A Novel Arsenate Reductase from the Arsenic Hyperaccumulating Fern <i>Pteris vittata</i>
Bibliographic record
Abstract
Pteris vittata sporophytes hyperaccumulate arsenic to 1% to 2% of their dry weight. Like the sporophyte, the gametophyte was found to reduce arsenate [As(V)] to arsenite [As(III)] and store arsenic as free As(III). Here, we report the isolation of an arsenate reductase gene (PvACR2) from gametophytes that can suppress the arsenate sensitivity and arsenic hyperaccumulation phenotypes of yeast (Saccharomyces cerevisiae) lacking the arsenate reductase gene ScACR2. Recombinant PvACR2 protein has in vitro arsenate reductase activity similar to ScACR2. While PvACR2 and ScACR2 have sequence similarities to the CDC25 protein tyrosine phosphatases, they lack phosphatase activity. In contrast, Arath;CDC25, an Arabidopsis (Arabidopsis thaliana) homolog of PvACR2 was found to have both arsenate reductase and phosphatase activities. To our knowledge, PvACR2 is the first reported plant arsenate reductase that lacks phosphatase activity. CDC25 protein tyrosine phosphatases and arsenate reductases have a conserved HCX5R motif that defines the active site. PvACR2 is unique in that the arginine of this motif, previously shown to be essential for phosphatase and reductase activity, is replaced with a serine. Steady-state levels of PvACR2 expression in gametophytes were found to be similar in the absence and presence of arsenate, while total arsenate reductase activity in P. vittata gametophytes was found to be constitutive and unaffected by arsenate, consistent with other known metal hyperaccumulation mechanisms in plants. The unusual active site of PvACR2 and the arsenate reductase activities of cell-free extracts correlate with the ability of P. vittata to hyperaccumulate arsenite, suggesting that PvACR2 may play an important role in this process.
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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".