Purine nucleoside phosphorylase deficiency induces p53-mediated intrinsic apoptosis in human induced pluripotent stem cell-derived neurons
Bibliographic record
Abstract
Purine nucleoside phosphorylase (PNP) is an important enzyme in the purine degradation and salvage pathway. PNP deficiency results in marked T lineage lymphopenia and severe immunodeficiency. Additionally, PNP-deficient patients and mice suffer from diverse non-infectious neurological abnormalities of unknown etiology. To further investigate the cause for these neurologic abnormalities, induced pluripotent stem cells (iPSC) from two PNP-deficient patients were differentiated into neurons. The iPSC-derived PNP-deficient neurons had significantly reduced soma and nuclei volumes. The PNP-deficient neurons demonstrated increased spontaneous and staurosporine-induced apoptosis, measured by cleaved caspase-3 expression, together with decreased mitochondrial membrane potential and increased cleaved caspase-9 expression, indicative of enhanced intrinsic apoptosis. Greater expression of tumor protein p53 was also observed in these neurons, and inhibition of p53 using pifithrin-α prevented the apoptosis. Importantly, treatment of the iPSC-derived PNP-deficient neurons with exogenous PNP enzyme alleviated the apoptosis. Inhibition of ribonucleotide reductase (RNR) in iPSC derived from PNP-proficient neurons with hydroxyurea or with nicotinamide and trichostatin A increased the intrinsic neuronal apoptosis, implicating RNR dysfunction as the potential mechanism for the damage caused by PNP deficiency. The findings presented here establish a potential mechanism for the neurological defects observed in PNP-deficient patients and reinforce the critical role that PNP has for neuronal viability.
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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 | 0.000 | 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".