Role of the Ste20‐like kinase <scp>SLK</scp> in podocyte adhesion
Bibliographic record
Abstract
SLK controls the cytoskeleton, cell adhesion, and migration. Podocyte-specific deletion of SLK in mice leads to podocyte injury as mice age and exacerbates injury in experimental focal segment glomerulosclerosis (FSGS; adriamycin nephrosis). We hypothesized that adhesion proteins may be substrates of SLK. In adriamycin nephrosis, podocyte ultrastructural injury was exaggerated by SLK deletion. Analysis of a protein kinase phosphorylation site dataset showed that podocyte adhesion proteins-paxillin, vinculin, and talin-1 may be potential SLK substrates. In cultured podocytes, deletion of SLK increased adhesion to collagen. Analysis of paxillin, vinculin, and talin-1 showed that SLK deletion reduced focal adhesion complexes (FACs) containing these proteins mainly in adriamycin-induced injury; there was no change in FAC turnover (focal adhesion kinase Y397 phosphorylation). In podocytes, paxillin S250 showed basal phosphorylation that was slightly enhanced by SLK; however, SLK did not phosphorylate talin-1. In adriamycin nephrosis, SLK deletion did not alter glomerular expression/localization of talin-1 and vinculin, but increased focal adhesion kinase phosphorylation modestly. Therefore, SLK decreases podocyte adhesion, but FAC proteins in podocytes are not major substrates of SLK in health and disease.
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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) | 0.002 | 0.001 |
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".