Factors impacting target-enriched long-read sequencing of resistomes and mobilomes

Ilya B. Slizovskiy, Nathalie Bonin, Jonathan E. Bravo, Peter M. Ferm, Jacob Singer, Christina Boucher, Noelle R. Noyes

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG–MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR.

Original languageEnglish (US)
Pages (from-to)2048-2060
Number of pages13
JournalGenome research
Volume34
Issue number11
DOIs
StatePublished - Nov 2024

Bibliographical note

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© 2024 Slizovskiy et al.

PubMed: MeSH publication types

  • Journal Article

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