Evaluation of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry and ClinPro Tools as a Rapid Tool for Typing Streptococcus pyogenes. (11th October 2019)

• Record Type: Journal Article
• Authors: Tadros, Manal; Cabrera, Ana; Matukas, Larissa M; Muller, Matthew
• Is Part Of: Open forum infectious diseases. Volume 6:Number 11(2019)
• Publication Date: 2019-10-11
• Subjects: GAS -- MALDI-TOF -- typing; Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofz441
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Timely strain typing of group A Streptococci (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. Methods: We developed and validated a typing model using 177 GAS isolates with known emm types. The typing model was created using 43 isolates, which included 8 different emm types, and then validated using 134 GAS isolates of known emm types that were not included in model generation. Results: 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 emm types 4, 59, and 74 but showed poor accuracy for emm 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; emm 74" compared with a non-outbreak group or an "outgroup " of other emm types. External validation of this model showed an accuracy of 91.4%. Conclusions: 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 emm typing for model generation should improve the accuracy of this approach in rapidly identifying related and unrelated GAS strains.