Seed-specific overexpression of <i>Arabidopsi</i>s <i>DGAT1</i> in Indian mustard (<i>Brassica juncea</i>) increases seed oil content and seed weight
Bibliographic record
Abstract
Oil content is an important yield trait in Brassica juncea (L.) Coss. Improvements to yield levels of mustard by conventional breeding methods have reached a plateau. The application of transgenic technology is an area that has not yet been explored for improving the oil content of B. juncea. In this study, the effect of overexpression of AtDGAT1 (a key gene involved in oil biosynthesis) on the seed oil content of B. juncea was investigated. For seed-specific overexpression, the gene was linked to Arabidopsis thaliana oleosin promoter and mobilized into mustard through Agrobacterium-mediated transformation. Transformants were selected on MS medium containing 50 mg/L kanamycin, and a transformation frequency of 10.5% was obtained. A total of 10 transgenic events were generated. Analyses of seed weight, oil content, and other yield traits in T 1 transgenics showed that seed-specific overexpression of AtDGAT1 significantly improved the oil content and seed weight. The maximum oil content increase observed in the transgenic seeds was 8.3% compared with the wild-type plants. Total fatty acid content was increased from 4% to 14% in six of the seven events. However, the content of oleic and linoleic acid was reduced and, of these two, oleic acid content showed drastic reduction.
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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".