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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".