{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T06:35:51Z","timestamp":1776926151591,"version":"3.51.2"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Myasthenia gravis (MG) is an autoimmune disorder of neuromuscular transmission.  Gut dysbiosis has been implicated in autoimmune pathogenesis, yet integrated microbial and metabolomic profiling in MG remains scarce.  To characterize gut microbiota and the fecal metabolome in MG, identify diagnostic biomarkers, and explore associations between microbial taxa, metabolites, and clinical severity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Fecal samples from 29 MG patients and 10 healthy controls underwent 16S rRNA sequencing and UHPLC-Q-TOF MS metabolomics. LEfSe identified differential taxa; metabolites were screened by VIP &amp;gt; 1.0,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05, FDR q &amp;lt; 0.05. Random Forest and Spearman correlation assessed biomarker performance and microbiota \u2013 metabolite \u2013 clinical associations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      MG patients showed significantly reduced alpha- and beta-diversity. LEfSe identified 232 discriminative taxa, with depletion of\n                      <jats:italic>butanoic acid<\/jats:italic>\n                      -producing commensals (\n                      <jats:italic>Faecalibacterium prausnitzii, Ruminococcus bromii, Bifidobacterium bifidum<\/jats:italic>\n                      ) and enrichment of\n                      <jats:italic>Klebsiella<\/jats:italic>\n                      . Metabolomics revealed 567 altered metabolites (424 downregulated), including reduced short-chain fatty acids and secondary bile acids (\n                      <jats:italic>lithocholic, isolithocholic<\/jats:italic>\n                      , and\n                      <jats:italic>allolithocholic acid<\/jats:italic>\n                      ). The Random Forest metabolite model achieved AUC\u00a0=\u00a01.0. Spearman analysis revealed that\n                      <jats:italic>lithocholic acid<\/jats:italic>\n                      (P &amp;lt; 0.05) and\n                      <jats:italic>allocholic acid<\/jats:italic>\n                      (P &amp;lt; 0.001) showed positive correlations with the QMGS, while\n                      <jats:italic>Ruminococcus<\/jats:italic>\n                      abundance was positively correlated with\n                      <jats:italic>butanoic acid<\/jats:italic>\n                      (P &amp;lt; 0.01). KEGG analysis implicated cholinergic synapse, bile secretion, sphingolipid signaling, and mTOR pathways.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>MG patients exhibit a distinct profile of gut dysbiosis and metabolic disturbances. The specific microbial and metabolic biomarkers identified in this study may offer novel insights for auxiliary diagnosis of MG and guide future microbiota-targeted intervention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1799199","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:36:35Z","timestamp":1776922595000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dysbiosis of gut microbiota and metabolomic alterations in myasthenia gravis: insights from 16S rRNA sequencing and untargeted metabolomics"],"prefix":"10.3389","volume":"17","author":[{"given":"Yunan","family":"Shan","sequence":"first","affiliation":[{"name":"Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and 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