Iron Modulation of Copper Uptake and Toxicity in a Green Alga (<i>Chlamydomonas reinhardtii</i>)
Bibliographic record
Abstract
Little attention has been paid to the role of essential trace elements on the toxicity of another element. In this work, we examined if low concentrations of essential elements (Co, Mn, Zn, and Fe) modified the response of a freshwater green alga ( Chlamydomonas reinhardtii ) to copper. To do so, we followed cell growth over 72 h in exposure media where the essential element concentrations were manipulated. Among these elements, iron proved to have a strong impact on the cells’ response to copper. The free Cu 2+ concentrations required to inhibit cellular growth by 50% (EC50) over 72 h decreased from 2 nM in regular Fe medium (10 –17.6 M Fe 3+ ) to 4 pM in low iron medium (10 –19.0 M Fe 3+ ); a 500-fold increase in toxicity. Moreover, at low Cu 2+ concentrations (10 –13.0 to 10 –10.5 M), Cu uptake increased under low iron conditions but remain relatively stable under regular iron conditions. These results show clearly that iron plays a protective role against copper uptake and toxicity to C. reinhardtii . In freshwaters, iron is always abundant but the expected free iron concentrations in surface waters can vary between 10 –14.0 to 10 –20.0 M, depending on pH (e.g., when pH increases from 6 to 8). We conclude that copper toxicity in natural waters can be modulated by iron and that, in some conditions, the Biotic Ligand Model may need to be further developed to account for the influence of iron.
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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".