mTORC1 inhibition induces pain via IRS-1-dependent feedback activation of ERK
Bibliographic record
Abstract
Mammalian target of rapamycin complex 1 (mTORC1) inhibitors are extensively used as immunosuppressants to prevent transplant rejection and in treatment of certain cancers. In patients, chronic treatment with rapamycin or its analogues (rapalogues) has been reported to lead to sensory hypersensitivity and pain conditions via an unknown mechanism. Here, we show that pharmacological or genetic inhibition of mTORC1 activates the extracellular signal-regulated kinase (ERK) pathway in sensory neurons via suppression of S6K1 to insulin receptor substrate 1 negative feedback loop. As a result, increased ERK activity induces sensory neuron sensitization, mechanical hypersensitivity, and spontaneous pain. The clinically available adenosine monophosphate-activated protein kinase activator, metformin, which is an antidiabetic drug, prevents rapamycin-induced ERK activation and the development of mechanical hypersensitivity and spontaneous pain. Taken together, our findings demonstrate that activation of the ERK pathway in sensory neurons as a consequence of mTORC1 inhibition leads to the development of pain. Importantly, this effect is abolished by co-treatment with metformin, thus providing a potential treatment option for rapalogue-evoked pain. Our findings highlight the physiological relevance of feedback signaling through mTORC1 inhibition and have important implications for development of pain therapeutics that target the mTOR pathway.
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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".