Systematic Proteome Profiling of Maternal Plasma for Development of Preeclampsia Biomarkers.

IF 6.1 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Molecular & Cellular Proteomics Pub Date : 2024-09-01 Epub Date: 2024-08-05 DOI:10.1016/j.mcpro.2024.100826
Ji Hyae Lim, Jae Min Lim, Hyeong Min Lee, Hyun Jung Lee, Dong Wook Kwak, You Jung Han, Moon Young Kim, Sang Hee Jung, Young Ran Kim, Hyun Mee Ryu, Kwang Pyo Kim
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引用次数: 0

Abstract

Preeclampsia (PE) is a hypertensive disorder of pregnancy with various clinical symptoms. However, traditional markers for the disease including high blood pressure and proteinuria are poor indicators of the related adverse outcomes. Here, we performed systematic proteome profiling of plasma samples obtained from pregnant women with PE to identify clinically effective diagnostic biomarkers. Proteome profiling was performed using TMT-based liquid chromatography-mass spectrometry (LC-MS/MS) followed by subsequent verification by multiple reaction monitoring (MRM) analysis on normal and PE maternal plasma samples. Functional annotations of differentially expressed proteins (DEPs) in PE were predicted using bioinformatic tools. The diagnostic accuracies of the biomarkers for PE were estimated according to the area under the receiver-operating characteristics curve (AUC). A total of 1307 proteins were identified, and 870 proteins of them were quantified from plasma samples. Significant differences were evident in 138 DEPs, including 71 upregulated DEPs and 67 downregulated DEPs in the PE group, compared with those in the control group. Upregulated proteins were significantly associated with biological processes including platelet degranulation, proteolysis, lipoprotein metabolism, and cholesterol efflux. Biological processes including blood coagulation and acute-phase response were enriched for down-regulated proteins. Of these, 40 proteins were subsequently validated in an independent cohort of 26 PE patients and 29 healthy controls. APOM, LCN2, and QSOX1 showed high diagnostic accuracies for PE detection (AUC >0.9 and p < 0.001, for all) as validated by MRM and ELISA. Our data demonstrate that three plasma biomarkers, identified by systematic proteomic profiling, present a possibility for the assessment of PE, independent of the clinical characteristics of pregnant women.

对母体血浆进行系统蛋白质组分析,以开发子痫前期生物标记物。
背景:子痫前期(PE)是一种妊娠期高血压疾病,具有各种临床症状。然而,该疾病的传统标志物(包括高血压和蛋白尿)并不能很好地反映相关的不良后果。在此,我们对患有 PE 的孕妇血浆样本进行了系统的蛋白质组分析,以确定临床上有效的诊断生物标志物:方法:使用基于 TMT 的液相色谱-质谱联用仪(LC-MS/MS)进行蛋白质组分析,随后通过多反应监测(MRM)分析对正常和 PE 孕妇血浆样本进行验证。利用生物信息学工具预测了 PE 中差异表达蛋白 (DEP) 的功能注释。根据接收者操作特征曲线下面积(AUC)估算了PE生物标志物的诊断准确性:结果:共鉴定出 1,307 种蛋白质,其中 870 种蛋白质从血浆样本中进行了定量。与对照组相比,PE 组 138 种 DEPs 蛋白质存在明显差异,其中 71 种 DEPs 蛋白质上调,67 种 DEPs 蛋白质下调。上调的蛋白质与包括血小板脱颗粒、蛋白水解、脂蛋白代谢和胆固醇外流在内的生物过程密切相关。包括血液凝固和急性期反应在内的生物过程富含下调蛋白。其中 40 个蛋白质随后在 26 名 PE 患者和 29 名健康对照者组成的独立队列中进行了验证。经 MRM 和 ELISA 验证,APOM、LCN2 和 QSOX1 对 PE 的检测显示出很高的诊断准确性(AUC > 0.9,P < 0.001):我们的数据表明,通过系统蛋白质组分析确定的三种血浆生物标记物可用于评估 PE,而不受孕妇临床特征的影响。
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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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