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Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

BACKGROUND: Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides...

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Autores principales: Lin, Chen-Ching, Hsiang, Jen-Tsung, Wu, Chia-Yi, Oyang, Yen-Jen, Juan, Hsueh-Fen, Huang, Hsuan-Cheng
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978157/
https://www.ncbi.nlm.nih.gov/pubmed/20950417
http://dx.doi.org/10.1186/1752-0509-4-138
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author Lin, Chen-Ching
Hsiang, Jen-Tsung
Wu, Chia-Yi
Oyang, Yen-Jen
Juan, Hsueh-Fen
Huang, Hsuan-Cheng
author_facet Lin, Chen-Ching
Hsiang, Jen-Tsung
Wu, Chia-Yi
Oyang, Yen-Jen
Juan, Hsueh-Fen
Huang, Hsuan-Cheng
author_sort Lin, Chen-Ching
collection PubMed
description BACKGROUND: Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. RESULTS: We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. CONCLUSIONS: We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.
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spelling pubmed-29781572010-11-17 Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy Lin, Chen-Ching Hsiang, Jen-Tsung Wu, Chia-Yi Oyang, Yen-Jen Juan, Hsueh-Fen Huang, Hsuan-Cheng BMC Syst Biol Research Article BACKGROUND: Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. RESULTS: We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. CONCLUSIONS: We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy. BioMed Central 2010-10-15 /pmc/articles/PMC2978157/ /pubmed/20950417 http://dx.doi.org/10.1186/1752-0509-4-138 Text en Copyright ©2010 Lin et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lin, Chen-Ching
Hsiang, Jen-Tsung
Wu, Chia-Yi
Oyang, Yen-Jen
Juan, Hsueh-Fen
Huang, Hsuan-Cheng
Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy
title Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy
title_full Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy
title_fullStr Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy
title_full_unstemmed Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy
title_short Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy
title_sort dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978157/
https://www.ncbi.nlm.nih.gov/pubmed/20950417
http://dx.doi.org/10.1186/1752-0509-4-138
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