Gene expression analysis in tubule interstitial compartments reveals candidate agents for IgA nephropathy

Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models

Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.

Formalin-fixed paraffin-embedded renal biopsy tissues: an underexploited biospecimen resource for gene expression profiling in IgA nephropathy

Primary IgA nephropathy (IgAN) diagnosis is based on IgA-dominant glomerular deposits and histological scoring is done on formalin-fixed paraffin embedded tissue (FFPE) sections using the Oxford classification. Our aim was to use this underexploited resource to extract RNA and identify genes that characterize active (endocapillary–extracapillary proliferations) and chronic (tubulo-interstitial) renal lesions in total renal cortex. RNA was extracted from archival FFPE renal biopsies of 52 IgAN patients, 22 non-IgAN and normal renal tissue of 7 kidney living donors (KLD) as controls. Genome-wide gene expression profiles were obtained and biomarker identification was carried out comparing gene expression signatures a subset of IgAN patients with active (N = 8), and chronic (N = 12) renal lesions versus non-IgAN and KLD. Bioinformatic analysis identified transcripts for active (DEFA4,TNFAIP6,FAR2) and chronic (LTB,CXCL6, ITGAX) renal lesions that were validated by RT-PCR and IHC. Finally, two of them (TNFAIP6 for active and CXCL6 for chronic) were confirmed in the urine of an independent cohort of IgAN patients compared with non-IgAN patients and controls. We have integrated transcriptomics with histomorphological scores, identified specific gene expression changes using the invaluable repository of archival renal biopsies and discovered two urinary biomarkers that may be used for specific clinical decision making.

Omics studies for comprehensive understanding of immunoglobulin A nephropathy: state-of-the-art and future directions

Immunoglobulin A nephropathy (IgAN) is the most common worldwide primary glomerulonephritis with a strong autoimmune component. The disease shows variability in both clinical phenotypes and endpoints and can be potentially subdivided into more homogeneous subtypes through the identification of specific molecular biomarkers. This review focuses on the role of omics in driving the identification of potential molecular subtypes of the disease through the integration of multilevel data from genomics, transcriptomics, epigenomics, proteomics and metabolomics. First, the identification of molecular biomarkers, including mapping of the full spectrum of common and rare IgAN risk alleles, could permit a more precise stratification of IgAN patients. Second, the analysis of transcriptomic patterns and their modulation by epigenetic factors like microRNAs has the potential to increase our understanding in the pathogenic mechanisms of the disease. Third, the specificity of urinary proteomic and metabolomic signatures and the understanding of their functional relevance may contribute to the development of new non-invasive biomarkers for a better molecular characterization of the renal damage and its follow-up. All these approaches can give information for targeted therapeutic decisions and will support novel clinical decision making. In conclusion, we offer a framework of omic studies and outline barriers and potential solutions that should be used for improving the diagnosis and treatment of the disease. The ongoing decade is exploiting novel high-throughput molecular technologies and computational analyses for improving the diagnosis (precision nephrology) and treatment (personalized therapy) of the IgAN subtypes.

An integrative systems biology approach for precision medicine in diabetic kidney disease

Current therapeutic approaches are ineffective in many patients with established diabetic kidney disease (DKD), an epidemic affecting one in three patients with diabetes. Early identification of patients at high risk for progression and individualizing therapies have the potential to mitigate kidney complications due to diabetes. To achieve this, a better understanding of the complex pathophysiology of DKD is needed. A system biology approach integrating large-scale omic data is well suited to unravel the molecular mechanisms driving DKD and may offer new perspectives how to personalize therapy. Recent studies indeed show that integrating genome scale data sets generated from prospectively designed clinical cohort studies with model systems using innovative bioinformatics analysis revealed critical molecular pathways in DKD and led to the development of candidate prognostic molecular biomarkers. This review seeks to provide an overview of the recent progress in the application of the integrative systems biology approaches specifically in the field of molecular biomarkers for DKD. We will mainly focus the discussion on how to use integrative system biology approach to first identify patients at high risk of progression, and second to identify patients who may or may not respond to treatment. Challenges and opportunities in applying precision medicine in DKD will also be discussed.

Association of Interleukin-10 Polymorphisms (rs1800872, rs1800871, and rs1800896) with Predisposition to IgA Nephropathy in a Chinese Han Population: A Case-Control Study

BACKGROUND/AIMS

IgA nephropathy (IgAN) is a common form of primary glomerulonephritis worldwide. Previous studies indicated that IL-10 single nucleotide polymorphisms (SNP) play an important role in IgAN pathogenesis, but the results were controversy. This study aimed to investigate the association between IL-10 SNPs (rs1800872, rs1800871, and rs1800896) with IgAN in a Chinese Han population.

METHODS

We conducted a case-control study that included 351 patients with IgAN and 310 age-, gender- and ethnicity-matched healthy controls. Three promoter SNPs (rs1800872, rs1800871, and rs1800896) of IL-10 were genotyped by Sequenom MassARRAY. Odds ratios (ORs) with 95% confidence intervals (CI) were used to assess the relationship with IgAN.

RESULTS

We found that the rs1800896 did not correlate with IgAN risk, whereas rs1800872 and rs1800871 were significantly associated with increased IgAN risk in all genetic models. The haplotype analysis indicated that the CCA haplotype was associated with increased IgAN risk (OR = 1.36; 95% CI = 1.05-1.75). Moreover, there were no associations between these SNPs and blood pressure or gender, whereas the rs1800896 variant was correlated with higher 24-hour urine protein in patients with IgAN.

CONCLUSION

Taken together, these results suggest that IL-10 is a susceptibility gene in patients with IgAN.