Genetic ablation of SLK exacerbates glomerular injury in adriamycin nephrosis in mice
Bibliographic record
Abstract
Ste20-like kinase SLK is critical for embryonic development and may play an important role in wound healing, muscle homeostasis, cell migration, and tumor growth. Mice with podocyte-specific deletion of SLK show albuminuria and damage to podocytes as they age. The present study addressed the role of SLK in glomerular injury. We induced adriamycin nephrosis in 3- to 4-mo-old control and podocyte SLK knockout (KO) mice. Compared with control, SLK deletion exacerbated albuminuria and loss of podocytes, synaptopodin, and podocalyxin. Glomeruli of adriamycin-treated SLK KO mice showed diffuse increases in the matrix and sclerosis as well as collapse of the actin cytoskeleton. SLK can phosphorylate ezrin. The complex of phospho-ezrin, Na+/H+exchanger regulatory factor 2, and podocalyxin in the apical domain of the podocyte is a key determinant of normal podocyte architecture. Deletion of SLK reduced glomerular ezrin and ezrin phosphorylation in adriamycin nephrosis. Also, deletion of SLK reduced the colocalization of ezrin and podocalyxin in the glomerulus. Cultured glomerular epithelial cells with KO of SLK showed reduced ezrin phosphorylation and podocalyxin expression as well as reduced F-actin. Thus, SLK deletion leads to podocyte injury as mice age and exacerbates injury in adriamycin nephrosis. The mechanism may at least in part involve ezrin phosphorylation as well as disruption of the cytoskeleton and podocyte apical membrane structure.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".