Genetic pathway analysis reveals a major role for extracellular matrix organization in inflammatory and neuropathic pain
Bibliographic record
Abstract
Chronic pain is a debilitating and poorly treated condition whose underlying mechanisms are poorly understood. Nerve injury and inflammation cause alterations in gene expression in tissues associated with pain processing, supporting molecular and cellular mechanisms that maintain painful states. However, it is not known whether transcriptome changes can be used to reconstruct a molecular pathophysiology of pain. In the current study, we identify molecular pathways contributing to chronic pain states through the analysis of global changes in the transcriptome of dorsal root ganglia, spinal cord, brain, and blood in mouse assays of nerve injury- and inflammation-induced pain. Comparative analyses of differentially expressed genes identified substantial similarities between 2 animal pain assays and with human low-back pain. Furthermore, the extracellular matrix (ECM) organization has been found the most commonly regulated pathway across all tested tissues in the 2 animal assays. Examination of human genome-wide association study data sets revealed an overrepresentation of differentially expressed genes within the ECM organization pathway in single nucleotide polymorphisms most strongly associated with human back pain. In summary, our comprehensive transcriptomics analysis in mouse and human identified ECM organization as a central molecular pathway in the development of chronic pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".