Inducible TDG knockout models to study epigenetic regulation
Bibliographic record
Abstract
Mechanistic and functional studies by gene disruption or editing approaches often suffer from confounding effects like compensatory cellular adaptations generated by clonal selection. These issues become particularly relevant when studying factors directly involved in genetic or epigenetic maintenance. To provide a genetic tool for functional and mechanistic investigation of DNA-repair mediated active DNA demethylation, we generated experimental models in mice and murine embryonic stem cells (ESCs) based on a minigene of the thymine-DNA glycosylase (TDG). The loxP -flanked miniTdg is rapidly and reliably excised in mice and ESCs by tamoxifen-induced Cre activation, depleting TDG to undetectable levels within 24 hours. We describe the functionality of the engineered miniTdg in mouse and ESCs (TDGiKO ESCs) and validate the pluripotency and differentiation potential of TDGiKO ESCs as well as the phenotype of induced TDG depletion. The controlled and rapid depletion of TDG allows for a precise manipulation at any point in time of multistep experimental procedures as presented here for neuronal differentiation in vitro . Thus, we provide a tested and well-controlled genetic tool for the functional and mechanistic investigation of TDG in active DNA (de)methylation and/or DNA repair with minimal interference from adaptive effects and clonal selection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".