Identification of MEF2-regulated genes during muscle differentiation
Bibliographic record
Abstract
Although a great deal has been elucidated concerning the mechanisms regulating muscle differentiation, little is known about transcription factor-specific gene regulation. Our understanding of the genetic mechanisms regulating cell differentiation is quite limited. Much of what has been defined centers on regulatory signaling cascades and transcription factors. Surprisingly few studies have investigated the association of genes with specific transcription factors. To address these issues, we have utilized a method coupling chromatin immunoprecipitation and CpG microarrays to characterize the genes associated with MEF2 in differentiating C(2)C(12) cells. Results demonstrated a defined binding pattern over the course of differentiation. Filtered data demonstrated 9 clones to be elevated at 0 h, 792 at 6 h, 163 by 1 day, and 316 at 3 days. Using unbiased selection parameters, we selected a subset of 291 prospective candidates. Clones were sequenced and filtered for removal of redundancy between clones and for the presence of repetitive elements. We were able to place 50 of these on the mouse genome, and 20 were found to be located near well-annotated genes. From this list, previously undefined associations with MEF2 were discovered. Many of these genes represent proteins involved in neurogenesis, neuromuscular junctions, signaling and metabolism. The remaining clones include many full-length cDNA and represent novel gene targets. The results of this study provides for the first time, a unique look at gene regulation at the level of transcription factor binding in differentiating muscle.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".