mTORC2 mediates structural plasticity in distal nociceptive endings that contributes to pain hypersensitivity following inflammation
Bibliographic record
Abstract
The encoding of noxious stimuli into action potential firing is largely mediated by nociceptive free nerve endings. Tissue inflammation, by changing the intrinsic properties of the nociceptive endings, leads to nociceptive hyperexcitability and thus to the development of inflammatory pain. Here, we showed that tissue inflammation-induced activation of the mammalian target of rapamycin complex 2 (mTORC2) triggers changes in the architecture of nociceptive terminals and leads to inflammatory pain. Pharmacological activation of mTORC2 induced elongation and branching of nociceptor peripheral endings and caused long-lasting pain hypersensitivity. Conversely, nociceptor-specific deletion of the mTORC2 regulatory protein rapamycin-insensitive companion of mTOR (Rictor) prevented inflammation-induced elongation and branching of cutaneous nociceptive fibers and attenuated inflammatory pain hypersensitivity. Computational modeling demonstrated that mTORC2-mediated structural changes in the nociceptive terminal tree are sufficient to increase the excitability of nociceptors. Targeting mTORC2 using a single injection of antisense oligonucleotide against Rictor provided long-lasting alleviation of inflammatory pain hypersensitivity. Collectively, we showed that tissue inflammation-induced activation of mTORC2 causes structural plasticity of nociceptive free nerve endings in the epidermis and inflammatory hyperalgesia, representing a therapeutic target for inflammatory 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 | 0.000 |
| 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".