An integrated systems approach to decipher cell-level gene-regulatory circuits necessary for developing precision medicine for cardiometabolic disorders and coronary artery disease

Traditional research approaches of complex traits like cardiometabolic disorders (CMDs) and coronary artery disease (CAD) suffer from being too candidate gene- or pathway-focused, failing to capture the true complexity of these disorders.

However with recent technology development allowing decephering regulatory mechanisms of gene expression in single cells, systems approches considering multiple layers  of omics data in disease-relevant tissues and cells responsible for the development of complex diseases can be considered.

In Aim 1, we will take advantage of the world-leading, gender-diverse and data-rich STARNET study of 1,706 well-characterized patients with and without CAD, to perform multi-modal profiling using the combined single nuclei (sn)ATAC/snRNA sequencing together with bulk RNAseq in seven vascular and metabolic tissues.

In Aim 2, we will infer multi-omic layered gene-regualtory networks (GRNs) and to these, apply deep learning modelling (“CADnet”) to identify and prioritize tissue- and cell-level gene-regulatory circuits driving CMDs and CAD.

In Aim 3 by assessing heritability contributions and the potential of poly-genetic risk scores to predict CMDs/CAD, the clinical impact of CADnet prioritized gene-regulatory circuits will be evaluated in large-scale population studies.

Our proposal holds strong potential to bring CMD and CAD understanding to a new systems, yet deeper, level, essentail for embarking on the promises of precision medicine.