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

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 regulat...

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Autores principales: Paci, Paola, Fiscon, Giulia, Conte, Federica, Wang, Rui-Sheng, Farina, Lorenzo, Loscalzo, Joseph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7819998/
https://www.ncbi.nlm.nih.gov/pubmed/33479222
http://dx.doi.org/10.1038/s41540-020-00168-0
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author Paci, Paola
Fiscon, Giulia
Conte, Federica
Wang, Rui-Sheng
Farina, Lorenzo
Loscalzo, Joseph
author_facet Paci, Paola
Fiscon, Giulia
Conte, Federica
Wang, Rui-Sheng
Farina, Lorenzo
Loscalzo, Joseph
author_sort Paci, Paola
collection PubMed
description 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.
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spelling pubmed-78199982021-01-28 Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery Paci, Paola Fiscon, Giulia Conte, Federica Wang, Rui-Sheng Farina, Lorenzo Loscalzo, Joseph NPJ Syst Biol Appl Article 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. Nature Publishing Group UK 2021-01-21 /pmc/articles/PMC7819998/ /pubmed/33479222 http://dx.doi.org/10.1038/s41540-020-00168-0 Text en © The Author(s) 2021 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Paci, Paola
Fiscon, Giulia
Conte, Federica
Wang, Rui-Sheng
Farina, Lorenzo
Loscalzo, Joseph
Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7819998/
https://www.ncbi.nlm.nih.gov/pubmed/33479222
http://dx.doi.org/10.1038/s41540-020-00168-0
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