Proteomic and lipidomics analyses of high fatty acid <i>AhDGAT3</i> transgenic soybean reveals the key <i>lipase</i> gene associated with the lipid internal mechanism
Bibliographic record
Abstract
Vegetable oil is one of the most important components of human nutrition. Soybean (Glycine max) is an important oil crop worldwide and contains rich unsaturated fatty acids. Diacylglycerol acyltransferase (DGAT) is a key rate-limiting enzyme in the Kennedy pathway from diacylglycerol (DAG) to triacylglycerol (TAG). In this study, we conducted further research using T3 AhDGAT3 transgenic soybean. A high-performance gas chromatography flame ionization detector showed that oleic acid (18:1) content and total fatty acid content of transgenic soybean were significantly higher than those of the wild type (WT). However, linoleic acid (18:2) was much lower than that in the WT. For further mechanistic studies, 20 differentially expressed proteins (DEPs) and 119 differentially expressed metabolites (DEMs) were identified between WT (JACK) and AhDGAT3 transgenic soybean mature seeds using proteomic and lipidomics analyses. Combined proteomic and lipidomics analyses showed that the upregulation of the key DEP (lipase GDSL domain-containing protein) in lipid transport and metabolic process induced an increase in the total fatty acid and 18:1 composition, but a decrease in the 18:2 composition of fatty acids. Our study provides new insights into the deep study of molecular mechanism underlying the enhancement of fatty acids in transgenic soybeans, especially oleic acid and total fatty acid, which are enhanced by over-expression of AhDGAT3.
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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) | 0.000 | 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 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".