Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for typing and characterizing foodborne pathogens. To evaluate the performance of existing bioinformatic tools for in silico prediction of antimicrobial resistance (AMR) and serotypes of Salmonella enterica, WGS-based genotype predictions were compared with the results of traditional phenotyping assays. A total of 111 S. enterica isolates recovered from a Canadian baseline study on broiler chicken conducted in 2012-2013 were selected based on phenotypic resistance to 15 different antibiotics and isolates were subjected to WGS. Both SeqSero2 and SISTR accurately determined S. enterica serotypes, with full matches to laboratory results for 87.4 and 89.2% of isolates, respectively, and partial matches for the remaining isolates. Antimicrobial resistance genes (ARGs) were identified using several bioinformatics tools including the Comprehensive Antibiotic Resistance Database - Resistance Gene Identifier (CARD-RGI), Center for Genomic Epidemiology (CGE) ResFinder web tool, Short Read Sequence Typing for Bacterial Pathogens (SRST2 v 0.2.0), and k-mer alignment method (KMA v 1.17). All ARG identification tools had ≥ 99% accuracy for predicting resistance to all antibiotics tested except streptomycin (accuracy 94.6%). Evaluation of ARG detection in assembled versus raw-read WGS data found minimal observable differences that were gene- and coverage- dependent. Where initial phenotypic results indicated isolates were sensitive, yet ARGs were detected, repeat AMR testing corrected discrepancies. All tools failed to find resistance-determining genes for one gentamicin- and two streptomycin-resistant isolates. Further investigation found a single nucleotide polymorphism (SNP) in the nuoF coding region of one of the isolates which may be responsible for the observed streptomycin-resistant phenotype. Overall, WGS-based predictions of AMR and serotype were highly concordant with phenotype determination regardless of computational approach used.

中文翻译：

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对基于全基因组序列的沙门氏菌血清型和抗菌素耐药性的系统评价。

全基因组测序（WGS）在公共卫生实验室中越来越多地用于对食源性病原体进行分类和表征。为了评估现有的生物信息学工具在计算机上预测肠炎沙门氏菌的抗菌素耐药性（AMR）和血清型的性能，将基于WGS的基因型预测与传统表型分析的结果进行了比较。根据对15种不同抗生素的表型抗性，从2012年至2013年对加拿大肉鸡进行的基线研究中选择了总共111株肠炎链球菌，并对其进行了WGS鉴定。SeqSero2和SISTR均可准确确定肠炎链球菌的血清型，分别与87.4％和89.2％的分离株的实验室结果完全匹配，而其余分离株的部分匹配。使用多种生物信息学工具鉴定了抗生素耐药性基因（ARG），包括综合抗生素耐药性数据库-耐药基因标识符（CARD-RGI），基因组流行病学中心（CGE）ResFinder网络工具，细菌病原体的短读序列分型（SRST2 v 0.2） .0）和k-mer对齐方式（KMA v 1.17）。除链霉素外，所有ARG鉴定工具均能准确预测≥99％的耐药性（除链霉素外，准确性为94.6％）。评估已组装的WGS数据与原始读取的WGS数据中的ARG检测结果，发现可观察到的最小差异是基因和覆盖范围依赖性的。如果最初的表型结果表明分离株是敏感的，但仍检测到ARG，则重复AMR测试可纠正差异。所有工具都未能找到一种抗庆大霉素和两种链霉素抗性分离株的耐药性决定基因。进一步研究发现，其中一种分离物的nuoF编码区存在单核苷酸多态性（SNP），这可能是所观察到的链霉素抗性表型的原因。总体而言，无论使用何种计算方法，基于WGS的AMR和血清型的预测与表型确定高度一致。