A fractionation method to identify qauntitative changes in protein expression mediated by IGF-1 on the proteome of murine C2C12 myoblasts
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Bibliographic record
Abstract
Although much is known about signal transduction downstream of insulin-like growth factor-1 (IGF-1), relatively little is known about the global changes in protein expression induced by this hormone. In this study, the acute effects of IGF-1 on the proteome of murine C2C12 cells were examined. Cells were treated with IGF-1 for up to 24 hours, lysed, and fractionated into cytosolic, nuclear, and insoluble portions. Proteins from the cytosolic fraction were further separated using a new batch ion-exchange chromatography method to reduce sample complexity, followed by two-dimensional (2D) electrophoresis, and identification of selected proteins by mass spectrometry. PDQuest software was utilized to identify and catalogue temporal changes in protein expression during IGF-1 stimulation. In response to IGF-1 stimulation, expression of 23 proteins increased at least three-fold and expression of 17 proteins decreased at least three-fold compared with control un-stimulated C2C12 cells. Changes in expression of selected proteins from each group, including Rho-GDI, cofillin, RAD50, enolase, IkappaB kinase b (IkappaBKb) and Hsp70 were confirmed by Western blotting. Additionally, the position of 136 'landmark' proteins whose expression levels and physicochemical properties did not change appreciably or consistently during IGF-1 treatment were mapped and identified. This characterization of large-scale changes in protein expression in response to growth factor stimulation of C2C12 cells will further help to establish a comprehensive understanding of the networks and pathways involved in the action of IGF-1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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