Disease- and sex-specific differences in patients with heart valve disease: a proteome study

Author:

Nordmeyer Sarah123ORCID,Kraus Milena4ORCID,Ziehm Matthias56ORCID,Kirchner Marieluise56ORCID,Schafstedde Marie1236ORCID,Kelm Marcus126,Niquet Sylvia56,Stephen Mariet Mathew4,Baczko Istvan7ORCID,Knosalla Christoph389ORCID,Schapranow Matthieu-P4ORCID,Dittmar Gunnar10ORCID,Gotthardt Michael3911ORCID,Falcke Martin12ORCID,Regitz-Zagrosek Vera913,Kuehne Titus1239,Mertins Philipp356ORCID

Affiliation:

1. Deutsches Herzzentrum der Charité – Medical Heart Center of Charité and German Heart Institute Berlin, Institute for Cardiovascular Computer-Assisted Medicine, Berlin, Germany

2. Deutsches Herzzentrum der Charité – Medical Heart Center of Charité and German Heart Institute Berlin, Department of Congenital Heart Disease – Pediatric Cardiology, Berlin, Germany

3. German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany

4. Hasso Plattner Institute for Digital Engineering, Digital Health Center, University of Potsdam, Potsdam, Germany

5. Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Proteomics Platform, Berlin, Germany

6. Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany

7. Department of Pharmacology and Pharmacotherapy, Interdisciplinary Excellence Centre, University of Szeged, Szeged, Hungary

8. Deutsches Herzzentrum der Charité – Medical Heart Center of Charité and German Heart, Department of Cardiothoracic and Vascular Surgery, Berlin, Germany

9. Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

10. Proteomics of Cellular Signaling, Luxembourg Institute of Health, Strassen, Luxembourg

11. Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Neuromuscular and Cardiovascular Cell Biology, Berlin, Germany

12. Max Delbrück Center for Molecular Medicine, Mathematical Cell Physiology, Berlin, Germany

13. Department of Cardiology, University Hospital Zürich, University of Zürich, Zürich, Switzerland

Abstract

Pressure overload in patients with aortic valve stenosis and volume overload in mitral valve regurgitation trigger specific forms of cardiac remodeling; however, little is known about similarities and differences in myocardial proteome regulation. We performed proteome profiling of 75 human left ventricular myocardial biopsies (aortic stenosis = 41, mitral regurgitation = 17, and controls = 17) using high-resolution tandem mass spectrometry next to clinical and hemodynamic parameter acquisition. In patients of both disease groups, proteins related to ECM and cytoskeleton were more abundant, whereas those related to energy metabolism and proteostasis were less abundant compared with controls. In addition, disease group–specific and sex-specific differences have been observed. Male patients with aortic stenosis showed more proteins related to fibrosis and less to energy metabolism, whereas female patients showed strong reduction in proteostasis-related proteins. Clinical imaging was in line with proteomic findings, showing elevation of fibrosis in both patient groups and sex differences. Disease- and sex-specific proteomic profiles provide insight into cardiac remodeling in patients with heart valve disease and might help improve the understanding of molecular mechanisms and the development of individualized treatment strategies.

Funder

German Federal Ministry of Education and Research

ERA-CVD

CRC1470

European Commission

Deutsche Forschungsgemeinschaft

Charité – Universitätsmedizin, Berlin

Ministry of Human Capacities, Hungary

Publisher

Life Science Alliance, LLC

Subject

Health, Toxicology and Mutagenesis,Plant Science,Biochemistry, Genetics and Molecular Biology (miscellaneous),Ecology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sex differences and estrogen effects in cardiac mitochondria in human aortic stenosis and in the mouse heart;Frontiers in Endocrinology;2023-10-17

2. Predicting Heart Disease Using Machine Learning Techniques on Electronic Health Records Data;2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA);2023-10-10

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