Department of Computer Science and Engineering, University of Nebraska-Lincoln.
Department of Food Science and Technology, University of Nebraska-Lincoln; Nebraska Food for Health Center, University of Nebraska-Lincoln.
J Vis Exp. 2021 Dec 7(178). doi: 10.3791/63115.
Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -> BAPS1 -> ST -> cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.
常规和系统地使用细菌全基因组测序 (WGS) 提高了公共卫生实验室和监管机构进行的流行病学调查的准确性和分辨率。大量公开的 WGS 数据可用于大规模研究病原体种群。最近，发布了一个免费的计算平台 ProkEvo，用于使用细菌 WGS 数据进行可重复、自动化和可扩展的基于层次的群体基因组分析。ProkEvo 的这种实现强调了将群体的标准基因型映射与对辅助基因组内容的挖掘相结合进行生态推断的重要性。特别是，这里强调的工作使用 ProkEvo 衍生的输出进行了基于群体的层次分析，使用 R 编程语言。主要目标是通过展示如何：i）使用层次基因型的系统发育引导映射；ii）评估基因型频率分布作为生态适应性的代理；iii）使用特定的基因型分类来确定亲缘关系和遗传多样性；iv）绘制区分谱系的辅助基因座。为了提高可重复性和可移植性，使用 R markdown 文件演示了整个分析方法。示例数据集包含了 2365 株动物源性食源性病原体沙门氏菌纽波特的基因组数据。基于系统发育的层次基因型（血清型 -> BAPS1 -> ST -> cgMLST）映射揭示了种群遗传结构，突出了序列类型 (ST) 作为区分基因型的关键。在三个最主要的谱系中，ST5 和 ST118 与高度克隆的 ST45 进化枝相比，具有更近的共同祖先。在基于 ST 的差异的基础上，进一步强调了辅助抗生素耐药性 (AMR) 基因座的分布。最后，使用基于系统发育的可视化将层次基因型和 AMR 内容结合起来，以揭示亲缘关系结构和谱系特异性基因组特征。综上所述，这种分析方法为使用泛基因组信息进行启发式细菌群体基因组分析提供了一些指导。
