Metagenomic frameworks for monitoring antibiotic resistance in aquatic environments

Abstract

Background: High-throughput genomic technologies offer new approaches for environmental health monitoring, including metagenomic surveillance of antibiotic resistance determinants (ARDs). Although natural environments serve as reservoirs for antibiotic resistance genes that can be transferred to pathogenic and human commensal bacteria, monitoring of these determinants has been infrequent and incomplete. Furthermore, surveillance efforts have not been integrated into public health decision making.

Objectives: We used a metagenomic epidemiology-based approach to develop an ARD index that quantifies antibiotic resistance potential, and we analyzed this index for common modal patterns across environmental samples. We also explored how metagenomic data such as this index could be conceptually framed within an early risk management context.

Methods: We analyzed 25 published data sets from shotgun pyrosequencing projects. The samples consisted of microbial community DNA collected from marine and freshwater environments across a gradient of human impact. We used principal component analysis to identify index patterns across samples.

Results: We observed significant differences in the overall index and index subcategory levels when comparing ecosystems more proximal versus distal to human impact. The selection of different sequence similarity thresholds strongly influenced the index measurements. Unique index subcategory modes distinguished the different metagenomes.

Conclusions: Broad-scale screening of ARD potential using this index revealed utility for framing environmental health monitoring and surveillance. This approach holds promise as a screening tool for establishing baseline ARD levels that can be used to inform and prioritize decision making regarding management of ARD sources and human exposure routes.

Citation: Port JA, Cullen AC, Wallace JC, Smith MN, Faustman EM. 2014. Metagenomic frameworks for monitoring antibiotic resistance in aquatic environments. Environ Health Perspect 122:222–228; http://dx.doi.org/10.1289/ehp.1307009

Figure 1

Bioinformatic framework for quantifying the index of ARDs. The index categories are shown in the cream-colored boxes and the subcategories in red. The gray boxes (e.g., commensal bacteria and virulence factors) represent subcategories that have not yet been incorporated into the index but may still play an important role in determining ARD potential. NCBI, National Center for Biotechnology Information.

Figure 2

Percentage of total sequenced reads per metagenome assigned to the ARD index. (A) Percentage of index-positive sequences per sample and ecosystem and (B–G) the percentage of sequence reads per sample and ecosystem assigned to each index subcategory [(B) ARG sequences; (C) MRG sequences; (D) plasmid sequences; (E) TE sequences; (F) phage sequences; (G) pathogen sequences]. The percentages are shown for four different sequence similarity thresholds [including high, medium-high, medium, and low stringencies (see Table 2)]. The number of pathogen-annotated sequences is shown instead of the percentage. The vertical bar in each plot separates ecosystems more distal versus more proximal to human impact. Filter sizes (i.e., 0.1 and 0.8 μm) are listed after the station names for the coastal ocean samples. The graph inserts for ARGs and plasmids in B and D are zoomed-in views of the abundance of each subcategory excluding the river sediment samples.

Figure 3

PCA score plot and corresponding loading values for the index subcategories by ecosystem. The medium-high sequence similarity threshold was used for this analysis (see Table 2). Sampling location P26 experiences increased mixing of oceanic waters relative to the other estuary samples. The red and purple circles indicate distinct coastal ocean and river sediment sample clusters, respectively, according to PC1.