James T. Morton, Dong-Min Jin, Robert H. Mills, Yan Shao, Gibraan Rahman, Daniel McDonald, Qiyun Zhu, Metin Balaban, Yueyu Jiang, Kalen Cantrell, Antonio Gonzalez, Julie Carmel, Linoy Mia Frankiensztajn, Sandra Martin-Brevet, Kirsten Berding, Brittany D. Needham, María Fernanda Zurita, Maude David, Olga V. Averina, Alexey S. Kovtun, Antonio Noto, Michele Mussap, Mingbang Wang, Daniel N. Frank, Gaspar Taroncher-Oldenburg
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 diferential ranking algorithm to identify
ASD-associated molecular and taxa profles across 10 cross-sectional
microbiome datasets and 15 other datasets, including dietary patterns,
metabolomics, cytokine profles and human brain gene expression profles.
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 profles predominantly encoded by
microbial species in the genera Prevotella, Bifdobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-infammatory cytokine profles. 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-defned cohorts and investigate how the GBA infuences ASD.