Evaluation of the bioMérieux EPISEQ-CS Software for wgMLST-Based Bacterial Strain Typing

Abstract

Background: Whole-genome sequencing (WGS) is becoming the method of choice for outbreak analysis of microbial pathogens. However, the main challenge with WGS for microbial strain typing is the conversion of raw sequencing data to actionable results for epidemiology and surveillance analysis. We evaluated the bioMrieux EPISEQ-CS, a cloud-based WGS data analysis software for outbreak detection to compare the results for 4 groups of different species previously characterized by strain typing and commonly isolated in hospital-acquired infections. Methods: In total, 30 methicillin-resistant Staphylococcus aureus (MRSA), 15 Clostridioides difficile (CDIFF), 17 Pseudomonas aeruginosa (PSA), and 10 Acinetobacter baumannii (ACB) isolates were included in this study. All isolates had been previously characterized by rep-PCR using the DiversiLab system (bioMrieux, France) and saved at 70C. Before testing, samples were thawed and plated, and DNA extraction was performed on the QIAcube (Qiagen, Hilden, Germany) using the DNEasy Ultra Clean Microbial kit extraction protocol. DNA libraries were prepared using the Nextera DNA Flex Kit and sequenced on the Illumina iSeq100 platform according to manufacturer’s recommendations (Illumina, San Diego, CA). Generated sequences were uploaded into EPISEQ-CS, and wgMLST-based analysis was performed. We compared clusters generated by the DiversiLab system and EPISEQ-CS. Results: DiversiLab identified 9 MRSA clusters among 30 isolates. EPISEQ-CS reclassified 14 of 30 isolates into 5 MRSA clusters and the remaining 16 isolates were unrelated. DiversiLab identified 2 CDIFF clusters among 15 isolates. EPISEQ-CS reclassified 3 isolates into 1 CDIFF cluster and determined the remaining 12 to be unrelated. DiversiLab identified 5 PSA clusters among 17 isolates, whereas EPISEQ-CS reclassified all 17 isolates as unrelated. DiversiLab identified 2 ACB clusters among 10 isolates, whereas EPISEQ-CS reclassified 2 ACB isolates into 1 cluster and determined 8 to be unrelated. Analysis using Simpson’s diversity index (D) suggested that the EPISEQ-CS showed increased diversity when compared to DiversiLab clustering across all bacterial species analyzed in this study. Conclusions: EPISEQ-CS enabled a comprehensive wgMLST analysis, including quality control and comparative epidemiological analysis, thereby providing a more reliable method for bacterial strain typing. As WGS becomes more affordable and applicable to routine epidemiological surveillance, EPISEQ-CS provides an informative tool in the monitoring of hospital-acquired infections.

Funding: None

Disclosures: None