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Blane B, Raven KE, Brown NM, Harrison EM, Coll F, Thaxter R, Enoch DA, Gouliouris T, Leek D, Girgis ST, Akram A, Matuszewska M, Rhodes P, Parkhill J, Peacock SJ. Evaluating the impact of genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) on hospital infection prevention and control decisions. Microb Genom 2024; 10. [PMID: 38630616 DOI: 10.1099/mgen.0.001235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
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.
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Affiliation(s)
- Beth Blane
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Kathy E Raven
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Nicholas M Brown
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Ewan M Harrison
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Francesc Coll
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Rachel Thaxter
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - David A Enoch
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Theodore Gouliouris
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Danielle Leek
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Sophia T Girgis
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Asha Akram
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Marta Matuszewska
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Paul Rhodes
- Next Gen Diagnostics, LLC, (NGD) Mountain View, CA, USA
- Broers Building, 21 JJ Thomson Ave., Cambridge, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, UK
| | - Sharon J Peacock
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
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Large-Scale Evaluation of a Rapid Fully Automated Analysis Platform to Detect and Refute Outbreaks Based on MRSA Genome Comparisons. mSphere 2022; 7:e0028322. [PMID: 36286527 PMCID: PMC9769837 DOI: 10.1128/msphere.00283-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
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.
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Higgs C, Sherry NL, Seemann T, Horan K, Walpola H, Kinsella P, Bond K, Williamson DA, Marshall C, Kwong JC, Grayson ML, Stinear TP, Gorrie CL, Howden BP. Optimising genomic approaches for identifying vancomycin-resistant Enterococcus faecium transmission in healthcare settings. Nat Commun 2022; 13:509. [PMID: 35082278 PMCID: PMC8792028 DOI: 10.1038/s41467-022-28156-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/07/2022] [Indexed: 11/08/2022] Open
Abstract
Vancomycin-resistant Enterococcus faecium (VREfm) is a major nosocomial pathogen. Identifying VREfm transmission dynamics permits targeted interventions, and while genomics is increasingly being utilised, methods are not yet standardised or optimised for accuracy. We aimed to develop a standardized genomic method for identifying putative VREfm transmission links. Using comprehensive genomic and epidemiological data from a cohort of 308 VREfm infection or colonization cases, we compared multiple approaches for quantifying genetic relatedness. We showed that clustering by core genome multilocus sequence type (cgMLST) was more informative of population structure than traditional MLST. Pairwise genome comparisons using split k-mer analysis (SKA) provided the high-level resolution needed to infer patient-to-patient transmission. The more common mapping to a reference genome was not sufficiently discriminatory, defining more than three times more genomic transmission events than SKA (3729 compared to 1079 events). Here, we show a standardized genomic framework for inferring VREfm transmission that can be the basis for global deployment of VREfm genomics into routine outbreak detection and investigation.
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Affiliation(s)
- Charlie Higgs
- Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Norelle L Sherry
- Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Department of Infectious Diseases, Austin Health, Melbourne, VIC, Australia
| | - Torsten Seemann
- Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Kristy Horan
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Hasini Walpola
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Paul Kinsella
- Department of Microbiology, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Katherine Bond
- Department of Microbiology, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Deborah A Williamson
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Department of Microbiology, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital, The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Caroline Marshall
- Victorian Infectious Diseases Service, The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Jason C Kwong
- Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Department of Infectious Diseases, Austin Health, Melbourne, VIC, Australia
| | - M Lindsay Grayson
- Department of Infectious Diseases, Austin Health, Melbourne, VIC, Australia
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC, Australia
| | - Timothy P Stinear
- Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Claire L Gorrie
- Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Benjamin P Howden
- Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
- Department of Infectious Diseases, Austin Health, Melbourne, VIC, Australia.
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Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12010208. [PMID: 35054375 PMCID: PMC8774325 DOI: 10.3390/diagnostics12010208] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/17/2022] Open
Abstract
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.
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Raven KE, Girgis ST, Akram A, Blane B, Leek D, Brown N, Peacock SJ. A common protocol for the simultaneous processing of multiple clinically relevant bacterial species for whole genome sequencing. Sci Rep 2021; 11:193. [PMID: 33420120 PMCID: PMC7794230 DOI: 10.1038/s41598-020-80031-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/09/2020] [Indexed: 12/02/2022] Open
Abstract
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.
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Affiliation(s)
- Kathy E Raven
- Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Road, Box 157, Cambridge, CB2 0QQ, UK
| | - Sophia T Girgis
- Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Road, Box 157, Cambridge, CB2 0QQ, UK
| | - Asha Akram
- Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Road, Box 157, Cambridge, CB2 0QQ, UK
| | - Beth Blane
- Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Road, Box 157, Cambridge, CB2 0QQ, UK
| | - Danielle Leek
- Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Road, Box 157, Cambridge, CB2 0QQ, UK
| | - Nicholas Brown
- Clinical Microbiology and Public Health Laboratory, Public Health England, Cambridge, UK
| | - Sharon J Peacock
- Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Road, Box 157, Cambridge, CB2 0QQ, UK.
- Clinical Microbiology and Public Health Laboratory, Public Health England, Cambridge, UK.
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6
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Tümmler B. Molecular epidemiology in current times. Environ Microbiol 2020; 22:4909-4918. [PMID: 32945108 DOI: 10.1111/1462-2920.15238] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 09/10/2020] [Accepted: 09/15/2020] [Indexed: 01/04/2023]
Abstract
Motivated to find options for prevention or intervention, molecular epidemiology aims to identify the host and microbial factors that determine the transmission, manifestation and progression of infectious disease. The genotyping of cultivatable bacterial strains is performed by either anonymous fingerprinting techniques or sequence-based exploration of variable genomic sites. Multilocus sequence typing of housekeeping genes and allele profiling of the core genome have become standard techniques of bacterial strain typing that may be supplemented by whole genome sequencing to explore all single nucleotide variants and/or the composition of the accessory genome. Next, novel protocols to investigate host and microbiome based upon smart third generation sequencing technologies are being developed for an effective surveillance, rapid diagnosis and real-time tracking of infectious diseases.
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Affiliation(s)
- Burkhard Tümmler
- Clinical Research Group, Clinic for Paediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Hannover, Germany
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Egli A, Schrenzel J, Greub G. Digital microbiology. Clin Microbiol Infect 2020; 26:1324-1331. [PMID: 32603804 PMCID: PMC7320868 DOI: 10.1016/j.cmi.2020.06.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 06/15/2020] [Accepted: 06/20/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. OBJECTIVE This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process. SOURCES We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology. CONTENT We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory. IMPLICATIONS We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.
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Affiliation(s)
- A Egli
- Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland; Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland.
| | - J Schrenzel
- Laboratory of Bacteriology, University Hospitals of Geneva, Geneva, Switzerland
| | - G Greub
- Institute of Medical Microbiology, University Hospital Lausanne, Lausanne, Switzerland
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Staphylococcus aureus whole genome sequence-based susceptibility and resistance prediction using a clinically amenable workflow. Diagn Microbiol Infect Dis 2020; 97:115060. [PMID: 32417617 DOI: 10.1016/j.diagmicrobio.2020.115060] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 03/23/2020] [Accepted: 04/05/2020] [Indexed: 01/21/2023]
Abstract
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.
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Raven KE, Blane B, Kumar N, Leek D, Bragin E, Coll F, Parkhill J, Peacock SJ. Defining metrics for whole-genome sequence analysis of MRSA in clinical practice. Microb Genom 2020; 6:e000354. [PMID: 32228804 PMCID: PMC7276698 DOI: 10.1099/mgen.0.000354] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
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.
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Affiliation(s)
- Kathy E. Raven
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Beth Blane
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Narender Kumar
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Danielle Leek
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Eugene Bragin
- Next Gen Diagnostics LLC (NGD), Mountain View, CA and Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Francesc Coll
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, CB3 0ES, UK
| | - Sharon J. Peacock
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
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González-Candelas F, Francés-Cuesta C, García-González N. The power and limitations of genomic surveillance of bacteria. Future Microbiol 2019; 14:1345-1348. [DOI: 10.2217/fmb-2019-0259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Fernando González-Candelas
- Institute for Integrative Systems Biology, I2SysBio (CSIC-UV), University of Valencia, Valencia, Spain
- Joint Research Unit ‘Infection & Public Health’ FISABIO-University of Valencia, Valencia, Spain
- CIBER in Epidemiology & Public Health, Valencia, Spain
| | - Carlos Francés-Cuesta
- Institute for Integrative Systems Biology, I2SysBio (CSIC-UV), University of Valencia, Valencia, Spain
- Joint Research Unit ‘Infection & Public Health’ FISABIO-University of Valencia, Valencia, Spain
| | - Neris García-González
- Institute for Integrative Systems Biology, I2SysBio (CSIC-UV), University of Valencia, Valencia, Spain
- Joint Research Unit ‘Infection & Public Health’ FISABIO-University of Valencia, Valencia, Spain
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