Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study
Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study
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DOI：
10.1099/mgen.0.000335
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发表时间：
2020-02-01
影响因子：
3.9
通讯作者：
Harris, Kathryn A.
中科院分区：
文献类型：
--
作者：
Doyle, Ronan M.;O'Sullivan, Denise M.;Harris, Kathryn A.
抗生素耐药性（AMR）对公众健康构成威胁。临床微生物实验室通常依赖于培养细菌进行抗菌药物敏感性测试（AST）。随着实施成本和技术障碍的降低，全基因组测序（WGS）已成为流行病学和预测AST结果的“一站式”测试。对于用于预测AMR的无数分析管道，几乎没有发表的比较。为了解决这个问题，我们进行了一项实验室间研究，为参与研究的研究人员提供了来自临床分离株的相同短读WGS数据，使我们能够评估参与者之间AMR生物信息学预测的重现性，并确定导致不一致结果的问题病例和因素。我们从在Illumina NextSeq或HiSeq仪器上测序的临床样本中获得的培养的碳青霉烯类耐药生物体中产生了10个不同质量的WGS数据集。九个参与团队（“参与者”）被提供这些序列数据，而没有任何其他上下文信息。每个参与者使用他们选择的管道来确定物种，耐药相关基因的存在，并预测对阿米卡星，庆大霉素，环丙沙星和头孢噻肟的敏感性或耐药性。我们发现，参与者从相同的临床样本中预测了不同数量的AMR相关基因和不同的基因变异。序列数据的质量，生物信息学管道的选择和结果的解释都导致了参与者之间的不一致。尽管许多不准确的基因变异注释不影响基因型耐药性预测，但与表型AST结果相比，我们观察到低特异性，但这在具有较高读取深度的样品中有所改善。如果这些结果被用来预测AST并指导治疗，那么至少有一名参与者会为每种分离株推荐不同的抗生素。在使用WGS预测AMR的最终分析阶段，这些挑战表明在临床环境中使用该技术时需要进行改进。全面的公共耐药序列数据库、关于序列数据质量的完整建议以及基因型和耐药表型之间比较的标准化，都将在临床微生物实验室使用WGS成功实施AST预测方面发挥重要作用。
Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories.