Different Properties of SEK1 and MKK7 in Dual Phosphorylation of Stress-induced Activated Protein Kinase SAPK/JNK in Embryonic Stem Cells
Bibliographic record
Abstract
Stress-activated protein kinase/c-Jun NH(2)-terminal kinase (SAPK/JNK), belonging to the mitogen-activated protein kinase family, plays an important role in stress signaling. SAPK/JNK activation requires the phosphorylation of both Thr and Tyr residues in its Thr-Pro-Tyr motif, and SEK1 and MKK7 have been identified as the dual specificity kinases. In this study, we generated mkk7(-/-) mouse embryonic stem (ES) cells in addition to sek1(-/-) cells and compared the two kinases in terms of the activation and phosphorylation of JNK. Although SAPK/JNK activation by various stress signals was markedly impaired in both sek1(-/-) and mkk7(-/-) ES cells, there were striking differences in the dual phosphorylation profile. The severe impairment observed in mkk7(-/-) cells was accompanied by a loss of the Thr phosphorylation of JNK without marked reduction in its Tyr-phosphorylated level. On the other hand, Thr phosphorylation of JNK in sek1(-/-) cells was also attenuated in addition to a decreased level of its Tyr phosphorylation. Analysis in human embryonic kidney 293T cells transfected with a kinase-dead SEK1 or a Thr-Pro-Phe mutant of JNK1 revealed that SEK1-induced Tyr phosphorylation of JNK1 was followed by additional Thr phosphorylation by MKK7. Furthermore, SEK1 but not MKK7 was capable of binding to JNK1 in 293T cells. These results indicate that the Tyr and Thr residues of SAPK/JNK are sequentially phosphorylated by SEK1 and MKK7, respectively, in the stress-stimulated ES cells.
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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".