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.

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.

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.

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.

Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan.

The multifaceted destructions caused by COVID-19 have been compared to that of World War II. What makes the situation even more complicated is the ambiguity about the duration and ultimate spread of the pandemic. It is especially critical for the governments, healthcare systems, and economic sectors to have an estimate of the future of this disaster. By using different mathematical approaches, including the classical susceptible-infected-recovered (SIR) model and its derivatives, many investigators have tried to predict the outbreak of COVID-19. In this study, we simulated the epidemic in Isfahan province of Iran for the period from Feb 14th to April 11th and also forecasted the remaining course with three scenarios that differed in terms of the stringency level of social distancing. Despite the prediction of disease course in short-term intervals, the constructed SIR model was unable to forecast the actual spread and pattern of epidemic in the long term. Remarkably, most of the published SIR models developed to predict COVID-19 for other communities, suffered from the same inconformity. The SIR models are based on assumptions that seem not to be true in the case of the COVID-19 epidemic. Hence, more sophisticated modeling strategies and detailed knowledge of the biomedical and epidemiological aspects of the disease are needed to forecast the pandemic.

Antibacterial Activity of Prunus Scoparia Root Methanol Extract against Most Common Burn Wound Pathogens.

BACKGROUND: Burn wound infection and sepsis are serious medical conditions requiring prompt intervention. Plants are a good natural source for the development of novel, safe, and cost-effective antibacterial agents. The objective of the present study was to assess the antibacterial potential of aqueous, chloroform, and methanol extracts of the Prunus scoparia (P. scoparia) root against the most common burn wound pathogens. METHODS: The present experimental study was conducted at Shiraz University of Medical Sciences (Shiraz, Iran) during 2018-2019. The antibacterial activity of the total plant extract was assayed using the broth microdilution method. Fractionation was performed using a separation funnel and solvents with different polarities. Broth microdilution and agar well diffusion assays were performed to determine the antibacterial potential of the obtained fractions. Quantitative and qualitative phytochemical analyses were performed to confirm the presence of secondary metabolites in both the total extract and the fractions. RESULTS: Methanolic extract of P. scoparia root exhibited antibacterial activity against all tested bacterial strains, especially against Methicillin-resistant Staphylococcus aureus (MRSA) isolates. This extract, compared to the aqueous and chloroformic extracts, exhibited the presence of active antibacterial compounds. The quantitative and qualitative results of phytochemical screening showed that phenols and flavonoids were the main antibacterial compounds in the methanolic extract of the plant. CONCLUSION: For the first time, we demonstrated the antibacterial activity of the P. scoparia root against MRSA isolates and other common burn wound pathogens.

Interspecific and intraspecific venom enzymatic variation among cobras (Naja sp. and Ophiophagus hannah).

The genera Ophiophagus and Naja comprise part of a clade of snakes referred to as cobras, dangerously venomous front-fanged snakes in the family Elapidae responsible for significant human mortality and morbidity throughout Asia and Africa. We evaluated venom enzyme variation for eleven cobra species and three N. kaouthia populations using SDS-PAGE venom fingerprinting and numerous enzyme assays. Acetylcholinesterase and PLA2 activities were the most variable between species, and PLA2 activity was significantly different between Malaysian and Thailand N. kaouthia populations. Venom metalloproteinase activity was low and significantly different among most species, but levels were identical for N. kaouthia populations; minor variation in venom L-amino acid oxidase and phosphodiesterase activities were seen between cobra species. Naja siamensis venom lacked the α-fibrinogenolytic activity common to other cobra venoms. In addition, venom from N. siamensis had no detectable metalloproteinase activity and exhibited an SDS-PAGE profile with reduced abundance of higher mass proteins. Venom profiles from spitting cobras (N. siamensis, N. pallida, and N. mossambica) exhibited similar reductions in higher mass proteins, suggesting the evolution of venoms of reduced complexity and decreased enzymatic activity among spitting cobras. Generally, the venom proteomes of cobras show highly abundant three-finger toxin diversity, followed by large quantities of PLA2s. However, PLA2 bands and activity were very reduced for N. haje, N. annulifera and N. nivea. Venom compositionalenzy analysis provides insight into the evolution, diversification and distribution of different venom phenotypes that complements venomic data, and this information is critical for the development of effective antivenoms and snakebite treatment.

Gene Delivery Using Lipoplexes and Polyplexes: Principles, Limitations and Solutions.

Gene therapy has attracted considerable attention for the treatment of genetic and acquired diseases. Successful gene therapy occurs when the therapeutic genes penetrate targeted cells and become available to the intracellular active site. Currently, a promising approach in gene delivery is the use of nonviral gene delivery vectors that lack immunogenicity but have low toxicity and potential tissue specificity. The widely used, existing nonviral gene vectors are cationic lipids and polymers that can pass across extracellular and intracellular barriers. However, the toxicity of these vectors is a barrier to their use. Currently, the disadvantages of nonviral vectors have been minimized by several modifications. The main purpose of this review is to describe the pros and cons of gene delivery using cationic lipids and polymers.

Beneficial effects and potential risks of tomato consumption for human health: An overview.

Tomato and its derived products have a very interesting nutritional value in addition to prominent antioxidant, anti-inflammatory, and anticancer activities. Tomatoes are generally quite safe to eat. However, overall consumption varies from individual to individual. Indeed, either beneficial or harmful effects of plants or their derived products are closely related to quality, including the presence of biologically active compounds. On the other hand, the synthesis and accumulation of these bioactive molecules depends on many other factors, such as environmental conditions. In this sense, this review briefly highlights the relationship between the chemistry of tomato and its derived products and their beneficial or harmful effects on human health, such as gastroesophageal reflux disease or heartburn, allergies, kidney and cardiovascular disorders, prostate cancer, irritable bowel syndrome, lycopenodermia, body aches, arthritis, and urinary problems.

The impact of genetic variation on metabolism of heavy metals: Genetic predisposition?

Genetic variations can be considered as internal contributing factors in susceptibility of individuals to heavy metals related toxicities. However, the exact mechanism of such inherent factors in body response to toxic materials, as well as their potentials to be considered as actual susceptibility factors are remaining to be more explored. So far, variations in different genes, which are directly/indirectly involving in the metabolism of heavy metals have been investigated by some experiments. Metallothioneins as one of the well-known groups of enzymes involving in detoxification of heavy metals, were shown to behave differentially among individuals. This phenomenon is due to the presence of some genetic variations in the middle or upper parts of their genomic sequences. The presence of different single nucleotide polymorphisms in metallothionein 2 A gene and the association of these variations with heavy metals body burden have been shown in different populations. Such genetic variations and their potential effects on heavy metal metabolisms and toxicities were shown in other genes, such as divalent metal transporter 1, glutathione related genes and methylenetetrahydrofolate reductase. However, the current data on different populations are challenging because of the presence of various other interference factors like different dietary and life habits, levels of exposure, as well as papulation related factors. Age, sex, smoking, dietary habits, ancestry differences and diverse metal exposure levels are seemed to be other effective variables in this area. In this review, we introduced several potential genes, their studied genetic variations and their impacts on heavy metal body burden, as well as body sensitivity in different populations.