Adding protein context to the human protein-protein interaction network to reveal meaningful interactions

Schaefer, M.H.; Lopes, T.J.S.; Mah, N.; Shoemaker, J.E.; Matsuoka, Y.; Fontaine, J.F.; Louis-Jeune, C.; Eisfeld, A.J.; Neumann, G.; Perez-Iratxeta, C.; Kawaoka, Y.; Kitano, H.; Andrade-Navarro, M.A.

Adding protein context to the human protein-protein interaction network to reveal meaningful interactions

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Item Type:	Article
Title:	Adding protein context to the human protein-protein interaction network to reveal meaningful interactions
Creators Name:	Schaefer, M.H., Lopes, T.J.S., Mah, N., Shoemaker, J.E., Matsuoka, Y., Fontaine, J.F., Louis-Jeune, C., Eisfeld, A.J., Neumann, G., Perez-Iratxeta, C., Kawaoka, Y., Kitano, H. and Andrade-Navarro, M.A.
Abstract:	Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.
Keywords:	Alzheimer Disease, Biocatalysis, Phosphorylation, Protein Binding, Proteins, Proteome, Signal Transduction, Viral Proteins
Source:	PLoS Computational Biology
ISSN:	1553-7358
Publisher:	Public Library of Science
Volume:	9
Number:	1
Page Range:	e1002860
Date:	3 January 2013
Official Publication:	https://doi.org/10.1371/journal.pcbi.1002860
PubMed:	View item in PubMed

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