Global analysis of <i>Saccharomyces cerevisiae</i> growth in mucin
Bibliographic record
Abstract
Metagenomic profiling of the human gut microbiome has discovered DNA from dietary yeasts like Saccharomyces cerevisiae. However, it is unknown if the S. cerevisiae detected by common metagenomic methods are from dead dietary sources, or from live S. cerevisiae colonizing the gut similar to their close relative Candida albicans. While S. cerevisiae can adapt to minimal oxygen and acidic environments, it has not been explored whether this yeast can metabolize mucin, the large, gel-forming, highly glycosylated proteins representing a major source of carbon in the gut mucosa. We reveal that S. cerevisiae can utilize mucin as their main carbon source, as well as perform both a transcriptome analysis and a chemogenomic screen to identify biological pathways required for this yeast to grow optimally in mucin. In total, 739 genes demonstrate significant differential expression in mucin culture, and deletion of 21 genes impact growth in mucin. Both screens suggest that mitochondrial function is required for proper growth in mucin, and through secondary assays we determine that mucin exposure induces mitogenesis and cellular respiration. We further show that deletion of an uncharacterized ORF, YCR095W-A, led to dysfunction in mitochondrial morphology and oxygen consumption in mucin. Finally, we demonstrate that Yps7, an aspartyl protease and homolog to mucin-degrading proteins in C. albicans, is important for growth on mucin. Collectively, our work serves as the initial step toward establishing how this common dietary fungus can survive in the mucus environment of the human gut.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".