深度蛋白质组学网络和机器学习分析日本脑炎病毒感染患者的脑脊液。

Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.

机构信息

Department of Biochemistry, University of Oxford, OX1 3QU, Oxford, U.K.

Kavli Institute for Nanoscience Discovery, University of Oxford, OX1 3QU, Oxford, U.K.

出版信息

J Proteome Res. 2023 Jun 2;22(6):1614-1629. doi: 10.1021/acs.jproteome.2c00563. Epub 2023 May 23.

Abstract

Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC-MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2-3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.

摘要

日本脑炎病毒是亚太地区导致神经感染的主要原因,在更偏远的地区则无法检测到。我们旨在检验人类脑脊液(CSF)中存在日本脑炎(JE)蛋白特征的假设,这些特征可用于快速诊断测试(RDT),有助于了解感染期间的宿主反应并预测结局。采用液相色谱-串联质谱(LC-MS/MS),通过广泛的离线分级和串联质量标签标记(TMT),比较了 JE 与其他确诊的神经感染(非 JE)患者的深部 CSF 蛋白质组。使用非依赖性采集(DIA)LC-MS/MS 进行了验证。鉴定出 5070 种蛋白质,包括 4805 种人类蛋白质和 265 种病原体蛋白质。使用 TMT 分析对 147 个患者样本进行特征选择和预测建模,从而开发了一种九种蛋白质的 JE 诊断特征。使用 DIA 分析对 16 个患者样本的独立样本组进行了测试,证明准确率为 82%。最终,在更大的患者群体和不同的地点进行验证,可以帮助将该列表精炼为用于 RDT 的 2-3 种蛋白质。该质谱蛋白质组学数据已通过 PRIDE 合作伙伴存储库,以数据集标识符 PXD034789 并以 10.6019/PXD034789 存入 ProteomeXchange 联盟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee6b/10246887/2978197af46f/pr2c00563_0002.jpg

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