Article Synopsis

  • Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with varying cognitive, behavioral, and communication challenges, and its relationship with the gut-brain axis (GBA) is being explored but lacks consistent findings.
  • A new Bayesian algorithm was used to analyze multiple datasets related to microbiomes, diets, and brain gene expression, revealing a distinct link between specific microbial communities (like Prevotella and Bifidobacterium) and the diverse characteristics of ASD, involving changes in metabolic profiles and inflammation.
  • The research indicates that this functional relationship along the GBA differs between age- and sex-matched individuals but not among siblings, highlighting how microbiome changes can be associated with ASD symptoms.

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Article Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322709PMC
http://dx.doi.org/10.1038/s41593-023-01361-0DOI Listing

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