Stimulation of Protein Kinase C Modulates Insulin-like Growth Factor-1-induced Akt Activation in PC12 Cells
Bibliographic record
Abstract
Activation of protein kinase C (PKC) plays an important role in the negative regulation of receptor signaling, but its effect on insulin-like growth factor-1 (IGF-1) receptor signaling remains unclear. In this study, we characterized the intracellular pathways involved in IGF-1-induced activation of Akt and evaluated the effects of the PKC activator phorbol 12-myristate 13-acetate (PMA) on the Akt activation by IGF-1. IGF-1 induced a time- and concentration-dependent activation of Akt. The effect of IGF-1 was blocked by the phosphatidylinositide 3-kinase (PI3K) inhibitors LY294002 (50 micrometer) and wortmannin (0.5 micrometer), but not by the MEK inhibitor PD98059 (50 micrometer) or the p70 S6 kinase pathway inhibitor rapamycin (50 nm), suggesting that the stimulation of Akt by IGF-1 is mediated by the PI3K pathway. Interestingly, cotreatment with PMA (400 nm) attenuated IGF-1-induced activation of Akt. The attenuation was blocked completely by the PKC inhibitor GO6983 (0.5 micrometer), but only partially by the MEK inhibitor PD98059 (50 micrometer), indicating that MAPK-dependent and -independent pathways are involved. PMA induced the activation of PKC in PC12 cells, and this induction was blocked by GO6983. These data further support the role of PKC in the effect of PMA. Moreover, PKCdelta is likely involved in the action of PMA on the basis of data obtained using isoform-specific inhibitors such as rottlerin. PMA also decreased IGF-1-induced tyrosine phosphorylation of insulin receptor substrate-1 and its association with PI3K. Taken together, these results suggest, for the first time, that stimulation of PKC modulates IGF-1-induced activation of Akt.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".