Mitochondrial DNA heteroplasmy distinguishes disease manifestation in <i>PINK1</i> / <i>PRKN-</i> linked Parkinson’s disease
Bibliographic record
Abstract
Biallelic mutations in PINK1/PRKN cause recessive Parkinson's disease. Given the established role of PINK1/Parkin in regulating mitochondrial dynamics, we explored mitochondrial DNA integrity and inflammation as disease modifiers in carriers of mutations in these genes. Mitochondrial DNA integrity was investigated in a large collection of biallelic (n = 84) and monoallelic (n = 170) carriers of PINK1/PRKN mutations, idiopathic Parkinson's disease patients (n = 67) and controls (n = 90). In addition, we studied global gene expression and serum cytokine levels in a subset. Affected and unaffected PINK1/PRKN monoallelic mutation carriers can be distinguished by heteroplasmic mitochondrial DNA variant load (area under the curve = 0.83, CI 0.74-0.93). Biallelic PINK1/PRKN mutation carriers harbour more heteroplasmic mitochondrial DNA variants in blood (P = 0.0006, Z = 3.63) compared to monoallelic mutation carriers. This enrichment was confirmed in induced pluripotent stem cell-derived (controls, n = 3; biallelic PRKN mutation carriers, n = 4) and post-mortem (control, n = 1; biallelic PRKN mutation carrier, n = 1) midbrain neurons. Last, the heteroplasmic mitochondrial DNA variant load correlated with IL6 levels in PINK1/PRKN mutation carriers (r = 0.57, P = 0.0074). PINK1/PRKN mutations predispose individuals to mitochondrial DNA variant accumulation in a dose- and disease-dependent manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".