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

Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) driven by a combination of genetic and environmental determinants. The gut microbiome of people with MS (pwMS) is distinct and postulated to influence disease through production of immunomodulatory metabolites. Systemic circulating metabolites are also altered in pwMS, including gut microbiota-derived tryptophan metabolites. However, defining microbial metabolic drivers of MS has remained challenging. We have previously shown that stable colonization by the gut commensal Limosilactobacillus reuteri (L. reuteri) can exacerbate disease in the autoimmune model of MS, experimental autoimmune encephalomyelitis (EAE), and that this effect is dependent on the availability of host dietary tryptophan. Here, we integrated microbiomic and metabolomic datasets from a longitudinal EAE study utilizing high and low tryptophan diets in mice colonized or not with L. reuteri.Results: We established the dynamics of the gut microbiome under short and long-term exposure to altered tryptophan bioavailability, identifying key features of the baseline microbiota affected by diet, microbiome context, or disease course. During short-term dietary intervention, L. reuteri colonization exerted a greater effect on the composition of the microbiota 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. Integration of serum metabolomics with microbiomic datasets using joint Robust Aitchison PCA revealed distinct clusters of tightly associated metabolites and microbiota enriched for specific functional pathways, including bile acid, nucleotide, nicotinate/nicotinamide, and tryptophan metabolism. Random Forest modeling revealed that metabolites, rather than the microbiota, performed better at predicting EAE severity and identified key metabolites associated with disease worsening, including p-cresols and indoles. Treatment with either p-cresol or 3-indoleglyoxylic acid in the EAE model resulted in exacerbation of disease, enhanced proinflammatory T cell responses in the CNS and increased cerebellar pathology.Conclusions: Together, these data demonstrate that dietary response is modulated by the composition of the gut microbiome and indicate that integrated analysis of microbiomic and metabolomic data can identify potential drivers of disease worsening in MS.