Reduction in hematopoietic stem cell numbers with in vivo drug selection can be partially abrogated by HOXB4 gene expression
Bibliographic record
Abstract
In vivo selection of hematopoietic stem cells (HSCs) offers an approach to enrichment of genetically modified blood cells in the context of gene therapy for blood disorders. We have previously demonstrated efficient HSC selection in mice using retroviral vectors expressing dihydrofolate reductase (DHFR) or methylguanine methyltransferase (MGMT) drug resistance genes. In this study, we examined whether drug selection was followed by subsequent HSC regeneration and, if not, whether regeneration could be augmented by enforced expression of HOXB4, which has previously been shown to enhance HSC regeneration after transplant. Using a murine competitive repopulation model, we found that selection using either the DHFR or the MGMT system was accompanied by a significant overall reduction in repopulating activity in secondary transplant assays, although hematopoiesis remained normal after recovery. Inclusion of a HOXB4 expression cassette in the DHFR vector resulted in a partial restoration of HSC numbers following selection and was associated with an increase in HSC selection efficiency. These results illustrate that while drug resistance vectors can protect transduced HSC from cytotoxic drugs, the self-renewal capacity of transduced HSCs is limited following in vivo selection. Strategies that increase self-renewal capacity could increase the efficiency and safety of in vivo selection.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".