Grb10 and Active Raf-1 Kinase Promote Bad-dependent Cell Survival
Why this work is in the frame
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Bibliographic record
Abstract
The proapoptotic protein Bad is a key player in cell survival decisions, and is regulated post-translationally by several signaling networks. We expressed Bad in mouse embryonic fibroblasts to sensitize them to apoptosis, and tested cell lines derived from knock-out mice to establish the significance of the interaction between the adaptor protein Grb10 and the Raf-1 protein kinase in anti-apoptotic signaling pathways targeting Bad. When compared with wild-type cells, both Grb10 and Raf-1-deficient cells exhibit greatly enhanced sensitivity to apoptosis in response to Bad expression. Structure-function analysis demonstrates that, in this cellular model, the SH2, proline-rich, and pleckstrin homology domains of Grb10, as well as its Akt phosphorylation site and consequent binding by 14-3-3, are all necessary for its anti-apoptotic functions. As for Raf-1, its kinase activity, its ability to be phosphorylated by Src on Tyr-340/341 and the binding of its Ras-associated domain to the Grb10 SH2 domain are all necessary to promote cell survival. Silencing the expression of either Grb10 or Raf-1 by small interfering RNAs as well as mutagenesis of specific serine residues on Bad, coupled with signaling inhibitor studies, all indicate that Raf-1 and Grb10 are required for the ability of both the phosphatidylinositol 3-kinase/Akt and MAP kinase pathways to modulate the phosphorylation and inactivation of Bad. Because total Raf-1, ERK, and Akt kinase activities are not impaired in the absence of Grb10, we propose that this adapter protein creates a subpopulation of Raf-1 with specific anti-apoptotic activity.
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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 | 0.000 |
| 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 | 0.000 |
| 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