Use of induced pluripotent stem cells to investigate the effects of purine nucleoside phosphorylase deficiency on neuronal development
Bibliographic record
Abstract
Background: Inherited defects in the function of the purine nucleoside phosphorylase (PNP) enzyme can cause severe T cell immune deficiency and early death from infection, autoimmunity, or malignancy. In addition, more than 50% of patients suffer diverse non-infectious neurological complications. However the cause for the neurological abnormalities are not known. Objectives: Differentiate induced pluripotent stem cells (iPSC) from PNP-deficient patients into neuronal cells to better understand the effects of impaired purine metabolism on neuronal development. Methods: Sendai virus was used to generate pluripotent stem cells from PNP-deficient and healthy control lymphoblastoid cells. Cells were differentiated into neuronal cells through the formation of embryoid bodies. Results: After demonstration of pluripotency, normal karyotype, and retention of the PNP deficiency state, iPSC were differentiated into neuronal cells. PNP-deficient neuronal cells had reduced soma and nuclei size in comparison to cells derived from healthy controls. Spontaneous apoptosis, determined by Caspase-3 expression, was increased in PNP-deficient cells. Conclusions: iPSC from PNP-deficient patients can be differentiated into neuronal cells, thereby providing an important tool to study the effects of impaired purine metabolism on neuronal development and potential treatments. Statement of novelty: We report here the first generation and use of neuronal cells derived from induced pluripotent stem cells to model human PNP deficiency, thereby providing an important tool for better understanding and management of this condition.
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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".