Endonuclease G depletion may improve efficiency of first generation adenovirus vector DNA replication in HeLa cells.
Bibliographic record
Abstract
First generation adenovirus (Ad5 ΔE1,E3) vectors are able to replicate their DNA in many tumour cells and can be used for oncotherapy. Highest rates of viral DNA replication occur in the G2/M transition of the cell cycle. In this study, we tried to increase the efficiency of Ad5 ΔE1,E3 DNA replication in the cervical carcinoma HeLa cells by using RNA interference (RNAi) to target endonuclease G (EndoG) whose depletion leads to an accumulation of cells in the G2/M transition. Targeting of EndoG by an shRNA encoded on an Ad5 ΔE1,E3 vector resulted in an early proliferation defect of cervical carcinoma HeLa cells. This effect coincided with enhanced DNA replication and encoded transgene expression of an Ad5 ΔE1,E3 vector. Applied in high concentrations, the EndoG-targeting Ad5 ΔE1,E3 vector showed enhanced HeLa cell killing ability relative to control Ad5 ΔE1,E3 vectors. These effects are most likely the result of EndoG depletion, which causes cells to accumulate in the G2/M transition of the cell cycle and extends favourable cellular conditions for Ad5 ΔE1,E3 DNA replication. Targeting of EndoG by RNAi may be a viable strategy for improving both the levels of transgene expression and the oncolytic properties of first generation adenovirus vectors.
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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 | 0.000 |
| 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".