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 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 | 0.001 |
| 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".