Endoplasmic reticulum stress in the dorsal root ganglia regulates large‐conductance potassium channels and contributes to pain in a model of multiple sclerosis
Bibliographic record
Abstract
Abstract Neuropathic pain is a common symptom of multiple sclerosis (MS) and current treatment options are ineffective. In this study, we investigated whether endoplasmic reticulum (ER) stress in dorsal root ganglia (DRG) contributes to pain hypersensitivity in the experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Inflammatory cells and increased levels of ER stress markers are evident in post‐mortem DRGs from MS patients. Similarly, we observed ER stress in the DRG of mice with EAE and relieving ER stress with a chemical chaperone, 4‐phenylbutyric acid (4‐PBA), reduced pain hypersensitivity. In vitro, 4‐PBA and the selective PERK inhibitor, AMG44, normalize cytosolic Ca 2+ transients in putative DRG nociceptors. We went on to assess disease‐mediated changes in the functional properties of Ca 2+ ‐sensitive BK‐type K + channels in DRG neurons. We found that the conductance‐voltage (GV) relationship of BK channels was shifted to a more positive voltage, together with a more depolarized resting membrane potential in EAE cells. Our results suggest that ER stress in sensory neurons of MS patients and mice with EAE is a source of pain and that ER stress modulators can effectively counteract this phenotype.
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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 | 0.001 |
| 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".