Nanopore metagenomic sequencing links clinically relevant resistance determinants to pathogens

Culture-independent metagenomics enables the detection of plasmid-encoded antimicrobial resistance (AMR) genes directly from clinical samples; however, the clinical significance of these genes depends on their bacterial host and genomic context, which metagenomics cannot fully infer. Nanopore sequencing technology intrinsically encodes epigenetic modifications such as methylation, which can be leveraged for plasmid–host associations from metagenomic data. Existing methods rely on the recovery of metagenome-assembled genomes (MAGs), which can introduce bias towards abundant taxa and leave clinically relevant, low-abundance pathogens unassociated. To address this limitation, we extended methylation-based plasmid–host association from the MAG level to individual assembly contigs and sequencing reads. The Contig- and Unassembled-read-based Pathogen Identification and Delineation (CUPID) pipeline implements the calculation of contig and read similarity scores, which compare weighted mean methylation rates across motifs genetically shared between any contig or read pair. We validated this approach on a mock metagenomic community composed of ten carbapenem-resistant Enterobacterales isolates, where we achieved 93.8% accuracy at the contig level and 100% at the read level for carbapenemase plasmid–host associations. When applied to metagenomic and quasimetagenomic data of 16 patient rectal swabs collected during routine hospital surveillance, our approach assigned every detected plasmid-encoded carbapenemase to its correct bacterial host at the contig level, using matched culture-based diagnostics and whole-genome sequencing as the ground truth. Read-level analysis identified additional associations that were missed at the contig level, including a multi-host plasmid confirmed by established diagnostics. These findings demonstrate a pathway from rapid AMR gene detection using metagenomics to actionable surveillance for infection prevention, transmission tracing and outbreak investigation.

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Publication Details

Journal
Microbial Genomics
Published
2026-09-21
DOI
https://doi.org/10.1099/mgen.0.001834
Primary Topic
Antibiotic Resistance in Bacteria
Type
article
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article

Nanopore metagenomic sequencing links clinically relevant resistance determinants to pathogens

Ela Sauerborn, Rhys T. White, Marc J. A. Stevens, Ebenezer Foster-Nyarko et al.
Microbial Genomics
Antibiotic Resistance in Bacteria
article

Nanopore metagenomic sequencing links clinically relevant resistance determinants to pathogens

Ela Sauerborn, Rhys T. White, Marc J. A. Stevens, Ebenezer Foster-Nyarko, Mads Albertsen, Silvio D. Brugger, Roger Stephan, Michael Biggel, Francis Muchaamba, Lara Urban, Søren Heidelbach, Tim Reska, Harika Ürel, Friedemann Gebhardt, Richard Fetherston
article en

Abstract

Culture-independent metagenomics enables the detection of plasmid-encoded antimicrobial resistance (AMR) genes directly from clinical samples; however, the clinical significance of these genes depends on their bacterial host and genomic context, which metagenomics cannot fully infer. Nanopore sequencing technology intrinsically encodes epigenetic modifications such as methylation, which can be leveraged for plasmid–host associations from metagenomic data. Existing methods rely on the recovery of metagenome-assembled genomes (MAGs), which can introduce bias towards abundant taxa and leave clinically relevant, low-abundance pathogens unassociated. To address this limitation, we extended methylation-based plasmid–host association from the MAG level to individual assembly contigs and sequencing reads. The Contig- and Unassembled-read-based Pathogen Identification and Delineation (CUPID) pipeline implements the calculation of contig and read similarity scores, which compare weighted mean methylation rates across motifs genetically shared between any contig or read pair. We validated this approach on a mock metagenomic community composed of ten carbapenem-resistant Enterobacterales isolates, where we achieved 93.8% accuracy at the contig level and 100% at the read level for carbapenemase plasmid–host associations. When applied to metagenomic and quasimetagenomic data of 16 patient rectal swabs collected during routine hospital surveillance, our approach assigned every detected plasmid-encoded carbapenemase to its correct bacterial host at the contig level, using matched culture-based diagnostics and whole-genome sequencing as the ground truth. Read-level analysis identified additional associations that were missed at the contig level, including a multi-host plasmid confirmed by established diagnostics. These findings demonstrate a pathway from rapid AMR gene detection using metagenomics to actionable surveillance for infection prevention, transmission tracing and outbreak investigation.

Microbial GenomicsVol. 12(9)
University of Zurich (CH), Helmholtz Zentrum München (DE), London School of Hygiene & Tropical Medicine (GB), University Hospital of Zurich (CH), Institute of Medical Microbiology and Hygiene (DE), Leibniz-Institute for Food Systems Biology at the Technical University of Munich (DE), Building Research Association of New Zealand (NZ), Oxford Nanopore Technologies (United Kingdom) (GB), Technical University of Munich (DE), Aalborg University (DK)
Openalex Percentile: Top 20%
Antibiotic Resistance in Bacteria
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