Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems.
Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems.
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DOI：
10.1371/journal.ppat.1000375
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发表时间：
2009-04
期刊：
影响因子：
6.7
通讯作者：
McDermott JE
中科院分区：
文献类型：
--
作者：
Samudrala R;Heffron F;McDermott JE
Ⅲ型分泌系统是许多革兰氏阴性菌致病性的重要组成部分。尽管分泌系统装置的组成成分是保守的，但其底物——效应蛋白却不是。我们采用了一种新的计算方法，通过整合基于蛋白质序列的特征，包括进化指标，如在一系列其他生物中的同源物模式、G+C含量、氨基酸组成以及蛋白质序列的N末端30个残基，来可靠地识别新的分泌型效应蛋白。该方法以植物病原体丁香假单胞菌的已知效应蛋白为训练集，并在去除具有可检测序列相似性的效应蛋白后，用动物病原体鼠伤寒沙门氏菌（S. Typhimurium）的一组效应蛋白进行验证。我们表明这种方法能够以高特异性和敏感性预测已知的分泌型效应蛋白。此外，通过考虑来自多种生物的大量效应蛋白，我们通过计算在分泌型效应蛋白的N末端20个残基中识别出一种常见的假定分泌信号。该信号可用于区分这两种生物中总共68个已知效应蛋白中的46个，表明它是一种适用于许多Ⅲ型分泌效应蛋白的真实的、共享的信号。我们利用该方法对鼠伤寒沙门氏菌中的分泌型效应蛋白进行了新的预测，其中一些已通过实验验证。我们还将该方法应用于预测遗传上难以处理的人类病原体沙眼衣原体中的分泌型效应蛋白，除了提供一些新的预测外，还识别出了大多数已知的分泌蛋白。这种方法为在广泛的致病细菌中识别分泌型效应蛋白提供了一种新途径，以便进一步进行实验表征，并深入了解Ⅲ型分泌信号的性质。
致病细菌释放许多不同的蛋白质，这些蛋白质的作用是干扰宿主防御，使细菌能够在宿主中入侵、持续存在和复制。在许多细菌病原体中，Ⅲ型分泌系统用于将这些毒力因子直接注入宿主细胞的细胞质。分泌的蛋白质没有高度保守的序列，也没有任何一种常见的可识别的信号序列来将它们靶向分泌。这使得在没有实验研究的情况下很难识别这类分泌蛋白，而在其他分泌系统中是可以做到的。在这项研究中，我们开发了一种从基因组蛋白质序列中检测分泌型毒力因子的计算方法。我们使用这种方法比较鼠伤寒沙门氏菌和一种植物病原体丁香假单胞菌的蛋白质N末端区域，并表明这种方法是迄今为止计算识别Ⅲ型分泌蛋白最有效的方法。我们进一步使用这种方法在这些蛋白质中识别出一种序列模式，该模式可能有助于将毒力蛋白导向Ⅲ型分泌装置。我们对这两种生物以及人类病原体沙眼衣原体中的分泌蛋白进行了新的预测。更好地理解病原体中的分泌型毒力因子将导致对抗重要传染病的新方法，并提供对病原体和宿主之间复杂相互作用的理解。
The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates—effector proteins—are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal. Pathogenic bacteria release a number of different proteins that function to interfere with host defenses and allow bacterial invasion, persistence, and replication in the host. In many bacterial pathogens, the type III secretion system is used to inject these virulence factors directly to the cytoplasm of the host cell. The secreted proteins do not have well-conserved sequences and do not have any kind of common identifiable signal sequence to target them for secretion. This makes it very difficult to identify secreted proteins of this kind without experimental investigation, as can be done in other secretion systems. In this study, we develop a computational approach to detect secreted virulence factors from genomic protein sequences. We use this method to compare the N-terminal regions of proteins from S. Typhimurium and a plant pathogen, P. syringae, and show that this approach is the most effective method of computational identification of type III secreted proteins to date. We further use this approach to identify a sequence pattern in these proteins that presumably helps direct virulence proteins to the type III secretion apparatus. We provide novel predictions of secreted proteins in these two organisms, as well as in the human pathogen C. trachomatis. Better understanding of secreted virulence factors in pathogens will lead to new ways of combating important infectious diseases and provide understanding of the complex interaction between pathogen and host.
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