A Systems-Level Multi-Omics Dissection of Syndromic and Idiopathic Autism Reveals Distinct Regulatory Architectures, Molecular Biomarkers, and Therapeutic Vulnerabilities

A Systems-Level Multi-Omics Dissection of Syndromic and Idiopathic Autism Reveals Distinct Regulatory Architectures, Molecular Biomarkers, and Therapeutic Vulnerabilities | 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 A Systems-Level Multi-Omics Dissection of Syndromic and Idiopathic Autism Reveals Distinct Regulatory Architectures, Molecular Biomarkers, and Therapeutic Vulnerabilities Ondippili Rudhra, Sanjeev Kumar Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9067110/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 Biological systems operate as self-organizing information networks in which genetic, epigenetic, and regulatory interactions collectively determine functional outcomes. Autism encompasses a heterogeneous set of neurodevelopmental conditions, including syndromic and idiopathic case. Despite extensive gene discovery efforts, how these autism subtypes differ in their underlying organization of biological information remains poorly understood. Here, we apply an integrative systems-level, multi-omics framework to compare syndromic and idiopathic autism as distinct regulatory systems. High-confidence autism risk genes were curated from the SFARI and AutismKB databases, and analyzed using functional enrichment, protein–protein interaction network modeling, graph-theoretic hub identification, brain-region, cell-type specific transcriptomic validation, experimentally supported miRNA regulatory network reconstruction, and deep learning–based drug target interaction analysis. Our analyses reveal clear differences in network organization between autism subtypes. Idiopathic autism is predominantly associated with synaptic signaling, ion channel activity, and transcriptional modulation, with hub genes KAT2B and AR enriched in basal ganglia associated regions and astrocytes. In contrast, syndromic autism shows enrichment for transcriptional regulation, chromatin remodeling, and dense miRNA-mediated control, with hub genes CHD3 and CSNK2A1 preferentially expressed in cerebellar, cortical regions and inhibitory neurons. Notably, master regulatory miRNAs differ completely between subtypes, indicating distinct post-transcriptional regulatory strategies. Deep learning-based screening further identifies subtype-specific therapeutic candidates with predicted central nervous system accessibility. Together, these findings demonstrate that syndromic and idiopathic autism differ in how regulatory information is structured and propagated across molecular networks, providing a systems-level perspective on autism heterogeneity and a general framework for analyzing biological information organization in complex systems. Bioinformatics Systems Biology Deep Learning Systems Biology Multi-Omics Integration Hub Gene miRNA Regulation Drug Repurposing Syndromic Autism Idiopathic Autism Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryFileS1.docx Documentation of the R (version 4.4.1) scripts used for GO and KEGG enrichment visualization, including the generation of horizontal bar plots and bubble plots. The file further outlines the custom Python-based analytical pipelines implemented for transcriptomic validation, single-cell enrichment analysis, deep learning–based drug–target interaction prediction, and miRNA regulatory network construction. SupplementaryFileS2.xlsx The list of autism risk genes includes those associated with syndromic and idiopathic cases. SupplementaryFileS3.xlsx A comprehensive, detailed report of the gene ontology and KEGG pathway analysis. SupplementaryFileS4.xlsx Identification of top 10 Hub Genes Using CytoHubba's Topological Algorithms in Cytoscape SupplementaryTableS1S2.docx Table S1. The detailed description of the top five syndromic, and idiopathic autism hub genes Table S2. Integrative multi-omics characterization of subtype-specific hub genes in autism. Hub genes identified from protein–protein interaction networks were evaluated using centrality metrics, regional brain expression (GTEx, HPA), cell-type enrichment (Allen Brain Atlas), miRNA regulatory architecture, predicted drug interactions with blood–brain barrier assessment, and biomarker suitability. Genes were stratified into primary, secondary, and low-specificity candidates based on combined systems-level evidence. SupplementaryFig12.pptx Supplementary Fig S1: The complete PPI network of (a) Syndromic autism (b) Idiopathic autism risk genes visualized using cytoscape. Supplementary Fig S2: Heatmaps illustrate the spatial expression profiles of selected hub genes across major human brain regions using data from the Human Protein Atlas (HPA). The top panel (a) represents hub genes identified in idiopathic autism (AR, RPS27A, UBA52, H3C1, KAT2B), while the bottom panel (b) represents hub genes associated with syndromic autism (SUPT16H, PHIP, CHD3, AGO2, CSNK2A1). Columns correspond to distinct brain regions, including amygdala, basal ganglia, cerebellum, cerebral cortex, choroid plexus, hippocampal formation, hypothalamus, medulla oblongata, midbrain, pons, thalamus, and white matter. Color intensity reflects log₂-transformed transcript per million values [log₂(TPM + 1)], with numerical values shown within each cell. Overall, idiopathic autism hub genes exhibit consistently higher and more uniform expression across brain regions compared with syndromic autism hub genes, which display greater regional variability, suggesting distinct neurobiological expression signatures between the two autism subtypes. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9067110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602752913,"identity":"b1200d30-c5fe-41bc-ad24-92bbda402770","order_by":0,"name":"Ondippili Rudhra","email":"","orcid":"","institution":"Alagappa university","correspondingAuthor":false,"prefix":"","firstName":"Ondippili","middleName":"","lastName":"Rudhra","suffix":""},{"id":602753090,"identity":"dc7d0572-9553-4265-84cf-1c7cf88480f4","order_by":1,"name":"Sanjeev Kumar Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACHjDJBsTMB4CEhAwpWtgSQFp4iNUCZhmg8nEB+Z4zxh9+7uCT45fI+fzqRo0FDwP74aMb8GkxONtjJtl7hs1YckbuNuucY0CH8aSl3cCrhZ/HjIG3jS1xw5mz24xz2IBaJHjM8GqR7+cx/vi3ja1+/5kzz4xz/hGhheFsj4E00JYEA/Ye5se5bURoMThzrExato3NcMbxNjPm3D4JHjZCfpHvSd788W3bMXn+ZubHn3O+1cnxsx8+ht9hDByg6DgGYrFJgEn8ykGA/QGQqAGxmD8QVj0KRsEoGAUjEQAA7VRD0+l3rQ0AAAAASUVORK5CYII=","orcid":"","institution":"Alagappa university","correspondingAuthor":true,"prefix":"","firstName":"Sanjeev","middleName":"Kumar","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2026-03-09 00:57:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9067110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9067110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104409554,"identity":"b7d49ad8-2e78-4d03-ad9e-d0ec653ab789","added_by":"auto","created_at":"2026-03-11 12:45:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2276392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9067110/v1_covered_c9eab3e1-bd0f-4ec3-839b-b8d4338a59c1.pdf"},{"id":104308992,"identity":"b5e9959e-d3d0-4107-bda4-d9918288a3ed","added_by":"auto","created_at":"2026-03-10 10:33:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39446,"visible":true,"origin":"","legend":"\u003cp\u003eDocumentation of the R (version 4.4.1) scripts used for GO and KEGG enrichment visualization, including the generation of horizontal bar plots and bubble plots. The file further outlines the custom Python-based analytical pipelines implemented for transcriptomic validation, single-cell enrichment analysis, deep learning–based drug–target interaction prediction, and miRNA regulatory network construction.\u003c/p\u003e","description":"","filename":"SupplementaryFileS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9067110/v1/e24c751f45218bf7c12a608b.docx"},{"id":104308994,"identity":"7e58f84b-8fc7-4829-b872-0d1a680d8cd2","added_by":"auto","created_at":"2026-03-10 10:33:19","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":91156,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;The list of autism risk genes includes those associated with syndromic and idiopathic cases.\u003c/p\u003e","description":"","filename":"SupplementaryFileS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9067110/v1/1f4004f5f9448083dc3ee9fd.xlsx"},{"id":104405412,"identity":"203578dd-ecd1-4e13-8bbf-79f984358c53","added_by":"auto","created_at":"2026-03-11 12:22:49","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":571985,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;A comprehensive, detailed report of \u0026nbsp;the gene ontology and KEGG pathway analysis.\u003c/p\u003e","description":"","filename":"SupplementaryFileS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9067110/v1/4d6c989c683e00953dc5943f.xlsx"},{"id":104405969,"identity":"af9c705d-e494-4231-ab2b-f47636e1d960","added_by":"auto","created_at":"2026-03-11 12:24:20","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12318,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of top 10 Hub Genes Using CytoHubba's Topological Algorithms in Cytoscape\u003c/p\u003e","description":"","filename":"SupplementaryFileS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9067110/v1/c26eb383379493fe715fae36.xlsx"},{"id":104308995,"identity":"a7c5a46e-a46a-4335-8723-25a703a62f98","added_by":"auto","created_at":"2026-03-10 10:33:19","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":25076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. The detailed description of the top five syndromic, and idiopathic autism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehub genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2. Integrative multi-omics characterization of subtype-specific hub genes in autism.\u003c/strong\u003e\u003cbr\u003e\nHub genes identified from protein–protein interaction networks were evaluated using centrality metrics, regional brain expression (GTEx, HPA), cell-type enrichment (Allen Brain Atlas), miRNA regulatory architecture, predicted drug interactions with blood–brain barrier assessment, and biomarker suitability. Genes were stratified into primary, secondary, and low-specificity candidates based on combined systems-level evidence.\u003c/p\u003e","description":"","filename":"SupplementaryTableS1S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9067110/v1/b0e6a1255056bf9b145fab25.docx"},{"id":104308996,"identity":"963d84fb-7ee0-4b47-a67f-9fe3099b46ac","added_by":"auto","created_at":"2026-03-10 10:33:19","extension":"pptx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1157332,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig S1: The complete PPI network of (a) Syndromic autism (b) Idiopathic autism risk genes visualized using cytoscape. Supplementary Fig S2: Heatmaps illustrate the spatial expression profiles of selected hub genes across major human brain regions using data from the Human Protein Atlas (HPA). The top panel (a) represents hub genes identified in idiopathic autism (AR, RPS27A, UBA52, H3C1, KAT2B), while the bottom panel (b) represents hub genes associated with syndromic autism (SUPT16H, PHIP, CHD3, AGO2, CSNK2A1). Columns correspond to distinct brain regions, including amygdala, basal ganglia, cerebellum, cerebral cortex, choroid plexus, hippocampal formation, hypothalamus, medulla oblongata, midbrain, pons, thalamus, and white matter. Color intensity reflects log₂-transformed transcript per million values [log₂(TPM + 1)], with numerical values shown within each cell. 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Autism encompasses a heterogeneous set of neurodevelopmental conditions, including syndromic and idiopathic case. Despite extensive gene discovery efforts, how these autism subtypes differ in their underlying organization of biological information remains poorly understood. Here, we apply an integrative systems-level, multi-omics framework to compare syndromic and idiopathic autism as distinct regulatory systems. High-confidence autism risk genes were curated from the SFARI and AutismKB databases, and analyzed using functional enrichment, protein\u0026ndash;protein interaction network modeling, graph-theoretic hub identification, brain-region, cell-type specific transcriptomic validation, experimentally supported miRNA regulatory network reconstruction, and deep learning\u0026ndash;based drug target interaction analysis. Our analyses reveal clear differences in network organization between autism subtypes. Idiopathic autism is predominantly associated with synaptic signaling, ion channel activity, and transcriptional modulation, with hub genes KAT2B and AR enriched in basal ganglia associated regions and astrocytes. In contrast, syndromic autism shows enrichment for transcriptional regulation, chromatin remodeling, and dense miRNA-mediated control, with hub genes CHD3 and CSNK2A1 preferentially expressed in cerebellar, cortical regions and inhibitory neurons. Notably, master regulatory miRNAs differ completely between subtypes, indicating distinct post-transcriptional regulatory strategies. Deep learning-based screening further identifies subtype-specific therapeutic candidates with predicted central nervous system accessibility. 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