High throughput screening identifies SOX2 as a super pioneer factor that inhibits DNA methylation maintenance at its binding sites
Bibliographic record
Abstract
Binding of mammalian transcription factors (TFs) to regulatory regions is hindered by chromatin compaction and DNA methylation of their binding sites. Nevertheless, pioneer transcription factors (PFs), a distinct class of TFs, have the ability to access nucleosomal DNA, leading to nucleosome remodelling and enhanced chromatin accessibility. Whether PFs can bind to methylated sites and induce DNA demethylation is largely unknown. Using a highly parallelized approach to investigate PF ability to bind methylated DNA and induce DNA demethylation, we show that the interdependence between DNA methylation and TF binding is more complex than previously thought, even within a select group of TFs displaying pioneering activity; while some PFs do not affect the methylation status of their binding sites, we identified PFs that can protect DNA from methylation and others that can induce DNA demethylation at methylated binding sites. We call the latter super pioneer transcription factors (SPFs), as they are seemingly able to overcome several types of repressive epigenetic marks. Finally, while most SPFs induce TET-dependent active DNA demethylation, SOX2 binding leads to passive demethylation, an activity enhanced by the co-binding of OCT4. This finding suggests that SPFs could interfere with epigenetic memory during DNA replication.
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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".