Proteomics to Metabolomics: A New Insight into the Pathogenesis of Hypertensive Nephropathy.

BACKGROUND: Hypertensive nephropathy (HN) is a high-burden disorder and a leading cause of end-stage renal disease. Despite huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to providing a comprehensive insight into this complex disorder. METHODS: Proteome profiles of kidney tubulointerstitium and outer and inner cortex from a rat model of HN were retrieved from the proteomics identification database, and the quality of the datasets was assessed. Proteins that exhibited differential expression were detected and their interactions were analyzed in the kidney sub-compartments. Furthermore, enzymes were linked to the attributed metabolites. Functional enrichment analyses were performed to identify key pathways and processes based on the differentially expressed proteins and predicted metabolites. RESULTS: Proteasome-mediated protein degradation, actin cytoskeleton organization, and Rho GTPase signaling pathway are involved in the pathogenesis of HN. Furthermore, tissue hypoxia and dysregulated energy homeostasis are among the key underlying events. The metabolism of purine and amino acids is also affected in HN. CONCLUSION: Although the proposed pathogenic mechanisms remain to be further validated in experimental studies, this study contributes to the understanding of the molecular mechanisms of HN through a systematic unsupervised approach. Considering the significant alterations of metabolic pathways, HN can be viewed as an "acquired error of metabolism."

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.

Regenerative potential of multinucleated cells: bone marrow adiponectin-positive multinucleated cells take the lead.

BACKGROUND: Polyploid cells can be found in a wide evolutionary spectrum of organisms. These cells are assumed to be involved in tissue regeneration and resistance to stressors. Although the appearance of large multinucleated cells (LMCs) in long-term culture of bone marrow (BM) mesenchymal cells has been reported, the presence and characteristics of such cells in native BM and their putative role in BM reconstitution following injury have not been fully investigated. METHODS: BM-derived LMCs were explored by time-lapse microscopy from the first hours post-isolation to assess their colony formation and plasticity. In addition, sub-lethally irradiated mice were killed every other day for four weeks to investigate the histopathological processes during BM regeneration. Moreover, LMCs from GFP transgenic mice were transplanted to BM-ablated recipients to evaluate their contribution to tissue reconstruction. RESULTS: BM-isolated LMCs produced mononucleated cells with characteristics of mesenchymal stromal cells. Time-series inspections of BM sections following irradiation revealed that LMCs are highly resistant to injury and originate mononucleated cells which reconstitute the tissue. The regeneration process was synchronized with a transient augmentation of adipocytes suggesting their contribution to tissue repair. Additionally, LMCs were found to be adiponectin positive linking the observations on multinucleation and adipogenesis to BM regeneration. Notably, transplantation of LMCs to myeloablated recipients could reconstitute both the hematopoietic system and BM stroma. CONCLUSIONS: A population of resistant multinucleated cells reside in the BM that serves as the common origin of stromal and hematopoietic lineages with a key role in tissue regeneration. Furthermore, this study underscores the contribution of adipocytes in BM reconstruction.

miR-802-5p is a key regulator in diabetic kidney disease.

Background: Diabetic kidney disease has substantial burden and limited therapeutic options. An inadequate understanding of the complex gene regulatory circuits underlying this disorder contributes to the insufficiency of current treatment strategies. MicroRNAs (miRNAs) play a crucial role as regulators of functionally related gene networks. Previously, mmu-mir-802-5p was identified as the sole dysregulated miRNA in both the kidney cortex and medulla of diabetic mice. This study aims to investigate the role of miR-802-5p in diabetic kidney disease. Materials and Methods: The validated and predicted targets of miR-802-5p were identified using miRTarBase and TargetScan databases, respectively. The functional role of this miRNA was inferred using gene ontology enrichment analysis. The expression of miR-802-5p and its selected targets were assessed by qPCR. The expression of the angiotensin receptor (Agtr1a) was measured by ELISA. Results: miR-802-5p exhibited dysregulation in both the kidney cortex and medulla of diabetic mice, with two- and four-fold over-expressions, respectively. Functional enrichment analysis of the validated and predicted targets of miR-802-5p revealed its involvement in the renin-angiotensin pathway, inflammation, and kidney development. Differential expression was observed in the Pten transcript and Agtr1a protein among the examined gene targets. Conclusion: These findings suggest that miR-802-5p is a critical regulator of diabetic nephropathy in the cortex and medulla compartments, contributing to disease pathogenesis through the renin-angiotensin axis and inflammatory pathways.

Cisplatin-Resistant Ovarian Cancer Cells Reveal a Polyploid Phenotype with Remarkable Activation of Nuclear Processes.

Background: Tumor recurrence as one of the main causes of cancer death is a big barrier to cancer complete treatment. Various studies denote the possible role of therapeutics in tumor relapse. Cisplatin as one of the generally used chemotherapy agents is supposed to be the source of therapy resistance through formation of polyploid giant cancer cells (PGCCs). Nevertheless, the mechanisms by which PGCCs promote tumor relapse are not fully understood. Materials and Methods: In this study, we performed experimental and bioinformatic investigations to recognize the mechanisms related to cisplatin resistance. A2780 and SCOV-3 cell lines were treated with cisplatin for 72 hours and were evaluated for their morphology by fluorescent microscopy and DNA content analysis. Furthermore, a microarray dataset of cisplatin-resistant ovarian cancer cells was re-analyzed to determine the significantly altered genes and signaling pathways. Results: Although cisplatin led to death of considerable fraction of cells in both cell lines, a significant number of survived cells became polyploid. On the other hand, our high throughput analysis determined significant change in expression of 1930 genes which mainly related to gene regulatory mechanisms and nuclear processes. Besides, mTOR, hypoxia, Hippo, and 14-3-3 signaling pathways previously shown to have role in PGCCs were determined. Conclusion: Taken together, results of this study demonstrated some key biological mechanisms related to cisplatin-resistant polyploid cancer cells.

A novel strategy for dynamic modeling of genome-scale interaction networks.

MOTIVATION: The recent availability of omics data allows the construction of holistic maps of interactions between numerous role-playing biomolecules. However, these networks are often static, ignoring the dynamic behavior of biological processes. On the other hand, dynamic models are commonly constructed on small scales. Hence, the construction of large-scale dynamic models that can quantitatively predict the time-course cellular behaviors remains a big challenge. RESULTS: In this study, a pipeline is proposed for the automatic construction of large-scale dynamic models. The pipeline uses a list of biomolecules and their time-course trajectories in a given phenomenon as input. First, the interaction network of the biomolecules is constructed. To state the underlying molecular events of each interaction, it is translated into a map of biochemical reactions. Next, to define the kinetics of the reactions, an ordinary differential equation (ODE) is generated for each involved biomolecule. Finally, the parameters of the ODE system are estimated by a novel large-scale parameter approximation method. The high performance of the pipeline is demonstrated by modeling the response of a colorectal cancer cell line to different chemotherapy regimens. In conclusion, Systematic Protein Association Dynamic ANalyzer constructs genome-scale dynamic models, filling the gap between large-scale static and small-scale dynamic modeling strategies. This simulation approach allows for holistic quantitative predictions which are critical for the simulation of therapeutic interventions in precision medicine. AVAILABILITY AND IMPLEMENTATION: Detailed information about the constructed large-scale model of colorectal cancer is available in supplementary data. The SPADAN toolbox source code is also available on GitHub (https://github.com/PooyaBorzou/SPADAN). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Zoledronic acid targets chemo-resistant polyploid giant cancer cells.

Although polyploid giant cancer cells (PGCCs) are known as a key source of failure of current therapies, sufficient drugs to target these cells are not yet introduced. Considering the similarities of polyploid cells in regeneration and cancer, we hypothesized that zoledronic acid (ZA), an osteoclast-targeting agent, might be used to eliminate PGCCs. The 5637-bladder cancer cell line was treated with various doses of cisplatin to enrich polyploid cells and the efficacy of different concentrations of ZA in reducing this population was assessed. The metabolic profile of PGCCs was investigated with gas chromatography-mass spectrometry. Lipid profiles, mitochondrial density, and ROS content were also measured to assess the response of the cells to ZA. Cancer cells surviving after three days of exposure with 6 µM cisplatin were mainly polyploid. These cells demonstrated special morphological features such as fusion with diploid or other polyploid cells and originated in daughter cells through budding. ZA could substantially eradicate PGCCs with the maximal effect observed with 50 µM which resulted in the drop of PGCC fraction from 60 ± 7.5 to 19 ± 1.7%. Enriched PGCCs after cisplatin-treatment demonstrated a drastic metabolic shift compared to untreated cancer cells with an augmentation of lipids. Further assays confirmed the high content of lipid droplets and cholesterol in these cells which were reduced after ZA administration. Additionally, the mitochondrial density and ROS increased in PGCCs both of which declined in response to ZA. Taken together, we propose that ZA is a potent inhibitor of PGCCs which alters the metabolism of PGCCs. Although this drug has been successfully exploited as adjuvant therapy for some malignancies, the current evidence on its effects on PGCCs justifies further trials to assess its potency for improving the success of current therapies for tackling tumor resistance and relapse.

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.

Olfactory receptors contribute to progression of kidney fibrosis.

Olfactory receptors (ORs) which are mainly known as odor-sensors in the olfactory epithelium are shown to be expressed in several non-sensory tissues. Despite the specified role of some of these receptors in normal physiology of the kidney, little is known about their potential effect in renal disorders. In this study, using the holistic view of systems biology, it was determined that ORs are significantly changed during the progression of kidney fibrosis. For further validation, common differentially expressed ORs resulted from reanalysis of two time-course microarray datasets were selected for experimental evaluation in a validated murine model of unilateral ureteral obstruction (UUO). Transcriptional analysis by real-time quantitative polymerase chain reaction demonstrated considerable changes in the expression pattern of Olfr433, Olfr129, Olfr1393, Olfr161, and Olfr622 during the progression of kidney fibrosis. For localization of these ORs, single-cell RNA-sequencing datasets of normal and UUO mice were reanalyzed. Results showed that Olfr433 is highly expressed in macrophages in day-2 and 7 post-injury in UUO mice and not in normal subgroups. Besides, like previous findings, Olfr1393 was shown to be expressed prominently in the proximal tubular cells of the kidney. In conclusion, our combinatorial temporal approach to the underlying mechanisms of chronic kidney disease highlighted the potential role of ORs in progression of fibrosis. The expression of Olfr433 in the macrophages provides some clue about its relation to molecular mechanisms promoted in the fibrotic kidney. The proposed ORs in this study could be the subject of further functional assessments in the future.

A deep learning approach to predict inter-omics interactions in multi-layer networks.

BACKGROUND: Despite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease. RESULTS: Here, as a novel nonlinear deep learning method, Data Integration with Deep Learning (DIDL) was proposed to predict inter-omics interactions. It consisted of an encoder that performs automatic feature extraction for biomolecules according to existing interactions coupled with a predictor that predicts unforeseen interactions. Applicability of DIDL was assessed on different networks, namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, validity of the novel predictions was evaluated by literature surveys. According to the results, the DIDL outperformed state-of-the-art methods. For all three networks, the areas under the curve and the precision-recall curve exceeded 0.85 and 0.83, respectively. CONCLUSIONS: DIDL offers several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to network sparsity. In addition, reliance solely on existing inter-layer interactions and independence of biochemical features of interacting molecules make this algorithm applicable for a wide variety of networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.

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.

Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.

Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.

Effect of melatonin supplementation on endothelial function in heart failure with reduced ejection fraction: A randomized, double-blinded clinical trial.

BACKGROUND: This study aimed to investigate the effect of melatonin supplementation on endothelial function in patients with heart failure with reduced ejection fraction (HFrEF). METHODS: This is an analysis of the MeHR trial, a randomized double-blinded placebo-controlled clinical trial with two parallel arms of 1:1. Oral 10 mg melatonin tablets or placebo was administered for 24 weeks. Deference in the percentage of flow-mediated dilatation (FMD) after the intervention was the primary outcome. RESULTS: Ninety-two patients were included in the study (age: 62.7±10.3 years, 87.0% male, ejection fraction (EF): 28.6±8.1). After adjustment for baseline FMD and age, a statistically significant difference in post-treatment FMD in favor of the melatonin group was seen (estimated marginal means [95%CI], melatonin: 7.84% [6.69-8.98], placebo: 5.98% [4.84-7.12], p = .027). There was no significant difference in the mean of post-treatment systolic/diastolic blood pressure, serum total antioxidant capacity, and serum malondialdehyde (MDA) between groups. Subgroup analysis showed significant improvement in FMD and MDA in the melatonin group in nondiabetic patients, while no difference was seen between study groups in diabetic patients. CONCLUSIONS: Melatonin supplementation in HFrEF might improve endothelial function; however, this beneficial effect might not be seen in diabetic patients.

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.

Optimization of Mouse-on-Mouse Immunohistochemistry by Utilizing Fluorescent-dye Conjugated Secondary Anti-Mouse Antibody.

The application of mouse monoclonal antibody for immunostaining the mouse tissues results in a high rate of background noise because of the interaction of the secondary antibody with endogenous immunoglobulins and other immune components. The most advised blocking strategy for the mouse-on-mouse immunostaining is the use of anti-mouse Fab fragments. Nevertheless, the commercial kits containing Fab fragment are costly and unavailable in many research laboratories. In this study, we provide evidence showing the potential of the fluorescent-dye conjugated secondary anti-mouse antibody for reducing the background noise in the mouse-on-mouse immunohistochemistry. Furthermore, our findings demonstrate the inadequacy of goat serum/protein-blocking solution alone as an immunohistochemistry blocking system for reducing the background noise.