Antimicrobial resistance prediction and phylogenetic analysis of Neisseria gonorrhoeae isolates using the Oxford Nanopore MinION sequencerShow others and affiliations
2018 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 8, no 1, article id 17596
Article in journal (Refereed) Published
Abstract [en]
Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is common, compromising gonorrhoea treatment internationally. Rapid characterisation of AMR strains could ensure appropriate and personalised treatment, and support identification and investigation of gonorrhoea outbreaks in nearly real-time. Whole-genome sequencing is ideal for investigation of emergence and dissemination of AMR determinants, predicting AMR, in the gonococcal population and spread of AMR strains in the human population. The novel, rapid and revolutionary long-read sequencer MinION is a small hand-held device that generates bacterial genomes within one day. However, accuracy of MinION reads has been suboptimal for many objectives and the MinION has not been evaluated for gonococci. In this first MinION study for gonococci, we show that MinION-derived sequences analysed with existing open-access, web-based sequence analysis tools are not sufficiently accurate to identify key gonococcal AMR determinants. Nevertheless, using an in house-developed CLC Genomics Workbench including de novo assembly and optimised BLAST algorithms, we show that 2D ONT-derived sequences can be used for accurate prediction of decreased susceptibility or resistance to recommended antimicrobials in gonococcal isolates. We also show that the 2D ONT-derived sequences are useful for rapid phylogenomic-based molecular epidemiological investigations, and, in hybrid assemblies with Illumina sequences, for producing contiguous assemblies and finished reference genomes.
Place, publisher, year, edition, pages
Nature Publishing Group, 2018. Vol. 8, no 1, article id 17596
National Category
Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:oru:diva-70622DOI: 10.1038/s41598-018-35750-4ISI: 000452084600004PubMedID: 30514867Scopus ID: 2-s2.0-85057604734OAI: oai:DiVA.org:oru-70622DiVA, id: diva2:1269373
Note
Funding Agencies:
Orebro County Council Research Committee
Foundation for Medical Research at Orebro University Hospital, Orebro, Sweden
SwissTransMed initiative (Translational Research Platforms in Medicine) from the Rectors' Conference of the Swiss Universities (CRUS) 25/2013
Pathogen Informatics Group at the Wellcome Sanger Institute
Wellcome Grant 098051
2018-12-102018-12-102025-02-07Bibliographically approved