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The resistome of confined built environments
Alexander Mahnert1, Christine Moissl-Eichinger2,3, Markus Zojer4, David Bogumil5, Itzhak Mizrahi5, Thomas Rattei4, and Gabriele Berg1
1Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria 2Department of Internal Medicine, Medical University Graz, Austria 3BioTechMed Graz, Austria 4Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Austria 5Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Israel
Introduction: Intensive care units are characterized by a defined microbial control and maintenance. Such actions were shown to drive selection processes and alter structure and function of the residing microbiome in these so-called confined built environments. Objectives: However, accompanying effects on the resistome were not sufficiently investigated. Therefore we examined different built environments, characterized by an increasing level of microbial control, (public buildings, public and private houses, intensive care units, cleanroom facilities) to understand coherences of microbial confinement and antibiotic resistance. Methods: Shotgun metagenomics were applied on large surface samples from different built environments and analyzed in genome centric as well as gene centric approaches. Binned genomes and extracted plasmids were further investigated for their resistance network to understand the transfer of mobile genetic resistance elements in microbial confined built environments. Results: While uncontrolled built environments were populated mainly by gram-positive bacteria with functions associated to carbohydrate and amino acid metabolism, confined built environments were characterized by discriminant profiles of mainly gram-negative bacteria with many functions associated to virulence, disease, defense and resistance. According to the Comprehensive Antibiotic Resistance Database, 377 different resistance features could be identified for 42 selected high quality binned genomes and 91 plasmids. Uncontrolled built environments showed a high proportion of tetracycline efflux proteins. On the other hand confined built environments showed a completely different resistome and comprised many proteins associated to multidrug resistances (e.g. mtrR and mexK) as well as resistance to novobiocin in genomes assigned to Acinetobacter, Agrobacterium or Pseudomonas. Conclusion: Deciphering the complex interaction network of microbes and their plasmids to exchange genetic resistant elements in confined compared to naturally uncontrolled built environments will help to identify and target microbial key players in intensive care units for an adapted microbial monitoring and biotechnological control to reduce resistance developments in the future.