Assessing compensation for loss of vacuolar function in<i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
We analyzed how Saccharomyces cerevisiae cells compensate for the lack of a functional vacuole, an acidic membrane-bound degradative and ion storage compartment. We hypothesized that cells lacking a functional vacuole would compensate for the loss of the functions of the vacuole by altering gene expression and (or) metabolic flux. We used gene expression profiling and Biolog phenotype microarray analysis to determine the compensatory mechanisms of cells lacking vacuolar function. In steady state, vps33 and vps41 cells changed the transcriptional profile of some genes, but no complete pathways were upregulated or downregulated. We treated vps41 cells with calcium to tease out cellular compensation for loss of vacuole function under ionic stress; however, changes in gene expression were not utilized to compensate for loss of vacuole function under stress either, as genes whose transcriptional profiles were changed did not function together in any one cellular process. Phenotype microarray analysis indicated that logarithmically growing vps33 or vps41 cells did not seem to compensate for loss of vacuolar function but instead demonstrated additional pleiotropic phenotypes due to the function of the vacuole. Under rich media conditions, yeast utilize the vacuole to regulate stress, ion response, and peptide degradation. However, loss of the vacuole does not lead to observable compensation mechanisms.
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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) | 0.000 | 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) | 0.001 | 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".