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开发与保护疟疾感染孕妇免受胎盘疟疾相关的抗体特征的多元预测模型。

Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria.

机构信息

Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia.

Department of Microbiology and Immunology, University of Melbourne, the Doherty Institute, Melbourne, Australia.

出版信息

Elife. 2021 Jun 29;10:e65776. doi: 10.7554/eLife.65776.

DOI:10.7554/eLife.65776
PMID:34181872
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8241440/
Abstract

BACKGROUND

causes placental malaria, which results in adverse outcomes for mother and child. -infected erythrocytes that express the parasite protein VAR2CSA on their surface can bind to placental chondroitin sulfate A. It has been hypothesized that naturally acquired antibodies towards VAR2CSA protect against placental infection, but it has proven difficult to identify robust antibody correlates of protection from disease. The objective of this study was to develop a prediction model using antibody features that could identify women protected from placental malaria.

METHODS

We used a systems serology approach with elastic net-regularized logistic regression, partial least squares discriminant analysis, and a case-control study design to identify naturally acquired antibody features mid-pregnancy that were associated with protection from placental malaria at delivery in a cohort of 77 pregnant women from Madang, Papua New Guinea.

RESULTS

The machine learning techniques selected 6 out of 169 measured antibody features towards VAR2CSA that could predict (with 86% accuracy) whether a woman would subsequently have active placental malaria infection at delivery. Selected features included previously described associations with inhibition of placental binding and/or opsonic phagocytosis of infected erythrocytes, and network analysis indicated that there are not one but multiple pathways to protection from placental malaria.

CONCLUSIONS

We have identified candidate antibody features that could accurately identify malaria-infected women as protected from placental infection. It is likely that there are multiple pathways to protection against placental malaria.

FUNDING

This study was supported by the National Health and Medical Research Council (Nos. APP1143946, GNT1145303, APP1092789, APP1140509, and APP1104975).

摘要

背景

引起胎盘疟疾,导致母婴不良后果。-表面表达寄生虫蛋白 VAR2CSA 的感染红细胞可与胎盘硫酸软骨素 A 结合。据推测,针对 VAR2CSA 的天然获得性抗体可预防胎盘感染,但已证明难以确定针对疾病的强大抗体保护相关性。本研究的目的是使用抗体特征开发一种预测模型,该模型可以识别免受胎盘疟疾感染的女性。

方法

我们使用系统血清学方法,结合弹性网络正则化逻辑回归、偏最小二乘判别分析和病例对照研究设计,在巴布亚新几内亚马当的 77 名孕妇队列中,确定妊娠中期与分娩时免受胎盘疟疾相关的天然获得性抗体特征。

结果

机器学习技术从 169 个测量的针对 VAR2CSA 的抗体特征中选择了 6 个,这些特征可以预测(准确率为 86%)一名女性随后是否会在分娩时发生活动性胎盘疟疾感染。选定的特征包括先前描述的与抑制胎盘结合和/或吞噬感染红细胞的功能有关的特征,网络分析表明,存在多种途径可以预防胎盘疟疾。

结论

我们已经确定了候选抗体特征,可以准确识别免受胎盘感染的疟疾感染女性。很可能存在多种预防胎盘疟疾的途径。

资金

本研究得到了澳大利亚国家卫生和医学研究委员会的支持(编号为 APP1143946、GNT1145303、APP1092789、APP1140509 和 APP1104975)。

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