Analysis of early C2C12 myogenesis identifies stably and differentially expressed transcriptional regulators whose knock-down inhibits myoblast differentiation
Bibliographic record
Abstract
Myogenesis is a tightly controlled process involving the transcriptional activation and repression of thousands of genes. Although many components of the transcriptional network regulating the later phases of myogenesis have been identified, relatively few studies have described the transcriptional landscape during the first 24 h, when myoblasts commit to differentiate. Through dense temporal profiling of differentiating C2C12 myoblasts, we identify 193 transcriptional regulators (TRs) whose expression is significantly altered within the first 24 h of myogenesis. A high-content shRNA screen of 77 TRs involving 427 stable lines identified 42 genes whose knockdown significantly inhibits differentiation of C2C12 myoblasts. Of the TRs that were differentially expressed within the first 24 h, over half inhibited differentiation when knocked down, including known regulators of myogenesis (Myod1, Myog, and Myf5), as well as 19 TRs not previously associated with this process. Surprisingly, a similar proportion (55%) of shRNAs targeting TRs whose expression did not change also inhibited C2C12 myogenesis. We further show that a subset of these TRs inhibits myogenesis by downregulating expression of known regulatory and structural proteins. Our findings clearly illustrate that several TRs critical for C2C12 myogenesis are not differentially regulated, suggesting that approaches that focus functional studies on differentially-expressed transcripts will fail to provide a comprehensive view of this complex process.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".