An in-silico approach to study the possible interactions of miRNA between human and SARS-CoV2. (October 2020)

• Record Type: Journal Article
• Authors: Sarma, Abhijit; Phukan, Homen; Halder, Neha; Madanan, Madathiparambil Gopalakrishnan
• Is Part Of: Computational biology and chemistry. Volume 88(2020)
• Publication Date: 2020-10
• Subjects: Nt nucleotide -- miRNA MicroRNA -- EBoV Ebola -- GO gene ontology; COVID19 -- SARS-CoV2 -- In-silico -- miRNA -- Hairpin -- Gene-ontology; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107352
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The rising epidemics of SARS-CoV2 has globally made a serious concern. Thus, understanding the virus-host relationship remains a serious concern. Here, we adopted the in-silico approach to picturized the similarities between the miRNAs of SARS-CoV2 genomes and human. Further, the assessments and prediction of miRNAs helped us to analyze and understand the mechanism of pathogenesis. Abstract: Background: The progressive SARS-CoV2 outbreaks worldwide have evoked global investigation. Despite the numerous in-silico approaches, the virus-host relationship remains a serious concern. MicroRNAs are the small non-coding RNAs that help in regulating gene profiling. The current study utilized miRNA prediction tools along with the PANTHER classification system to demonstrate association and sequence similarities shared between miRNAs of SARS-CoV2 and human host. Method: An in-silico approach was carried out using Vmir analyzer to predict miRNAs from SARS-CoV2 viral genomes. Predicted miRNAs from SARS-CoV2 viral genomes were used for effective hybridization sequence identification along the nucleotide similarities with human miRNAs from miRbase database. Further, it was proceeded to analyze the gene ontology using miRDB with PANTHER classification. Result: Based on the prediction and analysis, we have identified 22 potential miRNAs from five genomes of SARS-CoV2 linked with 12 human miRNAs. Analysis of human miRNAs hsa-mir-1267, hsa-mir-1-3p, hsa-mir-5683 were found shared between all the five viral SARS-CoV2 miRNAs. Further, PANTHER classification analyzed the gene-ontology being carried by these associations showed that 44 genes were involved in biological functions that includes genes specific for signaling pathway, immune complex generation, enzyme binding with effective role in the virus-host relationship. Conclusion: Our analysis concludes that the genes identified in this study can be effective in analyzing the virus-host interaction. It also provides a new direction to understand viral pathogenesis with a probable new way to link, that can be used to understand and relate the miRNAs of the virus to the host conditions.