CG methylated microarrays identify a novel methylated sequence bound by the CEBPB|ATF4 heterodimer that is active in vivo
Bibliographic record
Abstract
To evaluate the effect of CG methylation on DNA binding of sequence-specific B-ZIP transcription factors (TFs) in a high-throughput manner, we enzymatically methylated the cytosine in the CG dinucleotide on protein binding microarrays. Two Agilent DNA array designs were used. One contained 40,000 features using de Bruijn sequences where each 8-mer occurs 32 times in various positions in the DNA sequence. The second contained 180,000 features with each CG containing 8-mer occurring three times. The first design was better for identification of binding motifs, while the second was better for quantification. Using this novel technology, we show that CG methylation enhanced binding for CEBPA and CEBPB and inhibited binding for CREB, ATF4, JUN, JUND, CEBPD, and CEBPG. The CEBPB|ATF4 heterodimer bound a novel motif CGAT|GCAA 10-fold better when methylated. The electrophoretic mobility shift assay (EMSA) confirmed these results. CEBPB ChIP-seq data using primary female mouse dermal fibroblasts with 50× methylome coverage for each strand indicate that the methylated sequences well-bound on the arrays are also bound in vivo. CEBPB bound 39% of the methylated canonical 10-mers ATTGC|GCAAT in the mouse genome. After ATF4 protein induction by thapsigargin which results in ER stress, CEBPB binds methylated CGAT|GCAA in vivo, recapitulating what was observed on the arrays. This methodology can be used to identify new methylated DNA sequences preferentially bound by TFs, which may be functional in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".