BACTERIOLOGY crossm Pilot Evaluation of a Fully Automated Bioinformatics System for Analysis of Methicillin-Resistant Staphylococcus aureus Genomes and Detection of Outbreaks Nicholas M. Brown,a Beth Blane,b Kathy E. Raven,b Narender Kumar,b Danielle Leek,b Eugene Bragin,d Paul A. Rhodes,c Downloaded from David A. Enoch,a Rachel Thaxter,a Julian Parkhill,e Sharon J. Peacocka,b aClinical Microbiology and Public Health Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom bDepartment of Medicine, University of Cambridge, Cambridge, United Kingdom cNext Gen Diagnostics, Mountain View, California, USA dNext Gen Diagnostics, Hinxton, United Kingdom eWellcome Sanger Institute, Hinxton, United Kingdom http://jcm.asm.org/ ABSTRACT Genomic surveillance that combines bacterial sequencing and epidemi- ological 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) auto- mated 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 instru- on November 9, 2019 by guest ment. 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 con- cordance between the two analysis methods regarding species (S. aureus), detection of mecA, sequence type assignment, and detection of genetic determinants of resis- Citation Brown NM, Blane B, Raven KE, Kumar tance. Both analysis methods identified two MRSA clusters based on relatedness, N, Leek D, Bragin E, Rhodes PA, Enoch DA, one of which contained 3 cases that were involved in an outbreak linked to a clinic Thaxter R, Parkhill J, Peacock SJ. 2019. Pilot and ward associated with diabetic patient care. We conclude that, in this pilot study, evaluation of a fully automated bioinformatics system for analysis of methicillin-resistant the NGD system provided rapid and accurate data that could support infection con- Staphylococcus aureus genomes and detection trol practices. of outbreaks. J Clin Microbiol 57:e00858-19. https://doi.org/10.1128/JCM.00858-19. KEYWORDS microbiology, Staphylococcus aureus, whole-genome sequencing, Editor Yi-Wei Tang, Memorial Sloan Kettering bioinformatics Cancer Center Copyright © 2019 Brown et al. This is an open- access article distributed under the terms of etection of outbreaks associated with methicillin-resistant Staphylococcus aureus the Creative Commons Attribution 4.0 D(MRSA) in health care settings at the earliest opportunity supports early interven- International license. tions to bring these to a close. Outbreak detection in routine practice relies on the daily Address correspondence to Beth Blane, [email protected]. identification of patients who carry or are infected with MRSA and collation to deter- N.M.B. and B.B. are joint first authors. mine relatedness in time and place. In the event that 2 or more MRSA-positive patients Received 29 May 2019 have epidemiological links, all of the available information is then reviewed to deter- Returned for modification 25 June 2019 mine the probability of an outbreak and the need for further investigations, including Accepted 26 August 2019 screening of other patients, staff members, and/or equipment, and interventions such Accepted manuscript posted online 28 August 2019 as enhanced infection control measures and cleaning. MRSA outbreaks may take Published 23 October 2019 several days or weeks to detect and to investigate using this reactive approach. November 2019 Volume 57 Issue 11 e00858-19 Journal of Clinical Microbiology jcm.asm.org 1 Brown et al. Journal of Clinical Microbiology An alternative paradigm is to undertake routine, prospective, whole-genome se- quencing (WGS)-based surveillance of MRSA and to use comparisons of whole ge- nomes to drive infection control outbreak investigations and interventions (1). Follow- ing sequencing, the decision to investigate (or not) could be driven by a desk-based analysis of bacterial relatedness and patient movement data (1). There is growing evidence for the potential to transform infection control practices through the incor- poration of bacterial sequence data (2–6). MRSA sequencing, when used in combina- tion with patient movement data, provides a more accurate determination of trans- mission events and outbreak status than standard infection control methods alone (2–4). Furthermore, a study of genomic surveillance of MRSA strains isolated in a large clinical microbiology laboratory in the east of England over 12 months led to the identification of hundreds of transmission clusters that were not detected by standard Downloaded from infection control methods (4). Large routine microbiology laboratories are already capable of supporting pathogen sequencing, but a major impediment to its clinical translation is the lack of fully automated interpretation tools. Here, we report the findings of a prospective pilot study to evaluate the analysis of MRSA genomes using the Next Gen Diagnostics (NGD) automated bioinformatics system, which undertakes rapid pairwise comparison of genomes to provide a similarity matrix and has the potential to collate this matrix with patient movement data to provide infection control units with rapid outbreak visual- http://jcm.asm.org/ ization. MATERIALS AND METHODS Ethical approval. The study was conducted under ethical approval from the National Research Ethics Service (reference no. 11/EE/0499) and the Cambridge University Hospitals NHS Foundation Trust (CUH) Research and Development Department (reference no. A092428). Study setting, patients, and sample identification. The study was conducted at the Clinical Microbiology and Public Health Laboratory at the CUH in the United Kingdom. MRSA-positive patients with samples submitted over a period of 2 weeks in 2018 were identified using the hospital information technology system (EPIC Hyperspace 2014; Epic Systems Corp.). Putative or confirmed MRSA-positive on November 9, 2019 by guest culture plates were retrieved by the study team and were confirmed to be S. aureus using the Staph Latex kit (Pro-Lab Diagnostics). Laboratory data on the date and place of sampling, the sample type (screen or clinical sample), bacterial identification, and susceptibility test results were collected. Susceptibility testing using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) disc diffusion method is routinely performed in the laboratory (7), and such data were also collected. Epidemiological data on ward location (for inpatients), general practitioner (GP), and residential postal code were collected. Whole-genome sequencing. Patients and samples were renumbered with an anonymous study code, after which each patient had a single anonymized MRSA isolate processed for WGS. For each isolate, a 1-␮l loopful of growth was inoculated into phosphate-buffered saline to form a suspension and DNA was extracted using the Qiagen DNA mini extraction kit. Sequencing libraries were prepared using the Illumina Nextera DNA flex kit and sequenced with an Illumina MiniSeq system with a run time of 13 h, using the high-output 150-cycle MiniSeq cartridge and the Generate Fastq workflow. Each run contained 3 controls (no-template control, positive control [MRSA MPROS0386], and negative control [Escherichia coli NCTC12241]). Quality control metrics. Controls were required to pass the following metrics prior to further analysis: MRSA positive control—highest match to S. aureus using Kraken, Ͻ1% contamination with another species (equating to Ͻ0.4% match in Kraken) (8), assigned to sequence type 22 (ST22), mecA detected, minimum mean sequence depth of 20ϫ, and minimum of 80% mapping coverage of the MRSA reference genome (HO 5096 0412); E. coli negative control—highest species match to E. coli in Kraken, mec not detected, and no S. aureus ST assigned; no-template control—Ͻ1% contamination with any bacterial DNA (equating to Ͼ95,000 fragments) (8). MRSA isolates from the test panel were required to pass the following metrics prior to further analysis: highest match to S. aureus using Kraken, Ͻ10% contamination with another species (equating to Ͻ4% match in Kraken) (8), assigned to the correct ST, mec gene detected, minimum sequence depth of 20ϫ, and minimum of 80% mapping coverage of the MRSA reference genome (HO 5096 0412). Sequence data analysis using standard bioinformatics pipelines. Bacterial species were deter- mined using Kraken v1 (https://ccb.jhu.edu/software/kraken) and the miniKraken database (https://ccb .jhu.edu/software/kraken/dl/minikraken_20171019_8GB.tgz). Multilocus STs were identified for MRSA using ARIBA v2.12.1 (https://github.com/sanger-pathogens/ariba/wiki/MLST-calling-with-ARIBA). MRSA strains were screened for the presence of mecA (GenBank