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Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity

The current outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China threatened humankind worldwide. The coronaviruses contains the largest RNA genome among all other known RNA viruses, therefore the disease etiology can be understood by analyzing the genome sequence of SARS...

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Autores principales: Saini, Sandeep, Saini, Avneet, Thakur, Chander Jyoti, Kumar, Varinder, Gupta, Rishabh Dilip, Sharma, Jogesh Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Shiraz University 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382400/
https://www.ncbi.nlm.nih.gov/pubmed/32802902
http://dx.doi.org/10.22099/mbrc.2020.36507.1487
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author Saini, Sandeep
Saini, Avneet
Thakur, Chander Jyoti
Kumar, Varinder
Gupta, Rishabh Dilip
Sharma, Jogesh Kumar
author_facet Saini, Sandeep
Saini, Avneet
Thakur, Chander Jyoti
Kumar, Varinder
Gupta, Rishabh Dilip
Sharma, Jogesh Kumar
author_sort Saini, Sandeep
collection PubMed
description The current outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China threatened humankind worldwide. The coronaviruses contains the largest RNA genome among all other known RNA viruses, therefore the disease etiology can be understood by analyzing the genome sequence of SARS-CoV-2. In this study, we used an ab-intio based computational tool VMir to scan the complete genome of SARS-CoV-2 to predict pre-miRNAs. The potential pre-miRNAs were identified by ViralMir and mature miRNAs were recognized by Mature Bayes. Additionally, predicted mature miRNAs were analysed against human genome by miRDB server to retrieve target genes. Besides that we also retrieved GO (Gene Ontology) terms for pathways, functions and cellular components. We predicted 26 mature miRNAs from genome of SARS-CoV-2 that targets human genes involved in pathways like EGF receptor signaling, apoptosis signaling, VEGF signaling, FGF receptor signaling. Gene enrichment tool analysis and substantial literature evidences suggests role of genes like BMPR2 and p53 in pulmonary vasculature and antiviral innate immunity respectively. Our findings may help research community to understand virus pathogenesis.
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spelling pubmed-73824002020-08-13 Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity Saini, Sandeep Saini, Avneet Thakur, Chander Jyoti Kumar, Varinder Gupta, Rishabh Dilip Sharma, Jogesh Kumar Mol Biol Res Commun Original Article The current outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China threatened humankind worldwide. The coronaviruses contains the largest RNA genome among all other known RNA viruses, therefore the disease etiology can be understood by analyzing the genome sequence of SARS-CoV-2. In this study, we used an ab-intio based computational tool VMir to scan the complete genome of SARS-CoV-2 to predict pre-miRNAs. The potential pre-miRNAs were identified by ViralMir and mature miRNAs were recognized by Mature Bayes. Additionally, predicted mature miRNAs were analysed against human genome by miRDB server to retrieve target genes. Besides that we also retrieved GO (Gene Ontology) terms for pathways, functions and cellular components. We predicted 26 mature miRNAs from genome of SARS-CoV-2 that targets human genes involved in pathways like EGF receptor signaling, apoptosis signaling, VEGF signaling, FGF receptor signaling. Gene enrichment tool analysis and substantial literature evidences suggests role of genes like BMPR2 and p53 in pulmonary vasculature and antiviral innate immunity respectively. Our findings may help research community to understand virus pathogenesis. Shiraz University 2020-06 /pmc/articles/PMC7382400/ /pubmed/32802902 http://dx.doi.org/10.22099/mbrc.2020.36507.1487 Text en This is an Open Access article distributed under the terms of the Creative Commons Attribution License, (http://creativecommons.org/licenses/by/3.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Saini, Sandeep
Saini, Avneet
Thakur, Chander Jyoti
Kumar, Varinder
Gupta, Rishabh Dilip
Sharma, Jogesh Kumar
Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity
title Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity
title_full Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity
title_fullStr Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity
title_full_unstemmed Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity
title_short Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity
title_sort genome-wide computational prediction of mirnas in severe acute respiratory syndrome coronavirus 2 (sars-cov-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382400/
https://www.ncbi.nlm.nih.gov/pubmed/32802902
http://dx.doi.org/10.22099/mbrc.2020.36507.1487
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