Proteome dynamics reveal Leiomodin 1 as a key regulator of myogenic differentiation
Bibliographic record
Abstract
Abstract During myogenic differentiation the cellular architecture and proteome of muscle stem cells and myoblasts undergo extensive remodeling. These molecular processes are only partially understood and display alterations in disease conditions as well as during aging resulting in impaired regeneration. Here, we used mass spectrometry to quantify the temporal dynamics of more than 6000 proteins during myogenic differentiation. We identified the actin nucleator leiomodin 1 (LMOD1) among a restricted subset of cytoskeletal proteins increasing in abundance in early phases of myogenic differentiation. We show that LMOD1 is already expressed by muscle stem cells in vivo and displays increased abundance during skeletal muscle regeneration, especially during early regeneration suggesting that LMOD1 is important for induction of myotube formation. Of note, knockdown of LMOD1 in primary myoblasts and during skeletal muscle regeneration severely affects myogenic differentiation, while overexpression accelerates and improves the initiation of myotube formation suggesting that LMOD1 is a critical component regulating myogenic differentiation. Mechanistically, we show that LMOD1 physically and functionally interacts with the deacetylase sirtuin1 (SIRT1), a regulator of myogenic differentiation, especially at the onset of myogenic differentiation. We demonstrate that LMOD1 influences SIRT1 localization and the expression of a subset of its target genes. Consistently, depletion or pharmacological inhibition of SIRT1 partially rescues the impairment of myogenic differentiation observed after knockdown of LMOD1. Our work identifies a new regulator of myogenic differentiation that might be targeted to improve muscle regeneration in aging and disease.
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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".