The gut microbiota is a reservoir for antimicrobial resistance genes (ARGs). With current sequencing methods, it is difficult to assign ARGs to their microbial hosts, particularly if these ARGs are located on plasmids. Metagenomic chromosome conformation capture approaches (meta3C and Hi-C) have recently been developed to link bacterial genes to phylogenetic markers, thus potentially allowing the assignment of ARGs to their hosts on a microbiome-wide scale. Here, we generated a meta3C dataset of a human stool sample and used previously published meta3C and Hi-C datasets to investigate bacterial hosts of ARGs in the human gut microbiome. Sequence reads mapping to repetitive elements were found to cause problematic noise in, and may importantly skew interpretation of, meta3C and Hi-C data. We provide a strategy to improve the signal-to-noise ratio by discarding reads that map to insertion sequence elements and to the end of contigs. We also show the importance of using spike-in controls to quantify whether the cross-linking step in meta3C and Hi-C protocols has been successful. After filtering to remove artefactual links, 87 ARGs were assigned to their bacterial hosts across all datasets, including 27 ARGs in the meta3C dataset we generated. We show that commensal gut bacteria are an important reservoir for ARGs, with genes coding for aminoglycoside and tetracycline resistance being widespread in anaerobic commensals of the human gut.
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http://dx.doi.org/10.1099/mgen.0.001030 | DOI Listing |
Microb Genom
June 2023
Institute of Microbiology and Infection, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
The gut microbiota is a reservoir for antimicrobial resistance genes (ARGs). With current sequencing methods, it is difficult to assign ARGs to their microbial hosts, particularly if these ARGs are located on plasmids. Metagenomic chromosome conformation capture approaches (meta3C and Hi-C) have recently been developed to link bacterial genes to phylogenetic markers, thus potentially allowing the assignment of ARGs to their hosts on a microbiome-wide scale.
View Article and Find Full Text PDFMethods Mol Biol
January 2022
Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, Paris, France.
Microbial communities are key components of all ecosystems, but characterization of their complete genomic structure remains challenging. Typical analysis tends to elude the complexity of the mixes in terms of species, strains, as well as extrachromosomal DNA molecules. Recently, approaches have been developed that bins DNA contigs into individual genomes and episomes according to their 3D contact frequencies.
View Article and Find Full Text PDFFront Genet
August 2019
Institut Pasteur, Unité Régulation Spatiale des Génomes, UMR3525, CNRS, Paris, France.
Characterizing the complete genomic structure of complex microbial communities would represent a key step toward the understanding of their diversity, dynamics, and evolution. Current metagenomics approaches aiming at this goal are typically done by analyzing millions of short DNA sequences directly extracted from the environment. New experimental and computational approaches are constantly sought for to improve the analysis and interpretation of such data.
View Article and Find Full Text PDFMethods Enzymol
July 2019
Institut Pasteur, Département Génomes et Génétique, Groupe Régulation Spatiale des Génomes, Paris Cedex 15, France; CNRS, UMR 3525, Paris Cedex 15, France; Institut Pasteur, Center of Bioinformatics, Biostatistics and Integrative Biology (C3BI), Paris, France. Electronic address:
Microbial species thrive in very diverse environments and play fundamental roles in their equilibrium and dynamics. Metagenomics consists in extracting, sequencing, and studying the DNA present in ecosystems to better understand their regulation. Ideally, the maximal amount of information would be gathered from the full sequences of the genomes, episomes, and phages present in the microbial communities.
View Article and Find Full Text PDFGigascience
February 2018
The ithree institute, University of Technology Sydney, PO Box 123, Broadway, NSW 2077, Australia.
Background: Chromosome conformation capture (3C) and Hi-C DNA sequencing methods have rapidly advanced our understanding of the spatial organization of genomes and metagenomes. Many variants of these protocols have been developed, each with their own strengths. Currently there is no systematic means for simulating sequence data from this family of sequencing protocols, potentially hindering the advancement of algorithms to exploit this new datatype.
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