{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T11:16:19Z","timestamp":1788434179797,"version":"build-2803163510"},"reference-count":87,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","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>Objective<\/jats:title>\n                    <jats:p>The gut-kidney axis has emerged as a critical area of investigation. However, the compartment-specific regulatory mechanisms of gut microbiota metabolites within the glomerular and tubulointerstitial regions of diabetic kidney disease (DKD) remain incompletely understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The gutMGene, SEA, STP, GEO, Nephroseq v5, and KIT databases were interrogated. Multi-omics and experimental approaches were applied, involving differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, Mendelian randomization (MR), Gene Set Enrichment Analysis (GSEA), immune infiltration, gene set variation analysis (GSVA), clinical correlation, single-cell profiling, molecular docking, molecular dynamics simulation, CCK-8 assay, and RT-qPCR. The \u201cMicrobiota-Substrate-Metabolite-Target\u201d (M-S-M-T) network was constructed for each compartment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Machine learning identified six glomerular (\n                      <jats:italic>IGFBP6<\/jats:italic>\n                      ,\n                      <jats:italic>PLA2G4A<\/jats:italic>\n                      ,\n                      <jats:italic>CTSK<\/jats:italic>\n                      ,\n                      <jats:italic>HTR2B<\/jats:italic>\n                      ,\n                      <jats:italic>PDGFRA<\/jats:italic>\n                      ,\n                      <jats:italic>MMP7<\/jats:italic>\n                      ) and five tubulointerstitial (\n                      <jats:italic>CA2<\/jats:italic>\n                      ,\n                      <jats:italic>HSD11B2<\/jats:italic>\n                      ,\n                      <jats:italic>NQO2<\/jats:italic>\n                      ,\n                      <jats:italic>MMP7<\/jats:italic>\n                      ,\n                      <jats:italic>CYP24A1<\/jats:italic>\n                      ) core genes. Mendelian randomization, single-cell profiling, and clinical cohorts provided supportive genetic and clinical evidence linking these genes to the estimated glomerular filtration rate (eGFR) and to DKD. Immune infiltration analysis and GSVA indicated a higher estimated proportion of M2 macrophages and a shift of the estimated mast cell composition from activated to resting states in both compartments, which correlated with core gene expression. Via the \u201cM-S-M-T\u201d network, drug-likeness and toxicity screening, caffeic acid and naringenin chalcone were prioritized as shared candidate metabolites. Molecular docking and molecular dynamics simulations suggested favorable in silico binding to HTR2B and MMP7. Both metabolites attenuated high glucose (HG)-induced core gene dysregulation in HK-2 cells and podocytes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study proposed a compartment-specific \u201cM-S-M-T\u201d regulatory network in the DKD glomerulus and tubulointerstitium. The shared metabolites caffeic acid and naringenin chalcone, which targeted HTR2B and MMP7, produced a preliminary protective transcriptional response\n                      <jats:italic>in vitro<\/jats:italic>\n                      . These findings are hypothesis-generating and require validation in real-world microbiome, metabolomic and\n                      <jats:italic>in vivo<\/jats:italic>\n                      studies before any therapeutic inference can be drawn.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1925910","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:40:19Z","timestamp":1788154819000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative multi-omics analysis prioritizes compartment-specific candidate targets of gut microbiota metabolites in diabetic kidney disease"],"prefix":"10.3389","volume":"17","author":[{"given":"Fengwei","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Orthopedics, Renmin Hospital of Wuhan University","place":["Wuhan, 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