A base-resolution panorama of the <i>in vivo</i> impact of cytosine methylation on transcription factor binding
Bibliographic record
Abstract
ABSTRACT While methylation of CpG dinucleotides is traditionally considered antagonistic to the DNA-binding activity of most transcription factors (TFs), recent in vitro studies have revealed a more complex picture, suggesting that over a third of TFs may preferentially bind to methylated sequences. Expanding these in vitro observations to in vivo TF binding preferences, however, is challenging, as the effect of methylation of individual CpG sites cannot be easily isolated from the confounding effects of DNA accessibility and regional DNA methylation. As a result, the in vivo methylation preferences of most TFs remain uncharacterized. Here, we introduce joint accessibility-methylation-sequence (JAMS) models, which connect the strength of the binding signal observed in ChIP-seq to the DNA accessibility of the binding site, regional methylation level, DNA sequence, and base-resolution cytosine methylation. We show that JAMS models quantitatively explain the TF binding strength, recapitulate cell type-specific TF binding, and have high precision for inferring intra-motif methylation effects. Analysis of 2209 ChIP-seq experiments resulted in high-confidence JAMS models for 260 TFs, revealing that 45% of TFs are inhibited by intra-motif methylation in vivo . In contrast, only 16 TFs (6%) preferentially bind to methylated sites, including 11 novel methyl-binding TFs that are mostly from the multi-zinc finger family of TFs. Our study substantially expands the repertoire of in vivo methyl-binding TFs, but also suggests that most TFs that prefer methylated CpGs in vitro present themselves as methylation agnostic in vivo , potentially due to the balancing effect of competition with other methyl-binding proteins.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".