Pilot Evaluation of a Fully Automated Bioinformatics System for Analysis of Methicillin-Resistant Staphylococcus aureus Genomes and Detection of Outbreaks.
J Clin Microbiol
; 57(11)2019 11.
Article
em En
| MEDLINE
| ID: mdl-31462548
ABSTRACT
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.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Infecções Estafilocócicas
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Surtos de Doenças
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Genoma Bacteriano
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Biologia Computacional
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Automação Laboratorial
Tipo de estudo:
Diagnostic_studies
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Evaluation_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Revista:
J Clin Microbiol
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Reino Unido