Dataset Information

Multi-Scalar Data Integration Links Glomerular Angiopoietin-Tie Signaling Pathway Activation With Progression of Diabetic Kidney Disease.

ABSTRACT: Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD). Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of DKD. A three-marker panel was derived from a proteomics analysis of plasma samples by an unbiased machine learning approach from participants (N = 58) in the Clinical Phenotyping and Resource Biobank study. In combination with standard clinical parameters, this panel improved prediction of the composite outcome of ESKD or a 40% decline in glomerular filtration rate. The panel was validated in an independent group (N = 68), who also had kidney transcriptomic profiles. One marker, plasma angiopoietin 2 (ANGPT2), was significantly associated with outcomes in cohorts from the Cardiovascular Health Study (N = 3,183) and the Chinese Cohort Study of Chronic Kidney Disease (N = 210). Glomerular transcriptional angiopoietin/Tie (ANG-TIE) pathway scores, derived from the expression of 154 ANG-TIE signaling mediators, correlated positively with plasma ANGPT2 levels and kidney outcomes. Higher receptor expression in glomeruli and higher ANG-TIE pathway scores in endothelial cells corroborated potential functional effects in the kidney from elevated plasma ANGPT2 levels. Our work suggests that ANGPT2 is a promising prognostic endothelial biomarker with likely functional impact on glomerular pathogenesis in DKD.

SUBMITTER: Liu J

PROVIDER: S-EPMC9750948 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Multi-Scalar Data Integration Links Glomerular Angiopoietin-Tie Signaling Pathway Activation With Progression of Diabetic Kidney Disease.

Liu Jiahao J Nair Viji V Zhao Yi-Yang YY Chang Dong-Yuan DY Limonte Christine C Bansal Nisha N Fermin Damian D Eichinger Felix F Tanner Emily C EC Bellovich Keith A KA Steigerwalt Susan S Bhat Zeenat Z Hawkins Jennifer J JJ Subramanian Lalita L Rosas Sylvia E SE Sedor John R JR Vasquez Miguel A MA Waikar Sushrut S SS Bitzer Markus M Pennathur Subramaniam S Brosius Frank C FC De Boer Ian I Chen Min M Kretzler Matthias M Ju Wenjun W

Diabetes 20221201 12

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD). Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of DKD. A three-marker panel was derived from a proteomics analysis of plasma samples by an unbiased machine learning approach from participants (N = 58) in the Clinical Phenotyping and Resource Biobank study. In combination with standard clinical parameters, this panel improved prediction of the ...[more]

PMID: 36331122

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Project description:BackgroundChronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.MethodsGWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.ResultsA total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.ConclusionsOur integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

Project description:ObjectiveWe aimed to explore the relationship between the presence and intensity of glomerular IgG deposition and the occurrence of kidney progression events in IgA nephropathy (IgAN).MethodsThis retrospective study encompassed a total of 1207 patients with IgAN spanning the period from 2010 to 2022, and complete follow-up data were accessible for 736 patients. The IgG intensity was categorized as follows: low-level, defined as IgG (±) and IgG (+), and high-level, defined as IgG (++) and IgG (+++).ResultsWe found that the IgG-positive deposited group (N = 113, 9.36%) had significantly higher levels of ESR, TC, LDL, uric acid, proteinuria, and blood glucose, and lower serum albumin level compared to the IgG-negative deposited group (N = 1094, 90.64%). In terms of pathology, the IgG-positive deposited group had a significantly higher percentage of T2 score compared to the IgG-negative deposited group (p = 0.002). At the end of the follow-up period, the IgG-positive deposited group had a higher eGFR decline (-5.7 ± 4.37 ml/year) compared to the IgG-negative deposited group (-4 ± 2.52 ml/year), however, there was not a statistically significant difference between the two groups (p = 0.096). We observed that the high-IgG group had significantly higher level of TG compared to the low-IgG group (p = 0.042). Further analysis revealed that the group of patients with high level of IgG deposition in the kidney experienced a higher incidence of composite kidney outcomes compared to the group with low level of IgG deposition (p = 0.009). Logistic regression analyses showed that high level IgG deposition was an independent risk factor for kidney progression of IgAN (HR 13.419; 95% CI 2.690-66.943, P = 0.029). Further analyses for a solid conclusion using Cox regression that we found high level IgG deposition (HR 115.277; 95% CI 2.299-5.779E3, P = 0.017), eGFR (HR 0.932; 95% CI 0.870-0.999, P = 0.047), and urine protein excretion (HR 1.001; 95% CI 1.000-1.002, P = 0.015) were independent risk factor for kidney progression of IgAN.ConclusionsThe intensity of IgG deposition has been found to be associated with the progression of IgAN. Future prospective studies should provide more robust evidence on the impact of IgG deposition on kidney outcomes in patients with IgAN.

Dataset Information

Multi-Scalar Data Integration Links Glomerular Angiopoietin-Tie Signaling Pathway Activation With Progression of Diabetic Kidney Disease.

Publications

Multi-Scalar Data Integration Links Glomerular Angiopoietin-Tie Signaling Pathway Activation With Progression of Diabetic Kidney Disease.

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