Quantitative proteomics and reverse engineer analysis identified plasma exosome derived protein markers related to osteoporosis.
Quantitative proteomics and reverse engineer analysis identified plasma exosome derived protein markers related to osteoporosis.
复制标题
定量蛋白质组学和逆向工程分析确定了与骨质疏松症相关的血浆外泌体衍生的蛋白质标记物。
DOI：
10.1016/j.jprot.2020.103940
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发表时间：
2020-09-30
影响因子：
3.3
通讯作者：
Tang, Peifu
中科院分区：
文献类型：
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作者：
Chen, Ming;Li, Yi;Tang, Peifu
随着人口老龄化，骨质疏松症变得越来越普遍，成为一个主要的公共卫生问题。人类血液为强调疾病机制的病理靶点提供了主要基质。本研究比较了骨质疏松症、骨量减少和骨量正常患者血浆外泌体的蛋白质谱。该研究的目的是寻找潜在的新的诊断/治疗靶点，以进一步研究骨质疏松症。从PLAGH髋关节骨折数据库中共纳入60名参与者。进行定量蛋白质组学以分析分别来自诊断为骨质疏松症、骨质减少症和正常骨量的患者的血浆外泌体衍生蛋白。进一步进行平行反应监测（PRM）分析以验证所鉴定的蛋白质。生物信息学分析包括GO注释和基因调控网络的反向工程分析，用于注释所鉴定蛋白的生物学相关性。在发现数据集中鉴定了45个差异表达的蛋白质，其中4个，PSMB 9，阿尔斯，PCBP 2和VSIR在验证集中进一步验证。基于上述结果，构建了一个外泌体蛋白指数，用于骨质疏松症患者与非骨质疏松症患者的分类，分类性能评估的AUC为0.805（95%CI 0.620-0.926，p < 0.001）。此外，通过对调控网络的反向工程分析，确定并预测了可能与所确定的4个靶蛋白相互作用的蛋白质，为进一步研究骨质疏松症的病理机制提供了参考。
Alongside an aging population, osteoporosis has become increasingly common, representing a major public health problem. Human blood provides the predominant matrix for pathological targets underlining disease mechanisms. In the present study, the protein profiles of blood plasma exosomes from patients with osteoporosis, osteopenia, and those with normal bone mass were compared. The aim of the study was to search for potential novel diagnostic/therapeutic targets for further investigation in osteoporosis. A total of 60 participants were included from the PLAGH Hip Fracture Database. Quantitative proteomics was carried out to profile the plasma exosome derived proteins from patients diagnosed with osteoporosis, osteopenia, and normal bone mass, respectively. A Parallel reaction monitoring (PRM) analysis was further carried out to validate the identified proteins. Bio-informatics analyses including GO annotation and reverse engineering of gene regulatory networks analysis were applied in annotating the biological relevance of the identified proteins. Forty-five differentially expressed proteins were identified in the discovery dataset and four of them, PSMB9, AARS, PCBP2, and VSIR were further verified in a validation set. Based on the results, an exosomal-proteins index was constructed to classify individuals with osteoporosis from those without, an AUC of 0.805 (95% CI 0.620-0.926, p < 0.001) was achieved in classification performance assessment. Additionally, a reverse engineer of the regulatory network analysis identified and predicted the proteins which may interact with the four target proteins identified, providing references for further investigations into the pathological mechanisms of osteoporosis.