Contribution of Retinoid X Receptor Signaling to the Specification of Skeletal Muscle Lineage
Bibliographic record
Abstract
Pluripotent stem cells possess a tremendous potential for the treatment of many diseases because of their capacity to differentiate into a variety of cell lineages. However, they provide little promise for muscle-related diseases, mainly because of the lack of small molecule inducers to efficiently direct myogenic conversion. Retinoic acid, acting through the retinoic acid receptor (RAR) and retinoid X receptor (RXR), affects stem cell fate determination in a concentration-dependent manner, but it only has a modest efficacy on the commitment of ES cells into skeletal muscle lineage. The RXR is very important for embryonic development but is generally considered to act as a silent partner of RAR in a non-permissive mode. In this study, we have examined whether activation of the RXR by rexinoid or RXR-specific signaling play a role in the specification of stem cells into muscle lineage. Our findings demonstrate that mouse ES cells generate skeletal myocytes effectively upon treatment with rexinoid at the early stage of differentiation and that on a molecular level, rexinoid-enhanced myogenesis simulates the sequential events observed in vivo. Moreover, RXR-mediated myogenic conversion requires the function of β-catenin but not RAR. Our studies establish the feasibility of applying the RXR agonist in cell-based therapies to treat muscle-related diseases. The aptitude of mouse ES cells to generate skeletal myocytes following rexinoid induction also provides a model system to study the convergence of different signaling pathways in myogenesis.
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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".