Tadros Manal, Cabrera Ana, Matukas Larissa M, Muller Matthew
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
Division of Microbiology, Unity Health Toronto, Toronto, Ontario, Canada.
Open Forum Infect Dis. 2019 Oct 11;6(11):ofz441. doi: 10.1093/ofid/ofz441. eCollection 2019 Nov.
Timely strain typing of group A (GAS) is necessary to guide outbreak recognition and investigation. We evaluated the use of (matrix-assisted laser desorption ionization time-of-flight mass spectrometry) combined with cluster analysis software to rapidly distinguish between related and unrelated GAS isolates in real-time.
We developed and validated a typing model using 177 GAS isolates with known types. The typing model was created using 43 isolates, which included 8 different types, and then validated using 134 GAS isolates of known types that were not included in model generation.
Twelve spectra were generated from each isolate during validation. The overall accuracy of the model was 74% at a cutoff value of 80%. The model performed well with types 4, 59, and 74 but showed poor accuracy for types 1, 3, 12, 28, and 101. To evaluate the ability of this tool to perform typing in an outbreak situation, we evaluated a virtual outbreak model using a "virtual outbreak strain; 74" compared with a non-outbreak group or an "outgroup " of other types. External validation of this model showed an accuracy of 91.4%.
This approach has the potential to provide meaningful information that can be used in real time to identify and manage GAS outbreaks. Choosing isolates characterized by whole genome sequencing rather than typing for model generation should improve the accuracy of this approach in rapidly identifying related and unrelated GAS strains.
及时对A群链球菌（GAS）进行菌株分型对于指导疫情识别和调查至关重要。我们评估了使用基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）结合聚类分析软件实时快速区分相关和不相关GAS分离株的情况。
我们使用177株已知类型的GAS分离株开发并验证了一种分型模型。该分型模型使用43株分离株创建，其中包括8种不同类型，然后使用模型生成过程中未包含的134株已知类型的GAS分离株进行验证。
在验证过程中，每株分离株产生了12个光谱。在截断值为80%时，模型的总体准确率为74%。该模型对4型、59型和74型表现良好，但对1型、3型、12型、28型和101型的准确率较低。为了评估该工具在疫情情况下进行分型的能力，我们使用“虚拟疫情菌株；74型”与非疫情组或其他类型的“外群”评估了一个虚拟疫情模型。该模型的外部验证显示准确率为91.4%。
这种方法有可能提供有意义的信息，可实时用于识别和管理GAS疫情。选择以全基因组测序而非血清型分型为特征的分离株进行模型生成，应能提高该方法在快速识别相关和不相关GAS菌株方面的准确性。
