Evaluation of metagenomic assembly methods for the detection and characterization of antimicrobial resistance...

Area of Science:

• Environmental microbiology
• Genomics
• One Health research

Background:

• Antimicrobial resistance genes (ARGs) spread via mobile genetic elements (MGEs), posing risks to public health.
• Monitoring ARG-MGE linkages is crucial for predicting antimicrobial resistance (AMR) within a One Health framework.
• Existing methods lack standardization for detecting ARGs, MGEs, and their associations.

Purpose of the Study:

• To develop and evaluate a workflow for identifying and associating ARGs and MGEs using shotgun metagenomic data.
• To assess the efficacy of different assembly strategies for ARG-MGE linkage analysis.
• To analyze ARG and MGE prevalence across diverse environments within the One Health Continuum.

Main Methods:

• Utilized publicly available shotgun metagenomic DNA short-read data from various One Health samples (cattle feces, feedlot water, agricultural soil, wastewater influent).
• Compared ARG- and MGE-targeted assembly with de novo assembly of contigs for ARG-MGE association.
• Employed unassembled sequence data for estimating relative abundance of identified elements.

Main Results:

• ARG- and MGE-targeted assemblies were insufficient for establishing ARG-MGE linkages.
• De novo assembly of contigs provided adequate sequence context to successfully associate ARGs with MGEs.
• Discovery rates were maintained while improving association accuracy through de novo assembly.

Conclusions:

• De novo assembly is a superior strategy for identifying ARG-MGE linkages in metagenomic data.
• A combination of de novo assembly and analysis of unassembled reads is necessary for comprehensive ARG-MGE profiling.
• The developed workflow aids in understanding AMR transmission dynamics across the One Health Continuum.