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绘制肠道和上呼吸道感染时上皮-免疫细胞互作图谱。

Mapping the epithelial-immune cell interactome upon infection in the gut and the upper airways.

机构信息

Earlham Institute, Norwich Research Park, Norwich, UK.

Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.

出版信息

NPJ Syst Biol Appl. 2022 May 2;8(1):15. doi: 10.1038/s41540-022-00224-x.

Abstract

Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial-immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial-immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial-immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis.

摘要

越来越多的证据表明,上皮细胞在病毒感染(包括 SARS-CoV-2 感染)引起的全身性和过度激活的免疫反应中起着关键作用。然而,病毒感染如何改变调节炎症反应的上皮-免疫细胞相互作用,目前还不是很清楚。现有的实验方法不足以正确分析这个复杂的系统,因此需要计算预测和有针对性的数据整合作为替代方法。在这项工作中,我们提出了一个集成的计算生物学框架,该框架模拟了感染如何改变上皮细胞的细胞内信号转导,以及这种变化如何通过上皮细胞与局部免疫细胞群体之间的改变相互作用影响全身免疫反应。作为一个概念验证,我们专注于肠道和上呼吸道上皮感染的作用。为了描述修饰后的上皮-免疫互作网络,我们将细胞内和细胞间网络与来自 SARS-CoV-2 感染的人回肠和结肠类器官以及感染的气道纤毛上皮细胞的单细胞 RNA-seq 数据进行了整合。这种集成的方法学已被证明可用于指出在疾病反应过程中驱动炎症的特定上皮-免疫相互作用,并提出相关的分子靶点来指导有针对性的实验分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f90/9061772/6f6566f8a32a/41540_2022_224_Fig1_HTML.jpg

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