Showing papers by "Sushmita Paul published in 2021"
TL;DR: In this paper, the role and possible mechanisms of selenium, selenoproteins, and virally encoded selenophroteins in the pathogenicity of viral infections are discussed.
Abstract: The disruption of antioxidant defense has been demonstrated in severe acute respiratory syndrome due to SARS-CoV infection. Selenium plays a major role in decreasing the ROS produced in response to various viral infections. Selenoprotein enzymes are essential in combating oxidative stress caused due to excessive generation of ROS. Selenium also has a role in inhibiting the activation of NF-κB, thus alleviating inflammation. In viral infections, selenoproteins have also been found to inhibit type I interferon responses, modulate T cell proliferation and oxidative burst in macrophages, and inhibit viral transcriptional activators. Potential virally encoded selenoproteins have been identified by computational analysis in different viral genomes like HIV-1, Japanese encephalitis virus (JEV), and hepatitis C virus. This review discusses the role and the possible mechanisms of selenium, selenoproteins, and virally encoded selenoproteins in the pathogenicity of viral infections. Identification of potential selenoproteins in the COVID 19 genome by computational tools will give insights further into their role in the pathogenesis of viral infections.
12 citations
TL;DR: In this article, an algorithm named CorGO is introduced, that specifically deals with the identification of functionally similar gene-clusters, which can capture the functional similarity present between genes, which is very important from a biological perspective.
Abstract: Identification of groups of co-expressed or co-regulated genes is critical for exploring the underlying mechanism behind a particular disease like cancer. Condition-specific (disease-specific) gene-expression profiles acquired from different platforms are widely utilized by researchers to get insight into the regulatory mechanism of the disease. Several clustering algorithms are developed using gene expression profiles to identify the group of similar genes. These algorithms are computationally efficient but are not able to capture the functional similarity present between the genes, which is very important from a biological perspective. In this study, an algorithm named CorGO is introduced, that specifically deals with the identification of functionally similar gene-clusters. Two types of relationships are calculated for this purpose. Firstly, the Correlation (Cor) between the genes are captured from the gene-expression data, which helps in deciphering the relationship between genes based on its expression across several diseased samples. Secondly, Gene Ontology (GO)-based semantic similarity information available for the genes is utilized, that helps in adding up biological relevance to the identified gene-clusters. A similarity measure is defined by integrating these two components that help in the identification of homogeneous and functionally similar groups of genes. CorGO is applied to four different types of gene expression profiles of different types of cancer. Gene-clusters identified by CorGO, are further validated by pathway enrichment, disease enrichment, and network analysis. These biological analyses demonstrated significant connectivity and functional relatedness within the genes of the same cluster. A comparative study with commonly used clustering algorithms is also performed to show the efficacy of the proposed method.