Multiple mechanisms contribute to increased neutral lipid accumulation in yeast producing recombinant variants of plant diacylglycerol acyltransferase 1
Bibliographic record
Abstract
The apparent bottleneck in the accumulation of oil during seed development in some oleaginous plant species is the formation of triacylglycerol (TAG) by the acyl-CoA–dependent acylation of sn -1,2-diacylglycerol catalyzed by diacylglycerol acyltransferase (DGAT, EC 2.3.1.20). Improving DGAT activity using protein engineering could lead to improvements in seed oil yield ( e.g. in canola-type Brassica napus ). Directed evolution of B. napus DGAT1 (BnaDGAT1) previously revealed that one of the regions where amino acid residue substitutions lead to higher performance in BnaDGAT1 is in the ninth predicted transmembrane domain (PTMD9). In this study, several BnaDGAT1 variants with amino acid residue substitutions in PTMD9 were characterized. Among these enzyme variants, the extent of yeast TAG production was affected by different mechanisms, including increased enzyme activity, increased polypeptide accumulation, and possibly reduced substrate inhibition. The kinetic properties of the BnaDGAT1 variants were affected by the amino acid residue substitutions, and a new kinetic model based on substrate inhibition and sigmoidicity was generated. Based on sequence alignment and further biochemical analysis, the amino acid residue substitutions that conferred increased TAG accumulation were shown to be present in the DGAT1-PTMD9 region of other higher plant species. When amino acid residue substitutions that increased BnaDGAT1 enzyme activity were introduced into recombinant Camelina sativa DGAT1, they also improved enzyme performance. Thus, the knowledge generated from directed evolution of DGAT1 in one plant species can be transferred to other plant species and has potentially broad applications in genetic engineering of oleaginous crops and microorganisms.
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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 | 0.000 |
| 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 | 0.001 |
| 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".