Quantitative Protein Sulfenic Acid Analysis Identifies Platelet Releasate-Induced Activation of Integrin β<sub>2</sub> on Monocytes via NADPH Oxidase
Bibliographic record
Abstract
Physiological stimuli such as thrombin, or pathological stimuli such as lysophosphatidic acid (LPA), activate platelets. The activated platelets bind to monocytes through P-selectin–PSGL-1 interactions but also release the contents of their granules, commonly called “platelet releasate”. It is known that monocytes in contact with platelet releasate produce reactive oxygen species (ROS). Reversible cysteine oxidation by ROS is considered to be a potential regulator of protein function. In a previous study, we used THP-1 monocytic cells exposed to LPA- or thrombin-induced platelet releasate and a modified biotin switch assay to unravel the biological processes that are influenced by reversible cysteine oxidation. To gain a better understanding of the redox regulation of monocytes in atherosclerosis, we have now altered the modified biotin switch to selectively quantify protein sulfenic acid, a subpopulation of reversible cysteine oxidation. Using arsenite as reducing agent in the modified biotin switch assay, we were able to quantify 1161 proteins, in which more than 100 sulfenic acid sites were identified. Bioinformatics analysis of the quantified sulfenic acid sites highlighted the relevant, previously missed biological process of monocyte transendothelial migration, which included integrin β 2 . Flow cytometry validated the activation of LFA-1 (α L β 2 ) and Mac-1 (α M β 2 ), two subfamilies of integrin β 2 complexes, on human primary monocytes following platelet releasate treatment. The activation of LFA-1 was mediated by ROS from NADPH oxidase (NOX) activation. Production of ROS and activation of LFA-1 in human primary monocytes were independent of P-selectin–PSGL-1 interaction. Our results proved the modified biotin switch assay to be a powerful tool with the ability to reveal new regulatory mechanisms and identify new therapeutic targets.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".