Protein Disulfide Bond Formation in the Cytoplasm during Oxidative Stress
Bibliographic record
Abstract
The majority of disulfide-linked cytosolic proteins are thought to be enzymes that transiently form disulfide bonds while catalyzing oxidation-reduction (redox) processes. Recent evidence indicates that reactive oxygen species can act as signaling molecules by promoting the formation of disulfide bonds within or between select redox-sensitive proteins. However, few studies have attempted to examine global changes in disulfide bond formation following reactive oxygen species exposure. Here we isolate and identify disulfide-bonded proteins (DSBP) in a mammalian neuronal cell line (HT22) exposed to various oxidative insults by sequential nonreducing/reducing two-dimensional SDS-PAGE combined with mass spectrometry. By using this strategy, several known cytosolic DSBP, such as peroxiredoxins, thioredoxin reductase, nucleoside-diphosphate kinase, and ribonucleotide-diphosphate reductase, were identified. Unexpectedly, a large number of previously unknown DSBP were also found, including those involved in molecular chaperoning, translation, glycolysis, cytoskeletal structure, cell growth, and signal transduction. Treatment of cells with a wide range of hydrogen peroxide concentrations either promoted or inhibited disulfide bonding of select DSBP in a concentration-dependent manner. Decreasing the ratio of reduced to oxidized glutathione also promoted select disulfide bond formation within proteins from cytoplasmic extracts. In addition, an epitope-tagged version of the molecular chaperone HSP70 forms mixed disulfides with both beta4-spectrin and adenomatous polyposis coli protein in the cytosol. Our findings indicate that disulfide bond formation within families of cytoplasmic proteins is dependent on the nature of the oxidative insult and may provide a common mechanism used to control multiple physiological processes.
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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".