Phytochelatin induction by selenate in <i>Chlorella vulgaris,</i> and regulation of effect by sulfate levels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Phytochelatins (PCs) are short metal detoxification peptides made from the sulfur-rich molecule glutathione. The production of PCs by algae caused by Se exposure has never been studied, although many algae accumulate Se, forming Se-rich proteins and peptides, and higher plants have demonstrated PC production when treated with Se; therefore, a goal of the current study was to examine whether Se induces PC production in algae. Furthermore, selenate is thought to compete with sulfate in the S assimilation pathway, and sulfate therefore may have a protective effect against the toxic effects of high doses of Se in algae. Hence, the interaction of selenate and sulfate was investigated with respect to the induction of PCs. Chlorella vulgaris was cultured in media with either low (31.2 µM) or high (312 µM) concentrations of sulfate. These cultures were exposed to selenate in doses of 7, 35, and 70 nM for 48 h. In a separate treatment, Cd (890 nM) was added as a positive PC-inducing control, and one no-metal negative control was used. Total Se and Se speciation were determined, and glutathione, phytochelatin-2, and phytochelatin-3 were quantified in each of cell digests, cell medium, and cell lysates. We found that PCs and their precursor glutathione were induced by selenate as well as by a Cd control. The high concentration of sulfate was able to counter selenate-induced production of PCs and glutathione. These data support two possible mechanisms: a negative feedback system in the S assimilation pathway that affects PC production when sulfate is abundant, and competition for uptake at the ion transport level between selenate and sulfate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it