PDF(Pubmed)
Abstract：
The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.
摘要：
先前已评估了傅立叶变换红外（FT-IR）光谱对肺炎克雷伯菌分型和暴发控制的可靠性，但标准化和可重复性仍存在问题。我们开发并验证了具有衰减全反射（ATR）工作流程的可重复FT-IR，用于鉴定肺炎克雷伯菌谱系。我们使用293个代表多药耐药肺炎克雷伯菌谱系的分离株（2002-2021年），以训练基于荚膜（KL）类型区分的随机森林分类（RF）模型。该模型用280个同期分离株(2021-2022年)进行了验证，使用wzi测序和全基因组测序作为参考。在整个时间内在不同的培养基和仪器中测试可重复性和再现性。我们的RF模型基于对特定KL和O型组合的区分，允许对33种荚膜（KL）类型和多达36种临床相关肺炎克雷伯菌谱系进行分类。我们获得了很高的准确率(89%)，灵敏度（88%），和特异性（92%），包括直接从临床样本中获得的培养物，允许在识别细菌的同一天获得分型信息。该工作流程在不同的仪器中在整个时间内是可重复的(>98%的正确预测)。直接应用菌落,光谱采集,和自动KL预测通过三叶草MS数据分析软件允许短时间的结果（5分钟/分离）。我们证明了FT-IRATR光谱提供了有意义的，可重复,和准确的信息在一个非常早期的阶段（尽快细菌识别），以支持感染控制和公共卫生监测。高度健壮性以及自动化和灵活的数据分析工作流程为在全球范围内整合实时应用程序提供了机会。IMPORTANCEWE创建并验证了通过FT-IR光谱和机器学习识别临床相关肺炎克雷伯菌谱系的自动化和简单的工作流程，一种非常有用的方法，可以提供快速可靠的打字信息，以支持爆发管理和感染控制的实时决策。这种方法和工作流程对于支持临床微生物学诊断和帮助公共卫生监测是有意义的。
