Network topology reveals key cardiovascular disease genes.

The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of th...

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Autores principales:	Anida Sarajlić, Vuk Janjić, Neda Stojković, Djordje Radak, Nataša Pržulj
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/46c7cdf5165f411e9117b6cc9ce0dbdc
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spelling	oai:doaj.org-article:46c7cdf5165f411e9117b6cc9ce0dbdc2021-11-18T08:59:25ZNetwork topology reveals key cardiovascular disease genes.1932-620310.1371/journal.pone.0071537https://doaj.org/article/46c7cdf5165f411e9117b6cc9ce0dbdc2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23977067/?tool=EBIhttps://doaj.org/toc/1932-6203The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and "driver genes." We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies "key" genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.Anida SarajlićVuk JanjićNeda StojkovićDjordje RadakNataša PržuljPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e71537 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anida Sarajlić Vuk Janjić Neda Stojković Djordje Radak Nataša Pržulj Network topology reveals key cardiovascular disease genes.
description	The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and "driver genes." We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies "key" genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.
format	article
author	Anida Sarajlić Vuk Janjić Neda Stojković Djordje Radak Nataša Pržulj
author_facet	Anida Sarajlić Vuk Janjić Neda Stojković Djordje Radak Nataša Pržulj
author_sort	Anida Sarajlić
title	Network topology reveals key cardiovascular disease genes.
title_short	Network topology reveals key cardiovascular disease genes.
title_full	Network topology reveals key cardiovascular disease genes.
title_fullStr	Network topology reveals key cardiovascular disease genes.
title_full_unstemmed	Network topology reveals key cardiovascular disease genes.
title_sort	network topology reveals key cardiovascular disease genes.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/46c7cdf5165f411e9117b6cc9ce0dbdc
work_keys_str_mv	AT anidasarajlic networktopologyrevealskeycardiovasculardiseasegenes AT vukjanjic networktopologyrevealskeycardiovasculardiseasegenes AT nedastojkovic networktopologyrevealskeycardiovasculardiseasegenes AT djordjeradak networktopologyrevealskeycardiovasculardiseasegenes AT natasaprzulj networktopologyrevealskeycardiovasculardiseasegenes
_version_	1718421077883879424

Network topology reveals key cardiovascular disease genes.

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