In silico reconstruction of the gene network for cytokine regulation of ASD-associated
https://doi.org/10.18699/vjgb-25-105
Abstract
Accumulated evidence links dysregulated cytokine signaling to the pathogenesis of autism spectrum disorder (ASD), implicating genes, proteins, and their intermolecular networks. This paper systematizes these findings using bioinformatics analysis and machine learning methods.
The primary tool employed in the study was the AND-System cognitive platform, developed at the Institute of Cytology and Genetics, which utilizes artificial intelligence techniques for automated knowledge extraction from biomedical databases and scientific publications.
Using AND-System, we reconstructed a gene network of cytokine-mediated regulation of autism spectrum disorder (ASD)-associated genes and proteins. The analysis identified 110 cytokines that regulate the activity, degradation, and transport of 58 proteins involved in ASD pathogenesis, as well as the expression of 91 ASD-associated genes. Gene Ontology (GO) enrichment analysis revealed statistically significant associations of these genes with biological processes related to the development and function of the central nervous system. Furthermore, topological network analysis and functional significance assessment based on association with ASD-related GO biological processes allowed us to identify 21 cytokines exerting the strongest influence on the regulatory network. Among these, eight cytokines (IL-4, TGF-β1, BMP4, VEGFA, BMP2, IL-10, IFN-γ, TNF-α) had the highest priority, ranking at the top across all employed metrics. Notably, eight of the 21 prioritized cytokines (TNF-α, IL-6, IL-4, VEGFA, IL-2, IL-1β, IFN-γ, IL-17) are known targets of drugs currently used as immunosuppressants and antitumor agents. The pivotal role of these cytokines in ASD pathogenesis provides a rationale for potentially repurposing such inhibitory drugs for the treatment of autism spectrum disorders.
Keywords
About the Authors
N. M. LevanovaRussian Federation
Novosibirsk
E. G. Vergunov
Russian Federation
Novosibirsk
A. N. Savostyanov
Russian Federation
Novosibirsk
I. V. Yatsyk
Russian Federation
Novosibirsk
V. A. Ivanisenko
Russian Federation
Novosibirsk
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