Mitochondrial DNA heteroplasmy distinguishes disease manifestation in <i>PINK1</i> - and <i>PRKN</i> -linked Parkinson’s disease
Bibliographic record
Abstract
Abstract Biallelic mutations in PINK1 and PRKN cause recessively inherited Parkinson’s disease (PD). Though some studies suggest that PINK1 / PRKN monoallelic mutations may not contribute to risk, deep phenotyping assessment showed that PINK1 or PRKN monoallelic pathogenic variants were at a significantly higher rate in PD compared to controls. Given the established role of PINK1 and Parkin in regulating mitochondrial dynamics, we explored mitochondrial DNA (mtDNA) integrity and inflammation as potential disease modifiers in carriers of mutations in these genes. MtDNA integrity, global gene expression and serum cytokine levels were investigated in a large collection of biallelic (n=84) and monoallelic (n=170) carriers of PINK1 / PRKN mutations, iPD patients (n=67) and controls (n=90). Affected and unaffected PINK1 / PRKN monoallelic mutation carriers can be distinguished by heteroplasmic mtDNA variant load (AUC=0.83, CI:0.74-0.93). Biallelic PINK1 / PRKN mutation carriers harbor more heteroplasmic mtDNA variants in blood (p=0.0006, Z=3.63) compared to monoallelic mutation carriers. This enrichment was confirmed in iPSC-derived and postmortem midbrain neurons from biallelic PRKN -PD patients. Lastly, the heteroplasmic mtDNA variant load was found to correlate with IL6 levels in PINK1 / PRKN mutation carriers (r=0.57, p=0.0074). PINK1 / PRKN mutations predispose individuals to mtDNA variant accumulation in a dose- and disease-dependent manner. MtDNA variant load over time is a potential marker of disease manifestation in PINK1 / PRKN mutation carriers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".