Genomic epidemiology of methicillin-resistant and -susceptible Staphylococcus aureus from bloodstream infections
BMC Infectious Diseases volume 21, Article number: 589 (2021)
Bloodstream infections due to Staphylococcus aureus cause significant patient morbidity and mortality worldwide. Of major concern is the emergence and spread of methicillin-resistant S. aureus (MRSA) in bloodstream infections, which are associated with therapeutic failure and increased mortality.
We generated high quality draft genomes from 323 S. aureus blood culture isolates from patients diagnosed with bloodstream infection at the Dartmouth-Hitchcock Medical Center, New Hampshire, USA in 2010–2018.
In silico detection of antimicrobial resistance genes revealed that 133/323 isolates (41.18%) carry horizontally acquired genes conferring resistance to at least three antimicrobial classes, with resistance determinants for aminoglycosides, beta-lactams and macrolides being the most prevalent. The most common resistance genes were blaZ and mecA, which were found in 262/323 (81.11%) and 104/323 (32.20%) isolates, respectively. Majority of the MRSA (102/105 isolates or 97.14%) identified using in vitro screening were related to two clonal complexes (CC) 5 and 8. The two CCs emerged in the New Hampshire population at separate times. We estimated that the time to the most recent common ancestor of CC5 was 1973 (95% highest posterior density (HPD) intervals: 1966–1979) and 1946 for CC8 (95% HPD intervals: 1924–1959). The effective population size of CC8 increased until the late 1960s when it started to level off until late 2000s. The levelling off of CC8 in 1968 coincided with the acquisition of SCCmec Type IV in majority of the strains. The plateau in CC8 also coincided with the acceleration in the population growth of CC5 carrying SCCmec Type II in the early 1970s, which eventually leveled off in the early 1990s. Lastly, we found evidence for frequent recombination in the two clones during their recent clonal expansion, which has likely contributed to their success in the population.
We conclude that the S. aureus population was shaped mainly by the clonal expansion, recombination and co-dominance of two major MRSA clones in the last five decades in New Hampshire, USA. These results have important implications on the development of effective and robust strategies for intervention, control and treatment of life-threatening bloodstream infections.
Bacteremia refers to the presence of viable bacteria in the blood . Asymptomatic bacteremia can occur from normal daily activities, such as vigorous toothbrushing or from minor medical and dental procedures . Bacteremia is often transient and causes no further sequelae in healthy individuals . However, when bacteria are able to breach innate host defenses and gain access to deeper tissues and subcutaneous sites, they can cause debilitating and life-threatening infections such as systemic inflammatory response syndrome, septic shock and organ failure . Multiple bacterial species are implicated in bloodstream infections, of which Staphylococcus aureus cause 20.7% of total cases . The incidence of bloodstream infections due to S. aureus is 20–50 per 100,000 population per year, with case-fatality rates of approximately 10–30% . Bloodstream infection is notoriously hard to treat because it requires prompt source control and prolonged antimicrobial therapy . Of major concern is the emergence and spread of multidrug-resistant and methicillin-resistant S. aureus (MRSA) in bloodstream infections, which are associated with therapeutic failure and increased mortality .
Understanding host and pathogen factors that impact bloodstream infections are crucial to disease control and treatment options. Host-related factors that determine outcome from bloodstream infections due to S. aureus have been widely studied and include age, comorbidities, immune status and primary focus of infection (i.e., the site of infection considered most likely to be responsible for seeding bacteria into the bloodstream) [3, 5]. The most prominent risk factor for bloodstream infections due to S. aureus is the presence of prosthetic devices and other kinds of medical implantation of foreign bodies that allow the bacterium to invade the intravascular space [3, 5]. However, the contribution of the bacterium in bloodstream infections is less understood. Previous studies report that S. aureus in the blood often derive from colonizing isolates from other parts of the body acquired prior to bloodstream infection such as the nasopharynx and gastrointestinal tract [7,8,9]. Different S. aureus clones in bloodstream infections may have adopted different strategies to overcome host responses such as cytolytic toxicity and biofilm formation . An important gap in current knowledge is that relatively little is known of the genetic basis that underlies the successful adaptation and persistence of certain S. aureus lineages, in particular MRSA, in the larger population over the long term.
We generated high quality draft genomes from 323 S. aureus blood culture isolates from patients diagnosed with bloodstream infection at the Dartmouth-Hitchcock Medical Center, New Hampshire, USA in 2010–2018. We conclude that the S. aureus population was shaped mainly by the clonal expansion, recombination and co-dominance of two major MRSA clones in the last five decades.
Ethical approval was granted by the Committee for the Protection of Human Subjects of Dartmouth-Hitchcock Medical Center and Dartmouth College. This study protocol was deemed not to be human subjects research. Samples used in the study were subcultured bacterial isolates that had been archived in the routine course of clinical laboratory operations. No patient specimens were used and patient protected health information was not collected.
Archived MRSA and methicillin-susceptible S. aureus (MSSA) isolates from 325 unique pediatric and adult patient blood stream infections were included in this study. These archived isolates grew from clinical blood culture specimens submitted to the Department of Pathology and Laboratory Medicine at the Dartmouth-Hitchcock Medical Center, New Hampshire, USA from December 2010 – August 2018. The first significant blood culture isolate from each patient is routinely archived (freezer space permitting) in case of future need for patient care, epidemiologic, public health or laboratory quality studies. Upon subculture, isolates were assigned a study number and all patient identifiers were removed with only the date of collection and results of clinical antimicrobial susceptibility testing linked to the study number. A convenience sampling composed of approximately half of the unique patient isolates distributed throughout the study period were randomly selected and included in our analysis. All isolates had previously been tested in vitro on an automated broth microdilution testing platform (MicroScan Walkaway, Beckman Coulter, Inc., La Brea, CA) against a panel of antimicrobial agents including cefoxitin (screening well), daptomycin, oxacillin, penicillin and vancomycin. Results were interpreted per Clinical Laboratory Standards Institute (CLSI) guidelines . Methicillin resistance was determined per the manufacturer’s package insert and CLSI guidelines by growth in the cefoxitin screening well (> 4 μg/mL) and/or by an oxacillin minimum inhibitory concentration (MIC) of > 2 μg/mL. Methicillin-susceptible isolates were tested for beta-lactamase production using a nitrocefin disk (Remel, Lenexa, KS) using bacterial growth nearest to a 30 μg cefoxitin disk. Confirmatory penicillin zone-edge testing was not performed on beta-lactamase-negative isolates. Clinical breakpoints of other antimicrobials tested were interpreted per CLSI guidelines . All isolates were stored in DMSO solution in − 80 °C.
DNA extraction and whole genome sequencing
Isolates were subcultured from glycerol stocks onto commercially prepared tryptic soy agar with 10% sheep red blood cells (Remel, Lenexa, KS) and in brain heart infusion broth (BD Difco, Franklin Lakes, NJ) at 37 °C for 24 h. DNA was extracted and purified from the liquid culture using the Zymo Research QuickDNA Fungal/Bacterial Miniprep Kit (Irvine, CA) following manufacturer’s protocol. We used Qubit fluorometer (Invitrogen, Grand Island, NY) to measure DNA concentration. DNA libraries were prepared using the RipTide High Throughput Rapid DNA Library Prep kit (iGenomX, Carlsbad, CA). DNA samples were sequenced as multiplexed libraries on the Illumina HiSeq platform operated per the manufacturer’s instructions. Sequencing resulted to 250-nt long paired end reads. Sequencing was carried out at the UNH Hubbard Center for Genome Studies, Durham, NH, USA.
De novo genome assembly, annotation and pan-genome construction
Reads were assembled into contigs using the Shovill v.1.1.0 pipeline (https://github.com/tseemann/shovill) with the option --trim to produce high quality draft genomes. Shovill is a series of methods that includes subsampling read depth down to 150X, trimming adapters, correcting sequencing errors and assembling using SPAdes . We used the scaffolding and gap-filling programs SSPACE  and GapFiller  to improve the assemblies. Genome quality was assessed using the programs QUAST  and CheckM . Overall, genomes were at least 97.7% complete with no more than 2.86% contamination. Only genomes with < 200 contigs and N50 of > 40 kb were included. The number of contigs range from 16 to 115 and N50 of 77–1011 kb (Additional file 1: Table S1 and Additional file 2: Fig. S1). After filtering out the genomes with low coverage and of poor quality, a total of 323 genomes were used for downstream analyses. We calculated the genome-wide average nucleotide identity (ANI) for all possible pairs of genomes using the program FastANI v.1.0  and compared them to the reference strain (strain NCTC 8325; RefSeq assembly accession no. GCF_000013425.1). The resulting contigs were annotated using Prokka v.1.14.6 . We used Roary v.3.13.0  to characterize the totality of genes of all strains in a dataset or pan-genome .
In silico sequence typing and detection of antimicrobial resistance
The sequence type (ST) of each isolate was confirmed using the program MLST v.2.19.0 (https://github.com/tseemann/mlst), which extracts seven housekeeping genes (arcC, aroE, glpF, gmk, pta, tpi and yqiL) from the sequence contigs and compares sequence variation against previously characterized STs in the S. aureus PubMLST database (https://pubmlst.org/organisms/staphylococcus-aureus/). We used ARIBA v.2.14.5  and the ResFinder database  to identify resistance determinants due to horizontally acquired antimicrobial resistance genes. We also used VirulenceFinder  and the Virulence Factor Database (VFDB)  to screen all genomes for known virulence genes. We used SCCmecFinder v.1.2  to identify the presence and type of mecA-carrying staphylococcal chromosomal cassette (SCCmec) using minimum thresholds of > 60% for sequence coverage and > 90% sequence identity.
Phylogenetic and population structure analyses
Using the core genome alignment produced by Roary , we generated a maximum likelihood phylogeny using IQ-Tree v1.6.12 . We used ModelFinder to select the most appropriate nucleotide substitution model . We applied the general time reversible model of evolution  using nucleotide frequencies calculated directly from the sequence alignment and a FreeRate model of rate heterogeneity with three categories based on the ModelFinder results. We performed 1000 bootstrap replicates using ultrafast bootstrap approximation approach UFBoot2 . We partitioned the strains on the core genome phylogeny into sequence clusters consisting of genetically similar individuals using the Bayesian hierarchical clustering algorithm RhierBAPS v1.1.2 .
Construction of time-calibrated phylogenies
We identified single nucleotide polymorphisms (SNPs) in the full core genome length alignment using Snippy v.4.6.0 (https://github.com/tseemann/snippy) and mapped them to a reference genome (strain NCTC 8325). Nucleotide sites on the core SNP alignment of each sequence cluster that were identified by Gubbins v.2.4.1  as having experienced recombination were removed to generate a phylogeny free of the distorting signal of recombination. To test for the presence of a temporal signal, we used the recombination-free phylogenies as input in the roottotip function in BactDating v1.0.12  to perform a root-to-tip linear regression analysis and random permutation of sampling dates to assess significance. We used BactDating to estimate the dates of the most recent common ancestor. We carried out 108 iterations, removed the first half as burnin and the remainder was sampled every 100 iterations. Markov Chain Monte Carlo (MCMC) chain lengths were carried out until effective sample sizes of the inferred parameters α (coalescent time unit), μ (mean substitution rate) and σ (standard deviation of the per-branch substitution rates) parameters reached > 200 as suggested by the authors. To estimate the effective population size (Ne) for the time-scaled phylogenies, we used the R package  Skygrowth v. 0.3.1 that implements a Bayesian MCMC and maximum a posteriori algorithms . Skygrowth models the growth rate of Ne as a simple Brownian motion process.
Estimating recombination rates
Using the core genome alignment as input and 1000 bootstrapped replicates, we calculated the recombination rate using mcorr with default parameters . Mcorr uses a coalescent-based model to measure the degree to which any two loci separated by N bp have correlated substitutions and estimates six evolutionary parameters: θ – mutational divergence; ϕ – recombinational divergence; c – recombination coverage or proportion of sites in the genome whose diversity was derived from outside the sample through recombination; d – diversity; mean fragment size (f̅) of a recombination event; and θ/ϕ (or γ/μ) - relative rate of recombination to mutation . We used Welch’s t-test to compare the mcorr parameters between samples.
Unless otherwise noted, default parameters were used for all programs.
Phylogenetic relationships and population structure
We obtained a total of 325 convenience MRSA and MSSA isolates recovered from unique pediatric and adult patient blood cultures at the Dartmouth-Hitchcock Medical Center, USA which were sampled from December 2010 to August 2018. In vitro phenotypic susceptibility testing revealed a total of 105 isolates (32.31%) that were MRSA. We observed the occurrence of MRSA every year, with prevalence ranging from 14.3% in 2014 to 48.8% in 2018 (Fig. 1a). We retrieved high-quality draft genome sequences from 323 isolates (Additional file 1: Table S1 and Additional file 2: Fig. S1). De novo genome assembly produced sequences ranging from 2.67 to 2.97 Mb in length. Annotation of assembled genomes revealed a total of 8430 families of orthologous genes, which can be classified into 1698 core genes (genes present in 320–323 genomes), 171 soft core genes (present in 307–319 genomes), 1369 shell genes (present in 49–306 genomes) and 5192 cloud genes (present in ≤48 genomes) (Additional file 3: Table S2 and Additional file 4: Fig. S2). The number of genes per genome spanned from 2440 to 2814. The sizes of the pan- and core genomes were consistent with findings from previous genomic studies of S. aureus in clinical and non-clinical settings [36, 37].
Multilocus sequence typing (MLST) revealed high S. aureus diversity throughout the duration of our study period, with at least five sequence types (STs) present every year and several rare and previously unidentified STs (Fig. 1b and Additional file 1: Table S1). Those STs that were consistently observed from 2011 to 2018 were STs 5, 8 and 30, while STs 1 and 15 were observed in alternating years. ST398 appeared first in 2014 and continued until 2018. Other STs such as STs 45, 59, 72, 97 and 105 were less common and appeared intermittently over 8 years of our sampling period.
Population structure analysis using Bayesian hierarchical clustering of the core genome alignment showed eight distinct sequence clusters, ranging in size from 8 to 89 isolates (Fig. 1c). Sequence clusters consist of genetically related strains of the same or closely related STs. The population was dominated by two large clusters. Sequence cluster 5 consisted mainly of STs 5 and 105 (which are members of clonal complex (CC) 5 ), while sequence cluster 8 consisted mostly of ST 8 (which is a member of CC8 ). The majority of the MRSA identified using in vitro phenotypic testing were present in these two clusters [50/89 (56.18%) in CC5 and 52/76 (68.42%) in CC8]. We also identified one MRSA isolate in each of sequence clusters 2 (ST398), 6 (ST72) and an undelineated cluster consisting of ST1. Isolates that did not group with the eight sequence clusters made up the low frequency genotypes that were divergent from the rest of the population and with each other.
Resistance to multiple antimicrobial classes
We considered in silico evidence to identify resistance determinants in each isolate (Fig. 2a and Additional file 5: Table S3). We detected a total of 20 horizontally transferrable resistance genes. Two mechanisms mediate penicillin resistance in S. aureus. The blaZ gene encodes a narrow-spectrum penicillinase, which inactivates the penicillinase-labile penicillins (penicillin, amoxicillin, ampicillin, carbenicillin, ticarcillin, azlocillin and piperacillin) by hydrolysis of its β-lactam ring . The mecA gene encodes an alternative penicillin-binding protein PBP2a and confers resistance to all beta-lactam antimicrobial agents with the exception of ceftaroline . We detected blaZ and mecA in 262/323 isolates (81.11%) and 104/323 isolates (32.20%), respectively. We found a slight discrepancy between the in silico detection of the mecA gene and in vitro phenotypic testing for methicillin resistance. There were two isolates whose genomes contained the mecA gene but phenotypically tested as methicillin-susceptible. There were three isolates whose genomes did not contain the mecA gene but phenotypically tested as MRSA. These phenotype/genotype discrepancies may be due to sequencing errors, the existence of novel mecA homologs that are not recognized as yet in current databases or the presence of other yet unknown determinants of methicillin resistance. The mecA gene is carried by the mobile element staphylococcal cassette chromosome (SCCmec), of which there are currently 14 recognized types . In our study, SCCmec type II is associated mainly with CC5 (43/89 or 48.31% of total isolates in the cluster), while type IV with CC8 (52/76 or 68.42% of total isolates in the cluster). Each of the two CCs also contains isolates with SCCmec type from the other CC (i.e., five isolates in CC5 have SCCmec type IV and one isolate in CC8 have SCCmec type II). We also detected SCCmec type V in sequence cluster 6 (ST72). The three SCCmec types vary in terms of their gene content . SCCmec type II is a relatively large element (~ 52 kb) carrying a type 2 recombinase-encoding ccr gene complex and is commonly found among healthcare-acquired MRSA strains, including CC5 [43, 44]. It carries transposon Tn554, which contains the antimicrobial resistance genes ermA (erythromycin resistance) and spc (spectinomycin resistance). SCCmec Type IV is the smallest type (~ 24 kb), often circulating in community settings . Often, this element contains transposon Tn4001, carrying genes conferring resistance to aminoglycosides . Like type IV, SCCmec type V is smaller in length (~ 27 kb) and is often found in community-associated MRSA . It carries other resistance genes aside from mecA, but has a restriction modification system that may play a role in stabilizing the SCCmec .
We identified other horizontally acquired resistance genes  from the genome sequences. These were differentially distributed across the population and in some cases were associated mainly with specific sequence clusters. For example, the genes aadD (kanamycin resistance), ant(9)-Ia (aminoglycoside resistance) and ermA (erythromycin resistance) were mainly found in CC5 [30/89, 53/89 and 52/89 genomes, respectively]. The genes aph(3′)-IIIa (aminoglycoside resistance), mphC (macrolide resistance) and msrA (macrolide and streptogramin B resistance) were most frequently observed in isolates from CC8 (54/76, 56/76 and 55/76 genomes, respectively) . Although the prevalence of vancomycin resistance had been reported to be increasing in S. aureus in recent years , we did not detect vancomycin-resistant isolates using either phenotypic assays or in silico methods. Overall, we found that 133/323 isolates or 41.18% carry horizontally acquired genes conferring resistance to at least three antimicrobial classes (Fig. 2b), with resistance determinants for aminoglycosides (143/323), beta-lactams (277/323) and macrolides (168/323) being the most prevalent (Fig. 2c).
Virulence characteristics of S. aureus genomes
We used in silico screening of virulence genes that may play a critical role in bloodstream infections (Additional file 6: Fig. S3 and Additional file 7: Table S4). Several of these were detected among different sequence clusters. The hlgABC pore-forming leucotoxin genes were found in every genome, with the exception of two genomes missing the hlgC gene. We found the sak gene, which encodes a plasminogen activator protein staphylokinase , in 297/323 genomes (or 91.95%). The gene product of sak has two known roles in virulence. First, it forms a complex with plasmin to prevent biofilm formation in the blood, allowing for access to deeper tissues . It also prevents antimicrobial peptides excreted by the host to effectively target bacterial cells . Found in 309/323 genomes (or 95.67%), the virulence factor staphylococcal complement inhibitor encoded by the scn gene allows S. aureus to evade phagocytosis and killing by human neutrophils . The metalloproteinase-encoding aureolysin gene (aur) was found in 322/323 genomes (99.69%). Aureolysin has been implicated in the inactivation of host protease inhibitors and degradation of host antimicrobial peptides . Two allelic variants of aur have been previously reported  and we found both variants in our dataset: aur in 246/323 genomes (76.16%) and aur1 in 76/323 genomes (23.53%). We detected the aur variant in sequence clusters 4, 5, 6, 7, 8 and the unclustered strains, while aur1 was found in sequence clusters 1, 2 and 3. There appears to be little functional difference between the two aur variants and it remains unclear why they continue to persist in distinct S. aureus lineages .
We found differences in the distribution of virulence genes between CC5 and CC8. For example, CC5 has a much larger number of enterotoxin genes compared to CC8. Enterotoxins are proteins secreted by primarily by Staphylococcus spp. that target intestinal tissues and are commonly associated with food poisoning and toxic shock syndrome . Among these enterotoxin-encoding genes, we detected seg, sei, sem, sen, seo and seu in every CC5 genome with very few found in CC8 genomes. Other genes such as sed, sej and ser were also found more frequently in CC5 (46/89, 49/89 and 48/89 genomes, respectively). In contrast, the enterotoxin genes sek and seq were more common in CC8 (48/76 genomes for both). The gene splE, which encodes a serine protease , was also common in CC8 (71/76 genomes). The Panton-Valentine leukocidin (PVL) genes lukF and lukS were frequently associated with CC8 (48/76 and 47/76 genomes, respectively). The prophage-encoded PVL produces a pore-forming toxin capable of killing neutrophils, monocytes and macrophages and is closely linked to severe S. aureus infections . We also detected the arginine catabolic mobile element genomic island that can enhance the growth and survival of S. aureus  in 47/76 genomes in CC8.
Among the other sequence clusters, one notable finding is the presence of the gene that encodes the superantigen toxic-shock syndrome toxin-1 (TSST-1) among 39/43 or 90.70% of the genomes in ST30. TSST-1 activates T-cells, which results to the induction of a cytokine storm and can become fatal .
Estimating the date of clonal origins and effective population size
We sought to determine the dates of clonal origin of CCs 5 and 8, which make up majority of the MRSA genomes. Using the recombination-free alignment of core SNPs as input to BactDating , we first determined the presence of a temporal signal in each CC (Additional file 8: Fig. S4). We observed a significant positive correlation between the dates of isolation and root-to-tip distances (CC5: R2 = 0.06 and p value = 1.4 e-2 and CC8: R2 = 0.15 and p value = 3.0 e-4) indicating the presence of a clock-like signal. Results showed that the two CCs emerged in the New Hampshire population at separate times. We estimated that the time to the most recent common ancestor of CC5 was around 1973 (95% highest posterior density (HPD) intervals: 1966–1979) and around 1946 for CC8 (95% HPD intervals: 1924–1959) (Fig. 3ab). For each CC, we also estimated the change in the effective population size (Ne), which is a measure of the rate of change in population composition due to genetic drift . The effective population size of CC8 increased until the late 1960s when it started to level off until late 2000s. The onset of the levelling off of CC8 in 1968 coincided with the acquisition of SCCmec Type IV in majority of the strains. The plateau in CC8 also coincided with the acceleration in the population growth of CC5 in the early 1970s and which eventually leveled off in the early 1990s. The beginning of the levelling off of CC5 in 1993 overlaps with the acquisition of SCCmec Type II (Fig. 3 cd). These results indicate that the emergence of lineages each carrying distinct SCCmec types contributed to the success of the two MRSA CCs in the local population.
Variation in recombination rates
Homologous recombination, whereby similar tracts of DNA are acquired from closely related strains [57, 58], is known to impact the evolution and adaptation of S. aureus to its hosts [59,60,61]. The genetic diversity generated through recombination may be further disseminated in the population via subsequent recombination events with other strains or may be passed on to descendants through clonal descent. Here, we sought to determine the extent in which recombination has affected the genome evolution of the two dominant MRSA clones CCs 5 and 8. Using the core genome alignment as input and 1000 bootstrapped replicates, we used mcorr to calculate six recombination parameters (Fig. 4 and Additional file 9: Table S5) . The diversity (d) is the probability that a pair of genomes will differ at any locus and is estimated from the diversity generated from both recombination and accumulation of mutations of the clonal lineage. This parameter was estimated to be 7.63 × 10− 4 and 5.51 × 10− 4 in CC5 and CC8, respectively (p value = 0.0; Welch’s t test). The mutational divergence (θ), which refers to the mean number of mutations per locus since divergence of a pair of homologous sites, was estimated to be 0.020 for both CC5 and CC8 (p value = 0.120; Welch’s t test). Recombinational divergence (ϕ) was estimated to be 0.073 and 0.041 in CC5 and CC8, respectively (p value = 4.19 × 10− 58; Welch’s t test). The ratio ϕ/θ (or γ/μ), which gives the relative rate of recombination to mutation, was estimated to be 3.63 and 2.06 in CC5 and CC8, respectively (p value = 1.28 × 10− 53; Welch’s t test). The mean fragment size (f̅) of a recombination event was estimated to be 407.45 bp and 530.55 bp in CC5 and CC8, respectively (p value = 4.30 × 10− 107; Welch’s t test). Lastly, we calculated the recombination coverage c, which indicates the fraction of the genome whose diversity was derived from recombination events since the last common ancestor of the population, and which ranges from 0 (indicating clonal evolution) to 1 (indicating complete recombination) . Recombination coverage was estimated to be 0.035 and 0.026 in CC5 and CC8, respectively (p value = 1.10 × 10− 231; Welch’s t test), indicating that 3.5 and 2.6% of sites in any one genome from CC5 and CC8, respectively, originated from recombination events. Except for the mutational divergence (θ), we found significant differences in five recombination parameters between the two clonal complexes. We compared these values to those calculated by the authors of mcorr for a S. aureus genomic dataset of 308 invasive isolates from 26 countries in Europe and representing 10 CCs and six minor STs . Both CC5 and CC8 in the New Hampshire population have lower diversity (0.015 in Europe), mutational divergence (0.042 in Europe) and coverage (0.36 in Europe) than the European S. aureus, but higher γ/μ (1.0 in Europe). These were expected because the European dataset consisted of more diverse genotypes. Remarkably, the recombinational divergence and mean recombination size of the European S. aureus were 0.042 and 550, respectively, which were similar to the values of the New Hampshire CC8. CC5 had higher recombinational divergence and lower mean recombination size than the European S. aureus. Overall, these results indicate that the two MRSA clones in New Hampshire have experienced frequent recombination during their recent clonal expansion.
Elucidating the evolutionary context that has selected certain methicillin-resistant S. aureus genotypes is critical to developing effective surveillance, control strategies and treatment options for bloodstream infections. Using genome sequences of 323 S. aureus blood culture isolates spanning at least eight years of sampling, our analyses revealed the specific bacterial clones that predominate in bloodstream infections (CCs 5 and 8) and the genomic elements that contribute to their success as multidrug resistant pathogens in bloodstream infections.
Two mechanisms explain the spread of drug resistance in bacterial populations: clonal expansion of resistant lineages [63,64,65,66] and horizontal gene transfer of resistance genes [67,68,69]. Our study showed that both processes have shaped the long-term population dynamics of MRSA in New Hampshire. Our first major finding is that both CC5 and CC8 (each carrying different suites of resistance genes) have undergone clonal expansion in the last 50 years, which would partly explain the broad occurrence of multidrug and methicillin resistance in the S. aureus population. The clones that comprise these two lineages are the most prominent MRSA lineages worldwide and have been implicated in numerous healthcare-associated and community-acquired infections and outbreaks . CC8, which includes ST8, consists of several pandemic MRSA clones associated with healthcare- and community-associated infections [39, 44]. ST8 includes the notorious pandemic clone USA300 that emerged in Central Europe in the mid-nineteenth century and was exported to North America and elsewhere in the early twentieth century . Community and household transmission facilitated the widespread distribution of this clone in the United States [71,72,73]. ST8 also includes USA500 that emerged in the past 20 years and causes invasive infections in North America . On the other hand, CC5 includes multiple MRSA clones that cause hospital-associated infections in the Western Hemisphere . It is also the principal genetic background in S. aureus upon which full resistance to vancomycin, a key last-line bactericidal drug for treating MRSA infections, has arisen by acquisition of the Tn1546 carrying the van operon from Enterococcus donors . Although none of the New Hampshire isolates were vancomycin-resistant, reduced vancomycin susceptibility has been increasing in S. aureus causing bloodstream infections in other parts of the United States [46, 76] and it may therefore just be a matter of time before it emerges in New Hampshire.
The predominance of CCs 5 and 8 in bloodstream infections has been previously reported in other places. In Minnesota USA, the most common MRSA clones were CC5 (ST5, all SCCmec type II) and CC8 (ST8 and ST3342, mostly SCCmec type IV) . In a Colorado USA study that tracked S. aureus colonizing the nares and progressing to bacteremia within individual patients, STs 5 and 8 were also the most frequently observed . In a study of MRSA isolates causing bloodstream infections in England sampled from 2012 to 2013, ST22 (clonal complex 22) make up the majority of S. aureus isolates, while STs 5 and 8 were less common . In nine Latin American countries sampled in 2011–2014, the population of S. aureus from bloodstream infections was dominated by STs 5 and 8 as well as minor STs such as STs 30 and 72 . In contrast to our results, the Latin American study reported evidence for frequent clonal replacement of previously prevalent hospital-associated clones: ST8 replaced ST5 in Colombia and Ecuador, while ST5 replaced ST239 in Brazil . In our New Hampshire study, co-dominance rather than clonal replacement enabled both methicillin resistant CC5 (ST5) and CC8 (ST8) to persist in the last five decades. We can only postulate the reasons for such remarkable contrast in S. aureus population dynamics – clonal replacement in South America versus clonal co-dominance in North America – in a relatively short time span. Selective pressures due to ecological differences may explain this variation. Differences in the population dynamics of resistant bacteria could be due to regional differences in host immunity that selected for specific genes that happened to be resistant, differences in antimicrobial use or different patterns of transmission among specific host groups that receive more antimicrobial prescriptions such as children . Another important consideration is that our dataset included both pediatric and adult patients, while the Latin American study consisted only of adult patients. The size and diversity of the geographical regions being compared (New Hampshire as a single state in the United States versus multiple countries in Latin America) will also greatly influence these results. Lastly, while the sampling period (2010–2018 in our study versus 2011–2014 in the Latin American study) overlapped, making definitive comparison between the two remains challenging because of disparities in sampling strategies. Regardless, the co-dominance of two high-risk clones with distinct resistance and virulence characteristics is an important consideration in the epidemiology of S. aureus causing bloodstream infections in New Hampshire.
Our second major finding is that the second mechanism, i.e., horizontal acquisition of resistance genes, was also a major driver in the spread of resistance in S. aureus causing bloodstream infections. Although the mobile mecA was primarily found in CC5 and CC8, numerous horizontally-acquired resistance genes were also differentially distributed in other lineages. Two possible scenarios can happen. These rare lineages (i.e., isolates that were not part of CCs 5 and 8) can act as reservoir of resistance genes and their allelic variants from which the dominant lineages can draw from [44, 81, 82]. It may also be possible that the presence of these resistance genes can facilitate the clonal expansion and dissemination of the rare genotypes in the future [83, 84]. In either case, these rare genotypes carrying horizontally mobile resistance genes must be observed over the long term to ensure that risks due to either scenario are reduced. Moreover, both CC5 and CC8 appear to have had a history of frequent recombination during their recent clonal expansion. Frequent or large-scale genetic recombination events can significantly alter the genome structure of a strain, which can drive the emergence of lineages with unique traits such as ability to switch between animal and human hosts, epidemic features or enhanced growth in new ecological niches [61, 82, 85, 86].
We acknowledge several limitations of our study. First, only one isolate was obtained per patient. Multiple infections (either repeated or mixed infections) and the diversity of the S. aureus inoculum at the site of infection can greatly influence the S. aureus population within a single patient as the pathogen evolves during the course of infection . Second, characterization of mobile elements, resistance genes and virulence genes depend greatly on the composition and quality of existing databases used in in silico methods. This means that novel genetic variants (including unknown types of SCCmec) may not be recognized by current methods. Lastly, as in any phylogenetic and population demographic method, estimation of the dates of the last common ancestor is made difficult with increasing time between the ancestor and the observed descendants as well as the range of diversity of missing descendants. Hence, the dates of the last common ancestors of CC5 and CC8 represent only an approximation and may vary with the inclusion of additional isolates.
The results presented here open multiple avenues for future research. Continuous surveillance over many years is essential for documenting the long-term dynamics and drug resistance of S. aureus, including the potential for clonal replacement by other, less common lineages. Low frequency clones, which may harbor unique genomic elements or phenotypic features, may remain hidden from epidemiological and phylogenetic studies. They also have the capacity to replace one or both co-dominant lineages (CCs 5 and 8), which may be facilitated by changes in ecological conditions (e.g., change in antimicrobial use and demographic changes in the region). Future surveillance strategies will also be enhanced by the inclusion of other hospitals in the region. It is likely that a more heterogeneous assembly of S. aureus lineages are present in New Hampshire, but the lack of a systematic state- or region-wide S. aureus surveillance in bloodstream infections may lead to a subpopulation of genetic variants that we may have overlooked in the present analyses.
Second, future work should also focus on the relationship of clone-specific genomic features and the clinical outcomes of patients with bloodstream infection, which can be used as predictive factors in S. aureus mortality, clinical manifestation and disease severity. Once S. aureus enters the bloodstream, it replicates and disseminates to many different sites, causing severe disease manifestations such as sepsis, infective endocarditis, and deep-seated abscesses in virtually every organ tissue . It remains unclear if there is site-specific selection occurring for specific strains or genetic variants. This information will be particularly useful to precisely characterize protective vaccine antigens and the development of immune therapeutics to prevent disease or improve clinical outcomes. It will also inform appropriate patient management and therapeutics for persistent, relapse, metastatic or complicated S. aureus bloodstream infections. Unfortunately, our data set does not include an extensive amount of clinical, phenotypic or other epidemiological information for each isolate. Hence, we strongly advocate for the inclusion of such pertinent associated data in sampling and surveillance schemes of bloodstream infections in the state of New Hampshire and elsewhere.
We conclude that the S. aureus population causing bloodstream infections in New Hampshire, USA was shaped mainly by the clonal expansion, recombination and long-term co-dominance of two high-risk lineages with distinct genomic features, resistance characteristics and evolutionary histories. These results have important implications on the development of effective and robust approaches to identifying new bacterial targets for intervention, control and treatment strategies of life-threatening bloodstream infections.
Availability of data and materials
The dataset supporting the conclusions of this article is included within the article and its additional files. Genome sequence data of the S. aureus blood culture isolates have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under BioProject accession number PRJNA673382 with BioSample accession numbers for each genome listed in Additional file 1: Table S1. The reference strain NCTC 8325 (RefSeq assembly accession no. GCF_000013425.1) is available in NCBI.
Christaki E, Giamarellos-Bourboulis EJ. The complex pathogenesis of bacteremia: from antimicrobial clearance mechanisms to the genetic background of the host. Virulence. 2014;5(1):57–65. https://doi.org/10.4161/viru.26514.
Lockhart PB, Brennan MT, Sasser HC, Fox PC, Paster BJ, Bahrani-Mougeot FK. Bacteremia associated with toothbrushing and dental extraction. Circulation. 2008;117(24):3118–25. https://doi.org/10.1161/CIRCULATIONAHA.107.758524.
Thomer L, Schneewind O, Missiakas D. Pathogenesis of Staphylococcus aureus bloodstream infections. Annu Rev Pathol. 2016;11(1):343–64. https://doi.org/10.1146/annurev-pathol-012615-044351.
Diekema DJ, Hsueh P-R, Mendes RE, Pfaller MA, Rolston KV, Sader HS, et al. The microbiology of bloodstream infection: 20-year trends from the SENTRY antimicrobial surveillance program. Antimicrob Agents Chemother. 2019;63(7). https://doi.org/10.1128/AAC.00355-19.
van Hal SJ, Jensen SO, Vaska VL, Espedido BA, Paterson DL, Gosbell IB. Predictors of mortality in Staphylococcus aureus bacteremia. Clin Microbiol Rev. 2012;25(2):362–86. https://doi.org/10.1128/CMR.05022-11.
Akova M. Epidemiology of antimicrobial resistance in bloodstream infections. Virulence. 2016;7(3):252–66. https://doi.org/10.1080/21505594.2016.1159366.
Young BC, Golubchik T, Batty EM, Fung R, Larner-Svensson H, Votintseva AA, et al. Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease. Proc Natl Acad Sci U S A. 2012;109(12):4550–5. https://doi.org/10.1073/pnas.1113219109.
Méric G, Mageiros L, Pensar J, Laabei M, Yahara K, Pascoe B, et al. Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis. Nat Commun. 2018;9(1):5034. https://doi.org/10.1038/s41467-018-07368-7.
Benoit JB, Frank DN, Bessesen MT. Genomic evolution of Staphylococcus aureus isolates colonizing the nares and progressing to bacteremia. PLoS One. 2018;13(5):e0195860. https://doi.org/10.1371/journal.pone.0195860.
Recker M, Laabei M, Toleman MS, Reuter S, Saunderson RB, Blane B, et al. Clonal differences in Staphylococcus aureus bacteraemia-associated mortality. Nat Microbiol. 2017;2(10):1381–8. https://doi.org/10.1038/s41564-017-0001-x.
CLSI. Performance standards for antimicrobial susceptibility testing. CLSI supplement M100. 30th edition. Wayne, PA, USA: Clinical and Laboratory Standards Institute; 2020.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77. https://doi.org/10.1089/cmb.2012.0021.
Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics. 2011;27(4):578–9. https://doi.org/10.1093/bioinformatics/btq683.
Nadalin F, Vezzi F, Policriti A. GapFiller: a de novo assembly approach to fill the gap within paired reads. BMC Bioinformatics. 2012;13(Suppl 14):S8.
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29(8):1072–5. https://doi.org/10.1093/bioinformatics/btt086.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043–55. https://doi.org/10.1101/gr.186072.114.
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9(1):5114. https://doi.org/10.1038/s41467-018-07641-9.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9. https://doi.org/10.1093/bioinformatics/btu153.
Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31(22):3691–3. https://doi.org/10.1093/bioinformatics/btv421.
Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005;15(6):589–94. https://doi.org/10.1016/j.gde.2005.09.006.
Hunt M, Mather AE, Sánchez-Busó L, Page AJ, Parkhill J, Keane JA, et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom. 2017;3:e000131.
Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012;67(11):2640–4. https://doi.org/10.1093/jac/dks261.
Joensen KG, Scheutz F, Lund O, Hasman H, Kaas RS, Nielsen EM, et al. Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coli. J Clin Microbiol. 2014;52(5):1501–10. https://doi.org/10.1128/JCM.03617-13.
Liu B, Zheng D, Jin Q, Chen L, Yang J. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 2019;47(D1):D687–92. https://doi.org/10.1093/nar/gky1080.
Kaya H, Hasman H, Larsen J, Stegger M, Johannesen TB, Allesøe RL, et al. SCCmecFinder, a web-based tool for typing of Staphylococcal Cassette Chromosome mec in Staphylococcus aureus using whole-genome sequence data. mSphere. 2018;3:e00612–17.
Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32(1):268–74. https://doi.org/10.1093/molbev/msu300.
Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14(6):587–9. https://doi.org/10.1038/nmeth.4285.
Tavare S. Some Probabilistic and Statistical Problems in the Analysis of DNA Sequences. In: American Mathematical Society: Lectures on Mathematics in the Life Sciences. Rhode Island, USA: Amer Mathematical Society; 1986. p. 57–86.
Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35(2):518–22. https://doi.org/10.1093/molbev/msx281.
Tonkin-Hill G, Lees JA, Bentley SD, Frost SDW, Corander J. RhierBAPS: an R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res. 2018;3:93. https://doi.org/10.12688/wellcomeopenres.14694.1.
Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA, Bentley SD, et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 2015;43(3):e15. https://doi.org/10.1093/nar/gku1196.
Didelot X, Croucher NJ, Bentley SD, Harris SR, Wilson DJ. Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res. 2018;46(22):e134. https://doi.org/10.1093/nar/gky783.
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R: Foundation for Statistical Computing; 2013. http://www.R-project.org/
Volz EM, Didelot X. Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance. Syst Biol. 2018;67(4):719–28. https://doi.org/10.1093/sysbio/syy007.
Lin M, Kussell E. Inferring bacterial recombination rates from large-scale sequencing datasets. Nat Methods. 2019;16(2):199–204. https://doi.org/10.1038/s41592-018-0293-7.
Bosi E, Monk JM, Aziz RK, Fondi M, Nizet V, Palsson BØ. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc Natl Acad Sci U S A. 2016;113(26):E3801–9. https://doi.org/10.1073/pnas.1523199113.
Manara S, Pasolli E, Dolce D, Ravenni N, Campana S, Armanini F, et al. Whole-genome epidemiology, characterisation, and phylogenetic reconstruction of Staphylococcus aureus strains in a paediatric hospital. Genome Med. 2018;10(1):82. https://doi.org/10.1186/s13073-018-0593-7.
Challagundla L, Reyes J, Rafiqullah I, Sordelli DO, Echaniz-Aviles G, Velazquez-Meza ME, et al. Phylogenomic classification and the evolution of clonal complex 5 methicillin-resistant Staphylococcus aureus in the western hemisphere. Front Microbiol. 2018;9:1901. https://doi.org/10.3389/fmicb.2018.01901.
Bowers JR, Driebe EM, Albrecht V, McDougal LK, Granade M, Roe CC, et al. Improved subtyping of Staphylococcus aureus clonal complex 8 strains based on whole-genome phylogenetic analysis. mSphere. 2018;3:e00464–17.
Fuda CCS, Fisher JF, Mobashery S. Beta-lactam resistance in Staphylococcus aureus: the adaptive resistance of a plastic genome. Cell Mol Life Sci. 2005;62(22):2617–33. https://doi.org/10.1007/s00018-005-5148-6.
Hartman BJ, Tomasz A. Low-affinity penicillin-binding protein associated with beta-lactam resistance in Staphylococcus aureus. J Bacteriol. 1984;158(2):513–6. https://doi.org/10.1128/jb.158.2.513-516.1984.
International Working Group on the Classification of Staphylococcal Cassette Chromosome Elements (IWG-SCC). Classification of staphylococcal cassette chromosome mec (SCCmec): guidelines for reporting novel SCCmec elements. Antimicrob Agents Chemother. 2009;53:4961–7.
Liu J, Chen D, Peters BM, Li L, Li B, Xu Z, et al. Staphylococcal chromosomal cassettes mec (SCCmec): a mobile genetic element in methicillin-resistant Staphylococcus aureus. Microb Pathog. 2016;101:56–67. https://doi.org/10.1016/j.micpath.2016.10.028.
Monecke S, Coombs G, Shore AC, Coleman DC, Akpaka P, Borg M, et al. A field guide to pandemic, epidemic and sporadic clones of methicillin-resistant Staphylococcus aureus. PLoS One. 2011;6(4):e17936. https://doi.org/10.1371/journal.pone.0017936.
Haaber J, Penadés JR, Ingmer H. Transfer of antibiotic resistance in Staphylococcus aureus. Trends Microbiol. 2017;25(11):893–905. https://doi.org/10.1016/j.tim.2017.05.011.
Shariati A, Dadashi M, Moghadam MT, van Belkum A, Yaslianifard S, Darban-Sarokhalil D. Global prevalence and distribution of vancomycin resistant, vancomycin intermediate and heterogeneously vancomycin intermediate Staphylococcus aureus clinical isolates: a systematic review and meta-analysis. Sci Rep. 2020;10(1):12689. https://doi.org/10.1038/s41598-020-69058-z.
Nguyen LT, Vogel HJ. Staphylokinase has distinct modes of interaction with antimicrobial peptides, modulating its plasminogen-activation properties. Sci Rep. 2016;6(1):31817. https://doi.org/10.1038/srep31817.
Rooijakkers SHM, Ruyken M, Roos A, Daha MR, Presanis JS, Sim RB, et al. Immune evasion by a staphylococcal complement inhibitor that acts on C3 convertases. Nat Immunol. 2005;6(9):920–7. https://doi.org/10.1038/ni1235.
Pietrocola G, Nobile G, Rindi S, Speziale P. Staphylococcus aureus manipulates innate immunity through own and host-expressed proteases. Front Cell Infect Microbiol. 2017;7:166. https://doi.org/10.3389/fcimb.2017.00166.
Sabat A, Kosowska K, Poulsen K, Kasprowicz A, Sekowska A, van Den Burg B, et al. Two allelic forms of the aureolysin gene (aur) within Staphylococcus aureus. Infect Immun. 2000;68(2):973–6. https://doi.org/10.1128/IAI.68.2.973-976.2000.
Pinchuk IV, Beswick EJ, Reyes VE. Staphylococcal enterotoxins. Toxins (Basel). 2010;2(8):2177–97. https://doi.org/10.3390/toxins2082177.
Paharik AE, Salgado-Pabon W, Meyerholz DK, White MJ, Schlievert PM, Horswill AR. The Spl serine proteases modulate Staphylococcus aureus protein production and virulence in a rabbit model of pneumonia. mSphere. 2016;1:e00208–16.
Spaan AN, Henry T, van Rooijen WJM, Perret M, Badiou C, Aerts PC, et al. The staphylococcal toxin Panton-valentine Leukocidin targets human C5a receptors. Cell Host Microbe. 2013;13(5):584–94. https://doi.org/10.1016/j.chom.2013.04.006.
Diep BA, Stone GG, Basuino L, Graber CJ, Miller A, Des Etages S-A, et al. The arginine catabolic mobile element and staphylococcal chromosomal cassette mec linkage: convergence of virulence and resistance in the USA300 clone of methicillin-resistant Staphylococcus aureus. J Infect Dis. 2008;197(11):1523–30. https://doi.org/10.1086/587907.
Sharma H, Turner CE, Siggins MK, El-Bahrawy M, Pichon B, Kearns A, et al. Toxic shock syndrome toxin 1 evaluation and antibiotic impact in a transgenic model of staphylococcal soft tissue infection. mSphere. 2019;4:e00665–19.
Nei M, Tajima F. Genetic drift and estimation of effective population size. Genetics. 1981;98(3):625–40. https://doi.org/10.1093/genetics/98.3.625.
Didelot X, Maiden MCJ. Impact of recombination on bacterial evolution. Trends Microbiol. 2010;18(7):315–22. https://doi.org/10.1016/j.tim.2010.04.002.
Hanage WP. Not so simple after all: bacteria, their population genetics, and recombination. Cold Spring Harb Perspect Biol. 2016;8(7). https://doi.org/10.1101/cshperspect.a018069.
Castillo-Ramírez S, Corander J, Marttinen P, Aldeljawi M, Hanage WP, Westh H, et al. Phylogeographic variation in recombination rates within a global clone of methicillin-resistant Staphylococcus aureus. Genome Biol. 2012;13(12):R126. https://doi.org/10.1186/gb-2012-13-12-r126.
Driebe EM, Sahl JW, Roe C, Bowers JR, Schupp JM, Gillece JD, et al. Using whole genome analysis to examine recombination across diverse sequence types of Staphylococcus aureus. PLoS One. 2015;10(7):e0130955. https://doi.org/10.1371/journal.pone.0130955.
Spoor LE, Richardson E, Richards AC, Wilson GJ, Mendonca C, Gupta RK, et al. Recombination-mediated remodelling of host-pathogen interactions during Staphylococcus aureus niche adaptation. Microb Genom. 2015;1:e000036.
Aanensen DM, Feil EJ, Holden MTG, Dordel J, Yeats CA, Fedosejev A, et al. Whole-genome sequencing for routine pathogen surveillance in public health: a population snapshot of invasive Staphylococcus aureus in Europe. mBio. 2016;7:e00444–16.
Ward MJ, Gibbons CL, McAdam PR, van Bunnik BA, Girvan EK, Edwards GF, et al. Time-scaled evolutionary analysis of the transmission and antibiotic resistance dynamics of Staphylococcus aureus clonal complex 398. Appl Environ Microbiol. 2014;80:7275–82.
Gomez-Simmonds A, Greenman M, Sullivan SB, Tanner JP, Sowash MG, Whittier S, et al. Population structure of Klebsiella pneumoniae causing bloodstream infections at a new York City tertiary care hospital: diversification of multidrug-resistant isolates. J Clin Microbiol. 2015;53(7):2060–7. https://doi.org/10.1128/JCM.03455-14.
Challagundla L, Luo X, Tickler IA, Didelot X, Coleman DC, Shore AC, et al. Range expansion and the origin of USA300 North American epidemic methicillin-resistant Staphylococcus aureus. mBio. 2018;9:e02016–17.
Lam MMC, Wyres KL, Duchêne S, Wick RR, Judd LM, Gan Y-H, et al. Population genomics of hypervirulent Klebsiella pneumoniae clonal-group 23 reveals early emergence and rapid global dissemination. Nat Commun. 2018;9(1):2703. https://doi.org/10.1038/s41467-018-05114-7.
Lerminiaux NA, Cameron ADS. Horizontal transfer of antibiotic resistance genes in clinical environments. Can J Microbiol. 2019;65(1):34–44. https://doi.org/10.1139/cjm-2018-0275.
Dolejska M, Papagiannitsis CC. Plasmid-mediated resistance is going wild. Plasmid. 2018;99:99–111. https://doi.org/10.1016/j.plasmid.2018.09.010.
Evans DR, Griffith MP, Sundermann AJ, Shutt KA, Saul MI, Mustapha MM, et al. Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital. Elife. 2020;9. https://doi.org/10.7554/eLife.53886.
Strauß L, Stegger M, Akpaka PE, Alabi A, Breurec S, Coombs G, et al. Origin, evolution, and global transmission of community-acquired Staphylococcus aureus ST8. Proc Natl Acad Sci U S A. 2017;114(49):E10596–604. https://doi.org/10.1073/pnas.1702472114.
Alam MT, Read TD, Petit RA, Boyle-Vavra S, Miller LG, Eells SJ, et al. Transmission and microevolution of USA300 MRSA in U.S. households: evidence from whole-genome sequencing. mBio. 2015;6:e00054.
Jamrozy DM, Harris SR, Mohamed N, Peacock SJ, Tan CY, Parkhill J, et al. Pan-genomic perspective on the evolution of the Staphylococcus aureus USA300 epidemic. Microb Genom. 2016;2:e000058.
Uhlemann A-C, Dordel J, Knox JR, Raven KE, Parkhill J, Holden MTG, et al. Molecular tracing of the emergence, diversification, and transmission of S. aureus sequence type 8 in a New York community. Proc Natl Acad Sci U S A. 2014;111(18):6738–43. https://doi.org/10.1073/pnas.1401006111.
Frisch MB, Castillo-Ramírez S, Petit RA, Farley MM, Ray SM, Albrecht VS, et al. Invasive methicillin-resistant Staphylococcus aureus USA500 strains from the U.S. emerging infections program constitute three geographically distinct lineages. mSphere. 2018;3:e00571–17.
Kos VN, Desjardins CA, Griggs A, Cerqueira G, Van Tonder A, Holden MTG, et al. Comparative genomics of vancomycin-resistant Staphylococcus aureus strains and their positions within the clade most commonly associated with Methicillin-resistant S. aureus hospital-acquired infection in the United States. mBio. 2012;3:e00112–12.
Canty E, Carnahan B, Curley T, Anususinha E, Hamdy RF, Ericson JE. Reduced vancomycin susceptibility, MRSA and treatment failure in pediatric Staphylococcus aureus bloodstream infections. Pediatr Infect Dis J. 2021;40:429–33.
Park K-H, Greenwood-Quaintance KE, Uhl JR, Cunningham SA, Chia N, Jeraldo PR, et al. 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. 2017;12(6):e0179003. https://doi.org/10.1371/journal.pone.0179003.
Toleman MS, Reuter S, Jamrozy D, Wilson HJ, Blane B, Harrison EM, et al. Prospective genomic surveillance of methicillin-resistant Staphylococcus aureus (MRSA) associated with bloodstream infection, England, 1 October 2012 to 30 September 2013. Euro Surveill. 2019;24(4). https://doi.org/10.2807/1560-7917.ES.2019.24.4.1800215.
Arias CA, Reyes J, Carvajal LP, Rincon S, Diaz L, Panesso D, et al. A prospective cohort multicenter study of molecular epidemiology and phylogenomics of Staphylococcus aureus bacteremia in nine Latin american countries. Antimicrob Agents Chemother. 2017;61(10). https://doi.org/10.1128/AAC.00816-17.
Andam CP, Worby CJ, Gierke R, McGee L, Pilishvili T, Hanage WP. Penicillin resistance of nonvaccine type pneumococcus before and after PCV13 introduction, United States. Emerg Infect Dis. 2017;23(6):1012–5. https://doi.org/10.3201/eid2306.161331.
Planet PJ, LaRussa SJ, Dana A, Smith H, Xu A, Ryan C, et al. Emergence of the epidemic methicillin-resistant Staphylococcus aureus strain USA300 coincides with horizontal transfer of the arginine catabolic mobile element and speG-mediated adaptations for survival on skin. mBio. 2013;4:e00889–13.
Nimmo GR, Steen JA, Monecke S, Ehricht R, Slickers P, Thomas JC, et al. ST2249-MRSA-III: a second major recombinant methicillin-resistant Staphylococcus aureus clone causing healthcare infection in the 1970s. Clin Microbiol Infect. 2015;21(5):444–50. https://doi.org/10.1016/j.cmi.2014.12.018.
Copin R, Sause WE, Fulmer Y, Balasubramanian D, Dyzenhaus S, Ahmed JM, et al. Sequential evolution of virulence and resistance during clonal spread of community-acquired methicillin-resistant Staphylococcus aureus. Proc Natl Acad Sci U S A. 2019;116(5):1745–54. https://doi.org/10.1073/pnas.1814265116.
Packer S, Pichon B, Thompson S, Neale J, Njoroge J, Kwiatkowska RM, et al. Clonal expansion of community-associated meticillin-resistant Staphylococcus aureus (MRSA) in people who inject drugs (PWID): prevalence, risk factors and molecular epidemiology, Bristol, United Kingdom, 2012 to 2017. Euro Surveill. 2019;24(13). https://doi.org/10.2807/1560-7917.ES.2019.24.13.1800124.
Robinson DA, Enright MC. Evolution of Staphylococcus aureus by large chromosomal replacements. J Bacteriol. 2004;186(4):1060–4. https://doi.org/10.1128/JB.186.4.1060-1064.2004.
Richardson EJ, Bacigalupe R, Harrison EM, Weinert LA, Lycett S, Vrieling M, et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat Ecol Evol. 2018;2(9):1468–78. https://doi.org/10.1038/s41559-018-0617-0.
Didelot X, Walker AS, Peto TE, Crook DW, Wilson DJ. Within-host evolution of bacterial pathogens. Nat Rev Microbiol. 2016;14(3):150–62. https://doi.org/10.1038/nrmicro.2015.13.
The authors thank the UNH Resource Computing Center where all bioinformatics analyses were performed. We thank Anthony Westbrook for providing technical and bioinformatics assistance. We acknowledge the staff of DHMC Clinical Microbiology Laboratory for laboratory support and the UNH Hubbard Center for Genome Studies for sequencing support.
The study was supported by start-up funds from the UAlbany College of Arts and Sciences to C.P.A. and UNH Graduate School Dissertation Year Fellowship to J.T.S. The funders had no role in study design, data collection and analysis, decision to publish, and preparation of the manuscript and the findings do not necessarily reflect views and policies of the authors’ institutions and funders.
Ethics approval and consent to participate
Ethical approval was granted by the Committee for the Protection of Human Subjects of Dartmouth-Hitchcock Medical Center and Dartmouth College. This study protocol was deemed not to be human subjects research. Samples used in the study were subcultured bacterial isolates that had been archived in the routine course of clinical laboratory operations. No patient specimens were used and patient protected health information was not collected. Therefore, informed consent was not required.
Consent for publication
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Genome assembly, MLST, accession numbers and associated metadata of 323 S. aureus isolates
Assembly statistics of the S. aureus genomes
Distribution of genes in the S. aureus pan-genome identified using Roary
Pan-genome analysis of 323 S. aureus genomes. (a) Presence-absence matrix of gene clusters as determined by Roary, aligned to the phylogeny. Blue indicates presence. (b) Gene frequency histogram indicating the number of genomes each gene is present in. (c) The size of the pan-genome (blue), core genome (red), unique gene additions (purple) and new genes (green) as related to the number of individuals in the population. Proportional illustration of core, soft-core, shell, and cloud genes across the pangenome.
Distribution of horizontally acquired antimicrobial resistance genes identified using ResFinder
Distribution of virulence genes using VirulenceFinder and the Virulence Factor Database (VFDB). Details are shown in Supplementary Table S4.
Distribution of virulence genes identified using VirulenceFinder and the VFDB
Bactdating statistical tests and MCMC trace plots. (a) Initial rooted phylogeny and correlation test between date and root-to-tip distance withing the phylogeny for CC5. (b) Bactdating trace plots constructed by periodic sampling over the MCMC runs for CC5. (c) Initial phylogeny and correlation test for CC8. (d) Bactdating trace plots for CC8.
Recombination parameters inferred by mcorr for each of CC5 and CC8
About this article
Cite this article
Smith, J.T., Eckhardt, E.M., Hansel, N.B. et al. Genomic epidemiology of methicillin-resistant and -susceptible Staphylococcus aureus from bloodstream infections. BMC Infect Dis 21, 589 (2021). https://doi.org/10.1186/s12879-021-06293-3