Global analysis of protein phosphorylation networks in insulin signaling by sequential enrichment of phosphoproteins and phosphopeptides
Bibliographic record
Abstract
Although the important role of protein phosphorylation in insulin signaling networks is well recognized, its analysis in vivo has not been pursued in a systematic fashion through proteome-wide studies. Here we undertake a global analysis of insulin-induced changes in the rat liver cytoplasmic and endosomal phosphoproteome by sequential enrichment of phosphoproteins and phosphopeptides. After subcellular fractionation proteins were denatured and loaded onto iminodiacetic acid-modified Sepharose with immobilized Al³⁺ ions (IMAC-Al resin). Retained phosphoproteins were eluted with 50 mM phosphate and proteolytically digested. The digest was then loaded onto an IMAC-Al resin and phosphopeptides were eluted with 50 mM phosphate, and resolved by 2-dimensional liquid chromatography, which combined offline weak anion exchange and online reverse phase separations. The peptides were identified by tandem mass spectrometry, which also detected the phosphorylation sites. Non-phosphorylated peptides found in the flow-through of the IMAC-Al columns were also analyzed providing complementary information for protein identification. In this study we enriched phosphopeptides to ~85% purity and identified 1456 phosphopeptides from 604 liver phosphoproteins. Eighty-nine phosphosites including 45 novel ones in 83 proteins involved in vesicular transport, metabolism, cell motility and structure, gene expression and various signaling pathways were changed in response to insulin treatment. Together these findings could provide potential new markers for evaluating insulin action and resistance in obesity and 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) | 0.000 | 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 | 0.000 |
| 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".