Properties and Biotechnological Applications of Acyl‐CoA:diacylglycerol Acyltransferase and Phospholipid:diacylglycerol Acyltransferase from Terrestrial Plants and Microalgae
Bibliographic record
Abstract
Triacylglycerol (TAG) is the major storage lipid in most terrestrial plants and microalgae, and has great nutritional and industrial value. Since the demand for vegetable oil is consistently increasing, numerous studies have been focused on improving the TAG content and modifying the fatty-acid compositions of plant seed oils. In addition, there is a strong research interest in establishing plant vegetative tissues and microalgae as platforms for lipid production. In higher plants and microalgae, TAG biosynthesis occurs via acyl-CoA-dependent or acyl-CoA-independent pathways. Diacylglycerol acyltransferase (DGAT) catalyzes the last and committed step in the acyl-CoA-dependent biosynthesis of TAG, which appears to represent a bottleneck in oil accumulation in some oilseed species. Membrane-bound and soluble forms of DGAT have been identified with very different amino-acid sequences and biochemical properties. Alternatively, TAG can be formed through acyl-CoA-independent pathways via the catalytic action of membrane-bound phospholipid:diacylglycerol acyltransferase (PDAT). As the enzymes catalyzing the terminal steps of TAG formation, DGAT and PDAT play crucial roles in determining the flux of carbon into seed TAG and thus have been considered as the key targets for engineering oil production. Here, we summarize the most recent knowledge on DGAT and PDAT in higher plants and microalgae, with the emphasis on their physiological roles, structural features, and regulation. The development of various metabolic engineering strategies to enhance the TAG content and alter the fatty-acid composition of TAG is also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".