The FES Gene at the 15q26 Coronary-Artery-Disease Locus Inhibits Atherosclerosis

A cross-tissue transcriptome-wide association study identifies WDPCP as a potential susceptibility gene for coronary atherosclerosis

Background

Coronary atherosclerosis (CAS) is a complex chronic inflammatory disease with significant genetic and environmental contributions. While genome-wide association studies (GWAS) have pinpointed many risk loci, over 75 % are in non-coding regions, complicating functional analysis and understanding gene-disease mechanisms.

Methods

We conducted a cross-tissue transcriptome-wide association study (TWAS) using data from the GWAS Catalog (16,041 cases, 440,307 controls) and the Genotype-Tissue Expression (GTEx) v8 eQTL dataset. Initially, we used the Unified Test for Molecular Signatures (UTMOST) for analysis, followed by validation with Functional Summary-based Imputation (FUSION) and conditional and joint (COJO) analyses. Candidate genes were further refined using Multi-marker Analysis of Genomic Annotation (MAGMA). Causal relationships were assessed through Summary Data-Based Mendelian Randomization (SMR), colocalization analysis (COLOC), and Mendelian Randomization (MR). GeneMANIA was used to identify interacting genes, and Phenome-Wide Association Study (PheWAS) was employed to enhance the results.

Results

UTMOST identified 33 susceptibility genes for CAS. Out of these, 17 met stringent criteria in both UTMOST and FUSION analyses. Combining results from UTMOST, FUSION, and MAGMA, we identified four critical candidate genes. WDPCP was the only gene to pass SMR, COLOC, and MR analyses, confirming its causal role in CAS. GeneMANIA revealed additional interacting genes, and PheWAS validated WDPCP's role as a susceptibility gene.

Conclusion

WDPCP is a potential novel susceptibility gene for CAS, influencing endothelial function, lipid metabolism, and coronary artery development. This study extends GWAS findings, highlighting WDPCP's potential as a therapeutic target and its consistent expression across different tissues. Further validation studies are warranted.

Examining the potential causal relationships among smoking behaviors, blood DNA methylation profiles, and the development of coronary heart disease and myocardial infarction

BACKGROUND

Smoking has been identified as a standalone risk factor for coronary heart disease (CHD) and myocardial infarction (MI), but the precise underlying mechanisms remain incompletely elucidated.

RESULTS

In this study, we conducted a two-sample Mendelian randomization analysis to examine the impact of smoking behaviors (including smoking initiation, age of smoking initiation, cigarettes per day, and smoking cessation) and smoking-related DNA methylation at CpG sites on CHD and MI based on the UK Biobank dataset. Additionally, we included the FinnGen and Biobank Japan datasets as replications and performed a meta-analysis to combine the results from different data sources. We further validated our results using genetic colocalization analysis. In genomic analysis, we provided compelling evidence on the association between genetically predicted smoking initiation and increased susceptibility to CHD and MI. In epigenetic analysis, we identified 11 smoking-related CpG sites linked to CHD risk and 10 smoking-related CpG sites associated with the risk of MI based on the UK Biobank dataset. Subsequently, some of these CpG sites were further replicated using the FinnGen or BBJ datasets. Ultimately, a meta-analysis was conducted to integrate findings from various data sources (3 for CHD, and 2 for MI), revealing that 7 of 11 CpG sites were linked to CHD risk; whereas, 7 of 10 CpG sites were associated with MI risk. Furthermore, we performed genetic colocalization analysis and found that cg19744173 (FBLN7), cg00395063 (ARHGEF12), and cg16822035 (MCF2L) exhibited robust evidence of colocalization with coronary heart disease; whereas, cg19529732 (DIABLO), cg26405020 (FES), and cg08940075 (CNN3) demonstrated strong colocalization evidence with the risk of myocardial infarction.

CONCLUSIONS

Our research offers a novel insight into the impact of smoking on the susceptibility to CHD and MI through the lens of epigenetic DNA methylation.

GWAS breakthroughs: mapping the journey from one locus to 393 significant coronary artery disease associations

Coronary artery disease (CAD) poses a substantial threat to global health, leading to significant morbidity and mortality worldwide. It has a significant genetic component that has been studied through genome-wide association studies (GWAS) over the past 17 years. These studies have made progress with larger sample sizes, diverse ancestral backgrounds, and the discovery of multiple genomic regions related to CAD risk. In this review, we provide a comprehensive overview of CAD GWAS, including information about the genetic makeup of the disease and the importance of ethnic diversity in these studies. We also discuss challenges of identifying causal genes and variants within GWAS loci with a focus on non-coding regions. Additionally, we highlight tissues and cell types relevant to CAD, and discuss clinical implications of GWAS findings including polygenic risk scores, sex-specific differences in CAD genetics, ethnical aspects of personalized interventions, and GWAS guided drug development.

Exploring the genetic landscape of the brain-heart axis: A comprehensive analysis of pleiotropic effects between heart disease and psychiatric disorders

BACKGROUND

The genetic links between heart disease and psychiatric disorders are complex and not well understood. This study uses genome-wide association studies (GWAS) and advanced multilevel analyses to explore these connections.

METHODS

We analyzed GWAS data from seven psychiatric disorders and five types of heart disease. Genetic correlations and overlaps were examined using linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), and Genetic analysis incorporating Pleiotropy and Annotation (GPA). Pleiotropic single-nucleotide variations (SNVs) were identified with pleiotropic analysis under the composite null hypothesis (PLACO) and annotated via Functional mapping and annotation of genetic associations (FUMA). Potential pleiotropic genes were identified using Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary data-based Mendelian Randomization (SMR).

RESULTS

Among 35 trait pairs, 32 showed significant genetic correlations or overlaps. PLACO identified 15,077 SNVs, with 287 recognized as pleiotropic loci and 20 colocalization sites. MAGMA and SMR revealed 75 potential pleiotropic genes involved in diverse pathways, including cancer, neurodevelopment, and cellular organization. Mouse Genome Informatics (MGI) queries provided evidence linking multiple genes to heart or psychiatric disorders.

CONCLUSIONS

This analysis reveals loci and genes with pleiotropic effects between heart disease and psychiatric disorders, highlighting shared biological pathways. These findings illuminate the genetic mechanisms underlying the brain-heart axis and suggest shared biological foundations for these conditions, offering potential targets for future prevention and treatment strategies.

Genome-Wide and Exome-Wide Association Study Identifies Genetic Underpinning of Comorbidity between Myocardial Infarction and Severe Mental Disorders

BACKGROUND

Myocardial Infarction (MI) and severe mental disorders (SMDs) are two types of highly prevalent and complex disorders and seem to have a relatively high possibility of mortality. However, the contributions of common and rare genetic variants to their comorbidity arestill unclear.

METHODS

We conducted a combined genome-wide association study (GWAS) and exome-wide association study (EWAS) approach.

RESULTS

Using gene-based and gene-set association analyses based on the results of GWAS, we found the common genetic underpinnings of nine genes (GIGYF2, KCNJ13, PCCB, STAG1, HLA-C, HLA-B, FURIN, FES, and SMG6) and nine pathways significantly shared between MI and SMDs. Through Mendelian randomization analysis, we found that twenty-seven genes were potential causal genes for SMDs and MI. Based on the exome sequencing data of MI and SMDs patients from the UK Biobank, we found that MUC2 was exome-wide significant in the two diseases. The gene-set analyses of the exome-wide association study indicated that pathways related to insulin processing androgen catabolic process and angiotensin receptor binding may be involved in the comorbidity between SMDs and MI. We also found that six candidate genes were reported to interact with known therapeutic drugs based on the drug-gene interaction information in DGIdb.

CONCLUSIONS

Altogether, this study revealed the overlap of common and rare genetic underpinning between SMDs and MI and may provide useful insights for their mechanism study and therapeutic investigations.

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Meta-analysis and transcriptomic analysis reveal that NKRF and ZBTB17 regulate the NF-κB signaling pathway, contributing to the shared molecular mechanisms of Alzheimer's disease and atherosclerosis

INTRODUCTION

Alzheimer's disease (AD) and atherosclerosis (AS) are widespread diseases predominantly observed in the elderly population. Despite their prevalence, the underlying molecular interconnections between these two conditions are not well understood.

METHODS

Utilizing meta-analysis, bioinformatics methodologies, and the GEO database, we systematically analyzed transcriptome data to pinpoint key genes concurrently differentially expressed in AD and AS. Our experimental validations in mouse models highlighted the prominence of two genes, NKRF (NF-κB-repressing factor) and ZBTB17 (MYC-interacting zinc-finger protein 1).

RESULTS

These genes appear to influence the progression of both AD and AS by modulating the NF-κB signaling pathway, as confirmed through subsequent in vitro and in vivo studies.

CONCLUSIONS

This research uncovers a novel shared molecular pathway between AD and AS, underscoring the significant roles of NKRF and ZBTB17 in the pathogenesis of these disorders.

​Comprehensive mendelian randomization analysis of plasma proteomics to identify new therapeutic targets for the treatment of coronary heart disease and myocardial infarction

BACKGROUND

Ischemic heart disease is one of the leading causes of mortality worldwide, and thus calls for development of more effective therapeutic strategies. This study aimed to identify potential therapeutic targets for coronary heart disease (CHD) and myocardial infarction (MI) by investigating the causal relationship between plasma proteins and these conditions.

METHODS

A two-sample Mendelian randomization (MR) study was performed to evaluate more than 1600 plasma proteins for their causal associations with CHD and MI. The MR findings were further confirmed through Bayesian colocalization, Summary-data-based Mendelian Randomization (SMR), and Transcriptome-Wide Association Studies (TWAS) analyses. Further analyses, including enrichment analysis, single-cell analysis, MR analysis of cardiovascular risk factors, phenome-wide Mendelian Randomization (Phe-MR), and protein-protein interaction (PPI) network construction were conducted to verify the roles of selected causal proteins.

RESULTS

Thirteen proteins were causally associated with CHD, seven of which were also causal for MI. Among them, FES and PCSK9 were causal proteins for both diseases as determined by several analytical methods. PCSK9 was a risk factor of CHD (OR = 1.25, 95% CI: 1.13-1.38, P = 7.47E-06) and MI (OR = 1.36, 95% CI: 1.21-1.54, P = 2.30E-07), whereas FES was protective against CHD (OR = 0.68, 95% CI: 0.59-0.79, P = 6.40E-07) and MI (OR = 0.65, 95% CI: 0.54-0.77, P = 5.38E-07). Further validation through enrichment and single-cell analysis confirmed the causal effects of these proteins. Moreover, MR analysis of cardiovascular risk factors, Phe-MR, and PPI network provided insights into the potential drug development based on the proteins.

CONCLUSIONS

This study investigated the causal pathways associated with CHD and MI, highlighting the protective and risk roles of FES and PCSK9, respectively. FES. Specifically, the results showed that these proteins are promising therapeutic targets for future drug development.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

Integrative genomic analyses identify candidate causal genes for calcific aortic valve stenosis involving tissue-specific regulation

There is currently no medical therapy to prevent calcific aortic valve stenosis (CAVS). Multi-omics approaches could lead to the identification of novel molecular targets. Here, we perform a genome-wide association study (GWAS) meta-analysis including 14,819 cases among 941,863 participants of European ancestry. We report 32 genomic loci, among which 20 are novel. RNA sequencing of 500 human aortic valves highlights an enrichment in expression regulation at these loci and prioritizes candidate causal genes. Homozygous genotype for a risk variant near TWIST1, a gene involved in endothelial-mesenchymal transition, has a profound impact on aortic valve transcriptomics. We identify five genes outside of GWAS loci by combining a transcriptome-wide association study, colocalization, and Mendelian randomization analyses. Using cross-phenotype and phenome-wide approaches, we highlight the role of circulating lipoproteins, blood pressure and inflammation in the disease process. Our findings pave the way for the development of novel therapies for CAVS.

Involvement of Fgf2-mediated tau protein phosphorylation in cognitive deficits induced by sevoflurane in aged rats

OBJECTIVE

Anesthetics have been linked to cognitive alterations, particularly in the elderly. The current research delineates how Fibroblast Growth Factor 2 (Fgf2) modulates tau protein phosphorylation, contributing to cognitive impairments in aged rats upon sevoflurane administration.

METHODS

Rats aged 3, 12, and 18 months were subjected to a 2.5% sevoflurane exposure to form a neurotoxicity model. Cognitive performance was gauged, and the GEO database was employed to identify differentially expressed genes (DEGs) in the 18-month-old cohort post sevoflurane exposure. Bioinformatics tools, inclusive of STRING and GeneCards, facilitated detailed analysis. Experimental validations, both in vivo and in vitro, examined Fgf2's effect on tau phosphorylation.

RESULTS

Sevoflurane notably altered cognitive behavior in older rats. Out of 128 DEGs discerned, Fgf2 stood out as instrumental in regulating tau protein phosphorylation. Sevoflurane exposure spiked Fgf2 expression in cortical neurons, intensifying tau phosphorylation via the PI3K/AKT/Gsk3b trajectory. Diminishing Fgf2 expression correspondingly curtailed tau phosphorylation, neurofibrillary tangles, and enhanced cognitive capacities in aged rats.

CONCLUSION

Sevoflurane elicits a surge in Fgf2 expression in aging rats, directing tau protein phosphorylation through the PI3K/AKT/Gsk3b route, instigating cognitive aberrations.

Exploring the diagnostic value of blood circular RNA in atherosclerotic cardiovascular diseases by integrating bioinformatics and evidence-based medicine meta-analysis

This meta-analysis aimed to investigate the diagnostic value of blood circular RNA (circRNA) in atherosclerotic cardiovascular diseases (AS). Using bioinformatics and evidence-based medicine, we identified circ_0001900 as a potential biomarker for diagnosing AS-related cardiovascular diseases. Bioinformatics analysis indicated that circ_0001900 may participate in AS progression by regulating lipid and atherosclerosis-related genes on the MAPK1/3, SRC, TRAF6, and STAT3 signaling pathways. In vivo results showed that circ_0001900 was significantly up-regulated in AS mouse and AS patients' peripheral blood (PB), serum, serum serum extracellular vesicles (EVs), and peripheral blood mononuclear cells (PBMCs), with good diagnostic efficacy as evaluated by ROC curve analysis. Circ_0001900 knockout inhibited AS progression, which may be related to the regulation of these signaling pathways. These findings suggest that circ_0001900 may serve as a potential diagnostic and therapeutic target for AS-related cardiovascular diseases.

Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

BACKGROUND

It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.

METHODS

The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.

RESULTS

The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.

CONCLUSIONS

The implemented workflow could be used for other multifactorial diseases.

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

Integration of genetic fine-mapping and multi-omics data reveals candidate effector genes for hypertension

Genome-wide association studies of blood pressure (BP) have identified >1,000 loci, but the effector genes and biological pathways at these loci are mostly unknown. Using published association summary statistics, we conducted annotation-informed fine-mapping incorporating tissue-specific chromatin segmentation and colocalization to identify causal variants and candidate effector genes for systolic BP, diastolic BP, and pulse pressure. We observed 532 distinct signals associated with ≥2 BP traits and 84 with all three. For >20% of signals, a single variant accounted for >75% posterior probability, 65 were missense variants in known (SLC39A8, ADRB2, and DBH) and previously unreported BP candidate genes (NRIP1 and MMP14). In disease-relevant tissues, we colocalized >80 and >400 distinct signals for each BP trait with cis-eQTLs and regulatory regions from promoter capture Hi-C, respectively. Integrating mouse, human disorder, gene expression and tissue abundance data, and literature review, we provide consolidated evidence for 436 BP candidate genes for future functional validation and discover several potential drug targets.

Applications of Genome Editing Technologies in CAD Research and Therapy with a Focus on Atherosclerosis

Cardiovascular diseases, particularly coronary artery disease (CAD), remain the leading cause of death worldwide in recent years, with myocardial infarction (MI) being the most common form of CAD. Atherosclerosis has been highlighted as one of the drivers of CAD, and much research has been carried out to understand and treat this disease. However, there remains much to be better understood and developed in treating this disease. Genome editing technologies have been widely used to establish models of disease as well as to treat various genetic disorders at their root. In this review, we aim to highlight the various ways genome editing technologies can be applied to establish models of atherosclerosis, as well as their therapeutic roles in both atherosclerosis and the clinical implications of CAD.

The Genetics of Coronary Artery Disease: A Vascular Perspective

Genome-wide association studies (GWAS) have identified a large number of genetic loci for coronary artery disease (CAD), with many located close to genes associated with traditional CAD risk pathways, such as lipid metabolism and inflammation. It is becoming evident with recent CAD GWAS meta-analyses that vascular pathways are also highly enriched and present an opportunity for novel therapeutics. This review examines GWAS-enriched vascular gene loci, the pathways involved and their potential role in CAD pathogenesis. The functionality of variants is explored from expression quantitative trait loci, massively parallel reporter assays and CRISPR-based gene-editing tools. We discuss how this research may lead to novel therapeutic tools to treat cardiovascular disorders.

Exploring the genetic basis of coronary artery disease using functional genomics

Genome-wide Association Studies (GWAS) have identified more than 300 loci associated with coronary artery disease (CAD), defining the genetic risk map of the disease. However, the translation of the association signals into biological-pathophysiological mechanisms constitute a major challenge. Through a group of examples of studies focused on CAD, we discuss the rationale, basic principles and outcomes of the main methodologies implemented to prioritize and characterize causal variants and their target genes. Additionally, we highlight the strategies as well as the current methods that integrate association and functional genomics data to dissect the cellular specificity underlying the complexity of disease mechanisms. Despite the limitations of existing approaches, the increasing knowledge generated through functional studies helps interpret GWAS maps and opens novel avenues for the clinical usability of association data.

Establishing the role of the FES tyrosine kinase in the pathogenesis, pathophysiology, and severity of sepsis and its outcomes

Introduction

Sepsis is a result of initial over-activation of the immune system in response to an infection or trauma that results in reduced blood flow and life-threatening end-organ damage, followed by suppression of the immune system that prevents proper clearance of the infection or trauma. Because of this, therapies that not only limit the activation of the immune system early on, but also improve blood flow to crucial organs and reactivate the immune system in late-stage sepsis, may be effective treatments. The tyrosine kinase FES may fulfill this role. FES is present in immune cells and serves to limit immune system activation. We hypothesize that by enhancing FES in early sepsis and inhibiting its effects in late sepsis, the severity and outcome of septic illness can be improved.

Methods and analysis

In vitro and in vivo modeling will be performed to determine the degree of inflammatory signaling, cytokine production, and neutrophil extracellular trap (NET) formation that occurs in wild-type (WT) and FES knockout (FES-/- ) mice. Clinically available treatments known to enhance or inhibit FES expression (lorlatinib and decitabine, respectively), will be used to explore the impact of early vs. late FES modulation on outcomes in WT mice. Bioinformatic analysis will be performed to examine FES expression levels in RNA transcriptomic data from sepsis patient cohorts, and correlate FES expression data with clinical outcomes (diagnosis of sepsis, illness severity, hospital length-of-stay).

Ethics and dissemination

Ethics approval pending from the Queen's University Health Sciences & Affiliated Teaching Hospitals Research Ethics Board. Results will be disseminated through scientific publications and through lay summaries to patients and families.

Heterogeneity of macrophages in atherosclerosis revealed by single-cell RNA sequencing

Technology at the single-cell level has advanced dramatically in characterizing molecular heterogeneity. These technologies have enabled cell subtype diversity to be seen in all tissues, including atherosclerotic plaques. Critical in atherosclerosis pathogenesis and progression are macrophages. Previous studies have only determined macrophage phenotypes within the plaque, mainly by bulk analysis. However, recent progress in single-cell technologies now enables the comprehensive mapping of macrophage subsets and phenotypes present in plaques. In this review, we have updated and discussed the definition and classification of macrophage subsets in mice and humans using single-cell RNA sequencing. We summarized the different classification methods and perspectives: traditional classification with an updated scoring system, inflammatory macrophages, foamy macrophages, and atherosclerotic-resident macrophages. In addition, some special types of macrophages were identified by specific markers, including IFN-inducible and cavity macrophages. Furthermore, we discussed macrophage subset-specific markers and their functions. In the future, these novel insights into the characteristics and phenotypes of these macrophage subsets within atherosclerotic plaques can provide additional therapeutic targets for cardiovascular diseases.