Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease

Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease René A. J. Crans, Marta Fructuoso, Karen Bascón Cardozo, Hatice Recaioglu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7703742/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Extensive evidence suggests overlapping pathological mechanisms in the brain of individuals with Parkinson´s disease dementia, Down syndrome dementia, and Alzheimer´s disease. For these neurodegenerative dementias, we observed that the chronological age did not align with their biological age, which was determined based on hippocampal transcript levels (i.e., transcriptional age). Subsequently, we performed a transcriptomic analysis that corrected for the transcriptional age in the hippocampus of affected individuals, highlighting common underlying pathogenic mechanisms. There were 45 common differentially expressed genes (DEGs), whereas enriched functional terms were related to lysine N-methyltransferase activity and intermediate filament. Co-expression network analysis displayed a module that was significantly downregulated in the non-demented control group only. This module identified EHMT2 and LMNB2 as hub genes, which were also common DEGs. Overall, these findings uncover shared functional insights in the hippocampus, while specifically highlighting EHMT2 and LMNB2 as potential universal biomarkers or disease-altered targets across neurodegenerative dementias. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Dementia refers to a group of disorders causing a significant cognitive decline that is sufficient to interfere with daily life, including domestic, occupational, or social functioning 1 . The global prevalence is about 6% for individuals over the age of 60, where the number of people living with dementia is expected to increase from 57 to 152 million in the next 30 years 2 – 4 . Major risk factors to develop dementia are aging, genetics, and cardiovascular diseases 5 . Although Alzheimer’s disease (AD) has become almost synonymous with dementia, the latter can arise from multiple possible causes, such as neuropsychiatric, medical, and neurological conditions 6 . In older adults, dementia is mainly caused by neurodegenerative processes associated with various disorders 1 . A growing collection of evidence suggests overlapping pathogenic mechanisms for dementias with AD, Down syndrome (DS), and Parkinson’s disease (PD). Over 90% of DS individuals have a lifetime risk to develop AD-like dementia and this is currently the leading cause of death in this population 7 . AD entails a universal progression to dementia, whereas around 30–60% of the PD patients develop dementia in later stages of the disease 8 – 10 . The surviving neurons and neuronal processes in most patients with Parkinson´s disease dementia (PDD) contain Lewy body (LB) inclusions, which are abnormal accumulation and aggregation of α-synuclein proteins. This typical neuropathological hallmark for PD is frequently found in AD patients as well 11 . On the other hand, clinically diagnosed cases of PD demonstrate brain amyloid beta (Aβ) accumulation at levels typically associated with AD 12–14 . Similarly, almost all middle-aged individuals with DS present both the neuropathological hallmarks of AD, which are Aβ-containing senile plaques and phosphorylated tau-containing neurofibrillary tangles (NFT) 15 . This is partly explained by the overexpression of the amyloid precursor protein that is located on the extra copy of the human chromosome 21, resulting in higher levels of amyloid depositions in the brain of individuals with DS than in sporadic AD 16,17 . Moreover, cases of DS presenting pathologic changes of PD and LB formations have been reported 18 , 19 . Motor control deficits in DS individuals with parkinsonism could be efficiently reversed with L-DOPA, which is the most effective drug for the symptomatic treatment of PD 20 . Palat and colleagues suggested that psychomotor slowing in individuals with DS may be mistakenly attributed to AD, but can be in fact a sign of parkinsonism, as PD is underestimated in DS 20 . Despite differences in etiology and clinical presentation between AD, DS, and PD, they might converge in their pathogenic mechanisms leading to dementia. This study focuses on the hippocampus, a complex and plastic brain region embedded deep in the temporal lobe, playing a major role in learning and memory and which is highly susceptible to aging-related changes and dementia 21 , 22 . Longitudinal studies have shown increased rates of hippocampal atrophy in AD compared to age-matched non-demented controls, which has been accepted as a biomarker for sporadic AD 23 . Non-demented individuals with DS have significantly smaller volumes of the hippocampus, but not the amygdala. However, their hippocampal volume remains relatively constant in DS without dementia throughout the fifth decade. In contrast, the reduction of the hippocampal volume is considered as a clinical sign of dementia for individuals with DS over the age of 50 years 24 – 26 . In PDD patients, the hippocampus also shows increased atrophy with progression of the disease 27 . Hence, an increased knowledge of the underlying hippocampal mechanisms in dementia may lead to the design and application of diagnostic strategies or treatments. Aging has been associated with changes in transcriptional regulation, which is known to be a complex molecular mechanism, characterized by the intricate interplay between genetic variants, transcription factors, and DNA methylation 28 . The alterations in transcript levels may not be directly associated with the chronological age (i.e., the number of years a person has been alive), but impacted by the biological age of an organ or tissue 29 , 30 . The biological age is a measure of the apparent age based on a certain aspect (i.e., DNA methylation or transcript levels) and is influenced through intrinsic and external factors, such as genomic aberrations, diet, and stress 29 – 32 . In 1978, DS has been postulated as a segmental progeroid syndrome, as individuals with DS suffer from several age-associated disorders much earlier than euploid persons 33 , 34 . A recent study reported that the biological age in adults with DS is increased with approximately 18.8 years compared to their chronological age-matched controls, whereas the rate of aging does not increase throughout their lifespan 35 . However, biological age based on transcript levels (i.e., transcriptional age) has not yet been inferred and implemented in any analytical pipeline of transcriptomic studies on post-mortem DSD, AD, or PDD human brains. In this study, bulk RNA-sequencing (RNA-seq) was performed on total RNA (i.e., non-mRNA enriched) from post-mortem frozen human brain material of demented individuals diagnosed with AD, DS, and PD. Our investigation focused on assessing the hippocampus due to its pivotal role and involvement in learning and emotion, and it’s importance for spatial, episodic, and long-term memory formation 36 . To our best knowledge, this is the first RNA-seq analysis that considers transcriptional age acceleration (RNAAge) in the transcriptome analysis pipeline and detects common underlying pathogenic mechanisms for dementias from three different neurodegenerative disorders, highlighting potential universal biomarkers or disease-altering targets. Results Descriptive statistics A summarized description of the samples is shown in Table 1 , including sex, APOE genotype status, age at death, Braak stage, post-mortem interval (PMI), and RNA integrity number (RIN). There was a significant difference in mean age at death (years) between DS with dementia (DSD) and the other subject groups (One-way ANOVA, p-value = 0.002). There was no significant difference in the number of males and females (Fisher's exact test, p-value = 0.603), RIN (One-way ANOVA, p-value = 0.341) or PMI (One-way ANOVA, p-value = 0.179) between the non-demented controls (Control) and individuals with diagnosed dementias (i.e., PDD, AD, and DSD). Among the cases analyzed, there were ten individuals with ε3ε3 APOE genotype (n = 10), one with ε2ε3 APOE genotype (n = 1), one with ε2ε4 APOE genotype (n = 1), five with ε3ε4 APOE genotype (n = 5), and three with ε4ε4 APOE genotype (n = 3). Of note, no individuals were found to be ε2 homozygotes. The presence of the ε4 allele (ε4ε4/ε4ε2/ε4ε3) was observed in all the individuals with AD (6/6 = 100%) followed by less dominantly observations in PDD (2/4 = 50%), DSD (1/5 = 20%), and Control (0/5 = 0%) groups. Table 1 Summarized description of the cases used in this study for the human post-mortem hippocampal tissue. Abbreviations: Control = non-demented controls; PDD = Parkinson´s disease with dementia; AD = Alzheimer´s disease; DSD = Down syndrome with dementia; Braak = Braak stage; PMI = post-mortem interval; RIN = RNA integrity number; M = male; F = female. Control PDD AD DSD Sample size 5 4 6 5 Sex (M/F) 1/4 1/3 3/3 3/2 ApoE genotype ε4 – ε4 + 100% 0% 50% 50% 0% 100% 80% 20% Age at death (years) 80.4 ± 5.8 (73–89) 79.8 ± 8.3 (68–86) 72.5 ± 9.4 (62–90) 58.8 ± 6.4 (52–67) Braak I IV – V IV – VI V – VI PMI (h) 2.8 ± 1.7 (1.5–5.8) 14.1 ± 10.0 (3.3–24.0) 13.2 ± 9.3 (1.8–28.0) 11.5 ± 9.9 (3.0–28.5) RIN 8.1 ± 1.1 (6.6–9.2) 7.1 ± 0.6 (6.5–7.8) 7.2 ± 1.2 (5.8–8.5) 7.9 ± 0.8 (6.5–8.7) Transcriptional aging is accelerated in the hippocampus of Down syndrome with dementia The transcriptional age was estimated with the RNAAgeCalc algorithm, which is a machine learning-based transcriptomic clock. This algorithm predicts tissue-specific transcriptomic age based on a fixed set of coefficients from a pre-trained elastic net model, using 1,616 age-related genes identified from a meta-analysis of the GTEx database 32 . All our hippocampal samples were of Northwest European origin (i.e., Belgium, Northern France, and England). Therefore, the model trained in brain tissue of Caucasian origin was applied for this analysis on the Control, PDD, AD, and DSD samples. Only the dementia groups showed a significantly older transcriptional age than their chronological age (Fig. 1 a). The chronological age was significantly lower in the DSD group compared to the other groups (Fig. 1 b). However, the transcriptional ages did not differ significantly between all groups, including controls (Fig. 1 c). The mean transcriptional age in years was 92.5 ± 7.6, 102.7 ± 11.0, 96.8 ± 12.5, and 91.7 ± 8.2 for the Control, PDD, AD and DSD groups, respectively. Finally, age acceleration, as defined by the difference between biological age (e.g., transcriptional age) and chronological age (Fig. 1 d), showing a significant age acceleration in DSD individuals compared to the Control group (p-value = 0.0386). This result supports that the age of individuals with DS is more appropriately represented by their biological age than by their chronological age. Common DEGs are linked to neurological diseases Analysis of differential gene expression in the hippocampus was performed using five Control, four PDD, six AD, and five DSD samples. In bulk brain tissue, the gene expression profiles can be dramatically influenced by differences in cellular composition. This potential confounder might be due to the variation in grey/white matter ratios introduced during tissue extraction, inter-subject variability or represent disease related alterations 37 – 39 . To examine the contribution of different sources of biological and technical variations in our dataset, the proportions of major cell-type classes (i.e., astrocytes, endothelia, microglia, neurons, and oligodendrocytes) were first estimated in the samples. This result showed that the cell-type proportions did not differ between the groups ( Supplementary Fig. S1 ). Subsequently, the Kendall's Tau correlation was calculated between potential sources of variation in our data, such as chronological age (age at death), transcriptional age (RNAAge), PMI, RIN, and sex. The first principal component (PC1) captures the most variance and showed to be negatively correlated with RNAAge, APOE status, and PMI, while it was positively correlated with the RIN value ( Supplementary Fig. S2 ). In addition, chronological age, RNAAge, RIN, PMI, and APOE ε4 status were separately analyzed and plotted against the PC1 and second principal component (PC2). As indicated by a color gradient, mostly RIN values and transcriptional ages showed an opposite but gradual increase in these plots, which suggest that these variables causing the confounding that correlated with the first PC ( Supplementary Fig. S3 ). Further exploration showed that the Variance Inflation Factor for PMI and APOE ε4 status were higher than 5, indicating multicollinearity. To identify genes whose expression level changes in PDD, AD, and DSD, differential gene expression analysis was performed for a total of 24,186 transcripts with sex, RNAAge, and RIN as experimental covariates in Wald tests (see Methods). The exposed differences between the studied groups (i.e., PDD versus Control, AD versus Control, and DSD versus Control) are presented in volcano plots (Fig. 2 a). By setting a cutoff value with a false discovery rate (FDR) adjusted p-value ≤ 0.05 and |log 2 FC| of 0.3, a total of 2897, 657 and 196 DEGs were identified in the hippocampus of PDD, AD, and DSD individuals, respectively. Each set of DEGs correctly clustered the samples by their group, which is presented in heatmaps ( Supplementary Fig. S4 ). Moreover, the DEGs were directly compared between the three types of dementias. A total of 29 genes were commonly upregulated, and 16 genes were commonly downregulated among PDD, AD, and DSD samples (Fig. 2 b-c). The commonly up- and downregulated genes are listed in Table 2 . Table 2 Common up- and downregulated genes in the hippocampus of Parkinson´s disease dementia (PDD), Down syndrome dementia (DSD), and Alzheimer´s disease (AD) individuals. The genes were ranked based on their chromosomal location. Gene Description Expressed Chromosome Transcript length (bp) ENSEMBL ID ENTREZ ID ARHGEF10L Rho guanine nucleotide exchange factor 10 like Up Chr1 4625 ENSG00000074964 55160 HS6ST1P1 heparan sulfate 6-O-sulfotransferase 1 pseudogene 1 Up Chr1 1234 ENSG00000187952 N/A ADCY5 adenylate cyclase 5 Up Chr3 6643 ENSG00000173175 111 ZNF141 zinc finger protein 141 Down Chr4 536 ENSG00000131127 7700 DCAF16 DDB1 and CUL4 associated factor 16 Down Chr4 2633 ENSG00000163257 54876 CEP44 centrosomal protein 44 Down Chr4 600 ENSG00000164118 80817 TNPO1 transportin 1 Down Chr5 11040 ENSG00000083312 3842 TMEM161B transmembrane protein 161B Down Chr5 5735 ENSG00000164180 153396 N4BP3 NEDD4 binding protein 3 Up Chr5 5985 ENSG00000145911 23138 EHMT2 euchromatic histone lysine methyltransferase 2 Up Chr6 1517 ENSG00000204371 10919 FZD9 frizzled class receptor 9 Up Chr7 2343 ENSG00000188763 8326 SH2B2 SH2B adaptor protein 2 Up Chr7 490 ENSG00000160999 10603 SSPOP SCO-spondin, pseudogene Up Chr7 15799 ENSG00000197558 N/A SMARCD3 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 3 Up Chr7 468 ENSG00000082014 6604 LOC105376292 novel transcript Up Chr9 1860 ENSG00000227619 105376292 RALGDS ral guanine nucleotide dissociation stimulator Up Chr9 5596 ENSG00000160271 5900 NELFB negative elongation factor complex member B Up Chr9 2540 ENSG00000188986 25920 SYT15 synaptotagmin 15 Up Chr10 6428 ENSG00000204176 83849 ARHGAP19 Rho GTPase activating protein 19 Down Chr10 5525 ENSG00000213390 84986 PHRF1 PHD and ring finger domains 1 Up Chr11 5278 ENSG00000070047 57661 ATF7IP activating transcription factor 7 interacting protein Down Chr12 2298 ENSG00000171681 55729 ARID2 AT-rich interaction domain 2 Down Chr12 735 ENSG00000189079 196528 MIS18BP1 MIS18 binding protein 1 Down Chr14 4577 ENSG00000129534 55320 SPIRE2 spire type actin nucleation factor 2 Up Chr16 663 ENSG00000204991 84501 DEF8 differentially expressed in FDCP 8 homolog Up Chr16 3620 ENSG00000140995 54849 GPS2 G protein pathway suppressor 2 Up Chr17 1238 ENSG00000132522 2874 CACNA1G calcium voltage-gated channel subunit alpha1 G Up Chr17 7497 ENSG00000006283 8913 BAHCC1 BAH domain and coiled-coil containing 1 Up Chr17 10726 ENSG00000266074 57597 TPGS1 tubulin polyglutamylase complex subunit 1 Up Chr19 1114 ENSG00000141933 91978 LMNB2 lamin B2 Up Chr19 769 ENSG00000176619 84823 CACTIN cactin, spliceosome C complex subunit Up Chr19 699 ENSG00000105298 58509 ZNF121 zinc finger protein 121 Down Chr19 7177 ENSG00000197961 7675 ZNF653 zinc finger protein 653 Up Chr19 674 ENSG00000161914 115950 CYP4F3 cytochrome P450 family 4 subfamily F member 3 Down Chr19 2078 ENSG00000186529 4051 ZNF43 zinc finger protein 43 Down Chr19 5584 ENSG00000198521 7594 TTC9B tetratricopeptide repeat domain 9B Up Chr19 828 ENSG00000174521 148014 SPTBN4 spectrin beta, non-erythrocytic 4 Up Chr19 6105 ENSG00000160460 57731 GRIN2D glutamate ionotropic receptor NMDA type subunit 2D Up Chr19 5511 ENSG00000105464 2906 ZNF615 zinc finger protein 615 Down Chr19 4094 ENSG00000197619 284370 TRPM2 transient receptor potential cation channel subfamily M member 2 Up Chr21 5989 ENSG00000142185 7226 HIRA histone cell cycle regulator Up Chr22 3395 ENSG00000100084 7290 PRR5 proline rich 5 Up Chr22 1726 ENSG00000186654 55615 RAP2C RAP2C, member of RAS oncogene family Down ChrX 3341 ENSG00000123728 57826 MT-TC mitochondrially encoded tRNA-Cys (UGU/C) Down MT 66 ENSG00000210140 N/A MT-TY mitochondrially encoded tRNA-Tyr (UAU/C) Down MT 66 ENSG00000210144 N/A Next, a search was performed with the common DEGs using an expert-curated database (DisGeNET), which covers information on Mendelian and complex diseases (Fig. 3 ). This database prioritized and supported gene-disease associations for ADCY5 (adenylate cyclase 5), ARHGEF10L (Rho guanine nucleotide exchange factor 10 like), ARID2 (AT-rich interaction domain 2), ATF7IP (activating transcription factor 7 interacting protein), CACNA1G (calcium voltage-gated channel subunit alpha1 G), EHMT2 (euchromatic histone lysine methyltransferase 2), PHRF1 (PHD and ring finger domains 1), RALGDS (ral guanine nucleotide dissociation stimulator), GRIN2D (glutamate ionotropic receptor NMDA type subunit 2D), HIRA (histone cell cycle regulator), LMNB2 (lamin B2), SPIRE2 (spire type actin nucleation factor 2), SPTBN4 (spectrin beta, non-erythrocytic 4), TRPM2 (transient receptor potential cation channel subfamily M member 2), TTC9B (tetratricopeptide repeat domain 9B), ZNF141 (zinc finger protein 141), and ZNF43 (zinc finger protein 43). The top 3 disease associations were neoplasms, mental disorders, and congenital, hereditary, and neonatal disease and abnormalities (Fig. 3 a), as described with the comprehensive controlled vocabulary of Medical Subject Headings (MeSH). Furthermore, the gene associated with the most diseases was ADCY5 followed by fewer disease associations for CACNA1G , ARID2 , LMNB2 , EHMT2 , TPRM2 , GRIN2B , and SPTBN4 (Fig. 3 b). Although some other diseases were related with the common DEGs, the majority of associated diseases were related to neurological disorders. Consequently, the four largest gene-disease clusters (i.e., ADCY5 , CACNA1G , ARID2 , and LMNB2 ) were connected through neurodevelopment delay or neurodevelopmental disorders. Gene ontology reveals common molecular functions and cellular components among the dementias Gene Ontology (GO) analysis did not detect a commonly enriched or impaired biological process (BP) term among the different types of dementia (Fig. 4 a). The most overlapping enriched terms for molecular function (MF) were related to voltage-gated channel, ligand-gated channel, and lysine N -methyltransferase activities. In contrast, the oxidoreductase activity was impaired in PDD, AD, and DSD (Fig. 4 b). The enriched terms for cellular components (CC) were mainly associated with intermediate filaments, whereas commonly impaired CC terms corresponded with cilia and flagella structures (Fig. 4 c). Disease Ontology (DO) analysis associated DSD with characteristic terms (i.e., genetic disease and chromosomal disease), indicating an appropriate and accurate transcriptomic analysis. However, no single term was shared among the dementias in DO, while oxidative phosphorylation was the only term commonly impaired in the Kyoto Enrichment of Genes and Genomes (KEGG) analysis among the different types of dementia ( Supplementary Fig. S5 ). Chromatin organization module is altered in dementia For the weighted gene co-expression network analysis (WGCNA), the whole dataset was used to construct an adjacency matrix. Then, a network was constructed that was in line with the characteristics of a scale-free network. Therefore, the soft thresholding power (β) was set to seven to ensure a correlation coefficient above 0.85 (Fig. 5 a). The WGCNA R package was used to construct the co-expression network module and visually display the modules´ gene correlation. After merging different modules based on their similarities, a total of 22 co-expression modules were obtained with at least 50 genes in each module (Fig. 5 b). A heatmap was created based on module-trait relationship (Fig. 5 c), according to the Spearman correlation coefficient to evaluate the association between each module and the sample group (i.e., Control, PDD, AD, and DSD). Among the modules, darkseagreen4 (p-value ≤ 0.001) showed a high negative correlation with the non-demented samples (i.e., Control), while this association was not observed or even reverted for the groups with demented individuals (i.e., PDD, AD, and DSD). The genes of the darkseagreen4 module were selected for GO analysis, resulting that these genes were mainly associated with the biological process of chromatin organization. To explore gene-gene interactions, the edges and nodes (threshold 0.1) of this module were exported and visualized in Cytoscape (Fig. 6 ). Interestingly, a relatively high number of commonly upregulated genes among the dementias (5/29 = 17%) were present in the darkseagreen4 module (70 genes). Two of these genes ( EHMT2 and LMNB2 ) shown to be hub genes and were upregulated in the three types of dementia ( Supplementary Fig. S6 ), representing their aberrant expression plays a pivotal role in the disruption of chromatin structure within individuals with PDD, AD, and DSD. Discussion In line with previous studies, we found that the biological age was significantly accelerated in DS individuals compared to non-demented controls 35 , 40 . Overlapping transcriptomic analysis identified 45 commonly dysregulated genes (i.e., 29 up- and 16 downregulated) across PDD, AD, and DSD. For these neurodegenerative dementias, the top overlapping functional enriched terms were lysine N -methyltransferase activity, intermediate filament, and voltage-gated channel activity. WGCNA identified a module associated with chromatin organization that showed a strong negative correlation with control (i.e., non-demented) samples, whereas this association was reduced or even reversed in the dementia groups. This resulted in the identification of two hub genes: euchromatic histone lysine methyltransferase 2 ( EHMT2 ) and lamin B2 ( LMNB2 ), which were also common DEGs across the dementias. Previous studies have associated dysregulation of these genes with specific neurodegenerative and developmental disorders, while our findings extend their potential relevance across multiple dementias 41 – 43 . Neurodegenerative dementias are expected to be a major contributor to the global burden of disease. Hence, gaining more knowledge through transcriptomic studies to infer the pathogenic mechanisms involved will be key in addressing the expected increase in the number of individuals affected by dementia 2 . The strongest risk factor to develop dementia is aging, which might be better represented through people´s biological age (i.e., accumulation of cellular damage over time) than by one´s chronological age 44 . Many studies have already demonstrated accelerated aging in DS using algorithms based on various biomarkers (e.g., Horvath´s epigenetic clock, GlycoAgeTest, brain predicted age, and IgG-glycans) 35 , 40 , 45 , 46 . For the first time, we inferred accelerated aging based on transcript levels using an RNA-based algorithm for hippocampal tissues from three neurodegenerative dementias. Our research estimated a significant increase of biological (or transcriptional) age compared to their chronological age in all dementia types, but not in non-demented control cases. The accelerated age in the hippocampus of DSD individuals was significantly expedited between 20.3–45.2 years. Accelerated transcriptional aging has not only been shown to be disease-specific but to differ between brain regions and genetic backgrounds as well 32 , 35 , 47 . Consistent with our DSD results, Murray and colleagues observed an accelerated age of 20.4–31.1 years in DS individuals from the United Kingdom, whereas this age shift remained constant throughout their lifespan 35 . Together with our findings, this suggests that mainly DS is responsible for the accelerated aging and not dementia or only to a certain degree. Of note, accelerated transcriptional aging in DSD was independently confirmed with the BiT age clock algorithm, which is a binarized transcriptomic-based aging clock [data not shown] 48 . Importantly, the transcriptional age negatively correlated with the PC1, indicating it potentially affects the downstream transcriptomic analysis. Altogether, this led to incorporation of the transcriptomic ages as one of the covariates in our negative binomial generalized linear model to identify differential expressed transcripts between the dementias and non-demented cases, as the chronological age possibly did not reflect disease-specific aging processes. Cell composition and RNA quality have also been shown to be major confounders in transcriptomic studies 8 , 49 . Although we obtained high RIN values (5.8–9.2) from human post-mortem brains, these numbers significantly correlated with the PC1 values and were negatively correlated with the RNAAge, presenting an inverse relationship captured by PC1. This indicated that the transcriptional age and RIN have a strong but opposite contribution by capturing the variance and, thus, were incorporated in our gene expression model. The cell-type proportions were estimated with the MuSiC2 deconvolution algorithm 50 . In this study, there was no differences detected in cell-proportions between the dementias and control cases. Cell-type deconvolution depends on gene expression profiles, whereas cell composition differences in our neurodegenerative conditions may have been minimized through the implementation of aged controls in our study design 51 . Previous studies have shown changes in cell-type composition between DS and normal control brain tissues 52 , 53 . However, the direction of deregulation for certain cell-types was conflicting between these studies, indicating the limitations of these algorithms. Single-cell RNA-sequencing (scRNA-seq) and single-nucleus RNA-sequencing (snRNA-seq) methods might overcome the limitations of using cell-type estimation algorithms in bulk RNA-seq. Nevertheless, it is challenging to perform scRNA-seq in brain tissues due to the complex network of axons, dendrites, and glia that are lost and/or damaged after tissue dissection and cell dissociation 54 . In post-mortem brain tissues, snRNA-seq can be currently performed, however, at a cost of 50–80% transcriptomic reduction, which involves the complete loss of relatively low expressed transcripts 55 . Therefore, we used the scRNA-seq dataset from Darmanis et al. to estimate cell-type proportions in our bulk brain tissues, allowing us to detect also the ‘dark transcriptome’ (i.e., transcripts localized away from cell bodies) that scRNA-seq does not take into account 54 . Bulk RNA-seq was performed together with the ribosomal RNA depletion method, which allows sequencing of both coding and all non-coding transcripts. This approach has shown to result in a higher transcript coverage with unique transcriptome features compared to polyA + selection methods in human post-mortem tissue 49 , 56 . For instance, we identified the upregulation of LOC105376292 (i.e., a novel non-coding RNA transcript) and linked its potential involvement in neurodegenerative dementias, while a genome-wide significant locus for this transcript was recently associated with the brain arterial diameter (i.e., a biomarker for cerebrovascular disease, cognitive decline, and dementia) within a European population 57 . We acknowledge that the sample size for RNA-seq was small given the challenges associated with hippocampal tissue acquisition. Nonetheless, the unique combination of samples from different brain banks in this study allowed us to identify common transcriptional changes between potentially overlapping neurodegenerative dementias. In addition, the standardized neuropathological examination by experts ensured us with excellent and high-quality samples. Our transcriptomic analytic pipeline highlighted LMNB2 (lamin B2) and EHMT2 (euchromatic histone lysine methyltransferase 2) as hub genes in a chromatin organization module and showed them to be commonly upregulated transcripts in hippocampal tissue among the neurodegenerative dementia types. The intermediate filament protein LMNB2 plays a part in the formation of the nuclear lamina and the regulation of cellular processes, such as tissue development, cell cycle, cell proliferation, apoptosis, chromatin localization and stability, and DNA methylation. The influence of abnormal expression and mutations of LMNB2 has been gradually discovered in laminopathies and cancers 58 . Similarly, EHMT2 (also known as G9a) has been indicated to be involved in cell proliferation, apoptosis, cell invasion, and DNA methylation in neuroblastoma, a childhood neoplasm arising from neural crest cells 59 . Rapidly progressive dementia can be caused by certain types of cancers, which was also suggested in our gene-disease association study with the annotation of neoplasms as the highest MeSH class 60 . The main purpose of lamin B2 is to preserve nucleolus organization and stabilize nucleolin within the nucleolus, which is a nuclear compartment and is the site of ribosomal DNA transcription, processing, and ribosome biogenesis 61 . Over 40% of heterochromatin has shown to be associated with the nucleolar periphery, leading to the formation of nucleolus-associated chromatin domains 62 . These domains are enriched with heterochromatin from (peri)centromeric chromosomal regions and contain mostly repressive chromatin marks, such as dimethylation at lysine 9 of histone H3 (H3K9me2). Those regions can rearrange their configuration due to lamin levels, leading to a dynamic three-dimensional genomic architecture 63 . Our co-expression network analysis suggests LMNB2 to be a key player in chromatin organization. Defects in lamins A and C have been involved and classified as laminopathies, including muscular dystrophy, and progeria. Moreover, lamin B1 or B2 alteration has previously been linked to various neuropathies 64 . Consistent with our results, Gil and colleagues observed an increase of LMNB2 levels in pyramidal hippocampal neurons of AD patients at Braak stages V-VI, which was related with nucleoli displacement to the periphery and signs of neuronal attrition 43 . An AD model presented deregulation of lamin B that led to aberrant nucleo-cytoskeletal coupling and promoted heterochromatin relaxation and neuronal death, suggesting AD can be considered as an acquired neurodegenerative laminopathy linked to aging 65 . Furthermore, aneuploid chromosomes have been shown to be mis-localized in cell populations with depleted lamin B2, but not for other lamin subtypes, indicating together with our data a role for LMNB2 in trisomy 21 as well 66 . Nevertheless, the role of LMNB2 in dementia should be further explored with functional studies in models for PD, AD, and DS. The other hub gene, EHMT2, has recently been associated with PD in the European population through a genome-wide association study 67 . This methyltransferase specifically targets H3K9me2, which is associated with transcriptional gene repression 68 . Histone methylations have shown to be involved in the dysregulation of synaptic functions and associated with mental disorders, which was also the highest annotated MeSH class in this study 69 , 70 . However, future experimental studies (e.g., western blotting or immunohistochemistry) should validate the increase of H3K9me2 levels in the types of neurodegenerative dementias. Inhibition of upregulated EHMT2 levels has previously shown to decrease H3K9me2 levels, restore synaptic functions, prevent neuronal death, and rescue motor impairment without affecting the formation of α-synuclein in a mouse model for PD 41 . In a late-stage AD mouse model, the increase of EHMT2 expression led to augmented H3K9me2 levels, but not for this model at an early-stage, suggesting an age dependence of this epigenetic change happening later in life. Correspondingly, the inhibition of this methyltransferase rescued synaptic and cognitive functions, but failed to reduce the amyloid load in those AD mice 42 . In line with previous reports, our findings suggest EHMT2 dysregulation may play a central role in neurodegenerative dementias and highlight this methyltransferase as a potential therapeutic target warranting further experimental validation. Interestingly, different pharmaceutical interventions have been explored to alleviate cognitive impairment in DS, although with a limited success in clinical trials 71 , 72 . Until now, we are the first study that links EHTMT2 dysregulation with DSD, which suggests exploring the beneficial effects of EHMT2 inhibitors in preclinical DS models and to potentially ameliorate their cognitive impairment and synaptic dysfunction during adult life stages 73 . Although promising, our findings should still be interpreted with caution, as the study is limited by the modest sample size and the constraints of bulk RNA-seq. Nevertheless, the consistent signals observed among the different dementia groups suggest common molecular processes that may contribute to hippocampal vulnerability. Future studies using single-cell and spatial transcriptomic approaches will be essential to validate the role of these candidate genes and to determine whether they represent viable biomarkers or therapeutic targets. Overall, the transcriptomic analysis pipeline and unique approach presented in this work provides an original strategy to discover novel biomarkers or disease-altering targets in potentially overlapping neurological diseases and a promising research avenue for other diseases. Methods Sample Selection Post-mortem brain tissue of PDD, AD, and DSD individuals were pathologically confirmed and obtained from four European brain banks: National Brain Bank Neuro-CEB, Pitié-Salpêtrière Hospital (Paris, France); Institute of Psychiatry, King’s College London Brain Bank (London, United Kingdom); Cambridge Brain Bank, Addenbrooke's Hospital, Cambridge University Hospital (Cambridge, United Kingdom), and the Neurobiobank of the Institute Born-Bunge (Antwerp, Belgium). The cohort included five control cases (Control) from non-demented individuals who died without known neurological disorders, four samples from PDD patients, six samples from patients with sporadic AD, and five samples from DSD individuals with neuropathological signs of AD, including NFT and Aβ-containing senile plaques at histological examination 74 . The samples were collected at autopsy and stored at -80°C until further processing. Sample preparation Dissected hippocampi were weighted to calculate the homogenization volume of the buffer for obtaining a 20% concentrated suspension (weight/volume). The tissue was homogenized in ice-cold 50 mM Tris–HCl (pH 7.4) supplemented with Halt™ Protease and Phosphatase Inhibitor Cocktail (#78438, Thermo Fisher Scientific, Pittsburgh, PA, United States) by using the Bio-Gen PRO200 Homogenizer (#01-01200, PRO Scientific Inc., Oxford, CT, United States) at setting three for 30 seconds and then at full speed for one minute. Subsequently, the suspension was centrifuged at 3000 x g for 10 minutes (4°C). Then, the supernatant was used for RNA extraction. Total RNA was isolated with RNeasy Mini Kit (#74104, Qiagen, Hilden, Germany), according to manufacturer's instructions to achieve maximum yields of RNA. Next, the samples were treated with the Heat&Run® gDNA Removal Kit (#80200, ArticZymes, Tromsø, Norway) to avoid amplification of genomic DNA during further processing steps. The RNA concentration and purity were analyzed using the NanoDrop-1000 Spectrophotometer (Thermo Fisher Scientific, Pittsburgh, PA, United States). For each sample a total of 42–336 ng was obtained. The RIN was assessed using the RNA 6000 Pico Kit of Bioanalyzer 2100 system (#5067 − 1513, Agilent Technologies, Santa Clara, CA, United States). All RNA samples were stored at -80°C until further processing. RNA-sequencing and data quality control A total of 10 ng RNA was used for downstream RNA-seq application. First, the SMARTer® Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian (#634485, Takara Bio, Kusatsu, Japan) was used for library preparation, which was followed by a purification with AMPure XP beads (#A63880, Beckman Coulter Inc., Brea, CA, United States). Then, library fragments originating from rRNA (18S and 28S) and mitochondrial rRNA (m12S and m16S) were cleaved by ZapR v3 in the presence of mammalian specific R-Probes v3 (Takara Bio, Kusatsu, Japan). Paired-end sequencing for 50 bp each was performed on the NovaSeq 6000 platform (Illumina Inc., San Diego, CA, United States) to a depth of around 50 million reads (i.e., 25 million reads paired-end fragments). FASTQ output was assessed using MultiQC (version 1.10.1) with default settings prior to alignment and quantification. Quality was visually inspected to observe the “sequence quality”, “per tile sequencing quality”, “overrepresented sequences”, “adapter content” and other quality parameters 75 . RNA expression quantification and filtering Raw sequencing reads in the FASTQ files were mapped with STAR (version 2.7.8a) against the Gencode v41 transcriptome, which was based on the GRCh38.p13 reference genome 76 . BAM files were deduplicated with UMI-Tools dedup (version 1.1.2) using the --method = unique to retain one representative read per unique UMI. The generation of a table of counts with the subread R package (version 2.0.3) 77 . The options applied to quantify the abundance at gene level were -p to count read pairs, -t "exon" as features to be quantified, --largestOverlap to assign reads to the feature with the largest overlap and -g "gene_name" to collapse transcript-level quantification into gene-level counts. Deconvolution The raw count matrix obtained from STAR was imported in RStudio (version 2025.05.1 + 513) for performing bulk RNA-seq deconvolution using the MuSiC2 algorithm 50 . First, this matrix was converted to counts per million (cpm) by dividing with the total number of reads and multiplying by 10 ^ 6 . An ExpressionSet was created with the converted matrix and the single cell RNA sequencing (scRNA-seq) reference dataset from Darmanis et al. was imported through the scRNAseq R package (version 2.22.0) 78 . Subsequently, the scRNA-seq reference dataset was filtered for astrocytes, endothelia, microglia, neurons, and oligodendrocytes. The proportions of these cell-types for each sample were estimated with the function music2_prop_t_statistics from MuSiC2 (version 0.1.0) 50 . Then, a one-way ANOVA followed by the Tukey´s HSD multiple-comparisons post hoc test was performed to assess significance for each cell-type between the Control, PDD, AD and DSD individual groups ( Supplementary Fig. S1 ). Transcriptional age calculation The "predict_age" function of the RNAAgeCalc R package (version 1.20.0) was used to compute the transcriptional age for each RNA sample 32 . First, the transcript length for all transcripts were downloaded using the biomaRt R package (version 2.64.0) with Ensembl 114. Then, the Fragments Per Kilobase of exon per Million mapped reads (FPKM) was calculated by first dividing the raw counts matrix by the library size and then by gene length (i.e., transcript length). Finally, this FPKM counts matrix was provided to the "predict_age" function by setting the parameters as follows: exprtype "FPKM", tissue "brain", idtype "SYMBOL", stype "caucasian", signature "DESeq2" and maxp "30000". Covariate selection Sources of variation in the RNA-sequencing data were identified using principal component analysis (PCA) performed on gene-level expression filtered based on cpm values > 0.33 in at least 6 samples (24,186 genes). The filtered genes were normalized using Trimmed means of M values (TMM) method of the edgeR R package (version 4.6.3), which is a statistical package based on generalized linear models 79 , 80 . The transcriptional age (RNAAge), RIN, PMI and APOE ε4 status were significantly correlated with the first PC ( Supplementary Fig. S2 ). Samples were plotted by their first two principal components to determine how well the group (i.e., Control, PDD, AD, DSD), chronological age, transcriptional age, RIN, PMI, and APOE ε4 status separated ( Supplementary Fig. S3 ). Of note, none of the estimated cell proportion for each cell-type significantly differed between groups (see section deconvolution), resulting that those proportions were not included into the linear model as a covariate to adjust for cell composition variation. Moreover, PMI and APOE ε4 status suggested multicollinearity with other covariates in the model. Finally, the model for differential expression consisted of the groups (i.e., Control, PDD, AD, and DSD) and the covariates RNAAge, RIN, and sex. Although the sex difference was not significant between groups due to a small sample size, it was included as a covariate because the AD and DSD groups contained three times more males as the Control group. This was to exclude a bias for detecting DEGs due to their location on sex chromosomes. Differential gene expression Differential gene expression of the RNA-sequencing was assessed on gene expression level filtered based on cpm values > 0.33 in at least 6 samples (24,186 genes), using DESeq2 R Package (version 1.48.1) 81 . The Wald test was used to detect DEGs in a pairwise manner (i.e., PDD versus Control, AD versus Control, and DSD versus Control), controlling for covariates identified previously (see section 2.7). Prior to correction, covariates to be used in the model were scaled to ensure that continuous variables that are measured on different scales (i.e., RIN versus RNAAge) are comparable. Significant genes were obtained with a correction for FDR adjusted p-value ≤ 0.05 and |log 2 FC| of 0.3, which was identical to the settings from a recent bulk RNA-seq study performed with human DS hippocampal tissues 53 . The volcano plots and heatmaps ( Supplementary Fig. S4 ) depicting the DEGs were created with the ggplot2 (version 3.5.2) and pheatmap (version 1.0.13) R packages, respectively. Functional analysis Gene Set Enrichment Analysis (GSEA) was performed on the GO, DO and KEGG databases of a pre-ranked list according to the log 2 FC x -log 2 (p-value) metric, which penalize large fold changes that have large (non-significant) p-values. GSEA was used to test for enrichment of specific gene sets within the ranked list to define whether specific signaling pathways were enriched among upregulated or downregulated genes 82 . GO enrichment was performed to investigate the gene-related BP, MF, and CC. DO enrichment analysis was used to explore genes-related diseases. KEGG enrichment analysis was conducted to explore gene-related signaling pathways. The obtained results were visualized with the clusterProfiler R package (version 4.16.0) 83 . Statistical significance was set at an FDR-adjusted p-value ≤ 0.05. In addition, an enrichment analysis was performed with the common demented DEGs (45 genes) using the DisGeNET R package (version 1.0.7), which has genes’ association curated data to disease and phenotypic traits 84 , 85 . Weighted gene co-expression network analysis To screen for a potential group of genes that are specifically associated with dementia, the WGCNA R package (version 1.73) was used to perform a WGCNA to find modules in demented individuals and non-demented controls 86 . First, the raw counts matrix was filtered based on counts < 15 in more than 75% of the samples (23,150 genes). Subsequently, the Pearson correlation coefficient was calculated between each pair of genes to evaluate the expression similarity of genes and acquire a correlation matrix. The soft threshold function was applied to convert the correlation matrix into a weighted neighborhood matrix. An optimal soft power threshold of seven was selected through the soft connectivity algorithm, ensuring that the gene correlations were maximally consistent with the scale-free topology and a negative slope. Thereafter, a topological overlap matrix (TOM) was constructed from the adjacency matrix. The TOM was hierarchically clustered using average linkage hierarchical clustering with "1-TOM", which is a dissimilarity measure (dissTOM). The TOM indicates how similar two genes are in terms of their connectivity in the network (i.e., network interconnectedness), helping identify robust, biologically meaningful modules of co-expressed genes. Modules were defined as branches of a dendrogram and derived through the dynamic tree cutting method, applying a minimum module size of 50 and a maximum deep split. These initially generated modules were merged based on module eigengenes, using correlation-based adjacency as dissimilarity matrix. Furthermore, the modules with a smaller distance of less than 0.25 were merged into a single module. Each module was summarized by module eigengene (ME), representing the characteristic expression profile. Module-trait associations were computed with biological traits (i.e., Control, PDD, AD, DSD) and significant correlation indicated potential key modules, where key modules’ genes were considered key genes. The edges and nodes parameters were derived for significant modules in the module-trait associations and imported in Cytoscape (version 3.10.3) 87 . Key genes presented in these modules were used for generating an interaction network, gene ontology analysis and identification of hub genes. Finally, target genes were inferred through the intersection of DEGs with key modules’ genes. Declarations Data availability The RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus repository with the series record XXX. Code availability The R code and instructions for full reproduction of the results are available at https://github.com/renealbertjohan/UVic_HEROES. Acknowledgments The lab of M.D. is recognized by the Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement de la Generalitat de Catalunya (Grups consolidats 2023). We acknowledge the support of the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020‐001049‐S, MCIN/AEI/10.13039/501100011033), the Generalitat de Catalunya through the CERCA program and EMBL partnership. We gratefully acknowledge the expertise and technical support from the Escourolle Laboratory of the Neuropathology Department of Pitié-Salpetrière Hospital (Paris, France) and the CRG Genomics Unit for their support and assistance in this work (Barcelona, Spain). The CIBERER of Rare Diseases is an initiative of the Instituto Carlos III (ISCIII). We want to thank the Institute Born-Bunge (NBB-IBB; Antwerp, Belgium), Neuro-CEB (Paris, France), Institute of Psychiatry Kings College Brain Bank (London, United Kingdom), and the Cambridge Brain Bank (Cambridge, United Kingdom) for providing study materials. Funding information This study was funded by the Joint Program Neurodegenerative Diseases Agence Nationale de la Recherche (JPND ANR-17-JPCD-0003 HEROES and JPND HEROES AC170006), Agencia Estatal de Investigación (PID2019-110755RB-I00/AEI/10.13039/501100011033 and PID2022-141900OB-I00 INTO-DS), ZonMW project (733051072), Medical Research Council (MR/R024901/1), and the European Union's Horizon 2020 research and innovation program (848077 and 899986). Part of the experiments were also funded by the Centre Of Excellence Neurodegenerative Diseases Agence Nationale de la Recherche (COEN-0002 and COEN-4024) and the Investissement d’Avenir (ANR-10-AIHU-06). Moreover, the Jérôme-Lejeune Fondation funded M.F. and R.A.J.C with the Sisley d’Ornano-Lejeune postdoctoral fellowship 2019 and 2021 (12_PDC-2019 and 15_PDC-2021), respectively. R.A.J.C. received also funding by the ISCIII Sello Excelencia ISCIII-Health (IHMC22/00026) and Ministerio de Ciencia Innovación y Universidades (RTC2019-007230-1, RTC2019-007329-1, and CPP2022-009659). Author contributions Study concept and design: R.A.J.C. and M.D. Providing samples: M.C.P., M.F., P.P.D.D. and A.S. Conducted the experiments: R.A.J.C. Data analysis: R.A.J.C, K.B.C and H.C. Data interpretation: R.A.J.C., J.S.F. and B.R.M. Drafted the manuscript: R.A.J.C. and M.D. Critically revised and corrected the content: R.A.J.C, M.F., K.B.C., H.R., J.S.F., Y.V., A.S., D.V.D., P.P.D.D., B.R.M., M.C.P and M.D. Competing interests The authors declare no competing interests. Ethical approval This study was approved by the Ethics and Deontology Committee of the ICM Paris Brain Institute (COMETH-ICM). 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Supplementary Files SupplementarymaterialCransetal.2025.pdf EthicalapprovalBrainBanksMCPotier.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Submission checks completed at journal 06 Oct, 2025 First submitted to journal 24 Sep, 2025 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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1","display":"","copyAsset":false,"role":"figure","size":629694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of the chronological age, transcriptional age, and differential age.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003eTranscriptional age (RNAAge) was calculated with the RNAAgeCalc algorithm for each sample and compared against the chronological age (ChronAge) of the same individual, which are connected through a dotted line. All the dementia groups showed to have a higher transcriptional age than their biological age (Control, p-value = 0.427, PDD, p-value = 0.0211; AD, p-value = 0.0012; DSD, p-value = 0.00005). \u003cstrong\u003eb\u003c/strong\u003e Chronological age (or age at death) in years was 80.4 ± 5.8, 79.8 ± 8.3, 72.5 ± 9.4, and 58.8 ± 6.4 for the Control, PDD, AD, and DSD groups, respectively. The individuals with DS and dementia (DSD) were significantly younger at death compared to the other groups (Control \u003cem\u003eversus\u003c/em\u003eDSD, p-value = 0.00209; PDD \u003cem\u003eversus\u003c/em\u003e DSD, p-value = 0.00452; and AD \u003cem\u003eversus\u003c/em\u003eDSD, p-value = 0.0428). \u003cstrong\u003ec\u003c/strong\u003e There was no difference in transcriptional age between the groups (92.5 ± 7.6, 102.7 ± 11.0, 96.8 ± 12.5, and 91.7 ± 8.2 for the Control, PDD, AD, and DSD groups, respectively). \u003cstrong\u003ed\u003c/strong\u003e Age acceleration was calculated by the subtraction of the chronological age from the transcriptional age, which was represented as the differential age. Individuals with DSD had a significantly acceleration in age compared to non-demented controls (Control). After normal distribution was assessed with the Shapiro-Wilk test and the assumption for homoscedasticity controlled, statistical significance was tested using the two-way ANOVA or the one-way ANOVA followed by the Tukey´s HSD \u003cem\u003epost hoc\u003c/em\u003e test. Statistical significance was presented with * p-value ≤ 0.05; ** p-value ≤ 0.01; *** p-value ≤ 0.001.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/0b01dab6088acf333b936236.jpg"},{"id":94451706,"identity":"b99aaa38-f6fd-4ffc-ad32-83b165723969","added_by":"auto","created_at":"2025-10-27 14:40:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":721096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression analysis and overlapping differentially expressed genes for Parkinson´s disease dementia (PDD), Alzheimer´s disease (AD) and Down syndrome dementia (DSD).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003eVolcano plots showing the expression levels of differentially expressed genes in individuals with PDD, AD and DSD. Red dots indicate upregulated genes in these individuals compared to non-demented control cases (Control). In contrast, blue dots indicate downregulated genes in these individuals compared to Control. \u003cstrong\u003eb\u003c/strong\u003e Venn diagram illustrating the overlapping upregulated genes in the hippocampus of individuals with PDD, AD and DSD. In the diagram, colors represent the followings; PDD \u003cem\u003eversus\u003c/em\u003e Control (blue), AD \u003cem\u003eversus\u003c/em\u003eControl (yellow) and DSD \u003cem\u003eversus\u003c/em\u003e Control (red). \u003cstrong\u003ec\u003c/strong\u003e Venn diagram illustrating the overlapping downregulated genes in the hippocampus of individuals with PDD, AD and DSD. In the diagram, colors represent the followings; PDD \u003cem\u003eversus\u003c/em\u003e Control (blue), AD \u003cem\u003eversus\u003c/em\u003e Control (yellow) and DSD \u003cem\u003eversus\u003c/em\u003e Control (red).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/9359ea818eea72e88f66dc14.jpg"},{"id":94450475,"identity":"eb420eae-a4df-46bc-9b2c-6cdd91cad793","added_by":"auto","created_at":"2025-10-27 14:39:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":900316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene-Disease associations of the common DEGs in dementia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommon differentially expressed genes (DEGs) identified in individuals with Parkinson´s disease dementia (PDD), Alzheimer´s disease (AD), and Down syndrome dementia (DSD) were queried in the DisGeNET curated database. \u003cstrong\u003ea\u003c/strong\u003e Heatmap of Gene-Disease class associations for shared DEGs across PDD, AD, and DSD, organized according to the Medical Subject Headings (MeSH) disease classification. Color intensity reflects the level of gene expression or activity. \u003cstrong\u003eb\u003c/strong\u003e Gene-Disease network representing associations between the common DEGs in the dementia types and their corresponding diseases. Blue nodes represent diseases and pink nodes represent genes, while the thickness of the edges denoting the associations between the gene and the disease.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/f495547122a11b424917116f.jpg"},{"id":94450246,"identity":"a109e947-2e4a-4cb6-98f0-b9a905546fb1","added_by":"auto","created_at":"2025-10-27 14:39:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1070899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology (GO) enrichment analysis for DEG in dementia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO was performed through a gene set enrichment analysis for individuals with Parkinson´s disease dementia (PDD), Alzheimer´s disease (AD), and Down syndrome dementia (DSD). \u003cstrong\u003ea\u003c/strong\u003e Biological processes (BP) that were significantly enriched or impaired. \u003cstrong\u003eb\u003c/strong\u003e Molecular functions (MF) that were significantly enriched or impaired. \u003cstrong\u003ec\u003c/strong\u003e Cellular components (CC) that were significantly enriched or impaired. In each plot, dot size indicates the number of genes in that gene set by ratio (GeneRatio) and dot color reflects statistical significance based on FDR-adjusted p-values (p.adjust).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/b2882259096b82b9806a2570.jpg"},{"id":94451022,"identity":"05393ff9-4676-4825-86f4-19c530159f62","added_by":"auto","created_at":"2025-10-27 14:39:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":836609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted gene co-expression network analysis of dementia-associated gene modules.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003eSelection of the soft-thresholding powers (β) on scale-free topology fit index and mean connectivity. \u003cstrong\u003eb\u003c/strong\u003e Hierarchical clustering dendrogram of genes, with modules represented by different colors. Genes with the highest median absolute deviation enriched modules in a co-expression network. After merging high related modules (cutoff value ≤ 0.25), a total of 22 co-expression clusters were identified. \u003cstrong\u003ec\u003c/strong\u003e Heatmap of module-trait correlation across Control, PDD, AD, and DSD. The darkseagreen4 showed a strong negative correlation with the control (non demented) cases (p-value ≤ 0.001), while this association was absent or reversed in the demented individuals (i.e., PDD, AD, and DSD). Statistical significance was presented with * p-value ≤ 0.05; ** p-value ≤ 0.01; *** p-value ≤ 0.001.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/65b51c8616abc8b1bb234c4d.jpg"},{"id":94451003,"identity":"55a03876-635f-42a5-b118-5f28daa483f7","added_by":"auto","created_at":"2025-10-27 14:39:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":558422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork visualization and hub genes identification in the darkseagreen4 module.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe co-expression network of the darkseagreen4 module included 70 genes, where only the most strongly intra-connected genes are shown. Functional enrichment analysis showed that this module was significantly associated with chromatin organization (GO:0006325). Edges represent gene-gene correlation, while node size indicates number of connections (degree) for each gene. Darker green nodes represent the genes that were commonly upregulated across all dementia groups compared to the control group.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/c715a2ca9ad83f99aafe578b.jpg"},{"id":94489772,"identity":"e68cc12a-79dc-4d5a-b845-ce46fd9592f1","added_by":"auto","created_at":"2025-10-27 17:05:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6583994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/e8a016c9-21a7-49f6-8667-20beb6131ec6.pdf"},{"id":94451348,"identity":"3db744be-1892-4fb3-9ea4-593e18b30907","added_by":"auto","created_at":"2025-10-27 14:40:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2760365,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialCransetal.2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/a16f1e513b9f4286f397c797.pdf"},{"id":94451345,"identity":"8d4b178e-eef6-464a-900c-93a6adda2f61","added_by":"auto","created_at":"2025-10-27 14:40:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":511317,"visible":true,"origin":"","legend":"","description":"","filename":"EthicalapprovalBrainBanksMCPotier.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7703742/v1/d37661c3b2c9f8c1c132ff2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDementia refers to a group of disorders causing a significant cognitive decline that is sufficient to interfere with daily life, including domestic, occupational, or social functioning\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The global prevalence is about 6% for individuals over the age of 60, where the number of people living with dementia is expected to increase from 57 to 152\u0026nbsp;million in the next 30 years\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Major risk factors to develop dementia are aging, genetics, and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Although Alzheimer\u0026rsquo;s disease (AD) has become almost synonymous with dementia, the latter can arise from multiple possible causes, such as neuropsychiatric, medical, and neurological conditions\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In older adults, dementia is mainly caused by neurodegenerative processes associated with various disorders\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA growing collection of evidence suggests overlapping pathogenic mechanisms for dementias with AD, Down syndrome (DS), and Parkinson\u0026rsquo;s disease (PD). Over 90% of DS individuals have a lifetime risk to develop AD-like dementia and this is currently the leading cause of death in this population\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. AD entails a universal progression to dementia, whereas around 30\u0026ndash;60% of the PD patients develop dementia in later stages of the disease\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The surviving neurons and neuronal processes in most patients with Parkinson\u0026acute;s disease dementia (PDD) contain Lewy body (LB) inclusions, which are abnormal accumulation and aggregation of α-synuclein proteins. This typical neuropathological hallmark for PD is frequently found in AD patients as well\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. On the other hand, clinically diagnosed cases of PD demonstrate brain amyloid beta (Aβ) accumulation at levels typically associated with AD\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. Similarly, almost all middle-aged individuals with DS present both the neuropathological hallmarks of AD, which are Aβ-containing senile plaques and phosphorylated tau-containing neurofibrillary tangles (NFT)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This is partly explained by the overexpression of the amyloid precursor protein that is located on the extra copy of the human chromosome 21, resulting in higher levels of amyloid depositions in the brain of individuals with DS than in sporadic AD\u003csup\u003e16,17\u003c/sup\u003e. Moreover, cases of DS presenting pathologic changes of PD and LB formations have been reported\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Motor control deficits in DS individuals with parkinsonism could be efficiently reversed with L-DOPA, which is the most effective drug for the symptomatic treatment of PD\u003csup\u003e20\u003c/sup\u003e. Palat and colleagues suggested that psychomotor slowing in individuals with DS may be mistakenly attributed to AD, but can be in fact a sign of parkinsonism, as PD is underestimated in DS\u003csup\u003e20\u003c/sup\u003e. Despite differences in etiology and clinical presentation between AD, DS, and PD, they might converge in their pathogenic mechanisms leading to dementia.\u003c/p\u003e\u003cp\u003eThis study focuses on the hippocampus, a complex and plastic brain region embedded deep in the temporal lobe, playing a major role in learning and memory and which is highly susceptible to aging-related changes and dementia\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Longitudinal studies have shown increased rates of hippocampal atrophy in AD compared to age-matched non-demented controls, which has been accepted as a biomarker for sporadic AD\u003csup\u003e23\u003c/sup\u003e. Non-demented individuals with DS have significantly smaller volumes of the hippocampus, but not the amygdala. However, their hippocampal volume remains relatively constant in DS without dementia throughout the fifth decade. In contrast, the reduction of the hippocampal volume is considered as a clinical sign of dementia for individuals with DS over the age of 50 years\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In PDD patients, the hippocampus also shows increased atrophy with progression of the disease\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Hence, an increased knowledge of the underlying hippocampal mechanisms in dementia may lead to the design and application of diagnostic strategies or treatments.\u003c/p\u003e\u003cp\u003eAging has been associated with changes in transcriptional regulation, which is known to be a complex molecular mechanism, characterized by the intricate interplay between genetic variants, transcription factors, and DNA methylation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The alterations in transcript levels may not be directly associated with the chronological age (i.e., the number of years a person has been alive), but impacted by the biological age of an organ or tissue\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The biological age is a measure of the apparent age based on a certain aspect (i.e., DNA methylation or transcript levels) and is influenced through intrinsic and external factors, such as genomic aberrations, diet, and stress\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In 1978, DS has been postulated as a segmental progeroid syndrome, as individuals with DS suffer from several age-associated disorders much earlier than euploid persons\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. A recent study reported that the biological age in adults with DS is increased with approximately 18.8 years compared to their chronological age-matched controls, whereas the rate of aging does not increase throughout their lifespan\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. However, biological age based on transcript levels (i.e., transcriptional age) has not yet been inferred and implemented in any analytical pipeline of transcriptomic studies on post-mortem DSD, AD, or PDD human brains.\u003c/p\u003e\u003cp\u003eIn this study, bulk RNA-sequencing (RNA-seq) was performed on total RNA (i.e., non-mRNA enriched) from post-mortem frozen human brain material of demented individuals diagnosed with AD, DS, and PD. Our investigation focused on assessing the hippocampus due to its pivotal role and involvement in learning and emotion, and it\u0026rsquo;s importance for spatial, episodic, and long-term memory formation\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. To our best knowledge, this is the first RNA-seq analysis that considers transcriptional age acceleration (RNAAge) in the transcriptome analysis pipeline and detects common underlying pathogenic mechanisms for dementias from three different neurodegenerative disorders, highlighting potential universal biomarkers or disease-altering targets.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\u003cp\u003eA summarized description of the samples is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including sex, APOE genotype status, age at death, Braak stage, post-mortem interval (PMI), and RNA integrity number (RIN). There was a significant difference in mean age at death (years) between DS with dementia (DSD) and the other subject groups (One-way ANOVA, p-value\u0026thinsp;=\u0026thinsp;0.002). There was no significant difference in the number of males and females (Fisher's exact test, p-value\u0026thinsp;=\u0026thinsp;0.603), RIN (One-way ANOVA, p-value\u0026thinsp;=\u0026thinsp;0.341) or PMI (One-way ANOVA, p-value\u0026thinsp;=\u0026thinsp;0.179) between the non-demented controls (Control) and individuals with diagnosed dementias (i.e., PDD, AD, and DSD). Among the cases analyzed, there were ten individuals with ε3ε3 APOE genotype (n\u0026thinsp;=\u0026thinsp;10), one with ε2ε3 APOE genotype (n\u0026thinsp;=\u0026thinsp;1), one with ε2ε4 APOE genotype (n\u0026thinsp;=\u0026thinsp;1), five with ε3ε4 APOE genotype (n\u0026thinsp;=\u0026thinsp;5), and three with ε4ε4 APOE genotype (n\u0026thinsp;=\u0026thinsp;3). Of note, no individuals were found to be ε2 homozygotes. The presence of the ε4 allele (ε4ε4/ε4ε2/ε4ε3) was observed in all the individuals with AD (6/6\u0026thinsp;=\u0026thinsp;100%) followed by less dominantly observations in PDD (2/4\u0026thinsp;=\u0026thinsp;50%), DSD (1/5\u0026thinsp;=\u0026thinsp;20%), and Control (0/5\u0026thinsp;=\u0026thinsp;0%) groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSummarized description of the cases used in this study for the human post-mortem hippocampal tissue.\u003c/b\u003e Abbreviations: Control\u0026thinsp;=\u0026thinsp;non-demented controls; PDD\u0026thinsp;=\u0026thinsp;Parkinson\u0026acute;s disease with dementia; AD\u0026thinsp;=\u0026thinsp;Alzheimer\u0026acute;s disease; DSD\u0026thinsp;=\u0026thinsp;Down syndrome with dementia; Braak\u0026thinsp;=\u0026thinsp;Braak stage; PMI\u0026thinsp;=\u0026thinsp;post-mortem interval; RIN\u0026thinsp;=\u0026thinsp;RNA integrity number; M\u0026thinsp;=\u0026thinsp;male; F\u0026thinsp;=\u0026thinsp;female.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePDD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (M/F)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3/2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eApoE genotype\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eε4 \u0026ndash;\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eε4 +\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at death (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 (73\u0026ndash;89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 (68\u0026ndash;86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 (62\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 (52\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBraak\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIV \u0026ndash; V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIV \u0026ndash; VI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eV \u0026ndash; VI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMI (h)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 (1.5\u0026ndash;5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0 (3.3\u0026ndash;24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 (1.8\u0026ndash;28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 (3.0\u0026ndash;28.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRIN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (6.6\u0026ndash;9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 (6.5\u0026ndash;7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (5.8\u0026ndash;8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (6.5\u0026ndash;8.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTranscriptional aging is accelerated in the hippocampus of Down syndrome with dementia\u003c/h3\u003e\n\u003cp\u003eThe transcriptional age was estimated with the RNAAgeCalc algorithm, which is a machine learning-based transcriptomic clock. This algorithm predicts tissue-specific transcriptomic age based on a fixed set of coefficients from a pre-trained elastic net model, using 1,616 age-related genes identified from a meta-analysis of the GTEx database\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. All our hippocampal samples were of Northwest European origin (i.e., Belgium, Northern France, and England). Therefore, the model trained in brain tissue of Caucasian origin was applied for this analysis on the Control, PDD, AD, and DSD samples. Only the dementia groups showed a significantly older transcriptional age than their chronological age (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The chronological age was significantly lower in the DSD group compared to the other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). However, the transcriptional ages did not differ significantly between all groups, including controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The mean transcriptional age in years was 92.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6, 102.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0, 96.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5, and 91.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 for the Control, PDD, AD and DSD groups, respectively. Finally, age acceleration, as defined by the difference between biological age (e.g., transcriptional age) and chronological age (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), showing a significant age acceleration in DSD individuals compared to the Control group (p-value\u0026thinsp;=\u0026thinsp;0.0386). This result supports that the age of individuals with DS is more appropriately represented by their biological age than by their chronological age.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCommon DEGs are linked to neurological diseases\u003c/h3\u003e\n\u003cp\u003eAnalysis of differential gene expression in the hippocampus was performed using five Control, four PDD, six AD, and five DSD samples. In bulk brain tissue, the gene expression profiles can be dramatically influenced by differences in cellular composition. This potential confounder might be due to the variation in grey/white matter ratios introduced during tissue extraction, inter-subject variability or represent disease related alterations\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. To examine the contribution of different sources of biological and technical variations in our dataset, the proportions of major cell-type classes (i.e., astrocytes, endothelia, microglia, neurons, and oligodendrocytes) were first estimated in the samples. This result showed that the cell-type proportions did not differ between the groups (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Subsequently, the Kendall's Tau correlation was calculated between potential sources of variation in our data, such as chronological age (age at death), transcriptional age (RNAAge), PMI, RIN, and sex. The first principal component (PC1) captures the most variance and showed to be negatively correlated with RNAAge, APOE status, and PMI, while it was positively correlated with the RIN value (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). In addition, chronological age, RNAAge, RIN, PMI, and APOE ε4 status were separately analyzed and plotted against the PC1 and second principal component (PC2). As indicated by a color gradient, mostly RIN values and transcriptional ages showed an opposite but gradual increase in these plots, which suggest that these variables causing the confounding that correlated with the first PC (\u003cb\u003eSupplementary Fig. S3\u003c/b\u003e). Further exploration showed that the Variance Inflation Factor for PMI and APOE ε4 status were higher than 5, indicating multicollinearity. To identify genes whose expression level changes in PDD, AD, and DSD, differential gene expression analysis was performed for a total of 24,186 transcripts with sex, RNAAge, and RIN as experimental covariates in Wald tests (see Methods). The exposed differences between the studied groups (i.e., PDD \u003cem\u003eversus\u003c/em\u003e Control, AD \u003cem\u003eversus\u003c/em\u003e Control, and DSD \u003cem\u003eversus\u003c/em\u003e Control) are presented in volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). By setting a cutoff value with a false discovery rate (FDR) adjusted p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| of 0.3, a total of 2897, 657 and 196 DEGs were identified in the hippocampus of PDD, AD, and DSD individuals, respectively. Each set of DEGs correctly clustered the samples by their group, which is presented in heatmaps (\u003cb\u003eSupplementary Fig. S4\u003c/b\u003e). Moreover, the DEGs were directly compared between the three types of dementias. A total of 29 genes were commonly upregulated, and 16 genes were commonly downregulated among PDD, AD, and DSD samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c). The commonly up- and downregulated genes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCommon up- and downregulated genes in the hippocampus of Parkinson\u0026acute;s disease dementia (PDD), Down syndrome dementia (DSD), and Alzheimer\u0026acute;s disease (AD) individuals.\u003c/b\u003e The genes were ranked based on their chromosomal location.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpressed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChromosome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTranscript length (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSEMBL ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eENTREZ ID\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eARHGEF10L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRho guanine nucleotide exchange factor 10 like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000074964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHS6ST1P1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eheparan sulfate 6-O-sulfotransferase 1 pseudogene 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000187952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eADCY5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eadenylate cyclase 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000173175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZNF141\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ezinc finger protein 141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000131127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDCAF16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDDB1 and CUL4 associated factor 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000163257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCEP44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecentrosomal protein 44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000164118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e80817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTNPO1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etransportin 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000083312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTMEM161B\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etransmembrane protein 161B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000164180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e153396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN4BP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNEDD4 binding protein 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000145911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEHMT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eeuchromatic histone lysine methyltransferase 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000204371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10919\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFZD9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efrizzled class receptor 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000188763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSH2B2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSH2B adaptor protein 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000160999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSPOP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCO-spondin, pseudogene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000197558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSMARCD3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000082014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLOC105376292\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enovel transcript\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000227619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e105376292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRALGDS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eral guanine nucleotide dissociation stimulator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000160271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5900\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNELFB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enegative elongation factor complex member B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000188986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25920\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSYT15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esynaptotagmin 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000204176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e83849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eARHGAP19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRho GTPase activating protein 19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000213390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePHRF1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHD and ring finger domains 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000070047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eATF7IP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eactivating transcription factor 7 interacting protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000171681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eARID2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAT-rich interaction domain 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000189079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e196528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMIS18BP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMIS18 binding protein 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000129534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSPIRE2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003espire type actin nucleation factor 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000204991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDEF8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edifferentially expressed in FDCP 8 homolog\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000140995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGPS2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG protein pathway suppressor 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000132522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCACNA1G\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecalcium voltage-gated channel subunit alpha1 G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000006283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBAHCC1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBAH domain and coiled-coil containing 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000266074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57597\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTPGS1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etubulin polyglutamylase complex subunit 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000141933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e91978\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLMNB2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elamin B2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000176619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCACTIN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecactin, spliceosome C complex subunit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000105298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZNF121\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ezinc finger protein 121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000197961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZNF653\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ezinc finger protein 653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000161914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e115950\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCYP4F3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecytochrome P450 family 4 subfamily F member 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000186529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZNF43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ezinc finger protein 43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000198521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTTC9B\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etetratricopeptide repeat domain 9B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000174521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e148014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSPTBN4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003espectrin beta, non-erythrocytic 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000160460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGRIN2D\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eglutamate ionotropic receptor NMDA type subunit 2D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000105464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2906\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZNF615\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ezinc finger protein 615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000197619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e284370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTRPM2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etransient receptor potential cation channel subfamily M member 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000142185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIRA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehistone cell cycle regulator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000100084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePRR5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eproline rich 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChr22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000186654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRAP2C\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRAP2C, member of RAS oncogene family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChrX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000123728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57826\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMT-TC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emitochondrially encoded tRNA-Cys (UGU/C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000210140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMT-TY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emitochondrially encoded tRNA-Tyr (UAU/C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eENSG00000210144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNext, a search was performed with the common DEGs using an expert-curated database (DisGeNET), which covers information on Mendelian and complex diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This database prioritized and supported gene-disease associations for \u003cem\u003eADCY5\u003c/em\u003e (adenylate cyclase 5), \u003cem\u003eARHGEF10L\u003c/em\u003e (Rho guanine nucleotide exchange factor 10 like), \u003cem\u003eARID2\u003c/em\u003e (AT-rich interaction domain 2), \u003cem\u003eATF7IP\u003c/em\u003e (activating transcription factor 7 interacting protein), \u003cem\u003eCACNA1G\u003c/em\u003e (calcium voltage-gated channel subunit alpha1 G), \u003cem\u003eEHMT2\u003c/em\u003e (euchromatic histone lysine methyltransferase 2), \u003cem\u003ePHRF1\u003c/em\u003e (PHD and ring finger domains 1), \u003cem\u003eRALGDS\u003c/em\u003e (ral guanine nucleotide dissociation stimulator), \u003cem\u003eGRIN2D\u003c/em\u003e (glutamate ionotropic receptor NMDA type subunit 2D), \u003cem\u003eHIRA\u003c/em\u003e (histone cell cycle regulator), \u003cem\u003eLMNB2\u003c/em\u003e (lamin B2), \u003cem\u003eSPIRE2\u003c/em\u003e (spire type actin nucleation factor 2), \u003cem\u003eSPTBN4\u003c/em\u003e (spectrin beta, non-erythrocytic 4), \u003cem\u003eTRPM2\u003c/em\u003e (transient receptor potential cation channel subfamily M member 2), \u003cem\u003eTTC9B\u003c/em\u003e (tetratricopeptide repeat domain 9B), \u003cem\u003eZNF141\u003c/em\u003e (zinc finger protein 141), and \u003cem\u003eZNF43\u003c/em\u003e (zinc finger protein 43). The top 3 disease associations were neoplasms, mental disorders, and congenital, hereditary, and neonatal disease and abnormalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), as described with the comprehensive controlled vocabulary of Medical Subject Headings (MeSH). Furthermore, the gene associated with the most diseases was \u003cem\u003eADCY5\u003c/em\u003e followed by fewer disease associations for \u003cem\u003eCACNA1G\u003c/em\u003e, \u003cem\u003eARID2\u003c/em\u003e, \u003cem\u003eLMNB2\u003c/em\u003e, \u003cem\u003eEHMT2\u003c/em\u003e, \u003cem\u003eTPRM2\u003c/em\u003e, \u003cem\u003eGRIN2B\u003c/em\u003e, and \u003cem\u003eSPTBN4\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Although some other diseases were related with the common DEGs, the majority of associated diseases were related to neurological disorders. Consequently, the four largest gene-disease clusters (i.e., \u003cem\u003eADCY5\u003c/em\u003e, \u003cem\u003eCACNA1G\u003c/em\u003e, \u003cem\u003eARID2\u003c/em\u003e, and \u003cem\u003eLMNB2\u003c/em\u003e) were connected through neurodevelopment delay or neurodevelopmental disorders.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGene ontology reveals common molecular functions and cellular components among the dementias\u003c/h3\u003e\n\u003cp\u003eGene Ontology (GO) analysis did not detect a commonly enriched or impaired biological process (BP) term among the different types of dementia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The most overlapping enriched terms for molecular function (MF) were related to voltage-gated channel, ligand-gated channel, and lysine \u003cem\u003eN\u003c/em\u003e-methyltransferase activities. In contrast, the oxidoreductase activity was impaired in PDD, AD, and DSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The enriched terms for cellular components (CC) were mainly associated with intermediate filaments, whereas commonly impaired CC terms corresponded with cilia and flagella structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Disease Ontology (DO) analysis associated DSD with characteristic terms (i.e., genetic disease and chromosomal disease), indicating an appropriate and accurate transcriptomic analysis. However, no single term was shared among the dementias in DO, while oxidative phosphorylation was the only term commonly impaired in the Kyoto Enrichment of Genes and Genomes (KEGG) analysis among the different types of dementia (\u003cb\u003eSupplementary Fig. S5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eChromatin organization module is altered in dementia\u003c/h3\u003e\n\u003cp\u003eFor the weighted gene co-expression network analysis (WGCNA), the whole dataset was used to construct an adjacency matrix. Then, a network was constructed that was in line with the characteristics of a scale-free network. Therefore, the soft thresholding power (β) was set to seven to ensure a correlation coefficient above 0.85 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The WGCNA R package was used to construct the co-expression network module and visually display the modules\u0026acute; gene correlation. After merging different modules based on their similarities, a total of 22 co-expression modules were obtained with at least 50 genes in each module (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). A heatmap was created based on module-trait relationship (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), according to the Spearman correlation coefficient to evaluate the association between each module and the sample group (i.e., Control, PDD, AD, and DSD). Among the modules, darkseagreen4 (p-value\u0026thinsp;\u0026le;\u0026thinsp;0.001) showed a high negative correlation with the non-demented samples (i.e., Control), while this association was not observed or even reverted for the groups with demented individuals (i.e., PDD, AD, and DSD). The genes of the darkseagreen4 module were selected for GO analysis, resulting that these genes were mainly associated with the biological process of chromatin organization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore gene-gene interactions, the edges and nodes (threshold 0.1) of this module were exported and visualized in Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Interestingly, a relatively high number of commonly upregulated genes among the dementias (5/29\u0026thinsp;=\u0026thinsp;17%) were present in the darkseagreen4 module (70 genes). Two of these genes (\u003cem\u003eEHMT2\u003c/em\u003e and \u003cem\u003eLMNB2\u003c/em\u003e) shown to be hub genes and were upregulated in the three types of dementia (\u003cb\u003eSupplementary Fig. S6\u003c/b\u003e), representing their aberrant expression plays a pivotal role in the disruption of chromatin structure within individuals with PDD, AD, and DSD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn line with previous studies, we found that the biological age was significantly accelerated in DS individuals compared to non-demented controls\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Overlapping transcriptomic analysis identified 45 commonly dysregulated genes (i.e., 29 up- and 16 downregulated) across PDD, AD, and DSD. For these neurodegenerative dementias, the top overlapping functional enriched terms were lysine \u003cem\u003eN\u003c/em\u003e-methyltransferase activity, intermediate filament, and voltage-gated channel activity. WGCNA identified a module associated with chromatin organization that showed a strong negative correlation with control (i.e., non-demented) samples, whereas this association was reduced or even reversed in the dementia groups. This resulted in the identification of two hub genes: euchromatic histone lysine methyltransferase 2 (\u003cem\u003eEHMT2\u003c/em\u003e) and lamin B2 (\u003cem\u003eLMNB2\u003c/em\u003e), which were also common DEGs across the dementias. Previous studies have associated dysregulation of these genes with specific neurodegenerative and developmental disorders, while our findings extend their potential relevance across multiple dementias\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNeurodegenerative dementias are expected to be a major contributor to the global burden of disease. Hence, gaining more knowledge through transcriptomic studies to infer the pathogenic mechanisms involved will be key in addressing the expected increase in the number of individuals affected by dementia\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The strongest risk factor to develop dementia is aging, which might be better represented through people\u0026acute;s biological age (i.e., accumulation of cellular damage over time) than by one\u0026acute;s chronological age\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Many studies have already demonstrated accelerated aging in DS using algorithms based on various biomarkers (e.g., Horvath\u0026acute;s epigenetic clock, GlycoAgeTest, brain predicted age, and IgG-glycans)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. For the first time, we inferred accelerated aging based on transcript levels using an RNA-based algorithm for hippocampal tissues from three neurodegenerative dementias. Our research estimated a significant increase of biological (or transcriptional) age compared to their chronological age in all dementia types, but not in non-demented control cases. The accelerated age in the hippocampus of DSD individuals was significantly expedited between 20.3\u0026ndash;45.2 years. Accelerated transcriptional aging has not only been shown to be disease-specific but to differ between brain regions and genetic backgrounds as well\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Consistent with our DSD results, Murray and colleagues observed an accelerated age of 20.4\u0026ndash;31.1 years in DS individuals from the United Kingdom, whereas this age shift remained constant throughout their lifespan\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Together with our findings, this suggests that mainly DS is responsible for the accelerated aging and not dementia or only to a certain degree. Of note, accelerated transcriptional aging in DSD was independently confirmed with the BiT age clock algorithm, which is a binarized transcriptomic-based aging clock [data not shown]\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Importantly, the transcriptional age negatively correlated with the PC1, indicating it potentially affects the downstream transcriptomic analysis. Altogether, this led to incorporation of the transcriptomic ages as one of the covariates in our negative binomial generalized linear model to identify differential expressed transcripts between the dementias and non-demented cases, as the chronological age possibly did not reflect disease-specific aging processes.\u003c/p\u003e\u003cp\u003eCell composition and RNA quality have also been shown to be major confounders in transcriptomic studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Although we obtained high RIN values (5.8\u0026ndash;9.2) from human post-mortem brains, these numbers significantly correlated with the PC1 values and were negatively correlated with the RNAAge, presenting an inverse relationship captured by PC1. This indicated that the transcriptional age and RIN have a strong but opposite contribution by capturing the variance and, thus, were incorporated in our gene expression model. The cell-type proportions were estimated with the MuSiC2 deconvolution algorithm\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In this study, there was no differences detected in cell-proportions between the dementias and control cases. Cell-type deconvolution depends on gene expression profiles, whereas cell composition differences in our neurodegenerative conditions may have been minimized through the implementation of aged controls in our study design\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown changes in cell-type composition between DS and normal control brain tissues\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. However, the direction of deregulation for certain cell-types was conflicting between these studies, indicating the limitations of these algorithms. Single-cell RNA-sequencing (scRNA-seq) and single-nucleus RNA-sequencing (snRNA-seq) methods might overcome the limitations of using cell-type estimation algorithms in bulk RNA-seq.\u0026nbsp;Nevertheless, it is challenging to perform scRNA-seq in brain tissues due to the complex network of axons, dendrites, and glia that are lost and/or damaged after tissue dissection and cell dissociation\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In post-mortem brain tissues, snRNA-seq can be currently performed, however, at a cost of 50\u0026ndash;80% transcriptomic reduction, which involves the complete loss of relatively low expressed transcripts\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Therefore, we used the scRNA-seq dataset from Darmanis et al. to estimate cell-type proportions in our bulk brain tissues, allowing us to detect also the \u0026lsquo;dark transcriptome\u0026rsquo; (i.e., transcripts localized away from cell bodies) that scRNA-seq does not take into account\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBulk RNA-seq was performed together with the ribosomal RNA depletion method, which allows sequencing of both coding and all non-coding transcripts. This approach has shown to result in a higher transcript coverage with unique transcriptome features compared to polyA\u0026thinsp;+\u0026thinsp;selection methods in human post-mortem tissue\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. For instance, we identified the upregulation of \u003cem\u003eLOC105376292\u003c/em\u003e (i.e., a novel non-coding RNA transcript) and linked its potential involvement in neurodegenerative dementias, while a genome-wide significant locus for this transcript was recently associated with the brain arterial diameter (i.e., a biomarker for cerebrovascular disease, cognitive decline, and dementia) within a European population\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. We acknowledge that the sample size for RNA-seq was small given the challenges associated with hippocampal tissue acquisition. Nonetheless, the unique combination of samples from different brain banks in this study allowed us to identify common transcriptional changes between potentially overlapping neurodegenerative dementias. In addition, the standardized neuropathological examination by experts ensured us with excellent and high-quality samples.\u003c/p\u003e\u003cp\u003eOur transcriptomic analytic pipeline highlighted \u003cem\u003eLMNB2\u003c/em\u003e (lamin B2) and \u003cem\u003eEHMT2\u003c/em\u003e (euchromatic histone lysine methyltransferase 2) as hub genes in a chromatin organization module and showed them to be commonly upregulated transcripts in hippocampal tissue among the neurodegenerative dementia types. The intermediate filament protein LMNB2 plays a part in the formation of the nuclear lamina and the regulation of cellular processes, such as tissue development, cell cycle, cell proliferation, apoptosis, chromatin localization and stability, and DNA methylation. The influence of abnormal expression and mutations of LMNB2 has been gradually discovered in laminopathies and cancers\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Similarly, EHMT2 (also known as G9a) has been indicated to be involved in cell proliferation, apoptosis, cell invasion, and DNA methylation in neuroblastoma, a childhood neoplasm arising from neural crest cells\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Rapidly progressive dementia can be caused by certain types of cancers, which was also suggested in our gene-disease association study with the annotation of neoplasms as the highest MeSH class\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe main purpose of lamin B2 is to preserve nucleolus organization and stabilize nucleolin within the nucleolus, which is a nuclear compartment and is the site of ribosomal DNA transcription, processing, and ribosome biogenesis\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Over 40% of heterochromatin has shown to be associated with the nucleolar periphery, leading to the formation of nucleolus-associated chromatin domains\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. These domains are enriched with heterochromatin from (peri)centromeric chromosomal regions and contain mostly repressive chromatin marks, such as dimethylation at lysine 9 of histone H3 (H3K9me2). Those regions can rearrange their configuration due to lamin levels, leading to a dynamic three-dimensional genomic architecture\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Our co-expression network analysis suggests \u003cem\u003eLMNB2\u003c/em\u003e to be a key player in chromatin organization. Defects in lamins A and C have been involved and classified as laminopathies, including muscular dystrophy, and progeria. Moreover, lamin B1 or B2 alteration has previously been linked to various neuropathies\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Consistent with our results, Gil and colleagues observed an increase of LMNB2 levels in pyramidal hippocampal neurons of AD patients at Braak stages V-VI, which was related with nucleoli displacement to the periphery and signs of neuronal attrition\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. An AD model presented deregulation of lamin B that led to aberrant nucleo-cytoskeletal coupling and promoted heterochromatin relaxation and neuronal death, suggesting AD can be considered as an acquired neurodegenerative laminopathy linked to aging\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Furthermore, aneuploid chromosomes have been shown to be mis-localized in cell populations with depleted lamin B2, but not for other lamin subtypes, indicating together with our data a role for LMNB2 in trisomy 21 as well\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the role of \u003cem\u003eLMNB2\u003c/em\u003e in dementia should be further explored with functional studies in models for PD, AD, and DS.\u003c/p\u003e\u003cp\u003eThe other hub gene, EHMT2, has recently been associated with PD in the European population through a genome-wide association study\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. This methyltransferase specifically targets H3K9me2, which is associated with transcriptional gene repression\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Histone methylations have shown to be involved in the dysregulation of synaptic functions and associated with mental disorders, which was also the highest annotated MeSH class in this study\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. However, future experimental studies (e.g., western blotting or immunohistochemistry) should validate the increase of H3K9me2 levels in the types of neurodegenerative dementias. Inhibition of upregulated \u003cem\u003eEHMT2\u003c/em\u003e levels has previously shown to decrease H3K9me2 levels, restore synaptic functions, prevent neuronal death, and rescue motor impairment without affecting the formation of α-synuclein in a mouse model for PD\u003csup\u003e41\u003c/sup\u003e. In a late-stage AD mouse model, the increase of \u003cem\u003eEHMT2\u003c/em\u003e expression led to augmented H3K9me2 levels, but not for this model at an early-stage, suggesting an age dependence of this epigenetic change happening later in life. Correspondingly, the inhibition of this methyltransferase rescued synaptic and cognitive functions, but failed to reduce the amyloid load in those AD mice\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In line with previous reports, our findings suggest EHMT2 dysregulation may play a central role in neurodegenerative dementias and highlight this methyltransferase as a potential therapeutic target warranting further experimental validation. Interestingly, different pharmaceutical interventions have been explored to alleviate cognitive impairment in DS, although with a limited success in clinical trials\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Until now, we are the first study that links \u003cem\u003eEHTMT2\u003c/em\u003e dysregulation with DSD, which suggests exploring the beneficial effects of EHMT2 inhibitors in preclinical DS models and to potentially ameliorate their cognitive impairment and synaptic dysfunction during adult life stages\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough promising, our findings should still be interpreted with caution, as the study is limited by the modest sample size and the constraints of bulk RNA-seq.\u0026nbsp;Nevertheless, the consistent signals observed among the different dementia groups suggest common molecular processes that may contribute to hippocampal vulnerability. Future studies using single-cell and spatial transcriptomic approaches will be essential to validate the role of these candidate genes and to determine whether they represent viable biomarkers or therapeutic targets. Overall, the transcriptomic analysis pipeline and unique approach presented in this work provides an original strategy to discover novel biomarkers or disease-altering targets in potentially overlapping neurological diseases and a promising research avenue for other diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSample Selection\u003c/h2\u003e\u003cp\u003ePost-mortem brain tissue of PDD, AD, and DSD individuals were pathologically confirmed and obtained from four European brain banks: National Brain Bank Neuro-CEB, Piti\u0026eacute;-Salp\u0026ecirc;tri\u0026egrave;re Hospital (Paris, France); Institute of Psychiatry, King\u0026rsquo;s College London Brain Bank (London, United Kingdom); Cambridge Brain Bank, Addenbrooke's Hospital, Cambridge University Hospital (Cambridge, United Kingdom), and the Neurobiobank of the Institute Born-Bunge (Antwerp, Belgium). The cohort included five control cases (Control) from non-demented individuals who died without known neurological disorders, four samples from PDD patients, six samples from patients with sporadic AD, and five samples from DSD individuals with neuropathological signs of AD, including NFT and Aβ-containing senile plaques at histological examination\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. The samples were collected at autopsy and stored at -80\u0026deg;C until further processing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSample preparation\u003c/h2\u003e\u003cp\u003eDissected hippocampi were weighted to calculate the homogenization volume of the buffer for obtaining a 20% concentrated suspension (weight/volume). The tissue was homogenized in ice-cold 50 mM Tris\u0026ndash;HCl (pH 7.4) supplemented with Halt\u0026trade; Protease and Phosphatase Inhibitor Cocktail (#78438, Thermo Fisher Scientific, Pittsburgh, PA, United States) by using the Bio-Gen PRO200 Homogenizer (#01-01200, PRO Scientific Inc., Oxford, CT, United States) at setting three for 30 seconds and then at full speed for one minute. Subsequently, the suspension was centrifuged at 3000 x g for 10 minutes (4\u0026deg;C). Then, the supernatant was used for RNA extraction. Total RNA was isolated with RNeasy Mini Kit (#74104, Qiagen, Hilden, Germany), according to manufacturer's instructions to achieve maximum yields of RNA. Next, the samples were treated with the Heat\u0026amp;Run\u0026reg; gDNA Removal Kit (#80200, ArticZymes, Troms\u0026oslash;, Norway) to avoid amplification of genomic DNA during further processing steps. The RNA concentration and purity were analyzed using the NanoDrop-1000 Spectrophotometer (Thermo Fisher Scientific, Pittsburgh, PA, United States). For each sample a total of 42\u0026ndash;336 ng was obtained. The RIN was assessed using the RNA 6000 Pico Kit of Bioanalyzer 2100 system (#5067\u0026thinsp;\u0026minus;\u0026thinsp;1513, Agilent Technologies, Santa Clara, CA, United States). All RNA samples were stored at -80\u0026deg;C until further processing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRNA-sequencing and data quality control\u003c/h2\u003e\u003cp\u003eA total of 10 ng RNA was used for downstream RNA-seq application. First, the SMARTer\u0026reg; Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian (#634485, Takara Bio, Kusatsu, Japan) was used for library preparation, which was followed by a purification with AMPure XP beads (#A63880, Beckman Coulter Inc., Brea, CA, United States). Then, library fragments originating from rRNA (18S and 28S) and mitochondrial rRNA (m12S and m16S) were cleaved by ZapR v3 in the presence of mammalian specific R-Probes v3 (Takara Bio, Kusatsu, Japan). Paired-end sequencing for 50 bp each was performed on the NovaSeq 6000 platform (Illumina Inc., San Diego, CA, United States) to a depth of around 50\u0026nbsp;million reads (i.e., 25\u0026nbsp;million reads paired-end fragments). FASTQ output was assessed using MultiQC (version 1.10.1) with default settings prior to alignment and quantification. Quality was visually inspected to observe the \u0026ldquo;sequence quality\u0026rdquo;, \u0026ldquo;per tile sequencing quality\u0026rdquo;, \u0026ldquo;overrepresented sequences\u0026rdquo;, \u0026ldquo;adapter content\u0026rdquo; and other quality parameters\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRNA expression quantification and filtering\u003c/h2\u003e\u003cp\u003eRaw sequencing reads in the FASTQ files were mapped with STAR (version 2.7.8a) against the Gencode v41 transcriptome, which was based on the GRCh38.p13 reference genome\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. BAM files were deduplicated with UMI-Tools dedup (version 1.1.2) using the --method\u0026thinsp;=\u0026thinsp;unique to retain one representative read per unique UMI. The generation of a table of counts with the subread R package (version 2.0.3)\u003csup\u003e77\u003c/sup\u003e. The options applied to quantify the abundance at gene level were -p to count read pairs, -t \"exon\" as features to be quantified, --largestOverlap to assign reads to the feature with the largest overlap and -g \"gene_name\" to collapse transcript-level quantification into gene-level counts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDeconvolution\u003c/h2\u003e\u003cp\u003eThe raw count matrix obtained from STAR was imported in RStudio (version 2025.05.1\u0026thinsp;+\u0026thinsp;513) for performing bulk RNA-seq deconvolution using the MuSiC2 algorithm\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. First, this matrix was converted to counts per million (cpm) by dividing with the total number of reads and multiplying by 10\u003csup\u003e^\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. An ExpressionSet was created with the converted matrix and the single cell RNA sequencing (scRNA-seq) reference dataset from Darmanis et al. was imported through the scRNAseq R package (version 2.22.0)\u003csup\u003e78\u003c/sup\u003e. Subsequently, the scRNA-seq reference dataset was filtered for astrocytes, endothelia, microglia, neurons, and oligodendrocytes. The proportions of these cell-types for each sample were estimated with the function music2_prop_t_statistics from MuSiC2 (version 0.1.0)\u003csup\u003e50\u003c/sup\u003e. Then, a one-way ANOVA followed by the Tukey\u0026acute;s HSD multiple-comparisons \u003cem\u003epost hoc\u003c/em\u003e test was performed to assess significance for each cell-type between the Control, PDD, AD and DSD individual groups (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptional age calculation\u003c/h2\u003e\u003cp\u003eThe \"predict_age\" function of the RNAAgeCalc R package (version 1.20.0) was used to compute the transcriptional age for each RNA sample\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. First, the transcript length for all transcripts were downloaded using the biomaRt R package (version 2.64.0) with Ensembl 114. Then, the Fragments Per Kilobase of exon per Million mapped reads (FPKM) was calculated by first dividing the raw counts matrix by the library size and then by gene length (i.e., transcript length). Finally, this FPKM counts matrix was provided to the \"predict_age\" function by setting the parameters as follows: exprtype \"FPKM\", tissue \"brain\", idtype \"SYMBOL\", stype \"caucasian\", signature \"DESeq2\" and maxp \"30000\".\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCovariate selection\u003c/h2\u003e\u003cp\u003eSources of variation in the RNA-sequencing data were identified using principal component analysis (PCA) performed on gene-level expression filtered based on cpm values\u0026thinsp;\u0026gt;\u0026thinsp;0.33 in at least 6 samples (24,186 genes). The filtered genes were normalized using Trimmed means of M values (TMM) method of the edgeR R package (version 4.6.3), which is a statistical package based on generalized linear models\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. The transcriptional age (RNAAge), RIN, PMI and APOE ε4 status were significantly correlated with the first PC (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Samples were plotted by their first two principal components to determine how well the group (i.e., Control, PDD, AD, DSD), chronological age, transcriptional age, RIN, PMI, and APOE ε4 status separated (\u003cb\u003eSupplementary Fig. S3\u003c/b\u003e). Of note, none of the estimated cell proportion for each cell-type significantly differed between groups (see section deconvolution), resulting that those proportions were not included into the linear model as a covariate to adjust for cell composition variation. Moreover, PMI and APOE ε4 status suggested multicollinearity with other covariates in the model. Finally, the model for differential expression consisted of the groups (i.e., Control, PDD, AD, and DSD) and the covariates RNAAge, RIN, and sex. Although the sex difference was not significant between groups due to a small sample size, it was included as a covariate because the AD and DSD groups contained three times more males as the Control group. This was to exclude a bias for detecting DEGs due to their location on sex chromosomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDifferential gene expression\u003c/h2\u003e\u003cp\u003eDifferential gene expression of the RNA-sequencing was assessed on gene expression level filtered based on cpm values\u0026thinsp;\u0026gt;\u0026thinsp;0.33 in at least 6 samples (24,186 genes), using DESeq2 R Package (version 1.48.1)\u003csup\u003e81\u003c/sup\u003e. The Wald test was used to detect DEGs in a pairwise manner (i.e., PDD \u003cem\u003eversus\u003c/em\u003e Control, AD \u003cem\u003eversus\u003c/em\u003e Control, and DSD \u003cem\u003eversus\u003c/em\u003e Control), controlling for covariates identified previously (see section 2.7). Prior to correction, covariates to be used in the model were scaled to ensure that continuous variables that are measured on different scales (i.e., RIN \u003cem\u003eversus\u003c/em\u003e RNAAge) are comparable. Significant genes were obtained with a correction for FDR adjusted p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| of 0.3, which was identical to the settings from a recent bulk RNA-seq study performed with human DS hippocampal tissues\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The volcano plots and heatmaps (\u003cb\u003eSupplementary Fig. S4\u003c/b\u003e) depicting the DEGs were created with the ggplot2 (version 3.5.2) and pheatmap (version 1.0.13) R packages, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eFunctional analysis\u003c/h2\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA) was performed on the GO, DO and KEGG databases of a pre-ranked list according to the log\u003csub\u003e2\u003c/sub\u003eFC x -log\u003csub\u003e2\u003c/sub\u003e(p-value) metric, which penalize large fold changes that have large (non-significant) p-values. GSEA was used to test for enrichment of specific gene sets within the ranked list to define whether specific signaling pathways were enriched among upregulated or downregulated genes\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. GO enrichment was performed to investigate the gene-related BP, MF, and CC. DO enrichment analysis was used to explore genes-related diseases. KEGG enrichment analysis was conducted to explore gene-related signaling pathways. The obtained results were visualized with the clusterProfiler R package (version 4.16.0)\u003csup\u003e83\u003c/sup\u003e. Statistical significance was set at an FDR-adjusted p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05. In addition, an enrichment analysis was performed with the common demented DEGs (45 genes) using the DisGeNET R package (version 1.0.7), which has genes\u0026rsquo; association curated data to disease and phenotypic traits\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eWeighted gene co-expression network analysis\u003c/h2\u003e\u003cp\u003eTo screen for a potential group of genes that are specifically associated with dementia, the WGCNA R package (version 1.73) was used to perform a WGCNA to find modules in demented individuals and non-demented controls\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. First, the raw counts matrix was filtered based on counts\u0026thinsp;\u0026lt;\u0026thinsp;15 in more than 75% of the samples (23,150 genes). Subsequently, the Pearson correlation coefficient was calculated between each pair of genes to evaluate the expression similarity of genes and acquire a correlation matrix. The soft threshold function was applied to convert the correlation matrix into a weighted neighborhood matrix. An optimal soft power threshold of seven was selected through the soft connectivity algorithm, ensuring that the gene correlations were maximally consistent with the scale-free topology and a negative slope. Thereafter, a topological overlap matrix (TOM) was constructed from the adjacency matrix. The TOM was hierarchically clustered using average linkage hierarchical clustering with \"1-TOM\", which is a dissimilarity measure (dissTOM). The TOM indicates how similar two genes are in terms of their connectivity in the network (i.e., network interconnectedness), helping identify robust, biologically meaningful modules of co-expressed genes. Modules were defined as branches of a dendrogram and derived through the dynamic tree cutting method, applying a minimum module size of 50 and a maximum deep split. These initially generated modules were merged based on module eigengenes, using correlation-based adjacency as dissimilarity matrix. Furthermore, the modules with a smaller distance of less than 0.25 were merged into a single module. Each module was summarized by module eigengene (ME), representing the characteristic expression profile. Module-trait associations were computed with biological traits (i.e., Control, PDD, AD, DSD) and significant correlation indicated potential key modules, where key modules\u0026rsquo; genes were considered key genes. The edges and nodes parameters were derived for significant modules in the module-trait associations and imported in Cytoscape (version 3.10.3)\u003csup\u003e87\u003c/sup\u003e. Key genes presented in these modules were used for generating an interaction network, gene ontology analysis and identification of hub genes. Finally, target genes were inferred through the intersection of DEGs with key modules\u0026rsquo; genes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus repository with the series record XXX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R code and instructions for full reproduction of the results are available at https://github.com/renealbertjohan/UVic_HEROES.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lab of M.D. is recognized by the Secretaria d\u0026rsquo;Universitats i Recerca del Departament d\u0026rsquo;Economia I Coneixement de la Generalitat de Catalunya (Grups consolidats 2023). We acknowledge the support of the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020‐001049‐S, MCIN/AEI/10.13039/501100011033), the Generalitat de Catalunya through the CERCA program and EMBL partnership. We gratefully acknowledge the expertise and technical support from the Escourolle Laboratory of the Neuropathology Department of Piti\u0026eacute;-Salpetri\u0026egrave;re Hospital (Paris, France) and the CRG Genomics Unit for their support and assistance in this work (Barcelona, Spain). The CIBERER of Rare Diseases is an initiative of the Instituto Carlos III (ISCIII). We want to thank the Institute Born-Bunge (NBB-IBB; Antwerp, Belgium), Neuro-CEB (Paris, France), Institute of Psychiatry Kings College Brain Bank (London, United Kingdom), and the Cambridge Brain Bank (Cambridge, United Kingdom) for providing study materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Joint Program Neurodegenerative Diseases Agence Nationale de la Recherche (JPND ANR-17-JPCD-0003 HEROES and JPND HEROES AC170006), Agencia Estatal de Investigaci\u0026oacute;n (PID2019-110755RB-I00/AEI/10.13039/501100011033 and PID2022-141900OB-I00 INTO-DS), ZonMW project (733051072), Medical Research Council (MR/R024901/1), and the European Union\u0026apos;s Horizon 2020 research and innovation program (848077 and 899986). Part of the experiments were also funded by the Centre Of Excellence Neurodegenerative Diseases Agence Nationale de la Recherche (COEN-0002 and COEN-4024) and the Investissement d\u0026rsquo;Avenir (ANR-10-AIHU-06). Moreover, the J\u0026eacute;r\u0026ocirc;me-Lejeune Fondation funded M.F. and R.A.J.C with the Sisley d\u0026rsquo;Ornano-Lejeune postdoctoral fellowship 2019 and 2021 (12_PDC-2019 and 15_PDC-2021), respectively. R.A.J.C. received also funding by the ISCIII Sello Excelencia ISCIII-Health (IHMC22/00026) and Ministerio de Ciencia Innovaci\u0026oacute;n y Universidades (RTC2019-007230-1, RTC2019-007329-1, and CPP2022-009659).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: R.A.J.C. and M.D. Providing samples: M.C.P., M.F., P.P.D.D. and A.S. Conducted the experiments: R.A.J.C. Data analysis: R.A.J.C, K.B.C and H.C. Data interpretation: R.A.J.C., J.S.F. and B.R.M. Drafted the manuscript: R.A.J.C. and M.D. Critically revised and corrected the content: R.A.J.C, M.F., K.B.C., H.R., J.S.F., Y.V., A.S., D.V.D., P.P.D.D., B.R.M., M.C.P and M.D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics and Deontology Committee of the ICM Paris Brain Institute (COMETH-ICM). Brain tissue specimens are rare and precious and originate from donations for research purpose. The donors have signed an informed consent for research into diseases of the nervous system. The Biomedical Research Law (Law 14/2007, of July the 3rd, Biomedical Research) and (Royal Decree that developed after the Biomedical Research Law) were passed to regulate the proper collection, storage and use of biological samples of human origin, and to promote their use for biomedical research by following good ethical and scientific practices. All experiments were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGale, S. A., Acar, D. \u0026amp; Daffner, K. R. 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For these neurodegenerative dementias, we observed that the chronological age did not align with their biological age, which was determined based on hippocampal transcript levels (i.e., transcriptional age). Subsequently, we performed a transcriptomic analysis that corrected for the transcriptional age in the hippocampus of affected individuals, highlighting common underlying pathogenic mechanisms. There were 45 common differentially expressed genes (DEGs), whereas enriched functional terms were related to lysine N-methyltransferase activity and intermediate filament. Co-expression network analysis displayed a module that was significantly downregulated in the non-demented control group only. This module identified EHMT2 and LMNB2 as hub genes, which were also common DEGs. Overall, these findings uncover shared functional insights in the hippocampus, while specifically highlighting EHMT2 and LMNB2 as potential universal biomarkers or disease-altered targets across neurodegenerative dementias.\u003c/p\u003e","manuscriptTitle":"Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:31:20","doi":"10.21203/rs.3.rs-7703742/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T21:44:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-01T01:50:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158074792442271493730955242476672976320","date":"2026-01-14T20:02:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253109405946750393578653464580979872710","date":"2025-12-24T01:15:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T19:50:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312943843671932788570634116918437452586","date":"2025-11-19T04:35:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T08:24:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T00:54:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T10:05:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Dementia","date":"2025-09-24T12:29:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-dementia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Dementia](https://www.nature.com/npjdementia/)","snPcode":"44400","submissionUrl":"https://submission.springernature.com/new-submission/44400/3","title":"npj Dementia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e1ed5e19-5d61-431e-a846-1c95e531eea0","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56754515,"name":"Health sciences/Biomarkers"},{"id":56754516,"name":"Health sciences/Diseases"},{"id":56754517,"name":"Health sciences/Neurology"},{"id":56754518,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-03-05T16:54:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:31:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7703742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7703742","identity":"rs-7703742","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}