Inhibition of Raf-1 Alters Multiple Downstream Pathways to Induce Pancreatic β-Cell Apoptosis
Bibliographic record
Abstract
The serine threonine kinase Raf-1 plays a protective role in many cell types, but its function in pancreatic beta-cells has not been elucidated. In the present study, we examined whether primary beta-cells possess Raf-1 and tested the hypothesis that Raf-1 is critical for beta-cell survival. Using reverse transcriptase-PCR, Western blot, and immunofluorescence, we identified Raf-1 in human islets, mouse islets, and in the MIN6 beta-cell line. Blocking Raf-1 activity using a specific Raf-1 inhibitor or dominant-negative Raf-1 mutants led to a time- and dose-dependent increase in cell death, assessed by real-time imaging of propidium iodide incorporation, TUNEL, PCR-enhanced DNA laddering, and Caspase-3 cleavage. Although the rapid increase in apoptotic cell death was associated with decreased Erk phosphorylation, studies with two Mek inhibitors suggested that the classical Erk-dependent pathway could explain only part of the cell death observed after inhibition of Raf-1. An alternative Erk-independent pathway downstream of Raf-1 kinase involving the pro-apoptotic protein Bad has recently been characterized in other tissues. Inhibiting Raf-1 in beta-cells led to a striking loss of Bad phosphorylation at serine 112 and an increase in the protein levels of both Bad and Bax. Together, our data strongly suggest that Raf-1 signaling plays an important role regulating beta-cell survival, via both Erk-dependent and Bad-dependent mechanisms. Conversely, acutely inhibiting phosphatidylinositol 3-kinase Akt had more modest effects on beta-cell death. These studies identify Raf-1 as a critical anti-apoptotic kinase in pancreatic beta-cells and contribute to our understanding of survival signaling in this cell type.
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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) | 0.000 | 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) | 0.001 | 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".