Activated Thiol Sepharose‐based proteomic approach to quantify reversible protein oxidation
Bibliographic record
Abstract
Reactive oxygen species (ROS) can act as second messengers in various signaling pathways, and abnormal oxidation contributes to multiple diseases, including cancer. Detecting and quantifying protein oxidation is crucial for a detailed understanding of reduction‐oxidation reaction (redox) signaling. We developed an Activated Thiol Sepharose‐based proteomic (ATSP) approach to quantify reversible protein oxidation. ATSP can enrich H 2 O 2 ‐sensitive thiol peptides, which are more likely to contain reactive cysteines involved in redox signaling. We applied our approach to analyze hereditary leiomyomatosis and renal cell carcinoma (HLRCC), a type of kidney cancer that harbors fumarate hydratase (FH)‐inactivating mutations and has elevated ROS levels. Multiple proteins were oxidized in FH‐deficient cells, including many metabolic proteins such as the pyruvate kinase M2 isoform (PKM2). Treatment of HLRCC cells with dimethyl fumarate or PKM2 activators altered PKM2 oxidation levels. Finally, we found that ATSP could detect Src homology region 2 domain‐containing phosphatase‐2 and PKM2 oxidation in cells stimulated with platelet‐derived growth factor. This newly developed redox proteomics workflow can detect reversible oxidation of reactive cysteines and can be employed to analyze multiple physiologic and pathologic conditions.—Xu, Y., Andrade, J., Ueberheide, B., Neel, B. G. Activated Thiol Sepharose‐based proteomic approach to quantify reversible protein oxidation. FASEB J. 33, 12336–12347 (2019). www.fasebj.org
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".