The role of Nedd4L in the regulation of muscle stem cell function
Bibliographic record
Abstract
Muscle wasting diseases exist as a spectrum of diseases in which muscle function is impaired.Adult stem cells are the drivers of regeneration in damaged tissue.In patients with muscle degenerative diseases, the balance between the processes of muscle stem cell (MuSC) self-renewal and differentiation is perturbed; thus creating an environment that is not conducive to tissue homeostasis.Therefore, in order to assist in developing effective cell-based therapies for muscle wasting diseases, we must investigate the molecular mechanisms that are crucial for maintaining the critical balance that promotes normal MuSC function.E3 ubiquitin ligases target proteins for degradation through the proteasome, and they are known to be critical regulators of cell function.Interestingly, our data indicate that in muscle stem cells, Nedd4L (Neural Precursor Cell Expressed, Developmentally Down-Regulated 4-Like) is the only E3 ubiquitin ligase that is highly up regulated during a specific window following MuSC activation.Given this unique time frame, we hypothesize that Nedd4L is involved in a specific set of cellular functions that determine whether an activated satellite cell will self-renew or differentiate.In order to elucidate the function of Nedd4L in the regulation of MuSCs, I have utilized a series of in vitro and in vivo analyses.C2C12 mouse myoblasts were used to generate stable cell lines overexpressing Nedd4L and a mutated Nedd4L to assess the effect of Nedd4L on their proliferation and differentiation.Additionally, we deleted Nedd4L in MuSCs using the Cre/LoxP system to study the effect of the loss of Nedd4L on MuSC number and regenerative capacity of the whole muscle.Through these experiments, I have begun to characterize the role of Nedd4L in the MuSC context.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".