Bharat Amrita, Petkau Aaron, Avery Brent P, Chen Jessica C, Folster Jason P, Carson Carolee A, Kearney Ashley, Nadon Celine, Mabon Philip, Thiessen Jeffrey, Alexander David C, Allen Vanessa, El Bailey Sameh, Bekal Sadjia, German Greg J, Haldane David, Hoang Linda, Chui Linda, Minion Jessica, Zahariadis George, Domselaar Gary Van, Reid-Smith Richard J, Mulvey Michael R
National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada.
Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada.
Microorganisms. 2022 Jan 26;10(2):292. doi: 10.3390/microorganisms10020292.
Whole genome sequencing (WGS) of supports both molecular typing and detection of antimicrobial resistance (AMR). Here, we evaluated the correlation between phenotypic antimicrobial susceptibility testing (AST) and in silico prediction of AMR from WGS in ( = 1321) isolated from human infections in Canada. Phenotypic AMR results from broth microdilution testing were used as the gold standard. To facilitate high-throughput prediction of AMR from genome assemblies, we created a tool called Staramr, which incorporates the ResFinder and PointFinder databases and a custom gene-drug key for antibiogram prediction. Overall, there was 99% concordance between phenotypic and genotypic detection of categorical resistance for 14 antimicrobials in 1321 isolates (18,305 of 18,494 results in agreement). We observed an average sensitivity of 91.2% (range 80.5-100%), a specificity of 99.7% (98.6-100%), a positive predictive value of 95.4% (68.2-100%), and a negative predictive value of 99.1% (95.6-100%). The positive predictive value of gentamicin was 68%, due to seven isolates that carried , which conferred MICs just below the breakpoint of resistance. Genetic mechanisms of resistance in these 1321 isolates included 64 unique acquired alleles and mutations in three chromosomal genes. In general, in silico prediction of AMR in was reliable compared to the gold standard of broth microdilution. WGS can provide higher-resolution data on the epidemiology of resistance mechanisms and the emergence of new resistance alleles.
全基因组测序（WGS）既支持分子分型，也支持抗菌药物耐药性（AMR）检测。在此，我们评估了加拿大从人类感染中分离出的1321株菌株的表型抗菌药物敏感性试验（AST）与WGS预测AMR的计算机模拟结果之间的相关性。肉汤微量稀释试验的表型AMR结果用作金标准。为便于从基因组组装中高通量预测AMR，我们创建了一个名为Staramr的工具，该工具整合了ResFinder和PointFinder数据库以及用于抗菌谱预测的自定义基因-药物密钥。总体而言，1321株菌株中14种抗菌药物的分类耐药性表型和基因型检测之间的一致性为99%（18494个结果中的18305个结果一致）。我们观察到平均敏感性为91.2%（范围80.5 - 100%），特异性为99.7%（98.6 - 100%），阳性预测值为95.4%（68.2 - 100%），阴性预测值为99.1%（95.6 - 100%）。庆大霉素的阳性预测值为68%，原因是有7株携带了导致最低抑菌浓度略低于耐药断点的基因。这1321株菌株的耐药遗传机制包括64个独特的获得性等位基因和三个染色体基因中的突变。总体而言，与肉汤微量稀释的金标准相比，计算机模拟预测AMR在菌株中是可靠的。WGS可以提供关于耐药机制流行病学和新耐药等位基因出现的更高分辨率数据。
