Intracellular Delivery of Human Purine Nucleoside Phosphorylase by Engineered Diphtheria Toxin Rescues Function in Target Cells
Bibliographic record
Abstract
Despite a wealth of potential applications inside target cells, protein-based therapeutics are largely limited to extracellular targets due to the inability of proteins to readily cross biological membranes and enter the cytosol. Bacterial toxins, which deliver a cytotoxic enzyme into cells as part of their intoxication mechanism, hold great potential as platforms for delivering therapeutic protein cargo into cells. Diphtheria toxin (DT) has been shown to be capable of delivering an array of model proteins of varying sizes, structures, and stabilities into mammalian cells as amino-terminal fusions. Here, seeking to expand the utility of DT as a delivery vector, we asked whether an active human enzyme, purine nucleoside phosphorylase (PNP), could be delivered by DT into cells to rescue PNP deficiency. Using a series of biochemical and cellular readouts, we demonstrate that PNP is efficiently delivered into target cells in a receptor- and translocation-dependent manner. In patient-derived PNP-deficient lymphocytes and pluripotent stem cell-differentiated neurons, we show that human PNP is efficiently translocated into target cells by DT, where it is able to restore intracellular hypoxanthine levels. Further, through replacement of the native receptor-binding moiety of DT with single-chain variable fragments that were selected to bind mouse HBEGF, we show that PNP can be retargeted into mouse splenocytes from PNP-deficient mice, resulting in restoration of the proliferative capacity of T-cells. These findings highlight the versatility of the DT delivery platform and provide an attractive approach for the delivery of PNP as well as other cytosolic enzymes implicated in disease.
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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 | 0.000 | 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 | 0.000 | 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".