ArticleFrontiers in immunology2026
Integrative multi-omics analysis prioritizes compartment-specific candidate targets of gut microbiota metabolites in diabetic kidney disease.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: 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. Methods: 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 "Microbiota-Substrate-Metabolite-Target" (M-S-M-T) network was constructed for each compartment. Results: Machine learning identified six glomerular ( Conclusion: This study proposed a compartment-specific "M-S-M-T" 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
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