Multidimensional Response Surface Methodology for the Development of a Gene Editing Protocol for p67 <sup>phox</sup> -Deficient Chronic Granulomatous Disease
Bibliographic record
Abstract
Replacing a faulty gene with a correct copy has become a viable therapeutic option as a result of recent progress in gene editing protocols. Targeted integration of therapeutic genes in hematopoietic stem cells has been achieved for multiple genes using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 system and Adeno-Associated Virus (AAV) to carry a donor template. Although this is a promising strategy to correct genetic blood disorders, it is associated with toxicity and loss of function in CD34 + hematopoietic stem and progenitor cells, which has hampered clinical application. Balancing the maximum achievable correction against deleterious effects on the cells is critical. However, multiple factors are known to contribute, and the optimization process is laborious and not always clearly defined. We have developed a flexible multidimensional Response Surface Methodology approach for optimization of gene correction. Using this approach, we could rapidly investigate and select editing conditions for CD34 + cells with the best possible balance between correction and cell/colony-forming unit (CFU) loss in a parsimonious one-shot experiment. This method revealed that using relatively low doses of AAV2/6 and CRISPR/Cas9 ribonucleoprotein complex, we can preserve the fitness of CD34 + cells and, at the same time, achieve high levels of targeted gene insertion. We then used these optimized editing conditions for the correction of p67 phox -deficient chronic granulomatous disease (CGD), an autosomal recessive disorder of blood phagocytic cells resulting in severe recurrent bacterial and fungal infections and achieved rescue of p67 phox expression and functional correction of CD34 + -derived neutrophils from a CGD patient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".