Quantitative Profiling of Ubiquitylated Proteins Reveals Proteasome Substrates and the Substrate Repertoire Influenced by the Rpn10 Receptor Pathway
Bibliographic record
Abstract
The ubiquitin proteasome system (UPS) comprises hundreds of different conjugation/deconjugation enzymes and multiple receptors that recognize ubiquitylated proteins. A formidable challenge to deciphering the biology of ubiquitin is to map the networks of substrates and ligands for components of the UPS. Several different receptors guide ubiquitylated substrates to the proteasome, and neither the basis for specificity nor the relative contribution of each pathway is known. To address how broad of a role the ubiquitin receptor Rpn10 (S5a) plays in turnover of proteasome substrates, we implemented a method to perform quantitative analysis of ubiquitin conjugates affinity-purified from experimentally perturbed and reference cultures of Saccharomyces cerevisiae that were differentially labeled with 14N and 15N isotopes. Shotgun mass spectrometry coupled with relative quantification using metabolic labeling and statistical analysis based on q values revealed ubiquitylated proteins that increased or decreased in level in response to a particular treatment. We first identified over 225 candidate UPS substrates that accumulated as ubiquitin conjugates upon proteasome inhibition. To determine which of these proteins were influenced by Rpn10, we evaluated the ubiquitin conjugate proteomes in cells lacking either the entire Rpn10 (rpn10delta) (or only its UIM (ubiquitin-interacting motif) polyubiquitin-binding domain (uimdelta)). Twenty-seven percent of the UPS substrates accumulated as ubiquitylated species in rpn10delta cells, whereas only one-fifth as many accumulated in uimdelta cells. These findings underscore a broad role for Rpn10 in turnover of ubiquitylated substrates but a relatively modest role for its ubiquitin-binding UIM domain. This approach illustrates the feasibility of systems-level quantitative analysis to map enzyme-substrate networks in the UPS.
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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) | 0.000 | 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 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".