研究 NFkB 信号对单一、双重和三重 TLR 信号反应的计算和实验方法

Thalia Newman , Annarose Taylor , Sakhi Naik , Swati Pandey , Kimberly Manalang , Robert A. Kurt , Chun Wai Liew
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引用次数: 0

摘要

我们利用建模和实验数据评估了单核细胞对 TLR4/TLR5 和 TLR4/TLR7 双信号的反应,这类似于细胞对同时遇到不同类型病原体的反应。与通过单一 TLR 发出信号相比,TLR4/TLR5 和 TLR4/TLR7 信号都会导致 NFkB 反应减弱。当三种信号级联都被触发时,NFkB 反应也会降低。该模型表明,当细胞暴露于多种 TLR 激动剂时,信号通路之间的竞争导致了反应的减弱,然而在模型中调整 IRAKs 和 TABs 的水平不足以克服信号通路之间的竞争。为了在实验中检验改变 TLR 信号蛋白会如何影响 NFkB 对多种 TLR 激动剂的反应,我们用脂多糖预处理细胞,并跟踪细胞对单一、双重和三重 TLR 信号的反应。预处理降低了 NFkB 对所有三种激动剂的反应,这可能是 tlr4、tlr5、tlr7、nfkb、tab1、tab2 和 tab3 表达减少的结果。总之,该模型支持探索多种激动剂对信号通路的影响,以及调整 TLR 信号蛋白水平对改善 NFkB 反应的有效性。这些实验和数据表明,建立一个能够整合多种 TLR 信号级联的模型非常重要,因为单一 TLR 信号级联模型生成的数据无法预测当多种 TLR 信号级联被激活时细胞会如何反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A computational and experimental approach to studying NFkB signaling in response to single, dual, and triple TLR signaling

A computational and experimental approach to studying NFkB signaling in response to single, dual, and triple TLR signaling

Modeling and experimental data were used to evaluate how monocytes would respond to dual TLR4/TLR5 and dual TLR4/TLR7 signaling analogous to how the cells would respond to simultaneously encountering different types of pathogens. Both TLR4/TLR5 and TLR4/TLR7 signaling resulted in a decreased NFkB response relative to signaling through a single TLR. The NFkB response also decreased when all three signaling cascades were triggered. The model suggested that competition between the signaling pathways led to the impaired response when the cells were exposed to multiple TLR agonists, however adjusting the level of IRAKs and TABs in the model was insufficient to overcome competition between the signaling pathways. To experimentally examine how modifying TLR signaling proteins would impact the NFkB response to multiple TLR agonists, cells were pre-conditioned with lipopolysaccharide and the response to single, dual, and triple TLR signaling was followed. Pre-conditioning led to a reduction in the NFkB response to all three agonists, likely a consequence of decreased tlr4, tlr5, tlr7, nfkb, tab1, tab2, and tab3 expression. Collectively, the model supported exploration of the effects of multiple agonists on the signaling pathways and the effectiveness of adjusting the level of TLR signaling proteins in improving the NFkB response. These experiments and data show the importance of having a model capable of integrating multiple TLR signaling cascades since data generated by the model of a single TLR signaling cascade could not predict how the cells would respond when multiple TLR signaling cascades were activated.

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来源期刊
Immunoinformatics (Amsterdam, Netherlands)
Immunoinformatics (Amsterdam, Netherlands) Immunology, Computer Science Applications
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