Separation of cohorts on the basis of bacterial type IV conjugation systems identified from metagenomic assemblies

Abstract

AbstractConjugation enables the exchange of genetic elements throughout environments, including the human gut microbiome. Conjugative elements can carry and transfer clinically relevant metabolic pathways which makes precise identification of these systems in metagenomic samples clinically important. Here, we outline two distinct methods to identify conjugative systems in the human gut microbiome. We first show that conjugative systems exhibit strong population and age-level stratification. Additionally, we find that the total relative abundance of all conjugative systems present in a sample is not an informative metric to use, regardless of the method of identifying the systems. Finally, we demonstrate that the majority of assembled conjugative systems are not included within metagenomic bins, and that only a small proportion of the binned conjugative systems are included in “high-quality” metagenomic bins. Our findings highlight that conjugative systems differ between general North Americans and a cohort of North American pre-term infants, revealing a potential use as an age-related biomarker. Furthermore, conjugative systems can distinguish between other geographical-based cohorts. Our findings emphasize the need to identify and analyze conjugative systems outside of standard metagenomic binning pipelines.ImportanceThe human gut microbiome is increasingly being associated with human health outcomes through shotgun metagenomic sequencing. The usual approach of metagenomic-level analyses is to bin assembled sequences into approximations of bacterial genomes and perform further investigations on the resultant bins. Here, we show that type IV conjugative systems differ between age and geographically-based cohorts and that these systems are systematically excluded by binning algorithms. We suggest that analysis of type IV conjugative systems should be added to the current metagenomic analysis approaches as they contain much information that could explain differences between cohorts beyond those we investigated.