Bacterial whole-genome sequencing (WGS) provides clinical and public health laboratories an unprecedented level of information on species identification, antimicrobial resistance, and epidemiologic typing. However, multiple barriers to widespread adoption still exist. This research describes bacterial WGS using the Illumina iSeq 100 instrument to overcome some of these barriers. Using an in-house, high-quality single-nucleotide polymorphism analysis pipeline and a commercial whole-genome multilocus sequence typing program, the sequencing of Acinetobacter baumannii, Burkholderia cepacia, Clostridioides difficile, Enterococcus faecalis, Escherichia coli, Pseudomonas aeruginosa, Serratia marcescens, and Staphylococcus aureus isolates was validated. The genome coverage range was 17Â to 149Â, with a mean of 59Â. The limit of detection for single-nucleotide polymorphisms was 30Â. Overall platform base calling accuracy was >99.999%. Reproducibility and repeatability of base calling inferred from whole-genome multilocus sequence typing was species dependent and ranged from >97% similarity for P. aeruginosa to >99.9% similarity for S. aureus. Resistance gene and multilocus sequence typing allele identification was 100% concordant with expected results. A simple, modified library preparation reduces the per-sample cost by half to give overall theoretical sample costs ranging from approximately $50 to $100 for library preparation and sequencing. The iSeq 100 provides a cost-effective and easy-to-use platform for clinical and public health laboratories to sequence bacterial isolates for a wide range of potential applications.

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Background: Hospital-acquired infections pose a significant threat to patient health. Laboratories are starting to consider whole-genome sequencing (WGS) as a molecular method for outbreak detection and epidemiological surveillance. The objective of this study was to assess the use of the iSeq100 platform (Illumina, San Diego, CA) for accurate sequencing and WGS-based outbreak detection using the bioMérieux EPISEQ CS, a novel cloud-based software for sequence assembly and data analysis. Methods: In total, 25 isolates, including 19 MRSA isolates and 6 ATCC strains were evaluated in this study: A. baumannii ATCC 19606, B. cepacia ATCC 25416, E. faecalis ATCC 29212, E. coli ATCC 25922, P. aeruginosa ATCC 27853 and S. aureus ATCC 25923. DNA extraction of all isolates was performed on the QIAcube (Qiagen, Hilden, Germany) using the DNEasy Ultra Clean Microbial kit extraction protocol. DNA libraries were prepared for WGS using the Nextera DNA Flex Library Prep Kit (Illumina) and sequenced at 2×150-bp on the iSeq100 according to the manufacturer’s instructions. The 19 MRSA isolates were previously characterized by the DiversiLab system (bioMérieux, France). Upon validation of the iSeq100 platform, a new outbreak analysis was performed using WGS analysis using EPISEQ CS. ATCC sequences were compared to assembled reference genomes from the NCBI GenBank to assess the accuracy of the iSeq100 platform. The FASTQ files were aligned via BowTie2 version 2.2.6 software, using default parameters, and FreeBayes version 1.1.0.46-0 was used to call homozygous single-nucleotide polymorphisms (SNPs) with a minimum coverage of 5 and an allele frequency of 0.87 using default parameters. ATCC sequences were analyzed using ResFinder version 3.2 and were compared in silico to the reference genome. Results: EPISEQ CS classified 8 MRSA isolates as unrelated and grouped 11 isolates into 2 separate clusters: cluster A (5 isolates) and cluster B (6 isolates) with similarity scores of ≥99.63% and ≥99.50%, respectively. This finding contrasted with the previous characterization by DiversiLab, which identified 3 clusters of 2, 8, and 11 isolates, respectively. The EPISEQ CS resistome data detected the mecA gene in 18 of 19 MRSA isolates. Comparative analysis of the ATCCsequences to the reference genomes showed 99.9986% concordance of SNPs and 100.00% concordance between the resistance genes present. Conclusions: The iSeq100 platform accurately sequenced the bacterial isolates and could be an affordable alternative in conjunction with EPISEQ CS for epidemiological surveillance analysis and infection prevention.Funding: NoneDisclosures: None

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: NoneDisclosures: None