Role of oxidative stress and thiol antioxidant enzymes in nickel toxicity and resistance in strains of the green alga <i>Scenedesmus acutus</i> f. <i>alternans</i>
Bibliographic record
Abstract
Treatment with Ni(NO3)2 leads to the formation of reactive oxygen species (ROS) in the green alga Scenedesmus acutus f. alternans, causing lipid peroxidation. This effect was stronger in a Ni-sensitive strain, UTEX72, than in a Ni-resistant strain, B4. In the resistant strain, Ni induced an increased ratio of reduced to oxidized glutathione (GSH:GSSG), whereas it caused a lowered ratio in the sensitive strain. Enzymes involved in the control of ROS were studied in these strains as well as two others that have shown different degrees of nickel resistance. The resistant strain, B4, which grows while containing large amounts of internal Ni, had much higher levels of glutathione reductase and catalase than the other strains. The sensitive strain, UTEX72, had higher levels of glutathione peroxidase, superoxide dismutase, and glucose-6-phosphate dehydrogenase than did strain B4. The resistant strains, Ni-Tol and Cu-Tol, derived from strain UTEX72, which are partly able to exclude Ni, had enzyme profiles that resembled that of UTEX72 more closely than that of B4. Treatment with 10 and 100 microM Ni for 4 or 22 h had complex effects on enzyme levels in all four strains. Ni decreased glutathione reductase in B4, slightly increased it in Ni-Tol and Cu-Tol, and did not affect the low levels of this enzyme in UTEX72. Ni lowered glutathione peroxidase in B4 and either did not affect it or slightly raised it in the other strains. Ni lowered catalase in B4 and did not affect the other strains. Superoxide dismutase was raised in B4 and Ni-Tol and lowered in Cu-Tol and UTEX72, and glucose-6-phosphate dehydrogenase was lowered in all four strains. These results suggest that one major mechanism of Ni resistance, especially in strain B4, may be the ability to combat the formation of ROS when exposed to this metal, likely by maintaining a high GSH:GSSG ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".