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.

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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. 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.

Prolonged survival of clinically relevant pathogens on inanimate surfaces represents a major concern in healthcare facilities. Contaminated surfaces can serve as reservoirs of potential pathogens and greatly hinder the prevention of healthcare-associated infections. Probiotic cleaning using environmental microorganisms to promote inter-species competition has been proposed as an alternative to traditional chemical-based cleaning using antimicrobials. Probiotic cleaning seeks to take advantage of ecological principles such as competitive exclusion and utilize benign microorganisms to inhibit viable pathogens on indoor surfaces. However, limited mechanistic study has yielded direct evidence that enables the scientific community to understand the stress response, or microbe-microbe interactions between healthcare-associated pathogens and probiotic bacteria. Therefore, to bridge this knowledge gap, we combined transcriptomics and traditional microbiology techniques to investigate the differential impact of chemical-based and probiotic surface cleaners on the survival of Acinetobacter baumannii and Klebsiella pneumoniae, two clinically important pathogens. Although probiotic Bacillus included in a commercialized All-Purpose Probiotic Cleaner persisted on surfaces for an extended period of time, surfaces contaminated with A. baumannii cleaned using chemical-based detergent with and without probiotic Bacillus showed no statistical difference in the viable colony forming units (CFUs) of A. baumannii. Similarly, for Klebsiella pneumoniae, there were negligible statistical differences in CFUs between probiotic and detergent cleaning scenarios. The transcriptome of A. baumannii with and without probiotic addition shared a high degree of similarity in overall gene expression, while the transcriptome of K. pneumoniae with and without probiotic addition differed in overall gene expression. Together, these results highlight the need to fully understand the underlying biological and ecological mechanisms for different pathogens and practical implications of probiotic indoor cleaning.