全基因组网络揭示了沙门氏菌流行菌株的出现
Genome-wide networks reveal emergence of epidemic strains of Salmonella Enteritidis
摘要：
目的：为了加强对高负荷食源性病原体的监测，有机会将泛基因组数据与网络分析相结合。研究方法：2015年8月至2019年12月期间，肠道沙门氏菌亚种肠道血清型肠炎分离株被转介至新南威尔士州（新南威尔士州）肠道参考实验室（总共1033个分离株），其中包括确诊的疫情爆发。所有分离株均进行全基因组测序。通过计算机MLVA以及核心SNP量化基因组之间的距离，这为无向网络的构建提供了信息。由无向网络生成流行中心空间。无向SNP网络上的组分与系统发育树表示一起考虑。结果：暴发分离株可识别为MLVA和SNP网络上的不同组分。基于MLVA网络的中心性/患病率空间没有描绘爆发，而爆发在基于SNP网络的中心性/患病率空间中被清楚地描绘。基于系统发育分析，无向SNP网络上的组分与SNP聚类显示出高度一致性。结论：细菌全基因组数据的网络分析可以提高群体分析的分辨率。网络成分和SNP簇的高度一致性有望用于食源性沙门氏菌的快速群体分析。因为网络分析的开销很低。
英文摘要：
Objectives: To enhance monitoring of high-burden foodborne pathogens, there is opportunity to combine pangenome data with network analysis. Methods: Salmonella enterica subspecies Enterica serovar Enteritidis isolates were referred to the New South Wales (NSW) Enteric Reference Laboratory between August 2015 and December 2019 (1033 isolates in total), inclusive of a confirmed outbreak. All isolates underwent whole genome sequencing. Distances between genomes were quantified by in silico MLVA as well as core SNPs, which informed construction of undirected networks. Prevalence-centrality spaces were generated from the undirected networks. Components on the undirected SNP network were considered alongside a phylogenetic tree representation. Results: Outbreak isolates were identifiable as distinct components on the MLVA and SNP networks. The MLVA network based centrality/prevalence space did not delineate the outbreak, whereas the outbreak was clearly delineated in the SNP network based centrality/prevalence space. Components on the undirected SNP network showed a high concordance to the SNP clusters based on phylogenetic analysis. Conclusions: Bacterial whole genome data in network based analysis can improve the resolution of population analysis. High concordance of network components and SNP clusters is promising for rapid population analyses of foodborne Salmonella spp. due to the low overhead of network analysis.
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