Urmas Roostalu

BioStudies – one package for all the data supporting a study

BioStudies – one package for all the data supporting a study

BioStudies – one package for all the data supporting a study

BACKGROUND: Horses are one of the few animals that spontaneously develop atrial fibrillation (AF), making them a powerful model for studying AF mechanisms and treatment effects. Despite the initial effectiveness of treatment in horses and humans, AF-induced atrial remodeling compromises its long-term success. Observational studies have suggested that metformin may reduce the risk of AF, but its effects on progressive AF-induced atrial remodeling have yet to be evaluated in a high-fidelity large animal model. METHODS: Here, we used a longitudinal horse model of tachypacing-induced self-sustained AF to characterize the electrical, molecular, and metabolic atrial changes over 4 months of disease, with and without metformin treatment (30 mg/kg orally, twice daily; initiated before AF induction, N=24 horses). Electrophysiological and multiomic approaches were combined with histology, echocardiography, biochemical, and mitochondrial analyses to evaluate disease progression and treatment response. RESULTS: The horse model replicated critical aspects of AF-induced atrial remodeling observed in Humans, including electrical and structural changes. Despite upregulation of metabolic genes and proteins in AF, no significant ultrastructural mitochondrial changes were detected. Metformin plasma trough levels confirmed stable therapeutic exposure. Metformin-treated horses were protected against early AF stabilization and sustained a less complex AF substrate in the right atrium after 4 months of disease. These protective effects were associated with increased right atrial activity of the metabolic regulator, AMPK (AMP-activated protein kinase), changes in metabolic pathways, and modulation of ion-channel gene expression. CONCLUSIONS: Metformin treatment conferred protection against early AF stabilization and selectively attenuated right atrial substrate complexity in a translationally relevant preclinical model. These findings support metformin as a lead molecule for AF prevention, warranting further mechanistic and clinical studies.

Background and Purpose: Myocardial fibrosis (MF), a hallmark of structural cardiac remodelling, drives disease progression across most forms of heart failure and plays a central role in heart failure with preserved ejection fraction (HFpEF). Despite its clinical relevance, effective treatments remain scarce. In preclinical models, current methods for quantifying MF fail to capture its regional heterogeneity, limiting reliable assessment of novel anti‐fibrotic compounds. This study aimed to develop a whole‐heart imaging and deep learning (DL)‐based quantification pipeline for MF, and to validate its utility by evaluating the efficacy of a glucagon‐like peptide‐1 receptor (GLP‐1R) agonist in a mouse model of HFpEF. Experimental Approach: By utilising fluorescent collagen‐labelling dye, tissue clearing and three‐dimensional light sheet fluorescence microscopy (3D LSFM), we developed a high‐throughput imaging platform for MF. We established a DL framework to quantify interstitial, perivascular and replacement fibrosis, as well as hypertrophy, in 17 left ventricular (LV) segments. The antifibrotic efficacy of the GLP‐1R agonist semaglutide was evaluated in the db/db UNx‐ReninAAV mouse model, which exhibits diabetes, kidney failure, obesity and hypertension. Key Results: Whole‐heart 3D LSFM, combined with DL, enabled micrometre‐resolution mapping of MF in rodents. Using this approach, we observed that interstitial collagen content increases proportionally with cardiac hypertrophy. Chronic treatment with semaglutide reduced LV hypertrophy and perivascular fibrosis but did not affect the extent of replacement fibrosis. Conclusions and Implications: The established 3D imaging and quantification approach provides a powerful tool for evaluating the therapeutic efficacy of antifibrotic compounds and studying the pathological mechanisms underlying cardiovascular diseases.

The subthalamic nucleus (STN) is a major target for deep brain stimulation (DBS) in Parkinson’s disease (PD), yet its molecular organization and relationship to the adjacent para-subthalamic nucleus (pSTN) remain incompletely defined. Here, we performed comparative molecular mapping of the STN and pSTN in mouse and macaque, complemented by a human tissue sample, three-dimensional imaging, and publicly available spatial transcriptomic datasets. The STN showed a largely conserved molecular framework across species with gradients across rostrocaudal and dorsolateral axes. However, HTR2C was found enriched in the ventral STN of macaque and human tissue samples, while broadly distributed across the mouse STN. HTR2C showed opposite gradient of PVALB . In the mouse STN, Nts expression revealed an additional mediolateral gradient. Publicly available transcriptomic datasets showed concordant expression patterns. In both species, TAC1 characterized the canonical pSTN but also an extended population reaching dorsally along the medial aspect of the STN. TAC1 -positive cells were thus found in the region traversed by tyrosine hydroxylase-positive fibers of the medial forebrain bundle (MFB). Together, these findings reveal a spatially heterogeneous, multiaxial molecular organization of the STN and a spatially complex molecular organization of its neighboring brain areas, refining the molecular and anatomical framework of the subthalamic region.

Background: Extensive 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. Purpose: To 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 Approach: By 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 Results: Whole-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 Implications: The 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.

Recent advances in RNA analysis have deepened our understanding of cellular states in biological tissues. However, a substantial gap remains in integrating RNA expression data with spatial context across organs, primarily owing to the challenges associated with RNA detection within intact tissue volumes. Here, we developed Tris buffer–mediated retention of in situ hybridization chain reaction signal in cleared organs (TRISCO), an effective tissue-clearing method designed for whole-brain spatial three-dimensional (3D) RNA imaging. TRISCO resolved several crucial issues, including the preservation of RNA integrity, achieving uniform RNA labeling, and enhancing tissue transparency. We tested TRISCO using a broad range of cell-identity markers, noncoding and activity-dependent RNAs, within diverse organs of varying sizes and species. TRISCO thus emerges as a powerful tool for single-cell, whole-brain, 3D imaging that enables comprehensive transcriptional spatial analysis across the entire brain.

Overfeeding triggers homeostatic compensatory mechanisms that counteract weight gain. Here, we show that both lean and diet-induced obese (DIO) male mice exhibit a potent and prolonged inhibition of voluntary food intake following overfeeding-induced weight gain. We reveal that FGF21 is dispensable for this defense against weight gain. Targeted proteomics unveiled novel circulating factors linked to overfeeding, including the protease legumain (LGMN). Administration of recombinant LGMN lowers body weight and food intake in DIO mice. The protection against weight gain is also associated with reduced vascularization in the hypothalamus and sustained reductions in the expression of the orexigenic neuropeptide genes, Npy and Agrp, suggesting a role for hypothalamic signaling in this homeostatic recovery from overfeeding. Overfeeding of melanocortin 4 receptor (MC4R) KO mice shows that these mice can suppress voluntary food intake and counteract the enforced weight gain, although their rate of weight recovery is impaired. Collectively, these findings demonstrate that the defense against overfeeding-induced weight gain remains intact in obesity and involves mechanisms independent of both FGF21 and MC4R.