http://orcid.org/0000-0001-6344-4141
Figures
Abstract
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.
Citation: Park K-H, Greenwood-Quaintance KE, Uhl JR, Cunningham SA, Chia N, Jeraldo PR, et al. (2017) 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. PLoS ONE 12(6): e0179003. https://doi.org/10.1371/journal.pone.0179003
Editor: Herminia de Lencastre, Rockefeller University, UNITED STATES
Received: February 27, 2017; Accepted: May 22, 2017; Published: June 2, 2017
Copyright: © 2017 Park et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data is within the manuscript and the NCBI Repository (BioProject number PRJNA384623) at the following URL: https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP105427.
Funding: The authors received no specific funding for this work.
Competing interests: Dr. Patel reports grants from CD Diagnostics, BioFire, Check-Points, Curetis, Merck, Hutchison Biofilm Medical Solutions, Accelerate Diagnostics, Allergan, and The Medicines Company. Dr. Patel is a consultant to Curetis; monies are paid to Mayo Clinic. In addition, Dr. Patel has a patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an antibiofilm substance issued. Dr. Patel serves on an Actelion data monitoring board; any reimbursement is paid to Mayo Clinic. Dr. Patel receives travel reimbursement and an editor’s stipend from ASM and IDSA, and honoraria from the NBME, Up-to-Date and the Infectious Diseases Board Review Course. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Introduction
Staphylococcus aureus is responsible for a high percentage of hospital- and community-acquired infections worldwide. It is also a leading cause of bacteremia, often associated with metastatic infections and significant morbidity and mortality. The epidemiology of S. aureus infection has changed over the past decade and a half, with methicillin-resistant S. aureus (MRSA) being increasingly identified in community settings. This has led to interest in attempting to understand the genetic background of the pathogen across different geographic regions and care settings [1–3]. Since a retrospective, population-based, cohort study was done to evaluate initial episodes of S. aureus bacteremia (SAB) occurring in adult residents of Olmsted County, Minnesota, from 1998 through 2005 [4], there has been little data about the epidemiology of SAB from this area, and there has been no genetic characterization of involved isolates. In hospital settings, S. aureus may be transmitted from patient to patient via healthcare worker hands, contaminated equipment or through environmental contamination. Focused infection prevention and control (IPAC) measures guided by epidemiological investigations are necessary to prevent nosocomial transmission. To inform IPAC strategies within individual institutions, it is helpful to understand the molecular epidemiology of infection, including whether particular strains are prevalent, and whether or not there is evidence of patient to patient transmission.
In recent years, numerous tools have become available for typing of S. aureus, ranging from fingerprint-based methods such as pulsed-field gel electrophoresis (PFGE), to PCR-based methods such as multilocus variable-number tandem repeat analysis, to sequence-based methods such as multilocus sequencing typing (MLST), and most recently, to whole genome sequencing (WGS) [5]. The most widely used molecular typing method for defining MRSA epidemiology has traditionally been PFGE [6]. However, PFGE results can be challenging to compare between laboratories and to interpret; furthermore, PFGE is low throughput, is not suitable for long-term epidemiological investigations and assesses a limited amount of the microbial genome [7, 8]. A low mutation rate of the sequence fragments of seven housekeeping genes makes MLST most suitable for long-term and global epidemiological studies [9]. DNA sequencing of short sequence repeats of the polymorphic X region of the protein A gene, spa, consisting of a variable number of 21- to 27-bp sequences, is an alternative method for typing S. aureus [10, 11]. For MRSA, characterization of the resistance-conferring mobile genetic element, SCCmec is another method of typing. Recently, WGS-based typing has been made possible through next-generation DNA sequencing (NGS) technology; this approach allows detection of single nucleotide polymorphisms and core genome MLST (cgMLST), among analytical approaches. Through mapping of genome-wide variations, this approach theoretically provides optimal resolution to infer phylogenetic relatedness. WGS has been used in evolutionary studies, outbreak investigations, and phylogeographic distribution analyses [12–14].
The aim of the present study was to use WGS to investigate the molecular epidemiology of S. aureus isolates causing bacteremia in a single large Minnesota medical center. We also compared spa typing using data extracted from WGS to PCR/Sanger sequencing-based spa typing, and WGS-generated MLST- to cgMLST-based typing.
Materials and methods
Patient population
All episodes of S. aureus bacteremia in patients ≥16 years of age at the Mayo Clinic, in Rochester, Minnesota (2,059 hospital beds) from January 2015 through December 2015 were considered for inclusion. Isolates were prospectively collected. Only the first episode was analyzed in cases of recurrent SAB during the study period. Isolates associated with polymicrobial infection or suspected contamination, as well as those with insufficient medical record data were excluded. This non-interventional study was approved by the Mayo Clinic Institutional Review Board with a waiver of informed consent (IRB 15–001990).
Data collection and definitions
Patient demographics and clinical characteristics were recorded. Community-onset infections were defined as those either present or incubating at the time of hospital admission, or for which the first positive culture was obtained within 48 hours of admission. Community-onset infections were further classified as healthcare-associated (HCA) or community-associated (CA). If a patient fulfilled any of the following criteria, SAB was classified as HCA [15, 16]: (1) presence of an invasive device at the time of admission or at onset of infection; (2) history of MRSA infection or colonization in the antecedent 6 months; (3) history of surgery, hospitalization, dialysis, or residence in a long-term care facility in the antecedent 12 months; or (4) use of systemic antibiotics within 3 months prior to SAB. A case was classified as hospital onset (HO) if the first S. aureus blood culture was obtained 48 hours or more after hospital admission.
Identification and antimicrobial susceptibility testing
The BD BACTEC™ FX system (Becton, Dickinson and Company, Franklin Lakes, NJ) was used for blood culture. Blood culture bottles showing possible staphylococci were tested with the FilmArray® BCID panel (bioMérieux, Marcy l’Étoile, France), which includes rapid detection of mecA and S. aureus. Identification of S. aureus was by standard methods (biochemicals or matrix-assisted laser desorption ionization time-of-flight mass spectrometry); antimicrobial susceptibility testing was performed using agar dilution, with results interpreted using the Clinical and Laboratory Standards Institute guidelines [17]. Antibiotics tested included clindamycin, mupirocin, oxacillin, rifampin, trimethoprim-sulfamethoxazole, vancomycin, and daptomycin, with ceftaroline additionally tested against MRSA.
spa typing based on PCR/Sanger sequencing
Staphylococcal DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Valencia, CA). spa typing was performed with PCR followed by Sanger sequencing, as previously described [18]. Assignment of spa type and Based Upon Repeat Pattern (BURP) analysis for determination of spa clonal complex (spa CC) were performed using Ridom StaphType (Ridom GmbH, Germany).
DNA sequencing for WGS and cgMLST
Staphylococcal DNA was prepared for WGS using lysostaphin or achromopeptidase, the Maxwell® 16 Tissue DNA Purification kit (Promega Corporation, Madison, WI) and the Genomic DNA Clean & Concentrator™ kit (Zymo Research Corporation, Irvine, CA), performed according to the manufacturers’ instructions. WGS was performed on a MiSeq (Illumina, Inc., San Diego, CA) using the paired-end mode (2x250) in the Mayo Clinic Medical Genome Facility. Reads were trimmed using Trimmomatic. FASTQ files were imported into SeqSphere+ software version 2.3 (Ridom GmbH) for analysis. de novo assembly and application of a S. aureus cgMLST scheme was done using SeqSphere+ with default parameters and with 1,861 queried target genes, as described previously [19]. If genomes did not contain ≥95% of the 1,861 cgMLST target genes with a minimum of ten times global local coverage, WGS was repeated.
Determination of spa, MLST and SCCmec types from WGS data
In addition to performing a specific PCR followed by Sanger sequencing, spa type was determined using SeqSphere+ for spa typing with the WGS data. The WGS data was also used for MLST and SCCmec type determination. For MLST, seven target genes (arcC, aroE, glpF, gmk, pta, tpi, yqiL) were analyzed [20], also in SeqSphere+. If there was no matched allele in SeqSphere+, the sequence of the particular gene(s) from the S. aureus MLST scheme was manually extracted from the WGS data and compared to references in NCBI and the S. aureus MLST database (http://pubmlst.org/saureus) to assign the classical sequence type (ST). Novel sequences were submitted to the S. aureus MLST database http://pubmlst.org/saureus. The associated clonal complex (CC) was calculated using the eBURST algorithm (http://saureus.mlst.net/eBURST/), with CCs defined using a criterion of six common alleles [21]. SCCmec typing was determined by assessing ccr and mec gene complexes from WGS data. Sequences have been deposited in the NCBI Sequence Read Archive under bioproject number PRJNA384623.
Results
Of a total of 176 non-duplicate SAB isolates during the study period, 152 were included after excluding 13 which were associated with polymicrobial infection, 6 which were considered possible contaminants and 3 with insufficient medical record data. Two isolates failed to provide sufficient WGS data after multiple attempts. 63% were from male patients; the mean patient age was 61±16.8 (± standard deviation) years. Forty eight (32%) were methicillin–resistant, both phenotypically and by detection of mecA. Three were CA-MRSA, 25 HCA-MRSA, 20 HO-MRSA, 31 CA-methicillin-susceptible S. aureus (MSSA), 54 HCA-MSSA, and 19 HO-MSSA.
Comparison of antimicrobial susceptibility of MSSA and MRSA
54% of MRSA isolates and 14% of MSSA isolates were resistant to clindamycin. Clindamycin resistance was present 6% of CA isolates, 27% of HCA isolates, and 44% of HO isolates. Fewer than 5% of MRSA isolates were resistant to rifampin, mupirocin, or trimethoprim-sulfamethoxazole (Table 1). All MRSA isolates were susceptible to vancomycin, daptomycin, and ceftaroline.
spa typing, including a comparison between PCR/Sanger sequencing- and WGS-based spa typing
Using PCR/Sanger sequencing-based spa typing, there were 66 spa types, clustered into 11 spa clonal complexes (CCs) and 10 singleton spa types (Fig 1). Four isolates (t457, t586, t2465, t3736) were non-typeable as a result of their having fewer than five repeats.
MRSA, methicillin-resistant S. aureus; HO, hospital onset; HCA, healthcare-associated; CA, community-associated. Values in parentheses are the numbers of isolates of a given spa type.
spa typing of MSSA revealed wide genotypic diversity, with 61% distributed into four spa CCs (CC-002, CC-012, CC-008 and CC-084, Table 2). Of the 48 MRSA isolates, 42 (88%) belonged to spa CC-002 or CC-008.
Compared to PCR/Sanger sequencing-based spa typing, WGS-based spa typing revealed 26 isolates with apparently discrepant results. Using SeqSphere+, spa genes were not detected in 2 isolates and 24 isolates showed apparently different results than PCR/Sanger sequencing. When sequences were compared manually however, sequence differences were not confirmed for 18 isolates whereas they were confirmed in 6. For the 18 isolates for which the differences were not confirmed, WGS sequencing results showed several nucleotide differences in 12 isolates, and possible insertion, deletion or substitution errors in 6 isolates.
MLST from WGS
152 isolates were subjected to MLST analysis; one locus was not found in one isolate, which was not further analyzed. Using the S. aureus MLST database (http://pubmlst.org/saureus), there were 31 sequence types. The most prevalent sequence type was ST5, represented by 36 isolates (24% of all isolates); the next prevalent clone was ST8, represented by 24 isolates (16% of all isolates). Eighteen STs were divided into 6 CCs by eBURST and 13 STs were singletons. The singletons did not belong to any CC, and differed from all STs in the dataset at two or more MLST loci.
All CA-MRSA grouped into CC8 (2 ST8 and 1 ST3342); among HCA- and HO-MRSA (45 isolates), the most common sequence type was ST5 (15 isolates). There were no MRSA in CC45 or CC30. For MSSA, the common CCs were CC5 (23%), CC30 (19%), CC8 (15%), and CC15 (11%). Common MRSA CCs were CC5 (67%) and CC8 (25%).
SCCmec typing from WGS
Two SCCmec types were found among the 48 MRSA isolates, type II (56%) and type IV (44%). SCCmec type II isolates were all CC5, and SCCmec type IV was the most common type in CC8 (57%).
cgMLST from WGS
Fig 2 shows a minimum spanning tree based on the cgMLST allelic profiles with overlaid MLST clonal complex (CC) groupings. cgMLST enabled higher resolution within CCs than did MLST or spa typing (Fig 2).
Each circle represents a single genotype, i.e., an allelic profile based on up to 1,861 target genes present in the isolates with the “pairwise ignoring missing values” option turned on in the SeqSphere+ software during comparison. The circles are named with the sample ID and MLST ST and color coded as MSSA (white) or MRSA (black). The numbers on the connecting lines indicate the numbers of different alleles between the connected genotypes. MSSA, methicillin-susceptible S. aureus; MRSA, methicillin-resistant S. aureus; N.A, not available. The MRSA isolates with 9 allelic differences, MZ51 and MZ81, are highlighted in gray. CC30 by MLST corresponds to spa CC-012, CC5 corresponds to spa CC-002, and CC8 corresponds to spa CC-008.
By cgMLST analysis, there were 9 allelic differences between 2 isolates (MZ51 and MZ81), with the remaining 150 isolates differing from each other by over 40 allelic differences. MZ51 and MZ81 were both HO-MRSA bacteremia isolates, recovered from patients with peripheral inserted central catheters. The two bacteremias occurred 55 days apart. The patients from whom the isolates derived were both cared for on the same service and both had care provided by the same nurse practitioner.
A phylogenetic dendrogram of cgMLST data was generated to visualize the relatedness among isolates compared to MLST and PCR/Sanger sequencing-based spa typing (Fig 3).
cgMLST analysis was based on allelic profiles as determined using SeqSphere+ software. N.A., not available. Methicillin-resistant isolates are highlighted in black.
Discussion
In a single large Minnesota medical center, over 150 cases of SAB occurred in the year 2015, approximately one third of which were MRSA. spa typing of MSSA revealed wide genotypic diversity; the common MRSA clones were spa CC-002 and CC-008. By MLST analysis, CC45 and CC30 contained only MSSA isolates, and common MRSA clones were CC5 and CC8. cgMLST results yielded higher resolution typing results compared to MLST or spa typing, and demonstrated a single episode of possible hospital-based transmission of S. aureus bacteremia. Although there are no standardized thresholds for interpreting clonality with cgMSLT, we recently proposed that using the scheme applied herein for S. aureus, ≤8 allelic differences be considered related, 9–29 allelic differences be considered possibly related and 30 or more allelic differences be considered unrelated [22]. The findings presented herein support the use of these thresholds.
Outbreaks of CA-MRSA infections have been described worldwide with successful clones often associated with specific geographical locations [23]. Dominant clones of CA-MRSA in the United States are ST1 (PFGE type USA400) and ST8 (PFGE type USA300), and a common HCA-MRSA clone is ST5 (PFGE type USA100). In our study, only 3 cases were CA-MRSA of which 2 were ST8; the dominant clone for HCA- and HO-MRSA was ST5. Although MRSA isolates were more related with healthcare associated infection, HO MSSA bacteremia was surprisingly frequent. There were three ST398 isolates (t034, t571 in cluster 11 in Fig 1), one of which—MRSA isolate MZ105 (t571)—may have been livestock-associated.
Prevention of S. aureus bloodstream infections is compromised by incomplete understanding of transmission patterns of S. aureus, which in turn reflects, in part, suboptimal resolution offered by conventional typing methods. Analysis of every nucleotide in the genome provides a complete inventory of micro-evolutionary changes, but is impractical for large population samples. The use of cgMLST maps single nucleotide polymorphisms and insertion/deletions across a large number of genes to a reference sequence [24, 25]. cgMLST provides higher discrimination than ordinary MLST. In our study, only 2 of 152 isolates had a low number of allelic differences by cgMLST suggesting possible relatedness; these two cases were retroactively determined to be epidemiologically connected. cgMLST was used for the first time with next generation sequencing data for phylogenetic analysis of the German 2011 enterohemorrhagic Escherichia coli O104:H4 outbreak [26]. More recently, cgMLST was used to evaluate a meningococcal outbreak [27]. In a neonatal MRSA outbreak [24], a rapid sequencing platform revealed a distinct cluster of outbreak isolates and clear separation between outbreak and non-outbreak isolates with same sequence type (ST22). As the costs of sequencing have begun to approach those of conventional methods, high-resolution typing with WGS is now the state-of-the-art tool for routine epidemiological investigations.
We found that traditional spa typing using PCR/Sanger sequencing had better performance than WGS-based spa typing. Bletz et al. suggested that extraction of spa types from WGS with an optimized de novo assembly may enable nearly full compatibility with PCR/Sanger sequencing-based spa typing for MRSA [28]. However, assembly of repetitive areas based on short Illumina NGS read data may be prone to misassembling, resulting in failure or errors [29], as illustrated herein. Although it is not possible to compare data directly since different DNA extracts were used for PCR/Sanger sequencing- and WGS-spa typing herein, spa typing discrepancies between WGS data and traditional spa typing caution against WGS usage for spa typing. We note that we used a paired-end 250 base pair read length protocol for sequencing and that longer reads may improve WGS-based spa typing.
There are several limitations of our study. First, there was some missing data in the cgMLST targets. Second, we analyzed only S. aureus bacteremia patients and thus we cannot assess nosocomial transmission of non-bacteremia events or non-bacteremia to bacteremia events. Third, for our spa typing studies, we did not perform PCR/Sanger sequencing from the same DNA that had been used for WGS.
Conclusions
cgMLST analysis resulted in higher resolution epidemiological typing than did MLST or spa typing. MSSA isolates associated with SAB at a single large Minnesota medical center exhibited wider genotypic diversity than did MRSA isolates. By cgMLST analysis, only two of 152 SAB isolates appeared to be possibly related and conceivably associated with intra-hospital transmission.
Acknowledgments
The authors thank the Mayo Clinic Center for Individualized Medicine for generously supporting this study. We thank Dag Harmsen, University of Münster, for his thoughtful review of the manuscript draft.
Author Contributions
- Conceptualization: KP KG-Q JU SC NC PJ PS HN RP.
- Data curation: KP PJ RP PS HN.
- Formal analysis: KP PJ PS RP.
- Funding acquisition: RP.
- Investigation: KP KGQ JU SC NC PJ PS HN RP.
- Methodology: KP KGQ JU SC NC PJ PS HN RP.
- Project administration: RP.
- Resources: KP KGQ JU SC NC PJ PS HN RP.
- Software: RP.
- Supervision: KP KGQ RP.
- Validation: KP KGQ JU SC NC PJ PS HN RP.
- Visualization: KP KGQ JU SC NC PJ PS HN RP.
- Writing – original draft: KP.
- Writing – review & editing: KP KGQ JU SC NC PJ PS HN RP.
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