Quantitative proteomic analysis of S-nitrosated proteins in diabetic mouse liver with ICAT switch method
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Bibliographic record
Abstract
In this study we developed a quantitative proteomic method named ICAT switch by introducing isotope-coded affinity tag (ICAT) reagents into the biotin-switch method, and used it to investigate S-nitrosation in the liver of normal control C57BL/6J mice and type 2 diabetic KK-Ay mice. We got fifty-eight S-nitrosated peptides with quantitative information in our research, among which thirty-seven had changed S-nitrosation levels in diabetic mouse liver. The S-nitrosated peptides belonged to forty-eight proteins (twenty-eight were new S-nitrosated proteins), some of which were new targets of S-nitrosation and known to be related with diabetes. S-nitrosation patterns were different between diabetic and normal mice. Gene ontology enrichment results suggested that S-nitrosated proteins are more abundant in amino acid metabolic processes. The network constructed for S-nitrosated proteins by text-mining technology provided clues about the relationship between S-nitrosation and type 2 diabetes. Our work provides a new approach for quantifying S-nitrosated proteins and suggests that the integrative functions of S-nitrosation may take part in pathophysiological processes of type 2 diabetes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it