Pink1 regulates mitochondrial dynamics through interaction with the fission/fusion machinery
Bibliographic record
Abstract
Mitochondria form dynamic tubular networks that undergo frequent morphological changes through fission and fusion, the imbalance of which can affect cell survival in general and impact synaptic transmission and plasticity in neurons in particular. Some core components of the mitochondrial fission/fusion machinery, including the dynamin-like GTPases Drp1, Mitofusin, Opa1, and the Drp1-interacting protein Fis1, have been identified. How the fission and fusion processes are regulated under normal conditions and the extent to which defects in mitochondrial fission/fusion are involved in various disease conditions are poorly understood. Mitochondrial malfunction tends to cause diseases with brain and skeletal muscle manifestations and has been implicated in neurodegenerative diseases such as Parkinson's disease (PD). Whether abnormal mitochondrial fission or fusion plays a role in PD pathogenesis has not been shown. Here, we show that Pink1, a mitochondria-targeted Ser/Thr kinase linked to familial PD, genetically interacts with the mitochondrial fission/fusion machinery and modulates mitochondrial dynamics. Genetic manipulations that promote mitochondrial fission suppress Drosophila Pink1 mutant phenotypes in indirect flight muscle and dopamine neurons, whereas decreased fission has opposite effects. In Drosophila and mammalian cells, overexpression of Pink1 promotes mitochondrial fission, whereas inhibition of Pink1 leads to excessive fusion. Our genetic interaction results suggest that Fis1 may act in-between Pink1 and Drp1 in controlling mitochondrial fission. These results reveal a cell biological role for Pink1 and establish mitochondrial fission/fusion as a paradigm for PD research. Compounds that modulate mitochondrial fission/fusion could have therapeutic value in PD intervention.
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 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".