Silencing of gene expression: implications for design of retrovirus vectors
Bibliographic record
Abstract
Transcriptional silencing of retroviruses poses a major obstacle to their use as gene therapy vectors. Silencing is most pronounced in stem cells which are desirable targets for therapeutic gene delivery. Many vector designs combat silencing through cis-modifications of retroviral vector sequences. These designs include mutations of known retroviral silencer elements, addition of positive regulatory elements and insulator elements to protect the transgene from negative position effects. Similar strategies are being applied to lentiviral vectors that readily infect non-dividing quiescent stem cells. Collectively these cis-modifications have significantly improved vector design but optimal expression may require additional intervention to escape completely the trans-factors that scan for foreign DNA, establish silencing in stem cells and maintain silencing in their progeny. Cytosine methylation of CpG sites was proposed to cause retroviral silencing over 20 years ago. However, several studies provide evidence that retrovirus silencing acts through methylase-independent mechanisms. We propose an alternative silencing mechanism initiated by a speculative stem cell-specific "somno-complex". Further understanding of retroviral silencing mechanisms will facilitate better gene therapy vector design and raise new strategies to block transcriptional silencing in transduced stem cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".