H <sub>2</sub> O <sub>2</sub> -Induced Phosphorylation of ERK1/2 and PKB Requires Tyrosine Kinase Activity of Insulin Receptor and c-Src
Bibliographic record
Abstract
Hydrogen peroxide (H2O2) mimics many physiological responses of insulin, and increased H2O2 generation via the Nox-4 subunit of NAD(P)H oxidase was recently demonstrated to serve as a critical early step in the insulin signaling pathway. Exogenously added H2O2 has also been shown to activate several key components of the insulin signaling cascade. H2O2-induced signaling responses have been found to be associated with the activation of receptor and nonreceptor protein tyrosine kinases (PTK), including the insulin receptor (IR)-beta subunit. Therefore, in the present studies on Chinese hamster ovary cells overexpressing wild-type IR-PTK (CHO-IR) or a PTK-inactive form of IR (CHO-1018), we investigated whether IR-PTK plays a role in H2O2-induced signaling events. Treatment of CHO-IR cells with H2O2 increased the phosphorylation of extracellular signal-regulated kinases 1 and 2 (ERK1/2), protein kinase B (PKB), and glycogen synthase kinase-3beta while enhancing tyrosine phosphorylation of the IR-beta subunit and the p85 subunit of phosphatidylinositol 3-kinase (PI3K). Compared with CHO-IR cells, the stimulatory effect of H2O2 on ERK1/2 and PKB was partially reduced in CHO-1018 cells. However, pharmacological inhibition of Src family PTK by 4-amino-5-(4-chlorophenyl)-7-(tert-butyl)pyrazolo[3,4-d]pyrimidine (PP-2) almost completely blocked H2O2-stimulated phosphorylation of the p85 subunit of PI3K, ERK1/2, and PKB. Moreover, H2O2, but not insulin, induced Tyr-418 phosphorylation of Src, which was also suppressed by PP-2. Taken together, these data suggest that both IR-PTK and Src family PTKs contribute to H2O2-induced signaling in CHO-IR cells albeit IR-PTK has a less dominant role in this process.
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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 | 0.000 | 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) | 0.003 | 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".