Final Report Summary - CARDIOMICS (Cardiomics: Use of -omics methods in large populations for identification of novel drug targets and clinical biomarkers for coronary heart disease) We used proteomic and metabolomic profiling in large human cohorts both in a targeted manner, and as an unbiased approach to discover suitable drug targets and biomarkers from pathways that were not previously implicated in CHD pathophysiology. Targeted affinity-based proteomic profilingWe have analyzed 1,016 samples from the Swedish PIVUS-70 cohort, as well as 788 samples from the ULSAM-77 cohort for 82 cardiovascular candidate proteins with the emerging technology of proximity extension assay. So far, we have published two studies based on these data: In the first study, we used data from the PIVUS study, where the number of carotid arteries with plaques was recorded by ultrasound. The proximity extension technique enabled discovery of several new associations of candidate proteins with carotid artery plaque prevalence in a large human sample. Citation: Lind et al., Use of a proximity extension assay proteomics chip to discover new biomarkers for human atherosclerosis. Atherosclerosis. 2015;242(1):205-210). In the second study, we investigated the association with insulin resistance (IR), an important precursor of cardiovascular disease and our findings indicated cathepsin D as a new biomarker and suggested a causal effect on the endothelial marker t-PA. Citation: Nowak C et al., Protein biomarkers for insulin resistance and type 2 diabetes risk in two large community cohorts. Diabetes 2015 Sep; db150881.Mass spectrometry-based metabolomic profilingWe have worked with both the analytical and computational methods used to analyze a large UPLC–Q-TOF MS-based metabolomic profiling effort using plasma and serum samples from participants in three Swedish population-based studies of middle-aged and older human subjects: TwinGene, ULSAM and PIVUS. These studies represent a unique resource to explore and evaluate how metabolic variability across individuals affects human diseases. Citation: Ganna Large-scale non-targeted metabolomic profiling in three human population-based studies. Metabolomics. 2016. We thereafter performed a study for association with incident CHD events in 1,028 individuals with validation in 1,670 individuals and identified four lipid-related metabolites with evidence for clinical utility, as well as a causal role for monoglycerides in CHD development. Citation: Ganna et al., Large-scale Metabolomic Profiling Identifies Novel Biomarkers for Incident Coronary Heart Disease. PLoS Genet. In a second paper, we show that increase bile acid synthesis is related to lower LDL-cholesterol and increased type 2 diabetes risk. Citation: Fall T., Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism to be associated with incident type 2 diabetes, Diabetologia 2015 Sep; db150881.Functional follow-up of loci in zebrafishSince the CHD loci identified by genome-wide association studies were also associated with sub-clinical atherosclerosis, independently of traditional risk factors (den Hoed et al., 2015), we developed in vivo transgenic zebrafish model systems that allow visualization and quantification of early-stage atherosclerosis, as well as whole-body levels of total cholesterol and triglyceride levels – two established risk factors for CHD - in high-throughput. Proof-of-principal studies have so far shown that five days of overfeeding induces an atherogenic phenotype that can be prevented by concomitant treatment with lipid lowering drugs. Using a range of bioinformatics tools that make use of Encode and Roadmap Epigenomics data, amongst others, we have prioritized 117 genes in the 56 GWAS-identified loci. We have also selected genes encoding for the proteins identified using our proteomics screen for CHD. Zebrafish orthologues of these genes are currently being targeted using a multiplex CRISPR-Cas9 approach, in transgenic backgrounds that allow us to visualize the vascular atherogenic traits of interest. A pilot screen has shown that FLT1 is the culprit for CHD in the GWAS-identified locus on chromosome 13q12. With the ground work now in place, many more causal genes will be identified and characterized during the course of the next year.
