PINK1 deficiency impairs adult neurogenesis of dopaminergic neurons
Bibliographic record
Abstract
Abstract Recent evidence suggests neurogenesis is on-going throughout life but the relevance of these findings for neurodegenerative disorders such as Parkinson’s disease (PD) is poorly understood. Biallelic PINK1 mutations cause early onset, Mendelian inherited PD. We studied the effect of PINK1 deficiency on adult neurogenesis of dopaminergic (DA) neurons in two complementary model systems. Zebrafish are a widely-used model to study neurogenesis in development and through adulthood. Using EdU analyses and lineage-tracing studies, we first demonstrate that a subset of ascending DA neurons and adjacent local-projecting DA neurons are each generated into adulthood in wild type zebrafish at a rate that decreases with age. Pink1-deficiency impedes DA neurogenesis in these populations, most significantly in early adult life. Pink1 already exerts an early effect on Th1+ progenitor cells rather than on differentiated DA neurons only. In addition, we investigate the effect of PINK1 deficiency in a human isogenic organoid model. Global neuronal differentiation in PINK1-deficient organoids and isogenic controls is similar, but PINK1-deficient organoids display impeded DA neurogenesis. The observation of impaired adult dopaminergic neurogenesis in Pink1 deficiency in two complementing model systems may have significant consequences for future therapeutic approaches in human PD patients with biallelic PINK1 mutations.
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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 | 0.000 |
| 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".