Johan Björkegren is currently Professor of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA. He obtained his MD at the Karolinska University Hospital, and his PhD at the Karolinska Institutet, Sweden. His early work explored the role of triglyceride-rich lipoproteins in coronary artery disease, and 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. His subsequent research has focused on the use of multi-modal big data analysis to create reliable network models of human biology and cardiovascular disease. This has been achieved using a range of clinical datasets that combine detailed clinical characteristics, including imaging, genomics, and proteomics data. Together with Dr. Arno Ruusalepp, Tartu University Hospital, Estonia, Dr. Björkegren initiated the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) biobank from coronary artery disease (CAD) patients undergoing cardiac surgery. Subsequent work using this biobank has led to identification of RNA sequence data from up to nine CAD-relevant tissues, which will be critical to generating network models that predict the risk for and clinical outcomes of CAD.
Wednesday 03 June 08:30
Implementing systems biology to personalise medicine
Genome-wide association studies (GWAS) have led to the identification of over two hundred genetic loci that modulate inherited risk for CAD. However, given the complexity of the underlying molecular disease processes, it is evident that CAD cannot be understood nor cured by targeting isolated genes. Instead a focus on the regulatory-gene networks that control these processes is needed, to capture the combined effects of many genetic and environmental risk factors. These networks offer a framework to identify novel risk genes and study the molecular interactions within and across disease-relevant tissues leading to CAD.
Systems genetics offers a means to identify disease-driving networks and their genetic regulation, using genomic activity measures to define the underlying molecular processes and to integrate these with GWAS datasets. One innovative approach is transcriptome-wide association studies (TWAS), which offers the opportunity to prioritize candidate causal genes and tissues underlying GWAS loci, to identify gene-trait associations and likely causal genes in CAD.
Therefore, network models have enormous potential to improve the ability to predict CAD risk, and in turn to improve the application of precision medicine – the preventive and individual care of patients – in clinical practice. In addition, these models can also provide information to identify new therapeutic targets, and to monitor the effects of treatment for CAD.
Zeng L, Talukdar HA, Koplev S, Giannarelli C, Ivert T, Gan LM, Ruusalepp A, Schadt EE, Kovacic JC, Lusis AJ, Michoel T, Schunkert H, Björkegren JLM. Contribution of gene regulatory networks to heritability of coronary artery disease. J Am Coll Cardiol 2019;73:2946-57.
Glicksberg BS, Amadori L, Akers NK, Sukhavasi K, Franzén O, Li L, Belbin GM, Akers KL, Shameer K, Badgeley MA, Johnson KW, Readhead B, Darrow BJ, Kenny EE, Betsholtz C, Ermel R, Skogsberg J, Ruusalepp A, Schadt EE, Dudley JT, Ren H, Kovacic JC, Giannarelli C, Li SD, Björkegren JLM, Chen R. Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits. BMC Med Genomics 2019;12(Suppl 6):108.
Eales JM, Maan AA, Xu X, Michoel T, Hallast P, Batini C, Zadik D, Prestes PR, Molina E, Denniff M, Schroeder J, Bjorkegren JLM, Thompson J, Maffia P, Guzik TJ, Keavney B, Jobling MA, Samani NJ, Charchar FJ, Tomaszewski M. Human Y chromosome exerts pleiotropic effects on susceptibility to atherosclerosis. Arterioscler Thromb Vasc Biol 2019;39:2386-401.