Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradi...

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Autores principales:	Piñero, J., Berenstein, A., Gonzalez-Perez, A., Chernomoretz, A., Furlong, L.I.
Formato:	JOUR
Materias:	biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Pinero
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_20452322_v6_n_p_Pinero
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spelling	todo:paper_20452322_v6_n_p_Pinero2023-10-03T16:38:16Z Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing Piñero, J. Berenstein, A. Gonzalez-Perez, A. Chernomoretz, A. Furlong, L.I. biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules. Fil:Chernomoretz, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Furlong, L.I. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Pinero
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms
spellingShingle	biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms Piñero, J. Berenstein, A. Gonzalez-Perez, A. Chernomoretz, A. Furlong, L.I. Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
topic_facet	biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms
description	Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.
format	JOUR
author	Piñero, J. Berenstein, A. Gonzalez-Perez, A. Chernomoretz, A. Furlong, L.I.
author_facet	Piñero, J. Berenstein, A. Gonzalez-Perez, A. Chernomoretz, A. Furlong, L.I.
author_sort	Piñero, J.
title	Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
title_short	Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
title_full	Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
title_fullStr	Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
title_full_unstemmed	Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
title_sort	uncovering disease mechanisms through network biology in the era of next generation sequencing
url	http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Pinero
work_keys_str_mv	AT pineroj uncoveringdiseasemechanismsthroughnetworkbiologyintheeraofnextgenerationsequencing AT berensteina uncoveringdiseasemechanismsthroughnetworkbiologyintheeraofnextgenerationsequencing AT gonzalezpereza uncoveringdiseasemechanismsthroughnetworkbiologyintheeraofnextgenerationsequencing AT chernomoretza uncoveringdiseasemechanismsthroughnetworkbiologyintheeraofnextgenerationsequencing AT furlongli uncoveringdiseasemechanismsthroughnetworkbiologyintheeraofnextgenerationsequencing
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Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

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