ARGus: A Co-assembly workflow for MAG generation, ARG detection, and virulence analysis

Abstract

The emergence of antibiotic resistance among pathogenic bacteria is a significant global health challenge with multidrug resistance becoming increasingly common. Moreover, since antibiotic resistance genes (ARGs) can be transferred horizontally more bacteria are rapidly evolving resistance. In addition, emerging bacterial pathogens continue to arise from a combination of urbanization, animal agriculture, global movements of people, and inadequate sewage infrastructure. Researchers have begun applying deep sequencing and shotgun metagenomics to detect known and unknown pathogenic organisms and ARGs directly from environmental samples. Here, we describe a bioinformatics workflow that uses a co-assembly approach to assemble contigs across metagenomes and bin them into high coverage metagenomic assembled genomes (MAGs), while segregating out unbinned contigs that includes mobile elements (e.g., plasmids). The workflow includes annotation of coding sequences and differential determination of ARGs and virulence factors (VF) within the sets of both MAG genome bins and unbinned contigs and allows quantification of MAG, ARG and VF abundances for ecological (alpha and beta diversity) and network analyses. Workflow analysis of metagenomic samples collected from the heavily polluted Tijuana River identified hundreds of MAGs, including many high-quality bins and many novel potential pathogens, and found the vast majority of ARG sequence matches in the unbinned contigs. A combined network analysis found strong correlations (r > 0.90) between ARGs and specific MAGs, indicating which bacterial species is likely to contain the ARG. This workflow provides a powerful approach for public health metagenomics studies of emerging pathogens and ARGs.