{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T13:01:56Z","timestamp":1789650116462,"version":"4.0.0"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"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. Microbiol."],"abstract":"\n Introduction<\/jats:title>\n Diabetic kidney disease (DKD) has become the main cause of end-stage renal disease in China. Mounting evidence links microecological disorders to DKD progression. However, the composition and the functional characteristics of the renal microecology in DKD patients remain poorly defined. This study characterized renal tissue microbiota and metabolite profiles in DKD patients, aiming to provide insights for novel diagnostic strategies for DKD.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Renal tissue microbiome was analyzed in 42 DKD patients and 10 controls via 16S ribosomal DNA sequencing. Renal metabolomics was performed in 20 DKD patients and 10 controls using liquid chromatography-tandem mass spectrometry (LC-MS\/MS). Spearman correlation analysis clarified associations of clinical parameters with renal microbiome and metabolites. Biomarkers were identified by multi-omics integration.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n IHC, IF and TEM reveals the presence of bacteria in both glomeruli and renal tubules. A significant separation in microbial community composition was observed between DKD patients and controls (\n p<\/jats:italic>\n &lt; 0.05). The relative abundance of\n Acidobacteriota<\/jats:italic>\n ,\n Acinetobacter<\/jats:italic>\n and\n Afipia<\/jats:italic>\n genus were significantly elevated in DKD group, whereas\n Ralstonia<\/jats:italic>\n genus was decreased. Notably, the genus\n Acinetobacter<\/jats:italic>\n demonstrated a significant negative correlation with the eGFR, while\n Afipia<\/jats:italic>\n genus exhibited a significant positive correlation with blood glucose levels. Renal microbiota resembled urinary microbiota more closely than gut microbiota. Metabolomic analysis revealed significant difference in renal tissue metabolites between DKD patients and healthy controls. Lactate, hypoxanthine and phosphorylcholine were significantly positively correlated with the eGFR. Comprehensive multivariate analyses identified the genus\n Ralstonia<\/jats:italic>\n as a crucial biomarker associated with renal fibrotic injury in DKD, consistent across urinary and gut microbiomes. Decreased serum levels of stearylcarnitine (Car18:0), oleylcarnitine (Car18:1) and tryptophan were closely associated with renal injury, whereas decreased urinary serine and tyramine levels reflected the change of renal metabolites in DKD.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Our finding confirms the existence of microbiota within renal tissue and demonstrates that its structure and composition are significantly altered in DKD. These disruptions in renal microecology are closely associated with the progression of DKD. Multi-omics analysis further identified a panel of candidate markers for evaluation of DKDs.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1898177","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:03:20Z","timestamp":1789646600000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Renal tissue microbiota and metabolite profiling reveal dysregulated signatures in diabetic kidney disease"],"prefix":"10.3389","volume":"17","author":[{"given":"Na","family":"Zhao","sequence":"first","affiliation":[{"name":"The Basic Medical College of Shanxi Medical University, Taiyuan","place":["Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijia","family":"Leng","sequence":"additional","affiliation":[{"name":"Second Clinical Medical College of Shanxi Medical University, Taiyuan","place":["Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiqi","family":"Li","sequence":"additional","affiliation":[{"name":"The Basic Medical College of Shanxi Medical University, Taiyuan","place":["Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Wei","sequence":"additional","affiliation":[{"name":"The Basic Medical College of Shanxi Medical University, Taiyuan","place":["Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[{"name":"Second Clinical Medical College of Shanxi Medical University, Taiyuan","place":["Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cellular Physiology, Ministry of Education, Department of Physiology, Shanxi Medical University","place":["Taiyuan, China"]},{"name":"Shanxi Provincial Key Laboratory of Aging-related Diseases Mechanisms and Translation","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Second Clinical Medical College of Shanxi Medical University, Taiyuan","place":["Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"10509","DOI":"10.1038\/s41467-024-54432-6","article-title":"Cefazolin shifts the kidney microbiota to promote a lithogenic environment.","volume":"15","author":"Agudelo","year":"2024","journal-title":"Nat. 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