AtheroSpectrum Reveals Novel Macrophage Foam Cell Gene Signatures Associated With Atherosclerotic Cardiovascular Disease Risk.
AtheroSpectrum Reveals Novel Macrophage Foam Cell Gene Signatures Associated With Atherosclerotic Cardiovascular Disease Risk.
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动脉粥样硬化谱揭示与动脉粥样硬化性心血管疾病风险相关的新型巨噬细胞泡沫细胞基因特征 。
DOI：
10.1161/circulationaha.121.054285
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发表时间：
2022-01-18
期刊：
影响因子：
37.8
通讯作者：
Zhou B
中科院分区：
文献类型：
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作者：
Li C;Qu L;Matz AJ;Murphy PA;Liu Y;Manichaikul AW;Aguiar D;Rich SS;Herrington DM;Vu D;Johnson WC;Rotter JI;Post WS;Vella AT;Rodriguez-Oquendo A;Zhou B
虽然几种干预措施可以有效地降低动脉粥样硬化性心血管疾病(ASCVD)高危人群的血脂水平，但心血管事件(CVE)的风险仍然存在，这表明识别导致CVE风险的因素的医学需求尚未得到满足。单核细胞和巨噬细胞在动脉粥样硬化中发挥核心作用，但先前的工作尚未提供与ASCVD风险增加有关的巨噬细胞群体的详细信息。开发了一种新的巨噬细胞泡沫分析工具AtheroSpectrum，它使用两个定量指标来描述巨噬细胞的脂代谢和炎症状态。接下来，开发了一种机器学习算法来分析动脉粥样硬化参与者多种族研究(MESA-SET1，n=911)的外周血单核细胞转录组中的基因表达模式。生成了一个包含30个基因的列表，并将其与传统风险因素相结合，以创建ASCVD风险预测模型(CR-30)，该模型随后在其余的MESA-Set2(n=228)中得到验证；CR-30的性能也在两个独立的人类动脉粥样硬化组织转录组数据集(GTEx和GSE43292)中进行了测试。利用单细胞转录图谱(GSE97310、GSE116240、GSE97941、FR-FCM-Z23S)，AtheroSpectrum在斑块巨噬细胞中检测到两种不同的程序：稳态泡沫和炎性致病泡沫，后者与动脉粥样硬化的严重程度呈正相关。提取并筛选了2209个致病泡沫基因库，从中筛选出与Mesa-set1中的CVE相关的30个基因的子集。通过交叉验证泛化分析，将该基因集与传统的CVE易感变量整合到MESA-SET1中，建立了CVD风险评分模型(CR-30)。然后在MESA-SET 2(p=2.6×10−4，AuC=0.742)和两个独立的数据集(GTEx，p=7.32×10−17，AuC=0.664；GSE43292，p=7.04×10−2，AuC=0.633)上测试CR-30的性能。模型敏感性检验证实了30基因小组对预测模型的贡献(似然比检验，df=31，p=0.03)。我们的新计算程序(AtheroSpectrum)确定了与炎性巨噬泡沫细胞相关的特定基因表达谱。循环单核细胞中表达的30个基因的子集共同有助于预测症状性动脉粥样硬化性血管疾病。将致病泡沫基因集与已知的风险因素结合起来，可以显著增强预测ASCVD风险的能力。我们的计划可能会促进ASCVD风险的机械性研究和治疗和预后策略的发展。
While several interventions can effectively lower lipid levels in people at risk for atherosclerotic cardiovascular disease (ASCVD), cardiovascular event (CVE) risks remain, suggesting an unmet medical need to identify factors contributing to CVE risk. Monocytes and macrophages play central roles in atherosclerosis, but previous work has yet to provide a detailed view of macrophage populations involved in increased ASCVD risk. A novel macrophage foaming analytics tool, AtheroSpectrum, was developed using two quantitative indices depicting lipid metabolism and the inflammatory status of macrophages. Next, a machine-learning algorithm was developed to analyze gene expression patterns in the peripheral monocyte transcriptome of Multi-Ethnic Study of Atherosclerosis participants (MESA-set1, n=911). A list of 30 genes was generated and integrated with traditional risk factors to create an ASCVD risk prediction model (CR-30), which was subsequently validated in the remaining MESA-set2 (n=228); performance of CR-30 was also tested in two independent human atherosclerotic tissue transcriptome datasets (GTEx and GSE43292). Using single-cell transcriptomic profiles (GSE97310, GSE116240, GSE97941, FR-FCM-Z23S), AtheroSpectrum detected two distinct programs in plaque macrophages: homeostatic-foaming and inflammatory pathogenic-foaming, the latter was positively associated with severity of atherosclerosis in multiple studies. A pool of 2209 pathogenic foaming genes was extracted and screened to select a subset of 30 genes correlated with CVE in MESA-set1. A CVD risk score model (CR-30) was then developed by incorporating this gene-set with traditional variables sensitive to CVE in MESA-set1 after cross-validation generalizability analysis. The performance of CR-30 was then tested in MESA-set2 (p=2.60×10−4, AUC=0.742), and two independent datasets (GTEx, p=7.32×10−17, AUC=0.664; GSE43292, p=7.04×10−2, AUC=0.633). Model sensitivity tests confirmed the contribution of the 30-gene panel to the prediction model (likelihood ratio test, df=31, p=0.03). Our novel computational program (AtheroSpectrum) identified a specific gene expression profile associated with inflammatory macrophage foam cells. A subset of 30 genes expressed in circulating monocytes jointly contributed to prediction of symptomatic atherosclerotic vascular disease. Incorporating a pathogenic foaming gene-set with known risk factors can significantly strengthen the power to predict ASCVD risk. Our programs may facilitate both mechanistic investigations and development of therapeutic and prognostic strategies for ASCVD risk.