title: The pain interactome: connecting pain-specific protein interactions.
abstract: Understanding the molecular mechanisms associated with disease is a central goal of modern medical research. As such, many thousands of experiments have been published that detail individual molecular events that contribute to a disease. Here we use a semi-automated text mining approach to accurately and exhaustively curate the primary literature for chronic pain states. In so doing, we create a comprehensive network of 1,002 contextualized protein-protein interactions (PPIs) specifically associated with pain. The PPIs form a highly interconnected and coherent structure, and the resulting network provides an alternative to those derived from connecting genes associated with pain using interactions that have not been shown to occur in a painful state. We exploit the contextual data associated with our interactions to analyse subnetworks specific to inflammatory and neuropathic pain, and to various anatomical regions. Here, we identify potential targets for further study and several drug-repurposing opportunities. Finally, the network provides a framework for the interpretation of new data within the field of pain.
authors: Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK; Computer Science, Faculty of Engineering and Physical Sciences, University of Manchester, Manchester, UK.
authors: Daniel G
authors: DG
authors: Jamieson
authors: Neusentis, Pfizer, Worldwide Research & Development, Cambridge, UK.
authors: Andrew
authors: A
authors: Moss
authors: Neusentis, Pfizer, Worldwide Research & Development, Cambridge, UK.
authors: Michael
authors: M
authors: Kennedy
authors: Cancer Research UK Manchester Institute, University of Manchester, Manchester, UK.
authors: Sherrie
authors: S
authors: Jones
authors: Computer Science, Faculty of Engineering and Physical Sciences, University of Manchester, Manchester, UK; Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
authors: Goran
authors: G
authors: Nenadic
authors: Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK.
authors: David L
authors: DL
authors: Robertson
authors: Neusentis, Pfizer, Worldwide Research & Development, Cambridge, UK. Electronic address: benjamin.sidders@pfizer.com.
authors: Ben
authors: B
authors: Sidders
mesh_terms: D000818:Animals; D016208:Databases, Factual / Q000706:statistics & numerical data; D053263:Gene Regulatory Networks; D006801:Humans; D010146:Pain / Q000378:metabolism* / Q000473:pathology; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins
keywords: Gene expression; Inflammatory pain; Networks; Neuropathic pain; Protein–protein interactions; Text mining