Distinct roles for extracellular-signal-regulated protein kinase (ERK) mitogen-activated protein kinases and phosphatidylinositol 3-kinase in the regulation of Mcl-1 synthesis
Bibliographic record
Abstract
Alterations in the expression of various Bcl-2 family members may act as one means by which a cell's survival may be regulated. The mechanism by which cytokines regulate expression of Bcl-2 family members was examined in the haemopoietic cell line TF-1. Cytokine-induced Mcl-1 protein expression was shown to be controlled through a pathway dependent upon phosphatidylinositol 3-kinase (PI 3-kinase). The cytokine-induced increase in mRNA transcription was not dependent upon PI 3-kinase, thus dissociating the immediate-early transcription factors responsible for Mcl-1 transcription from the PI 3-kinase signalling pathway. In contrast, Mcl-1 mRNA levels were dependent upon MEK [mitogen-activated protein kinase (MAPK)/extracellular-signal-regulated protein kinase kinase] activation, suggesting a role for the Ras/MEK/MAPK pathway in Mcl-1 transcription. Activation of PI 3-kinase was shown to be necessary to stimulate Mcl-1 protein translation. This was not due to any effect on prolonging the half-life of the protein. Finally, the lipid second messenger ceramide was shown to cause a reduction in Mcl-1 protein translation, probably via its ability to inhibit protein kinase B activation, providing further clues regarding the death-inducing effect of this lipid.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".