Model system based proteomics to understand the host response during bacterial infections

IF 3.743 Q2 Biochemistry, Genetics and Molecular Biology Molecular BioSystems Pub Date : 2017-10-19 DOI:10.1039/C7MB00372B
Arumugam Kamaladevi, Shanmugam Marudhupandiyan and Krishnaswamy Balamurugan
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引用次数: 2

Abstract

Infectious diseases caused by bacterial pathogens pose a major concern to public health and, thus, greater attention must be given to providing insightful knowledge on host–pathogen interactions. There are several theories addressing the dynamics of complex mechanisms of host–pathogen interactions. The availability of an ample number of universally accepted model systems, including vertebrates, invertebrates, and mammalian cells, provides in-depth transcriptomics data to evaluate these complex mechanisms during host–pathogen interactions. Recent model system based proteomic studies have addressed the issues related to human diseases by establishing the protein profile of model animals that closely resemble the environment. As a result, model system based proteomics has been widely accepted as a powerful and effective approach to understand the highly complex host–pathogen interfaces at their protein levels. This review offers a snapshot of the contributions of selective model systems on host–bacterial pathogen interactions through proteomic approaches.

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模型系统为基础的蛋白质组学,以了解宿主在细菌感染期间的反应
由细菌病原体引起的传染病对公共卫生构成重大关切,因此,必须更加重视提供关于宿主-病原体相互作用的深刻知识。有几个理论解决宿主-病原体相互作用的复杂机制的动力学。大量普遍接受的模型系统(包括脊椎动物、无脊椎动物和哺乳动物细胞)的可用性为评估宿主-病原体相互作用过程中的这些复杂机制提供了深入的转录组学数据。最近基于模型系统的蛋白质组学研究通过建立与环境密切相似的模型动物的蛋白质谱来解决与人类疾病相关的问题。因此,基于模型系统的蛋白质组学已被广泛接受为一种在蛋白质水平上理解高度复杂的宿主-病原体界面的强大而有效的方法。本文综述了通过蛋白质组学方法对宿主-细菌病原体相互作用的选择性模型系统的贡献。
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来源期刊
Molecular BioSystems
Molecular BioSystems 生物-生化与分子生物学
CiteScore
2.94
自引率
0.00%
发文量
0
审稿时长
2.6 months
期刊介绍: Molecular Omics publishes molecular level experimental and bioinformatics research in the -omics sciences, including genomics, proteomics, transcriptomics and metabolomics. We will also welcome multidisciplinary papers presenting studies combining different types of omics, or the interface of omics and other fields such as systems biology or chemical biology.
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