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Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach

PURPOSE: The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach. MATERIALS AN...

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Autores principales: Sekaran, Tamizh Selvan Gnana, Kedilaya, Vishakh R., Kumari, Suchetha N., Shetty, Praveenkumar, Gollapalli, Pavan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society for Radiation Oncology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024183/
https://www.ncbi.nlm.nih.gov/pubmed/33794574
http://dx.doi.org/10.3857/roj.2021.00045
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author Sekaran, Tamizh Selvan Gnana
Kedilaya, Vishakh R.
Kumari, Suchetha N.
Shetty, Praveenkumar
Gollapalli, Pavan
author_facet Sekaran, Tamizh Selvan Gnana
Kedilaya, Vishakh R.
Kumari, Suchetha N.
Shetty, Praveenkumar
Gollapalli, Pavan
author_sort Sekaran, Tamizh Selvan Gnana
collection PubMed
description PURPOSE: The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach. MATERIALS AND METHODS: Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure. RESULTS: A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes. CONCLUSION: This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy.
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spelling pubmed-80241832021-04-15 Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach Sekaran, Tamizh Selvan Gnana Kedilaya, Vishakh R. Kumari, Suchetha N. Shetty, Praveenkumar Gollapalli, Pavan Radiat Oncol J Original Article PURPOSE: The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach. MATERIALS AND METHODS: Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure. RESULTS: A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes. CONCLUSION: This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy. The Korean Society for Radiation Oncology 2021-03 2021-03-24 /pmc/articles/PMC8024183/ /pubmed/33794574 http://dx.doi.org/10.3857/roj.2021.00045 Text en Copyright © 2021 The Korean Society for Radiation Oncology This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Sekaran, Tamizh Selvan Gnana
Kedilaya, Vishakh R.
Kumari, Suchetha N.
Shetty, Praveenkumar
Gollapalli, Pavan
Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
title Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
title_full Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
title_fullStr Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
title_full_unstemmed Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
title_short Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
title_sort exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024183/
https://www.ncbi.nlm.nih.gov/pubmed/33794574
http://dx.doi.org/10.3857/roj.2021.00045
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