Integrated multi-omics analysis identifies microbial and metabolic signatures and drivers of CNS autoimmunity 2305382

Introduction Multiple sclerosis (MS) is an autoimmune disease of the central nervous system driven by genetics and the environment. People with MS exhibit distinct gut microbiomes and altered systemic bacterial produced metabolites, including tryptophan-derived products. However, defining microbial metabolic drivers of MS remains challenging. We previously showed that colonization with Limosilactobacillus reuteri (L. reuteri) worsens disease in a model of MS, experimental autoimmune encephalomyelitis (EAE), in a tryptophan-dependent manner. Methods We integrated microbiomic and metabolomic datasets from a longitudinal EAE study using high and low tryptophan diets in mice colonized or not with L. reuteri to identify coordinated microbe—metabolite modules, and used Random Forest modeling to identify predictors of EAE severity. Candidate metabolites were tested in vivo for their effects on EAE and immune response. Results During short-term dietary intervention, L. reuteri colonization had a greater effect on the gut microbiome than did tryptophan bioavailability. With longer dietary exposure and EAE progression, high dietary tryptophan and L. reuteri colonization synergized to elicit profound changes in the microbiota, including altered abundance of distinct Lachnospiraceae, Blautia coccoides, and Akkermansia muciniphila. Multiomic integration revealed distinct clusters of metabolites and microbiota enriched for functional pathways, including bile acid and tryptophan metabolism. Metabolites outperformed microbiota in predicting EAE severity, identifying p-cresols and indoles as associated with disease worsening. Treatment with p-cresol or 3-indoleglyoxylic acid exacerbated EAE, enhanced proinflammatory T cell responses and increased cerebellar pathology. Conclusion These data indicate that dietary response is modulated by gut microbiome composition and that integrated microbiomic-metabolomic analyses can predict drivers of disease worsening in MS. Funding Source R01 NS097596 from NIH/NINDS and RG-2407-43682 from the NMSS to DNK and F31NS120381-01A1 from NIH/NINDS to TLM, training grant T32AI055402-16A1 to Dr.