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一种基于计算分析的内毒素诱导小鼠多器官动态炎症相互作用。

A computational analysis of dynamic, multi-organ inflammatory crosstalk induced by endotoxin in mice.

机构信息

Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

出版信息

PLoS Comput Biol. 2018 Nov 6;14(11):e1006582. doi: 10.1371/journal.pcbi.1006582. eCollection 2018 Nov.

DOI:10.1371/journal.pcbi.1006582
PMID:30399158
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6239343/
Abstract

Bacterial lipopolysaccharide (LPS) induces an acute inflammatory response across multiple organs, primarily via Toll-like receptor 4 (TLR4). We sought to define novel aspects of the complex spatiotemporal dynamics of LPS-induced inflammation using computational modeling, with a special focus on the timing of pathological systemic spillover. An analysis of principal drivers of LPS-induced inflammation in the heart, gut, lung, liver, spleen, and kidney to assess organ-specific dynamics, as well as in the plasma (as an assessment of systemic spillover), was carried out using data on 20 protein-level inflammatory mediators measured over 0-48h in both C57BL/6 and TLR4-null mice. Using a suite of computational techniques, including a time-interval variant of Principal Component Analysis, we confirm key roles for cytokines such as tumor necrosis factor-α and interleukin-17A, define a temporal hierarchy of organ-localized inflammation, and infer the point at which organ-localized inflammation spills over systemically. Thus, by employing a systems biology approach, we obtain a novel perspective on the time- and organ-specific components in the propagation of acute systemic inflammation.

摘要

细菌脂多糖(LPS)通过 Toll 样受体 4(TLR4)诱导多个器官的急性炎症反应。我们试图使用计算模型来定义 LPS 诱导的炎症的复杂时空动态的新方面,特别关注病理性全身性溢出的时间。使用在 C57BL/6 和 TLR4 缺失小鼠中测量的 20 种蛋白质水平炎症介质的 0-48 小时的数据,对心脏、肠道、肺、肝、脾和肾中的 LPS 诱导炎症的主要驱动因素进行分析,以评估器官特异性动力学,以及在血浆(作为全身性溢出的评估)中。使用一系列计算技术,包括主成分分析的时间间隔变体,我们确认了细胞因子(如肿瘤坏死因子-α和白细胞介素-17A)的关键作用,定义了器官局部炎症的时间层次结构,并推断出器官局部炎症向系统溢出的时间点。因此,通过采用系统生物学方法,我们获得了急性全身性炎症传播的时间和器官特异性成分的新视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/168eb5179da0/pcbi.1006582.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/b4c52b92968a/pcbi.1006582.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/7a4dd4b6bde6/pcbi.1006582.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/b6d06072a5e4/pcbi.1006582.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/39901a0e40a3/pcbi.1006582.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/168eb5179da0/pcbi.1006582.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/b4c52b92968a/pcbi.1006582.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/7a4dd4b6bde6/pcbi.1006582.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/b6d06072a5e4/pcbi.1006582.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/39901a0e40a3/pcbi.1006582.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b8b/6239343/168eb5179da0/pcbi.1006582.g005.jpg

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