双位点蛋白质组分析揭示心血管疾病的潜在药物靶点

Christopher Aldous Oldnall, Julian Ng-Kee-Kwong, Jimi Wills, A. Richmond, Tim Regan, Sara Clohisey Hendry, Archie Campbell, J. Baillie, A. Kriegsheim, C. Haley, A. Khamseh, S. Beentjes, Andrew Bretherick
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摘要

背景:尽管全基因组关联研究(GWAS)在揭示疾病病理生理学方面大有可为,但将疾病相关基因位点转化为临床可操作信息仍是一项挑战。孟德尔随机化(Mendelian randomisation,MR)将表达蛋白作为暴露因子,将疾病作为结果,是一种强大的分析方法,可利用 GWAS 数据,以数据驱动的方式大规模确定潜在的药物靶点。心血管疾病(CVD)是全球主要的健康负担,因此是确定潜在治疗靶点并对其进行优先排序的重要结果。研究方法在这项研究中,我们采用了基于数据摘要的通用磁共振成像(GSMR)技术,将从苏格兰世代(Generation Scotland)获得的外周血单核细胞(PBMC)和英国生物库(UK Biobank)获得的基于抗体的血浆蛋白测量结果作为暴露因子,并将英国生物库中的两种心血管疾病和三种心血管疾病相关风险因子作为结果。此外,我们还利用共定位来评估蛋白质与疾病结果之间是否存在共同的因果变异,从而为因果联系提供进一步的证据。结果:我们评估了 862 人的 PBMCs 中 5,114 个同工酶特异性蛋白质组的表达情况。利用这些数据进行的 GSMR 分析发现了 16 种可能的因果蛋白,涉及心血管疾病/心血管疾病相关风险因子中的三种,其中有 7 种得到了共定位分析的支持。在血浆 GSMR 分析中,发现了 761 种推测的病因蛋白,其中 145 种得到了共定位分析的支持。此外,我们还对结果的富集性进行了研究,发现与胆固醇代谢和血小板功能有关的通路得到了富集。在 PBMCs 和血浆中的重要 GSMR 结果之间有三个蛋白质重叠,其中两个蛋白质(COMT 和 SWAP70)确定了相关结果的相反影响方向,一个蛋白质确定了一致的影响方向(HLA-DRA)。讨论:本研究发现了一些可能与心血管疾病发病机制有关的蛋白质和通路。它还证明了蛋白质测量位置和量化方法的重要性。我们的研究有助于弥合基因型和表型之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dual site proteomic analyses reveal potential drug targets for cardiovascular disease
Background: While genome-wide association studies (GWAS) hold great promise for unravelling disease pathophysiology, the translation of disease-associated genetic loci into clinically actionable information remains a challenge. Mendelian randomisation (MR), using expressed proteins as exposures and disease as an outcome, stands as a powerful analytical approach for leveraging GWAS data to identify potential drug-targets--at scale--in a data-driven manner. Cardiovascular disease (CVD) is a major health burden worldwide, and therefore is an important outcome for which to establish and prioritise potential therapeutic targets. Methods: In this study, we utilised generalised summary-data-based MR (GSMR) with novel mass-spectrometry-based isoform-specific protein groups measured from peripheral-blood mononuclear cell (PBMC) obtained from Generation Scotland and antibody-based plasma protein measures from UK Biobank as exposures, and two CVD and three CVD-related risk-factors from UK Biobank as outcomes. Further, we used colocalisation to assess support for a shared causal variant between the proteins and the disease outcomes providing further evidence supporting a causal link. Results: We evaluate expression of 5,114 isoform-specific protein groups in PBMCs from 862 individuals. GSMR analysis, using this data, found 16 putative causal proteins across three of the CVD/CVD-related risk-factors with seven supported by colocalisation analysis. Within the plasma GSMR analysis, 761 putative causal proteins were identified, of which 145 were supported by colocalisation. In addition, we go on to examine enrichment amongst the results and find enrichment of pathways which relate to cholesterol metabolism and platelet function. There was an overlap of three proteins between significant GSMR results in PBMCs and plasma, with two proteins (COMT and SWAP70) identifying opposite directions of effect of the relevant outcome, and one identifying a concordant direction of effect (HLA-DRA). Discussion: This study identifies a number of proteins and pathways that may be involved in CVD pathogenesis. It also demonstrates the importance of the location of protein measurement and the methods by which it is quantified. Our research contributes to ongoing efforts to bridge the gap between genotype and phenotype.
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