Evidence map›Paper›PMID 24978826›Full record

ArticlePain2014

The pain interactome: connecting pain-specific protein interactions.

Daniel G Jamieson, Andrew Moss, Michael Kennedy, Sherrie Jones, Goran Nenadic, David L Robertson, Ben Sidders

Abstract read
In one paragraph

Article in Pain, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Mechanism-based nonopioid analgesic targets.The Journal of clinical investigation · 2025
    Review
  2. Large-Scale Plasma Proteomics to Profile Pathways and Prognosis of Chronic Pain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  3. Article
  4. Engineering Sensory Ganglion Multicellular System to Model Tissue Nerve Ingrowth.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Daniel G JamiesonComputational 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.
Andrew MossNeusentis, Pfizer, Worldwide Research & Development, Cambridge, UK.
Michael KennedyNeusentis, Pfizer, Worldwide Research & Development, Cambridge, UK.
Sherrie JonesCancer Research UK Manchester Institute, University of Manchester, Manchester, UK.
Goran NenadicComputer Science, Faculty of Engineering and Physical Sciences, University of Manchester, Manchester, UK; Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
David L RobertsonComputational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK.
Ben SiddersNeusentis, Pfizer, Worldwide Research & Development, Cambridge, UK. Electronic address: benjamin.sidders@pfizer.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Gene Regulatory NetworksAnimalsDatabases, FactualHumansPainProteinsProteinsGene expressionInflammatory painNetworksNeuropathic painProtein–protein interactionsText mining

Identifiers

PMID24978826
PMCPMC4247380

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.