Grb10 and Active Raf-1 Kinase Promote Bad-dependent Cell Survival
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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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) | 0.002 | 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".