Cocktail biosynthesis of triacylglycerol by rational modulation of diacylglycerol acyltransferases in industrial oleaginous Aurantiochytrium
Bibliographic record
Abstract
BACKGROUND: Triacylglycerol (TAG) is an important storage lipid in organisms, depending on the degree of unsaturation of fatty acid molecules attached to glycerol; it is usually used as the feedstock for nutrition or biodiesel. However, the mechanism of assembly of saturated fatty acids (SFAs) or polyunsaturated fatty acids (PUFAs) into TAGs remains unclear for industrial oleaginous microorganism. RESULTS: Diacylglycerol acyltransferase (DGAT) is a key enzyme for TAG synthesis. Hence, ex vivo (in yeast), and in vivo functions of four DGAT2s (DGAT2A, DGAT2B, DGAT2C, and DGAT2D) in industrial oleaginous thraustochytrid Aurantiochytrium sp. SD116 were analyzed. Results revealed that DGAT2C was mainly responsible for connecting PUFA to the sn-3 position of TAG molecules. However, DGAT2A and DGAT2D target SFA and/or MUFA. CONCLUSIONS: There are two specific TAG assembly routes in Aurantiochytrium. The "saturated fatty acid (SFA) TAG lane" primarily produces SFA-TAGs mainly mediated by DGAT2D whose function is complemented by DGAT2A. And, the "polyunsaturated fatty acid (PUFA) TAG lane" primarily produces PUFA-TAGs via DGAT2C. In this study, we demonstrated the functional distribution pattern of four DGAT2s in oleaginous thraustochytrid Aurantiochytrium, and provided a promising target to rationally design TAG molecular with the desired characteristics.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".