Selenomethionine and Total Methionine Ratio is Conserved in Seed Proteins of Selenium‐Treated and Nontreated Soybean, Flax, and Potato
Bibliographic record
Abstract
ABSTRACT Crop biofortification with Se is widely accepted. However, little is known about the efficiency of selenomethionine (Se‐Met) incorporation into the seed and tuber protein fraction of crops. Here, we report on the efficiency of Se incorporation into the seed and tuber protein fraction of soybean, flax, and potato and the putative identity of some of the proteins that are likely targets for this random pretranslation modification. Soybean, flax, and potato plants received Se using different sodium selenate application methods and rates, and Se‐Met was determined in seed, tuber, and their protein fractions. Selenium content in seed and tuber tissues and in protein fractions was increased by 2 to 86 times. The methionine (Met) pool was increased, as was the proportion of Se‐Met in the Met pool, but the ratio of Se‐Met:total Met was well conserved (1:1) between the treated and nontreated plants. After two‐dimensional (2D) protein electrophoresis, five protein spots were identified as glycinin, trypsin inhibitor (soybean), patatin‐3‐Kuras‐1, patain‐B2, and phosphoenol carboxy kinase (potato), none of which showed S to Se substitution in their primary structure. Linking Se and three stress markers in fresh tuber extracts, glutathione (GSH) level, glutathione peroxidase (GPx), and glutathione reductase (GR) activities were increased by 20 to 35%. The data showed low protein modification in these non‐Se‐accumulator crops and indicated that rates of 5 to 10 g ha–1 Se, through foliar application, may ensure not only an adequate daily intake levels for humans but may also activate antioxidant enzyme systems within the tuber tissue.
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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".