A genome-wide synthetic dosage lethality screen reveals multiple pathways that require the functioning of ubiquitin-binding proteins Rad23 and Dsk2
Bibliographic record
Abstract
BACKGROUND: Ubiquitin regulates a myriad of important cellular processes through covalent attachment to its substrates. A classic role for ubiquitin is to flag proteins for destruction by the proteasome. Recent studies indicate that ubiquitin-binding proteins (e.g. Rad23, Dsk2, Rpn10) play a pivotal role in transferring ubiquitylated proteins to the proteasome. However, the specific role of these ubiquitin receptors remains poorly defined. A key to unraveling the functions of these ubiquitin receptors is to identify their cellular substrates and biological circuits they are involved in. Although many strategies have been developed for substrate isolation, the identification of physiological targets of proteolytic pathways has proven to be quite challenging. RESULTS: Using a genome-wide functional screen, we have identified 11 yeast genes that cause slower growth upon their overexpression in cells lacking two ubiquitin-binding proteins Rad23 and Dsk2. Our results suggest that proper functioning of Rad23 and Dsk2 is required for efficient pheromone response, transcription, amino acid metabolism, and DNA damage response. Two proteins identified by the screen are shown to be proteolytic substrates of Dsk2, validating the large scale synthetic dosage lethality screen as a new strategy for identifying substrates of a specific degradation pathway. CONCLUSION: In conclusion, as proof-of-concept, we show that a synthetic dosage lethality screen, which is based on the toxicity induced by gene overexpression, offers an effective, complementary method to elucidating biological functions of proteolytic pathways.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".