BackgroundImmunoglobulin A nephropathy (IgAN) is the most common type of primary glomerular disease in adults worldwide. Several studies have reported that galactose-deficient IgA1 (Gd-IgA1) is involved in the pathogenesis of IgAN.MethodsThirty-five patients with IgAN diagnosed with renal biopsy for the first time served as the experimental group, who were hospitalized in our department. Twenty normal healthy cases in the physical examination center of our hospital served as the control group. Then the levels of Gd-IgA1 in serum and urine, and intestinal mucosal barrier injury indexes [diamine oxidase (DAO), serum soluble intercellular adhesion molecule-1 (sICAM-1), D-lactate (D-LAC), and lipopolysaccharide (LPS)] and inflammatory factors [interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α)] in the serum samples were detected. Fecal samples were collected to detect intestinal microbiota using 16 s rDNA sequencing. Then, we assessed possible correlations among clinical and laboratory findings.ResultsIn patients with IgAN, the levels of Gd-IgA1 both in the serum and urine were higher than that of the healthy control. Furthermore, urine Gd-IgA1 level was positively correlated with the serum creatinine level, 24 h urine protein, and M, S, and T parameters in the Oxford classification. ROC curve analysis showed that urine Gd-IgA1 has a greater diagnostic value (AUC = 0.9714, 95% CI, 0.932–1; P < 0.0001) for IgAN. The best cutoff value for urine Gd-IgA1 was 0.745 ng·l/ml·μmol (sensitivity, 94%; specificity, 95%). The intestinal mucosal barrier damage indexes (DAO, sICAM-1, D-LAC, and LPS) were increased in the patients with IgAN, which were positively correlated with Gd-IgA1 levels (P < 0.05) both in serum and urine. The levels of inflammatory factors in the patients with IgAN were increased. 16 s rDNA analysis showed that the intestinal microbiota in these patients was disordered compared to that observed in the healthy subjects. Actinobacteria, Bifidobacterium, Blautia, Bifidobacteriaceae, and Bifidobacteriales were decreased and Shigella was increased in IgAN. The decreased populations of these flora were negatively and significantly correlated with urine Gd-IgA1 and the levels of DAO, sICAM-1, D-LAC, and LPS.ConclusionThe urine Gd-IgA1 levels may be a non-invasive biological marker for evaluating kidney injury in IgAN. Gut flora dysbiosis and intestinal barrier dysfunction may be involved in Gd-IgA1 expression.

BackgroundLow protein supplemented with α-ketoacid diet (LKD) was recommended to be an essential intervention to delay the progression of chronic kidney disease (CKD) in patients who were not yet on dialysis. Aberrant gut microbiota and metabolism have been reported to be highly associated with CKD. However, the effect of LKD on gut microbiota and related fecal metabolism in CKD remains unclear.MethodsMice were fed with normal protein diet (NPD group), low protein diet (LPD group), and low protein diet supplemented with α-ketoacid (LKD group) after 5/6 nephrectomy. At the end of the study, blood, kidney tissues, and feces were collected for biochemical analyses, histological, 16S rRNA sequence of gut microbiome, and untargeted fecal metabolomic analyses.ResultsBoth LKD and LPD alleviate renal failure and fibrosis, and inflammatory statement in 5/6 nephrectomized mice, especially the LKD. In terms of gut microbiome, LKD significantly improved the dysbiosis induced by 5/6Nx, representing increased α-diversity and decreased F/B ratio. Compared with NPD, LKD significantly increased the abundance of g_Parasutterella, s_Parabacteroides_sp_CT06, f_Erysipelotrichaceae, g_Akkermansia, g_Gordonibacter, g_Faecalitalea, and s_Mucispirillum_sp_69, and decreased s_Lachnospiraceae_bacterium_28-4 and g_Lachnoclostridium. Moreover, 5/6Nx and LKD significantly altered fecal metabolome. Then, multi-omics analysis revealed that specific metabolites involved in glycerophospholipid, purine, vitamin B6, sphingolipid, phenylalanine, tyrosine and tryptophan biosynthesis, and microbes associated with LKD were correlated with the amelioration of CKD.ConclusionLKD had a better effect than LPD on delaying renal failure in 5/6 nephrectomy-induced CKD, which may be due to the regulation of affecting the gut microbiome and fecal metabolic profiles.

Introduction Kidney transplantation has surpassed dialysis as the optimal therapy for end-stage kidney disease (ESKD). Yet, most patients could suffer from a slow but continuous deterioration of kidney function leading to graft loss mostly due to chronic allograft nephropathy (CAN) after KT. The dysregulated gene expression for CAN is still poorly understood. Methods To explore the pathogenesis of genomics in CAN, we analyzed the differentially expressed genes (DEGs) of kidney transcriptome between CAN and non-rejecting patients by downloading gene expression microarrays from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to analyze the co-expression of DEGs to explore key modules, hub genes, and transcription factors in CAN. Functional enrichment analysis of key modules was performed to explore pathogenesis. ROC curve analysis was used to validate hub genes. Results As a result, 3 key modules and 15 hub genes were identified by WGCNA analysis. 3 key modules had 21 mutual Gene Ontology (GO) term enrichment functions. Extracellular structure organization, extracellular matrix organization, and extracellular region were identified as significant functions in CAN. Furthermore, transcription factor 12 was identified as the key transcription factor regulating key modules. All 15 hub genes: Yip1 interacting factor homolog B, membrane trafficking protein(YIF1B), toll like receptor 8 (TLR8), neutrophil cytosolic factor 4 (NCF4), glutathione peroxidase 8 (GPX8), mesenteric estrogen dependent adipogenesis (MEDAG), decorin(DCN), serpin family F member 1 (SERPINF1), integrin subunit beta like 1 (ITGBL1), SRY-box transcription factor 15 (SOX15), trophinin associated protein (TROAP), SRY-box transcription factor 1 (SOX1), metallothionein 3 (MT3), lysosomal protein transmembrane (5LAPTM5), FERM domain containing kindlin 3 (FERMT3), cathepsin S (CTSS) had a great diagnostic performance (AUC>0.7). Conclusion This study updates information and provides a new perspective for understanding the pathogenesis of CAN by bioinformatics means. More research is needed to validate and explore the results we have found to reveal the mechanisms underlying CAN.