Marcel den Hoed – Molecular epidemiology and translational genomics
The aim of my research programme is to identify and characterise causal genes for human disease, with a focus on cardiovascular and metabolic disorders. The work will increase our understanding of the underlying causes of human disease, and is anticipated to result in completely new ways to prevent and treat such diseases.
Results from large-scale genome-wide association studies (GWAS) have identified hundreds of loci that are robustly associated with the risk of cardiovascular and metabolic diseases, like coronary artery disease, diabetes and fatty liver disease. However, the causal variants and genes remain unknown for the vast majority of the identified loci. Before we can use results from GWAS in the clinic, for example as biomarkers or as novel drug targets, we need to identify causal variants and/or genes, and ideally also the tissues, cell types and pathways through which these variants and genes exert their effect.
Candidate genes are characterised in zebrafish model
My group takes findings from GWAS or other -omics efforts as a starting point, and uses bioinformatics approaches to predict which variants and genes are causal, and through which tissues, cell types and pathways they act. We subsequently use CRISPR/Cas9, live fluorescence imaging, and deep learning-based image analysis in zebrafish larvae to characterise the role of the prioritized genes in disease-related traits.
Zebrafish develop quickly post-fertilisation, and are transparent during the earliest stages of development. Thanks to advances in fluorescence imaging using labelled transgenes, non-embedded positioning and orienting of zebrafish larvae, automated and objective image quantification opportunities, and efficient mutagenesis using CRISPR/Cas9, it has now become possible to perform high-throughput, largely image-based genetic screens using zebrafish model systems.
Genes involved in a range of diseases are analysed
In recent years, my group has developed and validated such model systems for traits related to a range of common cardiometabolic diseases. We have since used these model systems to characterize the role of hundreds of genes predicted to play a role in lipid metabolism, obesity, insulin resistance, diabetes, atherosclerosis, coronary artery disease, non-alcoholic fatty liver disease and heart rhythm-related disorders. Results from these studies will increase our understanding of the molecular causes of disease, and will hopefully – in the long term – result in new or improved ways to treat or prevent them.