Retroviral vector silencing during iPS cell induction: An epigenetic beacon that signals distinct pluripotent states
Bibliographic record
Abstract
Retroviral vectors are transcriptionally silent in pluripotent stem cells. This feature has been potently applied in studies that reprogram somatic cells into induced pluripotent stem (iPS) cells. By delivering the four Yamanaka factors in retroviral vectors, high expression is obtained in fibroblasts to induce the pluripotent state. Partial reprogramming generates Class I iPS cells that express the viral transgenes and endogenous pluripotency genes. Full-reprogramming in Class II iPS cells silences the vectors as the endogenous genes maintain the pluripotent state. Thus, retroviral vector silencing serves as a beacon marking the fully reprogrammed pluripotent state. Here we review known silencer elements, and the histone modifying and DNA methylation pathways, that silence retroviral and lentiviral vectors in pluripotent stem cells. Both retroviral and lentiviral vectors are influenced by position effects and often exhibit variegated expression. The best vector designs facilitate full-reprogramming and subsequent retroviral silencing, which is required for directed-differentiation. Current retroviral reprogramming methods can be immediately applied to create patient-specific iPS cell models of human disease, however, future clinical applications will require novel chemical or other reprogramming methods that reduce or eliminate the integrated vector copy number load. Nevertheless, retroviral vectors will continue to play an important role in genetically correcting patient iPS cell models. We anticipate that novel pluripotent-specific reporter vectors will select for isolation of high quality human iPS cell lines, and select against undifferentiated pluripotent cells during regenerative medicine to prevent teratoma formation after transplantation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".