MYTHO: A novel regulator of autophagy and skeletal muscle health
Bibliographic record
Abstract
Rationale The loss of skeletal muscle function can have dramatic health consequence. Skeletal muscle dysfunction are found in a wide range of clinical conditions, including cancer cachexia, starvation, sepsis, and aging. Autophagy is an essential catabolic process that remove damaged cytosolic components. Accumulating evidence indicates that insufficient or excessive autophagy can contribute to the development of skeletal muscle atrophy. We recently identified a novel FoxO‐dependent gene, which we named MYTHO, with the potential to regulate autophagy in skeletal muscles. However, to date, the roles that MYTHO plays in skeletal muscles remain unknown. In this study, we evaluated the functional importance of MYTHO in skeletal muscle structure and function. Methods To study the functional importance of MYTHO in skeletal muscle mass and function, we used a combination of genetic strategies in mice to delete or overexpress MYTHO. Results MYTHO expression is induced in various muscle wasting conditions, including starvation, denervation, cancer cachexia, and sepsis. Using different genetics approaches, we confirmed that MYTHO is an important regulator of autophagy in skeletal muscle. Importantly, we also found that MYTHO knockdown in skeletal muscles caused various myopathic features, including muscle weakness, accumulation of tubular aggregates, a shift in myofiber type composition and nuclei mispositioning in myofibers. Using animals with muscle specific Atg7 deletion (a model of autophagy inactivation), we demonstrated that the pathological features associated with MYTHO knockdown are independent of its role in regulating autophagy. Conclusion We conclude that MYTHO is a central player in regulating skeletal muscle mass and integrity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".