CCAAT/enhancer binding protein beta protects muscle satellite cells from apoptosis after injury and in cancer cachexia
Bibliographic record
Abstract
CCAAT/enhancer binding protein beta (C/EBPβ), a transcription factor expressed in muscle satellite cells (SCs), inhibits the myogenic program and is downregulated early in differentiation. In a conditional null model in which C/EBPβ expression is knocked down in paired box protein 7+ (Pax7+) SCs, cardiotoxin (CTX) injury is poorly repaired, although muscle regeneration is efficient in control littermates. While myoblasts lacking C/EBPβ can differentiate efficiently in culture, after CTX injury poor regeneration was attributed to a smaller than normal Pax7+ population, which was not due to a failure of SCs to proliferate. Rather, the percentage of apoptotic SCs was increased in muscle lacking C/EBPβ. Given that an injury induced by BaCl2 is repaired with greater efficiency than controls in the absence of C/EBPβ, we investigated the inflammatory response following BaCl2 and CTX injury and found that the levels of interleukin-1β (IL-1β), a proinflammatory cytokine, were robustly elevated following CTX injury and could induce C/EBPβ expression in myoblasts. High levels of C/EBPβ expression in myoblasts correlated with resistance to apoptotic stimuli, while its loss increased sensitivity to thapsigargin-induced cell death. Using cancer cachexia as a model for chronic inflammation, we found that C/EBPβ expression was increased in SCs and myoblasts of tumor-bearing cachectic animals. Further, in cachectic conditional knockout animals lacking C/EBPβ in Pax7+ cells, the SC compartment was reduced because of increased apoptosis, and regeneration was impaired. Our findings indicate that the stimulation of C/EBPβ expression by IL-1β following muscle injury and in cancer cachexia acts to promote SC survival, and is therefore a protective mechanism for SCs and myoblasts in the face of inflammation.
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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) | 0.001 | 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) | 0.001 | 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".