Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic Islands. Issue 10 (1st October 2020)

• Record Type: Journal Article
• Authors: Maguire, Finlay; Jia, Baofeng; Gray, Kristen L.; Lau, Wing Yin Venus; Beiko, Robert G.; Brinkman, Fiona S. L.
• Is Part Of: Microbial genomics. Volume 6:Issue 10(2020)
• Publication Date: 2020-10-01
• Subjects: antimicrobial resistance -- genomic islands -- metagenomics -- metagenome-assembled genomes -- mobile genetic elements; Microbial genomics -- Periodicals; 572.8629
• DOI: 10.1099/mgen.0.000436
• Languages: English
• ISSNs: 2057-5858

Abstract

Abstract : Metagenomic methods enable the simultaneous characterization of microbial communities without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods aim to reassemble individual genomes from this data. However, the recovery of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), by binning has not been well characterized. Given the association of antimicrobial resistance (AMR) genes and virulence factor (VF) genes with MGEs, studying their transmission is a public-health priority. The variable copy number and sequence composition of MGEs makes them potentially problematic for MAG binning methods. To systematically investigate this issue, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated. While 82–94 % of chromosomes could be correctly recovered and binned, only 38–44 % of GIs and 1–29 % of plasmid sequences were found. Strikingly, no plasmid-borne VF nor AMR genes were recovered, and only 0–45 % of AMR or VF genes within GIs. We conclude that short-read MAG approaches, without further optimization, are largely ineffective for the analysis of mobile genes, including those of public-health importance, such as AMR and VF genes. We propose that researchers should explore developing methods that optimize for this issue and consider also using unassembled short reads and/or long-read approaches to more fully characterize metagenomic data.