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Web-based prediction of antimicrobial resistance in enterococcal clinical isolates by whole-genome sequencing

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Abstract

Besides phenotypic antimicrobial susceptibility testing (AST), whole genome sequencing (WGS) is a promising alternative approach for detection of resistance phenotypes. The aim of this study was to investigate the concordance between WGS-based resistance prediction and phenotypic AST results for enterococcal clinical isolates using a user-friendly online tools and databases. A total of 172 clinical isolates (34 E. faecalis, 138 E. faecium) received at the French National Reference Center for enterococci from 2017 to 2020 were included. AST was performed by disc diffusion or MIC determination for 14 antibiotics according to CA-SFM/EUCAST guidelines. The genome of all strains was sequenced using the Illumina technology (MiSeq) with bioinformatic analysis from raw reads using online tools ResFinder 4.1 and LRE-finder 1.0. For both E. faecalis and E. faecium, performances of WGS-based genotype to predict resistant phenotypes were excellent (concordance > 90%), particularly for antibiotics commonly used for treatment of enterococcal infections such as ampicillin, gentamicin, vancomycin, teicoplanin, and linezolid. Note that 100% very major errors were found for quinupristin-dalfopristin, tigecycline, and rifampicin for which resistance mutations are not included in databases. Also, it was not possible to predict phenotype from genotype for daptomycin for the same reason. WGS combined with online tools could be easily used by non-expert clinical microbiologists as a rapid and reliable tool for prediction of phenotypic resistance to first-line antibiotics among enterococci. Nonetheless, some improvements should be made such as the implementation of resistance mutations in the database for some antibiotics.

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Data availability

The genomic datasets generated and analyzed during the current study are available in GenBank as bioproject PRJNA875074.

Code availability

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Funding

This work was supported by “Santé Publique France,” the French national public health agency.

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Authors and Affiliations

  1. Service de Bactériologie-Hygiène Hospitalière, CHU de Rennes, Rennes, France

    Malo Penven, Asma Zouari, Sophie Nogues, Anaïs Collet, Maxime Lecourt, François Guerin, Gabriel Auger & Vincent Cattoir

  2. Université de Rennes 1, Inserm U1230, Rennes, France

    Malo Penven & Vincent Cattoir

  3. CNR de La Résistance Aux Antibiotiques (Laboratoire Associé ‘Entérocoques’), Rennes, France

    Asma Zouari, Sophie Nogues, Anaïs Collet, Maxime Lecourt, Aurélien Birer, François Guerin, Gabriel Auger & Vincent Cattoir

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  1. Malo Penven
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  2. Asma Zouari
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  3. Sophie Nogues
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Contributions

Malo Penven and Vincent Cattoir contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all the authors. The first draft of the manuscript was written by Malo Penven and Vincent Cattoir, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Vincent Cattoir.

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Supplementary Information

Figure S1.

Genetic relationship and resistance gene content among 34 E. faecalis clinical isolates. Neighbor-joining phylogenetic tree was constructed from aligned core-genome SNPs analysis based on SNPs and visualized together with ST affiliation (colored strips) and a heatmap for resistance mechanisms (black boxes) with iTOL v5. The scale bar represents 100 SNPs. (PNG 405 kb)

High resolution image (TIF 853 kb)

Figure S2.

Genetic relationship and resistance gene content among 138 E. faecium clinical isolates. Neighbor-joining phylogenetic tree was constructed from aligned core-genome SNPs analysis based on SNPs and visualized together with ST affiliation (colored strips) and a heatmap for resistance mechanisms (black boxes) with iTOL v5. The scale bar represents 1,000 SNPs. (PNG 914 kb)

High resolution image (TIF 2021 kb)

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Penven, M., Zouari, A., Nogues, S. et al. Web-based prediction of antimicrobial resistance in enterococcal clinical isolates by whole-genome sequencing. Eur J Clin Microbiol Infect Dis 42, 67–76 (2023). https://doi.org/10.1007/s10096-022-04527-z

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