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Peer reviewedOpen accessGonorrhea

Public health surveillance of multidrug-resistant clones of Neisseria gonorrhoeae in Europe: a genomic survey

The Lancet Infectious Diseases·

Simon R Harris, Michelle J Cole, Gianfranco Spiteri, Leonor Sánchez-Busó, Daniel Golparian, Susanne Jacobsson, Richard Goater, Khalil Abudahab, Corin A Yeats, Beatrice Bercot, Maria José Borrego, Brendan Crowley, Paola Stefanelli, Francesco Tripodo, Raquel Abad, David M Aanensen, Magnus Unemo, Jacinta Azevedo, Eszter Balla, Christopher Barbara, Thea Bergheim, Maria José Borrego, Viviane Bremer, Susanne Buder, Panayiota Maikanti-Charalambous, Stephanie Chisholm, Susan Cowan, Brendan Crowley, Tania Crucitti, Mercedes Diez, Mária Dudás, Kirstine Eastick, Agathe Goubard, Maria Haller, Guôrún Svanborg Hauksdóttir, Steen Hoffmann, Gwenda Hughes, Derval Igoe, Samo Jeverica, Irena Klavs, Hilde Kløvstad, Peter Kohl, Vasileia Konte, Ineke Linde, Violeta Mavcutko, Jackie Maistre Melillo, Gatis Pakarna, Peter Pavlik, Despo Pieridou, Guy La Ruche, Guôrún Sigmundsdóttir, Soteroulla Soteriou, Angelika Stary, Paola Stefanelli, Barbara Suligoi, Peter Truska, Eva Tzelepi, Magnus Unemo, Birgit Van Benthem, Alje Van Dam, Julio Vazquez, Inga Velicko, Ruth Verbrugge

DOI
10.1016/s1473-3099(18)30225-1
PMID
29776807
PMCID
PMC6010626
OpenAlex
W2803800822
Study type
Genomic study
Publisher
Elsevier BV
Article type
journal-article
Integrity
current

Why this research matters now

The study provides a framework for genomic surveillance of gonococcal infection using standardized sampling, whole-genome sequencing, and open-access software, demonstrating enhanced capacity to track antimicrobial resistance distribution and replacement patterns across risk groups nationally and regionally.

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Structured evidence summary

Research question

The study used whole-genome sequencing combined with epidemiological and phenotypic data to describe the gonococcal population across 20 European countries, assess changes in antimicrobial resistance levels and strain distribution, and evaluate WGS-based antimicrobial resistance prediction.

Study design

This was an observational genomic survey that sequenced gonococcal isolates collected from the European Gonococcal Antimicrobial Surveillance Programme across 20 countries during a defined period (September-November 2013). The study also developed a web platform for automated antimicrobial resistance prediction and molecular typing integrated with epidemiological data.

Population and setting

The study analyzed 1,054 gonococcal isolates from patients with gonorrhoea across 20 European countries.

Main findings

The multidrug-resistant NG-MAST genogroup G1407, previously associated with men who have sex men, became predominantly associated with heterosexual people (odds ratio 4.29) while its prevalence decreased from 23% (2009-10) to 17% (2013). Whole-genome sequencing provided improved resolution and accuracy compared to traditional typing methods, effectively predicted antimicrobial resistance, and identified discrepant results, mixed infections, contaminants, and drug-resistant clades linked to specific risk groups.

Public-health relevance

The study provides a framework for genomic surveillance of gonococcal infection using standardized sampling, whole-genome sequencing, and open-access software, demonstrating enhanced capacity to track antimicrobial resistance distribution and replacement patterns across risk groups nationally and regionally.

Important limitations

No explicit limitations were stated in the supplied abstract. This summary is limited to the supplied single-article abstract and does not include full-text details; interpretation would benefit from access to the original paper for complete context and methodological specifics.

GIDS interpretation

This study demonstrates the feasibility and value of integrating whole-genome sequencing into regional sexually transmitted infection surveillance programs. The observed shift in genogroup G1407 distribution toward heterosexual populations illustrates how genomic surveillance can identify changing epidemiological patterns that may inform public health response, though causal attribution to specific factors would require additional investigation beyond this descriptive survey.

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Related GIDS surveillance

Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.

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Evidence relationships

This article has 8 auditable classifier relationships to diseases, places, topics, and study design.

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