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.

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.

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission.

Understanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.

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.

Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis.

BACKGROUND: Whole-genome sequencing (WGS) can be used in genomic epidemiology investigations to confirm or refute outbreaks of bacterial pathogens, and to support targeted and efficient infection control interventions. We aimed to define a genetic relatedness cutoff, quantified as a number of single-nucleotide polymorphisms (SNP), for meticillin-resistant Staphylococcus aureus (MRSA), above which recent (ie, within 6 months) patient-to-patient transmission could be ruled out. METHODS: We did a retrospective genomic and epidemiological analysis of MRSA data from two prospective observational cohort studies in the UK to establish SNP cutoffs for genetic relatedness, above which recent transmission was unlikely. We used three separate approaches to calculate these thresholds. First, we applied a linear mixed model to estimate the S aureus substitution rate and 95th percentile within-host diversity in a cohort in which multiple isolates were sequenced per individual. Second, we applied a simulated transmission model to this same genomic dataset. Finally, in a second cohort, we determined the genetic distance (ie, the number of SNPs) that would capture 95% of epidemiologically linked cases. We applied the three approaches to both whole-genome and core-genome sequences. FINDINGS: In the linear mixed model, the estimated substitution rate was roughly 5 whole-genome SNPs (wgSNPs) or 3 core-genome SNPs (cgSNPs) per genome per year, and the 95th percentile within-host diversity was 19 wgSNPs or 10 cgSNPs. The combined SNP cutoffs for detection of MRSA transmission within 6 months per this model were thus 24 wgSNPs or 13 cgSNPs. The simulated transmission model suggested that cutoffs of 17 wgSNPs or 12 cgSNPs would detect 95% of MRSA transmission events within the same timeframe. Finally, in the second cohort, cutoffs of 22 wgSNPs or 11 cgSNPs captured 95% of epidemiologically linked cases within 6 months. INTERPRETATION: On the basis of our results, we propose conservative cutoffs of 25 wgSNPs or 15 cgSNPS above which transmission of MRSA within the previous 6 months can be ruled out. These cutoffs could potentially be used as part of a genomic sequencing approach to the management of outbreaks of MRSA in conjunction with traditional epidemiological techniques. FUNDING: UK Department of Health, Wellcome Trust, UK National Institute for Health Research.

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.

Evaluation of a fully automated bioinformatics tool to predict antibiotic resistance from MRSA genomes.

OBJECTIVES: The genetic prediction of phenotypic antibiotic resistance based on analysis of WGS data is becoming increasingly feasible, but a major barrier to its introduction into routine use is the lack of fully automated interpretation tools. Here, we report the findings of a large evaluation of the Next Gen Diagnostics (NGD) automated bioinformatics analysis tool to predict the phenotypic resistance of MRSA. METHODS: MRSA-positive patients were identified in a clinical microbiology laboratory in England between January and November 2018. One MRSA isolate per patient together with all blood culture isolates (total n = 778) were sequenced on the Illumina MiniSeq instrument in batches of 21 clinical MRSA isolates and three controls. RESULTS: The NGD system activated post-sequencing and processed the sequences to determine susceptible/resistant predictions for 11 antibiotics, taking around 11 minutes to analyse 24 isolates sequenced on a single sequencing run. NGD results were compared with phenotypic susceptibility testing performed by the clinical laboratory using the disc diffusion method and EUCAST breakpoints. Following retesting of discrepant results, concordance between phenotypic results and NGD genetic predictions was 99.69%. Further investigation of 22 isolate genomes associated with persistent discrepancies revealed a range of reasons in 12 cases, but no cause could be found for the remainder. Genetic predictions generated by the NGD tool were compared with predictions generated by an independent research-based informatics approach, which demonstrated an overall concordance between the two methods of 99.97%. CONCLUSIONS: We conclude that the NGD system provides rapid and accurate prediction of the antibiotic susceptibility of MRSA.

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. 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.

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

There is growing evidence for the value of bacterial whole-genome sequencing in hospital outbreak investigations. Our aim was to develop methods that support efficient and accurate low-throughput clinical sequencing of methicillin-resistant Staphylococcus aureus (MRSA) isolates. Using a test panel of 25 MRSA isolates previously associated with outbreak investigations, we devised modifications to library preparation that reduced the processing time by 1 hour. We determined the maximum number of isolates that could be sequenced per run using an Illumina MiniSeq platform and a 13-hour (overnight) run time, which equated to 21 MRSA isolates and 3 controls (no template, positive, and negative). Repeatability and reproducibility assays based on this sequencing methodology demonstrated 100% accuracy in assigning species and sequence type (ST) and in detecting mecA Established genetic relatedness between isolates was recapitulated. Quality control (QC) metrics were evaluated over nine sequencing runs. Of the test panel MRSA genomes, 168/173 (97%) passed QC metrics based on the correct species assigned, detection of mecA and ST, and depth/coverage metrics. An evaluation of contamination in these 9 runs showed that positive and negative controls and test MRSA sequence files contained <0.14% and <0.48% of fragments that matched another species, respectively. Deliberate contamination experiments confirmed that this was insufficient to impact data interpretation. These methods support reliable and reproducible clinical MRSA sequencing with a turnaround time (from DNA extraction to availability of data files) of 24 hours.