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Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putt...

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Autores principales: Saha, Sovan, Chatterjee, Piyali, Nasipuri, Mita, Basu, Subhadip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428263/
https://www.ncbi.nlm.nih.gov/pubmed/34567845
http://dx.doi.org/10.7717/peerj.12117
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author Saha, Sovan
Chatterjee, Piyali
Nasipuri, Mita
Basu, Subhadip
author_facet Saha, Sovan
Chatterjee, Piyali
Nasipuri, Mita
Basu, Subhadip
author_sort Saha, Sovan
collection PubMed
description The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.
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spelling pubmed-84282632021-09-24 Detection of spreader nodes in human-SARS-CoV protein-protein interaction network Saha, Sovan Chatterjee, Piyali Nasipuri, Mita Basu, Subhadip PeerJ Bioinformatics The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art. PeerJ Inc. 2021-09-06 /pmc/articles/PMC8428263/ /pubmed/34567845 http://dx.doi.org/10.7717/peerj.12117 Text en ©2021 Saha et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Saha, Sovan
Chatterjee, Piyali
Nasipuri, Mita
Basu, Subhadip
Detection of spreader nodes in human-SARS-CoV protein-protein interaction network
title Detection of spreader nodes in human-SARS-CoV protein-protein interaction network
title_full Detection of spreader nodes in human-SARS-CoV protein-protein interaction network
title_fullStr Detection of spreader nodes in human-SARS-CoV protein-protein interaction network
title_full_unstemmed Detection of spreader nodes in human-SARS-CoV protein-protein interaction network
title_short Detection of spreader nodes in human-SARS-CoV protein-protein interaction network
title_sort detection of spreader nodes in human-sars-cov protein-protein interaction network
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428263/
https://www.ncbi.nlm.nih.gov/pubmed/34567845
http://dx.doi.org/10.7717/peerj.12117
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