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Microbiome. 2018 Jan 15;6(1):11. doi: 10.1186/s40168-017-0387-y.

In-depth resistome analysis by targeted metagenomics.

Author information

1
Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain.
2
Joint Unit of Antibiotic Resistance and Bacterial Virulence associated with the Spanish National Research Council (CSIC), Madrid, Spain.
3
Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain.
4
National Center of Biotechnology, CSIC, Madrid, Spain.
5
Genomics Unit, Madrid Science Park, Madrid, Spain.
6
Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
7
IAME, UMR 1137, INSERM, Paris Diderot University, Sorbonne Paris Cité, Bacteriology Laboratory, Hospital Bichat, AP-HP, Paris, France.
8
MGP MetaGénoPolis, INRA, University of Paris-Saclay, Jouy-en-Josas, France.
9
Center of Host Microbiome Interactions, King's College, London, UK.
10
Present Address: Bioinformatics and Biostatistics HUB, C3BI and Biomics Pole, CITECH Pasteur Institute, Centre François Jacob, Paris, France.
11
Department of Medical Microbiology, University Medical Center, Utrecht, Netherlands.
12
Present Address: Institute of Microbiology and Infection, University of Birmingham, Birmingham, B15 2TT, UK.
13
Department of Molecular Biology, University of Cantabria, Santander, Spain.
14
Institute of Biomedicine and Biotechnology of Cantabria, IBBTEC (UC-CSIC), Santander, Spain.
15
Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain. mariateresa.coque@salud.madrid.org.
16
Joint Unit of Antibiotic Resistance and Bacterial Virulence associated with the Spanish National Research Council (CSIC), Madrid, Spain. mariateresa.coque@salud.madrid.org.
17
Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain. mariateresa.coque@salud.madrid.org.

Abstract

BACKGROUND:

Antimicrobial resistance is a major global health challenge. Metagenomics allows analyzing the presence and dynamics of "resistomes" (the ensemble of genes encoding antimicrobial resistance in a given microbiome) in disparate microbial ecosystems. However, the low sensitivity and specificity of available metagenomic methods preclude the detection of minority populations (often present below their detection threshold) and/or the identification of allelic variants that differ in the resulting phenotype. Here, we describe a novel strategy that combines targeted metagenomics using last generation in-solution capture platforms, with novel bioinformatics tools to establish a standardized framework that allows both quantitative and qualitative analyses of resistomes.

METHODS:

We developed ResCap, a targeted sequence capture platform based on SeqCapEZ (NimbleGene) technology, which includes probes for 8667 canonical resistance genes (7963 antibiotic resistance genes and 704 genes conferring resistance to metals or biocides), and 2517 relaxase genes (plasmid markers) and 78,600 genes homologous to the previous identified targets (47,806 for antibiotics and 30,794 for biocides or metals). Its performance was compared with metagenomic shotgun sequencing (MSS) for 17 fecal samples (9 humans, 8 swine). ResCap significantly improves MSS to detect "gene abundance" (from 2.0 to 83.2%) and "gene diversity" (26 versus 14.9 genes unequivocally detected per sample per million of reads; the number of reads unequivocally mapped increasing up to 300-fold by using ResCap), which were calculated using novel bioinformatic tools. ResCap also facilitated the analysis of novel genes potentially involved in the resistance to antibiotics, metals, biocides, or any combination thereof.

CONCLUSIONS:

ResCap, the first targeted sequence capture, specifically developed to analyze resistomes, greatly enhances the sensitivity and specificity of available metagenomic methods and offers the possibility to analyze genes related to the selection and transfer of antimicrobial resistance (biocides, heavy metals, plasmids). The model opens the possibility to study other complex microbial systems in which minority populations play a relevant role.

KEYWORDS:

Antimicrobial resistance; Differential abundance analysis; Metagenomics; Resistome; Targeted metagenomics

PMID:
29335005
PMCID:
PMC5769438
DOI:
10.1186/s40168-017-0387-y
[Indexed for MEDLINE]
Free PMC Article

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