Atherosclerosis最新文献

Vascular smooth muscle cells (VSMCs) in adult arteries maintain substantial phenotypic plasticity, which allows for the reversible cell state changes that enable vascular remodelling and homeostasis. In atherosclerosis, VSMCs dedifferentiate in response to lipid accumulation and inflammation, resulting in loss of their characteristic contractile state. Recent studies showed that individual, pre-existing VSMCs expand clonally and can acquire many different phenotypes in atherosclerotic lesions. The changes in gene expression underlying this phenotypic diversity are mediated by epigenetic modifications which affect transcription factor access and thereby gene expression dynamics. Additionally, epigenetic mechanisms can maintain cellular memory, potentially facilitating reversion to the contractile state. While technological advances have provided some insight, a comprehensive understanding of how VSMC phenotypes are governed in disease remains elusive. Here we review current literature in light of novel insight from studies at single-cell resolution. We also discuss how lessons from epigenetic studies of cellular regulation in other fields could help in translating the potential of targeting VSMC phenotype conversion into novel therapies in cardiovascular disease.

Cholesterol is a vital component of cellular membranes and is an essential molecule in mammalian physiology. Yet dysregulation of hepatic cholesterol metabolism and an increase in plasma cholesterol is linked to development of atherosclerotic cardiovascular disease. Maintaining tight regulation of cholesterol homeostasis is therefore essential, elegantly highlighted by the control of hepatic low-density lipoprotein receptor (LDLR) abundance and associated lipoprotein clearance. The LDLR was discovered in the 1970's in the seminal work of Brown and Goldstein. This was followed by the development of statins, which promote hepatic clearance of LDL via the LDLR pathway. The discovery two decades ago of Proprotein Convertase Subtilisin-Kexin Type 9 (PCSK9), a secreted protein that binds to the LDLR ectodomain and promotes its degradation, and the clinical development of PCSK9 inhibitors has ushered an effort to uncover additional mechanisms that govern the function and abundance of the LDLR. In recent years this has led to the identification of novel post-transcriptional and post-translational mechanisms that govern the LDLR. This review focuses on these emerging regulatory mechanisms and specifically discusses: (1) Regulation of the LDLR mRNA by RNA-binding proteins and microRNAs, (2) Ubiquitin-dependent degradation of the LDLR protein by the E3 ubiquitin ligases inducible degrader of the LDLR (IDOL) and GOLIATH (RNF130), (3) Control of the LDLR pathway by the asialoglycoprotein receptor 1 (ASGR1), and (4) The role of LDLR ectodomain shedding mediated by membrane-type 1 matrix metalloprotease (MT1-MMP), Bone morphogenetic protein 1 (BMP1), and γ-secretase. Understanding the contribution of these emerging mechanisms to regulation of the LDLR is important for the development of novel LDLR-focused lipid-lowering strategies.

Background and aims: Estimating the genetic risk of coronary artery disease (CAD) is now possible by aggregating data from genome-wide association studies (GWAS) into polygenic risk scores (PRS). Combining multiple PRS for specific circulating blood lipids could improve risk prediction. Here, we sought to evaluate the performance of PRS derived from CAD and blood lipids GWAS to predict the incidence of CAD.

Methods: This study included individuals aged between 40 and 69 from UK Biobank. We conducted GWAS for blood lipids measured by nuclear magnetic resonance in individuals without lipid-lowering treatments (n = 73,915). Summary statistics were used to derive PRS in the remaining participants (n = 318,051). A PRS_CAD was derived using the CARDIoGRAMplusC4D GWAS. Hazard ratios (HR) for CAD (n = 9017 out of 301,576; median follow-up: 12.6 years) were calculated per standard deviation increase in each PRS. Models' discrimination capacity and goodness-of-fit were evaluated.

Results: Out of 30 PRS, 27 were significantly associated with the incidence of CAD (p < 0.0017). The optimal combination of PRS included PRS for CAD, VLDL-C, total cholesterol and triglycerides. Discriminative capacities were significantly increased in the model including PRS_CAD and clinical risk factors (CRF) (C-statistic = 0.778 [0.773-0.782]) compared to the model with CRF only (C-statistic = 0.755 [0.751-0.760], difference = 0.022 [0.020-0.025]). Although the C-statistic remained similar when independent lipids PRS were added to the model with PRS_CAD and CRF (C-statistic = 0.778 [0.773-0.783]), the goodness-of-fit was significantly increased (chi-square test statistic = 20.18, p = 1.56e-04).

Conclusions: Although independently associated with CAD incidence, blood lipids PRS provide modest improvement in the predictive performance when added to PRS_CAD.