Many bacterial pathogens rely on dedicated secretion systems to translocate virulence proteins termed 'effectors' into host cells. These effectors engage and manipulate host cellular functions to support bacterial colonization and propagation. The secretion systems are molecular machines that recognize targeting 'features' in these effector proteins in vivo to selectively and efficiently secrete them. The joint analysis of whole genome sequencing data and computational predictions of amino acid characteristics of effector proteins has made available extensive lists of candidate effectors for many bacterial pathogens, among which Dot/Icm type IVB secretion system in Legionella pneumophila reigns with the largest number of effectors identified to-date. This system is also used by the causative agent of Q fever, Coxiella burnetii, to secrete a large pool of distinct effectors. By comparing these two pathogens, we provide an understanding of the rationale behind effector repertoire expansion. We will also discuss recent bioinformatic advances facilitating high-throughput discovery of secreted effectors through in silico 'feature' recognition, and the current challenge to substantiate the biological relevance and bona fide nature of effectors identified in silico.

中文翻译：

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绘制细菌效应子库的图谱：体内和计算机分析方法来定义决定细菌效应子分泌的蛋白质特征。

许多细菌病原体依靠专用的分泌系统将被称为“效应子”的毒力蛋白转运到宿主细胞中。这些效应子参与并操纵宿主细胞功能，以支持细菌的定殖和繁殖。分泌系统是识别体内这些效应蛋白的“特征”以选择性和有效地分泌它们的分子机器。对全基因组测序数据的联合分析和效应蛋白氨基酸特征的计算预测，为许多细菌病原体提供了广泛的候选效应子列表，其中嗜肺军团菌的Dot / Icm型IVB分泌系统占据了最多的效应子迄今已确定。Q发烧的致病菌Burnetii，分泌大量不同的效应子。通过比较这两种病原体，我们可以了解效应子库扩展的基本原理。我们还将讨论最近的生物信息学进展，这些进展有助于通过计算机模拟“特征”识别促进分泌效应子的高通量发现，以及当前为证实在计算机模拟中识别的效应子的生物学相关性和真正性质所面临的挑战。