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宿主-微生物群结构相互作用网络

Structural host-microbiota interaction networks.

作者信息

Guven-Maiorov Emine, Tsai Chung-Jung, Nussinov Ruth

机构信息

Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States of America.

Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.

出版信息

PLoS Comput Biol. 2017 Oct 12;13(10):e1005579. doi: 10.1371/journal.pcbi.1005579. eCollection 2017 Oct.

Abstract

Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

摘要

数百种不同的物种定殖于多细胞生物,使其成为“超级生物体”。越来越多的数据支持微生物群在健康和疾病中的作用。在分子水平上掌握宿主-微生物群相互作用(HMI)的原理很重要,因为这可能有助于深入了解感染机制。宿主与微生物群之间的相互作用可能有助于解决一些令人困惑的问题,比如一种微生物如何既能促进健康又能引发疾病。将宿主和微生物群视为一个整体的综合超级生物体网络,可能会揭示它们的密码,或许能阐明最基本的问题:它们如何调节免疫监视。在此框架内,结构性HMI网络能够独特地识别靶向不同宿主节点或干扰内源性宿主相互作用的潜在微生物效应物,以及宿主或微生物蛋白质上的突变如何影响相互作用。此外,结构性HMI有助于识别主要的宿主细胞调节节点和模块,微生物对这些节点和模块的调节会促进异常活动。总体而言,这些数据可以勾勒出致病机制,从而有助于优化有益的治疗方法。迄今为止,实验技术上的挑战限制了对HMI的大规模表征。在此,我们强调一个尚处于起步阶段的领域,我们认为该领域将越来越吸引计算领域的研究人员:预测跨物种的相互作用,并将这些相互作用映射到宿主细胞网络上,以弄清楚共生和致病微生物群如何调节宿主信号传导以及广泛的跨物种影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21d1/5638203/19cbc60ceb61/pcbi.1005579.g001.jpg

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