Depletion of DNMT1 in differentiated human cells highlights key classes of sensitive genes and an interplay with polycomb repression
Bibliographic record
Abstract
BACKGROUND: DNA methylation plays a vital role in the cell, but loss-of-function mutations of the maintenance methyltransferase DNMT1 in normal human cells are lethal, precluding target identification, and existing hypomorphic lines are tumour cells. We generated instead a hypomorphic series in normal hTERT-immortalised fibroblasts using stably integrated short hairpin RNA. RESULTS: Approximately two-thirds of sites showed demethylation as expected, with one-third showing hypermethylation, and targets were shared between the three independently derived lines. Enrichment analysis indicated significant losses at promoters and gene bodies with four gene classes most affected: (1) protocadherins, which are key to neural cell identity; (2) genes involved in fat homoeostasis/body mass determination; (3) olfactory receptors and (4) cancer/testis antigen (CTA) genes. Overall effects on transcription were relatively small in these fibroblasts, but CTA genes showed robust derepression. Comparison with siRNA-treated cells indicated that shRNA lines show substantial remethylation over time. Regions showing persistent hypomethylation in the shRNA lines were associated with polycomb repression and were derepressed on addition of an EZH2 inhibitor. Persistent hypermethylation in shRNA lines was, in contrast, associated with poised promoters. CONCLUSIONS: We have assessed for the first time the effects of chronic depletion of DNMT1 in an untransformed, differentiated human cell type. Our results suggest polycomb marking blocks remethylation and indicate the sensitivity of key neural, adipose and cancer-associated genes to loss of maintenance methylation activity.
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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) | 0.002 | 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".