Mining Bacterial Genomes for Secondary Metabolite Gene Clusters
Mining Bacterial Genomes for Secondary Metabolite Gene Clusters
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DOI：
10.1007/978-1-4939-6634-9_2
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发表时间：
2017-01-01
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影响因子：
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通讯作者：
Ziemert, Nadine
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文献类型：
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作者：
Adamek, Martina;Spohn, Marius;Ziemert, Nadine
随着细菌对常用抗生素耐药性的出现，迫切需要新的抗菌化合物。传统的生物活性导向的药物发现策略涉及费力的筛选工作，并显示出高的再发现率。随着新一代测序技术的发展以及临床使用的大多数抗生素是由细菌产生的次级代谢产物的知识，挖掘细菌基因组中具有抗菌活性的次级代谢产物是一种有前途的方法，可以指导更多时间和成本效益的新化合物的鉴定。然而，这听起来很容易实现，但也带来了一些挑战。迄今为止，已有几种用于预测次级代谢物基因簇的工具，其中一些是基于特征基因的检测，而另一些则是寻找基因内容或调控的特定模式，除了仅仅识别基因簇之外，其他一些因素如确定簇边界和评估检测到的簇的新奇也很重要。为了这个目的，预测的次级代谢产物基因与不同的集群和化合物数据库的比较是必要的。此外，建议将检测到的簇分类到基因簇家族中。到目前为止，基因组挖掘还没有标准化的程序;然而，存在克服所有这些挑战的不同方法，并在本章中进行了讨论。我们对次级代谢物基因簇识别的工作流程提供了实际指导，其中包括基因簇边界的确定，解决了使用基因组草案时出现的问题，并对基因簇分类的不同方法进行了展望。基于可理解的例子的协议设置，这应该使读者挖掘自己的基因组数据感兴趣的次级代谢产物。
With the emergence of bacterial resistance against frequently used antibiotics, novel antibacterial compounds are urgently needed. Traditional bioactivity-guided drug discovery strategies involve laborious screening efforts and display high rediscovery rates. With the progress in next generation sequencing methods and the knowledge that the majority of antibiotics in clinical use are produced as secondary metabolites by bacteria, mining bacterial genomes for secondary metabolites with antimicrobial activity is a promising approach, which can guide a more time and cost-effective identification of novel compounds. However, what sounds easy to accomplish, comes with several challenges. To date, several tools for the prediction of secondary metabolite gene clusters are available, some of which are based on the detection of signature genes, while others are searching for specific patterns in gene content or regulation.Apart from the mere identification of gene clusters, several other factors such as determining cluster boundaries and assessing the novelty of the detected cluster are important. For this purpose, comparison of the predicted secondary metabolite genes with different cluster and compound databases is necessary. Furthermore, it is advisable to classify detected clusters into gene cluster families. So far, there is no standardized procedure for genome mining; however, different approaches to overcome all of these challenges exist and are addressed in this chapter. We give practical guidance on the workflow for secondary metabolite gene cluster identification, which includes the determination of gene cluster boundaries, addresses problems occurring with the use of draft genomes, and gives an outlook on the different methods for gene cluster classification. Based on comprehensible examples a protocol is set, which should enable the readers to mine their own genome data for interesting secondary metabolites.