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Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples

View ORCID ProfileMaud Tournoud, Etienne Ruppé, Guillaume Perrin, Stéphane Schicklin, Ghislaine Guigon, Pierre Mahé, Vladimir Lazarevic, Sébastien Hauser, Caroline Mirande, Albrice Levrat, Karen Louis, Gaspard Gervasi, Jacques Schrenzel
doi: https://doi.org/10.1101/2020.02.26.966309
Maud Tournoud
1bioMérieux, Marcy l’Etoile, France
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  • For correspondence: maud.tournoud@biomerieux.com
Etienne Ruppé
2Genomic Research Laboratory, Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
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Guillaume Perrin
1bioMérieux, Marcy l’Etoile, France
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Stéphane Schicklin
1bioMérieux, Marcy l’Etoile, France
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Ghislaine Guigon
1bioMérieux, Marcy l’Etoile, France
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Pierre Mahé
1bioMérieux, Marcy l’Etoile, France
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Vladimir Lazarevic
2Genomic Research Laboratory, Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
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Sébastien Hauser
1bioMérieux, Marcy l’Etoile, France
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Caroline Mirande
1bioMérieux, Marcy l’Etoile, France
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Albrice Levrat
3CH Annecy Genevois, Epagny Metz-Tessy, France
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Karen Louis
4BioAster, Lyon, France
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Gaspard Gervasi
1bioMérieux, Marcy l’Etoile, France
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Jacques Schrenzel
2Genomic Research Laboratory, Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
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Abstract

Background Shortening the time-to-result for pathogen detection and identification and antibiotic susceptibility testing for patients with Hospital-Acquired and Ventilator-Associated pneumonia (HAP-VAP) is of great interest. For this purpose, clinical metagenomics is a promising non-hypothesis driven alternative to traditional culture-based solutions: when mature, it would allow direct sequencing all microbial genomes present in a BronchoAlveolar Lavage (BAL) sample with the purpose of simultaneously identifying pathogens and Antibiotic Resistance Genes (ARG). In this study, we describe a new bioinformatics method to detect pathogens and their ARG with good accuracy, both in mono- and polymicrobial samples.

Methods The standard approach (hereafter called TBo), that consists in taxonomic binning of metagenomic reads followed by an assembly step, suffers from lack of sensitivity for ARG detection. Thus, we propose a new bioinformatics approach (called TBwDM) with both models and databases optimized for HAP-VAP, that performs reads mapping against ARG reference database in parallel to taxonomic binning, and joint reads assembly.

Results In in-silico simulated monomicrobial samples, the recall for ARG detection increased from 51% with TBo to 97.3% with TBwDM; in simulated polymicrobial infections, it increased from 41.8% to 82%. In real sequenced BAL samples (mono and polymicrobial), detected pathogens were also confirmed by traditional culture approaches. Moreover, both recall and precision for ARG detection were higher with TBwDM than with TBo (35 points difference for recall, and 7 points difference for precision).

Conclusions We present a new bioinformatics pipeline to identify pathogens and ARG in BAL samples from patients with HAP-VAP, with higher sensitivity for ARG recovery than standard approaches and the ability to link ARG to their host pathogens.

  • Abbreviations

    HAP-VAP
    Hospital-Acquired and Ventilator-Associated Pneumonia
    DNA
    Desoxyribose Nucleic Acid
    BAL
    BronchoAlveolar Lavage
    ARG
    Antibiotic Resistance Gene
    RDB
    Reference DataBase
    TBwDM
    Taxonomic Binning with Determinant Mapping
    TBo
    Taxonomic Binning only
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    Posted February 27, 2020.
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    Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples
    Maud Tournoud, Etienne Ruppé, Guillaume Perrin, Stéphane Schicklin, Ghislaine Guigon, Pierre Mahé, Vladimir Lazarevic, Sébastien Hauser, Caroline Mirande, Albrice Levrat, Karen Louis, Gaspard Gervasi, Jacques Schrenzel
    bioRxiv 2020.02.26.966309; doi: https://doi.org/10.1101/2020.02.26.966309
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    Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples
    Maud Tournoud, Etienne Ruppé, Guillaume Perrin, Stéphane Schicklin, Ghislaine Guigon, Pierre Mahé, Vladimir Lazarevic, Sébastien Hauser, Caroline Mirande, Albrice Levrat, Karen Louis, Gaspard Gervasi, Jacques Schrenzel
    bioRxiv 2020.02.26.966309; doi: https://doi.org/10.1101/2020.02.26.966309

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