System Wide Views of Proteome Abundance and Dynamic Localization Reveals Novel Saccharomyces cerevisiae Stress Response and Replication Checkpoint Pathways
Bibliographic record
Abstract
Cells face a myriad of different environmental stressors and must respond appropriately, as failure to do so can result in disease states. To this end, cells engage intricate signaling pathways that typically involve cellular changes, such as alterations in transcription programs, translation rates, and protein function. Ultimately, protein activity is the final arbiter of function in most cellular pathways. In this work, I investigated the cellular response to stress in the model organism, Saccharomyces cerevisiae, focusing on two of the most important proteomic properties, protein abundance and localization. To begin, I evaluated 21 quantitative analyses of the S. cerevisiae proteome and converted all measurements of protein abundance into the intuitive measurement of absolute number of molecules per cell. I found that the previously described global environmental stress response (ESR) is not detected at the level of protein abundance. In addition, this rich dataset provided insight into proteins that are differentially regulated at the levels of mRNA abundance, mRNA translation, and protein abundance. Next, I developed a quantification method to measure protein localization dynamics in single cells and applied this pipeline to monitor 322 yeast proteins following MMS- and HU-induced replication stress. I discovered that proteins change localization at different rates and with high heterogeneity within a cell population. Moreover, this analysis revealed that Ydr132c/Mrx16 is an unrecognized component of intranuclear quality control (INQ) compartments. Finally, I defined protein factors that govern protein re-localization during conditions of stress, particularly in cells experiencing DNA replication stress. I discovered that Mec1 and Rad53 checkpoint kinases promote the proper subcellular localization of 131 proteins. Importantly, my screening efforts revealed a new checkpoint activation mechanism and gene involved in genome maintenance, RTG3. Together, my data show that a more complete characterization of the proteome, particularly during stressful conditions, provides great insight into cellular functions and reveals novel protein biology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".