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Here we performed a longitudinal and integrative multi-omics analysis of the gut microbiome, fecal metabolome and plasma metabolome in 169 KT recipients across 5 different transplantation centers, comprising discovery and validation cohorts. We observed KT-specific four kingdom microbiome dysbiosis, including bacteria, fungi, archaea and viruses, with the most pronounced shifts in bacterial and fungal communities. Furthermore, we identified 6 infection-associated co-abundance groups (CAGs) composed of 23 bacterial and 3 fungal species, highlighting extensive bacterial-fungal interactions. Interestingly, infection-associated fecal metabolomic pattern F1, enriched in N-acetylputrescine and hydroxyproline, was positively correlated with\n                    <jats:italic>Enterococcus<\/jats:italic>\n                    -,\n                    <jats:italic>Citrobacter<\/jats:italic>\n                    - and\n                    <jats:italic>Lactococcus<\/jats:italic>\n                    -dominated CAGs, as well as the plasma metabolite signature, represented by phenylacetyl-l-glutamine, indoxyl sulfate and leukotriene. In contrast, cholesterol sulfate and menadione in plasma were aligned with fecal indoleacetic acid and stachyose, a metabolic signature more characteristic of non-infected recipients. Finally, the combinatorial biomarkers of fungal and bacterial species achieved powerful diagnosis ability of KT infection in an independent validation cohort (area under the receiver operating characteristic curve (AUROC)\u2009=\u20090.80) with the fecal metabolites achieving high accuracy (AUROC\u2009=\u20090.83). Collectively, our findings not only uncovered the postoperative infection-specific multi-kingdom microbial network dynamics, but also revealed the microbial and its metabolic biomarkers with powerful diagnostic ability for postoperative infection in kidney transplantation.\n                  <\/jats:p>","DOI":"10.1038\/s41522-026-01099-0","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:25:29Z","timestamp":1784564729000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal multi-omics analysis identify multi-kingdom microbiome-host interaction dynamics and diagnostic biomarkers of postoperative infection after kidney transplantation"],"prefix":"10.1038","volume":"12","author":[{"given":"Yun-Xuan","family":"Guan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Xiang","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Yan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-An","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiu-Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-Jing","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Ping","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tu-Run","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si-Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Hui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruo-Lin","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Jun","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Zhi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Yin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning-Ning","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"1099_CR1","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/S0140-6736(20)30045-3","volume":"395","author":"GBD Chronic Kidney Disease Collaboration","year":"2020","unstructured":"GBD Chronic Kidney Disease Collaboration Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. 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