Evolutionarily divergent mTOR remodels the translatome to drive rapid wound closure and regeneration
Bibliographic record
Abstract
Abstract An outstanding mystery in biology is why some species, such as the axolotl, can scarlessly heal and regenerate tissues while most mammals cannot. Here, we demonstrate that rapid activation of protein synthesis is a unique, and previously uncharacterized, feature of the injury response critical for limb regeneration in the axolotl ( A. mexicanum) . By applying polysome sequencing, we identify hundreds of transcripts, including antioxidants and ribosome components, which do not change in their overall mRNA abundance but are selectively activated at the level of translation from pre-existing mRNAs in response to injury. In contrast, we show that protein synthesis is not activated in response to digit amputation in the non-regenerative mouse. We further identify the mTORC1 pathway as a key upstream signal that mediates this regenerative translation response in the axolotl. Inhibition of this pathway is sufficient to suppress translation and axolotl regeneration. Surprisingly, although mTOR is highly evolutionarily conserved, we discover unappreciated expansions in mTOR protein sequence among urodele amphibians. By engineering an axolotl mTOR in human cells, we demonstrate that this change creates a hypersensitive kinase that may allow axolotls to maintain this pathway in a highly labile state primed for rapid activation. This may underlie metabolic differences and nutrient sensing between regenerative and non-regenerative species that are key to regeneration. Together, these findings highlight the unanticipated impact of the translatome on orchestrating the early steps of wound healing in highly regenerative species and provide a missing link in our understanding of vertebrate regenerative potential.
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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".