In silico profiling of miRNAs and their target polymorphisms in leukemia associated genes

Single Nucleotide Polymorphisms (SNPs) within microRNA (miRNA) encoding regions of the genome are a large potential source for biologically relevant variation. SNPs along with miRNA act as a powerful tool to study the biology of a disease and also have the potential in monitoring disease prognosis and diagnosis. Therefore, evaluating the functional role of target mRNA will be a major challenge of future studies in the field of cancer biomarker research in leukemia. To assess, whether miRNA target SNPs are implicated in leukemia associated genes, we conducted an in silico approach along with the availability of publicly available web based tools for miRNA prediction and comprehensive genomic databases of SNPs. In this in-depth report, we attempted to use two computational approaches: prediction of miRNA in leukemia associated genes, and identifying the functional role of mRNAs targeted by miRNA. Our results from this study suggest that the application of in silico algorithms miRdSNP, PupaSuite and UTRScan analyses might provide an alternative approach to select target untranslated region (UTR) SNPs and understand the effect of SNPs on the functional attributes or molecular phenotype of a protein

Computational Refinement of Functional Single Nucleotide Polymorphisms Associated with ATM Gene

gene are the most common forms of genetic variations that account for various forms of cancer. However, the extent to which SNPs interferes with the gene regulation and affects cancer susceptibility remains largely unknown. gene. gene function can aid in better understanding of genetic differences in disease susceptibility

Path to Facilitate the Prediction of Functional Amino Acid Substitutions in Red Blood Cell Disorders – A Computational Approach

A major area of effort in current genomics is to distinguish mutations that are functionally neutral from those that contribute to disease. Single Nucleotide Polymorphisms (SNPs) are amino acid substitutions that currently account for approximately half of the known gene lesions responsible for human inherited diseases. As a result, the prediction of non-synonymous SNPs (nsSNPs) that affect protein functions and relate to disease is an important task.In this study, we performed a comprehensive analysis of deleterious SNPs at both functional and structural level in the respective genes associated with red blood cell metabolism disorders using bioinformatics tools. We analyzed the variants in Glucose-6-phosphate dehydrogenase (G6PD) and isoforms of Pyruvate Kinase (PKLR & PKM2) genes responsible for major red blood cell disorders. Deleterious nsSNPs were categorized based on empirical rule and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for evaluation of protein structure stability.We argue here that bioinformatics tools can play an important role in addressing the complexity of the underlying genetic basis of Red Blood Cell disorders. Based on our investigation, we report here the potential candidate SNPs, for future studies in human Red Blood Cell disorders. Current study also demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies. Our approach will present the application of computational tools in understanding functional variation from the perspective of structure, expression, evolution and phenotype

<i>In vitro</i> antiviral activity of the anti-HCV drugs daclatasvir and sofosbuvir against SARS-CoV-2, the aetiological agent of COVID-19

BackgroundCurrent approaches of drug repurposing against COVID-19 have not proven overwhelmingly successful and the SARS-CoV-2 pandemic continues to cause major global mortality. SARS-CoV-2 nsp12, its RNA polymerase, shares homology in the nucleotide uptake channel with the HCV orthologue enzyme NS5B. Besides, HCV enzyme NS5A has pleiotropic activities, such as RNA binding, that are shared with various SARS-CoV-2 proteins. Thus, anti-HCV NS5B and NS5A inhibitors, like sofosbuvir and daclatasvir, respectively, could be endowed with anti-SARS-CoV-2 activity.MethodsSARS-CoV-2-infected Vero cells, HuH-7 cells, Calu-3 cells, neural stem cells and monocytes were used to investigate the effects of daclatasvir and sofosbuvir. In silico and cell-free based assays were performed with SARS-CoV-2 RNA and nsp12 to better comprehend the mechanism of inhibition of the investigated compounds. A physiologically based pharmacokinetic model was generated to estimate daclatasvir's dose and schedule to maximize the probability of success for COVID-19.ResultsDaclatasvir inhibited SARS-CoV-2 replication in Vero, HuH-7 and Calu-3 cells, with potencies of 0.8, 0.6 and 1.1 μM, respectively. Although less potent than daclatasvir, sofosbuvir alone and combined with daclatasvir inhibited replication in Calu-3 cells. Sofosbuvir and daclatasvir prevented virus-induced neuronal apoptosis and release of cytokine storm-related inflammatory mediators, respectively. Sofosbuvir inhibited RNA synthesis by chain termination and daclatasvir targeted the folding of secondary RNA structures in the SARS-CoV-2 genome. Concentrations required for partial daclatasvir in vitro activity are achieved in plasma at Cmax after administration of the approved dose to humans.ConclusionsDaclatasvir, alone or in combination with sofosbuvir, at higher doses than used against HCV, may be further fostered as an anti-COVID-19 therapy

The Prediction Results of nsSNPs of human <i>ATM</i> Using SIFT, PolyPhen and I Mutant 3.0 algorithms.

<p>The Prediction <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034573#s2" target="_blank">Results</a> of nsSNPs of human <i>ATM</i> Using SIFT, PolyPhen and I Mutant 3.0 algorithms.</p

Schema representing the process of functional assessment of SNPs by <i>in silico</i> methods.

<p>SNPs were categorized based on its impact on coding region, regulatory region and post-translational modification sites. Once a tractable set of SNP is selected, <i>in silico</i> methods were used carefully to evaluate them based on the certain criteria specified by the users. Tools represented in shaded box were taken for our current analysis.</p

Integrative ranking system for nsSNPs in coding region.

<p>Predicted SNPs were categorized into four ranking groups based on the degree of deleterious effects. Coding SNPs were evaluated based on scores from SIFT, PolyPhen and I Mutant 3.0.</p

Concordance Analysis between the functional consequences of each nsSNP predicted by SIFT and PolyPhen.

<p>PolyPhen- Benign (0.00–0.99); Borderline (1.00–1.24); Potentially damaging (1.25–1.49); Possibly damaging (1.50-1.99); Probably damaging (≥2.00).</p><p>SIFT-Tolerated (1.00–0.201); Borderline (0.20 - 0.101); Potentially intolerant (0.100 - 0.050); Intolerant (0.040-0.000).</p

Summary of the multiple sequence alignment of different vertebrate sequences for PTM sites.

<p>Human <i>ATM</i> gene were compared with four different species. i) Mammals- <i>Mus musculus</i> (EDL25796.1) and <i>Bos Taurus</i> (NP_001192864.1), ii) Amphibia - <i>Xenopus tropicalis</i> (NP_001081968.1), iii) Aves - <i>Taeniopygia guttata</i> (XP_002197770.1) and Gallus gallus (NP_001155872.1), iv) Actinopterygii - <i>Danio rerio</i> (BAD91491.1). The consensus sequence is marked by an asterisk, conserved substitution by a double dot, and semi conserved substitution by a single dot. The different sequences are ordered as in aligned results from ClustalW.</p

Conservation score of amino acid residues analyzed by Consurf.

<p>Conservation Score: 1–4 Variable; 5–6 Intermediate; 7–9 Conserved.</p