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Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

ABSTRACT: Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

SUBMITTER: Sheng X

PROVIDER: S-EPMC7682409 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

Sheng Xin X Qiu Chengxiang C Liu Hongbo H Gluck Caroline C Hsu Jesse Y JY He Jiang J Hsu Chi-Yuan CY Sha Daohang D Weir Matthew R MR Isakova Tamara T Raj Dominic D Rincon-Choles Hernan H Feldman Harold I HI Townsend Raymond R Li Hongzhe H Susztak Katalin K

Proceedings of the National Academy of Sciences of the United States of America 20201103 46

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with ea ...[more]

PMID: 33144501

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Project description:PurposeDiabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD) worldwide. Elucidation of the molecular mechanisms underlying ferroptosis and immunity in DKD could aid the development of potentially effective therapeutics. This study aimed to perform an integrated analysis of ferroptosis and immune-related differentially expressed mRNAs (DEGs) in DKD.Materials and methodsGene expression profiles of samples obtained from patients with DKD and controls were downloaded from the Gene Expression Omnibus (GEO) database. The potential differentially expressed genes (DEGs) were screened using R software, and ferroptosis immune-related differentially expressed genes (FIRDEGs) were extracted from the DEGs. We performed functional enrichment analyses, and constructed protein-protein interaction (PPI) networks, transcription factor (TFs)-gene networks, and gene-drug networks to explore their potential biological functions. Correlation analysis and receiver operating characteristic curves were used for evaluating the FIRDEGs. We used the CIBERSORT algorithm to examine the composition of immune cells and determine the relationship between FIRDEG signatures and immune cells. Finally, the RNA expression of six FIRDEGs was validated in animal kidney samples using RT-PCR.ResultsWe identified 80 FIRDEGs and performed their functional analyses. We identified six hub genes (Ccl5, Il18, Cybb, Fcgr2b, Myd88, and Ccr2) using PPI networks and predicted potential TF gene networks and gene-drug pairs. Immune cells, including M2 macrophages, resting mast cells, and gamma-delta T cells, were altered in DKD; the FIRDEGs (Fcgr2b, Cybb, Ccr2, and Ccl5) were closely correlated with the infiltration abundance of M2 macrophages and gamma-delta T cells. Finally, the hub genes were verified in mouse kidney samples.ConclusionWe identified six hub FIRDEGs (Ccl5, Il18, Cybb, Fcgr2b, Myd88, and Ccr2) in DKD, and predicted the potential transcription factor gene networks and possible treatment targets for future research.

Project description:Background Gut microbiota has been reported to play an important role in diabetic kidney disease (DKD), however, the alterations of gut bacteria have not been determined. Methods Studies comparing the differences of gut microbiome between patients with DKD and non-DKD individuals using high-throughput sequencing technology, were systematically searched and reviewed. Outcomes were set as gut bacterial diversity, microbial composition, and correlation with clinical parameters of DKD. Qualitative data were summarized and compared through a funnel R script, and quantitative data were estimated by meta-analysis. Results A total of 15 studies and 1640 participants were included, the comparisons were conducted between DKD, diabetes mellitus (DM), non-diabetic kidney disease (NDKD), and healthy controls. There were no significant differences of α-diversity between DKD and DM, and between DKD and NDKD, however, significant lower microbial richness was found in DKD compared to healthy controls. Different bacterial compositions were found between DKD and non-DKD subjects. The phylum Actinobacteria were found to be enriched in DKD compared to healthy controls. At the genus level, we found the enrichment of Hungatella, Bilophila, and Escherichia in DKD compared to DM, patients with DKD showed lower abundances of Faecalibacterium compared to those with NDKD. The genera Butyricicoccus, Faecalibacterium, and Lachnospira were depleted in DKD compared to healthy controls, whereas Hungatella, Escherichia, and lactobacillus were significantly enriched. The genus Ruminococcus torques group was demonstrated to be inversely correlated with estimated glomerular filtration rate of DKD. Conclusions Gut bacterial alterations was demonstrated in DKD, characterized by the enrichment of the genera Hungatella and Escherichia, and the depletion of butyrate-producing bacteria, which might be associated with the occurrence and development of DKD. Further studies are still needed to validate these findings, due to substantial heterogeneity. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42022340870.

Project description:Background: Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease in China. Tubular injury contributes to the progression of DKD. Our study was conducted to explore the differential gene expression profiles between kidneys from patients with DKD and kidney living donors (LDs). Methods: In total, seven DKD and eighteen LD gene expression profiles from the GSE104954 dataset were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analyzed in R with the limma package. DEGs were uploaded to the g:Profiler online database to explore the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Ingenuity pathway analysis (IPA) was carried out using online IPA software. Weighted gene co-expression network analysis (WGCNA) was performed using the WGCNA R package. By integrating DEGs and genes from the top 1 phenotype-gene associated module, we determined the hub gene. We next tested the hub gene, VCAN, in the GSE30122 dataset. We also validated the versican levels in human kidney tissues, explored immune cell type enrichment using an online database xCell, and investigated the correlation between cell types and VCAN expression. Results: A total of 563 DEGs was identified. A large number of pathways were involved in the immune response process according to the results of GO, KEGG, and IPA. Using WGCNA, we selected the lightcyan module in which genes showed the strongest correlation with the phenotype and smallest P-value. We also identified VCAN as a hub gene by integrating DEG analysis and WGCNA. Versican expression was upregulated in human diabetic kidney tissue. Moreover, versican was speculated to play a role in immune injury according to the enrichment of functions and signaling pathways. VCAN transcript levels correlate with the assembly of immune cells in the kidney. Conclusion: Immune processes played an essential role in DKD tubulointerstitium injury. The hub gene VCAN contributed to this process.

Dataset Information

Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

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Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

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