Highly specific σ <sub>2</sub> R/TMEM97 ligand FEM-1689 alleviates neuropathic pain and inhibits the integrated stress response
Bibliographic record
Abstract
The sigma 2 receptor (σ 2 R) was described pharmacologically more than three decades ago, but its molecular identity remained obscure until recently when it was identified as transmembrane protein 97 (TMEM97). We and others have shown that σ 2 R/TMEM97 ligands alleviate mechanical hypersensitivity in mouse neuropathic pain models with a time course wherein maximal antinociceptive effect is approximately 24 h following dosing. We sought to understand this unique antineuropathic pain effect by addressing two key questions: do these σ 2 R/TMEM97 compounds act selectively via the receptor, and what is their downstream mechanism on nociceptive neurons? Using male and female conventional knockout mice for Tmem97, we find that a σ 2 R/TMEM97 binding compound, FEM-1689, requires the presence of the gene to produce antinociception in the spared nerve injury model in mice. Using primary mouse dorsal root ganglion neurons, we demonstrate that FEM-1689 inhibits the integrated stress response (ISR) and promotes neurite outgrowth via a σ 2 R/TMEM97-specific action. We extend the clinical translational value of these findings by showing that FEM-1689 reduces ISR and p-eIF2α levels in human sensory neurons and that it alleviates the pathogenic engagement of ISR by methylglyoxal. We also demonstrate that σ 2 R/TMEM97 is expressed in human nociceptors and satellite glial cells. These results validate σ 2 R/TMEM97 as a promising target for further development for the treatment of neuropathic 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".