探测虚拟蛋白质组以识别新型疾病生物标志物
Jonathan D Mosley1, Mark D Benson2,3, J Gustav Smith4
1Department of Medicine (J.D.M., C.M.S., J.F.F., J.C.D., T.J.W.), Vanderbilt University Medical Center, Nashville, TN.
Circulation
|December 21, 2018
概括
这项研究引入了一种虚拟蛋白质学方法,通过将遗传预测的蛋白质水平与大量患者的临床诊断联系起来,以确定新型疾病的生物标志物. 这种方法可以加速发现心血管疾病和其他疾病的生物标志物.
科学领域:
- 生物化学
- 遗传学
- 心血管医学
背景情况:
- 蛋白质组方法为生物标志物发现提供了巨大的潜力,但在大型生物库中面临着可扩展性的挑战.
- 高成本和实际障碍限制了蛋白质组技术对大规模数据集的应用.
- 提出了一种新的"虚拟蛋白质学"策略来克服这些局限性.
研究的目的:
- 开发和验证用于大规模生物标志物的虚拟蛋白质学方法.
- 在大量人群中将遗传预测的蛋白质水平与临床诊断联系起来.
- 确定心血管和其他疾病的新生物标志物.
主要方法:
- 来自Framingham心脏研究的全基因组关联数据被用来构建1129个血蛋白的遗传预测因子.
- 基因预测因子被验证并应用于估计来自eMERGE队列的41,288名患者的蛋白质水平.
- 测试了预测的蛋白质与1128种临床表型之间的关联,并使用MDCS队列中测量的蛋白质水平验证了关键发现.
主要成果:
- 在eMERGE队列中,虚拟蛋白质分析确定了55种与89种临床诊断相关的蛋白质 (q<0. 1).
- 13个显著的关联涉及脂质或动脉样表型.
- 在MDCS队列中的验证证实了阿波利波蛋白E异型与高脂血症和C型乳素域家族1成员B以及血小板衍生生长因子受体β与亚临床动脉样硬化相关.
结论:
- 这项研究使用虚拟蛋白质组方法展示了可行的生物标志物发现范式.
- 这种方法可以识别心血管疾病和其他疾病的候选生物标志物.
- 这些发现突显了将遗传数据与临床信息整合为大规模生物研究的潜力.
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