Acinetobacter baumannii Genomic Sequence-Based Core Genome Multilocus Sequence Typing Using Ridom SeqSphere+ and Antimicrobial Susceptibility Prediction in ARESdb.

Whole-genome sequencing (WGS) is rapidly replacing traditional typing methods for the investigation of infectious disease outbreaks. Additionally, WGS data are being used to predict phenotypic antimicrobial susceptibility. Acinetobacter baumannii, which is often multidrug-resistant, is a significant culprit in outbreaks in health care settings. A well-characterized collection of A. baumannii was studied using core genome multilocus sequence typing (cgMLST). Seventy-two isolates previously typed by PCR-electrospray ionization mass spectrometry (PCR/ESI-MS) provided by the Antimicrobial Resistance Leadership Group (ARLG) were analyzed using a clinical microbiology laboratory developed workflow for cgMLST with genomic susceptibility prediction performed using the ARESdb platform. Previously performed PCR/ESI-MS correlated with cgMLST using relatedness thresholds of allelic differences of ≤9 and ≤200 allelic differences in 78 and 94% of isolates, respectively. Categorical agreement between genotypic and phenotypic antimicrobial susceptibility across a panel of 11 commonly used drugs was 89%, with minor, major, and very major error rates of 8%, 11%, and 1%, respectively.

Metagenomic shotgun sequencing of blood to identify bacteria and viruses in leukemic febrile neutropenia.

Despite diagnostic advances in microbiology, the etiology of neutropenic fever remains elusive in most cases. In this study, we evaluated the utility of a metagenomic shotgun sequencing based assay for detection of bacteria and viruses in blood samples of patients with febrile neutropenia. We prospectively enrolled 20 acute leukemia patients and obtained blood from these patients at three time points: 1) anytime from onset of neutropenia until before development of neutropenic fever, 2) within 24 hours of onset of neutropenic fever, 3) 5-7 days after onset of neutropenic fever. Blood samples underwent sample preparation, sequencing and analysis using the iDTECT® Dx Blood v1® platform (PathoQuest, Paris, France). Clinically relevant viruses or bacteria were detected in three cases each by metagenomic shotgun sequencing and blood cultures, albeit with no concordance between the two. Further optimization of sample preparation methods and sequencing platforms is needed before widespread adoption of this technology into clinical practice.

Transcriptomic analysis of Streptococcus agalactiae periprosthetic joint infection.

Although Streptococcus agalactiae periprosthetic joint infection (PJI) is not as prevalent as staphylococcal PJI, invasive S. agalactiae infection is not uncommon. Here, RNA-seq was used to perform transcriptomic analysis of S. agalactiae PJI using fluid derived from sonication of explanted arthroplasties of subjects with S. agalactiae PJI, with results compared to those of S. agalactiae strain NEM316 grown in vitro. A total of 227 genes with outlier expression were found (164 upregulated and 63 downregulated) between PJI sonicate fluid and in vitro conditions. Functional enrichment analysis showed genes involved in mobilome and inorganic ion transport and metabolism to be most enriched. Genes involved in nickel, copper, and zinc transport, were upregulated. Among known virulence factors, cyl operon genes, encoding ß-hemolysin/cytolysin, were consistently highly expressed in PJI versus in vitro. The data presented provide insight into S. agalactiae PJI pathogenesis and may be a resource for identification of novel PJI therapeutics or vaccines against invasive S. agalactiae infections.

Porphyromonas somerae Invasion of Endometrial Cancer Cells.

Recent evidence suggests an association between endometrial cancer and the understudied bacterial species Porphyromonas somerae. This association was demonstrated in previous work that indicated a significantly enriched abundance of P. somerae in the uterine microbiome of endometrial cancer patients. Given the known associations of the Porphyromonas genus and oral cancer, we hypothesized that P. somerae may play a similar pathogenic role in endometrial cancer via intracellular activity. Before testing our hypothesis, we first characterized P. somerae biology, as current background data is limited. These novel characterizations include growth curves in liquid medium and susceptibility tests to antibiotics. We tested our hypothesis by examining growth changes in response to 17ß-estradiol, a known risk factor for endometrial cancer, followed by metabolomic profiling in the presence and absence of 17ß-estradiol. We found that P. somerae exhibits increased growth in the presence of 17ß-estradiol of various concentrations. However, we did not find significant changes in metabolite levels in response to 17ß-estradiol. To study direct host-microbe interactions, we used in vitro invasion assays under hypoxic conditions and found evidence for intracellular invasion of P. somerae in endometrial adenocarcinoma cells. We also examined these interactions in the presence of 17ß-estradiol but did not observe changes in invasion frequency. Invasion was shown using three lines of evidence including visualization via differential staining and brightfield microscopy, increased frequency of bacterial recovery after co-culturing, and in silico methods to detail relevant genomic and transcriptomic components. These results underscore potential intracellular phenotypes of P. somerae within the uterine microbiome. Furthermore, these results raise new questions pertaining to the role of P. somerae in the progression of endometrial cancer.

Comparative Transcriptomic Analysis of Staphylococcus aureus Associated with Periprosthetic Joint Infection under in Vivo and in Vitro Conditions.

Transcriptomic analysis can provide insight as to how Staphylococcus aureus adapts to the environmental niche of periprosthetic joint infection (PJI), a challenging clinical infection. Here, in vivo RNA expression of eight S. aureus PJIs was compared with expression of the corresponding isolates in planktonic culture using a total RNA-sequencing approach. Expression varied among isolates, with a common trend showing increased expression of several ica-independent biofilm formation genes, including sdr, fnb, ebpS, and aaa; genes encoding enzymes and toxins, including coa, nuc, hlb, and hlgA/B/C; and genes facilitating acquisition of iron via the iron-binding molecule siderophore B (snb) and heme consumption protein (isd) pathways in PJI. Several antimicrobial resistance determinants were detected; although their presence correlated with phenotypic susceptibility of the associated isolates, no difference in expression between in vivo and in vitro conditions was identified.

Plasmid Acquisition Alters Vancomycin Susceptibility in Clostridioides difficile.

The increasing incidence of primary and recurring Clostridioides difficile infections (CDI), which evade current treatment strategies, reflects the changing biology of C difficile. Here, we describe a putative plasmid-mediated mechanism potentially driving decreased sensitivity of C difficile to vancomycin treatment. We identified a broad host range transferable plasmid in a C difficile strain associated with lack of adequate response to vancomycin treatment. The transfer of this plasmid to a vancomycin-susceptible C difficile isolate decreased its susceptibility to vancomycin in vitro and resulted in more severe disease in a humanized mouse model. Our findings suggest plasmid acquisition in the gastrointestinal tract to be a possible mechanism underlying vancomycin treatment failure in patients with CDI, but further work is needed to characterize the mechanism by which plasmid genes determine vancomycin susceptibility in C difficile.

Staphylococcus aureus whole genome sequence-based susceptibility and resistance prediction using a clinically amenable workflow.

We used graphical user interface-based automated analytical tools from Next Gen Diagnostics (Mountain View, CA) and 1928 Diagnostics (Gothenburg, Sweden) to analyze whole genome sequence (WGS) data from 102 unique blood culture isolates of Staphylococcus aureus to predict antimicrobial susceptibly, with results compared to those of phenotypic susceptibility testing. Of 916 isolate/antibiotic combinations analyzed using the Next Gen Diagnostics tool, there were 9 discrepancies between WGS predictions and phenotypic susceptibility/resistance, including 8 for clindamycin and 1 for minocycline. Of 612 isolate/antibiotic combinations analyzed using the 1928 Diagnostics tool, there were 13 discrepancies between WGS predictions and phenotypic susceptibility/resistance, including 9 for clindamycin, 3 for trimethoprim-sulfamethoxazole, and 1 for rifampin. Trimethoprim-sulfamethoxazole was not assessed by Next Gen Diagnostics, and minocycline was not assessed by 1928 Diagnostics. There was complete concordance between phenotypic susceptibility/resistance and genotypic prediction of susceptibility/resistance using both analytical platforms for oxacillin, vancomycin, and mupirocin, as well as by the Next Gen Diagnostics analytical tool for levofloxacin (the 1928 Diagnostics tool did not assess levofloxacin). These results suggest that, from a performance standpoint, with some caveats, automatic bioinformatics tools may be acceptable to predict susceptibility and resistance to a panel of antibiotics for S. aureus.

Core genome MLST and resistome analysis of Klebsiella pneumoniae using a clinically amenable workflow.

Whole genome sequencing (WGS) is replacing traditional microbiological typing methods for investigation of outbreaks in clinical settings. Here, we used a clinical microbiology laboratory core genome multilocus sequence typing (cgMLST) workflow to analyze 40 isolates of K. pneumoniae which are part of the Antimicrobial Resistance Leadership Group (ARLG) isolate collection, alongside 10 Mayo Clinic K. pneumoniae isolates, comparing results to those of pulsed-field gel electrophoresis (PFGE). Additionally, we used the WGS data to predict phenotypic antimicrobial susceptibility (AST). Thirty-one of 40 ARLG K. pneumoniae isolates belonged to the same PFGE type, all of which, alongside 3 isolates of different PFGE types, formed a large cluster by cgMLST. PFGE and cgMLST were completely concordant for the 10 Mayo Clinic K. pneumoniae isolates. For AST prediction, the overall agreement between phenotypic AST and genotypic prediction was 95.6%.

Application of metagenomic shotgun sequencing to detect vector-borne pathogens in clinical blood samples.

BACKGROUND: Vector-borne pathogens are a significant public health concern worldwide. Infections with these pathogens, some of which are emerging, are likely under-recognized due to the lack of widely-available laboratory tests. There is an urgent need for further advancement in diagnostic modalities to detect new and known vector-borne pathogens. We evaluated the utility of metagenomic shotgun sequencing (MGS) as a pathogen agnostic approach for detecting vector-borne pathogens from human blood samples. METHODS: Residual whole blood samples from patients with known infection with Babesia microti, Borrelia hermsii, Plasmodium falciparum, Mansonella perstans, Anaplasma phagocytophilum or Ehrlichia chaffeensis were studied. Samples underwent DNA extraction, removal of human DNA, whole genome amplification, and paired-end library preparation, followed by sequencing on Illumina HiSeq 2500. Bioinformatic analysis was performed using the Livermore Metagenomics Analysis Toolkit (LMAT), Metagenomic Phylogenetic Analysis (MetaPhlAn2), Genomic Origin Through Taxonomic CHAllenge (GOTTCHA) and Kraken 2. RESULTS: Eight samples were included in the study (2 samples each for P. falciparum and A. phagocytophilum). An average of 27.5 million read pairs was generated per sample (range, 18.3-38.8 million) prior to removal of human reads. At least one of the analytic tools was able to detect four of six organisms at the genus level, and the organism present in five of eight specimens at the species level. Mansonella and Ehrlichia species were not detected by any of the tools; however, mitochondrial cytochrome c oxidase subunit I amino acid sequence analysis suggested the presence of M. perstans genetic material. CONCLUSIONS: MGS is a promising tool with the potential to evolve as a non-hypothesis driven diagnostic test to detect vector-borne pathogens, including protozoa and helminths.

Lack of correlation of virulence gene profiles of Staphylococcus aureus bacteremia isolates with mortality.

PURPOSE: Whole genome sequencing (WGS) analysis of Staphylococcus aureus is increasingly used in clinical practice. Although bioinformatics tools used in WGS analysis readily define the S. aureus virulome, the clinical value of this type of analysis is unclear. Here, virulence genes in S. aureus bacteremia (SAB) isolates were evaluated by WGS, with superantigens (SAgs) further evaluated by conventional PCR and functional assays, and results correlated with mortality. METHODS: 152 SAB isolates collected throughout 2015 at a large Minnesota medical center were studied and associated clinical data analyzed. Virulence genes were identified from previously-reported WGS data (https://doi.org/10.1371/journal.pone.0179003). SAg genes sea, seb, sec, sed, see, seg, seh, sei, sej, and tst were also assessed by individual PCR assays. Mitogenicity of SAgs was assessed using an in vitro proliferation assay with splenocytes from HLA-DR3 transgenic mice. RESULTS: Of the 152 SAB isolates studied, 106 (69%) were methicillin-susceptible S. aureus (MSSA). The number of deaths attributed and not attributed to SAB, and 30-day survivors were 24 (16%), 2 (1%), and 128 (83%), respectively. From WGS data, both MSSA and MRSA had high proportions of adhesion (>80%) and immune-evasion (>70%) genes. There was no difference in virulomes between survivor- and non-survivor-associated isolates. Although over 60% of SAB isolates produced functional SAgs, there were no differences in the distribution or prevalence of SAg genes between survivor- and non-survivor-associated isolates. CONCLUSION: In this study of one year of SAB isolates from a large medical center, the S. aureus virulome, as assessed by WGS, and also for SAgs using individual PCRs and phenotypic characterization, did not impact mortality.

Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis.

We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.

Whole-genome sequencing for methicillin-resistant Staphylococcus aureus (MRSA) outbreak investigation in a neonatal intensive care unit.

OBJECTIVE: To evaluate whole-genome sequencing (WGS) as a molecular typing tool for MRSA outbreak investigation. DESIGN: Investigation of MRSA colonization/infection in a neonatal intensive care unit (NICU) over 3 years (2014-2017). SETTING: Single-center level IV NICU.PatientsNICU infants and healthcare workers (HCWs). METHODS: Infants were screened for MRSA using a swab of the anterior nares, axilla, and groin, initially by targeted (ring) screening, and later by universal weekly screening. Clinical cultures were collected as indicated. HCWs were screened once using swabs of the anterior nares. MRSA isolates were typed using WGS with core-genome multilocus sequence typing (cgMLST) analysis and by pulsed-field gel electrophoresis (PFGE). Colonized and infected infants and HCWs were decolonized. Control strategies included reinforcement of hand hygiene, use of contact precautions, cohorting, enhanced environmental cleaning, and remodeling of the NICU. RESULTS: We identified 64 MRSA-positive infants: 53 (83%) by screening and 11 (17%) by clinical cultures. Of 85 screened HCWs, 5 (6%) were MRSA positive. WGS of MRSA isolates identified 2 large clusters (WGS groups 1 and 2), 1 small cluster (WGS group 3), and 8 unrelated isolates. PFGE failed to distinguish WGS group 2 and 3 isolates. WGS groups 1 and 2 were codistributed over time. HCW MRSA isolates were primarily in WGS group 1. New infant MRSA cases declined after implementation of the control interventions. CONCLUSION: We identified 2 contemporaneous MRSA outbreaks alongside sporadic cases in a NICU. WGS was used to determine strain relatedness at a higher resolution than PFGE and was useful in guiding efforts to control MRSA transmission.

Direct Detection and Identification of Prosthetic Joint Infection Pathogens in Synovial Fluid by Metagenomic Shotgun Sequencing.

Metagenomic shotgun sequencing has the potential to transform how serious infections are diagnosed by offering universal, culture-free pathogen detection. This may be especially advantageous for microbial diagnosis of prosthetic joint infection (PJI) by synovial fluid analysis since synovial fluid cultures are not universally positive and since synovial fluid is easily obtained preoperatively. We applied a metagenomics-based approach to synovial fluid in an attempt to detect microorganisms in 168 failed total knee arthroplasties. Genus- and species-level analyses of metagenomic sequencing yielded the known pathogen in 74 (90%) and 68 (83%) of the 82 culture-positive PJIs analyzed, respectively, with testing of two (2%) and three (4%) samples, respectively, yielding additional pathogens not detected by culture. For the 25 culture-negative PJIs tested, genus- and species-level analyses yielded 19 (76%) and 21 (84%) samples with insignificant findings, respectively, and 6 (24%) and 4 (16%) with potential pathogens detected, respectively. Genus- and species-level analyses of the 60 culture-negative aseptic failure cases yielded 53 (88%) and 56 (93%) cases with insignificant findings and 7 (12%) and 4 (7%) with potential clinically significant organisms detected, respectively. There was one case of aseptic failure with synovial fluid culture growth; metagenomic analysis showed insignificant findings, suggesting possible synovial fluid culture contamination. Metagenomic shotgun sequencing can detect pathogens involved in PJI when applied to synovial fluid and may be particularly useful for culture-negative cases.

Identification of Prosthetic Joint Infection Pathogens Using a Shotgun Metagenomics Approach.

Background: Metagenomic shotgun sequencing has the potential to change how many infections, particularly those caused by difficult-to-culture organisms, are diagnosed. Metagenomics was used to investigate prosthetic joint infections (PJIs), where pathogen detection can be challenging. Methods: Four hundred eight sonicate fluid samples generated from resected hip and knee arthroplasties were tested, including 213 from subjects with infections and 195 from subjects without infection. Samples were enriched for microbial DNA using the MolYsis basic kit, whole-genome amplified, and sequenced using Illumina HiSeq 2500 instruments. A pipeline was designed to screen out human reads and analyze remaining sequences for microbial content using the Livermore Metagenomics Analysis Toolkit and MetaPhlAn2 tools. Results: When compared to sonicate fluid culture, metagenomics was able to identify known pathogens in 94.8% (109/115) of culture-positive PJIs, with additional potential pathogens detected in 9.6% (11/115). New potential pathogens were detected in 43.9% (43/98) of culture-negative PJIs, 21 of which had no other positive culture sources from which these microorganisms had been detected. Detection of microorganisms in samples from uninfected aseptic failure cases was conversely rare (7/195 [3.6%] cases). The presence of human and contaminant microbial DNA from reagents was a challenge, as previously reported. Conclusions: Metagenomic shotgun sequencing is a powerful tool to identify a wide range of PJI pathogens, including difficult-to-detect pathogens in culture-negative infections.

Systematic Bias Introduced by Genomic DNA Template Dilution in 16S rRNA Gene-Targeted Microbiota Profiling in Human Stool Homogenates.

Variability in representation of microbial communities can be caused by differences in microbial composition or artifacts introduced at sample collection or processing. Alterations in community representation introduced by variations in starting DNA concentrations have not been systematically investigated in stool samples. The goal of this study was to evaluate the effect of the genomic DNA (gDNA) concentration in the resulting 16S rRNA gene library composition and compare its effect to other sample processing variables in homogenized human fecal material. Compared to a gDNA input of 1 ng/µl, inputs of ≤1.6 × 10-3 ng/µl resulted in a marked decrease in the concentration of the 16S rRNA gene amplicon (P < 0.001). Low gDNA concentrations (≤1.6 × 10-3 ng/µl) were also associated with a decrease (P < 0.001) in the number of operational taxonomic units and significant divergence in ß-diversity profiles (unweighted UniFrac distance, P < 0.001), as characterized by an overestimation of Proteobacteria and underestimation of Firmicutes. Even a gDNA concentration of 4 × 10-2 ng/µl showed a significant impact on the ß-diversity profile (unweighted UniFrac distance, P = 0.03). Overall, the gDNA concentration explained 22.4% to 38.1% of the microbiota variation based on various ß-diversity measures (P < 0.001). By comparison, the DNA extraction methods and PCR volumes tested did not significantly affect the microbial composition profile, and the PCR cycling method explained less than 3.7% of the microbiota variation (weighted UniFrac distance, P = 0.03). The 16S rRNA gene yield and the microbial community representation of human homogenized stool samples are significantly altered by gDNA template concentrations of ≤1.6 × 10-3 ng/µl. In addition, data from studies with a gDNA input of ≤4 × 10-2 ng/µl should be interpreted with caution. IMPORTANCE The genomic DNA input for stool samples utilized for microbiome composition has not been determined. In this study, we determined the reliable threshold level under which conclusions drawn from the data may be compromised. We also determined the type of microbial bias introduced by less-than-ideal genomic input.

A comprehensive analysis of breast cancer microbiota and host gene expression.

The inflammatory tumoral-immune response alters the physiology of the tumor microenvironment, which may attenuate genomic instability. In addition to inducing inflammatory immune responses, several pathogenic bacteria produce genotoxins. However the extent of microbial contribution to the tumor microenvironment biology remains unknown. We utilized The Cancer Genome Atlas, (TCGA) breast cancer data to perform a novel experiment utilizing unmapped and mapped RNA sequencing read evidence to minimize laboratory costs and effort. Our objective was to characterize the microbiota and associate the microbiota with the tumor expression profiles, for 668 breast tumor tissues and 72 non-cancerous adjacent tissues. The prominent presence of Proteobacteria was increased in the tumor tissues and conversely Actinobacteria abundance increase in non-cancerous adjacent tissues. Further, geneset enrichment suggests Listeria spp to be associated with the expression profiles of genes involved with epithelial to mesenchymal transitions. Moreover, evidence suggests H. influenza may reside in the surrounding stromal material and was significantly associated with the proliferative pathways: G2M checkpoint, E2F transcription factors, and mitotic spindle assembly. In summary, further unraveling this complicated interplay should enable us to better diagnose and treat breast cancer patients.

Molecular epidemiology of Staphylococcus aureus bacteremia in a single large Minnesota medical center in 2015 as assessed using MLST, core genome MLST and spa typing.

Staphylococcus aureus is a leading cause of bacteremia in hospitalized patients. Whether or not S. aureus bacteremia (SAB) is associated with clonality, implicating potential nosocomial transmission, has not, however, been investigated. Herein, we examined the epidemiology of SAB using whole genome sequencing (WGS). 152 SAB isolates collected over the course of 2015 at a single large Minnesota medical center were studied. Staphylococcus protein A (spa) typing was performed by PCR/Sanger sequencing; multilocus sequence typing (MLST) and core genome MLST (cgMLST) were determined by WGS. Forty-eight isolates (32%) were methicillin-resistant S. aureus (MRSA). The isolates encompassed 66 spa types, clustered into 11 spa clonal complexes (CCs) and 10 singleton types. 88% of 48 MRSA isolates belonged to spa CC-002 or -008. Methicillin-susceptible S. aureus (MSSA) isolates were more genotypically diverse, with 61% distributed across four spa CCs (CC-002, CC-012, CC-008 and CC-084). By MLST, there was 31 sequence types (STs), including 18 divided into 6 CCs and 13 singleton STs. Amongst MSSA isolates, the common MLST clones were CC5 (23%), CC30 (19%), CC8 (15%) and CC15 (11%). Common MRSA clones were CC5 (67%) and CC8 (25%); there were no MRSA isolates in CC45 or CC30. By cgMLST analysis, there were 9 allelic differences between two isolates, with the remaining 150 isolates differing from each other by over 40 alleles. The two isolates were retroactively epidemiologically linked by medical record review. Overall, cgMLST analysis resulted in higher resolution epidemiological typing than did multilocus sequence or spa typing.

Comparison of Whole-Genome Sequencing Methods for Analysis of Three Methicillin-Resistant Staphylococcus aureus Outbreaks.

Whole-genome sequencing (WGS) can provide excellent resolution in global and local epidemiological investigations of Staphylococcus aureus outbreaks. A variety of sequencing approaches and analytical tools have been used; it is not clear which is ideal. We compared two WGS strategies and two analytical approaches to the standard method of SmaI restriction digestion pulsed-field gel electrophoresis (PFGE) for typing S. aureus Forty-two S. aureus isolates from three outbreaks and 12 reference isolates were studied. Near-complete genomes, assembled de novo with paired-end and long-mate-pair (8 kb) libraries were first assembled and analyzed utilizing an in-house assembly and analytical informatics pipeline. In addition, paired-end data were assembled and analyzed using a commercial software package. Single nucleotide variant (SNP) analysis was performed using the in-house pipeline. Two assembly strategies were used to generate core genome multilocus sequence typing (cgMLST) data. First, the near-complete genome data generated with the in-house pipeline were imported into the commercial software and used to perform cgMLST analysis. Second, the commercial software was used to assemble paired-end data, and resolved assemblies were used to perform cgMLST. Similar isolate clustering was observed using SNP calling and cgMLST, regardless of data assembly strategy. All methods provided more discrimination between outbreaks than did PFGE. Overall, all of the evaluated WGS strategies yielded statistically similar results for S. aureus typing.