Preclinical evaluation of lentiviral gene therapy for adenosine deaminase 2 deficiency (DADA2): engraftment efficiency and biodistribution in humanised NBSGW mice
Bibliographic record
Abstract
Adenosine deaminase type 2 deficiency (DADA2) is caused by bi-allelic loss-of-function mutations in ADA2. While anti-TNF therapy is effective for the autoinflamatory and vasculitic components of the disease it does not correct marrow failure or immunodeficiency. Allogeneic stem cell transplantation (HSCT) offers a potential cure but is limited by challenges such as graft-versus-host-disease and donor availability. We previously demonstrated that lentiviral-mediated ADA2 gene therapy could restore ADA2 enzyme activity in patient-derived cells, correct macrophage inflammatory activation and reduce endothelial activation in vitro. Here, we evaluated the biodistribution and engraftment potential of lentivirally transduced healthy donor and patient-derived haematopoietic stem cells (HSC) in vivo using a humanised NBSGW mouse model. Transduced healthy HSC retained multilineage differentiation and engraftment capacity, without functional impairment. PCR analysis confirmed the absence of viral integration in non-haematopoietic organs, and histology showed no abnormal tissue changes, underscoring the safety and precision of this approach. In DADA2 patient-derived HSC, ADA2 transduction restored protein expression and enzyme activity, supporting improved cellular function and enhanced engraftment potential. These findings provide a strong foundation for advancing ADA2 gene therapy as a therapeutic strategy for DADA2, bringing it closer to clinical application.
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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) | 0.001 | 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".