Animal protein intake is directly associated with serum level of pentraxin 3 in hemodialysis patients.

Inflammation plays an important role in Cardiovascular disease (CVD) pathogenesis as the main cause of mortality in hemodialysis (HD) patients. Despite the relevance of nutrition and dietary intakes for inflammation status, the role of dietary protein sources remains unclear. The aim of this study was to evaluate the association between the different types of dietary protein and pentraxin 3 (PTX3) levels in HD patients. In this multi-center cross-sectional study, 227 adult patients undergoing HD for a minimum 90 days were recruited. A validated 168-item food frequency questionnaire was used to assess dietary intakes. Also, 5 ml blood samples were collected from each patient to measure the concentration of serum PTX3. Overall, 227 patients, including 63 women and 164 men, with a mean age of 58 years, participated in this study. There was a greater intake of animal protein per kilogram dry weight among patients with higher levels of PTX3 (0.46 vs. 0.54 g/kg; P = 0.035). In contrast, consumption of total protein and plant protein per kilogram dry weight was not different across PTX3 levels. Moreover, the chance of increased PTX3 concentration was directly associated with a one-unit increase in animal protein intake per kilogram dry weight, after adjusting for confounders. We did not observe any association between one-unit increases in plant protein intake per kilogram dry weight and chance of increased PTX3. In conclusion, animal protein intake was directly associated with circulating PTX3.

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.

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets / Identificación de genes clave y mecanismos reguladores biológicos en la nefropatía diabética: metaanálisis de conjuntos de datos de expresión génica

Background: Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN. Methods: After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network.(AU)

Antecedentes: La nefropatía diabética (ND), que se refiere a los casos con lesiones renales comprobadas por biopsia, es una de las principales complicaciones de la diabetes en todo el mundo. Sin embargo, los cambios biológicos subyacentes que causan la ND aún no se han entendido. Aquí realizamos un estudio de metaanálisis que incluyó perfiles de expresión génica de micromatrices provenientes de muestras glomerulares de pacientes con ND para adquirir una lista de genes expresados diferencialmente (meta-DEG) de consenso correlacionados con ND. Métodos: Después de los pasos de control de calidad y normalización, se ingresaron en el metaanálisis cinco conjuntos de datos de expresión génica (GES1009, GSE30528, GSE47183, GSE104948 y GSE93804). El metaanálisis se realizó mediante el método de tamaño de efecto aleatorio y los meta-DEG se sometieron a análisis de red y a diferentes pasos de análisis de enriquecimiento de ruta. Se utilizaron las bases de datos MiRTarBase y TRRUST para predecir los factores de transcripción y los miARN relacionados con los meta-DEG. Cytoscape construyó una red de corregulación que incluye DEG, factores de transcripción y miARN, y las moléculas principales se identificaron en función de las puntuaciones de centralidad en la red. (AU)

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.

A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures.

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer. METHODS: ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs. RESULTS: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes. CONCLUSION: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets.

BACKGROUND: Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN. METHODS: After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network. RESULTS: The identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, "immune system", "extracellular matrix organization", "hemostasis", "signal transduction", and "platelet activation" to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network. CONCLUSION: By performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

Candidate MicroRNA Biomarkers in Lupus Nephritis: A Meta-analysis of Profiling Studies in Kidney, Blood and Urine Samples.

CONTEXT: Lupus nephritis (LN) is a kidney disease caused by systemic lupus erythematosus in which kidneys are attacked by the immune system. So far, various investigations have reported altered miRNA expression profiles in LN patients and different miRNAs have been introduced as biomarkers and/or therapeutic targets in LN. The aim of this study was to introduce a consensus panel of potential miRNA biomarkers by performing a meta-analysis of miRNA profiles in the LN patients. MATERIALS AND METHODS: A comprehensive literature review approach was performed to find LN-related miRNA expression profiles in renal tissues, blood, and urine samples. After selecting the eligible studies and performing the data extraction, meta-analysis was done based on the vote-counting rank strategy as well as meta-analysis of p-values. The meta-miRNAs and their related genes were subjected to functional enrichment analyses and network construction. RESULTS: The results of the meta-analysis of 41 studies were three lists of consensus miRNAs with altered expression profiles in the various tissue samples of LN patients (meta-analysis of p-values < 0.05). Of the 13 studies on kidney tissue, the meta-miRNAs were let-7a, miR-198, let-7e, miR-145, and miR-26a. In addition, meta-miRNAs of miR-199a, miR-21, miR-423, miR-1260b, miR-589, miR-150, miR-155, miR-146a, and miR-183 from 21 studies on blood samples, and miR-146a, miR-204, miR-30c, miR-3201, and miR-1273e from 11 studies on urine samples can be considered as non-invasive biomarker panels for LN. Functional enrichment analysis on the meta-miRNA lists confirmed the involvement of their target genes in nephropathy-related signaling pathways. CONCLUSION: Using a meta-analytical approach, our study proposes three meta-miRNA panels that could be the target of further research to assess their potential as therapeutic targets/biomarkers in LN disease.

Discovering driver nodes in chronic kidney disease-related networks using Trader as a newly developed algorithm.

Thanks to the advances in the field of computational-based biology, a huge volume of disease-related data has been generated so far. From the existing data, the disease-related protein-protein interaction (PPI) networks seem to yield effective treatment plans due to the informative/systematic representation of diseases. Yet, a large number of previous studies have failed due to the complex nature of such disease-related networks. For addressing this limitation, in the present study, we combined Trader and the DFS algorithms to identify a minimal subset of nodes (driver nodes) whose removal produces a maximum number of disjoint sub-networks. We then screened the nodes in the disease-associated PPI networks and to evaluate the efficiency of the suggested method, it was applied to six PPI networks of differentially expressed genes in chronic kidney diseases. The performance of Trader was superior to other well-known algorithms in terms of identifying driver nodes. Besides, the proportion of proteins that were targeted by at least one FDA-approved drug was significantly higher among the identified driver nodes when compared with the rest of the proteins in the networks. The proposed algorithm could be applied for predicting future therapeutic targets in complex disorder networks. In conclusion, unlike the common methods, computationally efficient algorithms can generate more practical outcomes which are compatible with real-world biological facts.

Circulating ß2 and α1 microglobulins predict progression of nephropathy in diabetic patients: a meta-analysis of prospective cohort studies.

AIMS: To study the association of circulating ß2 (B2M) and α1 microglobulins (A1M) with diabetic nephropathy (DN) progression, a meta-analysis was performed on the prospective cohort studies. METHODS: Up to October 2021, a comprehensive search of the PubMed, EMBASE, Scopus, Web of Science, and Cochrane Library databases was performed. The primary outcome (progression of DN) was defined as a decrease in eGFR or the occurrence of end stage renal disease or DN-related mortality. Eligible studies were included in a pooled analysis that used either fixed-effect or random-effect models to compensate for variation in measurement standards between studies. The funnel plot and Egger's test were used to assess publication bias. RESULTS: The meta-analysis included 4398 people from 9 prospective trials (8 cohorts) for B2M and 3110 people from 4 prospective trials (3 cohorts) for A1M. Diabetic individuals with higher B2M levels had an increased risk for DN (relative risk [RR]: 1.81, 95% confidence interval [CI]: 1.56-2.09). Likewise, higher A1M was associated with augmented probability of DN (RR: 1.96, 95% CI: 1.46-2.62). The funnel plot and Egger's tests indicated no publication bias for A1M. Additionally, to compensate for putative publication bias for B2M, using trim and fill analysis, four studies were filled for this marker and the results remained significant (RR: 1.62, 95% CI: 1.37-1.92). CONCLUSIONS: The elevated serum levels of B2M and A1M could be considered as potential predictors of DN progression in diabetic patients. PROTOCOL REGISTRATION: PROSPERO CRD42021278300.

Carbon nanostructures: a comprehensive review of potential applications and toxic effects.

There is no doubt that nanotechnology has revolutionized our life since the 1970s when it was first introduced. Nanomaterials have helped us to improve the current products and services we use. Among the different types of nanomaterials, the application of carbon-based nanomaterials in every aspect of our lives has rapidly grown over recent decades. This review discusses recent advances of those applications in distinct categories, including medical, industrial, and environmental applications. The first main section introduces nanomaterials, especially carbon-based nanomaterials. In the first section, we discussed medical applications, including medical biosensors, drug and gene delivery, cell and tissue labeling and imaging, tissue engineering, and the fight against bacterial and fungal infections. The next section discusses industrial applications, including agriculture, plastic, electronic, energy, and food industries. In addition, the environmental applications, including detection of air and water pollutions and removal of environmental pollutants, were vastly reviewed in the last section. In the conclusion section, we discussed challenges and future perspectives.

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.

Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study.

BACKGROUND: Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm. RESULTS: GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease's most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes. CONCLUSIONS: The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm.

BACKGROUND: Diabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression. METHODS: The identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed. RESULTS: After normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes. CONCLUSIONS: The identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies.

AIM: Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN. DATA SYNTHESIS: To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers. CONCLUSION: The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease. PROSPERO REGISTRATION NUMBER: CRD42020197697.

Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents.

BACKGROUND: IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease are remained to be explored and still, there is an urgent need for the discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied to mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN. METHODS: Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database), and long-non coding RNAs (from LncTarD database) were used for the construction of a multilayer regulatory network via Cytoscape. RESULT: "GSE25590" (miRNA) and "GSE73953" (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with "Innate immune system", "Apoptosis", as well as "NGF signaling" pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN. CONCLUSION: Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

A meta-analysis of microRNA expression profiling studies in heart failure.

Heart failure (HF) is a major consequence of many cardiovascular diseases with high rate of morbidity and mortality. Early diagnosis and prevention are hampered by the lack of informative biomarkers. The aim of this study was to perform a meta-analysis of the miRNA expression profiling studies in HF to identify novel candidate biomarkers or/and therapeutic targets. A comprehensive literature search of the PubMed for miRNA expression studies related to HF was carried out. The vote counting and robust rank aggregation meta-analysis methods were used to identify significant meta-signatures of HF-miRs. The targets of HF-miRs were identified, and network construction and gene set enrichment analysis (GSEA) were performed to identify the genes and cognitive pathways most affected by the dysregulation of the miRNAs. The literature search identified forty-five miRNA expression studies related to CHF. Shared meta-signature was identified for 3 up-regulated (miR-21, miR-214, and miR-27b) and 13 down-regulated (miR-133a, miR-29a, miR-29b, miR-451, miR-185, miR-133b, miR-30e, miR-30b, miR-1, miR-150, miR-486, miR-149, and miR-16-5p) miRNAs. Network properties showed miR-29a, miR-21, miR-29b, miR-1, miR-16, miR-133a, and miR-133b have the most degree centrality. GESA identified functionally related sets of genes in signaling and community pathways in HF that are the targets of HF-miRs. The miRNA expression meta-analysis identified sixteen highly significant HF-miRs that are differentially expressed in HF. Further validation in large patient cohorts is required to confirm the significance of these miRs as HF biomarkers and therapeutic targets.

Electrochemical sensors and biosensors based on the use of polyaniline and its nanocomposites: a review on recent advances.

Polyaniline and its composites with nanoparticles have been widely used in electrochemical sensor and biosensors due to their attractive properties and the option of tuning them by proper choice of materials. The review (with 191 references) describes the progress made in the recent years in polyaniline-based biosensors and their applications in clinical sensing, food quality control, and environmental monitoring. A first section summarizes the features of using polyaniline in biosensing systems. A subsequent section covers sensors for clinical applications (with subsections on the detection of cancer cells and bacteria, and sensing of glucose, uric acid, and cholesterol). Further sections discuss sensors for use in the food industry (such as for sulfite, phenolic compounds, acrylamide), and in environmental monitoring (mainly pesticides and heavy metal ions). A concluding section summarizes the current state, highlights some of the challenges currently compromising performance in biosensors and nanobiosensors, and discusses potential future directions. Graphical abstract Schematic presentation of electrochemical sensor and biosensors applications based on polyaniline/nanoparticles in various fields of human life including medicine, food industry, and environmental monitoring. The simultaneous use of suitable properties polyaniline and nanoparticles can provide the fabrication of sensing systems with high sensitivity, short response time, high signal/noise ratio, low detection limit, and wide linear range by improving conductivity and the large surface area for biomolecules immobilization.

Identification of candidate microRNA biomarkers in diabetic nephropathy: a meta-analysis of profiling studies.

AIMS: The aim was to perform a meta-analysis on the miRNA expression profiling studies in diabetic nephropathy (DN) to identify candidate diagnostic biomarkers. METHODS: A comprehensive literature search was done in several databases and 53 DN miRNA expression studies were selected. To identify significant DN-miR meta-signatures, two meta-analysis methods were employed: vote-counting strategy and the robust rank aggregation method. The targets of DN-miRs were obtained and a gene set enrichment analysis was carried out to identify the pathways most strongly affected by dysregulation of these miRNAs. RESULTS: We identified a significant miRNA meta-signature common to both meta-analysis approaches of three up-regulated (miR-21-5p, miR-146a-5p, miR-10a-5p) and two down-regulated (miR-25-3p and miR-26a-5p) miRNAs. Besides that, subgroup analyses divided and compared the differentially expressed miRNAs according to species (human and animal), types of diabetes (T1DN and T2DN) and tissue types (kidney, blood and urine). Enrichment analysis confirmed that DN-miRs supportively target functionally related genes in signaling and community pathways in DN. CONCLUSION: Five highly significant and consistently dysregulated miRNAs were identified, and future studies should focus on discovering their potential effect on DN and their clinical value as DN biomarkers and therapeutic mediators.

Identification of candidate microRNA biomarkers in renal fibrosis: a meta-analysis of profiling studies.

The prognostic, diagnostic and therapeutic value of microRNA (miRNA) expression aberrations in renal fibrosis has been studied in recent years. However, the miRNA expression profiling efforts have led to inconsistent results between the studies. The aim of this study was to perform a meta-analysis on the renal fibrosis miRNA expression profiling studies to identify candidate diagnostic biomarkers. We performed comprehensive literature searches in several databases to identify miRNA expression studies of renal fibrosis in animal models and humans. The miRNAs expression data were extracted from 20 included studies, and both miRNA vote-counting strategy and Robust Rank Aggregation method were utilized to identify significant miRNA meta-signatures. The predicted and validated targets of miRNA meta-signature were obtained by using MultiMiR package in 11 databases. Then a gene set enrichment analysis (KEGG, PANTHER pathways and GO processes) were carried out with GeneCodis web tool to recognize pathways that are most strongly influenced by modified expressions of these miRNAs. We recognized in both meta-analysis approaches a significant miRNA meta-signature of five up-regulated (miR-142-3p, miR-223-3p, miR-21-5p, miR-142-5p and miR-214-3p) and two down-regulated (miR-29c-3p and miR-200a-3p) miRNAs. Enrichment analysis confirmed that miRNA meta-signature cooperatively target functionally related genes in signalling and developmental pathways in renal fibrosis. This meta-analysis identified seven highly significant and consistently dysregulated miRNAs from 20 datasets, as the focus of future investigations to discover their potential influence to renal fibrosis and their clinical utility as biomarkers and/or as therapeutic mediators against chronic kidney disease..