Pyruvate Released by Astrocytes Protects Neurons from Copper-Catalyzed Cysteine Neurotoxicity
Bibliographic record
Abstract
We have found previously that astrocytes can provide cysteine to neurons. However, cysteine has been reported to be neurotoxic although it plays a pivotal role in regulating intracellular levels of glutathione, the major cellular antioxidant. Here, we show that cysteine toxicity is a result of hydroxyl radicals generated during cysteine autoxidation. Transition metal ions are candidates to catalyze this process. Copper substantially accelerates the autoxidation rate of cysteine even at submicromolar levels, whereas iron and other transition metal ions, including manganese, chromium, and zinc, are less efficient. The autoxidation rate of cysteine in rat CSF is equal to that observed in the presence of approximately 0.2 microm copper. In tissue culture tests, we found that cysteine toxicity depends highly on its autoxidation rate and on the total amount of cysteine being oxidized, suggesting that the toxicity can be attributed to the free radicals produced from cysteine autoxidation, but not to cysteine itself. We have also explored the in vivo mechanisms that protect against cysteine toxicity. Catalase and pyruvate were each found to inhibit the production of hydroxyl radicals generated by cysteine autoxidation. In tissue culture, they both protected primary neurons against cysteine toxicity catalyzed by copper. This protection is attributed to their ability to react with hydrogen peroxide, preventing the formation of hydroxyl radicals. Pyruvate, but not catalase or glutathione peroxidase, was detected in astrocyte-conditioned medium and CSF. Our data therefore suggest that astrocytes can prevent cysteine toxicity by releasing pyruvate.
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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) | 0.001 | 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) | 0.001 | 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".