Phytochelatin and coumarin enrichment in root exudates of arsenic‐treated white lupin
Bibliographic record
Abstract
Abstract Soil contamination with toxic metalloids, such as arsenic, can represent a substantial human health and environmental risk. Some plants are thought to tolerate soil toxicity using root exudation, however, the nature of this response to arsenic remains largely unknown. Here, white lupin plants were exposed to arsenic in a semi‐hydroponic system and their exudates were profiled using untargeted liquid chromatography‐tandem mass spectrometry. Arsenic concentrations up to 1 ppm were tolerated and led to the accumulation of 12.9 μg As g −1 dry weight (DW) and 411 μg As g −1 DW in above‐ground and belowground tissues, respectively. From 193 exuded metabolites, 34 were significantly differentially abundant due to 1 ppm arsenic, including depletion of glutathione disulphide and enrichment of phytochelatins and coumarins. Significant enrichment of phytochelatins in exudates of arsenic‐treated plants was further confirmed using exudate sampling with strict root exclusion. The chemical tolerance toolkit in white lupin included nutrient acquisition metabolites as well as phytochelatins, the major intracellular metal‐binding detoxification oligopeptides which have not been previously reported as having an extracellular role. These findings highlight the value of untargeted metabolite profiling approaches to reveal the unexpected and inform strategies to mitigate anthropogenic pollution in soils around the world.
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.
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 | 0.000 | 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) | 0.003 | 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".