Metabolic engineering‐induced transcriptome reprogramming of lipid biosynthesis enhances oil composition in oat
Bibliographic record
Abstract
The endeavour to elevate the nutritional value of oat (Avena sativa) by altering the oil composition and content positions it as an optimal crop for fostering human health and animal feed. However, optimization of oil traits on oat through conventional breeding is challenging due to its quantitative nature and complexity of the oat genome. We introduced two constructs containing three key genes integral to lipid biosynthesis and/or regulatory pathways from Arabidopsis (AtWRI1 and AtDGAT1) and Sesame (SiOLEOSIN) into the oat cultivar 'Park' to modify the fatty acid composition. Four homozygous transgenic lines were generated with a transformation frequency of 7%. The expression of these introduced genes initiated a comprehensive transcriptional reprogramming in oat grains and leaves. Notably, endogenous DGAT, WRI1 and OLEOSIN genes experienced upregulation, while genes associated with fatty acid biosynthesis, such as KASII, SACPD and FAD2, displayed antagonistic expression patterns between oat grains and leaves. Transcriptomic analyses highlighted significant differential gene expression, particularly enriched in lipid metabolism. Comparing the transgenic oat plants with the wild type, we observed a remarkable increase of up to 34% in oleic acid content in oat grains. Furthermore, there were marked improvements in the total oil content in oat leaves, as well as primary metabolites changes in both oat grains and leaves, while maintaining homeostasis in the transgenic oat plants. These findings underscore the effectiveness of genetic engineering in manipulating oat oil composition and content, offering promising implications for human consumption and animal feeding through oat crop improvement programmes.
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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 | 0.001 | 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) | 0.000 | 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".