Role of the TWEAK-Fn14-cIAP1-NF-κB Signaling Axis in the Regulation of Myogenesis and Muscle Homeostasis
Bibliographic record
Abstract
Mammalian skeletal muscle maintains a robust regenerative capacity throughout life, largely due to the presence of a stem cell population known as "satellite cells" in the muscle milieu. In normal conditions, these cells remain quiescent; they are activated upon injury to become myoblasts, which proliferate extensively and eventually differentiate and fuse to form new multinucleated muscle fibers. Recent findings have identified some of the factors, including the cytokine TNFα-like weak inducer of apoptosis (TWEAK), which govern these cells' decisions to proliferate, differentiate, or fuse. In this review, we will address the functions of TWEAK, its receptor Fn14, and the associated signal transduction molecule, the cellular inhibitor of apoptosis 1 (cIAP1), in the regulation of myogenesis. TWEAK signaling can activate the canonical NF-κB signaling pathway, which promotes myoblast proliferation and inhibits myogenesis. In addition, TWEAK activates the non-canonical NF-κB pathway, which, in contrast, promotes myogenesis by increasing myoblast fusion. Both pathways are regulated by cIAP1, which is an essential component of downstream signaling mediated by TWEAK and similar cytokines. This review will focus on the seemingly contradictory roles played by TWEAK during muscle regeneration, by highlighting the interplay between the two NF-κB pathways under physiological and pathological conditions. We will also discuss how myogenesis is negatively affected by chronic conditions, which affect homeostasis of the skeletal muscle environment.
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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 | 0.001 | 0.001 |
| 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".