Control of ubiquitination of proteins in rat tissues by ubiquitin conjugating enzymes and isopeptidases
Bibliographic record
Abstract
The activity of the ubiquitin-dependent proteolytic system in differentiated tissues under basal conditions remains poorly explored. We measured rates of ubiquitination in rat tissue extracts. Accumulation of ubiquitinated proteins increased in the presence of ubiquitin aldehyde, indicating that deubiquitinating enzymes can regulate ubiquitination. Rates of ubiquitination varied fourfold, with the highest rate in the testis. We tested whether ubiquitin-activating enzyme (E1) or ubiquitin-conjugating enzymes (E2s) could be limiting for conjugation. Immunodepletion of the E2s UBC2 or UBC4 lowered rates of conjugation similarly. Supplementation of extracts with excess UBC2 or UBC4, but not E1, stimulated conjugation. However, UBC2-stimulated rates of ubiquitination still differed among tissues, indicating that tissue differences in E3s or substrate availability may also be rate controlling. UBC2 and UBC4 stimulated conjugation half-maximally at concentrations of 10-50 and 28-44 nM, respectively. Endogenous tissue levels of UBC2, but not UBC4, appeared saturating for conjugation, suggesting that in vivo modulation of UBC4 levels can likely control ubiquitin conjugation. Thus the pool of ubiquitin conjugates and therefore the rate of degradation of proteins by this system may be controlled by E2s, E3s, and isopeptidases. The regulation of the ubiquitin pathway appears complex, but precise.
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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 | 0.001 | 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".