Engineering of Saccharomyces cerevisiae for the accumulation of high amounts of triacylglycerol
Bibliographic record
Abstract
BACKGROUND: Fatty acid-based substances play an important role in many products, from food supplements to pharmaceutical products and biofuels. The production of fatty acids, mainly in their esterified form as triacylglycerol (TAG), has been intensively studied in oleaginous yeasts, whereas much less effort has been invested into non-oleaginous species. In the present work, we engineered the model yeast Saccharomyces cerevisiae, which is commonly regarded as non-oleaginous, for the storage of high amounts of TAG, comparable to the contents achieved in oleaginous yeasts. RESULTS: We investigated the effects of several mutations with regard to increased TAG accumulation and identified six of them as important for this phenotype: a point mutation in the acetyl-CoA carboxylase Acc1p, overexpression of the diacylglycerol acyltransferase Dga1p, deletions of genes coding for enzymes involved in the competing pathways glycogen and steryl ester synthesis and TAG hydrolysis, and a deletion of CKB1, the gene coding for one of the regulatory subunits of casein kinase 2. With the combination of these mutations in a S. cerevisiae strain with a relatively high neutral lipid level already in the non-engineered state, we achieved a TAG content of 65% in the dry biomass. High TAG levels were not only obtained under conditions that favor lipid accumulation, but also in defined standard carbon-limited media. CONCLUSIONS: Baker's yeast, which is usually regarded as inefficient in the storage of TAG, can be converted into a highly oleaginous strain that could be useful in processes aiming at the synthesis of fatty acid-based products. This work emphasizes the importance of strain selection in combination with metabolic engineering to obtain high product levels.
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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 | 0.000 | 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".