Lentivirus gene therapy for purine nucleoside phosphorylase deficiency
Bibliographic record
Abstract
BACKGROUND: Purine nucleoside phosphorylase (PNP) deficiency causes the accumulation of toxic purine metabolites and lethal T cell immune defects, which might be corrected by expressing PNP by transplanting bone marrow (BM) cells transduced with lentiviral vectors containing the human PNP gene (lentiPNP). METHODS: Lymphocytes from a single PNP-deficient patient as well as lymphocytes, fibroblasts and BM from PNP-deficient (PNP (-/-)) mice were transduced with lentiPNP. Female PNP (-/-) mice were transplanted with lentiPNP transduced BM cells from male PNP (-/-) mice or normal BM. RESULTS: LentiPNP transduction significantly increased PNP expression in PNP-deficient human lymphocytes, murine lymphocytes, fibroblasts and BM cells. LentiPNP transduction also significantly improved the proliferation of PNP (-/-) murine lymphocyte and survival of irradiated PNP (-/-) fibroblasts. Polymerase chain reaction analysis demonstrated efficient transduction of lentiPNP into total and lineage-depleted BM cells grown ex vivo. LentiPNP transduced PNP (-/-) BM cells transplanted into PNP (-/-) mice expressed PNP in vivo, partially restored urinary uric acid secretion, improved thymocytes maturation, increased weight gain and extended survival of the mice. However, 12 weeks after transplant, the benefit of lentiPNP transduced cells and normal BM diminished and the percentage of engrafted donor cells decreased. CONCLUSIONS: This short-term observational study provides the first in vivo proof that gene therapy may correct some of the abnormalities associated with PNP deficiency. Better gene transduction and expression, as well as improved cell engraftment, are required to further advance PNP gene therapy.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".