Quantitative Analysis of Global Protein Stability Rates in Tissues
Bibliographic record
Abstract
Abstract Protein degradation is an essential mechanism for maintaining homeostasis in response to internal and external perturbations. Disruption of this process is implicated in many human diseases, but quantitation of global stability rates has not yet been achieved in tissues. We have developed QUAD (Quantification of Azidohomoalanine Degradation), a technique to quantitate global protein degradation using mass spectrometry. Azidohomoalanine (AHA) is pulsed into mouse tissues through their diet. The mice are then returned to a normal diet and the decrease of AHA abundance can be quantitated in the proteome. QUAD analysis reveals that protein stability varied within tissues, but discernible trends in the data suggest that cellular environment is a major factor dictating stability. Within a tissue, different organelles, post-translation modifications, and protein functions were enriched with different stability patterns. Surprisingly, subunits of the TRIC molecular chaperonin possessed markedly distinct stability trajectories in the brain. Further investigation revealed that these subunits also possessed different subcellular localization and expression patterns that were uniquely altered with age and in Alzheimer’s disease transgenic mice, indicating a potential non-canonical chaperonin. Finally, QUAD analysis demonstrated that protein stability is enhanced with age in the brain but not in the liver. Overall, QUAD allows the first global quantitation of protein stability rates in tissues, which may lead to new insights and hypotheses in basic and translational research. Summary Protein degradation is an important component of the proteostasis network, but no techniques are available to globally quantitate degradation rates in tissues. In this study, we demonstrate a new method QUAD (Quantification of Azidohomoalanine Degradation) that can accurately quantitate degradation rates in tissues. QUAD analysis of mouse tissues reveal that unique degradation trends can define different tissue proteomes. Within a tissue, specific protein characteristics are correlated with different levels of protein stability. Further investigation of the TRIC chaperonin with strikingly different subunit stabilities suggests a non-canonical chaperonin in brain tissue. Consistent with the theory that the proteostasis network is compromised with age, we discovered that protein stability is globally enhanced in brains of old mice compared to young mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".