Retrovirus Silencing and Vector Design: Relevance to Normal and Cancer Stem Cells?
Bibliographic record
Abstract
An obstacle confronting gene therapy in stem cells is transcriptional silencing of the vector. Here, we discuss recent data indicating that oncoretrovirus and lentivirus vectors are silenced by multiple epigenetic pathways that result in DNA methylation and histone modifications. Both vector types can be variegated in stem cells and expression is often extinguished during differentiation. We propose a novel model of retrovirus silencing in which epigenetic pathways compete to recruit histone deacetylases, de novo methyltransferases, histone H1 and MeCP2 to the provirus. These chromatin modifications may act in concert with heterochromatin at or near the integration site to establish silencing or variegation respectively. Retrovirus vector designs for stem cells should delete virus silencer elements, incorporate strong positive regulatory elements and insulators, and avoid non-mammalian reporter genes. In addition, cancer stem cells that continually repopulate a growing tumour may share silencing pathways with normal stem cells. Ultimately, optimized vector designs may prove to be valuable tools for gene therapy of both normal and cancer 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".