Simple Methods to Detect Triacylglycerol Biosynthesis in a Yeast‐Based Recombinant System
Bibliographic record
Abstract
Standard methods to quantify the activity of triacylglycerol (TAG) synthesizing enzymes DGAT and PDAT (TAG-SE) require a sensitive but rather arduous laboratory assay based on radio-labeled substrates. Here we describe two straightforward methods to detect TAG production in baker's yeast Saccharomyces cerevisiae. First we demonstrate that a quadruple knockout yeast strain deficient in storage lipids has a reduced growth rate in a medium supplemented with fatty acids. This phenotype is rescued by restoring TAG biosynthesis and can be thus used to select yeast cells expressing a recombinant TAG-SE. In the second method, the activity of the recombinant enzyme is measured in a fluorescent in situ assay using Nile red dye that is specific for neutral lipids. Correlation between Nile red fluorescence and enzyme activity is demonstrated with several mutants of a TAG synthesizing enzyme. This yeast live-cell-based assay is rapid, inexpensive, sensitive, and is amenable to high-throughput applications. The methods can be used for a variety of applications such as isolation of novel genes, directed evolution, gene-specific drug screening and will facilitate novel approaches in the research of TAG-SE.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".