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用于急性中风诊断的血液蛋白生物标志物:基于发现的SWATH-MS蛋白质组学方法。

Blood-based protein biomarkers for the diagnosis of acute stroke: A discovery-based SWATH-MS proteomic approach.

作者信息

Misra Shubham, Singh Praveen, Nath Manabesh, Bhalla Divya, Sengupta Shantanu, Kumar Amit, Pandit Awadh K, Aggarwal Praveen, Srivastava Achal K, Mohania Dheeraj, Prasad Kameshwar, Vibha Deepti

机构信息

Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.

CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.

出版信息

Front Neurol. 2022 Sep 27;13:989856. doi: 10.3389/fneur.2022.989856. eCollection 2022.

DOI:10.3389/fneur.2022.989856
PMID:36237606
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9552908/
Abstract

BACKGROUND AND PURPOSES

Recent developments in high-throughput proteomic approach have shown the potential to discover biomarkers for diagnosing acute stroke and to elucidate the pathomechanisms specific to different stroke subtypes. We aimed to determine blood-based protein biomarkers to diagnose total stroke (IS+ICH) from healthy controls, ischemic stroke (IS) from healthy controls, and intracerebral hemorrhage (ICH) from healthy control subjects within 24 h using a discovery-based SWATH-MS proteomic approach.

METHODS

In this discovery phase study, serum samples were collected within 24 h from acute stroke (IS & ICH) patients and healthy controls and were subjected to SWATH-MS-based untargeted proteomics. For protein identification, a high-pH fractionated peptide library for human serum proteins (obtained from SCIEX) comprising of 465 proteins was used. Significantly differentially expressed (SDE) proteins were selected using the following criteria: >1.5-fold change for upregulated, < 0.67 for downregulated, value < 0.05, and confirmed/tentative selection using Boruta random forest. Protein-protein interaction network analysis and the functional enrichment analysis were conducted using STRING 11 online tool, g:Profiler tool and Cytoscape 3.9.0 software. The statistical analyses were conducted in R version 3.6.2.

RESULTS

Our study included 40 stroke cases (20 IS, 20 ICH) within 24 h and 40 age-, sex-, hypertension-, and diabetes-matched healthy controls. We quantified 375 proteins between the stroke cases and control groups through SWATH-MS analysis. We observed 31 SDE proteins between total stroke and controls, 16 SDE proteins between IS and controls, and 41 SDE proteins between ICH and controls within 24 h. Four proteins [ceruloplasmin, alpha-1-antitrypsin (SERPINA1), von Willebrand factor (vWF), and coagulation factor XIII B chain (F13B)] commonly differentiated total stroke, IS, and ICH from healthy control subjects. The most common significant pathways in stroke cases involved complement and coagulation cascades, platelet degranulation, immune-related processes, acute phase response, lipid-related processes, and pathways related to extracellular space and matrix.

CONCLUSION

Our discovery phase study identified potential protein biomarker candidates for the diagnosis of acute stroke and highlighted significant pathways associated with different stroke subtypes. These potential biomarker candidates warrant further validation in future studies with a large cohort of stroke patients to investigate their diagnostic performance.

摘要

背景与目的

高通量蛋白质组学方法的最新进展显示出发现用于诊断急性中风的生物标志物以及阐明不同中风亚型特异性病理机制的潜力。我们旨在使用基于发现的SWATH-MS蛋白质组学方法,在24小时内确定血液中的蛋白质生物标志物,以区分健康对照与总体中风(缺血性中风+脑出血)、健康对照与缺血性中风(IS)以及健康对照与脑出血(ICH)。

方法

在这项探索性研究中,在24小时内收集急性中风(IS和ICH)患者及健康对照的血清样本,并进行基于SWATH-MS的非靶向蛋白质组学分析。为进行蛋白质鉴定,使用了一个包含465种蛋白质的人血清蛋白高pH分级肽库(购自SCIEX)。采用以下标准选择显著差异表达(SDE)蛋白:上调倍数>1.5,下调倍数<0.67,P值<0.05,并使用Boruta随机森林进行确认/初步筛选。使用STRING 11在线工具、g:Profiler工具和Cytoscape 3.9.0软件进行蛋白质-蛋白质相互作用网络分析和功能富集分析。统计分析在R 3.6.2版本中进行。

结果

我们的研究纳入了24小时内的40例中风病例(20例IS,20例ICH)以及40例年龄、性别、高血压和糖尿病匹配的健康对照。通过SWATH-MS分析,我们在中风病例组和对照组之间定量了375种蛋白质。在24小时内,我们观察到总体中风与对照组之间有31种SDE蛋白,IS与对照组之间有16种SDE蛋白,ICH与对照组之间有41种SDE蛋白。四种蛋白质[铜蓝蛋白、α-1抗胰蛋白酶(SERPINA1)、血管性血友病因子(vWF)和凝血因子XIII B链(F13B)]可将总体中风、IS和ICH与健康对照区分开来。中风病例中最常见的显著通路涉及补体和凝血级联反应、血小板脱颗粒、免疫相关过程、急性期反应、脂质相关过程以及与细胞外空间和基质相关的通路。

结论

我们的探索性研究确定了用于诊断急性中风的潜在蛋白质生物标志物候选物,并突出了与不同中风亚型相关的显著通路。这些潜在的生物标志物候选物值得在未来对大量中风患者的研究中进一步验证,以研究其诊断性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/d8db7095e8e5/fneur-13-989856-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/ab6ae39e90ac/fneur-13-989856-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/a4a51bf896fe/fneur-13-989856-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/db6176eddfdb/fneur-13-989856-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/187bf70bbec4/fneur-13-989856-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/69c06c30fc00/fneur-13-989856-g0005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/d8db7095e8e5/fneur-13-989856-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/ab6ae39e90ac/fneur-13-989856-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/db6176eddfdb/fneur-13-989856-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/187bf70bbec4/fneur-13-989856-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/69c06c30fc00/fneur-13-989856-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/6170a2146c8f/fneur-13-989856-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/7b77c2d8381d/fneur-13-989856-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/f0c63a267fb6/fneur-13-989856-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/72f8a5d28a01/fneur-13-989856-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39bb/9552908/d8db7095e8e5/fneur-13-989856-g0010.jpg

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