Diphtheria toxin‐mediated transposon‐driven poly (A)‐trapping efficiently disrupts transcriptionally silent genes in embryonic stem cells
Bibliographic record
Abstract
Random gene trapping is the application of insertional mutagenesis techniques that are conventionally used to inactivate protein-coding genes in mouse embryonic stem (ES) cells. Transcriptionally silent genes are not effectively targeted by conventional random gene trapping techniques, thus we herein developed an unbiased poly (A) trap (UPATrap) method using a Tol2 transposon, which preferentially integrated into active genes rather than silent genes in ES cells. To achieve efficient trapping at transcriptionally silent genes using random insertional mutagenesis in ES cells, we generated a new diphtheria toxin (DT)-mediated trapping vector, DTrap that removed cells, through the expression of DT that was induced by the promoter activity of the trapped genes, and selected trapped clones using the neomycin-resistance gene of the vector. We found that a double-DT, the dDT vector, dominantly induced the disruption of silent genes, but not active genes, and showed more stable integration in ES cells than the UPATrap vector. The dDT vector disrupted differentiated cell lineage genes, which were silent in ES cells, and labeled trapped clone cells by the expression of EGFP upon differentiation. Thus, the dDT vector provides a systematic approach to disrupt silent genes and examine the cellular functions of trapped genes in the differentiation of target cells and development.
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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) | 0.001 | 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".