Ang II (Angiotensin II)–Induced FGFR1 (Fibroblast Growth Factor Receptor 1) Activation in Tubular Epithelial Cells Promotes Hypertensive Kidney Fibrosis and Injury
Bibliographic record
Abstract
BACKGROUND: Elevated Ang II (angiotensin II) level leads to a range of conditions, including hypertensive kidney disease. Recent evidences indicate that FGFR1 (fibroblast growth factor receptor 1) signaling may be involved in kidney injuries. In this study, we determined whether Ang II alters FGFR1 signaling to mediate renal dysfunction. METHODS: Human archival kidney samples from patients with or without hypertension were examined. Multiple genetic and pharmacological approaches were used to investigate FGFR1-mediated signaling in tubular epithelial NRK-52E cells in response to Ang II stimulation. C57BL/6 mice were infused with Ang II for 28 days to develop hypertensive kidney disease. Mice were treated with either adeno-associated virus expressing FGFR1 shRNA or FGFR1 inhibitor AZD4547. RESULTS: Kidney specimens from subjects with hypertension and mice challenged with Ang II have increased FGFR1 activity in renal epithelial cells. Renal epithelial cells in culture initiate extracellular matrix programming in response to Ang II, through the activation of FGFR1, which is independent of both AT1R (angiotensin II receptor type 1) and AT2R (angiotensin II receptor type 2). The RNA sequencing analysis indicated that disrupting FGFR1 suppresses Ang II-induced fibrogenic responses in epithelial cells. Mechanistically, Ang II-activated FGFR1 leads to STAT3 (signal transducer and activator of transcription 3) activation, which is responsible for fibrogenic factor expression in kidneys. In the mouse model of hypertensive kidney disease, genetic knockdown of FGFR1 or pharmacological inhibition of its activity protected kidneys from dysfunction and fibrosis upon Ang II challenge. CONCLUSIONS: Our studies uncover a novel mechanism causing renal fibrosis in hypertension and indicate FGFR1 as a potential target to preserve renal function and integrity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".