STAT3 inhibition attenuates the progressive phenotypes of Alport syndrome mouse model
Bibliographic record
Abstract
Background: Alport syndrome (AS) is a hereditary, progressive nephritis caused by mutation of type IV collagen. Previous studies have shown that activation of signal transducer and activator of transcription 3 (STAT3) exacerbates other renal diseases, but whether STAT3 activation exacerbates AS pathology is still unknown. Here we aim to investigate the involvement of STAT3 in the progression of AS. Method: Phosphorylated STAT3 expression was assessed by immunoblotting analysis of kidneys and glomeruli of an AS mouse model (Col4a5 G5X mutant). To determine the effect of blocking STAT3 signaling, we treated AS mice with the STAT3 inhibitor stattic (10 mg/kg i.p., three times per week for 10 weeks; n = 10). We assessed the renal function [proteinuria, blood urea nitrogen (BUN), serum creatinine] and analyzed the glomerular injury score, fibrosis and inflammatory cell invasion by histological staining. Moreover, we analyzed the gene expression of nephritis-associated molecules. Results: Phosphorylated STAT3 was upregulated in AS kidneys and glomeruli. Treatment with stattic ameliorated the progressive renal dysfunction, such as increased levels of proteinuria, BUN and serum creatinine. Stattic also significantly suppressed the gene expression levels of renal injury markers (Lcn2, Kim-1), pro-inflammatory cytokines (Il-6, KC), pro-fibrotic genes (Tgf-β, Col1a1, α-Sma) and Mmp9. Stattic treatment decreased the renal fibrosis congruently with the decrease of transforming growth factor beta (TGF-β) protein and increase of antifibrosis-associated markers p-Smad1, 5 and 8, which are negative regulators of TGF-β signaling. Conclusion: STAT3 inhibition significantly ameliorated the renal dysfunction in AS mice. Our finding identifies STAT3 as an important regulator in AS progression and provides a promising therapeutic target for AS.
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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 | 0.000 |
| 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".