Abstract
ObjectivesThe 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.MethodsMRSA-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.ResultsThe 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%.ConclusionsWe conclude that the NGD system provides rapid and accurate prediction of the antibiotic susceptibility of MRSA.
Highlights
Accuracy of the genetic prediction of phenotypic resistance depends on access to a comprehensive reference database but, if made available, sequence data could be used to support clinical care and provide an additional mechanism for the quality control (QC) of routine phenotypic testing
We report the findings of a large prospective evaluation of the Gen Diagnostics (NGD) automated bioinformatics tool to predict the phenotypic resistance of MRSA
We evaluated 778 isolates from 774 MRSA-positive individuals that were cultured from samples submitted to the laboratory between January and November 2018 from wards and clinics at three hospitals (n = 639) and 65 GP surgeries (n = 139)
Summary
There is growing evidence for the potential of pathogen sequencing to transform infection control practice and outbreak investigation.[1–6] As a result, sequencing technologies are becoming increasingly employed in diagnostic and public health microbiology laboratories for surveillance, outbreak investigation and transmission tracking of hospital and foodborne-associated outbreaks and emerging pathogens. As a result, sequencing technologies are becoming increasingly employed in diagnostic and public health microbiology laboratories for surveillance, outbreak investigation and transmission tracking of hospital and foodborne-associated outbreaks and emerging pathogens The reuse of such sequence data to detect genetic mutations and acquired genes associated with phenotypic antibiotic resistance could provide a rich source of surveillance information at little or no additional cost. As the cost and turnaround time of sequencing technologies fall and the databases necessary for genetic prediction expand, genome sequencing will become adopted as the primary method to detect genetic determinants of resistance This is already the case for Mycobacterium tuberculosis, with sequencing having entered into routine practice for prediction of resistance and outbreak investigation in several countries.[7]. This change in methodology is readily justified as susceptibility testing is laborious
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