DNA methylation is required to maintain DNA replication timing precision and 3D genome integrity
Bibliographic record
Abstract
Abstract DNA replication timing and three-dimensional (3D) genome organisation occur across large domains associated with distinct epigenome patterns to functionally compartmentalise genome regulation. However, it is still unclear if alternations in the epigenome, in particular cancer-related DNA hypomethylation, can directly result in alterations to cancer higher order genome architecture. Here, we use Hi-C and single cell Repli-Seq, in the colorectal cancer DNMT1 and DNMT3B DNA methyltransferases double knockout model, to determine the impact of DNA hypomethylation on replication timing and 3D genome organisation. First, we find that the hypomethylated cells show a striking loss of replication timing precision with gain of cell-to-cell replication timing heterogeneity and loss of 3D genome compartmentalisation. Second, hypomethylated regions that undergo a large change in replication timing also show loss of allelic replication timing, including at cancer-related genes. Finally, we observe the formation of broad ectopic H3K4me3-H3K9me3 domains across hypomethylated regions where late replication is maintained, that potentially prevent aberrant transcription and loss of genome organisation after DNA demethylation. Together, our results highlight a previously underappreciated role for DNA methylation in maintenance of 3D genome architecture.
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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 | 0.001 |
| 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".