TAT-mediated intracellular delivery of purine nucleoside phosphorylase corrects its deficiency in mice
Bibliographic record
Abstract
Defects in purine nucleoside phosphorylase (PNP) enzyme activity result in abnormal nucleoside homeostasis, severe T cell immunodeficiency, neurological dysfunction, and early death. Protein transduction domain (PTD) can transfer molecules into cells and may help restore PNP activity in cases of PNP deficiency. However, long-term use of PTD to replace enzymes in animal models or patients has not previously been described. We fused human PNP to the HIV-TAT PTD and found that the fusion with TAT changed the retention and distribution of PNP in PNP-deficient mice. TAT induced rapid intracellular delivery of PNP into tissues, including the brain, prevented urinary excretion of PNP, and protected PNP from neutralizing antibodies, resulting in significant extension of the enzyme's biological activity in vivo. Frequent TAT-PNP injections in PNP-deficient mice corrected the metabolic disorder and immune defects with no apparent toxicity. TAT-PNP remained effective over 24 weeks of treatment, resulting in continued improvement in immune function and extended survival. Our data demonstrate that TAT changes the properties of PNP in vivo and that long-term intracellular delivery of PNP by TAT corrects PNP deficiency in mice. We provide evidence to promote further use of PTD to treat diseases that require repeated intracellular enzyme or protein delivery.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".