Transcriptome meta-analysis and validation to discovery of hub genes and pathways in focal and segmental glomerulosclerosis.

BACKGROUND: Focal segmental glomerulosclerosis (FSGS), a histologic pattern of injury in the glomerulus, is one of the leading glomerular causes of end-stage renal disease (ESRD) worldwide. Despite extensive research, the underlying biological alterations causing FSGS remain poorly understood. Studying variations in gene expression profiles offers a promising approach to gaining a comprehensive understanding of FSGS molecular pathogenicity and identifying key elements as potential therapeutic targets. This work is a meta-analysis of gene expression profiles from glomerular samples of FSGS patients. The main aims of this study are to establish a consensus list of differentially expressed genes in FSGS, validate these findings, understand the disease's pathogenicity, and identify novel therapeutic targets. METHODS: After a thorough search in the GEO database and subsequent quality control assessments, seven gene expression datasets were selected for the meta-analysis: GSE47183 (GPL14663), GSE47183 (GPL11670), GSE99340, GSE108109, GSE121233, GSE129973, and GSE104948. The random effect size method was applied to identify differentially expressed genes (meta-DEGs), which were then used to construct a regulatory network (STRING, MiRTarBase, and TRRUST) and perform various pathway enrichment analyses. The expression levels of several meta-DEGs, specifically ADAMTS1, PF4, EGR1, and EGF, known as angiogenesis regulators, were analyzed using quantitative reverse transcription polymerase chain reaction (RT-qPCR). RESULTS: The identified 2,898 meta-DEGs, including 665 downregulated and 669 upregulated genes, were subjected to various analyses. A co-regulatory network comprising 2,859 DEGs, 2,688 microRNAs (miRNAs), and 374 transcription factors (TFs) was constructed, and the top molecules in the network were identified based on degree centrality. Part of the pathway enrichment analysis revealed significant disruption in the angiogenesis regulatory pathways in the FSGS kidney. The RT-qPCR results confirmed an imbalance in angiogenesis pathways by demonstrating the differential expression levels of ADAMTS1 and EGR1, two key angiogenesis regulators, in the FSGS condition. CONCLUSION: In addition to presenting a consensus list of differentially expressed genes in FSGS, this meta-analysis identified significant distortions in angiogenesis-related pathways and factors in the FSGS kidney. Targeting these factors may offer a viable strategy to impede the progression of FSGS.

Systematic immune cell dysregulation and molecular subtypes revealed by single-cell RNA-seq of subjects with type 1 diabetes.

BACKGROUND: Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting ß  cells. A comprehensive immune cell phenotype evaluation in T1DM has not been performed thus far at the single-cell level. METHODS: In this cross-sectional analysis, we generated a single-cell transcriptomic dataset of peripheral blood mononuclear cells (PBMCs) from 46 manifest T1DM (stage 3) cases and 31 matched controls. RESULTS: We surprisingly detected profound alterations in circulatory immune cells (1784 dysregulated genes in 13 immune cell types), far exceeding the count in the comparator systemic autoimmune disease SLE. Genes upregulated in T1DM were involved in WNT signaling, interferon signaling and migration of T/NK cells, antigen presentation by B cells, and monocyte activation. A significant fraction of these differentially expressed genes were also altered in T1DM pancreatic islets. We used the single-cell data to construct a T1DM metagene z-score (TMZ score) that distinguished cases and controls and classified patients into molecular subtypes. This score correlated with known prognostic immune markers of T1DM, as well as with drug response in clinical trials. CONCLUSIONS: Our study reveals a surprisingly strong systemic dimension at the level of immune cell network in T1DM, defines disease-relevant molecular subtypes, and has the potential to guide non-invasive test development and patient stratification.

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies.

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

The potential of cardiac biomarkers, NT-ProBNP and troponin T, in predicting the progression of nephropathy in diabetic patients: A meta-analysis of prospective cohort studies.

AIMS: A meta-analysis was done to investigate the association of two cardiac biomarkers of N-terminal prohormone of B-type natriuretic peptide (NT-proBNP) and circulating troponin T (TnT) with the progression of diabetic nephropathy (DN). METHODS: A thorough search of the PubMed, Scopus, and Web of Science databases was done until June 2022. The outcome (progression of DN) was described as either of the followings: a) eGFR decline, b) albuminuria, c) end-stage renal disease, or d) mortality. A pooled analysis of eligible studies was performed using random-effect models to compensate for the differences in measurement standards between the studies. We further carried out subgroup analyses to examine our results' robustness and find the source of heterogeneity. A sensitivity analysis was performed to assess the influence of individual studies on the pooled result and the funnel plot and Egger's test were used to assess publication bias. RESULTS: For NT-proBNP, 8741 participants from 14 prospective cohorts, and for TnT, 7292 participants from 9 prospective cohorts were included in the meta-analysis. Higher NT-proBNP levels in diabetic patients were associated with a higher probability of DN progression (relative risk [RR]: 1.67, 95% confidence interval [CI]: 1.44 to 1.92). Likewise, elevated levels of TnT were associated with an increased likelihood of DN (RR: 1.57, 95% CI: 1.34 to 1.83). The predictive power of both biomarkers for DN remained significant when the subgroup analyses were performed. The risk estimates were sensitive to none of the studies. The funnel plot and Egger's tests indicated publication bias for both biomarkers. Hence, trim and fill analysis was performed to compensate for this putative bias and the results remained significant both for NT-proBNP (RR: 1.50, 95% CI: 1.31 to 1.79) and TnT (RR: 1.35, 95% CI 1.15 to 1.60). CONCLUSIONS: The increased blood levels of TnT and NT-proBNP can be considered as predictors of DN progression in diabetic individuals. PROSPERO registration code: CRD42022350491.

An Integrative in silico Study to Discover Key Drivers in Pathogenicity of Focal and Segmental Glomerulosclerosis.

BACKGROUND: Focal and segmental glomerulosclerosis (FSGS) is a clinical-pathologic condition marked by segmental and localized glomerular damages. Despite investigations, the molecular mechanisms behind FSGS development remain to be more clarified. By a comprehensive analysis of an FSGS-related array set, the aim of this study was to unravel the top pathways and molecules involved in the pathogenesis of this disorder. METHODS: FSGS-related microarray dataset (GSE129973) from the Gene Expression Omnibus database was quality checked, analyzed, and its differentially expressed genes (DEGs) (log2 fold change > 1) were used for the construction of a protein-protein interaction (PPI) network (STRING). The degree of centrality was considered to select the hub molecules in the network. The weighted gene co-expression network analysis (WGCNA) was utilized to construct co-expression modules. Hub molecules were selected based on module membership and gene significance values in the disease's most correlated module. After spotting the key molecules considering both strategies, their expression pattern was checked in other FSGS microarray datasets. Gene ontology and Reactome pathway enrichment analyses were performed on the DEGs of the related module. RESULTS: After quality checking, normalization, and analysis of the dataset, 5,296 significant DEGs, including 2,469 upregulated and 2,827 downregulated DEGs were identified. The WGCNA algorithm clustered the DEGs into nine independent co-expression modules. The disease most correlated module (black module) was recognized and considered for further enrichment analysis. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the identified module's DEGs. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the black module's DEGs. The key molecules (BMP-2 and COL4A1) were identified as common hub molecules extracted from the two methods of PPI and the co-expressed networks. The two identified key molecules were validated in other FSGS datasets, where a similar pattern of expression was observed for both the genes. CONCLUSIONS: Two hub molecules (BMP-2 and COL4A) and some pathways (vesicle-mediated transport) were recognized as potential players in the pathogenesis of FSGS.