Neuropathic pain promotes adaptive changes in gene expression in brain networks involved in stress and depression
Bibliographic record
Abstract
Neuropathic pain is a complex chronic condition characterized by various sensory, cognitive, and affective symptoms. A large percentage of patients with neuropathic pain are also afflicted with depression and anxiety disorders, a pattern that is also seen in animal models. Furthermore, clinical and preclinical studies indicate that chronic pain corresponds with adaptations in several brain networks involved in mood, motivation, and reward. Chronic stress is also a major risk factor for depression. We investigated whether chronic pain and stress affect similar molecular mechanisms and whether chronic pain can affect gene expression patterns that are involved in depression. Using two mouse models of neuropathic pain and depression [spared nerve injury (SNI) and chronic unpredictable stress (CUS)], we performed next-generation RNA sequencing and pathway analysis to monitor changes in gene expression in the nucleus accumbens (NAc), the medial prefrontal cortex (mPFC), and the periaqueductal gray (PAG). In addition to finding unique transcriptome profiles across these regions, we identified a substantial number of signaling pathway-associated genes with similar changes in expression in both SNI and CUS mice. Many of these genes have been implicated in depression, anxiety, and chronic pain in patients. Our study provides a resource of the changes in gene expression induced by long-term neuropathic pain in three distinct brain regions and reveals molecular connections between pain and chronic stress.
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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".