Johan Björkegren

Professor Genetics and Genomic Sciences, Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York (2013)

Professor Medicine and Cardiology, Mount Sinai Hospital and the Cardiovascular Research Center of Icahn School of Medicine at Mount Sinai (2013)

Associate Professor of Molecular Medicine, Karolinska Institutet, Stockholm, Sweden (2003)

Visiting Professor, University of Tartu, Estonia (2015) and Tokyo College, Tokyo Univeristy, Japan (2024)

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, epigenetics, transcriptomics, proteomics, and metabolomics.

To this end, at Karolinska Institutet and subsequenrly at the Tartu University Hospital in Estonia much of my time has gone into gathering a large biobank of human arterial wall and metabolic tissues isolated from patients with coronary artery disease (CAD) who underwent 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, I have since 2013 been mainly active at the Department of Genetics and Genomic Sciences at Mount Sinai, New York where we have generated RNA sequence data from up to nine CAD-relevant tissues isolated from near 2000 clinically well–characterized patients. This powerful 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 use multi-modal big data analysis to create new and reliable network models of human molecular biology in health and disease that can lead to better disease prediction, monitoring and therapies. To this effort I have created clinical datasets of mainly cardiovascular disease patients in Sweden, Estonia and the US that are enriched for many of these data modalities including genetics, -omics (epigenetics, RNA, proteins, metabolites andlipids) combined with detailed clinical characteristics including imaging. I have a broad background in the design and analysis of clinical studies apprehending and applying the new screening and bioinformatics analysis tools to elucidate the deeper complexity of common diseases. The focus of these studies has been the role of functionally associated genes in molecular networks driving disease. Vital to this approach of a more granular understanding of complex disease biology is to originate these studies in humans suffering the disease whereas animal and cell disease model systems are used mainly for the purpose of validating key disease drivers and processes first identified in humans (i.e. a top-down vs. bottom-up approach).

This effort has now resulted in one of the world’s largest cardiometabolic disease (CMD)-related dataset, STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task), published in leading journals. Currently, we are expanding STARNET (v2) with >

Moving forward, I am highly driven, motivated and focused to expand the central theme of my research strategies: a mechanistic framework of gene-regulatory networks providing a global understanding of the regulatory landscape that 1) combines pathways into organ functions and 2) explain organ-organ interactions. In the end, this new knowledge will be required to enable diagnostics and therapies of molecularly-defined subcategories of CMD patients (i.e. enabling precision medicine).