Whole-genome sequencing for routine pathogen surveillance in public health: A population snapshot of invasive Staphylococcus aureus in Europe

David M. Aanensen, Edward J. Feil, Matthew T G Holden, Janina Dordel, Corin A. Yeats, Artemij Fedosejev, Richard Goater, Santiago Castillo-Ramírez, Jukka Corander, Caroline Colijn, Monika A. Chlebowicz, Leo Schouls, Max Heck, Gerlinde Pluister, Raymond Ruimy, Gunnar Kahlmeter, Jenny Åhman, Erika Matuschek, Alexander W. Friedrich, Julian ParkhillStephen D. Bentley, Brian G. Spratt, Hajo Grundmann*, Karina Krziwanek, Sabine Stumvoll, Walter Koller, Olivier Denis, Marc Struelens, Dimitr Nashev, Ana Budimir, Smilja Kalenic, Despo Pieridou-Bagatzouni, Vladislav Jakubu, Helena Zemlickova, Henrik Westh, Anders Rhod Larsen, Robert Skov, Frederic Laurent, Jerome Ettienne, Birgit Strommenger, Wolfgang Witte, Sofia Vourli, Alkis Vatopoulos, Anni Vainio, Jaana Vuopio-Varkila, Miklos Fuzi, Erika Ungvári, Stephan Murchan, Angela Rossney, Edvins Miklasevics, Arta Balode, Gunnsteinn Haraldsson, Karl G. Kristinsson, Monica Monaco, Annalisa Pantosti, Michael Borg, Marga Van Santen-Verheuvel, Xander Huijsdens, Lillian Marstein, Trond Jacobsen, Gunnar Skov Simonsen, Marta Airesde-Sousa, Herminia De Lencastre, Agnieszka Luczak-Kadlubowska, Waleria Hryniewicz, Monica Straut, Irina Codita, Maria Perez-Vazquez, Jesus Oteo Iglesias, Vesna Cvitkovic Spik, Manica Mueller-Premru, Sara Haeggman, Barbro Olsson-Liljequist, Matthew Ellington, Angela Kearns

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

111 Citations (Scopus)
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The implementation of routine whole-genome sequencing (WGS) promises to transform our ability to monitor the emergence and spread of bacterial pathogens. Here we combined WGS data from 308 invasive Staphylococcus aureus isolates corresponding to a pan-European population snapshot, with epidemiological and resistance data. Geospatial visualization of the data is made possible by a generic software tool designed for public health purposes that is available at the project URL (http:// www.microreact.org/project/EkUvg9uY?tt=rc). Our analysis demonstrates that high-risk clones can be identified on the basis of population level properties such as clonal relatedness, abundance, and spatial structuring and by inferring virulence and resistance properties on the basis of gene content. We also show that in silico predictions of antibiotic resistance profiles are at least as reliable as phenotypic testing. We argue that this work provides a comprehensive road map illustrating the three vital components for future molecular epidemiological surveillance: (i) large-scale structured surveys, (ii) WGS, and (iii) communityoriented database infrastructure and analysis tools. IMPORTANCE The spread of antibiotic-resistant bacteria is a public health emergency of global concern, threatening medical intervention at every level of health care delivery. Several recent studies have demonstrated the promise of routine wholegenome sequencing (WGS) of bacterial pathogens for epidemiological surveillance, outbreak detection, and infection control. However, as this technology becomes more widely adopted, the key challenges of generating representative national and international data sets and the development of bioinformatic tools to manage and interpret the data become increasingly pertinent. This study provides a road map for the integration of WGS data into routine pathogen surveillance. We emphasize the importance of large-scale routine surveys to provide the population context for more targeted or localized investigation and the development of open-access bioinformatic tools to provide the means to combine and compare independently generated data with publicly available data sets.

Original languageEnglish
Article numbere00444-16
Number of pages15
Issue number3
Publication statusPublished - 1 May 2016
MoE publication typeA1 Journal article-refereed

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