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.

Keywords: Gene expression; Inflammatory pain; Networks; Neuropathic pain; Protein–protein interactions; Text mining.

Fig. 1

The pain interaction network. (a) Workflow for creating a pain-specific protein–protein interaction (PPI) network. (b) The PPI network for all pain-associated proteins derived from the curated data. Proteins enriched against iRefIndex (P < .05) are highlighted in blue (3887 total; see Supplementary Table 13). Insets show the top 10 enriched proteins. Colored arrows refer to interaction type: Blue corresponds to positive regulation, red to negative regulation, turquoise to regulation, and yellow to binding (these edges are bi-directional).

Fig. 2

Bias in the pain interaction network. (a) Correlation between the number of publications and degree for nodes in our network showing a linear trend (rho = 0.83). (b) The average number of publications per interaction for a pain protein remains flat (rho = 0.08), suggesting that most interactions are reported individually (see Supplementary Figs. 1 and 2, and Supplementary Table 3). (c) There is an inverse relationship between the date of first publication on a protein’s interactions and the protein’s degree (rho = −0.4) (see Supplementary Table 4).

Fig. 3

Drug targets in the pain interaction network. (a) Drug targets are color coded by the contribution of pain to their primary indication (see Methods), as indicated in the key. The 10 most enriched nodes are enlarged and moved into the center for clarity. (b) Drug target profiles of each pain network. Proteins from each dataset are ranked by their enrichment P value and binned into quartiles. Numbers of associated drugs that target proteins in each quartile are then indicated. There is a significant relationship between the enrichment of a node in the text-mined network and the likelihood of it being a drug target for a pain specific indication (χ² test for trends in proportions, P = .002). However, neither the Pain Genes DB network nor the gene expression data show the same significant trend (P = .05 and P = 0.9, respectively).

Fig. 4

Protein regulation in the pain interaction network. (a) The top 10 most enriched genes in the pain network are shown with their regulation profiles broken down by incoming (is regulated, “I”) and outgoing (regulates others, “R”) interactions. Black denotes positive regulation, gray denotes negative regulation, and white denotes other types of interaction. Undirected binding interactions are excluded. (b) The distribution of net regulation for all proteins in the pain network shows a normal distribution with long tails. This indicates that only a few proteins act as master regulators. (c) These master regulators were determined using the exact binomial test (see Supplementary Table 16). The proteins that are significantly more regulated than they are regulators and vice versa are shown; nerve growth factor (NGF) is the most significant net regulator.

Fig. 5

Protein–protein interactions (PPIs) specific to neuropathic pain. (a) Neuropathic pain specific subnetwork. Blue edges represent those interactions that have been curated as increased in a neuropathic pain state, red edges decreased, and pink edges are those that have been denoted as both. Dark red nodes are those that are enriched against the general pain network (see Supplementary Table 17).

Fig. 6

Protein–protein interactions (PPIs) specific to inflammatory pain. (a) Inflammatory pain specific subnetwork. Blue edges are those interactions that have been curated as increased in a neuropathic pain state, red edges decreased, and pink edges are those that have been denoted as both. Dark red nodes are those that are enriched against the general pain network (see Supplementary Table 18).