Regnase-1 overexpression as a therapeutic approach of Marfan syndrome.

Rupture or dissection of thoracic aortic aneurysms is still the leading cause of death for patients diagnosed with Marfan syndrome. Inflammation and matrix digestion regulated by matrix metalloproteases (MMPs) play a major role in the pathological remodeling of the aortic media. Regnase-1 is an endoribonuclease shown to cleave the mRNA of proinflammatory cytokines, such as interleukin-6. Considering the major anti-inflammatory effects of regnase-1, here, we aimed to determine whether adeno-associated virus (AAV)-mediated vascular overexpression of the protein could provide protection from the development and progression of aortic aneurysms in Marfan syndrome. The overexpression of regnase-1 resulted in a marked decrease in inflammatory parameters and elastin degradation in aortic smooth muscle cells in vitro. Intravenous injection of a vascular-targeted AAV vector resulted in the efficient transduction of the aortic wall and overexpression of regnase-1 in a murine model of Marfan syndrome, associated with lower circulating levels of proinflammatory cytokines and decreased MMP expression and activity. Regnase-1 overexpression strongly improved elastin architecture in the media and reduced aortic diameter at distinct locations. Therefore, AAV-mediated regnase-1 overexpression may represent a novel gene therapy approach for inhibiting aortic aneurysms in Marfan syndrome.

Adapted clustering method for generic analysis of histological fibrosis staining as an open source tool.

Pathological remodeling of the extracellular matrix is a hallmark of cardiovascular disease. Abnormal fibrosis causes cardiac dysfunction by reducing ejection fraction and impairing electrical conductance, leading to arrhythmias. Hence, accurate quantification of fibrosis deposition in histological sections is of extreme importance for preclinical and clinical studies. Current automatic tools do not perform well under variant conditions. Moreover, users do not have the option to evaluate data from staining methods of their choice according to their purpose. To overcome these challenges, we underline a novel machine learning-based tool (FibroSoft) and we show its feasibility in a model of cardiac hypertrophy and heart failure in mice. Our results demonstrate that FibroSoft can identify fibrosis in diseased myocardium and the obtained results are user-independent. In addition, the results acquired using our software strongly correlate to those obtained by Western blot analysis of collagen 1 expression. Additionally, we could show that this method can be used for Masson's Trichrome and Picosirius Red stained histological images. The evaluation of our method also indicates that it can be used for any particular histology segmentation and quantification. In conclusion, our approach provides a powerful example of the feasibility of machine learning strategies to enable automatic analysis of histological images.