A Vacuolar Arsenite Transporter Necessary for Arsenic Tolerance in the Arsenic Hyperaccumulating Fern <i>Pteris vittata</i> Is Missing in Flowering Plants
Bibliographic record
Abstract
The fern Pteris vittata tolerates and hyperaccumulates exceptionally high levels of the toxic metalloid arsenic, and this trait appears unique to the Pteridaceae. Once taken up by the root, arsenate is reduced to arsenite as it is transported to the lamina of the frond, where it is stored in cells as free arsenite. Here, we describe the isolation and characterization of two P. vittata genes, ACR3 and ACR3;1, which encode proteins similar to the ACR3 arsenite effluxer of yeast. Pv ACR3 is able to rescue the arsenic-sensitive phenotypes of yeast deficient for ACR3. ACR3 transcripts are upregulated by arsenic in sporophyte roots and gametophytes, tissues that directly contact soil, whereas ACR3;1 expression is unaffected by arsenic. Knocking down the expression of ACR3, but not ACR3;1, in the gametophyte results in an arsenite-sensitive phenotype, indicating that ACR3 plays a necessary role in arsenic tolerance in the gametophyte. We show that ACR3 localizes to the vacuolar membrane in gametophytes, indicating that it likely effluxes arsenite into the vacuole for sequestration. Whereas single-copy ACR3 genes are present in moss, lycophytes, other ferns, and gymnosperms, none are present in angiosperms. The duplication of ACR3 in P. vittata and the loss of ACR3 in angiosperms may explain arsenic tolerance in this unusual group of ferns while precluding the same trait in angiosperms.
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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".