{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:35:41Z","timestamp":1789583741762,"version":"build-2803163510"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T00:00:00Z","timestamp":1726531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec><jats:title>Aims<\/jats:title><jats:p>Hyperglycemia is one of the adverse effects of tacrolimus (TAC), but the underlying mechanism is not fully identified. We used multi-omics analysis to evaluate the changes in the gut microbiota and metabolic profile of rats with TAC-induced diabetes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To establish a diabetic animal model, Sprague Dawley rats were divided randomly into two groups. Those in the TAC group received intraperitoneal injections of TAC (3 mg\/kg) for 8 weeks, and those in the CON group served as the control. 16S rRNA sequencing was used to analyze fecal microbiota. The metabolites of the two groups were detected and analyzed by nontargeted and targeted metabolomics, including amino acids (AAs), bile acids (BAs), and short-chain fatty acids (SCFAs).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The rats treated with TAC exhibited hyperglycemia as well as changes in the gut microbiota and metabolites. Specifically, their gut microbiota had significantly higher abundances of <jats:italic>Escherichia-Shigella<\/jats:italic>, <jats:italic>Enterococcus<\/jats:italic>, and <jats:italic>Allobaculum<\/jats:italic>, and significantly lower abundances of <jats:italic>Ruminococcus<\/jats:italic>, <jats:italic>Akkermansia<\/jats:italic>, and <jats:italic>Roseburia<\/jats:italic>. In addition, they had significantly reduced serum levels of AAs including asparagine, aspartic acid, glutamic acid, and methionine. With respect to BAs, they had significantly higher serum levels of taurocholic acid (TCA), and glycochenodeoxycholic acid (GCDCA), but significantly lower levels of taurodeoxycholic acid (TDCA) and tauroursodeoxycholic acid (TUDCA). There were no differences in the levels of SCFAs between the two groups. Correlations existed among glucose metabolism indexes (fasting blood glucose and fasting insulin), gut microbiota (<jats:italic>Ruminococcus<\/jats:italic> and <jats:italic>Akkermansia<\/jats:italic>), and metabolites (glutamic acid, hydroxyproline, GCDCA, TDCA, and TUDCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Both AAs and BAs may play crucial roles as signaling molecules in the regulation of TAC-induced diabetes.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcimb.2024.1436477","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T13:10:59Z","timestamp":1726665059000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Gut microbiota and metabolomic profile changes play critical roles in tacrolimus-induced diabetes in rats"],"prefix":"10.3389","volume":"14","author":[{"given":"Zhenwei","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minyan","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeng","family":"Zhen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caomei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li\u2019an","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingting","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenting","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s00726-011-1088-7","article-title":"Insulin resistance and the metabolism of branched-chain amino acids in humans","volume":"43","author":"Adeva","year":"2012","journal-title":"Amino Acids"},{"key":"B2","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1097\/FTD.0000000000000640","article-title":"Therapeutic drug monitoring of tacrolimus-personalized therapy: second consensus report","volume":"41","author":"Brunet","year":"2019","journal-title":"Ther. 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