Signaling modality within gp130 receptor enhances tissue regeneration
Bibliographic record
Abstract
Abstract Adult mammals are incapable of multi-tissue regeneration and augmentation of this potential may drastically shift current therapeutic paradigms. Here, we found that a common co-receptor of IL-6 cytokines, glycoprotein 130 (gp130), serves as a major nexus integrating various context-specific signaling inputs to either promote regenerative outcomes or aggravate disease progression. Via genetic and pharmacological experiments in vitro and in vivo , we demonstrated that a signaling tyrosine 814 (Y814) within gp130 serves as a major cellular stress sensor. Mice with constitutively inactivated Y814 (F814) exhibit regenerative, not reparative, responses after wounding in skin and anti-degenerative responses in the synovial joint. In addition, pharmacological inhibition of gp130 Y814 results in regeneration of multiple tissues in several species as well as disease modification in animal models of osteoarthritis. Our study characterizes a novel molecular mechanism that, if selectively manipulated, enhances the intrinsic regenerative capacity while preventing pathological outcomes in injury and disease. Summary Gp130 Y814 signaling module serves as a cellular stress sensor responsible for hindering tissue regeneration while triggering pathological outcomes after injury.
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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) | 0.003 | 0.001 |
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".