Methodology for Whole-Genome Sequencing of Methicillin-Resistant Staphylococcus aureus Isolates in a Routine Hospital Microbiology Laboratory

Evaluating the impact of genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) on hospital infection prevention and control decisions

Genomic epidemiology enhances the ability to detect and refute methicillin-resistant Staphylococcus aureus (MRSA) outbreaks in healthcare settings, but its routine introduction requires further evidence of benefits for patients and resource utilization. We performed a 12 month prospective study at Cambridge University Hospitals NHS Foundation Trust in the UK to capture its impact on hospital infection prevention and control (IPC) decisions. MRSA-positive samples were identified via the hospital microbiology laboratory between November 2018 and November 2019. We included samples from in-patients, clinic out-patients, people reviewed in the Emergency Department and healthcare workers screened by Occupational Health. We sequenced the first MRSA isolate from 823 consecutive individuals, defined their pairwise genetic relatedness, and sought epidemiological links in the hospital and community. Genomic analysis of 823 MRSA isolates identified 72 genetic clusters of two or more isolates containing 339/823 (41 %) of the cases. Epidemiological links were identified between two or more cases for 190 (23 %) individuals in 34/72 clusters. Weekly genomic epidemiology updates were shared with the IPC team, culminating in 49 face-to-face meetings and 21 written communications. Seventeen clusters were identified that were consistent with hospital MRSA transmission, discussion of which led to additional IPC actions in 14 of these. Two outbreaks were also identified where transmission had occurred in the community prior to hospital presentation; these were escalated to relevant IPC teams. We identified 38 instances where two or more in-patients shared a ward location on overlapping dates but carried unrelated MRSA isolates (pseudo-outbreaks); research data led to de-escalation of investigations in six of these. Our findings provide further support for the routine use of genomic epidemiology to enhance and target IPC resources.

Antimicrobial resistance and clonality of Staphylococcus aureus causing bacteraemia in children admitted to the Manhiça District Hospital, Mozambique, over two decades

BACKGROUND

Staphylococcus aureus is one of the main causes of bacteraemia, associated with high mortality, mainly due to the occurrence of multidrug resistant (MDR) strains. Data on antibiotic susceptibility and genetic lineages of bacteraemic S. aureus are still scarce in Mozambique. The study aims to describe the antibiotic susceptibility and clonality of S. aureus isolated from blood cultures of children admitted to the Manhiça District Hospital over two decades (2001-2019).

METHODS

A total of 336 S. aureus isolates detected in blood cultures of children aged <5 years were analyzed for antibiotic susceptibility by disk diffusion or minimal inhibitory concentration, and for the presence of resistance determinants by PCR. The clonality was evaluated by SmaI-PFGE, spa typing, and MLST. The SCCmec element was characterized by SCCmec typing.

RESULTS

Most S. aureus (94%, 317/336) were resistant to at least one class of antibiotics, and one quarter (25%) showed a MDR phenotype. High rates of resistance were detected to penicillin (90%) and tetracycline (48%); followed by erythromycin/clindamycin (25%/23%), and co-trimoxazole (11%), while resistance to methicillin (MRSA strains) or gentamicin was less frequent (≤5%). The phenotypic resistance to distinct antibiotics correlated well with the corresponding resistance determinants (Cohen's κ test: 0.7-1.0). Molecular typing revealed highly diverse clones with predominance of CC5 (17%, 58/336) and CC8 (16%), followed by CC15 (11%) and CC1 (11%). The CC152, initially detected in 2001, re-emerged in 2010 and became predominant throughout the remaining surveillance period, while other CCs (CC1, CC5, CC8, CC15, CC25, CC80, and CC88) decreased over time. The 16 MRSA strains detected belonged to clones t064-ST612/CC8-SCCmecIVd (69%, 11/16), t008-ST8/CC8-SCCmecNT (25%, 4/16) and t5351-ST88/CC88-SCCmecIVa (6%, 1/16). Specific clonal lineages were associated with extended length of stay and high in-hospital mortality.

CONCLUSION

We document the circulation of diverse MDR S. aureus causing paediatric bacteraemia in Manhiça district, Mozambique, requiring a prompt recognition of S. aureus bacteraemia by drug resistant clones to allow more targeted clinical management of patients.

Large-Scale Evaluation of a Rapid Fully Automated Analysis Platform to Detect and Refute Outbreaks Based on MRSA Genome Comparisons

Genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) could transform outbreak investigations, but its clinical introduction is hampered by the lack of automated data analysis tools to rapidly and accurately define transmission based on sequence relatedness. We aimed to evaluate a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline. We analyzed 781 MRSA genomes from 777 consecutive patients identified over a 9-month period in a clinical microbiology laboratory in the United Kingdom. Outputs were bacterial species identification, detection of mec genes, assignment to sequence types (STs), identification of pairwise relatedness using a definition of ≤25 single nucleotide polymorphisms (SNPs) apart, and use of genetic relatedness to identify clusters. There was full concordance between the two analysis methods for species identification, detection of mec genes, and ST assignment. A total of 3,311 isolate pairs ≤25 SNPs apart were identified by at least one method. These had a median (range) SNP difference between the two methods of 1.2 SNPs (0 to 22 SNPs), with most isolate pairs (87%) varying by ≤2 SNPs. This similarity increased when the research pipeline was modified to use a clonal-complex-specific reference (median 0 SNP difference, 91% varying by ≤2 SNPs). Both pipelines clustered 338 isolates/334 patients into 66 unique clusters based on genetic relatedness. We conclude that the automated transmission detection tool worked at least as well as a researcher-led manual analysis and indicates how such tools could support the rapid use of MRSA genomic epidemiology in infection control practice. IMPORTANCE It has been clearly established that genome sequencing of MRSA improves the accuracy of health care-associated outbreak investigations, including the confirmation and exclusion of outbreaks and identification of patients involved. This could lead to more targeted infection control actions but its use in clinical practice is prevented by several barriers, one of which is the availability of genome analysis tools that do not depend on specialist knowledge to analyze or interpret the results. We evaluated a prototype of a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline, using genomes from 777 patients over a period of 9 months. The performance was at least equivalent to the researcher-led manual genomic analysis. This indicates the feasibility of automated analysis and represents one more step toward the routine use of pathogen sequencing in infection prevention and control practice.

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

Staphylococcus aureus is an opportunistic pathogen responsible for a wide range of infections in humans, such as skin and soft tissue infections, pneumonia, food poisoning or sepsis. Historically, S. aureus was able to rapidly adapt to anti-staphylococcal antibiotics and become resistant to several classes of antibiotics. Today, methicillin-resistant S. aureus (MRSA) is a multidrug-resistant pathogen and is one of the most common bacteria responsible for hospital-acquired infections and outbreaks, in community settings as well. The rapid and accurate diagnosis of antimicrobial resistance in S. aureus is crucial to the early initiation of directed antibiotic therapy and to improve clinical outcomes for patients. In this narrative review, I provide an overview of recent phenotypic and molecular diagnostic methods for antimicrobial resistance detection in S. aureus, with a particular focus on MRSA detection. I consider methods for resistance detection in both clinical samples and isolated S. aureus cultures, along with a brief discussion of the advantages and the challenges of implementing such methods in routine diagnostics.

A common protocol for the simultaneous processing of multiple clinically relevant bacterial species for whole genome sequencing

Whole-genome sequencing is likely to become increasingly used by local clinical microbiology laboratories, where sequencing volume is low compared with national reference laboratories. Here, we describe a universal protocol for simultaneous DNA extraction and sequencing of numerous different bacterial species, allowing mixed species sequence runs to meet variable laboratory demand. We assembled test panels representing 20 clinically relevant bacterial species. The DNA extraction process used the QIAamp mini DNA kit, to which different combinations of reagents were added. Thereafter, a common protocol was used for library preparation and sequencing. The addition of lysostaphin, lysozyme or buffer ATL (a tissue lysis buffer) alone did not produce sufficient DNA for library preparation across the species tested. By contrast, lysozyme plus lysostaphin produced sufficient DNA across all 20 species. DNA from 15 of 20 species could be extracted from a 24-h culture plate, while the remainder required 48-72 h. The process demonstrated 100% reproducibility. Sequencing of the resulting DNA was used to recapitulate previous findings for species, outbreak detection, antimicrobial resistance gene detection and capsular type. This single protocol for simultaneous processing and sequencing of multiple bacterial species supports low volume and rapid turnaround time by local clinical microbiology laboratories.

High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies

Advances in nanopore-based sequencing techniques have enabled rapid characterization of genomes and transcriptomes. An emerging application of this sequencing technology is point-of-care characterization of pathogenic bacteria. However, genome assessments alone are unable to provide a complete understanding of the pathogenic phenotype. Genome-scale metabolic reconstruction and analysis is a bottom-up Systems Biology technique that has elucidated the phenotypic nuances of antimicrobial resistant (AMR) bacteria and other human pathogens. Combining these genome-scale models (GEMs) with point-of-care nanopore sequencing is a promising strategy for combating the emerging health challenge of AMR pathogens. However, the sequencing errors inherent to the nanopore technique may negatively affect the quality, and therefore the utility, of GEMs reconstructed from nanopore assemblies. Here we describe and validate a workflow for rapid construction of GEMs from nanopore (MinION) derived assemblies. Benchmarking the pipeline against a high-quality reference GEM of Escherichia coli K-12 resulted in nanopore-derived models that were >99% complete even at sequencing depths of less than 10× coverage. Applying the pipeline to clinical isolates of pathogenic bacteria resulted in strain-specific GEMs that identified canonical AMR genome content and enabled simulations of strain-specific microbial growth. Additionally, we show that treating the sequencing run as a mock metagenome did not degrade the quality of models derived from metagenome assemblies. Taken together, this study demonstrates that combining nanopore sequencing with GEM construction pipelines enables rapid, in situ characterization of microbial metabolism.

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.

Defining metrics for whole-genome sequence analysis of MRSA in clinical practice

Bacterial sequencing will become increasingly adopted in routine microbiology laboratories. Here, we report the findings of a technical evaluation of almost 800 clinical methicillin-resistant Staphylococcus aureus (MRSA) isolates, in which we sought to define key quality metrics to support MRSA sequencing in clinical practice. We evaluated the accuracy of mapping to a generic reference versus clonal complex (CC)-specific mapping, which is more computationally challenging. Focusing on isolates that were genetically related (<50 single nucleotide polymorphisms (SNPs)) and belonged to prevalent sequence types, concordance between these methods was 99.5 %. We use MRSA MPROS0386 to control for base calling accuracy by the sequencer, and used multiple repeat sequences of the control to define a permitted range of SNPs different to the mapping reference for this control (equating to 3 standard deviations from the mean). Repeat sequences of the control were also used to demonstrate that SNP calling was most accurate across differing coverage depths (above 35×, the lowest depth in our study) when the depth required to call a SNP as present was at least 4-8×. Using 786 MRSA sequences, we defined a robust measure for mec gene detection to reduce false-positives arising from contamination, which was no greater than 2 standard deviations below the average depth of coverage across the genome. Sequencing from bacteria harvested from clinical plates runs an increased risk of contamination with the same or different species, and we defined a cut-off of 30 heterozygous sites >50 bp apart to identify same-species contamination for MRSA. These metrics were combined into a quality-control (QC) flowchart to determine whether sequence runs and individual clinical isolates passed QC, which could be adapted by future automated analysis systems to enable rapid hands-off sequence analysis by clinical laboratories.

Phylogeographical Analysis Reveals the Historic Origin, Emergence, and Evolutionary Dynamics of Methicillin-Resistant Staphylococcus aureus ST228

BACKGROUND

Methicillin-resistant Staphylococcus aureus (MRSA) is a common healthcare-associated pathogen that remains a major public health concern. Sequence type 228 (ST228) was first described in Germany and spread to become a successful MRSA clone in several European countries. In 2000, ST228 emerged in Lausanne and has subsequently caused several large outbreaks. Here, we describe the evolutionary history of this clone and identify the genetic changes underlying its expansion in Switzerland.

MATERIALS AND METHODS

We aimed to understand the phylogeographic and demographic dynamics of MRSA ST228/ST111 by sequencing 530 representative isolates of this clone that were collected from 14 European countries between 1997 and 2012.

RESULTS

The phylogenetic analysis revealed distinct lineages of ST228 isolates associated with specific geographic origins. In contrast, isolates of ST111, which is a single locus variant of ST228 sharing the same spa type t041, formed a monophyletic cluster associated with multiple countries. The evidence points to a German origin of the sampled population, with the basal German lineage being characterized by spa type t001. The highly successful Swiss ST228 lineage diverged from this progenitor clone through the loss of the aminoglycoside-streptothricin resistance gene cluster and the gain of mupirocin resistance. This lineage was introduced first in Geneva and was subsequently introduced into Lausanne.

CONCLUSION

Our results reveal the radiation of distinct lineages of MRSA ST228 from a German progenitor, as the clone spread into different European countries. In Switzerland, ST228 was introduced first in Geneva and was subsequently introduced into Lausanne.

An Escherichia coli ST131 pangenome atlas reveals population structure and evolution across 4,071 isolates

Escherichia coli ST131 is a major cause of infection with extensive antimicrobial resistance (AMR) facilitated by widespread beta-lactam antibiotic use. This drug pressure has driven extended-spectrum beta-lactamase (ESBL) gene acquisition and evolution in pathogens, so a clearer resolution of ST131's origin, adaptation and spread is essential. E. coli ST131's ESBL genes are typically embedded in mobile genetic elements (MGEs) that aid transfer to new plasmid or chromosomal locations, which are mobilised further by plasmid conjugation and recombination, resulting in a flexible ESBL, MGE and plasmid composition with a conserved core genome. We used population genomics to trace the evolution of AMR in ST131 more precisely by extracting all available high-quality Illumina HiSeq read libraries to investigate 4,071 globally-sourced genomes, the largest ST131 collection examined so far. We applied rigorous quality-control, genome de novo assembly and ESBL gene screening to resolve ST131's population structure across three genetically distinct Clades (A, B, C) and abundant subclades from the dominant Clade C. We reconstructed their evolutionary relationships across the core and accessory genomes using published reference genomes, long read assemblies and k-mer-based methods to contextualise pangenome diversity. The three main C subclades have co-circulated globally at relatively stable frequencies over time, suggesting attaining an equilibrium after their origin and initial rapid spread. This contrasted with their ESBL genes, which had stronger patterns across time, geography and subclade, and were located at distinct locations across the chromosomes and plasmids between isolates. Within the three C subclades, the core and accessory genome diversity levels were not correlated due to plasmid and MGE activity, unlike patterns between the three main clades, A, B and C. This population genomic study highlights the dynamic nature of the accessory genomes in ST131, suggesting that surveillance should anticipate genetically variable outbreaks with broader antibiotic resistance levels. Our findings emphasise the potential of evolutionary pangenomics to improve our understanding of AMR gene transfer, adaptation and transmission to discover accessory genome changes linked to novel subtypes.

Pilot Evaluation of a Fully Automated Bioinformatics System for Analysis of Methicillin-Resistant Staphylococcus aureus Genomes and Detection of Outbreaks

Genomic surveillance that combines bacterial sequencing and epidemiological information will become the gold standard for outbreak detection, but its clinical translation is hampered by the lack of automated interpretation tools. We performed a prospective pilot study to evaluate the analysis of methicillin-resistant Staphylococcus aureus (MRSA) genomes using the Next Gen Diagnostics (NGD) automated bioinformatics system.

Genomic surveillance that combines bacterial sequencing and epidemiological information will become the gold standard for outbreak detection, but its clinical translation is hampered by the lack of automated interpretation tools. We performed a prospective pilot study to evaluate the analysis of methicillin-resistant Staphylococcus aureus (MRSA) genomes using the Next Gen Diagnostics (NGD) automated bioinformatics system. Seventeen unselected MRSA-positive patients were identified in a clinical microbiology laboratory in England over a period of 2 weeks in 2018, and 1 MRSA isolate per case was sequenced on the Illumina MiniSeq instrument. The NGD system automatically activated after sequencing and processed fastq folders to determine species, multilocus sequence type, the presence of a mec gene, antibiotic susceptibility predictions, and genetic relatedness based on mapping to a reference MRSA genome and detection of pairwise core genome single-nucleotide polymorphisms. The NGD system required 90 s per sample to automatically analyze data from each run, the results of which were automatically displayed. The same data were independently analyzed using a research-based approach. There was full concordance between the two analysis methods regarding species (S. aureus), detection of mecA, sequence type assignment, and detection of genetic determinants of resistance. Both analysis methods identified two MRSA clusters based on relatedness, one of which contained 3 cases that were involved in an outbreak linked to a clinic and ward associated with diabetic patient care. We conclude that, in this pilot study, the NGD system provided rapid and accurate data that could support infection control practices.

Rapid sequencing of MRSA direct from clinical plates in a routine microbiology laboratory

BACKGROUND

Routine sequencing of MRSA could bring about significant improvements to outbreak detection and investigation. Sequencing is commonly performed using DNA extracted from a pure culture, but overcoming the delay associated with this step could reduce the time to infection control interventions.

OBJECTIVES

To develop and evaluate rapid sequencing of MRSA using primary clinical cultures.

METHODS

Patients with samples submitted to the clinical laboratory at the Cambridge University Hospitals NHS Foundation Trust from which MRSA was isolated were identified, the routine laboratory culture plates obtained and DNA extraction and sequencing performed.

RESULTS

An evaluation of routine MRSA cultures from 30 patients demonstrated that direct sequencing from bacterial colonies picked from four different culture media was feasible. The 30 clinical MRSA isolates were sequenced on the day of plate retrieval over five runs and passed quality control metrics for sequencing depth and coverage. The maximum contamination detected using Kraken was 1.09% fragments, which were identified as Prevotella dentalis. The most common contaminants were other staphylococcal species (25 isolate sequences) and Burkholderia dolosa (11 isolate sequences). Core genome pairwise SNP analysis to identify clusters based on isolates that were ≤50 SNPs different was used to triage cases for further investigation. This identified three clusters, but more detailed genomic and epidemiological evaluation excluded an acute outbreak.

CONCLUSIONS

Rapid sequencing of MRSA from clinical culture plates is feasible and reduces the delay associated with purity culture prior to DNA extraction.