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While numerous environmental factors contribute to the spread of antibiotic resistance genes (ARGs), quantifying their relative contributions remains a fundamental challenge. Similarly, it is important to differentiate acute human health risks from environmental exposure, versus broader ecological risk of ARG evolution and spread across microbial taxa. Recent studies have proposed various methods for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon original MetaCompare pipeline by differentiating indicators of human health resistome risk (potential for human pathogens of acute resistance concern to acquire ARGs) from ecological resistome risk (overall mobility of ARGs and potential for pathogen acquisition). The updated pipeline's sensitivity was demonstrated by analyzing diverse publicly-available metagenomes from wastewater, surface water, soil, sediment, human gut, and synthetic microbial communities. MetaCompare 2.0 provided distinct rankings of the metagenomes according to both human health resistome risk and ecological resistome risk, with both scores trending higher when influenced by anthropogenic impact or other stress. We evaluated the robustness of the pipeline to sequence assembly methods, sequencing depth, contig count, and metagenomic library coverage bias. The risk scores were remarkably consistent despite variations in these technological aspects. We packaged the improved pipeline into a publicly-available web service (http://metacompare.cs.vt.edu/) that provides an easy-to-use interface for computing resistome risk scores and visualizing results.

Activated sludge is the centerpiece of biological wastewater treatment, as it facilitates removal of sewage-associated pollutants, fecal bacteria, and pathogens from wastewater through semi-controlled microbial ecology. It has been hypothesized that horizontal gene transfer facilitates the spread of antibiotic resistance genes within the wastewater treatment plant, in part because of the presence of residual antibiotics in sewage. However, there has been surprisingly little evidence to suggest that sewage-associated antibiotics select for resistance at wastewater treatment plants via horizontal gene transfer or otherwise. We addressed the role of sewage-associated antibiotics in promoting antibiotic resistance using lab-scale sequencing batch reactors fed field-collected wastewater, metagenomic sequencing, and our recently developed bioinformatic tool Kairos. Here, we found confirmatory evidence that fluctuating levels of antibiotics in sewage are associated with horizontal gene transfer of antibiotic resistance genes, microbial ecology, and microdiversity-level differences in resistance gene fate in activated sludge.

Polar regions are relatively isolated from human activity and thus could offer insight into anthropogenic and ecological drivers of the spread of antibiotic resistance. Plasmids are of particular interest in this context given the central role that they are thought to play in the dissemination of antibiotic resistance genes (ARGs). However, plasmidomes are challenging to profile in environmental samples. The objective of this study was to compare various aspects of the plasmidome associated with glacial ice and adjacent aquatic environments across the high Arctic archipelago of Svalbard, representing a gradient of anthropogenic inputs and specific treated and untreated wastewater outflows to the sea. We accessed plasmidomes by applying enrichment cultures, plasmid isolation and shotgun Illumina sequencing of environmental samples. We examined the abundance and diversity of ARGs and other stress‐response genes that might be co/cross‐selected or co‐transported in these environments, including biocide resistance genes (BRGs), metal resistance genes (MRGs), virulence genes (VGs) and integrons. We found striking differences between glacial ice and aquatic environments in terms of the ARGs carried by plasmids. We found a strong correlation between MRGs and ARGs in plasmids in the wastewaters and fjords. Alternatively, in glacial ice, VGs and BRGs genes were dominant, suggesting that glacial ice may be a repository of pathogenic strains. Moreover, ARGs were not found within the cassettes of integrons carried by the plasmids, which is suggestive of unique adaptive features of the microbial communities to their extreme environment. This study provides insight into the role of plasmids in facilitating bacterial adaptation to Arctic ecosystems as well as in shaping corresponding resistomes. Increasing human activity, warming of Arctic regions and associated increases in the meltwater run‐off from glaciers could contribute to the release and spread of plasmid‐related genes from Svalbard to the broader pool of ARGs in the Arctic Ocean.

With growing calls for increased surveillance of antibiotic resistance as an escalating global health threat, improved bioinformatic tools are needed for tracking antibiotic resistance genes (ARGs) across One Health domains. Most studies to date profile ARGs using sequence homology, but such approaches provide limited information about the broader context or function of the ARG in bacterial genomes. Here we introduce a new pipeline for identifying ARGs in genomic data that employs machine learning analysis of Protein-Protein Interaction Networks (PPINs) as a means to improve predictions of ARGs while also providing vital information about the context, such as gene mobility. A random forest model was trained to effectively differentiate between ARGs and nonARGs and was validated using the PPINs of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, andEnterobacter cloacae), which represent urgent threats to human health because they tend to be multi-antibiotic resistant. The pipeline exhibited robustness in discriminating ARGs from nonARGs, achieving an average area under the precision-recall curve of 88%. We further identified that the neighbors of ARGs, i.e., genes connected to ARGs by only one edge, were disproportionately associated with mobile genetic elements, which is consistent with the understanding that ARGs tend to be mobile compared to randomly sampled genes in the PPINs. This pipeline showcases the utility of PPINs in discerning distinctive characteristics of ARGs within a broader genomic context and in differentiating ARGs from nonARGs through network-based attributes and interaction patterns. The code for running the pipeline is publicly available athttps://github.com/NazifaMoumi/PPI-ARG-ESKAPE