Fibroblast-Specific β-Catenin Signaling Dictates the Outcome of AKI
Bibliographic record
Abstract
AKI is a devastating condition with high morbidity and mortality. The pathologic features of AKI are characterized by tubular injury, inflammation, and vascular impairment. Whether fibroblasts in the renal interstitium have a role in the pathogenesis of AKI is unknown. In this study, we investigated the role of fibroblast-specific β -catenin signaling in dictating the outcome of AKI, using conditional knockout mice in which β -catenin was specifically ablated in fibroblasts (Gli1- β -cat−/−). After ischemia-reperfusion injury (IRI), Gli1- β -cat−/− mice had lower serum creatinine levels and less morphologic injury than Gli1- β -cat+/+ littermate controls. Moreover, we detected fewer apoptotic cells, as well as decreased cytochrome C release; reduced expression of Bax, FasL, and p53; and increased phosphorylation of Akt, in the Gli1- β -cat−/− kidneys. Gli1- β -cat−/− kidneys also exhibited upregulated expression of proliferating cell nuclear antigen and Ki-67, which are markers of cell proliferation. Furthermore, Gli1- β -cat−/− kidneys displayed suppressed NF-κB signaling and cytokine expression and reduced infiltration of inflammatory cells. Notably, loss of β -catenin in fibroblasts induced renal expression of hepatocyte growth factor (HGF) and augmented the tyrosine phosphorylation of c-met receptor after IRI. In vitro , treatment with Wnt ligands or ectopic expression of active β -catenin inhibited HGF mRNA and protein expression and repressed HGF promoter activity. Collectively, these results suggest that fibroblast-specific β -catenin signaling can control tubular injury and repair in AKI by modulating HGF expression. Our studies uncover a previously unrecognized role for interstitial fibroblasts in the pathogenesis of AKI.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".