title: 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.
authors.affiliation: Computational Biology and Data Mining, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
authors.forename: Martin H
authors.initials: MH
authors.lastname: Schaefer
authors.forename: Tiago J S
authors.initials: TJ
authors.lastname: Lopes
authors.forename: Nancy
authors.initials: N
authors.lastname: Mah
authors.forename: Jason E
authors.initials: JE
authors.lastname: Shoemaker
authors.forename: Yukiko
authors.initials: Y
authors.lastname: Matsuoka
authors.forename: Jean-Fred
authors.initials: JF
authors.lastname: Fontaine
authors.forename: Caroline
authors.initials: C
authors.lastname: Louis-Jeune
authors.forename: Amie J
authors.initials: AJ
authors.lastname: Eisfeld
authors.forename: Gabriele
authors.initials: G
authors.lastname: Neumann
authors.forename: Carol
authors.initials: C
authors.lastname: Perez-Iratxeta
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Kawaoka
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Kitano
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Andrade-Navarro
mesh_terms: D000544:Alzheimer Disease / Q000378:metabolism; D055162:Biocatalysis; D006801:Humans; D010766:Phosphorylation; D011485:Protein Binding; D011506:Proteins / Q000378:metabolism*; D020543:Proteome; D015398:Signal Transduction; D014764:Viral Proteins / Q000378:metabolism
chemical_list: D011506:Proteins; D020543:Proteome; D014764:Viral Proteins