Retinoid X Receptor-selective Signaling in the Regulation of Akt/Protein Kinase B Isoform-specific Expression
Bibliographic record
Abstract
The differentiation and fusion of myoblasts into mature myotubes are complex processes responding to multiple signaling pathways. The function of Akt/PKB is critical for myogenesis, but less is clear as to the regulation of its isoform-specific expression. Bexarotene is a drug already used clinically to treat cancer, and it has the ability to enhance the commitment of embryonic stem cells into skeletal muscle lineage. Whereas bexarotene regulates fundamental biological processes through retinoid X receptor (RXR)-mediated gene expression, molecular pathways underlying its positive effects on myogenesis remain unclear. In this study, we have examined the signaling pathways that transmit bexarotene action in the context of myoblast differentiation. We show that bexarotene promotes myoblast differentiation and fusion through the activation of RXR and the regulation of Akt/PKB isoform-specific expression. Interestingly, bexarotene signaling appears to correlate with residue-specific histone acetylation and is able to counteract the detrimental effects of cachectic factors on myogenic differentiation. We also signify an isoform-specific role for Akt/PKB in RXR-selective signaling to promote and to retain myoblast differentiation. Taken together, our findings establish the viability of applying bexarotene in the prevention and treatment of muscle-wasting disorders, particularly given the lack of drugs that promote myogenic differentiation available for potential clinical applications. Furthermore, the model of bexarotene-enhanced myogenic differentiation will provide an important avenue to identify additional genetic targets and specific molecular interactions that we can study and apply for the development of potential therapeutics in muscle regeneration and repair.
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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) | 0.002 | 0.001 |
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".