JOURNAL ARTICLE

Exploring the Genetic and Molecular Connection between Autism and Huntington's Disease via Transcriptomics and Biological Interaction Networks Analysis.

  • Published In: Journal of Computational Biophysics & Chemistry, 2025, v. 24, n. 2. P. 215 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Naveed, Muhammad; Cheema, Sana Rehman; Aziz, Tariq; Makhdoom, Syeda Izma; Saleem, Urooj; Jamil, Hamza; Alhomrani, Majid; Alsanie, Walaa F.; Alamri, Abdulhakeem S. 3 of 3

Abstract

Autism Spectrum Disorder (ASD) and Huntington's Disease (HD) are distinct neurodevelopmental and neurodegenerative disorders, respectively, characterized by significant genetic and molecular alterations. ASD primarily affects early childhood and is associated with genetic mutations impacting brain development, while HD, an autosomal dominant disorder, leads to progressive neurodegeneration due to mutations in the HTT gene. Despite their differences, both disorders share common genetic pathways and molecular mechanisms. This study aims to explore the genetic and molecular connections between ASD and HD through a comprehensive analysis of differentially expressed genes (DEGs) and protein–protein interaction (PPI) networks to uncover shared pathways and potential overlapping mechanisms. Transcriptomic data were acquired from the NCBI-GEO database, specifically GSE180185 for ASD and GSE1751 for HD. DEGs were identified using thresholds of log2 fold change (FC)>1 and an adjusted p -value <0.05. Common DEGs between the two disorders were determined and analyzed using Cytoscape's STRING app to construct a PPI network with a confidence level of 0.7. Functional enrichment was conducted through KEGG and Gene Ontology (GO) analyses. Key regulatory modules and hubs were identified using CytoNCA and MCODE plugins. The ASD dataset revealed 565 DEGs, with 206 upregulated and 347 downregulated, while the HD dataset had 1091 DEGs, with 743 upregulated and 202 downregulated. Twelve genes were common to both conditions, including 4 upregulated and 8 downregulated. The PPI network comprised 62 nodes and 215 edges, with significant pathways including ascorbate metabolism and steroid hormone biosynthesis. Notably, Module 3, containing 12 nodes, was linked to EGFR tyrosine kinase resistance and apoptosis. This study identifies shared genetic and molecular pathways between ASD and HD, highlighting common regulatory mechanisms and potential targets for further research. The use of transcriptomic data and PPI network analysis reveals significant overlaps in the molecular mechanisms underlying these disorders. Further experimental validation and expanded dataset analyses could elucidate specific interactions and enhance our understanding of the shared pathways. Investigating these common mechanisms may also provide insights into potential therapeutic approaches for both ASD and HD. Differentially expressed genes (DEGs) in autism spectrum disorder (ASD) and Huntington's disease (HD) datasets reveal significant genetic overlaps, including 12 common DEGs identified. Protein-protein interaction (PPI) network analysis highlights key hub genes and functional modules enriched in EGFR tyrosine kinase resistance, apoptosis, and other critical pathways. Functional enrichment and gene ontology analyses indicate shared biological processes, cellular components, and molecular functions, offering potential therapeutic targets for both ASD and HD. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Computational Biophysics & Chemistry. 2025/03, Vol. 24, Issue 2, p215
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:2737-4165
  • DOI:10.1142/S2737416524500583
  • Accession Number:182580315
  • Copyright Statement:Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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