Stem Cell Pluripotency Genes Klf4 and Oct4 Regulate Complex SMC Phenotypic Changes Critical in Late-Stage Atherosclerotic Lesion Pathogenesis.

BACKGROUND: Rupture and erosion of advanced atherosclerotic lesions with a resultant myocardial infarction or stroke are the leading worldwide cause of death. However, we have a limited understanding of the identity, origin, and function of many cells that make up late-stage atherosclerotic lesions, as well as the mechanisms by which they control plaque stability. METHODS: We conducted a comprehensive single-cell RNA sequencing of advanced human carotid endarterectomy samples and compared these with single-cell RNA sequencing from murine microdissected advanced atherosclerotic lesions with smooth muscle cell (SMC) and endothelial lineage tracing to survey all plaque cell types and rigorously determine their origin. We further used chromatin immunoprecipitation sequencing (ChIP-seq), bulk RNA sequencing, and an innovative dual lineage tracing mouse to understand the mechanism by which SMC phenotypic transitions affect lesion pathogenesis. RESULTS: We provide evidence that SMC-specific Klf4- versus Oct4-knockout showed virtually opposite genomic signatures, and their putative target genes play an important role regulating SMC phenotypic changes. Single-cell RNA sequencing revealed remarkable similarity of transcriptomic clusters between mouse and human lesions and extensive plasticity of SMC- and endothelial cell-derived cells including 7 distinct clusters, most negative for traditional markers. In particular, SMC contributed to a Myh11-, Lgals3+ population with a chondrocyte-like gene signature that was markedly reduced with SMC-Klf4 knockout. We observed that SMCs that activate Lgals3 compose up to two thirds of all SMC in lesions. However, initial activation of Lgals3 in these cells does not represent conversion to a terminally differentiated state, but rather represents transition of these cells to a unique stem cell marker gene-positive, extracellular matrix-remodeling, "pioneer" cell phenotype that is the first to invest within lesions and subsequently gives rise to at least 3 other SMC phenotypes within advanced lesions, including Klf4-dependent osteogenic phenotypes likely to contribute to plaque calcification and plaque destabilization. CONCLUSIONS: Taken together, these results provide evidence that SMC-derived cells within advanced mouse and human atherosclerotic lesions exhibit far greater phenotypic plasticity than generally believed, with Klf4 regulating transition to multiple phenotypes including Lgals3+ osteogenic cells likely to be detrimental for late-stage atherosclerosis plaque pathogenesis.

Integrative Prioritization of Causal Genes for Coronary Artery Disease.

BACKGROUND: Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. METHODS: We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). RESULTS: We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. CONCLUSIONS: We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.