Robert H. Mills

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.

BACKGROUND: Crohn's disease (CD), an inflammatory bowel disease (IBD) subtype, results from pathologic interactions between host cells and its resident gut microbes. CD manifests in both isolated disease locations (ileum or colon) or a combination of locations (ileocolonic). To date, a comprehensive understanding of how isolated CD subtypes influence molecular profiles remains outstanding. To address this, we sought to define CD location signatures by leveraging a large cross-sectional feature set captured from the stool of over 200 IBD patients and healthy controls using metaproteomics, shotgun metagenomics, 16S rRNA sequencing, metabolomic profiling, and host genetics paired with clinical endoscopic assessments. RESULTS: Neither metagenomic nor host genetics alone distinguished CD location subtypes. In contrast, ileal and colonic CD were distinguished using mass spectrometry-based methods (metabolomics or metaproteomics) or a combined multi-omic feature set. This multi-omic feature set revealed colonic CD was strongly associated with neutrophil-related proteins. Additionally, colonic CD displayed a disease-severity-related association with Bacteroides vulgatus. Colonic CD and ulcerative colitis profiles harbored strikingly similar feature enrichments compared to ileal CD, including neutrophil-related protein enrichments. Compared to colonic CD, ileal CD profiles displayed increased primary and secondary bile acid levels and concomitant shifts in taxa with noted sensitivities such as Faecalibacterium prausnitzii or affinities for bile acid-rich environments, including Gammaproteobacteria and Blautia sp. Having shown robust molecular and microbial distinctions tied to CD locations, we leveraged these profiles to generate location-specific disease severity biomarkers that surpass the performance of Calprotectin. CONCLUSIONS: When compared using multi-omics features, colonic- and ileal-isolated CD subtypes display striking differences that suggest separate location-specific pathologies. Colonic CD's strong similarity to ulcerative colitis, including neutrophil and Bacteroides vulgatus involvement, is also evidence of a shared pathology for colonic-isolated IBD subtypes, while ileal CD maintains a unique, bile acid-driven profile. More broadly, this study demonstrates the power of multi-omics approaches for IBD biomarker discovery and elucidating the underlying biology. Video Abstract.