A community-driven resource for genomic epidemiology and antimicrobial resistance prediction of Neisseria gonorrhoeae at Pathogenwatch

• Type of Material: Article
• Authors: Leonor Sanchez-Buso, University of Oxford; Corin A. Yeats, University of Oxford; Benjamin Taylor, University of Oxford; Richard J. Goater, Wellcome Sanger Institute; Anthony Underwood, Wellcome Sanger Institute; Khalil Abudahab, Wellcome Sanger Institute; Silvia Argimon, Wellcome Sanger Institute; Kevin C. Ma, Harvard University; Tatum D. Mortimer, Harvard University; Daniel Golparian, Orebro University; Michelle J. Cole, Public Health England; Yonatan H. Grad, Harvard University; Irene Martin, Public Health Agency of Canada; Brian H. Raphael, Centers for Disease Control and Prevention; William Shafer, Emory University; Katy Town, Centers for Disease Control and Prevention; Teodora Wi, World Health Organization; Simon R. Harris, Biodata Innovation Center; Magnus Unemo, Orebro University; David M. Aanensen, University of Oxford
• Language: English
• Date: 2021-04-19
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s). 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13073-021-00858-2
• Title of Journal or Parent Work: Genome Medicine
• Volume: 13
• Issue: 1
• Start Page: 61
• End Page: 61
• Grant/Funding Information: YHG is supported by the NIH/NIAID grants R01 AI132606 and R01 AI153521. KCM is supported by the NSF GRFP grant number DGE1745303. TDM is supported by the National Institute of Allergy and Infectious Diseases at the National Institutes of Health [1 F32 AI145157-01].; At the time of preparation of this manuscript, LSB was supported by the Li Ka Shing Foundation (Big Data Institute, University of Oxford) and the Centre for Genomic Pathogen Surveillance (CGPS, https://www.pathogensurveillance.net/).; At the time of review and publication of this manuscript, LSB is funded by Plan GenT (CDEI-06/20-B), Conselleria de Sanitat Universal i Salut Pública, Generalitat Valenciana (Valencia, Spain).; WMS is a recipient of a Senior Research Career Scientist Award from the Biomedical Laboratory Research and Development Service of the Department of Veterans. Work on antibiotic resistance in his laboratory is supported by NIH grants R37 AI-021150 and R01 AI-147609.; DMA is supported by the Li Ka Shing Foundation (Big Data Institute, University of Oxford) and the Centre for Genomic Pathogen Surveillance (CGPS). DMA and SA are supported by the National Institute for Health Research (UK) Global Health Research Unit on Genomic Surveillance of AMR (16_136_111).; Pathogenwatch is developed with support from Li Ka Shing Foundation (Big Data Institute, University of Oxford) and Wellcome (099202).; The department of MJC receives funding from the European Centre for Disease Prevention and Control and the National Institute for Health Research (Health Protection Research Unit) for gonococcal whole-genome sequencing.
• Keywords: High-level resistance; Antimicrobial resistance; Azithromycin; Life Sciences & Biomedicine; Expanded spectrum; Pathogenwatch; Ribosomal RNA; Point Mutation; Tetracycline; MTRE Efflux pump; Public health; Reduced susceptibility; Neisseria gonorrhoeae; Identification; Genetics & Heredity; Science & Technology; Epidemiology; Genomics; Surveillance; Macrolide resistance
• Research Categories: Health Sciences, Epidemiology; Biology, Genetics; Health Sciences, Immunology; Health Sciences, Public Health

Abstract

Background Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. Methods Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch (https://pathogen.watch/ngonorrhoeae). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. Results AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. Conclusions The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.