The AIM2 inflammasome exacerbates atherosclerosis in clonal haematopoiesis.

Clonal haematopoiesis, which is highly prevalent in older individuals, arises from somatic mutations that endow a proliferative advantage to haematopoietic cells. Clonal haematopoiesis increases the risk of myocardial infarction and stroke independently of traditional risk factors1. Among the common genetic variants that give rise to clonal haematopoiesis, the JAK2V617F (JAK2VF) mutation, which increases JAK-STAT signalling, occurs at a younger age and imparts the strongest risk of premature coronary heart disease1,2. Here we show increased proliferation of macrophages and prominent formation of necrotic cores in atherosclerotic lesions in mice that express Jak2VF selectively in macrophages, and in chimeric mice that model clonal haematopoiesis. Deletion of the essential inflammasome components caspase 1 and 11, or of the pyroptosis executioner gasdermin D, reversed these adverse changes. Jak2VF lesions showed increased expression of AIM2, oxidative DNA damage and DNA replication stress, and Aim2 deficiency reduced atherosclerosis. Single-cell RNA sequencing analysis of Jak2VF lesions revealed a landscape that was enriched for inflammatory myeloid cells, which were suppressed by deletion of Gsdmd. Inhibition of the inflammasome product interleukin-1ß reduced macrophage proliferation and necrotic formation while increasing the thickness of fibrous caps, indicating that it stabilized plaques. Our findings suggest that increased proliferation and glycolytic metabolism in Jak2VF macrophages lead to DNA replication stress and activation of the AIM2 inflammasome, thereby aggravating atherosclerosis. Precise application of therapies that target interleukin-1ß or specific inflammasomes according to clonal haematopoiesis status could substantially reduce cardiovascular risk.

Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk States.

BACKGROUND: Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. They have been identified historically by the cell surface expression of CD14 and CD16. However, recent single-cell studies have revealed that they are much more heterogeneous than previously realized. METHODS: We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing to describe the comprehensive transcriptional and phenotypic landscape of 437 126 monocytes. RESULTS: This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi (major histocompatibility complex class II), monocyte-platelet aggregates, as well as nonclassical, and several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- nonclassical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique characteristics, developmental trajectories, transcriptional regulation, and tissue distribution. In addition, alterations associated with cardiovascular disease risk factors, including race, smoking, and hyperlipidemia were identified. Moreover, the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol. CONCLUSIONS: This integrative and cross-species comparative analysis provides a new perspective on the comparison of alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in cardiovascular disease and the potential for monocyte subpopulation targeted therapies.

Atherosclerosis Is a Smooth Muscle Cell-Driven Tumor-Like Disease.

BACKGROUND: Atherosclerosis, a leading cause of cardiovascular disease, involves the pathological activation of various cell types, including immunocytes (eg, macrophages and T cells), smooth muscle cells (SMCs), and endothelial cells. Accumulating evidence suggests that transition of SMCs to other cell types, known as phenotypic switching, plays a central role in atherosclerosis development and complications. However, the characteristics of SMC-derived cells and the underlying mechanisms of SMC transition in disease pathogenesis remain poorly understood. Our objective is to characterize tumor cell-like behaviors of SMC-derived cells in atherosclerosis, with the ultimate goal of developing interventions targeting SMC transition for the prevention and treatment of atherosclerosis. METHODS: We used SMC lineage tracing mice and human tissues and applied a range of methods, including molecular, cellular, histological, computational, human genetics, and pharmacological approaches, to investigate the features of SMC-derived cells in atherosclerosis. RESULTS: SMC-derived cells in mouse and human atherosclerosis exhibit multiple tumor cell-like characteristics, including genomic instability, evasion of senescence, hyperproliferation, resistance to cell death, invasiveness, and activation of comprehensive cancer-associated gene regulatory networks. Specific expression of the oncogenic mutant KrasG12D in SMCs accelerates phenotypic switching and exacerbates atherosclerosis. Furthermore, we provide proof of concept that niraparib, an anticancer drug targeting DNA damage repair, attenuates atherosclerosis progression and induces regression of lesions in advanced disease in mouse models. CONCLUSIONS: Our findings demonstrate that atherosclerosis is an SMC-driven tumor-like disease, advancing our understanding of its pathogenesis and opening prospects for innovative precision molecular strategies aimed at preventing and treating atherosclerotic cardiovascular disease.

High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis.

BACKGROUND: Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypic landscape of the cells within these lesions is limited. METHODS: To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. RESULTS: We identified 25 cell populations, each with a unique multiomic signature, including macrophages, T cells, NK (natural killer) cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the macrophages, we identified 2 proinflammatory subsets enriched in IL-1B (interleukin-1B) or C1Q expression, 2 TREM2-positive foam cells (1 expressing inflammatory genes), and subpopulations with a proliferative gene signature and SMC-specific gene signature with fibrotic pathways upregulated. Further characterization revealed various subsets of SMCs and fibroblasts, including SMC-derived foam cells. These foamy SMCs were localized in the deep intima of coronary atherosclerotic lesions. Utilizing cellular indexing of transcriptomes and epitopes by sequencing data, we developed a flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Lastly, we observed reduced proportions of efferocytotic macrophages, classically activated endothelial cells, and contractile and modulated SMC-derived cells, while inflammatory SMCs were enriched in plaques of clinically symptomatic versus asymptomatic patients. CONCLUSIONS: Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. These findings facilitate both the mapping of cardiovascular disease susceptibility loci to specific cell types and the identification of novel molecular and cellular therapeutic targets for the treatment of the disease.

Applications of single-cell genomics and computational strategies to study common disease and population-level variation.

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.

Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human tissues. Most existing methods remove batch effects in a low-dimensional embedding space. Although useful for clustering, batch effects are still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effects. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Methods such as Seurat 3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effects in gene expression, but MNN can only analyze two batches at a time, and it becomes computationally infeasible when the number of batches is large. Here, we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data while correcting batch effects both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC outperforms Scanorama, DCA + Combat, scVI, and MNN. With CarDEC denoising, non-highly variable genes offer as much signal for clustering as the highly variable genes (HVGs), suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC's denoised and batch-corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effects. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.

Progression of Coronary Artery Calcification and Risk of Clinical Events in CKD: The Chronic Renal Insufficiency Cohort Study.

RATIONALE & OBJECTIVE: Coronary artery calcification (CAC) progresses rapidly in people with chronic kidney disease (CKD) compared with the general population. We studied the association between CAC progression and higher risks of atherosclerotic cardiovascular disease (CVD), congestive heart failure, and all-cause mortality among adults with CKD. STUDY DESIGN: Prospective cohort study. SETTING: & Participants: 1,310 participants in the Chronic Renal Insufficiency Cohort (CRIC) Study who had at least one CAC scan with no prior history of CVD and with observed or imputed data on changes in CAC over time. EXPOSURE: Observed or imputed CAC progression, categorized as incident CAC among participants with zero CAC on the baseline scan, or progressive CAC when the baseline scan demonstrated CAC and there was an increase in CAC ≥50 Agatston units per year. OUTCOMES: Atherosclerotic CVD (myocardial infarction or stroke), congestive heart failure, and all-cause mortality. ANALYTICAL APPROACH: Cause-specific Cox proportional hazards regression, stratified by presence of CAC at baseline. RESULTS: A total of 545 participants without and 765 with prevalent CAC at baseline were included. During a mean 3.3 years between CAC assessments, 177 (32.5%) participants without baseline CAC developed incident CAC while 270 participants (35.3%) with baseline CAC developed a ≥50 Agatston units per year increase in CAC. After multivariable adjustment, incident CAC was associated with 2.42-fold higher rate of atherosclerotic CVD (95% confidence interval [CI]: 1.23-4.79) and 1.82-fold higher rate of all-cause mortality (95% CI: 1.03-3.22). Progressive CAC (≥50 units per year) was not associated with atherosclerotic CVD (hazard ratio [HR]: 1.42; 95% CI: 0.85-2.35) but was associated with a 1.73-fold higher rate of all-cause mortality (95% CI: 1.31-2.28). Progressive CAC was not associated with incident heart failure. LIMITATIONS: Residual confounding and limited statistical power for some outcomes. CONCLUSIONS: Among adults with CKD stages 2-4, CAC progression over a mean 3.3 years was associated with higher risk of atherosclerotic CVD and all-cause mortality. The associations were strongest among participants without CAC at baseline.

Signaling Pathways Associated With Prior Cardiovascular Events in Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy. A subset of patients experience major adverse cardiovascular events (MACEs), including arrhythmias, strokes and heart failure. However, the molecular mechanisms underlying MACEs in HCM are still not well understood. Therefore, we conducted a multicenter case-control study of patients with HCM, comparing those with and without prior histories of MACEs to identify dysregulated signaling pathways through plasma proteomics profiling. METHODS: We performed plasma proteomics profiling of 4986 proteins. We developed a proteomics-based discrimination model in patients enrolled at 1 institution (training set) and externally validated the model in patients enrolled at another institution (test set). We performed pathway analysis of proteins dysregulated in patients with prior MACEs. RESULTS: A total of 402 patients were included, with 278 in the training set and 124 in the test set. In this cohort, 257 (64%) patients had prior MACEs (172 in the training set and 85 in the test set). Using the proteomics-based model from the training set, the area under the receiver operating characteristic curve was 0.82 (95% confidence interval, 0.75-0.90) in the test set. Patients with prior MACEs demonstrated dysregulation in pathways known to be associated with MACEs (eg, TGF-ß) and novel pathways (eg, Ras-MAPK and associated pathways). CONCLUSIONS: In this multicenter study of 402 patients with HCM, we identified both known and novel pathways dysregulated in a subset of patients with more advanced disease.

Human Macrophage Long Intergenic Noncoding RNA, SIMALR, Suppresses Inflammatory Macrophage Apoptosis via NTN1 (Netrin-1).

BACKGROUND: Long noncoding RNAs (lncRNAs) have emerged as novel regulators of macrophage biology and inflammatory cardiovascular diseases. However, studies focused on lncRNAs in human macrophage subtypes, particularly human lncRNAs that are not conserved in rodents, are limited. METHODS: Through RNA-sequencing of human monocyte-derived macrophages, we identified suppressor of inflammatory macrophage apoptosis lncRNA (SIMALR). Lipopolysaccharide/IFNγ (interferon γ) stimulated human macrophages were treated with SIMALR antisense oligonucleotides and subjected to RNA-sequencing to investigate the function of SIMALR. Western blots, luciferase assay, and RNA immunoprecipitation were performed to validate function and potential mechanism of SIMALR. RNAscope was performed to identify SIMALR expression in human carotid atherosclerotic plaques. RESULTS: RNA-sequencing of human monocyte-derived macrophages identified SIMALR, a human macrophage-specific long intergenic noncoding RNA that is highly induced in lipopolysaccharide/IFNγ-stimulated macrophages. SIMALR knockdown in lipopolysaccharide/IFNγ stimulated THP1 human macrophages induced apoptosis of inflammatory macrophages, as shown by increased protein expression of cleaved PARP (poly[ADP-ribose] polymerase), caspase 9, caspase 3, and Annexin V+. RNA-sequencing of control versus SIMALR knockdown in lipopolysaccharide/IFNγ-stimulated macrophages showed Netrin-1 (NTN1) to be significantly decreased upon SIMALR knockdown. We confirmed that NTN1 knockdown in lipopolysaccharide/IFNγ-stimulated macrophages induced apoptosis. The SIMALR knockdown-induced apoptotic phenotype was rescued by adding recombinant NTN1. NTN1 promoter-luciferase reporter activity was increased in HEK293T (human embryonic kidney 293) cells treated with lentiviral overexpression of SIMALR. NTN1 promoter activity is known to require HIF1α (hypoxia-inducible factor 1 subunit alpha), and our studies suggest that SIMALR may interact with HIF1α to regulate NTN1 transcription, thereby regulating macrophages apoptosis. SIMALR was found to be expressed in macrophages in human carotid atherosclerotic plaques of symptomatic patients. CONCLUSIONS: SIMALR is a nonconserved, human macrophage lncRNA expressed in atherosclerosis that suppresses macrophage apoptosis. SIMALR partners with HIF1α (hypoxia-inducible factor 1 subunit alpha) to regulate NTN1, which is a known macrophage survival factor. This work illustrates the importance of interrogating the functions of human lncRNAs and exploring their translational and therapeutic potential in human atherosclerosis.

Prediction of New-onset Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy Using Plasma Proteomics Profiling.

BACKGROUND AND AIMS: Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), increasing symptom burden and stroke risk. We aimed to construct a plasma proteomics-based model to predict new-onset AF in patients with HCM and determine dysregulated signaling pathways. METHODS: In this prospective, multi-center cohort study, we conducted plasma proteomics profiling of 4,986 proteins at enrollment. We developed a proteomics-based machine learning (ML) model to predict new-onset AF using samples from one institution (training set) and tested its predictive ability using independent samples from another institution (test set). We performed a survival analysis to compare the risk of new-onset AF among high- and low-risk groups in the test set. We performed pathway analysis of proteins significantly (univariable p<0.05) associated with new-onset AF using a false discovery rate (FDR) threshold of 0.001. RESULTS: The study included 284 patients with HCM (training set: 193, test set: 91). Thirty-seven (13%) patients developed AF during median follow-up of 3.2 years [25-75 percentile: 1.8-5.2]. Using the proteomics-based prediction model developed in the training set, the area under the receiver-operating-characteristic curve (AUC) was 0.89 (95% confidence interval 0.78-0.99) in the test set. In the test set, patients categorized as high-risk had a higher rate of developing new-onset AF (log-rank p=0.002). The Ras-MAPK pathway was dysregulated in patients who developed incident AF during follow-up (FDR<1.0×10-6). CONCLUSION: This is the first study to demonstrate the ability of plasma proteomics to predict new-onset AF in HCM and identify dysregulated signaling pathways.

Prediction of cardiac death in patients with hypertrophic cardiomyopathy using plasma adipokine levels.

BACKGROUNDS AND AIMS: Hypertrophic cardiomyopathy (HCM) causes cardiac death through both sudden cardiac death (SCD) and death due to heart failure (HF). Although adipokines lead to adverse cardiac remodeling in HCM, the prognostic value of plasma adipokines in HCM remains unknown. We aimed to predict cardiac death in patients with HCM using plasma adipokines. METHODS AND RESULTS: We performed a multicenter prospective cohort study of patients with HCM. The outcome was cardiac death including heart transplant, death due to HF, and SCD. With data from 1 institution (training set), a prediction model was developed using random forest classification algorithm based on 10 plasma adipokines. The performance of the prediction model adjusted for 8 clinical parameters was examined in samples from another institution (test set). Time-to-event analysis was performed in the test set to compare the rate of outcome events between the low-risk and high-risk groups determined by the prediction model. In total, 389 (267 in the training set; 122 in the test set) patients with HCM were included. During the median follow-up of 2.7 years, 21 patients experienced the outcome event. The area under the covariates-adjusted receiver-operating characteristics curve was 0.89 (95 % confidence interval [CI] 0.71-0.99) in the test set. revealed the high-risk group had a significantly higher risk of cardiac death (hazard ratio 17.8, 95 % CI 2.1-148.3, P = 0.008). CONCLUSION: The present multicenter prospective study demonstrated that a panel of plasma adipokines predicts cardiac death in patients with HCM.

Clonal hematopoiesis of indeterminate potential and outcomes after heart transplantation: A multicenter study.

Cardiac allograft vasculopathy (CAV) is a leading cause of late graft failure and mortality after heart transplantation (HT). Sharing some features with atherosclerosis, CAV results in diffuse narrowing of the epicardial coronaries and microvasculature, with consequent graft ischemia. Recently, clonal hematopoiesis of indeterminate potential (CHIP) has emerged as a risk factor for cardiovascular disease and mortality. We aimed to investigate the relationship between CHIP and posttransplant outcomes, including CAV. We analyzed 479 HT recipients with stored DNA samples at 2 high-volume transplant centers, Vanderbilt University Medical Center and Columbia University Irving Medical Center. We explored the association between the presence of CHIP mutations with CAV and mortality after HT. In this case-control analysis, carriers of CHIP mutations were not at increased risk of CAV or mortality after HT. In a large multicenter genomics study of the heart transplant population, the presence of CHIP mutations was not associated with an increased risk of CAV or posttransplant mortality.

ADAMTS7: a Novel Therapeutic Target in Atherosclerosis.

PURPOSE OF REVIEW: Genome-wide association studies have repeatedly linked the metalloproteinase ADAMTS7 to coronary artery disease. Here we aim to highlight recent findings surrounding the human genetics of ADAMTS7, novel mouse models that investigate ADAMTS7 function, and potential substrates of ADAMTS7 cleavage. RECENT FINDINGS: Recent genome-wide association studies in coronary artery disease have replicated the GWAS signal for ADAMTS7 and shown that the signal holds true even across different ethnic groups. However, the direction of effect in humans remains unclear. A recent novel mouse model revealed that the proatherogenicity of ADAMTS7 is derived from its catalytic functions, while at the translational level, vaccinating mice against ADAMTS7 reduced atherosclerosis. Finally, in vitro proteomics approaches have identified extracellular matrix proteins as candidate substrates that may be causal for the proatherogenicity of ADAMTS7. ADAMTS7 represents an enticing target for therapeutic intervention. The recent studies highlighted here have replicated prior findings, confirming the genetic link between ADAMTS7 and atherosclerosis, while providing further evidence in mice that ADAMTS7 is a targetable proatherogenic enzyme.

Myeloid LXR (Liver X Receptor) Deficiency Induces Inflammatory Gene Expression in Foamy Macrophages and Accelerates Atherosclerosis.

BACKGROUND: Cholesterol loaded macrophage foam cells are a prominent feature of atherosclerotic plaques. Single-cell RNA sequencing has identified foam cells as TREM2 (triggering receptor expressed on myeloid cells 2) positive populations with low expression of inflammatory genes, resembling the TREM2 positive microglia of neurodegenerative diseases. Cholesterol loading of macrophages in vitro results in activation of LXR (liver X receptor) transcription factors and suppression of inflammatory genes. METHODS: To test the hypothesis that LXRs mediate anti-inflammatory effects in Trem2 expressing atherosclerotic plaque foam cells, we performed RNA profiling on plaque cells from hypercholesterolemic mice with myeloid LXR deficiency. RESULTS: Myeloid LXR deficiency led to a dramatic increase in atherosclerosis with increased monocyte entry, foam cell formation, and plaque inflammation. Bulk cell-RNA profiling of plaque myeloid cells showed prominent upregulation of inflammatory mediators including oxidative, chemokine, and chemotactic genes. Single-cell RNA sequencing revealed increased numbers of foamy TREM2-expressing macrophages; however, these cells had reduced expression of the Trem2 gene expression module, including phagocytic and cholesterol efflux genes, and had switched to a proinflammatory and proliferative phenotype. Expression of Trem2 was suppressed by inflammatory signals but not directly affected by LXR activation in bone marrow-derived macrophages. CONCLUSIONS: Our current studies reveal the key role of macrophage LXRs in promoting the Trem2 gene expression program and in suppressing inflammation in foam cells of atherosclerotic plaques.

ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing.

Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore failing to account for shared information across individuals. Failure to accommodate such shared information not only reduces power, but also makes it difficult to interpret results across individuals. However, when only RNA sequencing (RNA-seq) data are available, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to sample heterogeneity as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect to account for multi-SNP correlations within the same gene. ASEP only requires RNA-seq data, and is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre- versus post-treatment). Extensive simulations demonstrated the convincing performance of ASEP under a wide range of scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified ASE genes and validated our results with two published eQTL studies. We further applied ASEP to a human macrophage RNA-seq dataset, identified genes showing evidence of differential ASE between M0 and M1 macrophages, and confirmed our findings by results from cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level that only requires the use of RNA-seq data. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.

Long Noncoding RNA MIAT Controls Advanced Atherosclerotic Lesion Formation and Plaque Destabilization.

BACKGROUND: Long noncoding RNAs (lncRNAs) are important regulators of biological processes involved in vascular tissue homeostasis and disease development. The present study assessed the functional contribution of the lncRNA myocardial infarction-associated transcript (MIAT) to atherosclerosis and carotid artery disease. METHODS: We profiled differences in RNA transcript expression in patients with advanced carotid artery atherosclerotic lesions from the Biobank of Karolinska Endarterectomies. The lncRNA MIAT was identified as the most upregulated noncoding RNA transcript in carotid plaques compared with nonatherosclerotic control arteries, which was confirmed by quantitative real-time polymerase chain reaction and in situ hybridization. RESULTS: Experimental knockdown of MIAT, using site-specific antisense oligonucleotides (LNA-GapmeRs) not only markedly decreased proliferation and migration rates of cultured human carotid artery smooth muscle cells (SMCs) but also increased their apoptosis. MIAT mechanistically regulated SMC proliferation through the EGR1 (Early Growth Response 1)-ELK1 (ETS Transcription Factor ELK1)-ERK (Extracellular Signal-Regulated Kinase) pathway. MIAT is further involved in SMC phenotypic transition to proinflammatory macrophage-like cells through binding to the promoter region of KLF4 and enhancing its transcription. Studies using Miat-/- and Miat-/-ApoE-/- mice, and Yucatan LDLR-/- mini-pigs, as well, confirmed the regulatory role of this lncRNA in SMC de- and transdifferentiation and advanced atherosclerotic lesion formation. CONCLUSIONS: The lncRNA MIAT is a novel regulator of cellular processes in advanced atherosclerosis that controls proliferation, apoptosis, and phenotypic transition of SMCs, and the proinflammatory properties of macrophages, as well.

Cis-regulated expression of non-conserved lincRNAs associates with cardiometabolic related traits.

Many complex disease risk loci map to intergenic regions containing long intergenic noncoding RNAs (lincRNAs). The majority of these is not conserved outside humans, raising the question whether genetically regulated expression of non-conserved and conserved lincRNAs has similar rates of association with complex traits. Here we leveraged data from the Genotype-Tissue Expression (GTEx) project and multiple public genome-wide association study (GWAS) resources. Using an established transcriptome-wide association study (TWAS) tool, FUSION, we interrogated the associations between cis-regulated expression of lincRNAs and multiple cardiometabolic traits. We found that cis-regulated expression of non-conserved lincRNAs had a strikingly similar trend of association with complex cardiometabolic traits as conserved lincRNAs. This finding challenges the conventional notion of conservation that has led to prioritization of conserved loci for functional studies and calls attention to the need to develop comprehensive strategies to study the large number of non-conserved human lincRNAs that may contribute to human disease.

Nonconserved Long Intergenic Noncoding RNAs Associate With Complex Cardiometabolic Disease Traits.

OBJECTIVE: Transcriptome profiling of human tissues has revealed thousands of long intergenic noncoding RNAs (lincRNAs) at loci identified through large-scale genome-wide studies for complex cardiometabolic traits. This raises the question of whether genetic variation at nonconserved lincRNAs has any systematic association with complex disease, and if so, how different this pattern is from conserved lincRNAs. We evaluated whether the associations between nonconserved lincRNAs and 8 complex cardiometabolic traits resemble or differ from the pattern of association for conserved lincRNAs. Approach and Results: Our investigation of over 7000 lincRNA annotations from GENCODE Release 33-GRCh38.p13 for complex trait genetic associations leveraged several large, established meta-analyses genome-wide association study summary data resources, including GIANT (Genetic Investigation of Anthropometric Traits), UK Biobank, GLGC (Global Lipids Genetics Consortium), Cardiogram (Coronary Artery Disease Genome Wide Replication and Meta-Analysis), and DIAGRAM (Diabetes Genetics Replication and Meta-Analysis)/DIAMANTE (Diabetes Meta-Analysis of Trans-Ethnic Association Studies). These analyses revealed that (1) nonconserved lincRNAs associate with a range of cardiometabolic traits at a rate that is generally consistent with conserved lincRNAs; (2) these findings persist across different definitions of conservation; and (3) overall across all cardiometabolic traits, approximately one-third of genome-wide association study-associated lincRNAs are nonconserved, and this increases to about two-thirds using a more stringent definition of conservation. CONCLUSIONS: These findings suggest that the traditional notion of conservation driving prioritization for functional and translational follow-up of complex cardiometabolic genomic discoveries may need to be revised in the context of the abundance of nonconserved long noncoding RNAs in the human genome and their apparent predilection to associate with complex cardiometabolic traits.

Genome-wide analysis identifies novel susceptibility loci for myocardial infarction.

AIMS: While most patients with myocardial infarction (MI) have underlying coronary atherosclerosis, not all patients with coronary artery disease (CAD) develop MI. We sought to address the hypothesis that some of the genetic factors which establish atherosclerosis may be distinct from those that predispose to vulnerable plaques and thrombus formation. METHODS AND RESULTS: We carried out a genome-wide association study for MI in the UK Biobank (n∼472 000), followed by a meta-analysis with summary statistics from the CARDIoGRAMplusC4D Consortium (n∼167 000). Multiple independent replication analyses and functional approaches were used to prioritize loci and evaluate positional candidate genes. Eight novel regions were identified for MI at the genome wide significance level, of which effect sizes at six loci were more robust for MI than for CAD without the presence of MI. Confirmatory evidence for association of a locus on chromosome 1p21.3 harbouring choline-like transporter 3 (SLC44A3) with MI in the context of CAD, but not with coronary atherosclerosis itself, was obtained in Biobank Japan (n∼165 000) and 16 independent angiography-based cohorts (n∼27 000). Follow-up analyses did not reveal association of the SLC44A3 locus with CAD risk factors, biomarkers of coagulation, other thrombotic diseases, or plasma levels of a broad array of metabolites, including choline, trimethylamine N-oxide, and betaine. However, aortic expression of SLC44A3 was increased in carriers of the MI risk allele at chromosome 1p21.3, increased in ischaemic (vs. non-diseased) coronary arteries, up-regulated in human aortic endothelial cells treated with interleukin-1ß (vs. vehicle), and associated with smooth muscle cell migration in vitro. CONCLUSIONS: A large-scale analysis comprising ∼831 000 subjects revealed novel genetic determinants of MI and implicated SLC44A3 in the pathophysiology of vulnerable plaques.

Single-Cell Genomics Reveals a Novel Cell State During Smooth Muscle Cell Phenotypic Switching and Potential Therapeutic Targets for Atherosclerosis in Mouse and Human.

BACKGROUND: Smooth muscle cells (SMCs) play significant roles in atherosclerosis via phenotypic switching, a pathological process in which SMC dedifferentiation, migration, and transdifferentiation into other cell types. Yet how SMCs contribute to the pathophysiology of atherosclerosis remains elusive. METHODS: To reveal the trajectories of SMC transdifferentiation during atherosclerosis and to identify molecular targets for disease therapy, we combined SMC fate mapping and single-cell RNA sequencing of both mouse and human atherosclerotic plaques. We also performed cell biology experiments on isolated SMC-derived cells, conducted integrative human genomics, and used pharmacological studies targeting SMC-derived cells both in vivo and in vitro. RESULTS: We found that SMCs transitioned to an intermediate cell state during atherosclerosis, which was also found in human atherosclerotic plaques of carotid and coronary arteries. SMC-derived intermediate cells, termed "SEM" cells (stem cell, endothelial cell, monocyte), were multipotent and could differentiate into macrophage-like and fibrochondrocyte-like cells, as well as return toward the SMC phenotype. Retinoic acid (RA) signaling was identified as a regulator of SMC to SEM cell transition, and RA signaling was dysregulated in symptomatic human atherosclerosis. Human genomics revealed enrichment of genome-wide association study signals for coronary artery disease in RA signaling target gene loci and correlation between coronary artery disease risk alleles and repressed expression of these genes. Activation of RA signaling by all-trans RA, an anticancer drug for acute promyelocytic leukemia, blocked SMC transition to SEM cells, reduced atherosclerotic burden, and promoted fibrous cap stability. CONCLUSIONS: Integration of cell-specific fate mapping, single-cell genomics, and human genetics adds novel insights into the complexity of SMC biology and reveals regulatory pathways for therapeutic targeting of SMC transitions in atherosclerotic cardiovascular disease.