Mitogen- and Stress-Activated Protein Kinase 1 Mediates cAMP Response Element-Binding Protein Phosphorylation and Activation by Neurotrophins
Bibliographic record
Abstract
Activation of the transcription factor cAMP response element-binding protein (CREB) by neurotrophins is believed to regulate the survival, differentiation, and maturation of neurons in the CNS and PNS. Although phosphorylation of Ser133 is critical for the expression of CREB-regulated genes, the identity of neurotrophin-regulated Ser133 kinases has remained controversial. We show here that neurotrophin-induced CREB phosphorylation in CNS neurons depends exclusively on the extracellular signal-regulated kinase 1/2-activated kinase mitogen- and stress-activated protein kinase 1 (MSK1). Small interfering RNA directed against ribosomal S6 kinase 1 (RSK1) and RSK2 reduced phosphorylation of a RSK substrate but did not effect CREB-dependent transcription. However, expression of a selective inhibitory MSK1 mutant markedly attenuated BDNF-stimulated CREB phosphorylation and CREB-mediated transcription. Moreover, the ability of neurotrophins to stimulate CREB phosphorylation was abolished in CNS neurons from MSK1 knock-out mice. Consistent with a role for MSK1 in Ser133 phosphorylation, neurotrophin-induced expression of CREB-regulated genes was attenuated in MSK-deficient neurons. These results indicate that MSK1 is the major neurotrophin-activated Ser133 kinase in CNS neurons.
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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 | 0.000 |
| 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".