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      Bacterial colonization dynamics and antibiotic resistance gene dissemination in the hospital environment after first patient occupancy: a longitudinal metagenetic study

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          Abstract

          Background

          Humans spend the bulk of their time in indoor environments. This space is shared with an indoor ecosystem of microorganisms, which are in continuous exchange with the human inhabitants. In the particular case of hospitals, the environmental microorganisms may influence patient recovery and outcome. An understanding of the bacterial community structure in the hospital environment is pivotal for the prevention of hospital-acquired infections and the dissemination of antibiotic resistance genes. In this study, we performed a longitudinal metagenetic approach in a newly opened ward at the Charité Hospital (Berlin) to characterize the dynamics of the bacterial colonization process in the hospital environment after first patient occupancy.

          Results

          The sequencing data showed a site-specific taxonomic succession, which led to stable community structures after only a few weeks. This data was further supported by network analysis and beta-diversity metrics. Furthermore, the fast colonization process was characterized by a significant increase of the bacterial biomass and its alpha-diversity. The compositional dynamics could be linked to the exchange with the patient microbiota. Over a time course of 30 weeks, we did not detect a rise of pathogenic bacteria in the hospital environment, but a significant increase of antibiotic resistance determinants on the hospital floor.

          Conclusions

          The results presented in this study provide new insights into different aspects of the environmental microbiome in the clinical setting, and will help to adopt infection control strategies in hospitals and health care-related buildings.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40168-021-01109-7.

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          Most cited references87

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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              QIIME allows analysis of high-throughput community sequencing data.

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                Author and article information

                Contributors
                Tilman.Klassert@med.uni-jena.com
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                11 August 2021
                11 August 2021
                2021
                : 9
                : 169
                Affiliations
                [1 ]GRID grid.275559.9, ISNI 0000 0000 8517 6224, Jena University Hospital, ZIK Septomics, Host Septomics, ; Jena, Germany
                [2 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Institute for Hygiene and Environmental Medicine and Department for Medicine (Gastroenterology, Infectious diseases, Rheumatology), , Charité – Universitätsmedizin Berlin, ; Berlin, Germany
                [3 ]GRID grid.10041.34, ISNI 0000000121060879, University Institute of Tropical Diseases and Public Health of the Canary Islands, , University of La Laguna, ; San Cristóbal de La Laguna, Spain
                [4 ]BioControl Jena GmbH, Jena, Germany
                [5 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Institute for Hygiene and Environmental Medicine, , Charité–Universitätsmedizin, ; Berlin, Germany
                Author information
                http://orcid.org/0000-0002-4502-5450
                Article
                1109
                10.1186/s40168-021-01109-7
                8359561
                34380550
                6b6efbfe-4041-47b8-8279-30794d772139
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 November 2020
                : 2 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: network Zwanzig20 –InfectControl2020 (Teilvorhaben 3 – 03Z0818C)
                Award ID: network Zwanzig20 –InfectControl2020 (Teilvorhaben 3 – 03Z0818C)
                Award ID: network Zwanzig20 –InfectControl2020 (Teilvorhaben 3 – 03Z0818C)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010127, Jürgen Manchot Stiftung;
                Award ID: PhD Scholarship
                Award Recipient :
                Funded by: Friedrich-Schiller-Universität Jena (1010)
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

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