Bioactive lipid mediator class switching regulates myogenic cell progression and muscle regeneration
Bibliographic record
Abstract
The muscle stem cell niche is well-described as influencing myogenic cell fate decision; however, the intrinsic mechanisms driving muscle stem cell progression during myogenesis are not yet fully elucidated. Here, we demonstrate that bioactive lipid class switching, an auto-regulatory mechanism originally described during the inflammatory process, is conserved during myogenesis. During the transition from proliferation to differentiation, myogenic cells shift from pro-inflammatory to pro-resolution pathways, a process partially mediated by 15Δ-PGJ2 that promotes the expression of the prostaglandin inactivation enzyme 15-hydroxyprostaglandin dehydrogenase. Using pharmacological inhibitors and knockout models of the pro-resolution enzyme 15-lipoxygenase, we show that blocking the bioactive lipid class switching impairs myoblast differentiation in vitro and muscle regeneration in vivo. Administration of the pro-resolving mediator Protectin-D1 restores myogenesis, enhances muscle regeneration post-injury and improves muscle phenotype in a dystrophic mouse model. Overall, these findings provide a better comprehension of the mechanisms regulating myogenic progression, which opens new therapeutic avenues for muscle regeneration and dystrophies. Here they show that myogenic cells shift their bioactive lipid profile from pro-inflammatory to pro-resolving during myogenesis. Disrupting this transition impairs differentiation, whereas Protectin-D1 restores fusion and improves muscle phenotype in a dystrophic model.
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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".