Methylome profiling of SetDB1 deficient ESCs reveals diverse epigenetic cross-talk during pluripotency
Bibliographic record
Abstract
Abstract SetDB1 is best known as a chromatin modifier catalyzing H3K9me3. However, recent studies show that SetDB1 can promote H3K27me3-deposition and CTCF-binding, and potentially recruit de novo DNA methyltransferases. Given the tight connection with these processes, we hypothesized that DNA methylation (DNAme) may integrate these combined features of SetDB1. Thereto, we conducted time-course whole-genome bisulfite sequencing following Setdb1 knockout (KO) in mouse embryonic stem cells (ESCs). In serum-cultured ESCs, nearly half of SetDB1 binding sites are DNA methylated, coinciding with H3K9me3, mainly silencing retrotransposons and imprinting control regions. Both H3K9me3 and DNAme are lost upon Setdb1 KO, but while TET2 rapidly removes DNAme at many of these sites, some retrotransposons are shielded from TET2 and lose DNAme slowly via passive dilution. SetDB1-mediated regulation via H3K27me3, CTCF, SMAD3, and histone acetylation are uncoupled from the DNAme-H3K9me3 axis. Hypomethylated naïve ESCs show massive reactivation of retrotransposons upon Setdb1 KO, providing functional evidence that DNAme adds a protective layer against such activity. Altogether, our findings reveal how DNAme coordinates the multifaceted regulatory roles of SetDB1. Highlights In serum ESCs, SetDB1-dependent deposition of H3K9me3 and DNAme are tightly coupled, primarily silencing repeats and imprinted control regions; Loss of SetDB1 causes demethylation of a large range of repeat types, the pace of which is dependent on TET pre-loading; The regulatory modes of SetDB1 mediated by H3K27me3, CTCF, TGF-β signalling and histone acetylation are uncoupled from the SetDB1 DNAme-H3K9me3 axis and/or from each other; Loss of SetDB1-dependent DNAme in hypomethylated 2i ESCs reveals that DNAme serves as a buffering layer to repress SetDB1-mediated H3K9me3 targets.
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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".