Improving Erucic Acid Content in Rapeseed through Biotechnology: What Can the <i>Arabidopsis FAE1</i> and the Yeast <i>SLC1‐1</i> Genes Contribute?<sup>1,</sup><sup>2</sup>
Bibliographic record
Abstract
The main goal of our research is to produce, by genetic manipulation, Brassica napus L. cultivars with higher amounts of 22:1 in their seed oil than in present Canadian high erucic acid rapeseed (HEAR) cultivars developed through traditional breeding, ideally with proportions of 22:1 approaching 80 mol% (828 g kg−1). To probe some rate‐limiting steps in the accumulation of triacylglycerols containing very long chain fatty acids (VLCFAs), particularly erucic acid (22:1), we have taken a transgenic approach, studying the effect of expressing two target genes in HEAR B. napus cv. Hero. To study the role of the elongase complex, involved in elongation of C18 fatty acid moieties to produce VLCFAs, we expressed the Arabidopsis thaliana L., fatty acid elongase 1 (FAE1) gene under the control of a seed‐specific promoter (napin), in Hero. This resulted in increased proportions of 22:1 in the seed oil, rising from 430 g kg−1 in non‐transformed controls to 480 to 530 g kg−1 22:1 in FAE1 transgenic Hero lines. The FAE1 lines exhibited higher elongase activity in vitro compared to control lines. These data suggest that the level of active condensing enzyme in the native elongase complex is somewhat rate limiting for synthesis of erucic acid and other VLCFAs in HEAR. In small scale field trials, the VLCFA and 22:1 content of FAE1 transgenic lines were superior to field‐grown control lines. We report that in field plot trials, the progeny of our best T4 B. napus cv. Hero SLC1‐1 transgenic lines clearly out‐performed controls in terms of 22:1, oil content, and yield.
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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 | 0.001 | 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".