Regulation of Branched‐Chain Alpha‐Keto Acid Dehydrogenase During Muscle Cell Differentiation
Bibliographic record
Abstract
Skeletal muscles are critical to locomotion and whole‐body substrate metabolism; their mass and function also affect quality of life. Suboptimal muscle mass and function underlie or worsen chronic catabolic conditions like uncontrolled diabetes and several cancers. They are also predictive of treatment outcomes and survival. As a result, studies into mechanisms of muscle preservation and regeneration hold potential to improve patient outcomes. Muscle mass is a function of muscle cell number and protein balance. While muscle protein balance can be regulated by nutrition, especially the branched‐chain amino acids (BCAA: leucine, isoleucine and valine), the effect of nutrition on muscle cell formation and regeneration has received little attention. In addition, recent metabolomics studies have implicated metabolites of BCAA in both the activation of anabolic signaling and in prognosis of chronic disease, but little is known about the effects of these metabolites and the pathway that generate them on muscle cell formation. The first irreversible and rate limiting reaction involved in BCAA catabolism is regulated by an enzyme complex, branched‐chain alpha‐keto acid dehydrogenase (BCKD). Working with rodent muscle cells, we showed that the abundance of BCKDE1a subunit was upregulated during cell differentiation (up to 5X, P<0.05) without a corresponding change in mRNA level. BCKD activity is antagonistically regulated by a phosphatase (positively), and a kinase (negatively). BCKD kinase abundance tended to rise during differentiation along with a decrease in BCKD activity. Myoblasts depleted of BCKDE1a had impaired myotube formation and marked reduction in the expression of myofibrillar proteins. Collectively, these data highlight the significance of BCAA catabolism during cell differentiation and suggest that interventions that target BCKD abundance hold promise for muscle regeneration. Support or Funding Information NSERC, Faculty of Health at York University
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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".