Azidohomoalanine (AHA) Metabolic Labeling Reveals Unique Proteomic Insights into Protein Synthesis and Degradation in Response to Bortezomib Treatment
Bibliographic record
Abstract
BACKGROUND: Multiple myeloma (MM) is essentially an incurable cancer, but treatments with proteasome inhibitors are widely used clinically to extend patient survival. While the mechanisms of proteasome inhibition by Bortezomib are well known, the cellular responses to this proteotoxic stress that leads to sensitivity by MM are not fully elucidated. This study reports on the application of an emerging method to investigate proteostasis by proteomics. METHODS: We utilized metabolic labeling with azidohomoalanine (AHA) in a MM cell line in combination with Bortezomib treatment. AHA labeling facilitates the selective isolation and identification of proteins for investigations of protein synthesis or protein degradation. RESULTS: The data collected reveals significant changes in gene protein synthesis upon Bortezomib treatment, including protein neddylation. The data also reveals a global increase in protein degradation, which suggests the induction of an autophagy-related process. The resulting data collected reveals significant changes upon Bortezomib treatment in protein synthesis of genes, including protein neddylation, and protein degradation data reveals a global increase in protein degradation, suggesting an induction of an autophagy-related process. Subsequent cellular and proteomic analysis investigated the additional treatment of an autophagy inhibitor, hydroxychloroquine, in combination with Bortezomib treatment by label-free proteomics to further characterize the proteome-wide changes in these two proteotoxic stresses. CONCLUSIONS: AHA metabolic labeling proteomics to investigate protein synthesis and degradation enables novel complementary insights into complex cellular responses compared to that of traditional label-free proteomics.
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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) | 0.001 | 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".