Coronary Artery Disease CRISPR Cell Models for Drug Discovery

Coronary artery disease (CAD) remains the leading cause of death globally. According to the World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases in 2019, with CAD representing the majority of these cases. Key risk factors include hypertension, hyperlipidemia, smoking, diabetes, and a sedentary lifestyle. While acute myocardial infarction carries a high short-term mortality, the 5-year survival for patients with stable CAD is approximately 90% in high-income countries, though it declines significantly with comorbidities (NCI SEER data for heart disease). The economic burden is immense, with direct and indirect costs exceeding hundreds of billions of dollars annually.

CAD is an ideal disease for mechanistic studies due to its complex polygenic nature and well-characterized pathological progression from endothelial dysfunction to atherosclerotic plaque rupture. Public datasets from large GWAS consortia (e.g., CARDIoGRAMplusC4D) and transcriptomic studies of human plaques provide rich resources. Open questions include the role of smooth muscle cell phenotypic switching, macrophage foam cell formation, and the contribution of clonal hematopoiesis of indeterminate potential (CHIP) to CAD progression. Gene-edited cell models allow precise dissection of these pathways.

The pathogenesis of CAD involves several interconnected pathways:

1. Endothelial dysfunction: Reduced nitric oxide bioavailability leads to increased permeability and leukocyte adhesion.

2. Lipid retention and modification: Apolipoprotein B-containing lipoproteins (LDL) accumulate in the subendothelial space and undergo oxidation.

3. Foam cell formation: Macrophages take up oxidized LDL via scavenger receptors (e.g., CD36, SR-A), becoming lipid-laden foam cells.

4. Smooth muscle cell proliferation and migration: Intimal smooth muscle cells produce extracellular matrix, contributing to plaque growth.

5. Inflammation: Cytokines (IL-1beta, IL-6, TNF-alpha) and chemokines (MCP-1) drive a chronic inflammatory response.

6. Plaque rupture: Matrix metalloproteinases (MMPs) degrade the fibrous cap, leading to thrombosis.

While CAD is polygenic, certain loci have strong effect sizes. Key genes identified through GWAS and functional studies include:

Data sources: NCBI Gene, ClinVar, and large GWAS meta-analyses.

Key signaling networks implicated in CAD include:

• • NF-kB pathway: Central to inflammatory cytokine production in endothelial cells and macrophages.
• • Key nodes: IKK complex, p65, IkB-alpha.
• • MAPK/ERK pathway: Regulates smooth muscle cell proliferation and migration.
• • Key nodes: Ras, Raf, MEK1/2, ERK1/2.
• • PI3K/AKT/mTOR pathway: Promotes cell survival and lipid metabolism.
• • Key nodes: PI3K, AKT, mTOR, SREBP.
• • NLRP3 inflammasome: Mediates IL-1beta and IL-18 release in response to cholesterol crystals.
• • Key nodes: NLRP3, ASC, caspase-1.
• • Wnt/beta-catenin signaling: Involved in vascular calcification and smooth muscle cell differentiation.
• • Key nodes: Wnt, Frizzled, LRP5/6, beta-catenin.

Common cell lines used in CAD research include:

Organoid models: 3D vascular organoids derived from iPSCs can recapitulate vessel structure and allow study of cell-cell interactions in a more physiological context. They are increasingly used for drug screening and toxicity testing.

Animal models for CAD include:

• • ApoE knockout mice: Develop severe hypercholesterolemia and atherosclerosis on a Western diet.
• • LDLR knockout mice: Model for familial hypercholesterolemia.
• • PCSK9 gain-of-function mice: Exhibit high LDL levels.
• • Pig models (e.g., Yucatan minipigs): More human-like coronary anatomy and plaque morphology.
• • Rabbit models (e.g., Watanabe heritable hyperlipidemic rabbit): Spontaneous hypercholesterolemia.
• • PDX models: Not commonly used for CAD; primarily for cancer research.

CRISPR-Cas9 gene editing enables the creation of isogenic cell lines with precise genetic modifications. Examples include:

• • LDLR knockout in HepG2 cells: Models familial hypercholesterolemia for studying LDL uptake and cholesterol metabolism.
• • PCSK9 knockout in HepG2 cells: Increases LDL receptor expression, used to test PCSK9 inhibitor efficacy.
• • APOE knockout in THP-1 macrophages: Alters lipid metabolism and inflammatory response.
• • 9p21 locus deletion in HAoSMC: Investigates the role of this risk locus in smooth muscle cell proliferation.

These sequence-verified, commercially available models accelerate drug discovery by providing consistent, reproducible genetic backgrounds. Isogenic pairs (wild-type vs. edited) allow direct attribution of phenotypic changes to the specific mutation.

CRISPR knockout and knock-in lines are essential for validating candidate genes from GWAS. For example:

• • Knockout of SORT1 in hepatocytes confirmed its role in VLDL secretion.
• • Knock-in of the APOE epsilon4 allele in iPSC-derived macrophages demonstrated increased amyloid-beta uptake and inflammatory cytokine production.
• • Loss-of-function mutations in ANGPTL3 (identified in human genetics) were validated in edited hepatocytes, leading to the development of ANGPTL3 inhibitors.

Isogenic cell pairs are powerful tools for drug screening:

• • LDLR knockout cells can be used to screen for compounds that upregulate LDLR expression via alternative pathways.
• • PCSK9 knockout cells serve as a negative control for PCSK9 inhibitor assays.
• • Resistance modeling: Chronic exposure to statins can lead to compensatory upregulation of HMGCR; gene-edited cells with HMGCR overexpression can model this resistance.

CRISPR screens can identify novel biomarkers and therapeutic targets:

• • Genome-wide CRISPR knockout screens in macrophages identified genes regulating foam cell formation (e.g., ABCA1, ABCG1, LXR-alpha).
• • Synthetic lethality screens: Targeting genes that are essential only in the context of a specific mutation (e.g., LDLR deficiency) can reveal new drug targets.
• • Secretome analysis of edited cells can identify novel circulating biomarkers for CAD risk.