Biomedicine

Abstract

Introduction and Aim: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental disorder with complicated origins, and recent research points to a possible connection between dysbiosis of the gut microbiome and the pathophysiology of ASD. 

Materials and Methods: In the present study, gut microbiome samples from public repositories (NCBI BioProject IDs: PRJNA815491 and PRJNA642975) were meta-analyzed using an integrated computational methodology. The gut microbiome 16S rRNA samples (n = 98) were subjected to taxonomic classification, functional profiling, statistical analysis as well as LEfSE and T-test analysis to find microbial biomarkers. Lastly, Machine Learning (ML) was employed to find the important features related to ASD. 

Results: The results indicated nine significant features namely Sutterella, Prevotella, Blautia, Substance dependence pathway, Circulatory system pathway, Parasitic infectious disease, K02014 (TC.FEV.OM) gene, K03585 (acrA) gene, and K06147 (ABCB-BAC) gene. Moreover, complex relationships between microbial taxa, functional pathways, and genetic components were discovered by network analysis utilizing Cytoscape, which provided insight into possible microbial-host interactions and their relevance to the pathophysiology of ASD. 

Conclusion: Overall, our research sheds light on potential microbial biomarkers, pathways, and genes dysregulated in ASD, as well as the gut microbiome and functional changes linked to the disorder. These findings suggest interesting directions for future research and therapeutic approaches targeting the gut-brain axis in the management of ASD. They also add to a fuller knowledge of the intricate interactions between the gut microbiome, host genetics, and ASD pathogenesis.