Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

Abstract In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network tha...

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Autores principales:	Paola Paci, Giulia Fiscon, Federica Conte, Rui-Sheng Wang, Lorenzo Farina, Joseph Loscalzo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b8e7b0eeedcc4d0f8ee7cc64984fb627
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spelling	oai:doaj.org-article:b8e7b0eeedcc4d0f8ee7cc64984fb6272021-12-02T13:50:50ZGene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery10.1038/s41540-020-00168-02056-7189https://doaj.org/article/b8e7b0eeedcc4d0f8ee7cc64984fb6272021-01-01T00:00:00Zhttps://doi.org/10.1038/s41540-020-00168-0https://doaj.org/toc/2056-7189Abstract In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.Paola PaciGiulia FisconFederica ConteRui-Sheng WangLorenzo FarinaJoseph LoscalzoNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Paola Paci Giulia Fiscon Federica Conte Rui-Sheng Wang Lorenzo Farina Joseph Loscalzo Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
description	Abstract In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.
format	article
author	Paola Paci Giulia Fiscon Federica Conte Rui-Sheng Wang Lorenzo Farina Joseph Loscalzo
author_facet	Paola Paci Giulia Fiscon Federica Conte Rui-Sheng Wang Lorenzo Farina Joseph Loscalzo
author_sort	Paola Paci
title	Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
title_short	Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
title_full	Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
title_fullStr	Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
title_full_unstemmed	Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
title_sort	gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b8e7b0eeedcc4d0f8ee7cc64984fb627
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Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

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