Transcriptional regulation by FGF in the switch from pluripotency to skeletal muscle lineage commitment
Bibliographic record
Abstract
During development, key signalling pathways activate transcription factor regulators that direct pluripotent cells to specific cell fates.The bHLH transcription factor Myod was previously referred to as the "master regulator" of the muscle lineage due to its ability to convert fibroblasts to myoblasts.However, overexpression of Myod in mouse embryonic stem (ES) cells or Xenopus pluripotent explants is not sufficient for muscle differentiation.This indicates additional factors are needed for pluripotent cells to become competent to form muscle. Fibroblast growth factor (FGF) is required for mesodermal gene expression and Xenopus skeletal muscle development, and has been implicated in the progression of naïve pluripotency to lineage competence.My hypothesis is that FGF signalling promotes activation of genes required to allow cells to transition from pluripotency to cell lineage commitment.To investigate the role of FGF, a skeletal muscle inducing protocol was developed in Xenopus laevis pluripotent explants expressing Myod and treated with Fgf4.RNA-seq analysis allowed characterisation of the tissues induced by co-expression of Myod + Fgf4.In keeping with my hypothesis, a pattern of gene expression characteristic of fast twitch skeletal muscle was observed.RNA-seq analysis at three developmental time points allowed identification of potential regulators, which were tested for their ability to replace Fgf4 in the skeletal muscle protocol.Using this approach, tcf12 was identified as an FGF regulated gene that is a promising candidate for a myogenic feedforward transcriptional pathway with Myod, downstream of FGF.Differentiation of human skeletal muscle progenitors from H9 ES cells revealed that candidate gene expression was conserved in human myogenesis.Furthering our understanding of the regulation of skeletal muscle differentiation may help improve myoblast cell culture methods, or aid identification of potential therapeutic targets for future muscle wasting disease treatments.
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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".