Thiol and Metal Contents in Periphyton Exposed to Elevated Copper and Zinc Concentrations: A Field and Microcosm Study
Bibliographic record
Abstract
Phytochelatins are metal-binding polypeptides produced by algae under metal exposure. The aim of this study was to investigate the effects of metal concentration variations in natural systems on periphyton at the biochemical level by analyzing its intracellular thiol content, in particular phytochelatins. To that purpose, two field campaigns were conducted in a stream subject to an increase of dissolved metal concentrations (particularly Cu and Zn) during rain events, which results in an increase of their accumulation in periphyton. At background metal concentrations, several thiols were detectable in periphyton, namely, glutathione (GSH), gamma-glutamylcysteine (gammaGluCys), phytochelatins (PC2), and some unidentified thiols, U1 and U2. Glutathione and gammaGluCys contents were found to vary independently of the rain, as well as U1 and U2, whereas the phytochelatin content increased during the rain events. To investigate whether Cu or Zn may be responsible for this increase, microcosm experiments were carried out with natural water enriched with Cu, Zn, and Cd separately, and Cu and Zn in combination. In this study, GSH, PC2, and U1 were also detected, but not gammaGluCys. An increase in accumulated Cu content did not induce any changes in thiol content, whereas an increase of the Zn content induced a decrease in GSH content and an increase in phytochelatin content. Zinc rather than Cu may thus induce a phytochelatin content increase in periphyton in the field studies. Addition of Cu and Zn in combination also induced an increase in phytochelatin content. Cadmium was found to be the most effective inducer, with the production of larger phytochelatins (PC3-4). This study is the first one to report changes in thiol content in periphyton in response to an increase of the metal concentration in natural freshwaters.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".