Next-generation sequencing (NGS) is now widely used in microbiology to explore genome evolution and the structure of pathogen outbreaks. Bioinformatics pipelines readily detect single-nucleotide polymorphisms or short indels. However, bacterial genomes also evolve through the action of small transposable elements called insertion sequences (ISs), which are difficult to detect due to their short length and multiple repetitions throughout the genome. We designed panISa software for the ab initio detection of IS insertions in the genomes of prokaryotes. PanISa has been released as open source software (GPL3) available from https://github.com/bvalot/panISa. In this study, we assessed the utility of this software for evolutionary studies, by reanalysing five published datasets for outbreaks of human major pathogens in which ISs had not been specifically investigated. We reanalysed the raw data from each study, by aligning the reads against reference genomes and running panISa on the alignments. Each hit was automatically curated and IS-related events were validated on the basis of nucleotide sequence similarity, by comparison with the ISFinder database. In Acinetobacter baumannii , the panISa pipeline identified ISAba1 or ISAba125 upstream from the ampC gene, which encodes a cephalosporinase in all third-generation cephalosporin-resistant isolates. In the genomes of Vibrio cholerae isolates, we found that early Haitian isolates had the same ISs as Nepalese isolates, confirming the inferred history of the contamination of this island. In Enterococcus faecalis , panISa identified regions of high plasticity, including a pathogenicity island enriched in IS-related events. The overall distribution of ISs deduced with panISa was consistent with SNP-based phylogenic trees, for all species considered. The role of ISs in pathogen evolution has probably been underestimated due to difficulties detecting these transposable elements. We show here that panISa is a useful addition to the bioinformatics toolbox for analyses of the evolution of bacterial genomes. PanISa will facilitate explorations of the functional impact of ISs and improve our understanding of prokaryote evolution.

中文翻译：

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使用 panISa 软件破译插入序列在细菌流行病病原体进化中的作用。


新一代测序（NGS）现已广泛应用于微生物学领域，以探索基因组进化和病原体爆发的结构。生物信息学流程可以轻松检测单核苷酸多态性或短插入缺失。然而，细菌基因组也通过称为插入序列（IS）的小转座元件的作用而进化，这些转座元件由于其长度短且在整个基因组中多次重复而难以检测。我们设计了panISa软件，用于从头开始检测原核生物基因组中的 IS 插入。 PanISa已作为开源软件 (GPL3) 发布，可从 https://github.com/bvalot/panISa 获取。在这项研究中，我们通过重新分析五个已发表的人类主要病原体爆发数据集来评估该软件在进化研究中的实用性，其中尚未对 IS 进行专门研究。我们通过将读数与参考基因组进行比对并对比对运行panISa来重新分析每项研究的原始数据。每个命中都会自动策划，并通过与 ISFinder 数据库进行比较，根据核苷酸序列相似性验证 IS 相关事件。在鲍曼不动杆菌中， panISa管道鉴定了ampC基因上游的 IS Aba1或 IS Aba125 ，该基因在所有第三代头孢菌素耐药菌株中编码头孢菌素酶。在霍乱弧菌分离株的基因组中，我们发现早期海地分离株与尼泊尔分离株具有相同的IS，证实了该岛污染的推断历史。 在粪肠球菌中， panISa识别出了高可塑性区域，包括富含 IS 相关事件的致病岛。对于所有考虑的物种，用panISa推导的 IS 的总体分布与基于 SNP 的系统发育树一致。由于检测这些转座元件的困难，IS 在病原体进化中的作用可能被低估。我们在此证明panISa是生物信息学工具箱的一个有用补充，可用于分析细菌基因组的进化。 PanISa将促进对 IS 功能影响的探索，并提高我们对原核生物进化的理解。