Angiotensinogen gene polymorphisms and progression of chronic kidney disease in ADPKD patients.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is a multisystemic and progressive disorder characterized by cyst formation and kidney enlargement and ultimately renal failure. Reduction of CKD progression in the ADPKD by pharmacological blockade of the renin-angiotensin-aldosterone system (RAAS) using ACE inhibitors indicated the involvement of RAAS pathway in the progression of CKD. The aim of the present study is to investigate the role of angiotensinogen tag-single nucleotide polymorphisms (AGT tag-SNPs) in progression of CKD. METHODS: Twelve AGT tag-SNPs were genotyped in 102 ADPKD patients and 106 non-ADPKD subjects using FRET-based KASPar method. Genotypes and haplotypes were compared between ADPKD and controls. The effect of genotypes and hypertension on CKD progression was assessed using univariate and multivariate logistic regression. Mantel-Haenszel (M-H) stratified analysis was performed to study the interaction between CKD stages and hypertension. RESULTS: Of the twelve tag-SNPs analyzed, only rs11122578 SNP deviated Hardy-Weinberg equilibrium in controls. Significant association between two AGT polymorphisms (rs11122577 and rs4762) and ADPKD was observed. Analysis of linkage disequilibrium revealed two haplotype blocks and haplotypes are not associated with ADPKD. The univariate analysis revealed that the age, hypertension, family history of diabetes and AGT rs4762 contributed to the progression of CKD in ADPKD. The modifier effect of these factors remained even after controlling other variables in multivariate analysis. CONCLUSIONS: The results of our study suggest significant association between Thr207Met polymorphism of AGT and CKD progression and acts as an effect modifier of renal disease progression in ADPKD.

Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective.

A significant portion of the health burden of diabetic kidney disease (DKD) is caused by both type 1 and type 2 diabetes which leads to morbidity and mortality globally. It is one of the most common diabetic complications characterized by loss of renal function with high prevalence, often leading to acute kidney disease (AKD). Inflammation triggered by gut microbiota is commonly associated with the development of DKD. Interactions between the gut microbiota and the host are correlated in maintaining metabolic and inflammatory homeostasis. However, the fundamental processes through which the gut microbiota affects the onset and progression of DKD are mainly unknown. In this narrative review, we summarised the potential role of the gut microbiome, their pathogenicity between diabetic and non-diabetic kidney disease (NDKD), and their impact on host immunity. A well-established association has already been seen between gut microbiota, diabetes and kidney disease. The gut-kidney interrelationship is confirmed by mounting evidence linking gut dysbiosis to DKD, however, it is still unclear what is the real cause of gut dysbiosis, the development of DKD, and its progression. In addition, we also try to distinguish novel biomarkers for early detection of DKD and the possible therapies that can be used to regulate the gut microbiota and improve the host immune response. This early detection and new therapies will help clinicians for better management of the disease and help improve patient outcomes.

Investigation of differentially expressed genes and dysregulated pathways involved in multiple sclerosis.

Multiple Sclerosis (MS) is a neurodegenerative autoimmune and organ-specific demyelinating disorder, known to affect the central nervous system (CNS). While genetic studies have revealed several critical genes and diagnostic biomarkers associated with MS, the etiology of the disease remains poorly understood. This study is aimed at screening and identifying the key genes and canonical pathways associated with MS. Gene expression profiling of the microarray dataset GSE38010 was used to analyze two control brain samples (control 1; GSM931812, control 2; GSM931813), active inflammation stage samples (CAP1; GSM931815, CAP2; GSM931816) and late subsided stage samples (CP1; GSM931817, CP2; GSM931818) collected from patients ranging between 23 and 54years and both genders. This analysis yielded a list of 58,866 DEGs (29,433 for active-inflammation stage and 29,433 for late-subsided Stage). The interactions between the DEGs were then studied using STRING, Cytoscape software, and MCODE was employed to find the genes that form clusters. Functional enrichment and integrative analysis were performed using ClueGO/CluePedia and MetaCore™. Our data revealed dysregulated key canonical pathways in MS patients. In addition, we identified three hub genes (SCN2A, HTR2A, and HCN1) that may serve as potential biomarkers for the prognosis of MS. Furthermore, the expression patterns of HPCA and PLCB1 provide insights into the progressive stages of MS, indicating that these genes could be used in predicting MS progression. We were able to map potential biomarkers that could be used for the prognosis and diagnosis of MS.