Regulation of the Ste20-like Kinase, SLK
Bibliographic record
Abstract
Expression and activation of the Ste20-like kinase, SLK, is increased during kidney development and recovery from ischemic acute kidney injury. SLK promotes apoptosis, and it may regulate cell survival during injury or repair. This study addresses the role of phosphorylation in the regulation of kinase activity. We mutated serine and threonine residues in the putative activation segment of the SLK catalytic domain and expressed wild type (WT) and mutant proteins in COS-1 or glomerular epithelial cells. Compared with SLK WT, the T183A, S189A, and T183A/S189A mutants showed reduced in vitro kinase activity. SLK WT, but not mutants, increased activation-specific phosphorylation of c-Jun N-terminal kinase (JNK) and p38 kinase. Similarly, SLK WT stimulated activator protein-1 reporter activity, but activation of activator protein-1 by the three SLK mutants was ineffective. To test if homodimerization of SLK affects phosphorylation, the cDNA encoding SLK amino acids 1-373 (which include the catalytic domain) was fused with a cDNA for a modified FK506-binding protein, Fv (Fv-SLK 1-373). After transfection, the addition of AP20187 (an FK506 analog) induced regulated dimerization of Fv-SLK 1-373. AP20187-stimulated dimerization enhanced the kinase activity of Fv-SLK 1-373 WT. In contrast, kinase activity of Fv-SLK 1-373 T183A/S189A was weak and was not enhanced after dimerization. Finally, apoptosis was increased after expression of Fv-SLK 1-373 WT but not T183A/S189A. Thus, phosphorylation of Thr-183 and Ser-189 plays a key role in the activation and signaling of SLK and could represent a target for novel therapeutic approaches to renal injury.
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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 | 0.000 |
| 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".