MetaCompare: a computational pipeline for prioritizing environmental resistome risk

Abstract

The spread of antibiotic resistance is a growing public health concern. While numerous studies have highlighted the importance of environmental sources and pathways of the spread of antibiotic resistance, a systematic means of comparing and prioritizing risks represented by various environmental compartments is lacking. Here, we introduce MetaCompare, a publicly available tool for ranking 'resistome risk', which we define as the potential for antibiotic resistance genes (ARGs) to be associated with mobile genetic elements (MGEs) and mobilize to pathogens based on metagenomic data. A computational pipeline was developed in which each ARG is evaluated based on relative abundance, mobility, and presence within a pathogen. This is determined through the assembly of shotgun sequencing data and analysis of contigs containing ARGs to determine if they contain sequence similarity to MGEs or human pathogens. Based on the assembled metagenomes, samples are projected into a 3-dimensionalhazard space and assigned resistome risk scores. To validate, we tested previously published metagenomic data derived from distinct aquatic environments. Based on unsupervised machine learning, the test samples clustered in the hazard space in a manner consistent with their origin. The derived scores produced a well-resolved ascending resistome risk ranking of: wastewater treatment plant effluent, dairy lagoon, and hospital sewage.

Figure 1.

Overview of MetaCompare pipeline for ranking resistome risk based on shotgun metagenomic sequencing data obtained from a given environmental sample.

Figure 2.

Proposed categorization of an assembled contig according to annotation of its individual critical components.

Figure 3.

Illustration of the three-dimensional hazard space.

Figure 4.

Projection of the test metagenomic data sets into hazard space followed by silhouette analysis to evaluate cohesiveness of members assigned to each cluster. (a) Hazard space derived from abundances of reads annotated directly as ARGs, MGEs or pathogens. (b) Proposed hazard space derived from estimation of co-occurrence of critical components on assembled contigs. (c) Clustering coefficient (silhouette width) for each sample presented in (a) displayed in hazard space. The red dotted line with the red value indicates the average value of all silhouette widths. (d) Result of clustering coefficient analysis for the proposed hazard space based on estimation of co-occurrence of critical components on assembled contigs. WWTP- waste water treatment plant.

Figure 5.

Evaluation of clustering quality. Based on different means of measuring clustering quality: Purity, F-measure, Precision and Recall, as applied to k-means, c-means and k-medoids obtained using ‘Alternative’ (i.e. sequence read normalized) versus MetaCompare (i.e. co-occurrence of critical components on assembled contigs) approaches to project the samples into 3-D hazard space.