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A quantitative approach to study indirect effects among disease proteins in the human protein interaction network
BACKGROUND: Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lea...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2924296/ https://www.ncbi.nlm.nih.gov/pubmed/20670417 http://dx.doi.org/10.1186/1752-0509-4-103 |
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author | Nguyen, Thanh-Phuong Jordán, Ferenc |
author_facet | Nguyen, Thanh-Phuong Jordán, Ferenc |
author_sort | Nguyen, Thanh-Phuong |
collection | PubMed |
description | BACKGROUND: Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases. RESULTS: Based on the i2d and OMIM databases, we have constructed (i) a network of proteins causing five selected diseases (DP, disease proteins) plus their interacting partners (IP, non-disease proteins), the DPIP network and (ii) a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1) various cancers, (2) heart diseases, (3) obesity, (4) diabetes and (5) autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins. CONCLUSIONS: We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand particular pathways. We have found that the mediators between heart diseases and obesity, as well as heart diseases and diabetes are of relatively high functional importance in the cell. The mediator proteins suggested here should be experimentally tested as products of hypothetical disease-related proteins. |
format | Text |
id | pubmed-2924296 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29242962010-08-20 A quantitative approach to study indirect effects among disease proteins in the human protein interaction network Nguyen, Thanh-Phuong Jordán, Ferenc BMC Syst Biol Methodology Article BACKGROUND: Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases. RESULTS: Based on the i2d and OMIM databases, we have constructed (i) a network of proteins causing five selected diseases (DP, disease proteins) plus their interacting partners (IP, non-disease proteins), the DPIP network and (ii) a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1) various cancers, (2) heart diseases, (3) obesity, (4) diabetes and (5) autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins. CONCLUSIONS: We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand particular pathways. We have found that the mediators between heart diseases and obesity, as well as heart diseases and diabetes are of relatively high functional importance in the cell. The mediator proteins suggested here should be experimentally tested as products of hypothetical disease-related proteins. BioMed Central 2010-07-29 /pmc/articles/PMC2924296/ /pubmed/20670417 http://dx.doi.org/10.1186/1752-0509-4-103 Text en Copyright ©2010 Nguyen and Jordán; 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 | Methodology Article Nguyen, Thanh-Phuong Jordán, Ferenc A quantitative approach to study indirect effects among disease proteins in the human protein interaction network |
title | A quantitative approach to study indirect effects among disease proteins in the human protein interaction network |
title_full | A quantitative approach to study indirect effects among disease proteins in the human protein interaction network |
title_fullStr | A quantitative approach to study indirect effects among disease proteins in the human protein interaction network |
title_full_unstemmed | A quantitative approach to study indirect effects among disease proteins in the human protein interaction network |
title_short | A quantitative approach to study indirect effects among disease proteins in the human protein interaction network |
title_sort | quantitative approach to study indirect effects among disease proteins in the human protein interaction network |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2924296/ https://www.ncbi.nlm.nih.gov/pubmed/20670417 http://dx.doi.org/10.1186/1752-0509-4-103 |
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