Proteomic Characterization of Aggregating Proteins after the Inhibition of the Ubiquitin Proteasome System
Bibliographic record
Abstract
Protein aggregation, which is associated with the impairment of the ubiquitin proteasome system, is a hallmark of many neurodegenerative diseases. To better understand the contribution of proteasome inhibition in aggregation, we analyzed which proteins may potentially localize in chemically induced aggregates in human neuroblastoma tissue culture cells. We enriched for proteins in high-density structures by using a sucrose gradient in combination with stable isotope labeling with amino acids in cell culture (SILAC). The quantitative analysis allowed us to distinguish which proteins were specifically affected by the proteasome inhibition. We identified 642 potentially aggregating proteins, including the p62/sequestosome 1 and NBR1 ubiquitin-binding proteins involved in aggregation. We also identified the ubiquitin-associated protein 2 like (UBAP2L). We verified that it cofractionated with ubiquitin in the high-density fraction and that it was colocalized in the ubiquitin-containing aggregates after proteasome inhibition. In addition, we identified several chaperone proteins and used data from protein interaction networks to show that they potentially interact with distinct subgroups of proteins within the aggregating structures. Several other proteins associated with neurodegenerative diseases, like UCHL1, were identified, further underlining the potential of our analysis to better understand the aggregation process and proteotoxic stress caused by proteasome inhibition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".