DNA methylation shapes transcription factor binding beyond canonical CpG contexts
Bibliographic record
Abstract
Cytosine methylation is a key epigenetic modification that regulates transcription factor (TF) binding and gene expression. While most current understanding of methylation-sensitive TF binding derives from studies focused exclusively on fully methylated CpG sites, alternative forms-such as non-CpG and hemimethylation-are increasingly recognized as widespread and functionally important, particularly in embryonic stem cells and neurons. However, the direct impact of these alternative methylation contexts on TF-DNA interactions remains poorly defined, largely because current binding assays introduce methylation enzymatically, which precludes strand-specific and position-resolved measurements. Here, we systematically profile the methylation sensitivity of 18 human TFs spanning 11 structural families using chemically synthesized DNA libraries containing position-specific 5-methylcytosines (5mC) in CpG, non-CpG, and hemimethylated contexts, measured via high-throughput protein-binding microarrays. Our results reveal extensive TF sensitivity to methylation state, position, and strand orientation, including strong binding of several TFs to non-CpG and hemimethylated sites. The presence of 5mC can dramatically alter TF-DNA interactions: transforming low-affinity sites into high-affinity ones by enabling new contacts or silencing otherwise favorable motifs through steric hindrance. Genomic analyses further show that the methylation-sensitive sequences identified in vitro are represented within enhancers and regulatory elements, exhibiting distinct methylation patterns across cell types. Together, our findings uncover a previously hidden layer of methylation-dependent TF-DNA recognition, broadening the understanding of epigenetics in transcriptional regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".