Analysis of sulfur and selenium assimilation in <i>Astragalus</i> plants with varying capacities to accumulate selenium
Bibliographic record
Abstract
Several Astragalus species have the ability to hyperaccumulate selenium (Se) when growing in their native habitat. Given that the biochemical properties of Se parallel those of sulfur (S), we examined the activity of key S assimilatory enzymes ATP sulfurylase (ATPS), APS reductase (APR), and serine acetyltransferase (SAT), as well as selenocysteine methyltransferase (SMT), in eight Astragalus species with varying abilities to accumulate Se. Se hyperaccumulation was found to positively correlate with shoot accumulation of S-methylcysteine (MeCys) and Se-methylselenocysteine (MeSeCys), in addition to the level of SMT enzymatic activity. However, no correlation was observed between Se hyperaccumulation and ATPS, APR, and SAT activities in shoot tissue. Transgenic Arabidopsis thaliana overexpressing both ATPS and APR had a significant enhancement of selenate reduction as a proportion of total Se, whereas SAT overexpression resulted in only a slight increase in selenate reduction to organic forms. In general, total Se accumulation in shoots was lower in the transgenic plants overexpressing ATPS, PaAPR, and SAT. Root growth was adversely affected by selenate treatment in both ATPS and SAT overexpressors and less so in the PaAPR transgenic plants. Such observations support our conclusions that ATPS and APR are major contributors of selenate reduction in planta. However, Se hyperaccumulation in Astragalus is not driven by an overall increase in the capacity of these enzymes, but rather by either an increased Se flux through the S assimilatory pathway, generated by the biosynthesis of the sink metabolites MeCys or MeSeCys, or through an as yet unidentified Se assimilation pathway.
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 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".