Transcriptomic Response of Purple Willow (Salix purpurea) to Arsenic Stress
Bibliographic record
Abstract
Arsenic (As) is a toxic element for plants and one of the most common anthropogenic pollutants found at contaminated sites. Despite its severe effects on plant metabolism, several species can accumulate substantial amounts of arsenic and endure the associated stress. However, the genetic mechanisms involved in arsenic tolerance remains obscure in many model plant species used for land decontamination (phytoremediation), including willows. The present study assesses the potential of Salix purpurea cv. ‘Fish Creek’ for arsenic phytoextraction and reveals the genetic responses behind arsenic tolerance, phytoextraction and metabolism. Four weeks of hydroponic exposure to 0, 5, 30 and 100 mg/L revealed that plants were able to tolerate up to 5 mg/L arsenic. Concentrations of 0 and 5 mg/L of arsenic treatment were then used to compare alterations in gene expression of roots, stems and leaves using RNA sequencing. Differential gene expression revealed transcripts encoding proteins putatively involved in entry of arsenic into the roots, storage in vacuoles and potential transport through the plant as well as primary and secondary (indirect) toxicity tolerance mechanisms. A major role for tannin as a compound used to relieve cellular toxicity is implicated as well as unexpected expression of the cadmium transporter CAX2, providing a potential means for internal arsenic mobility. These insights into the underpinning genetics of a successful phytoremediating species present novel opportunities for selection of dedicated arsenic tolerant crops as well as the potential to integrate such tolerances into a wider Salix ideotype alongside traits including biomass yield, biomass quality, low agricultural inputs and phytochemical production.
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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".