Faculty Opinions recommendation of Mitochondrial Dysfunction Combined with High Calcium Load Leads to Impaired Antioxidant Defense Underlying the Selective Loss of Nigral Dopaminergic Neurons.
Bibliographic record
Abstract
Mitochondrial dysfunction is critically involved in Parkinson's disease, characterized by loss of dopaminergic neurons (DaNs) in the substantia nigra (SNc), whereas DaNs in the neighboring ventral tegmental area (VTA) are much less affected.In contrast to VTA, SNc DaNs engage calcium channels to generate action potentials, which lead to oxidant stress by yet unknown pathways.To determine the molecular mechanisms linking calcium load with selective cell death in the presence of mitochondrial deficiency, we analyzed the mitochondrial redox state and the mitochondrial membrane potential in mice of both sexes with genetically induced, severe mitochondrial dysfunction in DaNs (MitoPark mice), at the same time expressing a redox-sensitive GFP targeted to the mitochondrial matrix.Despite mitochondrial insufficiency in all DaNs, exclusively SNc neurons showed an oxidized redox-system, i.e., a low reduced/oxidized glutathione (GSH-GSSG) ratio.This was mimicked by cyanide, but not by rotenone or antimycin A, making the involvement of reactive oxygen species rather unlikely.Surprisingly, a high mitochondrial inner membrane potential was maintained in MitoPark SNc DaNs.Antagonizing calcium influx into the cell and into mitochondria, respectively, rescued the disturbed redox ratio and induced further hyperpolarization of the inner mitochondrial membrane.Our data therefore show that the constant calcium load in SNc DaNs is counterbalanced by a high mitochondrial inner membrane potential, even under conditions of severe mitochondrial dysfunction, but triggers a detrimental imbalance in the mitochondrial redox system, which will lead to neuron death.Our findings thus reveal a new mechanism, redox imbalance, which underlies the differential vulnerability of DaNs to mitochondrial defects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.453 | 0.231 |
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".