Project description:Rationale: Epicardial adipose tissue (EAT) has been independently associated with non-calcified, high-risk coronary plaques in low-to intermediate risk subjects. Recently, a bidirectional communication was shown between EAT and diseased coronary arteries. In high-risk patients it is unknown whether quantitative measures of EAT can capture, and which molecular players are involved in this mutual interplay. Objective: In a high-risk population, we aimed to determine how the volume of EAT is linked to coronary artery disease (CAD) and to identify potential EAT-deregulated pathways in CAD patients specifically related to coronary artery calcification (CAC). Methods and Results: In a prospective cohort of 574 degenerative severe aortic stenosis patients referred to cardiac surgery, we quantified fat depots by computed tomography (CT) and performed a comparative quantitative proteomics of thoracic fat, including EAT, mediastinal (MAT) and subcutaneous (SAT) adipose tissues. We did not find an independent association of EAT volume with the severity, distribution and complexity of coronary stenosis in invasive coronary angiography. Although, EAT volume was correlated with high CAC, its cardiovascular risk factors-adjusted association was not significant. Taking as reference non-CAD matched-patients and compared to MAT and SAT, EAT proteomic signature of CAD was characterized by up-regulation of pro-calcifying annexins (Annexin A2, ANXA2), fatty acid binding transporters (FABP4) and inflammatory signaling proteins, and by down-regulation of fetuin-A and redox state regulatory enzymes. In EAT, ANXA2 regulation was positively correlated with CAC. EAT gene expression studies confirmed overexpression of ANXA2 and FABP4 in CAD, but no expression of FETUA was detected. Compared with non-CAD, fetuin-A circulating levels were higher in CAD, whereas no fetuin-A pericardial fluid differences were found. Conclusions: In this high-risk cohort, EAT presented an imbalance of pro-calcifying, pro-inflammatory and lipid transporters mediators. These local EAT-mediated regulatory mechanisms were not reflected by the CT volume of EAT alone.

Project description:While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remain poorly understood. Single cell RNA sequencing (scRNAseq) data generated with SmartSeq2 (~8000 genes/cell) in nearly 19,000 single cells isolated during atherosclerosis progression in mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes. For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease (CAD) patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study.Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by three smooth-muscle cells (SMC), and three macrophage (MP) subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type pro-inflammatory/Trem2-high lipid-associated (MP) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of three arterial wall GRNs: GRN33 (MP), GRN39 (SMC) and GRN122(MP) with major contributions to CAD heritability and strong associations with clinical scores of coronary atherosclerosis severity (SYNTAX/Duke scores). The presence and pathophysiological relevance of GRN39 was verified in five independent RNAseq datasets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM, in cultured human vascular SMCs.By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a CAD framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced-stage, symptomatic atherosclerosis.

Project description:Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target in order to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised ‘convex analysis of mixtures’ (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired Abdominal Aorta (AA) and Left Anterior Descending Coronary Artery (LAD) specimens (N=198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N=62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of ‘pure’ fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) analysis quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N=46) or free from apparent CAD (control, N=40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (Confidence Interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogenous tissue types.

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Single-cell and bulk RNA sequencing of mouse atherosclerosis disease stage course [scRNASeq_WAT_Mono]

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