Mobilome-driven segregation of the resistome in biological wastewater treatment

Needles in haystacks: monitoring the potential escape of bioaerosolised antibacterial resistance genes from wastewater treatment plants with air and phyllosphere sampling

Wastewater treatment plants are well-known point sources of emissions of antibacterial resistance genes (ARGs) into the environment. Although most work to date has focused on ARG dispersal via effluent, aerial dispersal in bioaerosols is a poorly understood, but likely important vector for ARG dispersal. Recent evidence suggests that ARG profiles of the conifer needle phyllosphere could be used to measure bioaerosol dispersal from anthropogenic sources. Here, we assessed airborne dispersal of ARGs from wastewater treatment plants in Wales, UK and Quebec, Canada, using conifer needles as passive bioaerosol monitors. ARG profiles of wastewater were compared to those of conifer phyllosphere using high-throughput qPCR. ARG richness was significantly lower in conifer phyllosphere samples than wastewater samples, though no differences were observed across the dispersal gradients. Mean copy number of ARGs followed a similar trend. ARG profiles showed limited, but consistent patterns with increasing distance from wastewater treatment plants, but these did not align with those of wastewater samples. For example, proportional abundance of aminoglycosides decreased over the dispersal gradient in Wales, whereas mobile genetic elements showed the inverse relationship. In summary, while distinct ARG profiles exist along dispersal gradients, links to those of wastewater were not apparent.

An in-depth evaluation of metagenomic classifiers for soil microbiomes

BACKGROUND

Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.

RESULTS

In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.

CONCLUSION

This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Genome-centric analyses of 165 metagenomes show that mobile genetic elements are crucial for the transmission of antimicrobial resistance genes to pathogens in activated sludge and wastewater

Wastewater is considered a reservoir of antimicrobial resistance genes (ARGs), where the abundant antimicrobial-resistant bacteria and mobile genetic elements facilitate horizontal gene transfer. However, the prevalence and extent of these phenomena in different taxonomic groups that inhabit wastewater are still not fully understood. Here, we determined the presence of ARGs in metagenome-assembled genomes (MAGs) and evaluated the risks of MAG-carrying ARGs in potential human pathogens. The potential of these ARGs to be transmitted horizontally or vertically was also determined. A total of 5,916 MAGs (completeness >50%, contamination <10%) were recovered, covering 68 phyla and 279 genera. MAGs were dereplicated into 1,204 genome operational taxonomic units (gOTUs) as a proxy for species ( average nucleotide identity >0.95). The dominant ARG classes detected were bacitracin, multi-drug, macrolide-lincosamide-streptogramin (MLS), glycopeptide, and aminoglycoside, and 10.26% of them were located on plasmids. The main hosts of ARGs belonged to Escherichia, Klebsiella, Acinetobacter, Gresbergeria, Mycobacterium, and Thauera. Our data showed that 253 MAGs carried virulence factor genes (VFGs) divided into 44 gOTUs, of which 45 MAGs were carriers of ARGs, indicating that potential human pathogens carried ARGs. Alarmingly, the MAG assigned as Escherichia coli contained 159 VFGs, of which 95 were located on chromosomes and 10 on plasmids. In addition to shedding light on the prevalence of ARGs in individual genomes recovered from activated sludge and wastewater, our study demonstrates a workflow that can identify antimicrobial-resistant pathogens in complex microbial communities.

IMPORTANCE

Antimicrobial resistance (AMR) threatens the health of humans, animals, and natural ecosystems. In our study, an analysis of 165 metagenomes from wastewater revealed antibiotic-targeted alteration, efflux, and inactivation as the most prevalent AMR mechanisms. We identified several genera correlated with multiple ARGs, including Klebsiella, Escherichia, Acinetobacter, Nitrospira, Ottowia, Pseudomonas, and Thauera, which could have significant implications for AMR transmission. The abundance of bacA, mexL, and aph(3")-I in the genomes calls for their urgent management in wastewater. Our approach could be applied to different ecosystems to assess the risk of potential pathogens containing ARGs. Our findings highlight the importance of managing AMR in wastewater and can help design measures to reduce the transmission and evolution of AMR in these systems.

Insight into antimicrobial resistance at a new beef cattle feedlot in western Canada

IMPORTANCE

A better understanding of how environmental reservoirs of ARGs in the feedlot relate to those found in animal pathogens will help inform and improve disease management, treatment strategies, and outcomes. Monitoring individual cattle or small groups is invasive, logistically challenging, expensive, and unlikely to gain adoption by the beef cattle industry. Wastewater surveillance has become standard in public health studies and has inspired similar work to better our understanding of AMR in feedlots. We derived our insights from sampling water bowls in a newly established feedlot: a unique opportunity to observe AMR prior to animal arrival and to monitor its development over 2 months. Importantly, the bacterial community of a single water bowl can be influenced by direct contact with hundreds of animals. Our results suggest that water bowl microbiomes are economical and pragmatic sentinels for monitoring relevant AMR mechanisms.

Interplay Between Antimicrobial Resistance and Global Environmental Change

Antibiotic resistance genes predate the therapeutic uses of antibiotics. However, the current antimicrobial resistance crisis stems from our extensive use of antibiotics and the generation of environmental stressors that impose new selective pressure on microbes and drive the evolution of resistant pathogens that now threaten human health. Similar to climate change, this global threat results from human activities that change habitats and natural microbiomes, which in turn interact with human-associated ecosystems and lead to adverse impacts on human health. Human activities that alter our planet at global scales exacerbate the current resistance crisis and exemplify our central role in large-scale changes in which we are both protagonists and architects of our success but also casualties of unanticipated collateral outcomes. As cognizant participants in this ongoing planetary experiment, we are driven to understand and find strategies to curb the ongoing crises of resistance and climate change.

Adapt or perish

Microbial communities in wastewater treatment plants provide insights into the development and mechanisms of antimicrobial resistance.