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

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.
AuthorsMartin H Schaefer, Tiago J S Lopes, Nancy Mah, Jason E Shoemaker, Yukiko Matsuoka, Jean-Fred Fontaine, Caroline Louis-Jeune, Amie J Eisfeld, Gabriele Neumann, Carol Perez-Iratxeta, Yoshihiro Kawaoka, Hiroaki Kitano, Miguel A Andrade-Navarro
JournalPLoS computational biology (PLoS Comput Biol) Vol. 9 Issue 1 Pg. e1002860 ( 2013) ISSN: 1553-7358 [Electronic] United States
PMID23300433 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
Chemical References
  • Proteins
  • Proteome
  • Viral Proteins
Topics
  • Alzheimer Disease (metabolism)
  • Biocatalysis
  • Humans
  • Phosphorylation
  • Protein Binding
  • Proteins (metabolism)
  • Proteome
  • Signal Transduction
  • Viral Proteins (metabolism)

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