Potentially damaging variants’ analysis in autism subgroups uncovers early brain-expressed gene modules relevant to autism pathophysiology

Potentially damaging variants’ analysis in autism subgroups uncovers early brain-expressed gene modules relevant to autism pathophysiology | 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 Research Article Potentially damaging variants’ analysis in autism subgroups uncovers early brain-expressed gene modules relevant to autism pathophysiology Gaia Scaccabarozzi, Luca Fumagalli, Maddalena Mambretti, Roberto Giorda, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5534869/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Understanding the functional implications of genes’ variants related to autism heterogeneity represents a crucial challenge. Gene set analysis examines the combined effect of multiple genes with convergent biological functions. Here we explored whether a multi-step analysis could identify gene sets relevant to autism subtyping in terms of different loads of possibly damaging variants (PDVs) among two subgroups of autistic children. Methods After subdividing our sample of 71 autistic children (3-12 years) in two subgroups with higher (>80; n=43) and lower (≤80; n=28) intelligence quotient (IQ), a gene set variant enrichment analysis identified gene sets with significantly different incidence of PDVs between the two subgroups. Significant gene sets were then clustered into modules of genes. Their brain expression was investigated according to the BrainSpan Atlas of the Developing Human Brain. Next, we extended each module by selecting the genes that were spatio-temporally co-expressed in the developing brain and physically interacting with those in the modules. Last, we explored the incidence of autism susceptibility genes within the original and extended modules. Results Our analysis identified 38 significant gene sets (FDR, q<0.05), which clustered in four gene modules involved in ion cell communication, neurocognition, gastrointestinal function, and immune system. Those modules were highly expressed in specific brain structures across different developmental stages. Spatio-temporal brain co-expression across development and physical protein interactions identified extended clusters of genes where we found an over-representation of autism susceptibility genes. Limitations The sample size of this work is limited. Our analysis was also limited to a disease-associated subsection of the exome. Conclusions Our unbiased approach identified modules of genes functionally relevant to autism pathophysiology in a relatively small set of participants, providing evidence of their implication in the phenotypic differences of autism subgroups. The findings of interconnections between different modules and with autism susceptibility genes suggest that diversity in autism likely originates from multiple interacting pathways. Future research could leverage the present approach to identify genetic pathways relevant to autism subtyping. autism heterogeneity genetics potentially damaging variants brain expression Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Autism is a neurodevelopmental condition characterised by relevant heterogeneity across multiple levels of analysis. Its phenotypic presentation can drastically differ depending on the degree of both core and co-occurring features, such as language abilities (1), intellectual profile (2), motor coordination difficulties (3,4), and the overall adaptive functioning (5) (with this respect, see also the LIMA approach, (6). Critical heterogeneity has been also found at deeper levels of investigation, including neurophysiology (7,8) and genetics (9,10). Along with those aspects, it is also important to consider the dimension of chronogeneity, i.e. the variation in autism manifestation in relation to the dimension of time and, markedly, of development (11). Despite this multi-level heterogeneity, the role of genetic factors in autism aetiology is unquestioned. The heritability of the condition is high, with estimates ranging between 64% and 91% (12). Over recent years, hundreds of de novo and rare inherited variants have been identified as major contributors to individual autism risk (13–19), for a review, see (20). The expression of those autism-associated genes have been reported to be preeminent at prenatal and early postnatal stages, substantiating the early onset of the condition (21,22). However, although with smaller effect in comparison to that of de novo and rare inherited variants, several studies have demonstrated that heritability in autism is largely due to common variants, i.e. to the genetic variations that are commonly present in the general population (23–25). Thus, as for other neurodevelopmental conditions, there is considerable evidence that indicates the high heritability of autism and its polygenic nature, with both rare and common variants having a critical role (26). In the last 10 years, a body of research has leveraged genetic heterogeneity to parse autism phenotypic presentations (27–30). With this respect, specific genotypes have been associated with well-defined clinical manifestations of autism, potentially via distinct intermediate brain endophenotypes (31). Nevertheless, given the heterogeneity of the condition, the identification of the functional implications of genes related to autism requires large-scale studies, mostly involving thousands of participants. An alternative perspective is shifting the focus from single genes to networks of genes that converge in functionally relevant biological processes, e.g. by using gene set analysis. Gene sets are groups of genes that share a common biological function and are pre-defined on the basis of prior biological knowledge (32). Gene set approach permits to examine the combined effect of multiple DNA variants that cumulatively impact on biological pathways potentially relevant to autism (9). In this study, we explored the hypothesis of whether a multi-step unbiased analysis could identify gene sets relevant to autism subtyping and functionally characterise them in terms of biological processes and their spatio-temporal co-expression in the brain. Since we intended to evaluate the gene sets’ cumulative effects we considered all the potentially damaging variants (PDVs), regardless of both their frequency in the population and their effect on the proteins. The present work is split into different objectives. First, after dividing our clinical sample of autistic children into two subgroups on the basis of their standardised measures of intelligence quotient (IQ), we identified gene sets that presented a different load of PDVs between participants with higher IQ and lower IQ, using a data-driven enrichment analysis. With this regard, previous research has reported significant associations between genetic variations in autism and cognitive heterogeneity (33,14). The obtained gene sets were hierarchically clustered into modules based on their PDVs enrichment. We then characterised each of those modules with a label representative of the biological processes that best characterised them. Next, to explore the functional implications of the modules, we assessed their expression profiles in the brain according to the BrainSpan Atlas of the Developing Human Brain (34). More specifically, we determined whether genes in the modules were expressed above expectations in brain structures through different developmental stages, to gain insights about their potential involvement in brain development. Afterwards, to explore more extensively the functional interplay between the modules, we extended each module by selecting those genes which both physically interact at the protein level and show a highly correlated spatio-temporal brain expression profile with those in the modules. Among the extended set of genes, we assessed their spatial and temporal co-expression in the brain according to the BrainSpan Atlas of the Developing Human Brain and their physical interaction at the protein level according to the bioGRID database (35). Understanding how genes relate to each other during neurodevelopment is essential to identify what are potential shared biological processes (36) and how they could interact along their functional pathways to impact the final outcome. Genes that are co-expressed in the brain are indeed more likely to act together in converging processes. In this sense, the evidence of the corresponding proteins’ interactions represents a confirmation of this functional association (37). Lastly, we investigated whether the genes in the modules and those in the extended set of their co-expressed interactors were enriched in candidate genes highly associated with autism susceptibility according to the Simons Foundation Autism Research Initiative (SFARI) database (38), which constantly integrates genetic information from multiple research studies. METHODS We summarised our methods in a workflow diagram (Fig. 1 ). Participants A total of 71 autistic children aged 3–12 years (61 males, 10 females) were involved in this cross-sectional study. Participants were consecutively recruited at the Child Psychopathology Unit of Scientific Institute, IRCCS Eugenio Medea (Bosisio Parini, Italy), over a 36-month period between July 2016 and July 2019, as a part of a larger study (39). Autistic children were admitted to inpatient/outpatient units of our institute either for assessment or for a comprehensive rehabilitation program. All participants had been previously diagnosed at our institute on the basis of a consensus "best estimate" DSM-5 clinical diagnostic process informed by, but not dependent on, scores on the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) (40). Participants were excluded in case that a well-defined genetic disorder was detected. Further exclusion criteria were the use of medication affecting the central nervous system, the presence of significant sensory impairment (e.g., blindness, deafness), abnormalities detected by MRI, and suffering from chronic or acute medical illness. All participants were drug-naïve. This research was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and later amendments and was approved by the Ethics Committee of our Institute “Comitato Etico IRCCS E. Medea—Sezione Scientifica Associazione La Nostra Famiglia” (Prot. N.33/18—CE). Informed written consent was collected by all of the participants’ parents or legal guardians before participation. Measures All children underwent an assessment of the IQ level. The specific assessment tool was chosen according to the participants’ developmental level among the Griffiths Mental Development Scales (41), Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III) (42), and Wechsler Intelligence Scale for Children–IV (WISC-IV) (43). The diagnoses were confirmed using the Autism Diagnostic Observation Schedule–Second Edition (ADOS-2) (40). Total raw scores and social affect (SA) and restricted repetitive behaviour (RRB) raw scores were separately converted into calibrated severity scores following the procedure by Gotham et al. (44) and Hus et al. (45) to allow comparisons across individuals with different developmental levels. Information about the familial socioeconomic status (SES) was also collected using the Hollingshead scale for parental employment (46). Biological samples Blood or saliva samples (7 and 64, respectively) were obtained from participants for DNA extraction, which was in-house performed at the Molecular Biology Laboratory of our Institute. Blood was collected in tubes with EDTA. Genomic DNA was extracted from blood samples through the GenElute Blood Genomic DNA kit (Sigma). DNA from each sample was eluted in 50µl of 10 mM Tris (pH 9.0) / 0.5 mM EDTA and stored at -20°C. The Oragene OG-500 kit (DNA Genotek) was used to collect saliva samples. Genomic DNA was extracted from saliva samples by precipitation in ethanol with the manufacturer's protocol, resuspended in 50 µl of 10 mM Tris (pH 9.0)/1 mM EDTA and stored at -20°C. Variants identification Samples’ DNA were screened by using a targeted next generation sequencing approach with a targeted design that enables analysis of only the disease-associated targets, the SureSelect Focused Exome, with approximately 5,200 disease-associated genes (see supplementary table S1 for a complete list of genes). The sequencing libraries were prepared from genomic DNA by using a Sure Select enrichment system (Agilent Technologies). Targeted libraries were run on NextSeq platform according to the manufacturer's instructions (Illumina, San Diego, CA, USA). The sequenced reads were then aligned to reference target regions and variants were called with BWA enrichment application (which include also GATK for variant calling) available on BaseSpace Onsite (Illumina, San Diego, CA, USA). Erroneous variant calls were discarded. Furthermore, we did not consider the Y chromosome because some variations were retrieved also for women. Variants’ annotation was performed by ANNOVAR (47) with a refGene database generated by converting the human reference genome (GRCh37/hg19) GFF3 and FASTA files to ANNOVAR file format using the gff3toGenePred package (48). Potentially damaging variants (PDVs) were then selected as those occurring in the panel exons and annotated as indels, stop codon gain, stop codon loss or non-synonymous. Gene sets definition We obtained 29227 gene sets corresponding to ontology terms or pathways and their associated genes. To this purpose we interrogated multiple sources, namely: Gene Ontology (49) (version 2019-03), HPO Phenotype (50) (version 2019-03), Kegg Pathway Database (50) (2019-2-25) and Reactome Pathway Database (51) (Physical Entity Identifier mapping files, updated to 2019-03). Evaluation of gene sets variants abundance in a group of subjects To keep into account variants zygosity, we calculated the number of variants V for a given gene set S and a group of subjects G as follows: \(\:V=\frac{\left({\sum\:}_{i\in\:G}{\sum\:}_{g\in\:{S}_{A}}{V}_{ig}\right)\left({\sum\:}_{i\in\:G}{\sum\:}_{g\in\:{S}_{A}}2{L}_{g}\right)+\left({\sum\:}_{i\in\:G}{\sum\:}_{g\in\:{S}_{X}}{V}_{ig}\right)\left({\sum\:}_{i\in\:G}{\sum\:}_{g\in\:{S}_{A}}{N}_{i}{L}_{g}\right)}{{\sum\:}_{i\in\:G}{\sum\:}_{g\in\:{S}_{A}}2{L}_{g}+{\sum\:}_{i\in\:G}{\sum\:}_{g\in\:{S}_{A}}{N}_{i}{L}_{g}}\) [1] Where V ig is the number of alternative alleles observed for subject i in gene g , L g is the length of gene g and N i is 2 if the subject i is a female or 1 otherwise. Summations are over the subjects in the group G and the genes over the gene-set S ( S A for genes in autosomal chromosomes and S X for genes on the X chromosome). The same procedure was used to evaluate the number of variants in a gene set for a single subject (in this case G only contains one subject). We define a gene set enrichment score for a group or a single subject as the number of variants in the gene set calculated following [1] divided by the total number of variants for the group or for that specific subject. Gene set variants enrichment analysis (GSVEA) The goal of the GSVEA is to determine whether the abundance of variants observed in a given gene-set differs in two subject groups. To assess whether the variants abundance in a gene set S is significantly different between two groups of participants G1 and G2 , a Fisher’s exact test is applied to the contingency table defined as: Number of variants in the gene set Number of variants not in the gene set G1 V G1 V A1 - V G1 G2 V G2 V A2 - V G2 V G1 and V G2 are calculated according to [1] for the gene set S while V A1 and V A2 are calculated according to [1] but for the gene set comprising all the genes in the exome panel. When multiple gene sets are tested, false discovery rate (FDR) is applied to the Fisher test p values and q values are reported. Gene sets and subjects clustering Hierarchical clustering has been consistently performed in this study using the Ward algorithm (52) applied to the euclidean distance. Optimal ranks have been determined by silhouette analysis (53). Gene expression in the brain during neurodevelopmental stages We used the ABAEnrichment R package (version 1.20.0) (54) to characterise the brain expression of the genes included in the modules according to the BrainSpan Atlas of the Developing Human Brain (34). BrainSpan Atlas includes five developmental stages, namely prenatal, infant (0–2 years), child (3–11 years), adolescent (12–19 years) and adult (> 19 years). According to the study’s aims, we limited our analysis to the four developmental stages. Genes Spatio-Temporal co-expression in the developing brain For each gene in the exome panel, we built an expression vector extracting from the BrainSpan Atlas of the Developing Human Brain the expression levels measured in each available structure for the first four developmental stages. For any pair of genes in a set, we estimated their co-expression in time and space by calculating the Pearson correlation coefficient (p) between their expression vectors. Correlation p-values are also computed and corrected for multiple testing (FDR). Gene pairs with p > 0.7 and q-values < 0.05 are considered to be co-expressed. Genes protein-protein interactions The bioGRID database (35) was used to identify whether couples of genes had physical protein-protein interactions. Genes having at least one physical interaction with the genes in the modules were considered as interactors in the present study. Incidence of autism-related genes Lastly, we analysed the incidence of genes with a gene score of 1 (High Confidence category) in the SFARI database ( https://sfari.org/ accessed July 9th, 2024) in our gene modules and their extended networks. Fisher exact tests were performed considering all the genes listed in the exome panel and, in case of multiple testing, p values were corrected according to Bonferroni. RESULTS Sample description Table 1 summarises the sociodemographic and clinical characteristics of the total sample and for the two subgroups of participants with lower ( ≤ 80, n=28) and higher IQ (>80, n=43), respectively. The two subgroups were balanced in autism severity as assessed with the ADOS-2 calibrated severity scores and in the male to female ratio. Participants with higher IQ had significantly higher scores on SES (p=0.035) and were slightly older than subjects with lower IQ (p=0.057). Demographically, the entire sample was Caucasian. Table 1 . Sample description. Autism, TOT (n=71) Autism, IQ ≤ 80 (n=28) Autism, IQ>80 (n=43) p-value Age 7.6 (2.4) [3.2-11.9] 6.9 (2.6) [3.2-11.9] 8.1 (2.1) [3.8-11.6] 0.057 Sex (M:F) 61:10 25:3 36:7 0.510 IQ 86.1 (21.8) [47-134] 64.7 (9.6) [47-80] 100.0 (15.0) [81-134] <0.001 SES 58.5 (18.1) [20-90] 52.7 (16.9) [20-90] 62.5 (18.1) [30-90] 0.035 ADOS-2 total CSS 6.2 (1.7) [3-10] 6.1 (1.7) [4-10] 6.2 (1.7) [3-9] 0.505 ADOS-2 SA CSS 6.4 (1.9) [3-10] 6.5 (1.9) [3-10] 6.4 (1.9) [3-10] 0.696 ADOS-2 RRB CSS 6.3 (2.1) [1-10] 5.9 (2.7) [1-10] 6.5 (1.6) [1-9] 0.756 Note. Data are expressed as Mean (SD) [range]. Mann-Whitney U test was used for quantitative variables, chi-squared test for the categorical variable sex. SES: socio-economic status; ADOS: autism diagnostic observation schedule; CSS: calibrated severity scores; SA: social affect; RRB: restricted repetitive behaviours. Gene sets showing differential PDVs enrichment between autism subgroups are involved in ion cell communication, neurocognition, gastrointestinal function, and immune system. After splitting our sample into two subgroups, we performed a gene set variants enrichment analysis (GSVEA). In brief, the goal of this analysis was to determine whether the abundance of PDVs observed in a given gene set differed in the two subgroups. We performed the analysis for nearly 30.000 functionally characterised gene sets obtained from public databases of ontologies and pathways (see methods). Among the considered gene sets, 38 showed a significantly different incidence of PDVs between the two subgroups of autistic participants (false discovery rate, FDR, q < 0.05). For each subject we calculated the proportion of PDVs —enrichment score— in each significant gene set. We then applied an optimal hierarchical clustering procedure to the resulting matrix at both gene sets and subjects level (Fig 2A). The significant gene sets resulted grouped in five clusters (terms’ clusters, TC). Two of them —TC2 and TC3— were merged in a single cluster, given their high intersection level (Fig 2B), resulting in a total of four modules, which were operationally defined as the union of the corresponding significantly enriched gene sets. These four modules grouped together genes involved in a variety of processes and functions related to ion cell communication, neurocognition, gastrointestinal function, and immune system, respectively, and were thus labelled accordingly (Table 3). The data-driven identification of these four modules was based on the initial top-down splitting of our sample in two subgroups on the basis of their standardised measures of IQ. With a bottom-up approach, we also explored whether the modules’ enrichment effectively identified clusters of participants with phenotypic differences. At the subject level, participants were clustered in three groups (subjects’ clusters, SC). These SCs had a non-random distribution of subjects with IQ>80 (Fisher exact test; p<0.001), which resulted particularly represented in SC2 (Fig 2C). A logistic regression analysis based on the five TCs enrichment scores was also performed. With a leave-one-out cross validation approach, the regression model showed an accuracy of 0.704 (area under the receiver operating characteristic curve, ROC AUC=0.74) in predicting IQ group membership (Fig 2D). Participants in the three SCs did not differ significantly in ADOS total or subscales scores (Kruskal-Wallis test; p>0.05). Genes in the modules are expressed in the brain in the developmental period We then aimed to understand whether the genes included in the four modules could be relevantly expressed in brain regions throughout the development. To this end, we leveraged the spatial gene expression data provided by the BrainSpan Atlas of the Developing Human Brain to link our gene modules to brain structures across development. Given the childhood-onset nature of the condition, we specifically considered the first four developmental stages listed in BrainSpan Atlas: prenatal, infancy (0-2 years), childhood (3-11 years), adolescence (12-19 years). Results of the ABAEnrichment analysis revealed that genes in most of the modules were significantly more expressed across specific brain structures and developmental stages, except for the immune system module. An overview of the significantly enriched brain structures in each developmental stage can be found in Table 2. Genes in the ion cell communication module appear to be particularly expressed in the prenatal period and in adolescence across different brain structures. Genes in the neurocognition module are mainly expressed in the hippocampus and in various primary and associative cortical areas across infancy and childhood. Genes of the gastrointestinal module are extensively expressed from infancy to adolescence in cerebellum, subcortical nuclei and cortical structures. Last, genes included in the immune system module are not expressed higher than expected in either any brain structure or developmental stage. Table 2. Modules’ expression enrichment across brain structures and developmental stages. ION CELL COMMUNICATION NEUROCOGNITION IMMUNE SYSTEM GASTRO-INTESTINAL Pre-natal Infant 0-2y Child 3-11y Adol 12-19y Pre-natal Infant 0-2y Child 3-11y Adol 12-19y Pre-natal Infant 0-2y Child 3-11y Adol 12-19y Pre-natal Infant 0-2y Child 3-11y Adol 12-19y CBC_cerebellar cortex 0.032 1.138 0.028 1.156 0.043 1.177 MD_mediodorsal nucleus of thalamus 0.01 3.25 0.031 2.884 0.037 2.78 STR_striatum 0.01 1.476 0.021 3.06 0.037 1.302 AMY_amygdaloid complex 0.042 1.23 0.035 1.216 HIP_hippocampus (hippocampal formation) 0.016 2.36 0.037 2.552 0.018 1.246 0.005 1.268 0.023 1.454 M1C_primary motor cortex (area M1, area 4) 0.03 1.389 0.016 2.98 0.025 2.001 0.04 1.249 0.013 1.465 S1C_primary somatosensory cortex (area S1, areas 3,1,2) 0.013 2.997 0.022 2.265 0.041 1.237 V1C_primary visual cortex (striate cortex, area V1/17) 0.029 2.945 0.013 3.015 0.042 1.185 A1C_primary auditory cortex (core) 0.004 1.56 0.049 1.2 0.024 1.215 OFC_orbital frontal cortex 0.037 2.78 0.017 2.331 0.049 2.524 VFC_ventrolateral prefrontal cortex 0.034 2.863 0.019 2.01 0.012 1.706 0 1.468 DFC_dorsolateral prefrontal cortex 0.04 2.775 0.038 1.614 0.008 3.292 MFC_anterior (rostral) cingulate (medial prefrontal) cortex 0.008 1.75 0.004 1.307 0.008 1.531 ITC_inferolateral temporal cortex (area TEv, area 20) 0.047 1.437 0.035 2.821 0.01 2.398 STC_posterior (caudal) superior temporal cortex (area 22c) 0.018 2.321 0.023 1.666 IPC_posteroventral (inferior) parietal cortex 0.004 1.56 0.008 2.408 0.025 1.201 Note. Sampled brain structures are listed on the left and investigated age ranges are reported in columns. P-values (bold text) and fold values (regular text) are reported for brain structures and age ranges where a significant enrichment for that module was found. Identification of extended modules through physical protein-protein interactions and brain spatio-temporal co-expression Next, we aimed at identifying the genes which are both interacting with those in the four modules and showing a high level of co-expression in the developing brain. To this purpose, we used the protein-protein interaction database bioGRID, and the expression data from the BrainSpan Atlas of the Developing Human Brain. For each gene in a module we selected its direct physical interactors which also presented a significant level of spatio–temporal co-expression in the brain with that gene (see methods). Each module was then extended with its co-expressed interactors (Table 3, see supplementary table S2 for a complete list of genes). Table 3. Number of genes in each module and in each module extended with its co-expressed interactors. Number of genes in each module Number of genes in each extended module Ion cell communication 36 70 Neurocognition 20 149 Gastrointestinal function 94 331 Immune system 80 207 Total 219 590 We then collected all the genes in the four extended modules (n=590) and built a co-expression matrix by calculating the spatio-temporal co-expression for each pair (Fig 3A). Hierarchical clustering identified three genes’ clusters (GC) among this extended set of genes. Genes in clusters GC1 and GC2 showed high within-cluster and low between-clusters co-expression levels, while those in cluster GC3 showed a general intermediate level of co-expression both internally and with the other two clusters. Figure 3B depicts the annotated protein-protein interaction network for the three clusters, displaying high connection levels for clusters GC1 and GC2. SFARI genes are overrepresented in the extended networks of genes in the modules Looking at the incidence of autism-related genes, nine out of the 219 genes in the modules (4.11%) were also annotated in the SFARI database in the high confidence category. This proportion was higher than expected in a random sample of genes, but did not reach the level of significance (OR=1.534; p=0.2063). When considering the four extended modules, high-confidence SFARI genes were significantly overrepresented (32 out of 590, 5.42%; OR=2.296, p=0.0001; see supplementary table S3 for a complete list of genes). Looking at the distribution of these SFARI genes in the three co-expression clusters, they were significantly overrepresented in cluster GC1 (28 out of 362, 7.73%; OR=3.40; corrected p<0.001) while not in GC2 (1 out of 153, 0.65%) and GC3 (3 out of 63, 4.76%). Moreover, within GC1, the enrichment of SFARI genes was significant in the extended networks of neurocognition module (10 out of 128, 7.81%; OR=3.114; Bonferroni corrected p=0.033) and gastrointestinal module (16 out of 246, 6.50%; OR=2.615; Bonferroni corrected p=0.014) but not in the immune system and ion cell communication modules. DISCUSSION In this work, we identified four modules of genes with different possibly damaging variants’ load in two autism subgroups with higher and lower IQ levels by using an unsupervised approach. Importantly, the identified modules grouped together genes involved in a variety of biological processes 一ion cell communication, neurocognition, gastrointestinal function, and immune system一 that have been previously reported to be atypical in autism. Here, for the first time to our knowledge, we provide preliminary evidence of the possible implication of these biological processes in the differential phenotypic manifestations of two discrete autism subgroups. The first gene module 一ion cell communication一 includes genes involved in ion homeostasis, transport, and signalling (e.g., ATP1A2 and ATP1A3一subunits of a sodium-potassium pump, CACNA1C一subunit of a calcium voltage-gated channel). Those genes have been extensively associated with autism (55–57), mostly for their crucial role in synaptic functioning (13,58,19). In the very early developmental stages, genes within this module encode for proteins with major contributions in neural proliferation, migration, and differentiation (59), including voltage-gated ion channels that regulate the propagation of action potentials and pacemaking, also in relation to the cardiac membrane excitability. With this respect, an association between autism and congenital heart disease has been reported (60–62) and functionally confirmed based on the evidence of convergent genetic pathways (13,63). Genes in this module were significantly enriched in many brain regions during the prenatal stage according to our brain expression analysis, confirming their early involvement in development. Brain expression results also identified a significant enrichment of those genes during adolescence. With this respect, an association between the expression of ion-related genes in adolescence and neuropsychiatric conditions have been observed (64). Interestingly, our results also align with previous research indicating an association between variants in ion channels-related genes and both higher IQ and lower levels of repetitive behaviours in autism (65), confirming the relevance of this gene module in distinguishing different phenotypic manifestations of the condition. The neurocognition module encompasses gene sets related with various alterations in cognitive functions, including lack of insight, anomia, agnosia, alexia, as well as some core autistic features such as circumscribed behaviours and collectionism. Coherently with their involvement in cognitive functions, we found that the brain expression of these genes is mainly localised in cortical regions. Interestingly, many of the included genes are involved in the causation of different forms of neurodegenerative dementia (e.g. TREM2, GRN, PSEN1, C9ORF72, MAPT), including frontotemporal dementia (66–68). Common etiopathological mechanisms have been hypothesised between neurodegenerative dementia and neurodevelopmental conditions, including autism (69–71), based on the observations of partially overlapping symptoms (70,72) and increased occurrence of dementia in autistic adults (73). Moreover, these genes are also involved in biological processes specific for neurodevelopment, such as the production of neurotrophic factors (74), synaptic development and refinement (75,76), and broad synaptic functioning (77), which is coherent with their potential involvement in autism. Third, the finding of a gene module related to gastrointestinal function is in line with the relatively recent hypothesis of gut involvement in the etiological mechanisms of autism (78,79). Gastrointestinal symptoms are frequent in a large percentage of autistic individuals (80) and are associated with the severity of the condition (81). Alterations in gut microbiome are also commonly reported in autism (82,83). The impact of the gut microbiota on the brain, mainly via the bidirectional microbiota-gut-brain axis, have been broadly investigated in neurodevelopmental conditions (84). Our expression analyses revealed that genes in the gastrointestinal module were extensively expressed in the brain across all developmental stages. The present result therefore extends the previous finding of a surprisingly high proportion of autism-associated genes expressed both in the brain and in the gastrointestinal tract (85), further indicating a common genetic pathway for alterations in these two systems. Lastly, the immune system has been extensively involved in autism, both as a candidate etiological mechanism, e.g., through maternal immune activation, and as potential endophenotype, with converging evidence of immune dysregulation in autistic individuals (86–91). Moreover, genes controlling innate and adaptive immunity have been previously associated with autism (92,93), but also with autistic traits in the general population (94). Interestingly, some of the immune genes possibly involved in autism are also implicated in neurodevelopmental processes such as neuronal plasticity, with their strongest expression in the brain during gestational and early postnatal age (93,95). In contrast with those findings, we did not find here genes within the immune module to be particularly expressed in the brain along the neurodevelopmental stages. With this respect, it is important to have in mind that the gene expression profiles considered in this work are those reported in the BrainSpan Atlas of the Developing Human Brain for typically developing individuals. It is therefore possible that the presence of genetic variants in autistic subjects contributes to change the typical gene expression patterns, leading to the immune genes’ over-expression in the brain that is reported elsewhere in the literature (96,97). Furthermore, this result could also suggest a systemic rather than brain-localised role of the immune system in autism. Indeed, immune dysregulation can impact brain function through different pathways, including the gut-brain axis where the immune system is a crucial mediator (98,99). Moreover, some circulating cytokines can reach the brain and inhibit neurogenesis or promote neuron death, whereas endogenous anti-brain antibodies may be produced, altering the development or function of neurons (86,95). After having identified the four modules with a data-driven analysis based on the initial top-down splitting of our sample on IQ, we applied a bottom-up approach to explore whether the variants enrichment could effectively identify different clusters of participants. Coherently with our initial stratification, we found that participants with IQ > 80 were particularly represented in one of the three subjects’ clusters, as also confirmed by a predictive logistic regression model. While this result aligns with our criterion for subgrouping participants, the moderate accuracy of the regression model suggests that other factors are implicated in the between-groups differences associated with the modules’ enrichment. Nevertheless, the subjects’ clusters were not highly differentiated in terms of autism symptom severity. Future extensions of this work should combine different units of analysis (e.g., cells, circuits/networks, neurobiology) to further clarify linkages between genotype and clinical phenotypes. In addition to pinpointing the gene modules and discussing their relationship to phenotypic characteristics of autism, we thoroughly explored the functional interplay between the modules by considering an extended set of genes including the co-expressed interactors of the genes in each module. We discovered that most of the genes within this extended set presented high levels of spatio-temporal brain co-expression across development. Consistently, genes with higher co-expression correlation levels also highly interacted at the level of protein-protein interactions. Likewise, genes from different modules were found to be mutually interacting. Complementing earlier reports (13,100,101), this work therefore provides further evidence supporting the idea that the biology of autism could likely implicate altered interactions between different modules rather than the existence of independent disrupted pathways, which cumulatively contribute to the clinical outcome. Lastly, we found that SFARI high-confidence autism-related genes were overrepresented in the total set of genes in the extended modules, but not in the restricted group of genes in the modules. This suggests a significant connection, but no intersection, between the four pinpointed modules and genes previously associated with autism susceptibility. Nonetheless, this result also indicates that our unbiased method could effectively identify sets of genes in close relationship with autism-related genes, uncovering their potential role in shaping the heterogenous autistic phenotype. Importantly, the distribution of SFARI high-confidence genes within the total set of genes in the four extended modules was not stochastic. Indeed, based on their spatio-temporal co-expression levels, we found three different gene clusters: one with higher interaction levels but relatively few SFARI genes, a second one with less interactions and fewer SFARI genes, and a third cluster displaying high levels of interaction and overrepresented SFARI genes. In this last cluster, the autism-related genes were specifically overrepresented within the extended networks of the neurocognition, and gastrointestinal genes. Interestingly, the interaction between SFARI and neurocognition-related genes is in line with a previous large-scale exome sequencing study in autism (13), which found a significant protein-protein interaction between autism genes and MAPT, a gene implicated in neurodegenerative disorders, that is also part of our neurocognition module. Previous work has also highlighted that above 90% of the 62 highest-ranking autism risk genes in the SFARI database are expressed in both brain and gastrointestinal tissues (85), substantiating their interactions. LIMITATIONS Some limitations of the present study should be considered. First, our group of participants was limited in size. Nevertheless, the application of an analytical method based on gene sets allowed the identification of significant results even in the present relatively small set of participants. With respect to the genetic data, our analysis was limited to a subsection of the whole exome (~ 5200 genes) screened with a panel that exclusively enables analysis of disease-associated target genes. Notwithstanding this limitation, the present preliminary findings align with previous evidence from whole-exome sequencing studies. However, it should be noted that whole-exome/genome analysis could have yielded an even more comprehensive pattern of results. Moreover, by only considering the load of PDVs in a gene set, we neglect possible differences between each variant effect on the gene set associated function. The availability of this information could have facilitated functional connections between genetics and phenotypic manifestations. Lastly, at the present stage, the analysis of differences in the load of PDVs between subgroups of participants relies solely on IQ scores. Future extensions of this work should use the present multi-step approach to investigate genetic differences also between autism subtypes defined by their core and non-core features in motor, language, intellectual, and adaptive functioning. CONCLUSIONS In the present study, we have shown evidence that an unbiased, multi-step analysis could identify sets of genes involved in neurocognition, ion cell communication, gastrointestinal function, and immune system that are potentially related to the phenotypic differences among autistic children with different IQ levels. Besides being early expressed in the brain, such biological pathways are spatio-temporally co-expressed and highly interconnected with each other and with many autism-related genes. These findings therefore support the hypothesis that the diversity in autism likely originates from multiple interacting pathways that could be altered at various levels rather than deriving from a series of independent functional cascades with cumulative impact on the final outcome. Although these observations should be considered with caution, future research could leverage the present approach to identify genetic pathways relevant to autism subtyping, investigating a link between genetic profiles and distinct biotypes of autistic individuals. Declarations Author contributions Conceptualization: LF, UP, AC. Methodology: GS, LF, MMa, RG, MV, UP, AC. Software: LF, MMa, UP. Formal analysis: GS, LF, MMa, UP. Investigation: RG, MV, SBC, LV, EM, MN, MMo, AC. Data curation: GS, LF, SBC, UP, AC. Writing—original draft preparation: GS, UP, AC. Writing—review and editing: GS, LF, UP, AC. Visualization: GS, LF, UP, AC. Supervision: UP, AC. Project administration: UP, AC. Funding acquisition: AC. Funding This project has received funding from the Italian Ministry of Health to AC (Ricerca Finalizzata GR-2011-02348929; Ricerca Corrente 2024). References Naigles LR, Johnson R, Mastergeorge A, Ozonoff S, Rogers SJ, Amaral DG, et al. Neural correlates of language variability in preschool‐aged boys with autism spectrum disorder. Autism Research 2017;10(6):1107–1119. Wolff N, Stroth S, Kamp-Becker I, Roepke S, Roessner V. Autism Spectrum Disorder and IQ–A complex interplay, Front Psychiatry. Frontiers in psychiatry 2022;13(856084). Craig F, Crippa A, Ruggiero M, Rizzato V, Russo L, Fanizza I, et al. Characterization of Autism Spectrum Disorder (ASD) subtypes based on the relationship between motor skills and social communication abilities. Human Movement Science 2021;77:102802. Wang LA, Petrulla V, Zampella CJ, Waller R, Schultz RT. Gross motor impairment and its relation to social skills in autism spectrum disorder: A systematic review and two meta-analyses. Psychol Bull 2022;148(3-4):273. Mandelli V, Landi I, Busuoli EM, Courchesne E, Pierce K, Lombardo MV. Prognostic early snapshot stratification of autism based on adaptive functioning. Nature Mental Health 2023;1(5):327–336. Mandelli V, Severino I, Eyler L, Pierce K, Courchesne E, Lombardo MV. A 3D approach to understanding heterogeneity in early developing autisms. Molecular Autism 2024;15(1):41. Geschwind DH, Levitt P. Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol 2007;17(1):103–111. Di Martino A, Yan C, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 2014;19(6):659–667. An JY, Claudianos C. Genetic heterogeneity in autism: From single gene to a pathway perspective. Neuroscience & Biobehavioral Reviews 2016;68:442–453. Havdahl A, Niarchou M, Starnawska A, Uddin M, van der Merwe C, Warrier V. Genetic contributions to autism spectrum disorder. Psychol Med 2021;51(13):2260–2273. Georgiades S, Bishop SL&F, T. Editorial Perspective: Longitudinal research in autism–introducing the concept of ‘chronogeneity’. Journal of Child Psychology and Psychiatry 2017;58(5):634–636. Tick B, Bolton P, Happé F, Rutter M, Rijsdijk F. Heritability of autism spectrum disorders: a meta‐analysis of twin studies. Journal of Child Psychology and Psychiatry 2016;57(5):585–595. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Ercument Cicek A, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 2014;515(7526):209–215. Iossifov I, O’roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 2014;515(7526):216–221. Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 2015;87(6):1215–1233. Yuen RK, Merico D, Bookman M, Howe J, Thiruvahindrapuram B, Patel RV, et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci 2017;20(4):602–611. Ruzzo EK, Pérez-Cano L, Jung J, Wang L, Kashef-Haghighi D, Hartl C, et al. Inherited and de novo genetic risk for autism impacts shared networks. Cell 2019;178(4):850–866. e26. Doan RN, Lim ET, De Rubeis S, Betancur C, Cutler DJ, Chiocchetti AG, et al. Recessive gene disruptions in autism spectrum disorder. Nat Genet 2019;51(7):1092–1098. Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An J, et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 2020;180(3):568–584. e23. Choi L, An J. Genetic architecture of autism spectrum disorder: Lessons from large-scale genomic studies. Neuroscience & Biobehavioral Reviews 2021;128:244–257. Willsey AJ, Sanders SJ, Li M, Dong S, Tebbenkamp AT, Muhle RA, et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 2013;155(5):997–1007. Courchesne E, Pramparo T, Gazestani VH, Lombardo MV, Pierce K, Lewis NE. The ASD Living Biology: from cell proliferation to clinical phenotype. Mol Psychiatry 2019;24(1):88–107. Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. Most genetic risk for autism resides with common variation. Nat Genet 2014;46(8):881–885. de la Torre-Ubieta L, Won H, Stein JL, Geschwind DH. Advancing the understanding of autism disease mechanisms through genetics. Nat Med 2016;22(4):345–361. Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 2019;51(3):431–444. Cirnigliaro M, Chang TS, Arteaga SA, Pérez-Cano L, Ruzzo EK, Gordon A, et al. The contributions of rare inherited and polygenic risk to ASD in multiplex families. Proceedings of the National Academy of Sciences 2023;120(31):e2215632120. Jeste SS, Geschwind DH. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nature Reviews Neurology 2014;10(2):74–81. Chawner SJ, Doherty JL, Anney RJ, Antshel KM, Bearden CE, Bernier R, et al. A genetics-first approach to dissecting the heterogeneity of autism: phenotypic comparison of autism risk copy number variants. Am J Psychiatry 2021;178(1):77–86. Warrier V, Zhang X, Reed P, Havdahl A, Moore TM, Cliquet F, et al. Genetic correlates of phenotypic heterogeneity in autism. Nat Genet 2022;54(9):1293–1304. Di Giovanni D, Enea R, Di Micco V, Benvenuto A, Curatolo P, Emberti Gialloreti L. Using machine learning to explore shared genetic pathways and possible endophenotypes in autism spectrum disorder. Genes 2023;14(2):313. Bertelsen N, Landi I, Bethlehem RA, Seidlitz J, Busuoli EM, Mandelli V, et al. Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry. Communications biology 2021;4(1):574. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 2005;102(43):15545–15550. Wang HZ, Qin H, Guo W, Samuels J, Shugart YY. New insights into the genetic mechanism of IQ in autism spectrum disorders. Frontiers in Genetics 2013;4:195. BrainSpan Atlas of the Developing Human Brain. Available at: https://www.brainspan.org Stark C, Breitkreutz B, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006;34(suppl_1):D535–D539. Mahfouz A, Ziats MN, Rennert OM, Lelieveldt BP, Reinders MJ. Shared pathways among autism candidate genes determined by co-expression network analysis of the developing human brain transcriptome. Journal of Molecular Neuroscience 2015;57(4):580–594. Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, et al. Functional organization of the transcriptome in human brain. Nat Neurosci 2008;11(11):1271–1282. Simons Foundation Autism Research Initiative (SFARI) database. Available at: https://sfari.org Accessed July 9th, 2024. Crippa A, Craig F, Busti Ceccarelli S, Mauri M, Grazioli S, Scionti N, et al. A multimethod approach to assessing motor skills in boys and girls with autism spectrum disorder. Autism 2021;25(5):1481–1491. Lord C, Rutter M, Goode S, Heemsbergen J, Jordan H, Mawhood L, et al. Autism diagnostic observation schedule. J Autism Dev Disord 2012. Griffiths R. The abilities of young children. A study in mental measurement. 1970. Wechsler D. The Wechsler Preschool and Primary Scale of Intelligence. 3rd ed.: The Psychological Corporation Canadian Journal of School Psychology; 2002. Wechsler D. Wechsler Intelligence Scale for Children–IV (WISCIV) Italian Edition. Florence: Organizzazioni Speciali 2012. Gotham K, Pickles A, Lord C. Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J Autism Dev Disord 2009;39:693–705. Hus V, Gotham K, Lord C. Standardizing ADOS domain scores: Separating severity of social affect and restricted and repetitive behaviors. J Autism Dev Disord 2014;44:2400–2412. Hollingshead AB. Four factor index of social status . : Yale University; 1975. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38(16):e164. Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature methods 2018;15(7):475–476. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet 2000;25(1):25–29. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 2010;38(suppl_1):D355–D360. Fabregat A, Korninger F, Viteri G, Sidiropoulos K, Marin-Garcia P, Ping P, et al. Reactome graph database: Efficient access to complex pathway data. PLoS computational biology 2018;14(1):e1005968. Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? Journal of classification 2014;31:274–295. Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987;20:53–65. Grote S, Prüfer K, Kelso J, Dannemann M. ABAEnrichment: an R package to test for gene set expression enrichment in the adult and developing human brain. Bioinformatics 2016;32(20):3201–3203. Schmunk G, Gargus JJ. Channelopathy pathogenesis in autism spectrum disorders. Frontiers in genetics 2013;4:222. Daghsni M, Rima M, Fajloun Z, Ronjat M, Brusés JL, M'rad R, et al. Autism throughout genetics: Perusal of the implication of ion channels. Brain and Behavior 2018;8(8):e00978. Evangelho VGO, Bello ML, Castro HC, Amorim MR. Gene set enrichment analysis indicates convergence in the mTOR signalling pathway between syndromic and non-syndromic autism. Neuroscience Informatics 2023;3(2):100119. Bourgeron T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nature Reviews Neuroscience 2015;16(9):551–563. Smith RS, Walsh CA. Ion channel functions in early brain development. Trends Neurosci 2020;43(2):103–114. Gu S, Katyal A, Zhang Q, Chung W, Franciosi S, Sanatani S. The association between congenital heart disease and autism spectrum disorder: A systematic review and meta-analysis. Pediatr Cardiol 2023;44(5):1092–1107. Sigmon ER, Kelleman M, Susi A, Nylund CM, Oster ME. Congenital heart disease and autism: a case-control study. Pediatrics 2019;144(5). Splawski I, Timothy KW, Sharpe LM, Decher N, Kumar P, Bloise R, et al. CaV1. 2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell 2004;119(1):19–31. Rosenthal SB, Willsey HR, Xu Y, Mei Y, Dea J, Wang S, et al. A convergent molecular network underlying autism and congenital heart disease. Cell systems 2021;12(11):1094–1107. e6. Clifton NE, Collado-Torres L, Burke EE, Pardiñas AF, Harwood JC, Di Florio A, et al. Developmental profile of psychiatric risk associated with voltage-gated cation channel activity. Biol Psychiatry 2021;90(6):399–408. Lee J, Ha S, Ahn J, Lee S, Choi JR, Cheon K. The role of ion channel-related genes in autism spectrum disorder: a study using next-generation sequencing. Frontiers in Genetics 2021;12:595934. Firdaus Z, Li X. Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches. International journal of molecular sciences 2024;25(4):2320. Hinz FI, Geschwind DH. Molecular genetics of neurodegenerative dementias. Cold Spring Harbor perspectives in biology 2017;9(4):a023705. DeJesus-Hernandez M, Mackenzie IR, Boeve BF, Boxer AL, Baker M, Rutherford NJ, et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 2011;72(2):245–256. Li C, Pang D, Lin J, Yang T, Shang H. Shared genetic links between frontotemporal dementia and psychiatric disorders. BMC medicine 2022;20(1):131. Nadeem MS, Hosawi S, Alshehri S, Ghoneim MM, Imam SS, Murtaza BN, et al. Symptomatic, genetic, and mechanistic overlaps between autism and Alzheimer’s disease. Biomolecules 2021;11(11):1635. Fumagalli GG, Crippa A. The search for a link between autism spectrum disorder and neurodegenerative dementia. MINERVA PSYCHIATRY 2022;63(3):189–196. Rhodus EK, Barber J, Abner EL, Duff DM, Bardach SH, Caban-Holt A, et al. Behaviors characteristic of autism spectrum disorder in a geriatric cohort with mild cognitive impairment or early dementia. Alzheimer Disease & Associated Disorders 2020;34(1):66–71. Vivanti G, Tao S, Lyall K, Robins DL, Shea LL. The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder. Autism Research 2021;14(10):2189–2199. Wang L, Chen J, Hu Y, Liao A, Zheng W, Wang X, et al. Progranulin improves neural development via the PI3K/Akt/GSK-3β pathway in the cerebellum of a VPA-induced rat model of ASD. Translational Psychiatry 2022;12(1):114. Tian Y, Xiao X, Liu W, Cheng S, Qian N, Wang L, et al. TREM2 improves microglia function and synaptic development in autism spectrum disorders by regulating P38 MAPK signaling pathway. Molecular Brain 2024;17(1):12. Filipello F, Morini R, Corradini I, Zerbi V, Canzi A, Michalski B, et al. The microglial innate immune receptor TREM2 is required for synapse elimination and normal brain connectivity. Immunity 2018;48(5):979–991. e8. Saura CA, Servián-Morilla E, Scholl FG. Presenilin/γ-secretase regulates neurexin processing at synapses. PloS one 2011;6(4):e19430. Dargenio VN, Dargenio C, Castellaneta S, De Giacomo A, Laguardia M, Schettini F, et al. Intestinal barrier dysfunction and microbiota–gut–brain axis: Possible implications in the pathogenesis and treatment of autism spectrum disorder. Nutrients 2023;15(7):1620. Bjørklund G, Pivina L, Dadar M, Meguid NA, Semenova Y, Anwar M, et al. Gastrointestinal alterations in autism spectrum disorder: What do we know? Neuroscience & Biobehavioral Reviews 2020;118:111–120. Chaidez V, Hansen RL, Hertz-Picciotto I. Gastrointestinal problems in children with autism, developmental delays or typical development. J Autism Dev Disord 2014;44:1117–1127. Adams JB, Johansen LJ, Powell LD, Quig D, Rubin RA. Gastrointestinal flora and gastrointestinal status in children with autism–comparisons to typical children and correlation with autism severity. BMC gastroenterology 2011;11:1–13. Xu M, Xu X, Li J, Li F. Association between gut microbiota and autism spectrum disorder: a systematic review and meta-analysis. Frontiers in psychiatry 2019;10:473. Liu F, Li J, Wu F, Zheng H, Peng Q, Zhou H. Altered composition and function of intestinal microbiota in autism spectrum disorders: a systematic review. Translational psychiatry 2019;9(1):43. Morais LH, Schreiber IV HL, Mazmanian SK. The gut microbiota–brain axis in behaviour and brain disorders. Nature Reviews Microbiology 2021;19(4):241–255. Niesler B, Rappold GA. Emerging evidence for gene mutations driving both brain and gut dysfunction in autism spectrum disorder. Mol Psychiatry 2021;26(5):1442–1444. Meltzer A, Van de Water J. The role of the immune system in autism spectrum disorder. Neuropsychopharmacology 2017;42(1):284–298. Onore C, Careaga M, Ashwood P. The role of immune dysfunction in the pathophysiology of autism. Brain Behav Immun 2012;26(3):383–392. Ormstad H, Bryn V, Saugstad OD, Skjeldal O, Maes M. Role of the immune system in autism spectrum disorders (ASD). CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2018;17(7):489–495. Lombardo MV, Moon HM, Su J, Palmer TD, Courchesne E, Pramparo T. Maternal immune activation dysregulation of the fetal brain transcriptome and relevance to the pathophysiology of autism spectrum disorder. Mol Psychiatry 2018;23(4):1001–1013. Careaga M, Rogers S, Hansen RL, Amaral DG, Van de Water J, Ashwood P. Immune endophenotypes in children with autism spectrum disorder. Biol Psychiatry 2017;81(5):434–441. Masi A, Glozier N, Dale R, Guastella AJ. The immune system, cytokines, and biomarkers in autism spectrum disorder. Neuroscience bulletin 2017;33:194–204. Horiuchi F, Yoshino Y, Kumon H, Hosokawa R, Nakachi K, Kawabe K, et al. Identification of aberrant innate and adaptive immunity based on changes in global gene expression in the blood of adults with autism spectrum disorder. Journal of Neuroinflammation 2021;18:1–12. Arenella M, Matuleviciute R, Tamouza R, Leboyer M, McAlonan G, Bralten J, et al. Immunogenetics of autism spectrum disorder: a systematic literature review. Brain Behav Immun 2023. Arenella M, Cadby G, De Witte W, Jones RM, Whitehouse AJ, Moses EK, et al. Potential role for immune-related genes in autism spectrum disorders: Evidence from genome-wide association meta-analysis of autistic traits. Autism 2022;26(2):361–372. Estes ML, McAllister AK. Immune mediators in the brain and peripheral tissues in autism spectrum disorder. Nature Reviews Neuroscience 2015;16(8):469–486. Garbett K, Ebert PJ, Mitchell A, Lintas C, Manzi B, Mirnics K, et al. Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol Dis 2008;30(3):303–311. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 2011;474(7351):380–384. Azhari A, Azizan F, Esposito G. A systematic review of gut‐immune‐brain mechanisms in Autism Spectrum Disorder. Dev Psychobiol 2019;61(5):752–771. Powell N, Walker MM, Talley NJ. The mucosal immune system: master regulator of bidirectional gut–brain communications. Nature reviews Gastroenterology & hepatology 2017;14(3):143–159. Lombardo MV, Courchesne E, Lewis NE, Pramparo T. Hierarchical cortical transcriptome disorganization in autism. Molecular Autism 2017;8:1–17. Gupta S, Ellis SE, Ashar FN, Moes A, Bader JS, Zhan J, et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nature communications 2014;5(1):5748. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additional file 1.xlsx: Table S1. Panel of genes under analysis in the present work. Additionalfile2.xlsx Additional file 2.xlsx: Table S2. Genes in the modules and their co-expressed interactors (extended modules). Additionalfile3.xlsx Additional file 3.xlsx: Table S3. Genes in the modules and their SFARI interactors. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Medea","correspondingAuthor":false,"prefix":"","firstName":"Uberto","middleName":"","lastName":"Pozzoli","suffix":""},{"id":385171982,"identity":"5c28688e-0147-48b9-bf87-6eafb7657001","order_by":11,"name":"Alessandro Crippa","email":"data:image/png;base64,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","orcid":"","institution":"IRCCS Eugenio Medea","correspondingAuthor":true,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Crippa","suffix":""}],"badges":[],"createdAt":"2024-11-27 11:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5534869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5534869/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70485814,"identity":"8b735766-02ef-4a7b-b2e7-fceb5ae8f82b","added_by":"auto","created_at":"2024-12-03 15:46:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46608,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the present multi-step study design.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5534869/v1/0c42a54e425784a2391fb788.png"},{"id":70486871,"identity":"cd27f31d-fc1a-4d2b-b0e1-3e9f71c81c94","added_by":"auto","created_at":"2024-12-03 15:54:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":345437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePanel A\u003c/strong\u003e: Heatmap showing the incidence of potentially damaging variants (PDVs) for each subject (columns) in the gene sets (rows) differentially enriched in PDVs between the two subgroups of autistic children. Data-driven optimal hierarchical clustering procedure applied to both gene sets and subjects revealed five terms’ clusters (TC) and three subjects’ clusters (SC).\u003cstrong\u003e Panel B\u003c/strong\u003e: Eulero-Venn diagram representing the genes included in the modules with their intersections. Given their significant proportion of overlapping genes, TC2 and TC3 were merged into a single module Neurocognition. \u003cstrong\u003ePanel C\u003c/strong\u003e: Count of subjects in each SC according to their IQ class membership. Proportion of subjects with IQ\u0026gt;80 was non-random (Fisher exact test; p\u0026lt;0.001), specifically higher in SC2. \u003cstrong\u003ePanel D\u003c/strong\u003e: AUROC of the IQ group classification based on TCs’ enrichment scores.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5534869/v1/314952a834c3cc5ed6efb2c2.png"},{"id":70487254,"identity":"356d59ca-9581-43fd-a919-7c0ddccbe6a1","added_by":"auto","created_at":"2024-12-03 16:02:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":525999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePanel A.\u003c/strong\u003e Heatmap showing levels of spatio-temporal co-expression in terms of correlation (colour scale) among the total group of genes in the four extended modules. Data-driven hierarchical clustering revealed 3 genes’ clusters (GCs) based on the levels of co-expression. \u003cstrong\u003ePanel B\u003c/strong\u003e. Interaction network including genes from the four modules (coloured background) and their co-expressed interactors (white background). Outline colours refer to the 3 GCs, while topology depends on the number of physical interactions reported in bioGRID; genes without any reported interaction are omitted. Circles identify SFARI genes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5534869/v1/9fc3aec26a9f5bdd11825fd9.png"},{"id":70488178,"identity":"d3bab07b-384c-4e8b-baa7-ee0362f692f8","added_by":"auto","created_at":"2024-12-03 16:18:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1849587,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5534869/v1/b1d81bf5-1660-4e2d-bf3a-5569de9e3c2f.pdf"},{"id":70485815,"identity":"e992b39f-3492-4f47-8820-6cbfbd1963ec","added_by":"auto","created_at":"2024-12-03 15:46:31","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":71068,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1.xlsx: Table S1. Panel of genes under analysis in the present work.\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5534869/v1/8c954f108607ad6bb2f874b9.xlsx"},{"id":70485818,"identity":"c6d15b4b-6672-49bc-ba11-c21849247481","added_by":"auto","created_at":"2024-12-03 15:46:31","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":41445,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2.xlsx: Table S2. Genes in the modules and their co-expressed interactors (extended modules).\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5534869/v1/6c7f947922bf85aaa0b7c860.xlsx"},{"id":70485828,"identity":"a0d8448b-79c4-495d-a828-0af558095cc1","added_by":"auto","created_at":"2024-12-03 15:46:32","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14689,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3.xlsx: Table S3. Genes in the modules and their SFARI interactors.\u003c/p\u003e","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5534869/v1/456d4c63a1239c76b3d8a394.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potentially damaging variants’ analysis in autism subgroups uncovers early brain-expressed gene modules relevant to autism pathophysiology","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAutism is a neurodevelopmental condition characterised by relevant heterogeneity across multiple levels of analysis. Its phenotypic presentation can drastically differ depending on the degree of both core and co-occurring features, such as language abilities (1), intellectual profile (2), motor coordination difficulties (3,4), and the overall adaptive functioning (5) (with this respect, see also the LIMA approach, (6). Critical heterogeneity has been also found at deeper levels of investigation, including neurophysiology (7,8) and genetics (9,10). Along with those aspects, it is also important to consider the dimension of chronogeneity, i.e. the variation in autism manifestation in relation to the dimension of time and, markedly, of development (11). Despite this multi-level heterogeneity, the role of genetic factors in autism aetiology is unquestioned. The heritability of the condition is high, with estimates ranging between 64% and 91% (12). Over recent years, hundreds of de novo and rare inherited variants have been identified as major contributors to individual autism risk (13\u0026ndash;19), for a review, see (20). The expression of those autism-associated genes have been reported to be preeminent at prenatal and early postnatal stages, substantiating the early onset of the condition (21,22). However, although with smaller effect in comparison to that of de novo and rare inherited variants, several studies have demonstrated that heritability in autism is largely due to common variants, i.e. to the genetic variations that are commonly present in the general population (23\u0026ndash;25). Thus, as for other neurodevelopmental conditions, there is considerable evidence that indicates the high heritability of autism and its polygenic nature, with both rare and common variants having a critical role (26).\u003c/p\u003e \u003cp\u003eIn the last 10 years, a body of research has leveraged genetic heterogeneity to parse autism phenotypic presentations (27\u0026ndash;30). With this respect, specific genotypes have been associated with well-defined clinical manifestations of autism, potentially via distinct intermediate brain endophenotypes (31). Nevertheless, given the heterogeneity of the condition, the identification of the functional implications of genes related to autism requires large-scale studies, mostly involving thousands of participants.\u003c/p\u003e \u003cp\u003eAn alternative perspective is shifting the focus from single genes to networks of genes that converge in functionally relevant biological processes, e.g. by using gene set analysis. Gene sets are groups of genes that share a common biological function and are pre-defined on the basis of prior biological knowledge (32). Gene set approach permits to examine the combined effect of multiple DNA variants that cumulatively impact on biological pathways potentially relevant to autism (9).\u003c/p\u003e \u003cp\u003eIn this study, we explored the hypothesis of whether a multi-step unbiased analysis could identify gene sets relevant to autism subtyping and functionally characterise them in terms of biological processes and their spatio-temporal co-expression in the brain. Since we intended to evaluate the gene sets\u0026rsquo; cumulative effects we considered all the potentially damaging variants (PDVs), regardless of both their frequency in the population and their effect on the proteins.\u003c/p\u003e \u003cp\u003eThe present work is split into different objectives. First, after dividing our clinical sample of autistic children into two subgroups on the basis of their standardised measures of intelligence quotient (IQ), we identified gene sets that presented a different load of PDVs between participants with higher IQ and lower IQ, using a data-driven enrichment analysis. With this regard, previous research has reported significant associations between genetic variations in autism and cognitive heterogeneity (33,14). The obtained gene sets were hierarchically clustered into modules based on their PDVs enrichment. We then characterised each of those modules with a label representative of the biological processes that best characterised them.\u003c/p\u003e \u003cp\u003eNext, to explore the functional implications of the modules, we assessed their expression profiles in the brain according to the BrainSpan Atlas of the Developing Human Brain (34). More specifically, we determined whether genes in the modules were expressed above expectations in brain structures through different developmental stages, to gain insights about their potential involvement in brain development.\u003c/p\u003e \u003cp\u003eAfterwards, to explore more extensively the functional interplay between the modules, we extended each module by selecting those genes which both physically interact at the protein level and show a highly correlated spatio-temporal brain expression profile with those in the modules. Among the extended set of genes, we assessed their spatial and temporal co-expression in the brain according to the BrainSpan Atlas of the Developing Human Brain and their physical interaction at the protein level according to the bioGRID database (35). Understanding how genes relate to each other during neurodevelopment is essential to identify what are potential shared biological processes (36) and how they could interact along their functional pathways to impact the final outcome. Genes that are co-expressed in the brain are indeed more likely to act together in converging processes. In this sense, the evidence of the corresponding proteins\u0026rsquo; interactions represents a confirmation of this functional association (37).\u003c/p\u003e \u003cp\u003e Lastly, we investigated whether the genes in the modules and those in the extended set of their co-expressed interactors were enriched in candidate genes highly associated with autism susceptibility according to the Simons Foundation Autism Research Initiative (SFARI) database (38), which constantly integrates genetic information from multiple research studies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eWe summarised our methods in a workflow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 71 autistic children aged 3\u0026ndash;12 years (61 males, 10 females) were involved in this cross-sectional study. Participants were consecutively recruited at the Child Psychopathology Unit of Scientific Institute, IRCCS Eugenio Medea (Bosisio Parini, Italy), over a 36-month period between July 2016 and July 2019, as a part of a larger study (39). Autistic children were admitted to inpatient/outpatient units of our institute either for assessment or for a comprehensive rehabilitation program. All participants had been previously diagnosed at our institute on the basis of a consensus \"best estimate\" DSM-5 clinical diagnostic process informed by, but not dependent on, scores on the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) (40). Participants were excluded in case that a well-defined genetic disorder was detected. Further exclusion criteria were the use of medication affecting the central nervous system, the presence of significant sensory impairment (e.g., blindness, deafness), abnormalities detected by MRI, and suffering from chronic or acute medical illness. All participants were drug-na\u0026iuml;ve.\u003c/p\u003e \u003cp\u003e This research was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and later amendments and was approved by the Ethics Committee of our Institute \u0026ldquo;Comitato Etico IRCCS E. Medea\u0026mdash;Sezione Scientifica Associazione La Nostra Famiglia\u0026rdquo; (Prot. N.33/18\u0026mdash;CE). Informed written consent was collected by all of the participants\u0026rsquo; parents or legal guardians before participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eAll children underwent an assessment of the IQ level. The specific assessment tool was chosen according to the participants\u0026rsquo; developmental level among the Griffiths Mental Development Scales (41), Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III) (42), and Wechsler Intelligence Scale for Children\u0026ndash;IV (WISC-IV) (43). The diagnoses were confirmed using the Autism Diagnostic Observation Schedule\u0026ndash;Second Edition (ADOS-2) (40). Total raw scores and social affect (SA) and restricted repetitive behaviour (RRB) raw scores were separately converted into calibrated severity scores following the procedure by Gotham et al. (44) and Hus et al. (45) to allow comparisons across individuals with different developmental levels. Information about the familial socioeconomic status (SES) was also collected using the Hollingshead scale for parental employment (46).\u003c/p\u003e\n\u003ch3\u003eBiological samples\u003c/h3\u003e\n\u003cp\u003eBlood or saliva samples (7 and 64, respectively) were obtained from participants for DNA extraction, which was in-house performed at the Molecular Biology Laboratory of our Institute. Blood was collected in tubes with EDTA. Genomic DNA was extracted from blood samples through the GenElute Blood Genomic DNA kit (Sigma). DNA from each sample was eluted in 50\u0026micro;l of 10 mM Tris (pH 9.0) / 0.5 mM EDTA and stored at -20\u0026deg;C. The Oragene OG-500 kit (DNA Genotek) was used to collect saliva samples. Genomic DNA was extracted from saliva samples by precipitation in ethanol with the manufacturer's protocol, resuspended in 50 \u0026micro;l of 10 mM Tris (pH 9.0)/1 mM EDTA and stored at -20\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eVariants identification\u003c/h3\u003e\n\u003cp\u003eSamples\u0026rsquo; DNA were screened by using a targeted next generation sequencing approach with a targeted design that enables analysis of only the disease-associated targets, the SureSelect Focused Exome, with approximately 5,200 disease-associated genes (see supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for a complete list of genes). The sequencing libraries were prepared from genomic DNA by using a Sure Select enrichment system (Agilent Technologies). Targeted libraries were run on NextSeq platform according to the manufacturer's instructions (Illumina, San Diego, CA, USA). The sequenced reads were then aligned to reference target regions and variants were called with BWA enrichment application (which include also GATK for variant calling) available on BaseSpace Onsite (Illumina, San Diego, CA, USA). Erroneous variant calls were discarded. Furthermore, we did not consider the Y chromosome because some variations were retrieved also for women.\u003c/p\u003e \u003cp\u003eVariants\u0026rsquo; annotation was performed by ANNOVAR (47) with a refGene database generated by converting the human reference genome (GRCh37/hg19) GFF3 and FASTA files to ANNOVAR file format using the gff3toGenePred package (48). Potentially damaging variants (PDVs) were then selected as those occurring in the panel exons and annotated as indels, stop codon gain, stop codon loss or non-synonymous.\u003c/p\u003e\n\u003ch3\u003eGene sets definition\u003c/h3\u003e\n\u003cp\u003eWe obtained 29227 gene sets corresponding to ontology terms or pathways and their associated genes. To this purpose we interrogated multiple sources, namely: Gene Ontology (49) (version 2019-03), HPO Phenotype (50) (version 2019-03), Kegg Pathway Database (50) (2019-2-25) and Reactome Pathway Database (51) (Physical Entity Identifier mapping files, updated to 2019-03).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of gene sets variants abundance in a group of subjects\u003c/h2\u003e \u003cp\u003eTo keep into account variants zygosity, we calculated the number of variants \u003cem\u003eV\u003c/em\u003e for a given gene set \u003cem\u003eS\u003c/em\u003e and a group of subjects \u003cem\u003eG\u003c/em\u003e as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:V=\\frac{\\left({\\sum\\:}_{i\\in\\:G}{\\sum\\:}_{g\\in\\:{S}_{A}}{V}_{ig}\\right)\\left({\\sum\\:}_{i\\in\\:G}{\\sum\\:}_{g\\in\\:{S}_{A}}2{L}_{g}\\right)+\\left({\\sum\\:}_{i\\in\\:G}{\\sum\\:}_{g\\in\\:{S}_{X}}{V}_{ig}\\right)\\left({\\sum\\:}_{i\\in\\:G}{\\sum\\:}_{g\\in\\:{S}_{A}}{N}_{i}{L}_{g}\\right)}{{\\sum\\:}_{i\\in\\:G}{\\sum\\:}_{g\\in\\:{S}_{A}}2{L}_{g}+{\\sum\\:}_{i\\in\\:G}{\\sum\\:}_{g\\in\\:{S}_{A}}{N}_{i}{L}_{g}}\\)\u003c/span\u003e \u003c/span\u003e [1]\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eig\u003c/em\u003e\u003c/sub\u003e is the number of alternative alleles observed for subject \u003cem\u003ei\u003c/em\u003e in gene \u003cem\u003eg\u003c/em\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e is the length of gene \u003cem\u003eg\u003c/em\u003e and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is 2 if the subject \u003cem\u003ei\u003c/em\u003e is a female or 1 otherwise. Summations are over the subjects in the group \u003cem\u003eG\u003c/em\u003e and the genes over the gene-set \u003cem\u003eS\u003c/em\u003e (\u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eA\u003c/em\u003e\u003c/sub\u003e for genes in autosomal chromosomes and \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eX\u003c/em\u003e\u003c/sub\u003e for genes on the X chromosome). The same procedure was used to evaluate the number of variants in a gene set for a single subject (in this case G only contains one subject). We define a gene set enrichment score for a group or a single subject as the number of variants in the gene set calculated following [1] divided by the total number of variants for the group or for that specific subject.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene set variants enrichment analysis (GSVEA)\u003c/h3\u003e\n\u003cp\u003eThe goal of the GSVEA is to determine whether the abundance of variants observed in a given gene-set differs in two subject groups. To assess whether the variants abundance in a gene set \u003cem\u003eS\u003c/em\u003e is significantly different between two groups of participants \u003cem\u003eG1\u003c/em\u003e and \u003cem\u003eG2\u003c/em\u003e, a Fisher\u0026rsquo;s exact test is applied to the contingency table defined as:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of variants in the gene set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of variants not in the gene set\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eG1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eA1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e- V\u003c/em\u003e\u003csub\u003e\u003cem\u003eG1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eG2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eA2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e- V\u003c/em\u003e\u003csub\u003e\u003cem\u003eG2\u003c/em\u003e\u003c/sub\u003e\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\u003e \u003cem\u003eV\u003c/em\u003e \u003csub\u003e \u003cem\u003eG1\u003c/em\u003e \u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eG2\u003c/em\u003e\u003c/sub\u003e are calculated according to [1] for the gene set \u003cem\u003eS\u003c/em\u003e while \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eA1\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eA2\u003c/em\u003e\u003c/sub\u003e are calculated according to [1] but for the gene set comprising all the genes in the exome panel. When multiple gene sets are tested, false discovery rate (FDR) is applied to the Fisher test p values and q values are reported.\u003c/p\u003e\n\u003ch3\u003eGene sets and subjects clustering\u003c/h3\u003e\n\u003cp\u003eHierarchical clustering has been consistently performed in this study using the Ward algorithm (52) applied to the euclidean distance. Optimal ranks have been determined by silhouette analysis (53).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGene expression in the brain during neurodevelopmental stages\u003c/h2\u003e \u003cp\u003e We used the ABAEnrichment R package (version 1.20.0) (54) to characterise the brain expression of the genes included in the modules according to the BrainSpan Atlas of the Developing Human Brain (34). BrainSpan Atlas includes five developmental stages, namely prenatal, infant (0\u0026ndash;2 years), child (3\u0026ndash;11 years), adolescent (12\u0026ndash;19 years) and adult (\u0026gt;\u0026thinsp;19 years). According to the study\u0026rsquo;s aims, we limited our analysis to the four developmental stages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenes Spatio-Temporal co-expression in the developing brain\u003c/h2\u003e \u003cp\u003eFor each gene in the exome panel, we built an expression vector extracting from the BrainSpan Atlas of the Developing Human Brain the expression levels measured in each available structure for the first four developmental stages. For any pair of genes in a set, we estimated their co-expression in time and space by calculating the Pearson correlation coefficient (p) between their expression vectors. Correlation p-values are also computed and corrected for multiple testing (FDR). Gene pairs with p\u0026thinsp;\u0026gt;\u0026thinsp;0.7 and q-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered to be co-expressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenes protein-protein interactions\u003c/h2\u003e \u003cp\u003eThe bioGRID database (35) was used to identify whether couples of genes had physical protein-protein interactions. Genes having at least one physical interaction with the genes in the modules were considered as interactors in the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of autism-related genes\u003c/h2\u003e \u003cp\u003eLastly, we analysed the incidence of genes with a gene score of 1 (High Confidence category) in the SFARI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sfari.org/\u003c/span\u003e\u003cspan address=\"https://sfari.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed July 9th, 2024) in our gene modules and their extended networks. Fisher exact tests were performed considering all the genes listed in the exome panel and, in case of multiple testing, p values were corrected according to Bonferroni.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cem\u003eSample description\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 summarises the sociodemographic and clinical characteristics of the total sample and for the two subgroups of participants with lower (\u003cstrong\u003e\u0026le;\u003c/strong\u003e80, n=28) and higher IQ (\u0026gt;80, n=43), respectively. The two subgroups were balanced in autism severity as assessed with the ADOS-2 calibrated severity scores and in the male to female ratio. Participants with higher IQ had significantly higher scores on\u0026nbsp;SES (p=0.035) and were slightly older than subjects with lower IQ (p=0.057). Demographically, the entire sample was Caucasian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Sample description.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutism, TOT\u003c/strong\u003e (n=71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutism, IQ\u003c/strong\u003e\u003cstrong\u003e\u0026le;\u003c/strong\u003e\u003cstrong\u003e80\u003c/strong\u003e (n=28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutism, IQ\u0026gt;80\u003c/strong\u003e (n=43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e7.6 (2.4) [3.2-11.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e6.9 (2.6) [3.2-11.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e8.1 (2.1) [3.8-11.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (M:F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e61:10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e25:3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e36:7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e86.1 (21.8) [47-134]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e64.7 (9.6) [47-80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e100.0 (15.0) [81-134]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e58.5 (18.1) [20-90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e52.7 (16.9) [20-90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e62.5 (18.1) [30-90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADOS-2 total CSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e6.2 (1.7) [3-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e6.1 (1.7) [4-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e6.2 (1.7) [3-9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADOS-2 SA CSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e6.4 (1.9) [3-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e6.5 (1.9) [3-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e6.4 (1.9) [3-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2691%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADOS-2 RRB CSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e6.3 (2.1) [1-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4252%;\"\u003e\n \u003cp\u003e5.9 (2.7) [1-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7542%;\"\u003e\n \u003cp\u003e6.5 (1.6) [1-9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1262%;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Data are expressed as Mean (SD) [range]. Mann-Whitney U test was used for quantitative variables, chi-squared test for the categorical variable sex. SES: socio-economic status; ADOS: autism diagnostic observation schedule; CSS: calibrated severity scores; SA: social affect; RRB: restricted repetitive behaviours.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGene sets showing differential PDVs enrichment between autism subgroups are involved in ion cell communication, neurocognition, gastrointestinal function, and immune system.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter splitting our sample into two subgroups, we performed a gene set variants enrichment analysis (GSVEA). In brief, the goal of this analysis was to determine whether the abundance of PDVs observed in a given gene set differed in the two subgroups. We performed the analysis for nearly 30.000 functionally characterised gene sets obtained from public databases of ontologies and pathways (see methods). Among the considered gene sets, 38 showed a significantly different incidence of PDVs between the two subgroups of autistic participants (false discovery rate, FDR, q \u0026lt; 0.05). For each subject we calculated the proportion of PDVs \u0026mdash;enrichment score\u0026mdash; in each significant gene set. We then applied an optimal hierarchical clustering procedure to the resulting matrix at both gene sets and subjects level (Fig 2A). The significant gene sets resulted grouped in five clusters (terms\u0026rsquo; clusters, TC). Two of them \u0026mdash;TC2 and TC3\u0026mdash; were merged in a single cluster, given their high intersection level (Fig 2B), resulting in a total of four modules, which were operationally defined as the union of the corresponding significantly enriched gene sets. These four modules grouped together genes involved in a variety of processes and functions related to ion cell communication, neurocognition, gastrointestinal function, and immune system, respectively, and were thus labelled accordingly (Table 3). The data-driven identification of these four modules was based on the initial top-down splitting of our sample in two subgroups on the basis of their standardised measures of IQ. With a bottom-up approach, we also explored whether the modules\u0026rsquo; enrichment effectively identified clusters of participants with phenotypic differences. At the subject level, participants were clustered in three groups (subjects\u0026rsquo; clusters, SC). These SCs had a non-random distribution of subjects with IQ\u0026gt;80 (Fisher exact test; p\u0026lt;0.001), which resulted particularly represented in SC2 (Fig 2C). A logistic regression analysis based on the five TCs enrichment scores was also performed. With a leave-one-out cross validation approach, the regression model showed an accuracy of 0.704 (area under the receiver operating characteristic curve, ROC AUC=0.74) in predicting IQ group membership (Fig 2D). Participants in the three SCs did not differ significantly in ADOS total or subscales scores (Kruskal-Wallis test; p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenes in the modules are expressed in the brain in the developmental period\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe then aimed to understand whether the genes included in the four modules could be relevantly expressed in brain regions throughout the development. To this end, we leveraged the spatial gene expression data provided by the BrainSpan Atlas of the Developing Human Brain to link our\u0026nbsp;gene modules to brain structures across development. Given the childhood-onset\u0026nbsp;nature of the condition, we specifically considered the first four developmental stages listed in BrainSpan Atlas: prenatal, infancy (0-2 years), childhood (3-11 years), adolescence (12-19 years).\u003c/p\u003e\n\u003cp\u003eResults of the ABAEnrichment analysis revealed that genes in most of the modules were significantly more expressed across specific brain structures and developmental stages, except for the immune system module. An overview of the significantly enriched brain structures in each developmental stage can be found in Table 2. Genes in the ion cell communication module appear to be particularly expressed in the prenatal period and in adolescence across different brain structures. Genes in the neurocognition module are mainly expressed in the hippocampus and in various primary and associative cortical areas across infancy and childhood. Genes of the gastrointestinal module are extensively expressed from infancy to adolescence in cerebellum, subcortical nuclei and cortical structures. Last, genes included in the immune system module are not expressed higher than expected in either any brain structure or developmental stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Modules\u0026rsquo; expression enrichment across brain structures and developmental stages.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eION CELL COMMUNICATION\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEUROCOGNITION\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMMUNE SYSTEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGASTRO-INTESTINAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003ePre-natal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eInfant 0-2y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eChild 3-11y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eAdol 12-19y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003ePre-natal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eInfant 0-2y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eChild 3-11y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAdol 12-19y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003ePre-natal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eInfant 0-2y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eChild 3-11y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAdol 12-19y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003ePre-natal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eInfant 0-2y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eChild 3-11y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eAdol 12-19y\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCBC_cerebellar cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMD_mediodorsal nucleus of thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSTR_striatum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAMY_amygdaloid complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHIP_hippocampus (hippocampal formation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eM1C_primary motor cortex (area M1, area 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eS1C_primary somatosensory cortex (area S1, areas 3,1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eV1C_primary visual cortex (striate cortex, area V1/17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e3.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eA1C_primary auditory cortex (core)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eOFC_orbital frontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eVFC_ventrolateral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDFC_dorsolateral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e3.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMFC_anterior (rostral) cingulate (medial prefrontal) cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eITC_inferolateral temporal cortex (area TEv, area 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSTC_posterior (caudal) superior temporal cortex (area 22c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eIPC_posteroventral (inferior) parietal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eSampled brain structures are listed on the left and investigated age ranges are reported in columns. P-values (bold text) and fold values (regular text) are reported for brain structures and age ranges where a significant enrichment for that module was found.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIdentification of extended modules through physical protein-protein interactions and brain spatio-temporal co-expression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNext, we aimed at identifying the genes which are both interacting with those in the four modules and showing a high level of co-expression in the developing brain. To this purpose, we used the protein-protein interaction database bioGRID, and the expression data from the BrainSpan Atlas of the Developing Human Brain.\u0026nbsp;For each gene in a module we selected its direct physical interactors which also presented a significant level of spatio\u0026ndash;temporal co-expression in the brain with that gene (see methods). Each module was then extended with its co-expressed interactors (Table 3, see supplementary table S2 for a complete list of genes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Number of genes in each module and in each module extended with its co-expressed interactors.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8251%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNumber of genes in each module\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNumber of genes in each extended module\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIon cell communication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8251%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4448%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeurocognition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8251%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4448%;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGastrointestinal function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8251%;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4448%;\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmune system\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8251%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4448%;\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8251%;\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4448%;\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe then collected all the genes in the four extended modules (n=590) and built a co-expression matrix by calculating the spatio-temporal co-expression for each pair (Fig 3A). Hierarchical clustering identified three genes\u0026rsquo; clusters (GC) among this extended set of genes. Genes in clusters GC1 and GC2 showed high within-cluster and low between-clusters co-expression levels, while those in cluster GC3 showed a general intermediate level of co-expression both internally and with the other two clusters. Figure 3B depicts the annotated protein-protein interaction network for the three clusters, displaying high connection levels for clusters GC1 and GC2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSFARI genes are overrepresented in the extended networks of genes in the modules\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLooking at the incidence of autism-related genes, nine out of the 219 genes in the modules (4.11%)\u0026nbsp;were also annotated in the SFARI database in the high confidence category. This proportion was higher than expected in a random sample of genes, but did not reach the level of significance (OR=1.534; p=0.2063). When considering the four extended modules, high-confidence SFARI genes were significantly overrepresented (32 out of 590, 5.42%; OR=2.296, p=0.0001; see supplementary table S3 for a complete list of genes).\u003c/p\u003e\n\u003cp\u003eLooking at the distribution of these SFARI genes in the three co-expression clusters, they were significantly overrepresented in cluster GC1 (28 out of 362, 7.73%; OR=3.40; corrected p\u0026lt;0.001) while not in GC2 (1 out of 153, 0.65%) and GC3 (3 out of 63, 4.76%). Moreover, within GC1, the enrichment of SFARI genes was significant in the extended networks of neurocognition module (10 out of 128, 7.81%; OR=3.114; Bonferroni corrected p=0.033) and gastrointestinal module (16 out of 246, 6.50%; OR=2.615; Bonferroni corrected p=0.014) but not in the immune system and ion cell communication modules.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this work, we identified four modules of genes with different possibly damaging variants\u0026rsquo; load in two autism subgroups with higher and lower IQ levels by using an unsupervised approach. Importantly, the identified modules grouped together genes involved in a variety of biological processes 一ion cell communication, neurocognition, gastrointestinal function, and immune system一 that have been previously reported to be atypical in autism. Here, for the first time to our knowledge, we provide preliminary evidence of the possible implication of these biological processes in the differential phenotypic manifestations of two discrete autism subgroups.\u003c/p\u003e \u003cp\u003eThe first gene module 一ion cell communication一 includes genes involved in ion homeostasis, transport, and signalling (e.g., ATP1A2 and ATP1A3一subunits of a sodium-potassium pump, CACNA1C一subunit of a calcium voltage-gated channel). Those genes have been extensively associated with autism (55\u0026ndash;57), mostly for their crucial role in synaptic functioning (13,58,19). In the very early developmental stages, genes within this module encode for proteins with major contributions in neural proliferation, migration, and differentiation (59), including voltage-gated ion channels that regulate the propagation of action potentials and pacemaking, also in relation to the cardiac membrane excitability. With this respect, an association between autism and congenital heart disease has been reported (60\u0026ndash;62) and functionally confirmed based on the evidence of convergent genetic pathways (13,63). Genes in this module were significantly enriched in many brain regions during the prenatal stage according to our brain expression analysis, confirming their early involvement in development. Brain expression results also identified a significant enrichment of those genes during adolescence. With this respect, an association between the expression of ion-related genes in adolescence and neuropsychiatric conditions have been observed (64). Interestingly, our results also align with previous research indicating an association between variants in ion channels-related genes and both higher IQ and lower levels of repetitive behaviours in autism (65), confirming the relevance of this gene module in distinguishing different phenotypic manifestations of the condition.\u003c/p\u003e \u003cp\u003eThe neurocognition module encompasses gene sets related with various alterations in cognitive functions, including lack of insight, anomia, agnosia, alexia, as well as some core autistic features such as circumscribed behaviours and collectionism. Coherently with their involvement in cognitive functions, we found that the brain expression of these genes is mainly localised in cortical regions. Interestingly, many of the included genes are involved in the causation of different forms of neurodegenerative dementia (e.g. TREM2, GRN, PSEN1, C9ORF72, MAPT), including frontotemporal dementia (66\u0026ndash;68). Common etiopathological mechanisms have been hypothesised between neurodegenerative dementia and neurodevelopmental conditions, including autism (69\u0026ndash;71), based on the observations of partially overlapping symptoms (70,72) and increased occurrence of dementia in autistic adults (73). Moreover, these genes are also involved in biological processes specific for neurodevelopment, such as the production of neurotrophic factors (74), synaptic development and refinement (75,76), and broad synaptic functioning (77), which is coherent with their potential involvement in autism.\u003c/p\u003e \u003cp\u003eThird, the finding of a gene module related to gastrointestinal function is in line with the relatively recent hypothesis of gut involvement in the etiological mechanisms of autism (78,79). Gastrointestinal symptoms are frequent in a large percentage of autistic individuals (80) and are associated with the severity of the condition (81). Alterations in gut microbiome are also commonly reported in autism (82,83). The impact of the gut microbiota on the brain, mainly via the bidirectional microbiota-gut-brain axis, have been broadly investigated in neurodevelopmental conditions (84). Our expression analyses revealed that genes in the gastrointestinal module were extensively expressed in the brain across all developmental stages. The present result therefore extends the previous finding of a surprisingly high proportion of autism-associated genes expressed both in the brain and in the gastrointestinal tract (85), further indicating a common genetic pathway for alterations in these two systems.\u003c/p\u003e \u003cp\u003eLastly, the immune system has been extensively involved in autism, both as a candidate etiological mechanism, e.g., through maternal immune activation, and as potential endophenotype, with converging evidence of immune dysregulation in autistic individuals (86\u0026ndash;91). Moreover, genes controlling innate and adaptive immunity have been previously associated with autism (92,93), but also with autistic traits in the general population (94). Interestingly, some of the immune genes possibly involved in autism are also implicated in neurodevelopmental processes such as neuronal plasticity, with their strongest expression in the brain during gestational and early postnatal age (93,95). In contrast with those findings, we did not find here genes within the immune module to be particularly expressed in the brain along the neurodevelopmental stages. With this respect, it is important to have in mind that the gene expression profiles considered in this work are those reported in the BrainSpan Atlas of the Developing Human Brain for typically developing individuals. It is therefore possible that the presence of genetic variants in autistic subjects contributes to change the typical gene expression patterns, leading to the immune genes\u0026rsquo; over-expression in the brain that is reported elsewhere in the literature (96,97). Furthermore, this result could also suggest a systemic rather than brain-localised role of the immune system in autism. Indeed, immune dysregulation can impact brain function through different pathways, including the gut-brain axis where the immune system is a crucial mediator (98,99). Moreover, some circulating cytokines can reach the brain and inhibit neurogenesis or promote neuron death, whereas endogenous anti-brain antibodies may be produced, altering the development or function of neurons (86,95).\u003c/p\u003e \u003cp\u003eAfter having identified the four modules with a data-driven analysis based on the initial top-down splitting of our sample on IQ, we applied a bottom-up approach to explore whether the variants enrichment could effectively identify different clusters of participants. Coherently with our initial stratification, we found that participants with IQ\u0026thinsp;\u0026gt;\u0026thinsp;80 were particularly represented in one of the three subjects\u0026rsquo; clusters, as also confirmed by a predictive logistic regression model. While this result aligns with our criterion for subgrouping participants, the moderate accuracy of the regression model suggests that other factors are implicated in the between-groups differences associated with the modules\u0026rsquo; enrichment. Nevertheless, the subjects\u0026rsquo; clusters were not highly differentiated in terms of autism symptom severity. Future extensions of this work should combine different units of analysis (e.g., cells, circuits/networks, neurobiology) to further clarify linkages between genotype and clinical phenotypes.\u003c/p\u003e \u003cp\u003eIn addition to pinpointing the gene modules and discussing their relationship to phenotypic characteristics of autism, we thoroughly explored the functional interplay between the modules by considering an extended set of genes including the co-expressed interactors of the genes in each module. We discovered that most of the genes within this extended set presented high levels of spatio-temporal brain co-expression across development. Consistently, genes with higher co-expression correlation levels also highly interacted at the level of protein-protein interactions. Likewise, genes from different modules were found to be mutually interacting. Complementing earlier reports (13,100,101), this work therefore provides further evidence supporting the idea that the biology of autism could likely implicate altered interactions between different modules rather than the existence of independent disrupted pathways, which cumulatively contribute to the clinical outcome.\u003c/p\u003e \u003cp\u003eLastly, we found that SFARI high-confidence autism-related genes were overrepresented in the total set of genes in the extended modules, but not in the restricted group of genes in the modules. This suggests a significant connection, but no intersection, between the four pinpointed modules and genes previously associated with autism susceptibility. Nonetheless, this result also indicates that our unbiased method could effectively identify sets of genes in close relationship with autism-related genes, uncovering their potential role in shaping the heterogenous autistic phenotype. Importantly, the distribution of SFARI high-confidence genes within the total set of genes in the four extended modules was not stochastic. Indeed, based on their spatio-temporal co-expression levels, we found three different gene clusters: one with higher interaction levels but relatively few SFARI genes, a second one with less interactions and fewer SFARI genes, and a third cluster displaying high levels of interaction and overrepresented SFARI genes. In this last cluster, the autism-related genes were specifically overrepresented within the extended networks of the neurocognition, and gastrointestinal genes. Interestingly, the interaction between SFARI and neurocognition-related genes is in line with a previous large-scale exome sequencing study in autism (13), which found a significant protein-protein interaction between autism genes and MAPT, a gene implicated in neurodegenerative disorders, that is also part of our neurocognition module. Previous work has also highlighted that above 90% of the 62 highest-ranking autism risk genes in the SFARI database are expressed in both brain and gastrointestinal tissues (85), substantiating their interactions.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eSome limitations of the present study should be considered. First, our group of participants was limited in size. Nevertheless, the application of an analytical method based on gene sets allowed the identification of significant results even in the present relatively small set of participants. With respect to the genetic data, our analysis was limited to a subsection of the whole exome (~\u0026thinsp;5200 genes) screened with a panel that exclusively enables analysis of disease-associated target genes. Notwithstanding this limitation, the present preliminary findings align with previous evidence from whole-exome sequencing studies. However, it should be noted that whole-exome/genome analysis could have yielded an even more comprehensive pattern of results. Moreover, by only considering the load of PDVs in a gene set, we neglect possible differences between each variant effect on the gene set associated function. The availability of this information could have facilitated functional connections between genetics and phenotypic manifestations. Lastly, at the present stage, the analysis of differences in the load of PDVs between subgroups of participants relies solely on IQ scores. Future extensions of this work should use the present multi-step approach to investigate genetic differences also between autism subtypes defined by their core and non-core features in motor, language, intellectual, and adaptive functioning.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn the present study, we have shown evidence that an unbiased, multi-step analysis could identify sets of genes involved in neurocognition, ion cell communication, gastrointestinal function, and immune system that are potentially related to the phenotypic differences among autistic children with different IQ levels. Besides being early expressed in the brain, such biological pathways are spatio-temporally co-expressed and highly interconnected with each other and with many autism-related genes. These findings therefore support the hypothesis that the diversity in autism likely originates from multiple interacting pathways that could be altered at various levels rather than deriving from a series of independent functional cascades with cumulative impact on the final outcome. Although these observations should be considered with caution, future research could leverage the present approach to identify genetic pathways relevant to autism subtyping, investigating a link between genetic profiles and distinct biotypes of autistic individuals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: LF, UP, AC.\u0026nbsp;Methodology: GS, LF, MMa, RG, MV, UP, AC. Software: LF, MMa, UP.\u0026nbsp;Formal analysis: GS, LF, MMa, UP. Investigation: RG, MV, SBC, LV, EM, MN, MMo, AC.\u0026nbsp;Data curation: GS, LF, SBC, UP, AC.\u0026nbsp;Writing\u0026mdash;original draft preparation: GS, UP, AC. Writing\u0026mdash;review and editing: GS, LF, UP, AC. Visualization: GS, LF, UP, AC. Supervision: UP, AC. Project administration: UP, AC. Funding acquisition: AC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project has received funding from the Italian Ministry of Health to AC (Ricerca Finalizzata GR-2011-02348929; Ricerca Corrente 2024).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNaigles LR, Johnson R, Mastergeorge A, Ozonoff S, Rogers SJ, Amaral DG, et al. Neural correlates of language variability in preschool‐aged boys with autism spectrum disorder. Autism Research 2017;10(6):1107\u0026ndash;1119.\u003c/li\u003e\n\u003cli\u003eWolff N, Stroth S, Kamp-Becker I, Roepke S, Roessner V. Autism Spectrum Disorder and IQ\u0026ndash;A complex interplay, Front Psychiatry. Frontiers in psychiatry 2022;13(856084).\u003c/li\u003e\n\u003cli\u003eCraig F, Crippa A, Ruggiero M, Rizzato V, Russo L, Fanizza I, et al. Characterization of Autism Spectrum Disorder (ASD) subtypes based on the relationship between motor skills and social communication abilities. Human Movement Science 2021;77:102802.\u003c/li\u003e\n\u003cli\u003eWang LA, Petrulla V, Zampella CJ, Waller R, Schultz RT. Gross motor impairment and its relation to social skills in autism spectrum disorder: A systematic review and two meta-analyses. Psychol Bull 2022;148(3-4):273.\u003c/li\u003e\n\u003cli\u003eMandelli V, Landi I, Busuoli EM, Courchesne E, Pierce K, Lombardo MV. Prognostic early snapshot stratification of autism based on adaptive functioning. Nature Mental Health 2023;1(5):327\u0026ndash;336.\u003c/li\u003e\n\u003cli\u003eMandelli V, Severino I, Eyler L, Pierce K, Courchesne E, Lombardo MV. A 3D approach to understanding heterogeneity in early developing autisms. Molecular Autism 2024;15(1):41.\u003c/li\u003e\n\u003cli\u003eGeschwind DH, Levitt P. Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol 2007;17(1):103\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eDi Martino A, Yan C, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 2014;19(6):659\u0026ndash;667.\u003c/li\u003e\n\u003cli\u003eAn JY, Claudianos C. Genetic heterogeneity in autism: From single gene to a pathway perspective. Neuroscience \u0026amp; Biobehavioral Reviews 2016;68:442\u0026ndash;453.\u003c/li\u003e\n\u003cli\u003eHavdahl A, Niarchou M, Starnawska A, Uddin M, van der Merwe C, Warrier V. Genetic contributions to autism spectrum disorder. Psychol Med 2021;51(13):2260\u0026ndash;2273.\u003c/li\u003e\n\u003cli\u003eGeorgiades S, Bishop SL\u0026amp;F, T. Editorial Perspective: Longitudinal research in autism\u0026ndash;introducing the concept of \u0026lsquo;chronogeneity\u0026rsquo;. Journal of Child Psychology and Psychiatry 2017;58(5):634\u0026ndash;636.\u003c/li\u003e\n\u003cli\u003eTick B, Bolton P, Happ\u0026eacute; F, Rutter M, Rijsdijk F. Heritability of autism spectrum disorders: a meta‐analysis of twin studies. Journal of Child Psychology and Psychiatry 2016;57(5):585\u0026ndash;595.\u003c/li\u003e\n\u003cli\u003eDe Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Ercument Cicek A, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 2014;515(7526):209\u0026ndash;215.\u003c/li\u003e\n\u003cli\u003eIossifov I, O\u0026rsquo;roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 2014;515(7526):216\u0026ndash;221.\u003c/li\u003e\n\u003cli\u003eSanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 2015;87(6):1215\u0026ndash;1233.\u003c/li\u003e\n\u003cli\u003eYuen RK, Merico D, Bookman M, Howe J, Thiruvahindrapuram B, Patel RV, et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci 2017;20(4):602\u0026ndash;611.\u003c/li\u003e\n\u003cli\u003eRuzzo EK, P\u0026eacute;rez-Cano L, Jung J, Wang L, Kashef-Haghighi D, Hartl C, et al. Inherited and de novo genetic risk for autism impacts shared networks. Cell 2019;178(4):850\u0026ndash;866. e26.\u003c/li\u003e\n\u003cli\u003eDoan RN, Lim ET, De Rubeis S, Betancur C, Cutler DJ, Chiocchetti AG, et al. Recessive gene disruptions in autism spectrum disorder. Nat Genet 2019;51(7):1092\u0026ndash;1098.\u003c/li\u003e\n\u003cli\u003eSatterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An J, et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 2020;180(3):568\u0026ndash;584. e23.\u003c/li\u003e\n\u003cli\u003eChoi L, An J. Genetic architecture of autism spectrum disorder: Lessons from large-scale genomic studies. Neuroscience \u0026amp; Biobehavioral Reviews 2021;128:244\u0026ndash;257.\u003c/li\u003e\n\u003cli\u003eWillsey AJ, Sanders SJ, Li M, Dong S, Tebbenkamp AT, Muhle RA, et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 2013;155(5):997\u0026ndash;1007.\u003c/li\u003e\n\u003cli\u003eCourchesne E, Pramparo T, Gazestani VH, Lombardo MV, Pierce K, Lewis NE. The ASD Living Biology: from cell proliferation to clinical phenotype. Mol Psychiatry 2019;24(1):88\u0026ndash;107.\u003c/li\u003e\n\u003cli\u003eGaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. Most genetic risk for autism resides with common variation. Nat Genet 2014;46(8):881\u0026ndash;885.\u003c/li\u003e\n\u003cli\u003ede la Torre-Ubieta L, Won H, Stein JL, Geschwind DH. Advancing the understanding of autism disease mechanisms through genetics. Nat Med 2016;22(4):345\u0026ndash;361.\u003c/li\u003e\n\u003cli\u003eGrove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 2019;51(3):431\u0026ndash;444.\u003c/li\u003e\n\u003cli\u003eCirnigliaro M, Chang TS, Arteaga SA, P\u0026eacute;rez-Cano L, Ruzzo EK, Gordon A, et al. The contributions of rare inherited and polygenic risk to ASD in multiplex families. Proceedings of the National Academy of Sciences 2023;120(31):e2215632120.\u003c/li\u003e\n\u003cli\u003eJeste SS, Geschwind DH. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nature Reviews Neurology 2014;10(2):74\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eChawner SJ, Doherty JL, Anney RJ, Antshel KM, Bearden CE, Bernier R, et al. A genetics-first approach to dissecting the heterogeneity of autism: phenotypic comparison of autism risk copy number variants. Am J Psychiatry 2021;178(1):77\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eWarrier V, Zhang X, Reed P, Havdahl A, Moore TM, Cliquet F, et al. Genetic correlates of phenotypic heterogeneity in autism. Nat Genet 2022;54(9):1293\u0026ndash;1304.\u003c/li\u003e\n\u003cli\u003eDi Giovanni D, Enea R, Di Micco V, Benvenuto A, Curatolo P, Emberti Gialloreti L. Using machine learning to explore shared genetic pathways and possible endophenotypes in autism spectrum disorder. Genes 2023;14(2):313.\u003c/li\u003e\n\u003cli\u003eBertelsen N, Landi I, Bethlehem RA, Seidlitz J, Busuoli EM, Mandelli V, et al. Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry. Communications biology 2021;4(1):574.\u003c/li\u003e\n\u003cli\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 2005;102(43):15545\u0026ndash;15550.\u003c/li\u003e\n\u003cli\u003eWang HZ, Qin H, Guo W, Samuels J, Shugart YY. New insights into the genetic mechanism of IQ in autism spectrum disorders. Frontiers in Genetics 2013;4:195.\u003c/li\u003e\n\u003cli\u003eBrainSpan Atlas of the Developing Human Brain. Available at: https://www.brainspan.org\u003c/li\u003e\n\u003cli\u003eStark C, Breitkreutz B, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006;34(suppl_1):D535\u0026ndash;D539.\u003c/li\u003e\n\u003cli\u003eMahfouz A, Ziats MN, Rennert OM, Lelieveldt BP, Reinders MJ. Shared pathways among autism candidate genes determined by co-expression network analysis of the developing human brain transcriptome. Journal of Molecular Neuroscience 2015;57(4):580\u0026ndash;594.\u003c/li\u003e\n\u003cli\u003eOldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, et al. Functional organization of the transcriptome in human brain. Nat Neurosci 2008;11(11):1271\u0026ndash;1282.\u003c/li\u003e\n\u003cli\u003eSimons Foundation Autism Research Initiative (SFARI) database. Available at: https://sfari.org Accessed July 9th, 2024.\u003c/li\u003e\n\u003cli\u003eCrippa A, Craig F, Busti Ceccarelli S, Mauri M, Grazioli S, Scionti N, et al. A multimethod approach to assessing motor skills in boys and girls with autism spectrum disorder. Autism 2021;25(5):1481\u0026ndash;1491.\u003c/li\u003e\n\u003cli\u003eLord C, Rutter M, Goode S, Heemsbergen J, Jordan H, Mawhood L, et al. Autism diagnostic observation schedule. J Autism Dev Disord 2012.\u003c/li\u003e\n\u003cli\u003eGriffiths R. The abilities of young children. A study in mental measurement. 1970.\u003c/li\u003e\n\u003cli\u003eWechsler D. The Wechsler Preschool and Primary Scale of Intelligence. 3rd ed.: The Psychological Corporation Canadian Journal of School Psychology; 2002.\u003c/li\u003e\n\u003cli\u003eWechsler D. Wechsler Intelligence Scale for Children\u0026ndash;IV (WISCIV) Italian Edition. Florence: Organizzazioni Speciali 2012.\u003c/li\u003e\n\u003cli\u003eGotham K, Pickles A, Lord C. Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J Autism Dev Disord 2009;39:693\u0026ndash;705.\u003c/li\u003e\n\u003cli\u003eHus V, Gotham K, Lord C. Standardizing ADOS domain scores: Separating severity of social affect and restricted and repetitive behaviors. J Autism Dev Disord 2014;44:2400\u0026ndash;2412.\u003c/li\u003e\n\u003cli\u003eHollingshead AB. Four factor index of social status . : Yale University; 1975.\u003c/li\u003e\n\u003cli\u003eWang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38(16):e164.\u003c/li\u003e\n\u003cli\u003eGr\u0026uuml;ning B, Dale R, Sj\u0026ouml;din A, Chapman BA, Rowe J, Tomkins-Tinch CH, et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature methods 2018;15(7):475\u0026ndash;476.\u003c/li\u003e\n\u003cli\u003eAshburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet 2000;25(1):25\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eKanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 2010;38(suppl_1):D355\u0026ndash;D360.\u003c/li\u003e\n\u003cli\u003eFabregat A, Korninger F, Viteri G, Sidiropoulos K, Marin-Garcia P, Ping P, et al. Reactome graph database: Efficient access to complex pathway data. PLoS computational biology 2018;14(1):e1005968.\u003c/li\u003e\n\u003cli\u003eMurtagh F, Legendre P. Ward\u0026rsquo;s hierarchical agglomerative clustering method: which algorithms implement Ward\u0026rsquo;s criterion? Journal of classification 2014;31:274\u0026ndash;295.\u003c/li\u003e\n\u003cli\u003eRousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987;20:53\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eGrote S, Pr\u0026uuml;fer K, Kelso J, Dannemann M. ABAEnrichment: an R package to test for gene set expression enrichment in the adult and developing human brain. Bioinformatics 2016;32(20):3201\u0026ndash;3203.\u003c/li\u003e\n\u003cli\u003eSchmunk G, Gargus JJ. Channelopathy pathogenesis in autism spectrum disorders. Frontiers in genetics 2013;4:222.\u003c/li\u003e\n\u003cli\u003eDaghsni M, Rima M, Fajloun Z, Ronjat M, Brus\u0026eacute;s JL, M\u0026apos;rad R, et al. Autism throughout genetics: Perusal of the implication of ion channels. Brain and Behavior 2018;8(8):e00978.\u003c/li\u003e\n\u003cli\u003eEvangelho VGO, Bello ML, Castro HC, Amorim MR. Gene set enrichment analysis indicates convergence in the mTOR signalling pathway between syndromic and non-syndromic autism. Neuroscience Informatics 2023;3(2):100119.\u003c/li\u003e\n\u003cli\u003eBourgeron T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nature Reviews Neuroscience 2015;16(9):551\u0026ndash;563.\u003c/li\u003e\n\u003cli\u003eSmith RS, Walsh CA. Ion channel functions in early brain development. Trends Neurosci 2020;43(2):103\u0026ndash;114.\u003c/li\u003e\n\u003cli\u003eGu S, Katyal A, Zhang Q, Chung W, Franciosi S, Sanatani S. The association between congenital heart disease and autism spectrum disorder: A systematic review and meta-analysis. Pediatr Cardiol 2023;44(5):1092\u0026ndash;1107.\u003c/li\u003e\n\u003cli\u003eSigmon ER, Kelleman M, Susi A, Nylund CM, Oster ME. Congenital heart disease and autism: a case-control study. Pediatrics 2019;144(5).\u003c/li\u003e\n\u003cli\u003eSplawski I, Timothy KW, Sharpe LM, Decher N, Kumar P, Bloise R, et al. CaV1. 2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell 2004;119(1):19\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eRosenthal SB, Willsey HR, Xu Y, Mei Y, Dea J, Wang S, et al. A convergent molecular network underlying autism and congenital heart disease. Cell systems 2021;12(11):1094\u0026ndash;1107. e6.\u003c/li\u003e\n\u003cli\u003eClifton NE, Collado-Torres L, Burke EE, Pardi\u0026ntilde;as AF, Harwood JC, Di Florio A, et al. Developmental profile of psychiatric risk associated with voltage-gated cation channel activity. Biol Psychiatry 2021;90(6):399\u0026ndash;408.\u003c/li\u003e\n\u003cli\u003eLee J, Ha S, Ahn J, Lee S, Choi JR, Cheon K. The role of ion channel-related genes in autism spectrum disorder: a study using next-generation sequencing. Frontiers in Genetics 2021;12:595934.\u003c/li\u003e\n\u003cli\u003eFirdaus Z, Li X. Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches. International journal of molecular sciences 2024;25(4):2320.\u003c/li\u003e\n\u003cli\u003eHinz FI, Geschwind DH. Molecular genetics of neurodegenerative dementias. Cold Spring Harbor perspectives in biology 2017;9(4):a023705.\u003c/li\u003e\n\u003cli\u003eDeJesus-Hernandez M, Mackenzie IR, Boeve BF, Boxer AL, Baker M, Rutherford NJ, et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 2011;72(2):245\u0026ndash;256.\u003c/li\u003e\n\u003cli\u003eLi C, Pang D, Lin J, Yang T, Shang H. Shared genetic links between frontotemporal dementia and psychiatric disorders. BMC medicine 2022;20(1):131.\u003c/li\u003e\n\u003cli\u003eNadeem MS, Hosawi S, Alshehri S, Ghoneim MM, Imam SS, Murtaza BN, et al. Symptomatic, genetic, and mechanistic overlaps between autism and Alzheimer\u0026rsquo;s disease. Biomolecules 2021;11(11):1635.\u003c/li\u003e\n\u003cli\u003eFumagalli GG, Crippa A. The search for a link between autism spectrum disorder and neurodegenerative dementia. MINERVA PSYCHIATRY 2022;63(3):189\u0026ndash;196.\u003c/li\u003e\n\u003cli\u003eRhodus EK, Barber J, Abner EL, Duff DM, Bardach SH, Caban-Holt A, et al. Behaviors characteristic of autism spectrum disorder in a geriatric cohort with mild cognitive impairment or early dementia. Alzheimer Disease \u0026amp; Associated Disorders 2020;34(1):66\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eVivanti G, Tao S, Lyall K, Robins DL, Shea LL. The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder. Autism Research 2021;14(10):2189\u0026ndash;2199.\u003c/li\u003e\n\u003cli\u003eWang L, Chen J, Hu Y, Liao A, Zheng W, Wang X, et al. Progranulin improves neural development via the PI3K/Akt/GSK-3\u0026beta; pathway in the cerebellum of a VPA-induced rat model of ASD. Translational Psychiatry 2022;12(1):114.\u003c/li\u003e\n\u003cli\u003eTian Y, Xiao X, Liu W, Cheng S, Qian N, Wang L, et al. TREM2 improves microglia function and synaptic development in autism spectrum disorders by regulating P38 MAPK signaling pathway. Molecular Brain 2024;17(1):12.\u003c/li\u003e\n\u003cli\u003eFilipello F, Morini R, Corradini I, Zerbi V, Canzi A, Michalski B, et al. The microglial innate immune receptor TREM2 is required for synapse elimination and normal brain connectivity. Immunity 2018;48(5):979\u0026ndash;991. e8.\u003c/li\u003e\n\u003cli\u003eSaura CA, Servi\u0026aacute;n-Morilla E, Scholl FG. Presenilin/\u0026gamma;-secretase regulates neurexin processing at synapses. PloS one 2011;6(4):e19430.\u003c/li\u003e\n\u003cli\u003eDargenio VN, Dargenio C, Castellaneta S, De Giacomo A, Laguardia M, Schettini F, et al. Intestinal barrier dysfunction and microbiota\u0026ndash;gut\u0026ndash;brain axis: Possible implications in the pathogenesis and treatment of autism spectrum disorder. Nutrients 2023;15(7):1620.\u003c/li\u003e\n\u003cli\u003eBj\u0026oslash;rklund G, Pivina L, Dadar M, Meguid NA, Semenova Y, Anwar M, et al. Gastrointestinal alterations in autism spectrum disorder: What do we know? Neuroscience \u0026amp; Biobehavioral Reviews 2020;118:111\u0026ndash;120.\u003c/li\u003e\n\u003cli\u003eChaidez V, Hansen RL, Hertz-Picciotto I. Gastrointestinal problems in children with autism, developmental delays or typical development. J Autism Dev Disord 2014;44:1117\u0026ndash;1127.\u003c/li\u003e\n\u003cli\u003eAdams JB, Johansen LJ, Powell LD, Quig D, Rubin RA. Gastrointestinal flora and gastrointestinal status in children with autism\u0026ndash;comparisons to typical children and correlation with autism severity. BMC gastroenterology 2011;11:1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eXu M, Xu X, Li J, Li F. Association between gut microbiota and autism spectrum disorder: a systematic review and meta-analysis. Frontiers in psychiatry 2019;10:473.\u003c/li\u003e\n\u003cli\u003eLiu F, Li J, Wu F, Zheng H, Peng Q, Zhou H. Altered composition and function of intestinal microbiota in autism spectrum disorders: a systematic review. Translational psychiatry 2019;9(1):43.\u003c/li\u003e\n\u003cli\u003eMorais LH, Schreiber IV HL, Mazmanian SK. The gut microbiota\u0026ndash;brain axis in behaviour and brain disorders. Nature Reviews Microbiology 2021;19(4):241\u0026ndash;255.\u003c/li\u003e\n\u003cli\u003eNiesler B, Rappold GA. Emerging evidence for gene mutations driving both brain and gut dysfunction in autism spectrum disorder. Mol Psychiatry 2021;26(5):1442\u0026ndash;1444.\u003c/li\u003e\n\u003cli\u003eMeltzer A, Van de Water J. The role of the immune system in autism spectrum disorder. Neuropsychopharmacology 2017;42(1):284\u0026ndash;298.\u003c/li\u003e\n\u003cli\u003eOnore C, Careaga M, Ashwood P. The role of immune dysfunction in the pathophysiology of autism. Brain Behav Immun 2012;26(3):383\u0026ndash;392.\u003c/li\u003e\n\u003cli\u003eOrmstad H, Bryn V, Saugstad OD, Skjeldal O, Maes M. Role of the immune system in autism spectrum disorders (ASD). CNS \u0026amp; Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS \u0026amp; Neurological Disorders) 2018;17(7):489\u0026ndash;495.\u003c/li\u003e\n\u003cli\u003eLombardo MV, Moon HM, Su J, Palmer TD, Courchesne E, Pramparo T. Maternal immune activation dysregulation of the fetal brain transcriptome and relevance to the pathophysiology of autism spectrum disorder. Mol Psychiatry 2018;23(4):1001\u0026ndash;1013.\u003c/li\u003e\n\u003cli\u003eCareaga M, Rogers S, Hansen RL, Amaral DG, Van de Water J, Ashwood P. Immune endophenotypes in children with autism spectrum disorder. Biol Psychiatry 2017;81(5):434\u0026ndash;441.\u003c/li\u003e\n\u003cli\u003eMasi A, Glozier N, Dale R, Guastella AJ. The immune system, cytokines, and biomarkers in autism spectrum disorder. Neuroscience bulletin 2017;33:194\u0026ndash;204.\u003c/li\u003e\n\u003cli\u003eHoriuchi F, Yoshino Y, Kumon H, Hosokawa R, Nakachi K, Kawabe K, et al. Identification of aberrant innate and adaptive immunity based on changes in global gene expression in the blood of adults with autism spectrum disorder. Journal of Neuroinflammation 2021;18:1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eArenella M, Matuleviciute R, Tamouza R, Leboyer M, McAlonan G, Bralten J, et al. Immunogenetics of autism spectrum disorder: a systematic literature review. Brain Behav Immun 2023.\u003c/li\u003e\n\u003cli\u003eArenella M, Cadby G, De Witte W, Jones RM, Whitehouse AJ, Moses EK, et al. Potential role for immune-related genes in autism spectrum disorders: Evidence from genome-wide association meta-analysis of autistic traits. Autism 2022;26(2):361\u0026ndash;372.\u003c/li\u003e\n\u003cli\u003eEstes ML, McAllister AK. Immune mediators in the brain and peripheral tissues in autism spectrum disorder. Nature Reviews Neuroscience 2015;16(8):469\u0026ndash;486.\u003c/li\u003e\n\u003cli\u003eGarbett K, Ebert PJ, Mitchell A, Lintas C, Manzi B, Mirnics K, et al. Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol Dis 2008;30(3):303\u0026ndash;311.\u003c/li\u003e\n\u003cli\u003eVoineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 2011;474(7351):380\u0026ndash;384.\u003c/li\u003e\n\u003cli\u003eAzhari A, Azizan F, Esposito G. A systematic review of gut‐immune‐brain mechanisms in Autism Spectrum Disorder. Dev Psychobiol 2019;61(5):752\u0026ndash;771.\u003c/li\u003e\n\u003cli\u003ePowell N, Walker MM, Talley NJ. The mucosal immune system: master regulator of bidirectional gut\u0026ndash;brain communications. Nature reviews Gastroenterology \u0026amp; hepatology 2017;14(3):143\u0026ndash;159.\u003c/li\u003e\n\u003cli\u003eLombardo MV, Courchesne E, Lewis NE, Pramparo T. Hierarchical cortical transcriptome disorganization in autism. Molecular Autism 2017;8:1\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eGupta S, Ellis SE, Ashar FN, Moes A, Bader JS, Zhan J, et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nature communications 2014;5(1):5748.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"autism, heterogeneity, genetics, potentially damaging variants, brain expression","lastPublishedDoi":"10.21203/rs.3.rs-5534869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5534869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding the functional implications of genes’ variants related to autism heterogeneity represents a crucial challenge. Gene set analysis examines the combined effect of multiple genes with convergent biological functions. Here we explored whether a multi-step analysis could identify gene sets relevant to autism subtyping in terms of different loads of possibly damaging variants (PDVs)\u003cstrong\u003e \u003c/strong\u003eamong two subgroups of autistic children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter subdividing our sample of 71 autistic children (3-12 years) in two subgroups with higher (\u0026gt;80; n=43) and lower (≤80; n=28) intelligence quotient (IQ), a gene set variant enrichment analysis identified gene sets with significantly different incidence of PDVs between the two subgroups. Significant gene sets were then clustered into modules of genes. Their brain expression was investigated according to the BrainSpan Atlas of the Developing Human Brain. Next, we extended each module by selecting the genes that were spatio-temporally co-expressed in the developing brain and physically interacting with those in the modules. Last, we explored the incidence of autism susceptibility genes within the original and extended modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis identified 38 significant gene sets (FDR, q\u0026lt;0.05), which clustered in four gene modules involved in ion cell communication, neurocognition, gastrointestinal function, and immune system. Those modules were highly expressed in specific brain structures across different developmental stages. Spatio-temporal brain co-expression across development and physical protein interactions identified extended clusters of genes where we found an over-representation of autism susceptibility genes.\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThe sample size of this work is limited. Our analysis was also limited to a disease-associated subsection of the exome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur unbiased approach identified modules of genes functionally relevant to autism pathophysiology in a relatively small set of participants, providing evidence of their implication in the phenotypic differences of autism subgroups. The findings of interconnections between different modules and with autism susceptibility genes suggest that diversity in autism likely originates from multiple interacting pathways. Future research could leverage the present approach to identify genetic pathways relevant to autism subtyping.\u003c/p\u003e","manuscriptTitle":"Potentially damaging variants’ analysis in autism subgroups uncovers early brain-expressed gene modules relevant to autism pathophysiology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 15:46:27","doi":"10.21203/rs.3.rs-5534869/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3dba2fff-6bf5-4b20-8e20-de071ddfe6b3","owner":[],"postedDate":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-03T15:46:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-03 15:46:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5534869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5534869","identity":"rs-5534869","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}