A fractionation method to identify qauntitative changes in protein expression mediated by IGF-1 on the proteome of murine C2C12 myoblasts
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".