Multi-omic characterization of the immune mechanisms driving human atheroprogression

动脉粥样硬化性心血管疾病(CVD)是世界范围内导致死亡和发病率的主要原因。近几十年来，随着有效的降胆固醇策略的发展和对其他血管危险因素的积极管理，动脉粥样硬化的医疗管理得到了极大的改善。然而，心血管疾病的残留率仍然高得令人无法接受，因此需要新的治疗范例来降低风险。一系列广泛的研究支持动脉壁内的免疫反应推动动脉粥样硬化的进展，最近的试验提供了免疫治疗剂可以降低心血管疾病风险的概念证明。然而，由于缺乏精确调节动脉粥样硬化进展背后的免疫反应的药物，以及缺乏可用于个性化治疗的动脉粥样硬化炎症的特定生物标志物，动脉粥样硬化保护性免疫疗法的临床翻译工作滞后。这项提议的重点是解决这两个关键挑战。首要目标是发现免疫治疗的新药物靶点，并揭示动脉粥样硬化炎症的内表型。扩展和扩展我以前的工作建立的管道，我将首先致力于通过利用大规模蛋白质组、单细胞转录和代谢组数据并将它们锚定在遗传信息上，剖析心血管疾病潜在的免疫路径。结合来自人类动脉粥样硬化样本的组学数据，我将探索动脉粥样硬化的蛋白质组、转录和细胞内表型特征，这些特征介导了调节有希望的药物靶点对心血管疾病风险的影响。利用尖端的单细胞和空间转录技术，然后我将探索反映动脉粥样硬化进展的人类颈动脉粥样硬化斑块的分子免疫特征。最后，通过在机器学习框架中将这些高分辨率分子数据与颈动脉MRI和外周血液蛋白质组分析相结合，我的目标是检测动脉粥样硬化免疫图景的体内可访问生物标记物。这项拟议研究的结果将为临床翻译更精确和个性化的免疫治疗策略开辟道路，最终目标是降低全球心血管疾病的负担。

Atherosclerotic cardiovascular disease (CVD) is the leading cause of mortality and morbidity worldwide. The medical management of atherosclerosis has dramatically improved in recent decades with the development of effective cholesterol-lowering strategies and the aggressive management of other vascular risk factors. However, the residual rates of CVD continue being unacceptably high thus calling for new treatment paradigms in lowering risk. An extensive line of research supports that an immune response within the arterial wall drives atheroprogression and recent trials provided proof-of-concept that immunotherapeutic agents can lower CVD risk. However, the clinical translation of atheroprotective immunotherapies is lagging behind due to the lack of drugs that precisely modulate the immune response underlying atheroprogression and the lack of specific biomarkers of atheroinflammation that could be used to personalize treatments. This proposal is focused on addressing these two key challenges. The overarching goals are to detect novel drug targets for immunotherapies and uncover endophenotypes of atheroinflammation. Extending and scaling a pipeline established by my previous work, I will first aim to dissect immune pathways underlying CVD by leveraging large-scale proteomic, single-cell transcriptomic, and metabolomic data and anchoring them to genetic information. Integrating omics data from human atherosclerotic samples I will explore proteomic, transcriptomic, and cellular endophenotypic signatures of atheroinflammation that mediate the effects of modulating promising drug targets on risk of CVD. Utilizing cutting-edge single-cell and spatial transcriptomic technologies, I will then explore the molecular immune signatures of human carotid atherosclerotic plaques that reflect atheroprogression. Finally, by integrating these high-resolution molecular data with carotid MRI and peripheral blood proteomic analyses in a machine learning framework, I will aim to detect accessible in vivo biomarkers of the immune landscape of atherosclerosis. The findings from the proposed research will open the road for the clinical translation of more precise and personalized immunotherapeutic strategies with the ultimate goal of lowering the global burden of CVD.