Chemical Genomic Screening of a Saccharomyces cerevisiae Genomewide Mutant Collection Reveals Genes Required for Defense against Four Antimicrobial Peptides Derived from Proteins Found in Human Saliva
Bibliographic record
Abstract
To compare the effects of four antimicrobial peptides (MUC7 12-mer, histatin 12-mer, cathelicidin KR20, and a peptide containing lactoferricin amino acids 1 to 11) on the yeast Saccharomyces cerevisiae, we employed a genomewide fitness screen of combined collections of mutants with homozygous deletions of nonessential genes and heterozygous deletions of essential genes. When an arbitrary fitness score cutoffs of 1 (indicating a fitness defect, or hypersensitivity) and -1 (indicating a fitness gain, or resistance) was used, 425 of the 5,902 mutants tested exhibited altered fitness when treated with at least one peptide. Functional analysis of the 425 strains revealed enrichment among the identified deletions in gene groups associated with the Gene Ontology (GO) terms "ribosomal subunit," "ribosome biogenesis," "protein glycosylation," "vacuolar transport," "Golgi vesicle transport," "negative regulation of transcription," and others. Fitness profiles of all four tested peptides were highly similar, particularly among mutant strains exhibiting the greatest fitness defects. The latter group included deletions in several genes involved in induction of the RIM101 signaling pathway, including several components of the ESCRT sorting machinery. The RIM101 signaling regulates response of yeasts to alkaline and neutral pH and high salts, and our data indicate that this pathway also plays a prominent role in regulating protective measures against all four tested peptides. In summary, the results of the chemical genomic screens of S. cerevisiae mutant collection suggest that the four antimicrobial peptides, despite their differences in structure and physical properties, share many interactions with S. cerevisiae cells and consequently a high degree of similarity between their modes of action.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".