Johan Bjorkegren, MD, PhD
Focusing on cardiovascular diseases, the goal of my research is to use multi-modal big data analysis to create reliable network models of human biology and disease. Network models have enormous potential to improve our ability to predict disease risk, identify new therapeutic targets, and to monitor molecular effects of treatments. To achieve this goal, I have designed and generated a range of clinical datasets of cardiovascular disease that combine detailed clinical characteristics with imaging, genomics, proteomics, and other types of data. My research has long focused on cardiovascular disease. My early work explored the role of triglyceride-rich lipoproteins in coronary artery disease (CAD), and my postdoctoral studies in mouse models established the hepatic gene microsomal triglyceride transfer protein as a key target to lower plasma cholesterol levels and reduce atherosclerosis. Since then, my primary focus has been systems analyses to generate network models from large genomic datasets—both from CAD patients in the clinic and from cellular and mouse models of atherosclerosis progression and regression in the laboratory. Throughout the last decade, I have designed and led a range of clinical and mouse model studies to elucidate the inherit complexity of CAD. As one of the first clinical scientists to apply the emerging technologies of molecular profiling to large patient cohorts, I have revealed the role of functionally associated genes in several molecular networks that drive CAD. A common complex disease such as CAD cannot be understood nor cured by targeting isolated genes. Rather, the focus needs to be on molecular disease processes mirrored by regulatory-gene networks that capture the combined effects of many genetic and environmental risk factors. To this end, and before my arrival at Mount Sinai, much of my time has gone into gathering a truly unique biobank from CAD patients undergoing different forms of heart surgery. The Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) is a joint study initiative between the cardiovascular chief surgeon at the Tartu University Hospital in Estonia, Dr. Arno Ruusalepp, and myself. Using the STARNET bio-bank in collaboration with the Department of Genetics and Genomic Sciences here at Mount Sinai, we have now generated RNA sequence data from up to nine CAD-relevant tissues isolated from over 500 hundred clinically well–characterized patients. This unprecedented dataset is the main resource for our current efforts to generate network models that predict the risk for and clinical outcomes of CAD. My entrepreneurial ambitions have focused on translating the results of our systems genetic research into new therapies and diagnostics for patients at risk for or suffering CAD. I have launched several entrepreneurial projects. Of particular importance is Clinical Gene Networks AB—the first Bio-IT company in Sweden, founded in 2003 with the goal of exploring “clinical” networks to generate the next generation of diagnostics and therapies based on network models of complex diseases. I am exited to be a member of the exceptional team at the Department of Genetics and Genomic Sciences, led by Eric E. Schadt. Eric and I have a longstanding collaboration based on similar ideas despite wide difference in our basic training. I am privileged to be part of this world-leading research center. It allows me to achieve my goals through access to a diversity of skills and talented collaborators.
Multi-Disciplinary Training Areas
Genetics and Genomic Sciences [GGS]