Quantitative proteomic analysis of S-nitrosated proteins in diabetic mouse liver with ICAT switch method
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".