{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:59:37Z","timestamp":1790085577616,"version":"4.0.1"},"reference-count":55,"publisher":"American Society for Microbiology","issue":"9","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102065, 62271353"],"award-info":[{"award-number":["62102065, 62271353"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Funds for the innovation of science and Technology, Fujian province","award":["2022J05055"],"award-info":[{"award-number":["2022J05055"]}]},{"name":"Fujian Medical University Research Fundation of Talented Scholars","award":["XRCZX2022003"],"award-info":[{"award-number":["XRCZX2022003"]}],"id":[{"id":"https:\/\/ror.org\/050s6ns64","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["mSystems"],"published-print":{"date-parts":[[2026,9,22]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition and function being closely associated with its onset and progression. However, the high inter-individual variability in gut microbiota complicates the identification of pathogenic mechanisms using traditional methods. In contrast, the smaller variability in gut microbial metabolites offers a more reliable and consistent basis for cross-individual comparisons. Parsimonious flux balance analysis (pFBA), integrated with double machine learning (DoubleML), identified 17 metabolites significantly associated with hypertension (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05, robustness value [RV] &gt;0.1). These included meso-2,6-diaminoheptanedioate, p-hydroxyphenylacetic acid, cellobiose, dextran 40 (1,6-\u03b1-D-glucan), L-glutamic acid, and kestopentaose, among others. Differential microbial correlation network analysis identified a key microbial subnetwork, termed the FERM guild, consisting of 19 species, with prominent genera including\n                      <jats:italic toggle=\"yes\">Faecalibacterium, Enterobacter, Roseburia<\/jats:italic>\n                      , and\n                      <jats:italic toggle=\"yes\">Methanobrevibacter<\/jats:italic>\n                      . Using Gene Set Enrichment Analysis (GSEA), the dysregulation of this guild was found to be strongly associated with a set of 17 hypertension-related metabolites (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.017). Further analysis revealed that the contribution of FERM genera to key metabolites is significantly associated with blood pressure (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05), even without significant differences in their abundance; additionally, an imbalance exists between FERM genera and other species. Our findings reveal that hypertension is associated with a disruption of gut microbial diversity, structure, and metabolic function. Seventeen key metabolites related to blood pressure regulation were identified, exhibiting pro- or anti-hypertensive potential and linked to functional microbial modules. These results highlight the gut microbiota and its metabolites as promising targets for therapeutic intervention in hypertension.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>Hypertension remains a major global public health burden; however, most studies on its relationship with the gut microbiota rely on traditional species-abundance analyses, which are limited by substantial inter-individual variability. In contrast, microbial metabolites show greater stability across individuals and thus offer a more reliable entry point for mechanistic research. By integrating metabolic modeling, causal inference, and network analysis, this study identified 17 key metabolites significantly associated with blood pressure and uncovered a functionally coordinated microbial community (FERM) whose contribution to critical metabolic fluxes (rather than its taxonomic abundance) was closely linked to hypertension. These findings reveal a metabolite-centered mechanism connecting microbial functions to host blood pressure regulation and provide new potential targets for microbiome-based interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/msystems.00358-26","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T13:03:16Z","timestamp":1785848596000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["The FERM guild: a differentially correlated microbial module drives hypertension via metabolic flux perturbations"],"prefix":"10.1128","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4274-4884","authenticated-orcid":false,"given":"Wenkai","family":"Lai","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/050s6ns64","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University","place":["Fuzhou, China"]},{"id":[{"id":"https:\/\/ror.org\/050s6ns64","id-type":"ROR","asserted-by":"publisher"}],"name":"School of Medical Imaging, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/050s6ns64","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoping","family":"Huang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/050s6ns64","id-type":"ROR","asserted-by":"publisher"}],"name":"Clinical College of Obstetrics and Gynecology and Pediatrics, Fujian Medical 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