Dynamic cross talk model of the epithelial innate immune response to double-stranded RNA stimulation: coordinated dynamics emerging from cell-level noise.
Dynamic cross talk model of the epithelial innate immune response to double-stranded RNA stimulation: coordinated dynamics emerging from cell-level noise.
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DOI：
10.1371/journal.pone.0093396
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Kimmel M
中科院分区：
文献类型：
--
作者：
Bertolusso R;Tian B;Zhao Y;Vergara L;Sabree A;Iwanaszko M;Lipniacki T;Brasier AR;Kimmel M
我们提出了一个集成的动态串扰模型的上皮先天免疫反应（IIR）纳入RIG-I和TLR 3作为两个主要的模式识别受体（PRR）收敛于RelA和IRF 3转录效应。bioPN模拟在群体和单细胞水平上响应于时间进程、基因沉默和剂量-响应扰动而再现生物学相关基因和蛋白质丰度测量。我们的计算预测表明，RelA和IRF 3是在自动和交叉调节。我们预测，并证实实验，RIG-I mRNA的表达是由IRF 7控制。我们还预测存在TLR 3依赖性、IRF 3非依赖性转录因子，其控制MAVS、IRF 3和IKK家族成员的表达。我们的模型证实了在单细胞中观察到的dsRNA剂量依赖性振荡模式，周期为1-3 hr. Model拟合到时间序列，与敲低数据匹配，表明NF-κB模块在与TNFα刺激实验不同的方案（具有不同的系数值）中运行。在未来的研究中，该模型将作为一个基础，识别病毒编码的IIR拮抗剂和检查病毒复制的随机效应。我们的模型生成模拟的时间序列，再现了在单个细胞中观察到的嘈杂的振荡活动模式（1-3小时）。我们的工作支持的假设，IIR是一种现象，出现了进化，尽管在单个细胞水平的高度可变的反应。
We present an integrated dynamical cross-talk model of the epithelial innate immune reponse (IIR) incorporating RIG-I and TLR3 as the two major pattern recognition receptors (PRR) converging on the RelA and IRF3 transcriptional effectors. bioPN simulations reproduce biologically relevant gene-and protein abundance measurements in response to time course, gene silencing and dose-response perturbations both at the population and single cell level. Our computational predictions suggest that RelA and IRF3 are under auto- and cross-regulation. We predict, and confirm experimentally, that RIG-I mRNA expression is controlled by IRF7. We also predict the existence of a TLR3-dependent, IRF3-independent transcription factor (or factors) that control(s) expression of MAVS, IRF3 and members of the IKK family. Our model confirms the observed dsRNA dose-dependence of oscillatory patterns in single cells, with periods of 1–3 hr. Model fitting to time series, matched by knockdown data suggests that the NF-κB module operates in a different regime (with different coefficient values) than in the TNFα-stimulation experiments. In future studies, this model will serve as a foundation for identification of virus-encoded IIR antagonists and examination of stochastic effects of viral replication. Our model generates simulated time series, which reproduce the noisy oscillatory patterns of activity (with 1–3 hour period) observed in individual cells. Our work supports the hypothesis that the IIR is a phenomenon that emerged by evolution despite highly variable responses at an individual cell level.
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影响因子：
9.9
作者：
Cheong, Raymond;Hoffmann, Alexander;Levchenko, Andre
通讯作者：
Levchenko, Andre
影响因子：
64.8
作者：
Alexopoulou, L;Holt, AC;Flavell, RA
通讯作者：
Flavell, RA
影响因子：
1.1
作者：
Girolami, Mark
通讯作者：
Girolami, Mark
影响因子：
3.7
作者：
Du, Cheng-Jin;Marcello, Marco;Bretschneider, Till
通讯作者：
Bretschneider, Till
DOI：
10.1016/s1357-2725(96)00169-0
发表时间：
1997-07-01
影响因子：
4
作者：
Clemens, MJ
通讯作者：
Clemens, MJ