A quick and reliable method for genome-wide host factor screening of Saccharomyces cerevisiae killer toxins
Bibliographic record
Abstract
Numerous yeasts produce toxic compounds to fight the competitors. Such compounds include small molecules (like antibiotics), antibiotic peptides, and also larger proteins, including killer toxins. Their ability to affect the cell depends on the host factors modulating the killing activity. Here we describe a robotics-based method to advance the genome-wide screening for the host factors affecting sensitivity of budding yeast to the killer toxins using the K2 system as the model. We demonstrate that arraying the mutant library on the agar plates containing the K2 killer toxinproducing strain and/or purified toxin (“survival” assay) increases the sensitivity and speed of the screen and decreases the costs compared to the traditional “killer” assay. We show the applicability of a new screening method of searching for the host factors using a killer strain isolated from agricultural plant environment. In addition, the “survival” assay allows identification of previously undetected factors that could be the “missing links” in the pathways of toxininduced cellular responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".