The function of PINK1 and parkin in mitochondrial quality control (234.2)
Bibliographic record
Abstract
Parkinson’s disease (PD) is a common, devastating neurodegenerative disorder. Both genetic and environmental models strongly implicate mitochondrial dysfunction in PD. In particular, PINK1 and Parkin, two recessive PD genes, function in a common pathway regulating mitochondrial quality‐control. In healthy mitochondria, PINK1, a mitochondrial kinase, is rapidly degraded in a process involving both mitochondrial proteases and the cytosolic proteasome. This process is highly dependent upon the membrane potential across the mitochondrial inner membrane (ΔΨm), which drives PINK1 import into mitochondria. Indeed, mitochondrial damage that dissipates ΔΨm blocks PINK1 import and leads to its accumulation on the surface of mitochondria. PINK1 accumulation triggers the translocation of parkin, an E3 ubiquitin ligase, from the cytosol to mitochondria, where it mediates the elimination of dysfunctional mitochondria by autophagy (mitophagy). From these studies, a concept of PD pathogenesis is emerging whereby defects in PINK1 or parkin function reduce the efficiency with which damaged mitochondria, a major source of toxic reactive oxygen species, are eliminated. We will present recent work, based on the crystal structure of parkin and on genetic screens to identify regulators of parkin function, exploring how parkin is activated upon its recruitment to damaged mitochondria.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".