Glycogen Synthase Kinase-3β Induces Neuronal Cell Death via Direct Phosphorylation of Mixed Lineage Kinase 3
Bibliographic record
Abstract
Mixed lineage kinase 3 (MLK3) is a mitogen-activated protein kinase kinase kinase member that activates the c-Jun N-terminal kinase (JNK) pathway. Aberrant activation of MLK3 has been implicated in neurodegenerative diseases. Similarly, glycogen synthase kinase (GSK)-3beta has also been shown to activate JNK and contribute to neuronal apoptosis. Here, we show a functional interaction between MLK3 and GSK-3beta during nerve growth factor (NGF) withdrawal-induced cell death in PC-12 cells. The protein kinase activities of GSK-3beta, MLK3, and JNK were increased upon NGF withdrawal, which paralleled increased cell death in NGF-deprived PC-12 cells. NGF withdrawal-induced cell death and MLK3 activation were blocked by a GSK-3beta-selective inhibitor, kenpaullone. However, the MLK family inhibitor, CEP-11004, although preventing PC-12 cell death, failed to inhibit GSK-3beta activation, indicating that induction of GSK-3beta lies upstream of MLK3. In GSK-3beta-deficient murine embryonic fibroblasts, ultraviolet light was unable to activate MLK3 kinase activity, a defect that was restored upon ectopic expression of GSK-3beta. The activation of MLK3 by GSK-3beta occurred via phosphorylation of MLK3 on two amino acid residues, Ser(789) and Ser(793), that are located within the C-terminal regulatory domain of MLK3. Furthermore, the cell death induced by GSK-3beta was mediated by MLK3 in a manner dependent on its phosphorylation of the specific residues within the C-terminal domain by GSK-3beta. Taken together, our data provide a direct link between GSK-3beta and MLK3 activation in a neuronal cell death pathway and identify MLK3 as a direct downstream target of GSK-3beta. Inhibition of GSK-3 is thus a potential therapeutic strategy for neurodegenerative diseases caused by trophic factor deprivation.
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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) | 0.001 | 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".