Podocyte YAP and TAZ hyperactivation drives glomerular epithelial proliferative diseases in mice and humans
Bibliographic record
Abstract
Kidney diseases characterized by glomerular epithelial cell proliferation are rare but often devastating, frequently leading to progressive scarring and renal failure. Ranging from autoimmune-induced crescentic glomerulonephritis to HIV infection-induced collapsing glomerulopathy, these diseases are triggered by a wide variety of insults and have generally been thought of as different entities. Here, using immunostaining and spatial transcriptomics, we profiled human kidney biopsies collected from patients with two of these diseases, collapsing glomerulopathy and antineutrophil cytoplasmic antibody (ANCA) vasculitis-induced crescentic glomerulonephritis, to identify common disease-causing molecules. Although triggered by different insults, we identified abnormal hyperactivation of the transcription cofactors Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) in podocytes as a potential common driver of these diseases. To test this hypothesis, we genetically activated podocyte YAP and TAZ in cultured human cells and in mice by deleting the YAP and TAZ inhibitory large tumor suppressor kinases (LATSs). LATS deficiency in mouse podocytes induced a phenotypic transition in vitro, characterized by a highly distorted structure and an increase in matrix gene expression, mimicking many features of the podocytopathy seen in diseases characterized by glomerular epithelial proliferation. In mice, LATS-deficient podocytes orchestrated a profibrotic and pro-proliferative response in surrounding glomerular cells, a characteristic phenomenon of glomerular epithelial proliferative diseases. This response was attenuated when we also deleted podocyte YAP or TAZ in these mice. Together, our findings point to podocyte YAP-TAZ hyperactivation as a previously unrecognized and unifying driver of glomerular epithelial proliferative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".