Antibiotic resistance has been recognized as a major challenge worldwide for humans. “One Health” has been recognized as a key concept for containment of antibiotic resistance. Under the framework, the role of the environment in the development of antibiotic resistance genes (ARGs) has become increasingly obvious. Despite numerous efforts, response to antibiotic resistance is considered to be inadequate, which is probably due to the lack of a clear roadmap. Here, we propose a “One Health” roadmap to combat antibiotic resistance in the environment through (1) understanding environmental resistome. The environmental gene pool has long been recognized as the single largest reservoir of both known and novel ARGs. (2) Standardizing ARG quantification. Systematic joint efforts based on standardized quantification are urgently needed to understand the true tempospatial profiles of the environmental resistome. (3) Identifying mechanisms of resistome development. Horizontal gene transfer and co‐selection have been recognized as the two main mechanisms contributing to the environmental resistome. (4) Establishing a risk‐assessment framework. The first critical step for large‐scale cost‐effective targeted ARG management in the environment is the risk assessment to identify the priority ARGs for control. (5) Formulating regulatory standards. By correlating the environmental ARG profile with public health, we may identify the indicator ARGs that can be integrated into current environmental quality standards. (6) Developing control strategies. Systematic analysis of available control technologies is required to identify the most feasible ones to curtail the spread of ARGs in the environment. The proposed roadmap under the “One Health” framework provides a guide to tackle antibiotic resistance in the environment.

Wastewater Treatment Plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for Horizontal Gene Transfer (HGT) of antimicrobial resistance determinants. This facilitates the transmission of clinically important genes between, e.g., enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped. In this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e., the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 (95% CI: 0.44, 0.55), 0.43 for pKJK5 (0.95% CI: 0.41, 0.49) and 0.53 for RP4 (0.95% CI: 0.48, 0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of antimicrobial resistance (AMR) pollution in wastewater systems. The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.

The increasing global burden of antimicrobial resistance (AMR) has been identified as a critical public health crisis, necessitating the development of robust, real‐time surveillance frameworks to evaluate AMR dynamics. Sewage surveillance is emerging as a promising tool that utilizes sewage fingerprinting to provide comprehensive and unbiased information on antibiotic resistance genes (ARGs) within human populations. Here, we conducted a large‐scale, year‐long field surveillance of resistome in the community sewage using both short‐ and long‐read metagenomic sequencing. We examined samples collected from 95 geographically distributed sites across Hong Kong, covering a population of 4.8 million residents, during summer and winter seasons. Our findings revealed distinct seasonal patterns through high‐resolution resistome profiling. We found that the resistome structures shifted from the community sewage collected at sewer manholes to the influent of wastewater treatment plants (WWTPs), driven by taxonomic variation. Notably, community sewage exhibited a significantly higher similarity to the resistome of human feces than WWTP influent, which provides insights for selecting suitable sampling sites for epidemiological ARG surveillance. The application of long‐read sequencing markedly enhanced our understanding of the phylogenetic diversity of ARG hosts and uncovered a broad spectrum of potentially mobile ARGs with varied genetic backgrounds. Furthermore, we observed multiple local ARG transmission patterns and subsequently evaluated their potential threats to public health based on the gene trees to inform future epidemiological control strategies. Overall, this work expands our current understanding of community sewage for population‐level AMR monitoring and establishes a baseline for advancing sewage surveillance efforts to better combat AMR.