Yttergren, S. T. | bioRxiv Science

bioRxiv2026

Circulating soluble urokinase-type plasminogen activator receptor reflects disease severity in a mouse model of diabetic kidney disease and heart failure with preserved ejection fraction

Circulating biomarkers are increasingly used for patient risk stratification in chronic kidney disease (CKD) and heart failure with preserved ejection fraction (HFpEF). However, clinically relevant circulating biomarkers remain insufficiently characterized in rodent models recapitulating diabetic cardiorenal disease with HFpEF. To address this gap, we evaluated 20 translationally relevant inflammation-associated biomarkers in the diabetic db/db uninephrectomized (UNx)-ReninAAV mouse model of CKD and HFpEF. db/db UNx-ReninAAV mice exhibited marked increases in circulating soluble urokinase-type plasminogen activator receptor (suPAR) and monocyte chemoattractant protein-1 (MCP-1), and in interleukin 10 (IL-10) at late stages of disease. Histological analyses confirmed increased tissue expression of suPAR in the heart and kidney and of MCP-1 in the heart. Notably, circulating suPAR levels correlated with disease severity, including systolic and diastolic cardiac dysfunction and albuminuria. Together, these results provide a systematic analysis of biomarkers in a rodent model of diabetes, CKD and HFpEF and identify suPAR as the biomarker most closely associated with disease severity.

physiology↗

bioRxiv2025

Deep Learning-Enhanced Light Sheet Microscopy Unveils Semaglutide Impact on Cardiac Fibrosis

BackgroundExtensive preclinical research aims to develop novel therapeutics for myocardial fibrosis (MF), a condition marked by collagen accumulation that impairs cardiac function. MF is particularly relevant in heart failure with preserved ejection fraction (HFpEF), a growing clinical challenge with limited treatment options. However, current methods for quantifying MF in mouse models struggle to accurately capture its heterogeneous regional distribution, creating a significant barrier to reliably assessing the efficacy of therapeutics. PurposeTo develop a whole-heart fibrosis imaging and deep learning (DL)-based quantification method and validate the workflow by assessing the efficacy of a glucagon-like peptide-1 receptor (GLP-1R) agonist in mouse HFpEF model. Experimental ApproachBy utilizing a fluorescent collagen-labelling dye, tissue clearing and 3D light sheet microscopy, we developed a high-throughput imaging platform for MF. We established DL framework to quantify perivascular and replacement fibrosis, as well as hypertrophy, in 17 left ventricular (LV) segments. The antifibrotic effects of the GLP-1R agonist semaglutide were evaluated in the db/db UNx-ReninAAV mouse model, which exhibits diabetes, kidney failure, obesity, and hypertension. Key ResultsWhole-heart 3D light sheet microscopy, combined with artificial intelligence, enables micrometer-resolution analysis of MF distribution in rodents. This approach allows for detailed characterization of distinct regional fibrosis patterns. Chronic semaglutide treatment significantly reduced LV hypertrophy and perivascular fibrosis but had no significant effect on replacement fibrosis. Conclusions and ImplicationsThe established 3D imaging and quantification approach provides a powerful tool for evaluating the therapeutic efficacy of antifibrotic compounds and studying the cellular and pathological mechanisms underlying cardiovascular diseases.

pathology↗