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Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease

Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying...

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Autores principales: García-Vaquero, Marina L., Gama-Carvalho, Margarida, Pinto, Francisco R., De Las Rivas, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304429/
https://www.ncbi.nlm.nih.gov/pubmed/35891788
http://dx.doi.org/10.1016/j.csbj.2022.07.006
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author García-Vaquero, Marina L.
Gama-Carvalho, Margarida
Pinto, Francisco R.
De Las Rivas, Javier
author_facet García-Vaquero, Marina L.
Gama-Carvalho, Margarida
Pinto, Francisco R.
De Las Rivas, Javier
author_sort García-Vaquero, Marina L.
collection PubMed
description Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.
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spelling pubmed-93044292022-07-25 Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease García-Vaquero, Marina L. Gama-Carvalho, Margarida Pinto, Francisco R. De Las Rivas, Javier Comput Struct Biotechnol J Research Article Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods. Research Network of Computational and Structural Biotechnology 2022-07-15 /pmc/articles/PMC9304429/ /pubmed/35891788 http://dx.doi.org/10.1016/j.csbj.2022.07.006 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
García-Vaquero, Marina L.
Gama-Carvalho, Margarida
Pinto, Francisco R.
De Las Rivas, Javier
Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease
title Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease
title_full Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease
title_fullStr Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease
title_full_unstemmed Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease
title_short Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease
title_sort biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304429/
https://www.ncbi.nlm.nih.gov/pubmed/35891788
http://dx.doi.org/10.1016/j.csbj.2022.07.006
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