Anthropogenic antimicrobial micropollutants and their implications for agriculture.

Antibiotics and disinfectants have saved millions of human lives and cured uncountable animal diseases, but their activity is not limited to the site of application. Downstream, these chemicals become micropollutants, contaminating water at trace levels, resulting in adverse impacts on soil microbial communities and threatening crop health and productivity in agricultural settings and perpetuating the spread of antimicrobial resistance. Especially as resource scarcity drives increased reuse of water and other waste streams, considerable attention is needed to characterize the fate of antibiotics and disinfectants and to prevent or mitigate environmental and public health impacts. In this review, we hope to provide an overview of why increasing concentrations of micropollutants such as antibiotics are concerning in the environment, how they can pose health risks for humans, and how they can be countered using bioremediation strategies.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics.

BACKGROUND: Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources. RESULTS: The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Clinically relevant pathogens on surfaces display differences in survival and transcriptomic response in relation to probiotic and traditional cleaning strategies.

Indoor surfaces are paradoxically presumed to be both colonized by pathogens, necessitating disinfection, and "microbial wastelands." In these resource-poor, dry environments, competition and decay are thought to be important drivers of microbial community composition. However, the relative contributions of these two processes have not been specifically evaluated. To bridge this knowledge gap, we used microcosms to evaluate whether interspecies interactions occur on surfaces. We combined transcriptomics and traditional microbiology techniques to investigate whether competition occurred between two clinically important pathogens, Acinetobacter baumannii and Klebsiella pneumoniae, and a probiotic cleaner containing a consortium of Bacillus species. Probiotic cleaning seeks to take advantage of ecological principles such as competitive exclusion, thus using benign microorganisms to inhibit viable pathogens, but there is limited evidence that competitive exclusion in fact occurs in environments of interest (i.e., indoor surfaces). Our results indicate that competition in this setting has a negligible impact on community composition but may influence the functions expressed by active organisms. Although Bacillus spp. remained viable on surfaces for an extended period of time after application, viable colony forming units (CFUs) of A. baumannii recovered following exposure to a chemical-based detergent with and without Bacillus spp. showed no statistical difference. Similarly, for K. pneumoniae, there were small statistical differences in CFUs between cleaning scenarios with or without Bacillus spp. in the chemical-based detergent. The transcriptome of A. baumannii with and without Bacillus spp. exposure shared a high degree of similarity in overall gene expression, but the transcriptome of K. pneumoniae differed in overall gene expression, including reduced response in genes related to antimicrobial resistance. Together, these results highlight the need to fully understand the underlying biological and ecological mechanisms for community assembly and function on indoor surfaces, as well as having practical implications for cleaning and disinfection strategies for infection prevention.