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用于儿童结核病新型生物标志物发现的血浆蛋白质组学

Plasma proteomics for novel biomarker discovery in childhood tuberculosis.

作者信息

Fossati Andrea, Wambi Peter, Jaganath Devan, Calderon Roger, Castro Robert, Mohapatra Alexander, McKetney Justin, Luiz Juaneta, Nerurkar Rutuja, Nkereuwem Esin, Franke Molly F, Mousavian Zaynab, Collins Jeffrey M, Sigal George B, Segal Mark R, Kampman Beate, Wobudeya Eric, Cattamanchi Adithya, Ernst Joel D, Zar Heather J, Swaney Danielle L

机构信息

J. David Gladstone Institutes, San Francisco, 94158, California,USA.

Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, 94158,California,USA.

出版信息

medRxiv. 2025 Mar 20:2024.12.05.24318340. doi: 10.1101/2024.12.05.24318340.

DOI:10.1101/2024.12.05.24318340
PMID:39677468
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11643232/
Abstract

Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, and identifying novel non-sputum-based TB biomarkers to improve pediatric TB diagnosis is a global priority. We sought to develop a plasma biosignature for TB by probing the plasma proteome of 511 children stratified by TB diagnostic classification and HIV status from sites in four low- and middle-income countries, using high-throughput data-independent acquisition mass-spectrometry (DIA-PASEF-MS). We identified 47 proteins differentially regulated (BH adjusted p-values < 1%) between children with microbiologically confirmed TB and children with non-TB respiratory diseases (Unlikely TB). We further employed machine learning to derive three parsimonious biosignatures encompassing 4, 5, or 6 proteins that achieved AUCs of 0.86-0.88 all of which exceeded the minimum WHO target product profile accuracy thresholds for a TB screening test (70% specificity at 90% sensitivity, PPV 0.65-0.74, NPV 0.92-0.95). This work provides insights into the unique host response in pediatric TB disease, as well as a non-sputum biosignature that could reduce delays in TB diagnosis and improve detection and management of TB in children worldwide.

摘要

未能快速诊断结核病(TB)并启动治疗是儿童结核病成为主要死因的一个驱动因素。目前的结核病诊断检测方法在儿童中的表现不佳,识别新型非痰液结核病生物标志物以改善儿童结核病诊断是全球优先事项。我们试图通过使用高通量数据非依赖采集质谱法(DIA-PASEF-MS),对来自四个低收入和中等收入国家站点的511名按结核病诊断分类和艾滋病毒状态分层的儿童的血浆蛋白质组进行探测,来开发一种结核病血浆生物标志物。我们确定了47种在微生物学确诊的结核病儿童和非结核病呼吸道疾病(不太可能是结核病)儿童之间差异调节的蛋白质(BH校正p值<1%)。我们进一步采用机器学习得出了三种简约的生物标志物,分别包含4、5或6种蛋白质,其曲线下面积(AUC)达到0.86 - 0.88,所有这些都超过了世界卫生组织结核病筛查测试目标产品概况的最低准确性阈值(90%灵敏度下特异性为70%,阳性预测值为0.65 - 0.74,阴性预测值为0.92 - 0.95)。这项工作为儿童结核病中独特的宿主反应提供了见解,以及一种非痰液生物标志物,可减少结核病诊断的延迟,并改善全球儿童结核病的检测和管理。

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Plasma proteomics for novel biomarker discovery in childhood tuberculosis.用于儿童结核病新型生物标志物发现的血浆蛋白质组学
medRxiv. 2025 Mar 20:2024.12.05.24318340. doi: 10.1101/2024.12.05.24318340.
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本文引用的文献

1
Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis.血浆免疫谱分析结合机器学习有助于活动性肺结核的诊断和预后。
Emerg Microbes Infect. 2024 Dec;13(1):2370399. doi: 10.1080/22221751.2024.2370399. Epub 2024 Jul 4.
2
Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers.整合血浆蛋白质组学鉴定出结核病特异性诊断生物标志物。
JCI Insight. 2024 Mar 21;9(8):e173273. doi: 10.1172/jci.insight.173273.
3
From simple to complex: Protein-based biomarker discovery in tuberculosis.
从简单到复杂:结核病中基于蛋白质的生物标志物发现。
Eur J Immunol. 2023 Dec;53(12):e2350485. doi: 10.1002/eji.202350485. Epub 2023 Oct 8.
4
Diagnostic performance of host protein signatures as a triage test for active pulmonary TB.宿主蛋白标志物作为活动性肺结核的分诊检测的诊断性能。
J Clin Microbiol. 2023 Oct 24;61(10):e0026423. doi: 10.1128/jcm.00264-23. Epub 2023 Sep 19.
5
Predicting Pediatric Tuberculosis: The Need for Age-Specific Host Biosignatures.预测儿童结核病:对特定年龄宿主生物标志物的需求。
Clin Infect Dis. 2023 Aug 14;77(3):450-452. doi: 10.1093/cid/ciad270.
6
Proteomic analysis identifies a signature of disease severity in the plasma of COVID-19 pneumonia patients associated to neutrophil, platelet and complement activation.蛋白质组学分析确定了与中性粒细胞、血小板和补体激活相关的新冠肺炎患者血浆中疾病严重程度的特征。
Clin Proteomics. 2022 Nov 8;19(1):38. doi: 10.1186/s12014-022-09377-7.
7
Tuberculosis in Children.儿童结核病。
Infect Dis Clin North Am. 2022 Mar;36(1):49-71. doi: 10.1016/j.idc.2021.11.008.
8
Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.在大规模蛋白质组学研究中进行批次效应的诊断和校正:教程。
Mol Syst Biol. 2021 Aug;17(8):e10240. doi: 10.15252/msb.202110240.
9
Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion.通过正交蛋白酶消化实现全面的血浆蛋白质组学
J Proteome Res. 2021 Aug 6;20(8):4031-4040. doi: 10.1021/acs.jproteome.1c00357. Epub 2021 Jul 28.
10
System-Wide Profiling of Protein Complexes Via Size Exclusion Chromatography-Mass Spectrometry (SEC-MS).通过尺寸排阻色谱-质谱联用技术(SEC-MS)进行蛋白质复合物的全系统分析。
Methods Mol Biol. 2021;2259:269-294. doi: 10.1007/978-1-0716-1178-4_18.