In Silico Prediction of Functional SNPs Interrupting Antioxidant Defense Genes in Relation to COVID-19 Progression.

The excessive production of reactive oxygen species and weakening of antioxidant defense system play a pivotal role in the pathogenesis of different diseases. Extensive differences observed among individuals in terms of affliction with cancer, cardiovascular disorders, diabetes, bacterial, and viral infections, as well as response to treatments can be partly due to their genomic variations. In this work, we attempted to predict the effect of SNPs of the key genes of antioxidant defense system on their structure, function, and expression in relation to COVID-19 pathogenesis using in silico tools. In addition, the effect of SNPs on the target site binding efficiency of SNPs was investigated as a factor with potential to change drug response or susceptibility to COVID-19. According to the predicted results, only six missense SNPs with minor allele frequency (MAF) ≥ 0.1 in the coding region of genes GPX7, GPX8, TXNRD2, GLRX5, and GLRX were able to strongly affect their structure and function. Our results predicted that 39 SNPs with MAF ≥ 0.1 led to the generation or destruction of miRNA-binding sites on target antioxidant genes from GPX, PRDX, GLRX, TXN, and SOD families. The results obtained from comparing the expression profiles of mild vs. severe COVID-19 patients using GEO2R demonstrated a significant change in the expression of approximately 250 miRNAs. The binding efficiency of 21 of these miRNAs was changed due to the elimination or generation of target sites in these genes. Altogether, this study reveals the fundamental role of the SNPs of antioxidant defense genes in COVID-19 progression and susceptibility of individuals to this virus. In addition, different responses of COVID-19 patients to antioxidant defense system enhancement drugs may be due to presence of these SNPs in different individuals.

Meta-analysis of microarray data to determine gene indicators involved in cisplatin resistance in non-small cell lung cancer.

BACKGROUND: Lung cancer is a major cause of cancer-related mortality worldwide, with a 5-year survival rate of approximately 22%. Cisplatin is one of the standard first-line chemotherapeutic agents for non-small cell lung cancer (NSCLC), but its efficacy is often limited by the development of resistance. Despite extensive research on the molecular mechanisms of chemoresistance, the underlying causes remain elusive and complex. AIMS: We analyzed three microarray datasets to find the gene signature and key pathways related to cisplatin resistance in NSCLC. METHODS AND RESULTS: We compared the gene expression of sensitive and resistant NSCLC cell lines treated with cisplatin. We found 274 DEGs, including 111 upregulated and 163 downregulated genes, in the resistant group. Gene set enrichment analysis showed the potential roles of several DEGs, such as TUBB2B, MAPK7, TUBAL3, MAP2K5, SMUG1, NTHL1, PARP3, NTRK1, G6PD, PDK1, HEY1, YTHDF2, CD274, and MAGEA1, in cisplatin resistance. Functional analysis revealed the involvement of pathways, such as gap junction, base excision repair, central carbon metabolism, and Notch signaling in the resistant cell lines. CONCLUSION: We identified several molecular factors that contribute to cisplatin resistance in NSCLC cell lines, involving genes and pathways that regulate gap junction communication, DNA damage repair, ROS balance, EMT induction, and stemness maintenance. These genes and pathways could be targets for future studies to overcome cisplatin resistance in NSCLC.

Meta-analysis of microarray data to determine gene indicators involved in the cisplatin resistance in ovarian cancer.

BACKGROUND: Significant miss-expressed gene indicators contributing to cisplatin resistance in ovarian cancer have not been completely understood. It seems that several regulatory genes and signaling pathways are associated with the emergence of the chemo-resistant phenotype. AIMS: Here, a meta-analysis approach was adopted to assess deregulated genes involved in relapse after the first line of chemotherapy (cisplatin). METHODS AND RESULTS: To do so, six ovarian cancer libraries were gathered from GEO repository. Batch effect removal and quality assessment, and boxplots and PCA were performed using SVA and ggplot2 packages in R, respectively. Cisplatin-resistant and -sensitive ovarian cancer groups were compared with find genes with significant expression changes using linear regression models in the LIMMA R package. The significance threshold for DEGs was taken as adj p-value < .05 and - 1 > logFC > 1. A total of 261 genes were identified to have significant differential expression levels in the cisplatin-resistant versus cisplatin-sensitive group. Among the 10 top up-regulated and down-regulated genes, PITX2, SNCA, and EPHA7 (up), as well as TMEM98 (down) are indirect upstream regulators of PI3K/AKT signaling pathway, contributing greatly to the development of chemo-resistance in cancer via promoting cell proliferation, survival, and cell cycle progression as well as inhibiting apoptosis. Moreover, a comprehensive assessment of DEGs revealed the dysregulation of not only membrane ion channels KCa1.1, Kv4, and CACNB4, affecting cell excitability, proliferation, and apoptosis but also cell adhesion proteins COL4A6, EPHA3, and CD9, affecting the attachment of normal cells to ECM and apoptosis, introducing good options to reverse cisplatin resistance. CONCLUSION: Our results predict and suggest that upstream regulators of PI3K/AKT signaling pathway, ion channels, and cell adhesion proteins play important roles in cisplatin resistance development in ovarian cancer.

Acute Kidney Injury (AKI) in COVID-19: In silico Identification of LncRNA-MiRNA-Gene Networks and Key Transcription Factors.

PURPOSE: Acute kidney injury (AKI) accounts for up to 29% of severe COVID-19 cases and increases mortality among these patients. Viral infections participate in the pathogenesis of diseases by changing the expression profile of normal transcriptome. This study attempts to identify LncRNA-miRNA-gene and TF-gene networks as gene expression regulating networks in the kidney tissues of COVID-19 patients. METHODS: In this analysis, four kidney libraries from the GEO repository were considered. To conduct the preprocessing, Deseq2 software in R was used for the purpose of data normalization and log2 transformation. In addition, pre- and post-normalization, PCA and box plots were developed using ggplot2 software in R for quality control. The expression profiles of the kidney samples of COVID-19 patients and control individuals were compared using DEseq2 software in R. The considered significance thresholds for DEGs were Adj P value < 0.05 and |logFC| >2. Then, to predict molecular interactions in lncRNA-miRNA-gene networks, different databases, including DeepBase v3.0, miRNATissueAtlas2, DIANA-LncBase v3, and miRWalk, were used. Furthermore, by employing ChEA databases, interactions at the TF-Gene level were obtained. Finally, the obtained networks were plotted using Stringdb and Cytoscape v8. RESULTS: Results obtained from the comparison of the post-mortem kidney tissue samples of the COVID-19 patients with the healthy kidney tissue samples showed significant changes in the expression of more than 2000 genes. In addition, predictions regarding the miRNA-gene interaction network based on DEGs obtained from this meta-analysis showed that 11 miRNAs targeted the obtained DEGs. Interestingly, in the kidney tissue, these 11 miRNAs interacted with LINC01874, LINC01788, and LINC01320, which have high specificity for this tissue. Moreover, four transcription factors of EGR1, SMAD4, STAT3, and CHD1 were identified as key transcription factors regulating DEGs. Taken together, the current study showed several dysregulated genes in the kidney of patients affected with COVID-19. CONCLUSION: This study suggests lncRNA-miRNA-gene networks and key TFs as new diagnostic and therapeutic targets for experimental and preclinical studies.

In silico identification of potential miRNAs -mRNA inflammatory networks implicated in the pathogenesis of COVID-19.

COVID-19 has been found to affect the expression profile of several mRNAs and miRNAs, leading to dysregulation of a number of signaling pathways, particularly those related to inflammatory responses. In the current study, a systematic biology procedure was used for the analysis of high-throughput expression data from blood specimens of COVID-19 and healthy individuals. Differentially expressed miRNAs in blood specimens of COVID-19 vs. healthy specimens were then identified to construct and analyze miRNA-mRNA networks and predict key miRNAs and genes in inflammatory pathways. Our results showed that 171 miRNAs were expressed as outliers in box plot and located in the critical areas according to our statistical analysis. Among them, 8 miRNAs, namely miR-1275, miR-4429, miR-4489, miR-6721-5p, miR-5010-5p, miR-7110-5p, miR-6804-5p and miR-6881-3p were found to affect expression of key genes in NF-KB, JAK/STAT and MAPK signaling pathways implicated in COVID-19 pathogenesis. In addition, our results predicted that 25 genes involved in above-mentioned inflammatory pathways were targeted not only by these 8 miRNAs but also by other obtained miRNAs (163 miRNAs). The results of the current in silico study represent candidate targets for further studies in COVID-19.

Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI.

This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of [Formula: see text] named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs.

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis.

Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated-in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching ("method of moments"), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.