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Network analysis of atherosclerotic genes elucidates druggable targets

BACKGROUND: Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pa...

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Autores principales: Banik, Sheuli Kangsa, Baishya, Somorita, Das Talukdar, Anupam, Choudhury, Manabendra Dutta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8893053/
https://www.ncbi.nlm.nih.gov/pubmed/35241081
http://dx.doi.org/10.1186/s12920-022-01195-y
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author Banik, Sheuli Kangsa
Baishya, Somorita
Das Talukdar, Anupam
Choudhury, Manabendra Dutta
author_facet Banik, Sheuli Kangsa
Baishya, Somorita
Das Talukdar, Anupam
Choudhury, Manabendra Dutta
author_sort Banik, Sheuli Kangsa
collection PubMed
description BACKGROUND: Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis. RESULT: From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool. CONCLUSION: Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01195-y.
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spelling pubmed-88930532022-03-04 Network analysis of atherosclerotic genes elucidates druggable targets Banik, Sheuli Kangsa Baishya, Somorita Das Talukdar, Anupam Choudhury, Manabendra Dutta BMC Med Genomics Research BACKGROUND: Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis. RESULT: From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool. CONCLUSION: Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01195-y. BioMed Central 2022-03-03 /pmc/articles/PMC8893053/ /pubmed/35241081 http://dx.doi.org/10.1186/s12920-022-01195-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Banik, Sheuli Kangsa
Baishya, Somorita
Das Talukdar, Anupam
Choudhury, Manabendra Dutta
Network analysis of atherosclerotic genes elucidates druggable targets
title Network analysis of atherosclerotic genes elucidates druggable targets
title_full Network analysis of atherosclerotic genes elucidates druggable targets
title_fullStr Network analysis of atherosclerotic genes elucidates druggable targets
title_full_unstemmed Network analysis of atherosclerotic genes elucidates druggable targets
title_short Network analysis of atherosclerotic genes elucidates druggable targets
title_sort network analysis of atherosclerotic genes elucidates druggable targets
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8893053/
https://www.ncbi.nlm.nih.gov/pubmed/35241081
http://dx.doi.org/10.1186/s12920-022-01195-y
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