Differential production of superoxide by neuronal mitochondria
Bibliographic record
Abstract
BACKGROUND: Mitochondrial DNA (mtDNA) mutations, which are present in all mitochondria-containing cells, paradoxically cause tissue-specific disease. For example, Leber's hereditary optic neuropathy (LHON) results from one of three point mutations mtDNA coding for complex I components, but is only manifested in retinal ganglion cells (RGCs), a central neuron contained within the retina. Given that RGCs use superoxide for intracellular signaling after axotomy, and that LHON mutations increase superoxide levels in non-RGC transmitochondrial cybrids, we hypothesized that RGCs regulate superoxide levels differently than other neuronal cells. To study this, we compared superoxide production and mitochondrial electron transport chain (METC) components in isolated RGC mitochondria to mitochondria isolated from cerebral cortex and neuroblastoma SK-N-AS cells. RESULTS: In the presence of the complex I substrate glutamate/malate or the complex II substrate succinate, the rate of superoxide production in RGC-5 cells was significantly lower than cerebral or neuroblastoma cells. Cerebral but not RGC-5 or neuroblastoma cells increased superoxide production in response to the complex I inhibitor rotenone, while neuroblastoma but not cerebral or RGC-5 cells dramatically decreased superoxide production in response to the complex III inhibitor antimycin A. Immunoblotting and real-time quantitative PCR of METC components demonstrated different patterns of expression among the three different sources of neuronal mitochondria. CONCLUSION: RGC-5 mitochondria produce superoxide at significantly lower rates than cerebral and neuroblastoma mitochondria, most likely as a result of differential expression of complex I components. Diversity in METC component expression and function could explain tissue specificity in diseases associated with inherited mtDNA abnormalities.
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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 | 0.000 | 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 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".