Hydrogen sulfide regulates arsenic-induced cell death in yeast cells by modulating the antioxidative system
Bibliographic record
Abstract
Arsenic (As) is a metal with potentially toxic effects on different organisms. Hydrogen sulfide (H 2 S) plays a vital role in mitigating heavy metal toxicity by reducing oxidative stress in plants and animals. However, the role of H 2 S in alleviating arsenic toxicity in yeast cells remains unclear. In this study, the role of NaHS (exogenous physiological H 2 S) in alleviating As-induced yeast cell death was investigated. Yeast cells in the logarithmic phase were pretreated with 0.05 mmol/L NaHS for 6 h, and then incubated in the YPD medium with or without 1 mmol/L As. After 12 h of treatment, relative survival rate, H 2 S content, oxidative stress biomarkers, and antioxidant machinery were measured. Our results showed that sodium arsenite-induced yeast cell death and pretreatment with 0.05 mmol/L NaHS significantly alleviated sodium arsenite-induced cell death. Under sodium arsenite conditions, the levels of intracellular reactive oxygen species (ROS) and malondialdehyde (MDA) increased, accompanied by the inhibition of the catalase (CAT) activity and the downregulation of CTT1 expression. However, the activities of the superoxide dismutase (SOD) and glutathion peroxidase (GPX) increased, and the expression of SOD1 and GPX2 was markedly upregulated in the group treated with sodium arsenite. When yeast cells were pretreated with NaHS, the intracellular ROS and MDA levels decreased significantly, and the activities of SOD, CAT, and GPX increased significantly. This was associated with a significant increase in relative survival rate and H 2 S content compared to the arsenic treatment alone. Our findings indicate that NaHS alleviates sodium arsenite-induced yeast cell death, mainly by enhancing the antioxidant defense system.
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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) | 0.001 | 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".