Versmessen Nick, Mispelaere Marieke, Vandekerckhove Marjolein, Hermans Cedric, Boelens Jerina, Vranckx Katleen, Van Nieuwerburgh Filip, Vaneechoutte Mario, Hulpiau Paco, Cools Piet
Laboratory Bacteriology Research, Department of Diagnostic Sciences, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium.
Department of Diagnostic Sciences, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium.
Diagnostics (Basel). 2024 Aug 17;14(16):1800. doi: 10.3390/diagnostics14161800.
Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8-81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and typing accuracy. Our protocol allows the integration of PromethION technology in clinical laboratories, with ANI and DDH proving to be accurate and robust alternative typing methods, potentially offering superior resolution. WGS-based antibiotic resistance prediction holds promise for specific antibiotic classes.
全基因组测序（WGS）正在彻底改变临床细菌学。然而，细菌分型仍通过存在固有局限性的参考技术进行研究。这凸显了对提供可靠且准确的序列类型（ST）分类的替代方法的需求。本研究优化并评估了一种适用于PromethION平台的GridION纳米孔测序方案。对48株具有不同ST的临床分离株进行测序，以评估两种替代分型方法和耐药性分析应用。多位点序列分型（MLST）用作参考分型方法。使用平均核苷酸同一性（ANI）和数字DNA-DNA杂交（DDH）评估基因组相关性，并评估区分菌株分辨率的阈值。将基于WGS的抗生素耐药性预测与参考最低抑菌浓度（MIC）测定进行比较。我们发现ANI和DDH的阈值分别为99.3%和94.1%，这与MLST分类相关性良好，并显示出比MLST潜在更高的区分分辨率。基于WGS的抗生素耐药性预测与阿莫西林、头孢他啶、阿米卡星、妥布霉素和甲氧苄啶-磺胺甲恶唑的MIC测定的分类一致性≥93%。对于阿莫西林-克拉维酸、头孢吡肟、氨曲南和环丙沙星，性能次优（68.8-81.3%）。需要至少12倍的测序覆盖度来维持基本的基因组特征和分型准确性。我们的方案允许将PromethION技术整合到临床实验室中，ANI和DDH被证明是准确且可靠的替代分型方法，可能提供更高的分辨率。基于WGS的抗生素耐药性预测对特定抗生素类别具有前景。
