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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".