caused significant expression changes of sub-sets of genes among candidate down-stream regulatory targets of human embryo regulatory LTRs (Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). These significant changes of gene expression were documented in multiple independent experiments employing various human cell lines, which were genetically engineered using either shRNA interference or CRISPR-guided epigenetic silencing tools tailored for selective targeting of distinct regulatory elements of HERVs (including regulatory LTRs and long non-coding RNAs, lncRNAs). While identified proportions of genes comprising high-confidence down-stream regulatory targets varied from 14\u0026ndash;36% in different experimental settings, cumulative sets of identified herein high-confidence down-stream regulatory targets constitute 67% of candidate regulatory LTR-regulated genes (Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e; \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). Based on these observations, it has been concluded that high-confidence regulatory LTR-regulated genes defined by expression profiling experiments comprise a significant majority of 5444 genes and 709 genes, which represent, respectively, cumulative and common sets of identified herein putative down-stream regulatory targets of LTR7; MLT2A1; and MLT2A2 human embryo regulatory LTR loci.\u003c/p\u003e\n\u003cp\u003eIt was of interest to determine whether the inferences of potential global phenotypic impacts of human embryo regulatory LTR elements on human development and pathophysiology could be reproduced by GSEA of high-confidence down-stream regulatory target genes of regulatory LTRs. To address this question, significantly enriched records identified by GSEA of 709 genes and 473 genes during analyses of corresponding genomics and proteomics databases were retrieved and catalogued at the statistical significance threshold of adjusted P value (FDR q value)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). Side by side comparisons of the results demonstrate that GSEA of 473 high-confidence down-stream regulatory target genes of regulatory LTRs essentially recapitulated the quantitative and qualitative GSEA patterns of 703 genes representing a common set of candidate down-stream regulatory targets of regulatory LTRs (Table \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e; \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). Therefore, the inferences of potential global phenotypic impacts of human embryo regulatory LTR elements on human development and pathophysiology that have been made based on GO-guided annotations and GSEA of candidate down-stream target genes are recapitulated by GSEA of gene expression profiling experiments-validated high-confidence down-stream regulatory target genes of HERVs.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGSEA of phenotypic impacts of common sets of LTR7, MLT2A1, and MLT2A2 down-stream target genes defined by proximity placement analyses (709 genes) and by genetic/epigenetic silencing of regulatory LTR loci (473 genes).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProximity placement defined genes*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenetic/epigenetic silencing defined genes*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenomics \u0026amp; proteomics databases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e709 genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e473 genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisGeNET database of human diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrphanet Augmented 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases AutoRIF ARCHS4 Predictions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases GeneRIF ARCHS4 Predictions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases GeneRIF Gene Lists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRare Diseases AutoRIF Gene Lists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWikiPathway 2021 Human\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKEGG 2021 Human\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioPlanet 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Cellular Component 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Molecular Function 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO Biological Process 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMGI Mammalian Phenotype Level 4 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanglaoDB Augmented 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCellMarker Augmented 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAzimuth Cell Types 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTEx Tissue Expression Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTEx Tissue Expression Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Gene Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARCHS4 Human Tissues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAllen Brain Atlas up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAllen Brain Atlas down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Perturbations from GEO down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Perturbations from GEO up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNAseq Automatic GEO Signatures Human Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNAseq Automatic GEO Signatures Human Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTranscription Factor PPIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReactome 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARCHS4 Kinases Co-expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanther 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCI-Nature 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuBMAP ASCTplusB augmented 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARCHS4 TF Co-expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteomicsDB 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eLegend: * Numbers of significantly enriched records identified by GSEA of 709 genes and 473 genes during analyses of corresponding genomics and proteomics databases to define entries at the statistical significance threshold of adjusted P value (FDR q value)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eHuman embryo regulatory LTRs derived from distinct families of pan-primate endogenous retroviruses and human-specific genomic regulatory sequences (HSRS) share common sets of candidate down-stream target genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of important conclusions that could be derived from reported herein observations is that human embryo regulatory LTR elements could have contributed to primate evolution by driving the emergence of human-specific phenotypic traits. To test this hypothesis and gauge potential impacts of regulatory LTRs on emergence of human-specific phenotypic traits, identified and functionally characterized in this study candidate down-stream regulatory targets of human embryo regulatory LTR elements were cross-referenced with previously reported down-stream target genes of 110,339 human-specific genomic regulatory sequences (HSRS), which are fixed in human population and creation of which was attributed to several distinct mechanisms (Glinsky, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e\u0026ndash;2022; Methods). For each binary category of distinct regulatory loci and corresponding down-stream regulatory targets, common sets of down-stream target genes were identified and evaluated based on two quantitative metrics: a) enrichment of observed numbers of common target genes compared to expected numbers based on the random target distribution model; b) evaluations of relative abundance profiles of common sets of down-stream target genes calculated as the percentage of all down-stream target genes. It has been surmised that identification of significantly enriched common sets of genes independently defined as putative down-stream regulatory targets of HSRS and human embryo regulatory LTRs may reveal common phenotypic traits affected by different classes of genomic regulatory elements.\u003c/p\u003e\n\u003cp\u003eConsistent with the idea of overlapping sets of down-stream targets for different classes of genomic regulatory elements, genes identified as down-stream regulatory targets of MLT2A2 and MLT2A1 loci are significantly enriched among LTR7 target genes, while genes identified as down-stream regulatory targets of LTR7 loci are significantly enriched among MLT2A2 and MLT2A1 target genes (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). Furthermore, genome-wide connectivity profiles of human embryo regulatory LTRs and putative target genes are significantly recapitulated within genomic regulatory networks presumably governed by 72,349 human-specific mutations of regulatory DNA sequences, including 17,777 fixed human-specific insertions and deletions; 35,074 fixed human-specific neuro-regulatory single nucleotide mutations shared with archaic humans; and 7,236 fixed human-specific regulatory regions devoided of archaic humans\u0026rsquo; DNA (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn several instances it was possible to dissect common patterns of down-stream regulatory targets\u0026rsquo; enrichment of genes linked with different families of human embryo regulatory LTRs vis-a-vis defined sets of human-specific sequences previously identified as components of different GRNs (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). These set of analyses included the pairwise comparisons of the relative enrichment profiles of genes comprising down-stream targets of Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers and genes linked with 1619 human-specific Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers (Barakat et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky and Barakat, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); genes comprising down-stream targets of transposable elements (TE) loci expressed in dorsolateral prefrontal cortex (DLPFC) of the adult human brain and genes linked with 4690 human-specific TE loci expressed in human adult DLPFC (Guffanti et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); genes identified as down-stream targets of a highly-diverse set of 59,732 HSRS and genes associated with 12,262 created de novo HSRS (Glinsky, 2020). Notably, in all instances genes comprising down-stream targets of bona fide human-specific components of genomic regulatory networks appear significantly enriched among genes defined as down-stream targets of different families of human embryo regulatory LTR elements (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab12\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEnrichment of genes linked to distinct families of Human-Specific Regulatory Sequences (HSRS) among putative down-stream target genes of distinct families of human embryo regulatory LTR elements.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpected\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnrichment/Depletion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGenes associated with LTR7 regulatory elements\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with 1619 human-specific Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67E-37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with TE loci expressed in human adult DLPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.085E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with 4690 human-specific TE loci expressed in human adult DLPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.04E-76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with 59,732 HSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.732E-68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with 12,262 created de novo HSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.274E-83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with HSRS (except genes linked with de novo HSRS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.634E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGenes linked with MLT2A1 \u0026amp; MLT2A2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with 1619 human-specific Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.30E-41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with TE loci expressed in human adult DLPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30E-67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with 4690 human-specific TE loci expressed in human adult DLPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15E-117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with 59,732 HSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66E-94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with 12,262 created de novo HSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12E-120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with HSRS (except genes linked with de novo HSRS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.35E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGenes linked with LTR7 \u0026amp; MLT2A1 \u0026amp; MLT2A2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with 1619 human-specific Na\u0026iuml;ve \u0026amp; Primed hESC functional enhancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26E-43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with TE loci expressed in human adult DLPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes linked with 4690 human-specific TE loci expressed in human adult DLPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.80E-119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with 59,732 HSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.12E-114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with 12,262 created de novo HSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.66E-112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes associated with HSRS (except genes linked with de novo HSRS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0046903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eLegend: *, 2-tail p values were estimated using the Fisher\u0026apos;s exact test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIdentified herein consistent patterns of significant enrichment of common sets of genes comprising down-stream regulatory targets of HSRS and human embryo regulatory LTRs seem related to the 3D topological domains\u0026rsquo; architecture of linear chromatin fibers as well as chromatin state signatures. This notion is supported by observations of significant enrichments of genes associated with rapidly evolving in humans topologically associating domains (TADs) among genes comprising down-stream regulatory targets of human embryo regulatory LTRs (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). In a similar fashion, evaluation of genes associated with dynamic TADs, which are TADs undergoing marked changes of their genomic boundaries during differentiation of human cells, documented their significant enrichment among down-stream target genes of human embryo regulatory LTR elements (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). It has been observed that enrichment score values of these categories of common down-stream target genes appeared placed consistently among top-scoring significantly-enriched gene sets within panels of down-stream regulatory targets of distinct families of human embryo regulatory LTRs.\u003c/p\u003e\n\u003cp\u003eGenes identified herein as candidate down-stream regulatory targets of distinct families of human embryo regulatory LTR elements appear related to a significant degree to down-stream target genes of DNA sequences defined as genomic regulatory elements based on their chromatin state\u0026rsquo;s signatures, namely 8,297 fixed human-specific regulatory regions (Marnetto et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and 7,598 hESC developmental enhancers (Rada-Iglesias et al., 2011). Results of these analyses documented significant enrichment of genes comprising down-stream targets of chromatin state-defined regulatory DNA sequences, revealing genome-wide connectivity components of human embryo regulatory LTR elements and their putative target genes that are recapitulated, in part, within genomic regulatory networks governed by 8,297 fixed human-specific regulatory regions and 7,598 hESC developmental enhancers defined by chromatin state signatures. These findings imply that subsets of genes comprising down-stream targets of regulatory LTRs represent down-stream target genes regulated by hESC developmental enhancers and/or fixed human-specific regulatory regions implicated in control of differentiation phenotypes of human cells.\u003c/p\u003e\n\u003cp\u003eOne of biological processes affecting the integrity of genomic DNA sequences during evolution is colonization and functional/structural remodeling of primates\u0026rsquo; genomes by mobile genetic elements (MGE), including various families of TE and HERVs, most of which lost their mobility and have been co-opted to perform a variety of genomic regulatory functions. L1, Alu and SVA elements are the only transposable elements that have unequivocally been shown to be currently active in humans, as shown by de novo insertions that are responsible for genetic disorders (Cordaux and Batzer, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Inevitable mutational losses of essential regulatory features of transposon insertions dictate the necessity of continuous retrotransposition during evolution. Latest examples of structural/functional exaptation of MGEs likely reflecting relatively recent events of genomic evolution of present day Modern Humans have been mapped based on identification and characterization of fixed insertions of MGEs that were most recently active in human genome (Autio et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has been observed that down-stream target genes of 1937 genomic regulatory elements constituting fixed DNA insertions of most recently active MGEs are significantly enriched among down-stream target genes of human embryo regulatory LTRs of distinct HERV families. Overall, a majority of genes (536 of 926 genes; 58%) comprising down-stream regulatory targets of fixed MGE loci most recently active in human genome appears selected from genes identified in this study as putative down-stream targets of distinct families of human embryo regulatory LTR elements. These findings indicate that GRNs governed by different families of human embryo regulatory LTRs and insertions of MGEs that were most recently active in human genome share a common set of down-stream regulatory targets comprising 536 genes.\u003c/p\u003e\n\u003cp\u003eComparative analyses of genome-wide connectivity maps of genomic coordinates of human embryo regulatory LTRs, human-specific regulatory elements, and their candidate down-stream target genes imply concordant patterns of their inferred phenotypic impacts and suggest a genomic regulatory mechanism contributing to emergence of human-specific traits during primate evolution. Considering evolutionary timelines of introduction and expansion of distinct HERVs\u0026rsquo; families within the primate lineage vis-\u0026agrave;-vis human-specific genomic regulatory sequences, these observations are consistent with the idea that human embryo regulatory LTR elements have made important contribution to creation of genomic fingerprints guiding the evolution of regulatory DNA features facilitating the emergence of human-specific phenotypic traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDown-stream target genes of human embryo regulatory LTR elements are significantly enriched among genes manifesting species-specific expression mapping bias in Human-Chimpanzee hybrids of induced pluripotent stem cells and brain organoids.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDissection of genomic and molecular anatomy of regulatory mechanisms defining development and manifestations of human-specific phenotypic traits remains a highly significant challenge despite recent significant progress in explorations of species-specific developmental features using primate-derived induced pluripotent stem cells (iPSC) and brain organoids, which were particularly useful for identification of species-specific differences of gene expression (Camp et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gallego Romero. et al., 2015; Glinsky, 2020; Kanton, et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kronenberg et al., 2017; Lancaster et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mora-Bermudez et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Muchnik et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Otani et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pasca et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pasca, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pollen et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prescott et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Qian et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sloan et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Most recently, human-chimpanzee hybrid iPSC (hyiPSC) has been developed and utilized to study species-specific gene expression differences in hyiPSC and hyiPS cells differentiated into region-specific brain organoids termed hybrid cortical spheroids, hyCS (Agoglia et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gokhman et al., 2021). Stable tetraploid hybrids of human and chimpanzee cells enabled to control for non-genetic confounding factors such as different timing of developmental dynamics and differentiation processes, micro-environmental fluctuations, differences in cellular compositions, experimental batch effects, which are limited the utility of other experimental approaches aiming to disentangle species-specific divergence of gene expression. Significantly, cross-species tetraploid hybrid cells allowed to unambiguously identify species-specific gene expression differences caused specifically by genomic \u003cem\u003ecis\u003c/em\u003e-regulatory divergence between species, which include promoters and enhancers affecting only down-stream target genes residing on the same species-specific DNA molecules (Agoglia et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gokhman et al., 2021). Therefore, it was logical to test the hypothesis that human embryo regulatory LTR elements may contribute to emergence of human-specific phenotypic traits by evaluating their potential regulatory effects on genes manifesting species-specific expression differences in human-chimpanzee hybrid cells.\u003c/p\u003e\n\u003cp\u003eTo this end, individual binary categories of 5444 down-stream regulatory targets of human embryo regulatory LTRs with three different sets of genes manifesting species-specific gene expression biases in human-chimpanzee hybrid cells were selected, then common (overlapping) sets of genes were identified and significance of enrichment was estimated of observed numbers of common genes compared to corresponding expected numbers based on the random distribution model (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e). Notably, consistent patterns of significant enrichment of human embryo regulatory LTR target genes among three different categories of genes manifesting species-specific biases of gene expression in human-chimpanzee hybrid cells (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e). There are 95 genes putatively regulated by human embryo LTRs among 374 genes with species-specific expression mapping bias in hyiPSC of human-chimpanzee hybrids (Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), which constitute 2.2-fold enrichment compared to expected numbers of common genes (p\u0026thinsp;=\u0026thinsp;9.58E-14). Similarly, there was observed the 1.9-fold (p\u0026thinsp;=\u0026thinsp;5.06E-07) enrichment of human embryo LTRs\u0026rsquo; down-stream target genes among genes exhibiting species-specific expression mapping bias in human-chimpanzee hybrids of Cortical Spheroid (Brain Organoids), hyCS (Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Finally, a total of 56 down-stream target genes of human embryo regulatory LTRs constitute 30.6% of genes manifesting species allele-specific expression profiles in human-chimpanzee hybrid cells (Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e), which represents a 2.7-fold enrichment (p\u0026thinsp;=\u0026thinsp;4.36E-12) compared to expected by chance alone numbers. Examination of expression profiles of genes with species-specific expression bias in human-chimpanzee hybrids revealed the apparently discordant patterns of their expression in both hyiPSC (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; panels A and B) and hyCS brain organoids\u0026rsquo; settings (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; panels C and D) supporting the idea that species-specific differences in gene expression may contribute to phenotypic divergence of Humans and Chimpanzee in regulation and manifestation of stemness phenotype and cortical differentiation.\u003c/p\u003e\n\u003ctable id=\"Tab13\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEnrichment of human embryo regulatory LTR down-stream target genes among genes with species-specific expression mapping bias in hybrids of Human and Chimpanzee cells.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes with expression mapping bias in hybrid induced Pluripotent Stem (iPS) Cells of Human and Chimpanzee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes regulated by human embryo regulatory LTR elements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpected by chance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnrichment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.58E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes with expression mapping bias in hybrid Cortical Spheroids (Brain Organoids) of Human and Chimpanzee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes regulated by human embryo regulatory LTR elements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpected by chance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnrichment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.06E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes manifesting allele-specific expression in hybrids of Human and Chimpanzee cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes regulated by human embryo regulatory LTR elements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpected by chance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnrichment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.36E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eLegend: * p values were estimated using the hypergeometric distribution test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eOverall, these analyses revealed that a significant fraction of genes with species-specific expression bias in human-chimpanzee hybrid cells represents down-stream target genes of human embryo regulatory LTRs (177 of 676 genes; 26%), indicating that regulatory effects of regulatory LTR elements may have affected species segregation during the evolution of Great Apes by contributing to phenotypic divergence of Modern Humans. It was of interest to determine what morphological structures and phenotypic traits were preferred targets of regulatory LTR elements during Modern Human divergence during primate evolution. This question was addressed by performing GSEA of identified herein 177 genes (\u003cstrong\u003eSupplementary Table S5\u003c/strong\u003e) across multiple genomic database (Methods). GSEA of 177 genes employing the ARCHS4 Human Tissues database identified 23 records with significant enrichment of human embryo regulatory LTRs\u0026rsquo; target genes (adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), among which the Dentate Granule Cells were the top-scoring term (adjusted p value\u0026thinsp;=\u0026thinsp;3.62E-09). A total of 53 genes linked with 223 human embryo regulatory LTRs were identified among genes constituting genetic markers of the Dentate Granule Cells (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eE). In addition to the Dentate Granule Cells, prefrontal cortex (52 genes), cingulate gyrus (51 genes), cerebellum (48 genes), and cerebral cortex (48 genes) were identified among the top 5 scoring records (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eTo gain insights into the broader developmental context of biological functions of genes associated with 223 human embryo regulatory LTR elements, the DAG analyses were carried out employing the GREAT algorithm (Methods). Notably, GO annotations of the Mouse Single KO database generated local DAG which identified abnormal synaptic transmission (MP:0003635; linked with 66 LTRs and 17 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;1.49E-15); abnormal learning/memory/conditioning (MP:0002063) and abnormal cognition (MP:0014114; both terms linked with 49 LTRs and 14 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;7.98E-08); abnormal excitatory postsynaptic currents (MP:0002910; 19 LTRs linked with 6 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;2.08E-06); abnormal synaptic vesicle morphology (MP:0004769; 11 LTRs linked with 5 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;5.37E-06); and abnormal CNS synaptic transmission (MP:0002206; linked with 65 LTRs and 16 genes; Binominal FDR Q-value\u0026thinsp;=\u0026thinsp;3.07E-17) among 250 significantly enriched functional and developmental anomalies caused by knockout of individual genes associated with 223 human embryo regulatory LTRs (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; panels F and G).\u003c/p\u003e\n\u003cp\u003eTaken together, reported observations indicate that genes manifesting species-specific expression bias in human-chimpanzee hybrid cells and linked by putative regulatory associations with human embryo regulatory LTR elements are implicated in higher cognitive functions of learning and memory; biological processes of CNS synaptic transmission, excitatory postsynaptic currents, and neuroactive ligands-receptors interactions; gene expression markers of numerous morphological components of human brain, including the Dentate Granule Cells, prefrontal cortex, cingulate gyrus, cerebellum, cerebral cortex, superior frontal gyrus, dorsal striatum, motor and sensory neurons, midbrain, oligodendrocytes, and neuronal epithelium (Table \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S5\u003c/strong\u003e). Additionally, this family of genes represents gene expression markers of adult human somatic tissues (small intestine; testis; alpha cells; retina) and morphological elements of human embryogenesis namely fetal brain cortex, oocyte, and zygote (Table \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e; \u003cstrong\u003eSupplementary Table S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab14\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of significantly enriched phenotypic traits and morphological elements associated with 177 down-stream target genes of human embryo regulatory LTRs manifesting species-specific expression bias in human-chimpanzee hybrids.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKEGG 2021\u003c/p\u003e\n \u003cp\u003eHuman Database\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHuman embryo regulatory LTR target genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted P-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenes manifesting species-specific expression bias\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted P-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent, LTR regulated\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelative enrichment*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroactive ligand-receptor interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuBMAP ASCTplusB augmented 2022 Database\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman embryo regulatory LTR target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes manifesting species-specific expression bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent, LTR regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelative enrichment*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInh L1 SST DEFB108B - Brain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.29E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.387543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInh L1-2 VIP PTGER3 - Brain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.29E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInh L1-3 SST FAM20A - Brain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInh L1-2 PVALB CDK20 - Brain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInh L1-2 VIP SCML4 - Brain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARCHS4 Human Tissues Database\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman embryo regulatory LTR target genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenes manifesting species-specific expression bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent, LTR regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelative enrichment*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDENTATE GRANULE CELLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.62E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.25E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePREFRONTAL CORTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.03E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCINGULATE GYRUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEREBELLUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEREBRAL CORTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPINAL CORD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPINAL CORD (BULK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUPERIOR FRONTAL GYRUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAIN (BULK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.62E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.29E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDORSAL STRIATUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.31E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOTOR NEURON\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.55E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.16E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIDBRAIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSMALL INTESTINE (BULK TISSUE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFETAL BRAIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLIGODENDROCYTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTESTIS (BULK TISSUE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.16E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALPHA CELLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFETAL BRAIN CORTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRETINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSENSORY NEURON\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEURONAL EPITHELIUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOOCYTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHUMAN ZYGOTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPERM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.514091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eLegend: *Relative enrichment values were estimated by dividing observed values by expected values in corresponding category based on 26% representation of all regulatory LTR down-stream target genes among all genes manifesting species-specific expression bias in human-chimpanzee hybrids.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe hypothesis that down-stream target genes of human embryo regulatory LTRs may affect human-specific gene expression networks operating in adult human brain was further validated by observations that human embryo regulatory LTRs\u0026rsquo; target genes are significantly enriched among genes expression of which demarcates human-specific features of spatial gene expression profiles delineated by comparative analyses of eight brain regions of humans, chimpanzees, gorillas, a gibbon, and macaques (Xu et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Overall, a significant fraction of genes comprising down-stream target of human embryo regulatory LTRs represents genes implicated in differential transcriptomic patterns of eight regions of primates\u0026rsquo; brains (1968 of 5444 genes; 36.2%; p\u0026thinsp;=\u0026thinsp;1.03604E-20; hypergeometric distribution test). It has been observed that human embryo regulatory LTRs\u0026rsquo; target genes constitute 35.2% of genes manifesting human-specific expression differences in examined eight brain regions in contrast to 25.4% genes exhibiting chimpanzee-specific expression differences (p\u0026thinsp;=\u0026thinsp;0.026; 2-tail Fisher\u0026rsquo;s exact test). Furthermore, detailed analyses of expression profiles of genes assigned to 26 distinct gene expression modules of primates\u0026rsquo; brain identified 3 modules (modules 22; 16; and 15; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eH) that manifest most significant relative enrichment of human embryo regulatory LTR\u0026rsquo;s target genes in contrast to 7 modules (modules 4; 7\u0026ndash;11; 18) exhibiting most significant relative depletion (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eH). Notably, two of three modules with most significant relative enrichment (human-specific module 22 and module 16) of human embryo regulatory LTRs\u0026rsquo; down-stream target genes are known to contain the disproportionally high numbers of genes with hippocampus-specific expression (Xu et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings indicate that hippocampus-specific genes may constitute one of preferred regulatory targets of human embryo regulatory LTR elements.\u003c/p\u003e\n\u003cp\u003eCollectively, reported herein observations support the hypothesis that genes comprising putative down-stream regulatory targets of human embryo regulatory LTRs contributed to phenotypic divergence of Modern Humans during the primate evolution and may have impacted the emergence of uniquely humans\u0026rsquo; phenotypic traits. Identification of hippocampus and more specifically Dentate Granule Cells among significant morphological targets of preference of regulatory effects of human embryo regulatory LTR elements may assist in precise experimental definition of important morphological structures of human brain having evolutionary shaped critical impacts on human-specific features of higher cognitive functions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTransitions of exogenous retroviral infections to the state of endogenous retroviruses have persisted within the primate lineage throughout the past 30\u0026ndash;40\u0026nbsp;million years. Concurrently with transitions of exogenous retroviral infections to the state of endogenous retroviruses (ERVs), sophisticated and highly complex molecular mechanisms are co-evolved in mammalian cells to identify and control endogenous retroviral sequences (Dopkins et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One of the outcomes of these processes was a large-scale colonization of human genome by HERVs, which expanded during evolution to represent\u0026thinsp;~\u0026thinsp;8% of DNA sequences in human cells. Colonization of human genome by HERVs is likely to represent the end result of long-term germ-line reinfections during primate evolution, rather than retrotransposition in cis or complementation in trans following a single infection cycle (Belshaw et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Resulting large-scale expansion of highly homologous sequences derived from individual retroviruses and high diversity of numerous retroviral families created formidable scientific challenges of both analytical and conceptual dimensions. In recent years, several bioinformatics pipelines have been developed and implemented which are dedicated to highly accurate identification and quantification at the level of individual genetic loci resolution of DNA and RNA sequences originated from endogenous retroviruses in various experimental settings, including DNA and RNA extracted from human tissues and cells (Guffanti et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), ChIP-seq and HiC sequencing outputs (Lerat, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as well as from single-cell RNA sequencing experiments (Berrens et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These advances facilitated significant progress in our understanding of biological roles of DNA sequences derived from individual families of HERVs and revealed novel epigenetic, transcriptional, and posttranscriptional mechanisms of their locus-specific regulation. However, advances in development and implementation of analytical and conceptual approaches aiming to dissect the potential cross-talks of distinct families of HERVs that colonized human genomes during primate evolution and their contribution to emergence of human-specific phenotypic traits remain limited.\u003c/p\u003e \u003cp\u003ePresent analyses were focused on human embryo regulatory LTR loci defined herein as LTR loci with experimentally documented genomic regulatory functions affecting phenotypes deemed essential for human embryonic development, in particular, preimplantation stages of human embryogenesis. To this end, patterns of evolutionary origins and global effects of 8839 human embryo regulatory LTR elements derived from distinct families of pan-primate endogenous retroviruses on genome-wide transcriptional regulatory networks were evaluated with emphasis on the assessment of their potential impacts on developmental processes and pathological conditions of Modern Humans. Phenotypic patterns of regulatory effects on pathophysiology of Modern Humans were validated by differential GSEA of high confidence down-stream target genes expression of which is altered following genetic and/or epigenetic targeting of human embryo regulatory LTR elements. Collectively, present analyses demonstrate a global scale of potential phenotypic impacts of human embryo regulatory LTR elements on pathophysiology of Modern Humans.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSequential patterns of evolutionary origins and quantitative expansion during primate evolution of distinct families of human embryo regulatory LTR elements.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOverall, analyzed in this contribution three distinct LTR families, namely MLT2A1 (2416 loci), MLT2A2 (3069 loci), and LTR7 (3354 loci), appear to have similar evolutionary histories defined by evolutionary patterns of expansion, conservation, and divergence during primate evolution. However, they manifest clearly discernable distinct timelines of evolutionary origins, which were defined as the estimated timelines when retroviruses infect primates\u0026rsquo; germlines and transition from the state of exogenous infection agents to endogenous retroviral sequences integrated into host genomes of primate lineage. Present analyses indicate that both MLT2A1/HERVL and MLT2A2/HERVL retroviruses have infected primates\u0026rsquo; germlines before the segregation of Old World Monkeys (OWM) and New World Monkeys (NWM) lineages. This conclusion is based on the evidence that the consistent earliest presence of highly-conserved pan-primate MLT2A1 and MLT2A2 loci has been mapped to genomes of both NWM and OWM. In contrast, the consistent earliest presence in primates\u0026rsquo; genomes of LTR7 loci could be mapped to genomes of OWM but not to genomes of NWM. These observations suggest that LTR7/HERVH retroviruses have entered germlines of the primate lineage after the separation of the OWM and NWM lineages. Pan-primate endogenous MLT2A1/HERVL, MLT2A2/HERVL, and LTR7/HERVH retroviruses appear to achieve a significant expansion in genomes of Great Apes, which is ranging from ~\u0026thinsp;2-fold expansion for MLT2A2 loci to ~\u0026thinsp;5-fold expansion for LTR7 loci. Interestingly, the consistent earliest presence of highly-conserved LTR5_Hs loci has been mapped to the Gibbon\u0026rsquo;s genome (Glinsky, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), consistent with the primate evolution model stating that LTR5_Hs/HERVK retroviruses successfully colonized germlines of the primate lineage after the segregation of Gibbons\u0026rsquo; species. Subsequently, MLT2A1/HERVL; MLT2A2/HERVL; LTR7/HERVH; and LTR5_Hs/HERVK retroviruses appear to undergo a marked expansion in genomes of Great Apes. Interestingly, the consistent patterns of increasing cognitive and behavioral complexities during primate evolution have been observed on the OWM evolutionary branch, which continually evolved in Africa, but not on geographically segregated to South America the NWM branch.\u003c/p\u003e \u003cp\u003eConsistent with the previously reported findings for highly-conserved LTR7 and LTR5_Hs loci (Glinsky, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), patterns of quantitative expansion in different primate species of human embryo regulatory LTR elements appear to replicate the consensus evolutionary sequence of increasing cognitive and behavioral complexities during primate evolution. The apparent concordance of increasing cognitive and behavioral complexities during the evolution of primate species with clearly discernable evidence of sequential introduction and expansion of distinct families of regulatory LTR elements in genomes of the primate lineage suggest that human embryo regulatory LTRs originated from distinct retroviruses may have contributed to the guidance of primate evolution toward the emergence of human-specific phenotypic traits. This hypothesis seems particularly appealing when considered together with the evidence of global impacts on exceedingly broad spectrum of developmental phenotypes of genes constituting down-stream regulatory targets of human embryo regulatory LTR elements.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEmergence during primate evolution of the global Genomic Regulatory Dominion (GRD) governed by human embryo regulatory LTR elements.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eObservations reported in this contribution provide the foundation for the hypothesis that evolution of human embryo regulatory LTR elements created the global retroviral Genomic Regulatory Dominion (GRD) consisting of 26 gene ontology enrichment-defined genome-wide Genomic Regulatory Networks (GRNs). Decoded in this study the hierarchical super-structure of regulatory LTR-associated GRD and GRNs represents an intrinsically integrated developmental compendium of 69,573 Genomic Regulatory Modules (GRMs) each of which is congregated along specific experimentally-defined genotype-phenotypic trait associations. On the levels of individual genomic loci, described in this contribution retroviral GRD integrates 8839 highly conserved LTR elements linked to 5444 down-stream target genes. These scattered across human genome individual genomic loci were forged by evolution into a functionally-consonant constellation of 26 genome-wide multimodular GRNs, each of which harbors hundreds significantly enriched single gene ontology-specific traits. Genomic coordinates of GRNs are scattered across chromosomes to occupy from 5.5\u0026ndash;15.09% of the human genome. Each GRN acquired during evolution similar regulatory architecture: it harbors from 529 to 1486 human embryo regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences that are quantitatively balanced corresponding to their genome-wide abundance. GRNs integrate activities of functionally and structurally highly diverse panels of down-stream regulatory targets comprising from 199 to 805 genes. These include genes encoding transcription factors, chromatin-state remodelers, signal sensing and signal transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. Compositions of individual GRNs consist of several hundred to thousands smaller gene ontology enrichment analysis-defined genomic regulatory modules (GRMs), each of which combines from a dozen to hundreds regulatory LTRs and down-stream target genes. A total of 69,573 statistically significant regulatory LTR-linked GRMs (Binominal FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) have been identified in this contribution, including 27,601 GRMs that have been validated by the single ontology-specific directed acyclic graph (DAG) analyses across 6 gene ontology annotations databases.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsights into putative concurrent mechanisms of biological functions and global organism-level impacts of human embryo regulatory LTR elements.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo corroborate and extend findings documented based on GO-guided analytical approaches, comprehensive series of Gene Set Enrichment Analyses (GSEA) of regulatory LTRs down-stream target genes were carried out employing more than 70 genomics and proteomics databases, including a large panel of databases developed from single-cell resolution studies of healthy and diseased human\u0026rsquo;s organs and tissues. It has been observed that distinct GRNs and GRMs appear to operate on individuals\u0026rsquo; life-span timescale along specific phenotypic venues selected from an exceedingly broad spectrum of down-stream gene ontology-defined and signaling pathways-guided frameworks. GRNs and GRMs governed by human embryo regulatory LTRs seem functionally designed to exert profound effects on patterns of transcription, protein-protein interactions, developmental phenotypes, physiological traits, and pathological conditions of Modern Humans.\u003c/p\u003e \u003cp\u003eGO analyses of Mouse phenotype databases and GSEA of the MGI Mammalian Phenotype Level 4 2021 database revealed that down-stream regulatory targets of human embryo regulatory LTRs are enriched for genes that are essential for faithful execution of developmental processes and functionally-adequate states of all major tissues, organs, and organ systems. These conclusions are supported by observations documenting profound developmental defects in corresponding single gene KO mouse models. Furthermore, genes comprising candidate down-stream regulatory targets of human embryo regulatory LTRs are engaged in PPI networks that have been implicated in pathogenesis of human common and rare disorders (3298 and 2071 significantly enriched records, respectively), in part, by impacting PPIs that are significantly enriched in 1783 multiprotein complexes recorded in the NURSA Human Endogenous Complexome database and 6584 records of virus-host PPIs documented in Virus-Host PPI P-HIPSTer 2020 database.\u003c/p\u003e \u003cp\u003eCollectively, results of multiple analytical experiments reported in this contribution consistently demonstrate that genes comprising candidate down-stream regulatory targets of human embryo regulatory LTR elements affect development and functions of essentially all major tissues, organs, and organ systems, including central and peripheral nervous systems, cardio-vascular (circulatory) and lymphatic systems, gastrointestinal (digestive) and urinary (excretory) systems, musculoskeletal and respiratory systems, endocrine and immune systems, as well as integumentary and reproductive systems. In-depth follow-up analyses of single gene knockout models of defined regulatory LTR down-stream targets revealed a multitude of profound developmental defects and numerous examples of organogenesis failures, indicating that activities of human embryo regulatory LTR down-stream target genes are essential for development and functions of major organs and organ systems.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalytical inference of a cell-type-level preferred morphological targets of regulatory effects of human embryo regulatory LTR elements.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eApplications of GO annotations and GSEA approaches revealed exceedingly broad impacts of genes comprising putative regulatory targets of human embryo regulatory LTRs on the levels of tissues, organs, and organ systems of the human body. Similarly, GSEA of single-cell genomic databases (for example, PanglaoDB Augmented 2021 database; CellMarker Augmented 2021 database; Azimuth Cell Types 2021 database) documented a very large number of distinct cell types among significantly enriched records, including a multitude of specific types of neurons, radial glia and Bergmann glia cells, alpha and beta cells, oligodendrocytes, astrocytes, multiple types of stem cells and precursor cells, and anterior pituitary gland cells among others. However, targets of preference interrogations among 177 LTR target genes manifesting species-specific biases in human-chimpanzee hybrids employing the ARCHS4 Human Tissues database (Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e) enabled identification of some specific cell types that appear to represent the preferred regulatory targets of human embryo regulatory LTR elements, specifically Dentate Granule Cells and neuronal epithelium. Identification of Dentate Granule Cells is of particular interest because Dentate Granule Cells have been implicated in the parallel emergence of both dynamic and stable memory engrams in the hippocampus (Hainmueller and Bartos, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the contribution of adult-born Dentate Granule Cells of Dentate Gyrus to the downstream firing dynamics of neocortical information flow to the hippocampus (McHugh et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These latest findings are highly consistent with the critical role of the adult hippocampal neurogenesis, which occurs precisely only in the Dentate Gyrus, in higher cognitive functions, especially learning, memory, and emotions (Kempermann et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kuhn et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese cell types remain consistently among top-scoring significantly enriched records throughout the layered multistage process of GSEA-guided interrogations of different sets of regulatory LTR down-stream target genes, including 5444 genes comprising the cumulative set of regulatory LTR regulatory targets (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e); 709 genes representing the common set of regulatory LTR regulatory targets (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e); 473 genes comprising high-confidence down-stream regulatory targets of regulatory LTRs (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e); and 177 regulatory LTR down-stream target genes manifesting species-specific expression bias in human-chimpanzee hybrid cells (Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e; \u003cb\u003eSupplementary Table S5\u003c/b\u003e). For example, Radial Glia Cells (78 regulatory LTR target genes; adjusted p value\u0026thinsp;=\u0026thinsp;8.80E-18; PanglaoDB Augmented 2021 database); Neuronal epithelium (937 regulatory LTR target genes; adjusted p value\u0026thinsp;=\u0026thinsp;2.24E-47; ARCHS4 Human Tissues database); and Dentate Granule Cells (863 regulatory LTR target genes; adjusted p value\u0026thinsp;=\u0026thinsp;2.45E-28; ARCHS4 Human Tissues database), have been identified among top-scoring significantly enriched terms during GSEA of 5444 genes comprising a cumulative set of putative down-stream regulatory targets of human embryo regulatory LTR elements (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). It has been observed that overall human embryo regulatory LTRs\u0026rsquo; down-stream target genes constitute large fractions of all gene expression markers assigned in the respective genomic databases to corresponding cell types: they constitute 68% of genes for Radial Glia Cells; 40% of genes for Neuronal epithelium; and 37% of genes for Dentate Granule Cells.\u003c/p\u003e \u003cp\u003eCollectively, these observations strongly argue that specific cell types, namely neuronal epithelium, Radial Glia Cells, and Dentate Granule Cells, may represent the cellular targets of preference of genomic regulatory networks governed by human embryo regulatory LTR elements and suggest that embryonic and adult neurogenesis could have been among the principal biological processes contributing to striking phenotypic divergence of Modern Humans during the evolution of Great Apes. In agreement with this notion, GSEA of human embryo regulatory LTRs down-stream target genes comprising of sets of 709 genes; 473 genes; or 177 genes employing the GTEx Human Tissue Expression database of up-regulated genes consistently demonstrated that all top 100 significantly enriched records constitute the brain tissues of male or female individuals (\u003cb\u003eSupplementary Tables S2; S5\u003c/b\u003e). In contrast, there were no significantly enriched records of human brain tissue samples identified by GSEA of human embryo regulatory LTRs down-stream target genes employing the GTEx Human Tissue Expression database of down-regulated genes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsights into molecular mechanisms of global genome-wide and proteome-wide actions of human embryo regulatory LTR elements.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHuman embryo regulatory LTR elements undergo programmed epigenetic activation and silencing during human embryonic development starting from the earliest stages of preimplantation embryogenesis (Introduction). Mechanistically, activation of human endogenous regulatory LTR elements may function as epigenetic switch facilitating targeted reversible transitions of the 3D regulatory chromatin architecture within topologically associating domains from dominant single enhancer modes of active regulatory LTRs to the state of agile host super-enhancer loci of the regulatory chromatin microenvironment. Epigenetic silencing of regulatory LTR loci could create distribution patterns of silenced chromatin domains of H3K9me3 repressive histone marks. Conversely, active regulatory LTR elements may contribute to creation of transcriptionally active chromatin state by impacting deposition of H3K4me3 histone marks and expansion of broad H3K4me3 domains.\u003c/p\u003e \u003cp\u003eDeposition of H3K9me3 histone marks is significantly enriched on LTR retrotransposons throughout human preimplantation embryogenesis (Yu et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), indicating that human embryo regulatory LTR elements facilitate genome-wide heterochromatin patterning. Genes that are marked by or positioned near H3K9me3 peaks throughout the human development are related to cell fate specification, which is consistent with the idea that one of important functions of H3K9me3 chromatin modifications is to block the premature expression of genes essential for regulation of developmental stages (Yu et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Embryonic stage-specific establishment of H3K9me3 histone modifications silences targeted LTR loci of different families during human embryogenesis and 8-cell-specific H3K9me3 domains demarcate enhancer-like genomic regions marked by hallmarks of enhancers H3K4me1 and H3K27Ac at the later developmental stages and in adult human somatic tissues (Xu et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn human blastocysts, H3K4me3/H3K9me3 and H3K4me3/H3K27me3 bivalent chromatin domains have been identified that contributed to heterochromatin patterning primed for lineage segregation and likely involved in lineage differentiation (Xu et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been reported that H3K4me3/H3K9me3 bivalent histone methylation signature denotes poised differentiation master regulatory genes in trophoblast stem cells, extraembryonic endoderm stem cells, and preadipocytes (Matsumura et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rugg-Gunn et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Genes occupied by H3K4me3/H3K9me3 bivalent chromatin domains were implicated in various metabolic processes and are not significantly expressed during early embryonic development (Xu et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while nearly 30% of LTRs were occupied by H3K4me3/H3K9me3 bivalent chromatin modifications indicating that some human embryo regulatory LTR loci may be poised for activation at the later developmental stages. These observations suggest that human embryo regulatory LTR elements may contribute to creation of bivalent H3K4me3/H3K9me3 chromatin domains, thus affecting the genome-wide patterns of both H3K9me3 repressive chromatin marks and H3K4me3 active chromatin modifications. I\u003c/p\u003e \u003cp\u003eInterestingly, CRISPRi-mediated silencing of the LTR7Y/B loci in hESC documented significant effects on expression of eight chromatin writers (methyltransferases) and erasers (demethylases) regulating methylation status of the H3K4 histone, suggesting a mechanism of selective activation of the \u003cem\u003eKMT2D\u003c/em\u003e methyltransferase gene expression by LTR7Y/B (Glinsky, data not shown and manuscript in preparation). These findings imply that human embryo regulatory LTRs, in particular, LTR7Y/B loci, may facilitate the establishment of the broad H3K4me3 chromatin domains. Consistent with this hypothesis, the KMT2D methyltransferase plays the principal role in establishing the broad H3K4me3 chromatin domains and constitutes an intrinsic components of enhancers-forming multimolecular liquid-liquid phase-separated condensates, which are particularly prominent at super-enhancers (reviewed in Kent et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, activation/repression cycles of regulatory LTR actions may function as an epigenetic state transitions' shuttle between on/off epigenetic control of super-enhancer activities at the selected topological domains and nanodomains of the interphase chromatin. Aberrations of these epigenetic reprogramming patterns may contribute to developmental defects and pathogenesis of human disorders.\u003c/p\u003e \u003cp\u003eObservations reported in this contribution may have important implications for our understanding of the molecular pathophysiology of human diseases linked with malfunctions of endogenous multimolecular complexes tagged by PPI networks of protein products encoded by regulatory LTR-target genes. Present analyses suggest that one of widely shared mechanisms underlying development of a broad spectrum of human pathologies of common, rare, or orphan classifications is associated with altered engagements of protein products of disease-causing genes within PPI networks of proteins encoded by down-stream target genes of regulatory LTR elements. Multiple malfunctions\u0026rsquo; focal points of these human pathology-driving PPI networks may operate by impacting protein-protein interactions that are significantly enriched in identified herein 1783 multiprotein complexes recorded in the NURSA Human Endogenous Complexome database and 6594 records of virus-host protein-protein interactions reported in the Virus-Host PPI P-HIPSTer 2020 database.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsights into mechanisms of potential impacts of human embryo regulatory LTR elements during primate evolution.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis contribution highlights potential multidimensional impacts of retroviral GRD on emergence of human-specific traits during primate evolution. Genome-wide candidate regulatory targets and high-confidence down-stream target genes of human embryo regulatory LTR elements identified and functionally characterized in this study were cross-referenced with putative down-stream target genes of more than 100 thousands human-specific genomic regulatory elements. These analyses revealed consistent patterns of significant enrichment of common sets of genes comprising down-stream regulatory targets of HSRS and human embryo regulatory LTRs that are related to the 3D topological domains\u0026rsquo; architecture of linear chromatin fibers as well as regulatory chromatin state signatures. Overall, identification of significantly enriched sets of intersecting genes independently defined as putative down-stream regulatory targets of different families of HSRS and human embryo regulatory LTR elements depicts common phenotypic traits potentially affected by evolutionary and structurally distinct classes of genomic regulatory elements. Future follow-up studies would be required to dissect genetic and molecular mechanisms of connectivity, coopetition, and discordancy effects on down-stream target genes of human embryo regulatory LTRs of distinct retroviral families and human-specific genomic regulatory loci.\u003c/p\u003e \u003cp\u003eCollectively, observations reported in this contribution indicate that two critically important classes of genetic loci appear co-segregated within identified herein human embryo regulatory LTR-governed genomic regulatory units of various architecture: a). Genes essential for the integrity of nervous system development and functions; b). Genes essential for mammalian offspring survival. It seems likely that this striking co-segregation within genomic regulatory units of the GRD governed by regulatory LTRs may reflect co-regulation of these two classes of genes by common sets of regulatory LTR elements. Since knockout of many genes within these regulatory LTR-governed GRNs causes offspring lethality, it is logical to expect that their sustained activity would be essential for offspring survival. Sustained activity of MOS genes within regulatory LTR-governed GRNs could be achieved either by relieving the repressor actions of regulatory LTRs or maintaining the activator effects of regulatory LTRs. Alignments of this genomic regulatory architecture of human embryo regulatory LTR-governed GRNs with control of genes essential for development and functions of nervous system may have contributed to acceleration of central nervous system\u0026rsquo;s dramatic changes during primate evolution.\u003c/p\u003e \u003cp\u003eThese findings suggest that during primate evolution functionally essential fertility genes (that is, genes knockout of which causes reduced fertility and/or infertility phenotypes) and genes essential for development and functions of nervous system were placed under regulatory control of human embryo regulatory LTR elements. It follows that biologically meaningful repressor effects on both classes of genes during ontogenesis would cause defects in development and functions of nervous system as well as would result in reduced fertility and/or infertility phenotypes, thus directly affecting the survivability of the offspring lineage and evolutionary fitness of species.\u003c/p\u003e \u003cp\u003eFurthermore, present observations indicate that altered functions of genes essential for mammalian offspring survival (that is, genes knockout of which causes the offspring lethality phenotypes) may represent one of critically important genetic elements contributing to reduced fertility and/or infertility phenotypes. Reported herein observations suggest that during primate evolution functionally essential fertility genes, genes essential for offspring survival, and genes essential for development and functions of nervous system may have been placed under regulatory control of human embryo regulatory LTR elements. This implied genomic regulatory connectivity may constitute a key element of evolutionary concordance linking the requirement of balanced and coherent development and functions of nervous system to the survivability of the offspring lineage and evolutionary fitness of primate species.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion: Human embryo regulatory LTR elements function as a pan-primate epigenetic architect contributing to evolution, development, physiological traits, and pathological phenotypes of Modern Humans.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring millions years of primate evolution, two distinct families of pan-primate endogenous retroviruses, namely HERVL and HERVH, infected primates\u0026rsquo; germline, colonized host genomes and evolved to contribute to creation of the global retroviral genomic regulatory dominion (GRD) performing vital regulatory and morphogenetic functions during human embryogenesis. Retroviral GRD constitutes of 8839 highly conserved LTR elements linked to 5444 down-stream target genes forged by evolution into a functionally-consonant constellation of 26 genome-wide multimodular genomic regulatory networks (GRNs) each of which is defined by significant enrichment of numerous single gene ontology-specific traits. Locations of GRNs appear scattered across chromosomes to occupy from 5.5\u0026ndash;15.09% of the human genome. Each GRN harbors from 529 to 1486 human embryo regulatory LTR elements derived from LTR7, MLT2A1, and MLT2A2 sequences that are quantitatively balanced according to their genome-wide abundance. GRNs integrate activities from 199 to 805 down-stream target genes, including transcription factors, chromatin-state remodelers, signal sensing and signal transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. GRN\u0026rsquo;s compositions consist of several hundred to thousands smaller gene ontology enrichment analysis-defined genomic regulatory modules (GRMs), each of which combines from a dozen to hundreds LTRs and down-stream target genes. Overall, this study identifies 69,573 statistically significant regulatory LTR-linked GRMs (Binominal FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), including 27,601 GRMs validated by the single ontology-specific directed acyclic graph (DAG) analyses across 6 gene ontology annotations databases. These observations were corroborated and extended by execution of a comprehensive series of Gene Set Enrichment Analyses (GSEA) of regulatory LTRs down-stream target genes employing more than 70 genomics and proteomics databases, including a large panel of databases developed from single-cell resolution studies of healthy and diseased human\u0026rsquo;s organs and tissues. Genes assigned to distinct GRNs and GRMs appear to operate on individuals\u0026rsquo; life-span timescale along specific phenotypic avenues selected from a multitude of down-stream gene ontology-defined and signaling pathways-guided frameworks to exert profound effects on patterns of transcription, protein-protein interactions, developmental phenotypes, physiological traits, and pathological conditions of Modern Humans. GO analyses of Mouse phenotype databases and GSEA of the MGI Mammalian Phenotype Level 4 2021 database revealed that down-stream regulatory targets of human embryo regulatory LTRs are enriched for genes making essential contributions to development and functions of all major tissues, organs, and organ systems, including central and peripheral nervous systems, cardio-vascular (circulatory) and lymphatic systems, gastrointestinal (digestive) and urinary (excretory) systems, musculoskeletal and respiratory systems, endocrine and immune systems, as well as integumentary and reproductive systems. These observations were corroborated by numerous developmental defects documented in a single gene KO mouse models. Genes comprising candidate down-stream regulatory targets of human embryo regulatory LTRs are engaged in protein-protein interaction (PPI) networks that have been implicated in pathogenesis of human common and rare disorders (3298 and 2071 significantly enriched records, respectively), in part, by impacting PPIs that are significantly enriched in 1783 multiprotein complexes recorded in the NURSA Human Endogenous Complexome database and 6584 records of virus-host PPIs documented in Virus-Host PPI P-HIPSTer 2020 database. GSEA-guided analytical inference of the preferred cellular targets of human embryo regulatory LTR elements supported by analyses of genes with species-specific expression mapping bias in Human-Chimpanzee hybrids identified Neuronal epithelium, Radial Glia, and Dentate Granule Cells as cell-type-specific marks within a Holy Grail sequence of embryonic and adult neurogenesis. Observations reported in this contribution support the hypothesis that evolution of human embryo regulatory LTR elements created the global GRD consisting of 26 gene ontology enrichment-defined genome-wide GRNs. Decoded herein the hierarchical super-structure of regulatory LTR-associated GRD and GRNs represents an intrinsically integrated developmental compendium of thousands GRMs congregated on specific genotype-phenotypic trait associations. Many highlighted in this contribution GRMs may represent the evolutionary selection units driven by inherent genotype-phenotype associations affecting primate species\u0026rsquo; fitness and survival by exerting control over mammalian offspring survival genes implicated in reduced fertility and infertility phenotypes. Mechanistically, programmed activation during embryogenesis and ontogenesis of genomic constituents of human embryo retroviral GRD coupled with targeted epigenetic silencing may guide genome-wide heterochromatin patterning within nanodomains and topologically-associated domains during differentiation, thus affecting 3D folding dynamics of linear chromatin fibers and active transcription compartmentalization within interphase chromatin of human cells.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eData source and analytical protocols\u003c/h2\u003e\n \u003cp\u003ePresent analyses were focused on human embryo regulatory LTR loci (see Introduction) the evolutionary origins of which were mapped to genomes of Old World Monkeys (OWM), indicating that these genomic loci could be defined as highly-conserved pan-primate regulatory sequences because they have been present in genomes of primate species for ~\u0026thinsp;30\u0026ndash;40 MYA. Three distinct LTR families meet these criteria, namely MLT2A1 (2416 loci), MLT2A2 (3069 loci), and LTR7 (3354 loci). A total of 8839 fixed non-polymorphic sequences of human embryo regulatory LTR elements residing in genomes of Modern Humans (hg38 human reference genome database) were retrieved as described in recent studies (Hashimoto et al., 2021; Carter et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the number of highly conserved orthologous loci in genomes of sixteen non-human primates (NHP) were determined exactly as previously reported (Glinsky, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Briefly, fixed non-polymorphic regulatory LTR loci residing in the human genome (hg38 human reference genome database) has been considered highly conserved in the genome of non-human primates (NHP) only if the following two requirements are met:\u003c/p\u003e\n \u003cp\u003e(1) During the direct LiftOver test (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.ucsc.edu/cgi-bin/hgLiftOver\u003c/span\u003e\u003c/span\u003e ), the human LTR sequence has been mapped in the NHP genome to the single orthologous locus with a threshold of at least 95% sequence identity;\u003c/p\u003e\n \u003cp\u003e(2) During the reciprocal LiftOver test, the NHP sequence identified in the direct LiftOver test has been remapped with at least 95% sequence identity threshold to the exactly same human orthologous sequence which was queried during the direct LiftOver test.\u003c/p\u003e\n \u003cp\u003eTo ascertain the potential regulatory cross-talks of human embryo regulatory LTRs and human-specific regulatory sequences (HSRS), analyses of a total of 110,339 candidate HSRS were carried-out, including 59,732 HSRS (Glinsky, \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e); 35,074 neuro-regulatory human-specific SNCs (Kanton et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Glinsky, \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e); 8,297 fixed human-specific regulatory regions defined by chromatin state signatures (Marnetto et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e); 7,236 fixed human-specific regulatory regions devoided of archaic humans\u0026rsquo; DNA (Schaefer et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Detailed catalogues of candidate HSRS including descriptions of diverse origins and regulatory features of different families of HSRS and corresponding references of original contributions are reported in the Supplementary Materials and earlier publications [Barakat et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fuentes et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e; 2018; 2019; \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003ec\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guffanti et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky and Barakat, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; McLean et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pontis et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTo evaluate the potential impacts of human embryo regulatory LTRs on genomic regulatory networks implicated in pluripotency and stemness phenotypes as well as developmental processes, analyses have been performed of 7,598 hESC developmental enhancers defined by chromatin state signatures (Rada-Iglesias et al., 2011); 32,353 and 36,417 functional enhancers operating in Primed and Na\u0026iuml;ve hESC, respectively (Barakat et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), as well as 1,619 human-specific hESC functional enhancers (Glinsky and Barakat, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Solely publicly available datasets and resources were used in this contribution. The significance of the differences in the expected and observed numbers of events was calculated using two-tailed Fisher\u0026rsquo;s exact test. Multiple proximity placement enrichment tests were performed for individual families and sub-sets of LTRs and HSRS taking into account the size in bp of corresponding genomic regions, size distributions in human cells of topologically associating domains, distances to putative regulatory targets, bona fide regulatory targets identified in targeted genetic interference and/or epigenetic silencing experiments. Additional details of methodological and analytical approaches are provided in the Supplementary Materials and previously reported contributions [Barakat et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fuentes et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e; 2018; 2019; \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003ec\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guffanti et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky and Barakat, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; McLean et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pontis et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eGene set enrichment and genome-wide proximity placement analyses\u003c/h2\u003e\n \u003cp\u003eGene set enrichment analyses were carried-out using the Enrichr bioinformatics platform, which enables the interrogation of nearly 200,000 gene sets from more than 100 gene set libraries. The Enrichr API (January 2018 through January 2023 releases) [Chen et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kuleshov et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xie et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e] was used to test genes linked to regulatory LTR elements, HSRS, or other regulatory loci of interest for significant enrichment in numerous functional categories. When technically feasible, larger sets of genes comprising several thousand entries were analyzed. Regulatory connectivity maps between HSRS, regulatory LTRs and coding genes and additional functional enrichment analyses were performed with the Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm [McLean et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e] at default settings. The reproducibility of the results was validated by implementing two releases of the GREAT algorithm: GREAT version 3.0.0 (02/15/2015 to 08/18/2019) and GREAT version 4.0.4 (08/19/2019) applying default settings at differing maximum extension thresholds as previously reported (Glinsky \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003ec\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The GREAT algorithm allows investigators to identify and annotate the genome-wide connectivity networks of user-defined distal regulatory loci and their putative target genes. Concurrently, the GREAT algorithm performs functional Gene Ontology (GO) annotations and analyses of statistical enrichment of GO annotations of identified genomic regulatory elements (GREs) and target genes, thus enabling the inference of potential biological impacts of interrogated genomic regulatory networks. The Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm was employed to identify putative down-stream target genes of human embryo regulatory LTRs. Concurrently with the identification of putative regulatory target genes of GREs, the GREAT algorithm performs stringent statistical enrichment analyses of functional annotations of identified down-stream target genes, thus enabling the inference of potential significance of phenotypic impacts of interrogated GRNs. Importantly, the assignment of phenotypic traits as putative statistically valid components of GRN actions entails the assessments of statistical significance of the enrichment of both GREs and down-stream target genes by applying independent statistical tests.\u003c/p\u003e\n \u003cp\u003eThe validity of statistical definitions of genomic regulatory networks (GRNs) and genomic regulatory modules (GRMs) based on the binominal (regulatory elements) and hypergeometrc (target genes) FDR Q values was evaluated using a directed acyclic graph (DAG) test based on the enriched terms from a single ontology-specific table generated by the GREAT algorithm (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Sets S1: GRN1-GRN26\u003c/strong\u003e). DAG test draws patterns and directions of connections between significantly enriched GO modules based on the experimentally-documented temporal logic of developmental processes and structural/functional relationships between gene ontology enrichment analysis-defined statistically significant terms. A specific DAG test utilizes only a sub-set of statistically significant GRMs from a single gene ontology-specific table generated by the GREAT algorithm by extracting GRMs manifesting connectivity patterns defined by experimentally documented developmental and/or structure/function/activity relationships. These GRMs are deemed valid observations and visualized as a consensus hierarchy network of the ontology-specific DAGs (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cstrong\u003eSupplementary Data Sets S1: GRN1-GRN26\u003c/strong\u003e). Based on these considerations, the DAG algorithm draws the developmental and structure/function/activity relationships-guided hierarchy of connectivity between statistically significant gene ontology enriched GRMs.\u003c/p\u003e\n \u003cp\u003eGenome-wide Proximity Placement Analysis (GPPA) of down-stream target genes and distinct genomic features co-localizing with regulatory LTRs and HSRS was carried out as described previously and originally implemented for human-specific transcription factor binding sites [Glinsky et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e, 2018, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020c\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guffanti et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eDifferential GSEA to infer the relative contributions of distinct subsets of regulatory LTR elements and down-stream target genes on phenotypes of interest.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eWhen technically and analytically feasible, different sets of regulatory LTRs and candidate down-stream target genes defined at several significance levels of statistical metrics and comprising from dozens to several thousand individual genetic loci were analyzed using differential GSEA. This approach was utilized to gain insights into biological effects of regulatory LTRs and down-stream target genes and infer potential mechanisms of phenotype affecting activities. Previously, this approach was successfully implemented for identification and characterization of human-specific regulatory networks governed by human-specific transcription factor-binding sites [Glinsky et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e, 2018, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020c\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guffanti et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e] and functional enhancer elements [Barakat et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glinsky and Barakat \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Glinsky \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e, 2018, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020c\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e]. Differential GSEA approach has been utilized for characterization of phenotypic impacts of 13,824 genes associated with 59,732 human-specific regulatory sequences [Glinsky, \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e], 8,405 genes associated with 35,074 human-specific neuroregulatory single-nucleotide changes [Glinsky, \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e], 8,384 genes regulated by stem cell-associated retroviral sequences (SCARS) [Glinsky \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e], as well as human genes and medicinal molecules affecting the susceptibility to SARS-CoV-2 coronavirus [Glinsky, \u003cspan class=\"CitationRef\"\u003e2020c\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eInitial GSEA entail interrogations of each specific set of candidate down-stream target genes using\u0026thinsp;~\u0026thinsp;70 distinct genomic databases, including comprehensive pathway enrichment Gene Ontology (GO) analyses. Upon completion, these analyses were followed by in-depth interrogations of the identified significantly-enriched gene sets employing selected genomic databases deemed most statistically informative at the initial GSEA. In all reported tables and plots (unless stated otherwise), in addition to the nominal p values and adjusted p values, the Enrichr software calculate the \u0026ldquo;combined score\u0026rdquo;, which is a product of the significance estimate and the magnitude of enrichment (combined score c\u0026thinsp;=\u0026thinsp;log(p) * z, where p is the Fisher\u0026rsquo;s exact test p-value and z is the z-score deviation from the expected rank).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analyses of the Publicly Available Datasets\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses of the publicly available genomic datasets, including error rate estimates, background and technical noise measurements and filtering, feature peak calling, feature selection, assignments of genomic coordinates to the corresponding builds of the reference human genome, and data visualization, were performed exactly as reported in the original publications and associated references linked to the corresponding data visualization tracks (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genome.ucsc.edu/\u003c/span\u003e\u003c/span\u003e ). Additional elements or modifications of statistical analyses are described in the corresponding sections of the Results. Statistical significance of the Pearson correlation coefficients was determined using GraphPad Prism version 6.00 software. Both nominal and Bonferroni adjusted p values were estimated and considered as reported in corresponding sections of the Results. The significance of the differences in the numbers of events between the groups was calculated using two-sided Fisher\u0026rsquo;s exact and Chi-square test, and the significance of the overlap between the events was determined using the hypergeometric distribution test [Tavazoie et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eThis work was made possible by the open public access policies of major grant funding agencies and international genomic databases and the willingness of many investigators worldwide to share their primary research. Author would like to thank you Victoria Glinskii for invaluable expert assistance with graphical presentation of the results of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a single author contribution. All elements of this work, including the conception of ideas, formulation, and development of concepts, execution of experiments, analysis of data, and writing of the paper, were performed by the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn part, this work was supported by OncoScar, LLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Glinsky is co-founder of the OncoScar, LLC, an early-stage biotechnology company dedicated to exploration of the potential translational utility of stem cell-associated retroviral sequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the reported observations and required to reproduce the findings are provided in the main body of the paper and Supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgoglia RM, Sun D, Birey F, Yoon SJ, Miura Y, Sabatini K, Pașca SP, Fraser HB. 2021. 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Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells. \u003cem\u003eNature Genetics\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e51\u003c/strong\u003e:1380\u0026ndash;1388. doi:10.1038/s41588-019-0479-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"human endogenous retrovirus type H (HERVH), human endogenous retrovirus type L (HERVL), LTR7, MLT2A1, MLT2A2, retrotransposition, primate evolution, mammalian offspring survival genes, human embryogenesis, reduced fertility, infertility, synaptic transmission, protein-protein interactions at synapses, human endogenous complexomes, viral-host protein-protein interactions, neoplasm metastasis, neurodevelopmental disorders, neurodegenerative diseases, human-specific phenotypic traits, human-specific regulatory sequences","lastPublishedDoi":"10.21203/rs.3.rs-4020067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4020067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTwo distinct families of pan-primate endogenous retroviruses, namely HERVL and HERVH, infected primates germline, colonized host genomes, and evolved into the global genomic regulatory dominion (GRD) operating during human embryogenesis (HE). Analyzed in this contribution GRD derived from HERV sequences constitutes 8839 highly conserved fixed LTR elements linked to 5444 down-stream target genes forged by evolution into a functionally-consonant constellation of 26 genome-wide multimodular genomic regulatory networks (GRNs), each of which is defined by significant enrichment of numerous single gene ontology (GO)-specific traits. Locations of GRNs appear scattered across chromosomes to occupy from 5.5%-15.09% of human genome. Each GRN harbors from 529\u0026ndash;1486 regulatory LTRs derived from LTR7, MLT2A1, and MLT2A2 sequences that appear quantitatively balanced according to their genome-wide abundance. GRNs integrate activities from 199\u0026ndash;805 down-stream target genes, including transcription factors, chromatin-state remodelers, signal-sensing and signal-transduction mediators, enzymatic and receptor binding effectors, intracellular complexes and extracellular matrix elements, and cell-cell adhesion molecules. GRNs compositions consist of several hundred to thousands smaller GO enrichment-defined genomic regulatory modules (GRMs) combining from a dozen to hundreds LTRs and down-stream target genes, which appear to operate on individuals life-span timescale along specific phenotypic avenues to exert profound effects on patterns of transcription, protein-protein interactions, developmental phenotypes, physiological traits, and pathological conditions of Modern Humans. Overall, this study identifies 69,573 statistically significant regulatory LTR-linked GRMs (Binominal FDR q-value threshold of 0.001), including 27,601 GRMs validated by the single GO-specific directed acyclic graph (DAG) analyses across six GO annotations. Collectively, reported herein observations support the hypothesis that pan-primate ERVs have made significant impacts on evolution of GRNs affecting physiological traits and pathological conditions of Modern Humans, including phenotypes manifestation of which considered human-specific.\u003c/p\u003e","manuscriptTitle":"Gene ontology-guided analyses of pan-primate regulatory LTR elements contributing to embryogenesis, development of physiological traits and pathological phenotypes of Modern Humans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-12 16:15:26","doi":"10.21203/rs.3.rs-4020067/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a15dcc99-9f6a-4229-a1df-71ca74e66893","owner":[],"postedDate":"March 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-16T01:42:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-12 16:15:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4020067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4020067","identity":"rs-4020067","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}