{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T23:51:14Z","timestamp":1768434674265,"version":"3.49.0"},"posted":{"date-parts":[[2026,1,9]]},"group-title":"Bioinformatics","reference-count":113,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,1,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) driven by genetic and environmental determinants. The gut microbiome of people with MS (pwMS) is distinct and influences disease through immunomodulatory metabolite production. Circulating metabolites are altered in pwMS, but identifying microbial-metabolic drivers remains challenging. We previously showed that colonization by the gut commensal\n                  <jats:italic>Limosilactobacillus reuteri<\/jats:italic>\n                  (\n                  <jats:italic>L. reuteri<\/jats:italic>\n                  ) exacerbates disease in the experimental autoimmune encephalomyelitis (EAE) model of MS, in a tryptophan-dependent manner. Here, we integrated microbiomic and metabolomic datasets from a longitudinal EAE study utilizing high and low tryptophan diets in mice colonized or not with\n                  <jats:italic>L. reuteri<\/jats:italic>\n                  . Gut microbiome dynamics under short- and long-term alterations in tryptophan bioavailability, were affected by diet, microbiome context, or disease. During short-term dietary intervention,\n                  <jats:italic>L. reuteri<\/jats:italic>\n                  colonization exerted a greater impact on microbiome composition than tryptophan bioavailability. With longer dietary exposure and EAE progression, high dietary tryptophan and\n                  <jats:italic>L. reuteri<\/jats:italic>\n                  colonization synergized to elicit profound microbiota changes, including alterations in Lachnospiraceae,\n                  <jats:italic>Blautia<\/jats:italic>\n                  , and\n                  <jats:italic>Akkermansia<\/jats:italic>\n                  . Integration of metabolomic and microbiomic datasets using joint Robust Aitchison PCA revealed clusters of associated 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 top disease-associated metabolites. Treatment with p-cresol or 3-indoleglyoxylic acid exacerbated EAE, enhanced proinflammatory T cell responses, and increased cerebellar pathology. These data demonstrate that dietary responses are shaped by gut microbiome composition and that integrated microbiomic-metabolomic analyses can identify drivers of disease worsening in MS.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>IMPORTANCE<\/jats:title>\n                  <jats:p>MS is a multifactorial disease influenced not only by genetics but also by environmental factors, potentially including diet and the composition of the gut microbiome. We show that interactions between diet and commensal gut microbiota profoundly impact levels of immunomodulatory systemic metabolites, including several that are associated with disease in pwMS. Importantly, we demonstrate that individual gut microbiota produced metabolites are sufficient to worsen disease in a mouse model of MS. Integration of gut microbiome and blood metabolite datasets combined with subsequent predictive modeling, may bolster biomarker identification and the capacity to predict disease severity in pwMS, as compared to performance of individual datasets alone. These findings highlight metabolites as key mediators linking diet and the gut microbiota to neuroinflammation. Importantly, this work suggests that targeting microbial metabolites or modifying diet\u2013microbiome interactions may represent new strategies to reduce disease activity in MS and related autoimmune disorders.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.01.08.698420","type":"posted-content","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T15:35:19Z","timestamp":1767972919000},"source":"Crossref","is-referenced-by-count":0,"title":["Integrated multi-omics analysis identifies microbial and metabolic signatures and drivers of CNS autoimmunity"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0950-9618","authenticated-orcid":false,"given":"Theresa L.","family":"Montgomery","sequence":"first","affiliation":[{"name":"Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT 05401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily A.","family":"Nelson","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT 05401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lauren A.","family":"Downs","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT 05401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eamonn R.","family":"Heney","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT 05401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margaret Frances J.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT 05401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cameron","family":"Martino","sequence":"additional","affiliation":[{"name":"Leaven Foods Inc., San Diego, CA, 92126 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"McDonald","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gibraan","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA"},{"name":"Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0975-9019","authenticated-orcid":false,"given":"Rob","family":"Knight","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA"},{"name":"Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA"},{"name":"Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA"},{"name":"Hal\u0131c\u0131o\u011flu Data Science Institute, University of California, San Diego, La Jolla, CA, USA"},{"name":"Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitry N.","family":"Krementsov","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT 05401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026011201400483000_2026.01.08.698420v1.1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra052130"},{"key":"2026011201400483000_2026.01.08.698420v1.2","doi-asserted-by":"publisher","DOI":"10.1002\/dneu.20387"},{"key":"2026011201400483000_2026.01.08.698420v1.3","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000007035"},{"issue":"14","key":"2026011201400483000_2026.01.08.698420v1.4","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1177\/1352458520970841","article-title":"Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition","volume":"26","year":"2020","journal-title":"Multiple Sclerosis Journal"},{"key":"2026011201400483000_2026.01.08.698420v1.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2018.01.021"},{"key":"2026011201400483000_2026.01.08.698420v1.6","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra052130"},{"issue":"1","key":"2026011201400483000_2026.01.08.698420v1.7","first-page":"1","article-title":"Multiple Sclerosis: Pathogenesis, Symptoms, Diagnoses and Cell-Based Therapy","volume":"19","year":"2017","journal-title":"Cell journal"},{"key":"2026011201400483000_2026.01.08.698420v1.8","doi-asserted-by":"crossref","unstructured":"Lobeck, L.J. , Multiple Sclerosis, in Encyclopedia of the Human Brain, V.S. 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