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      Cleaning and Disinfection of Biofilms Composed of Listeria monocytogenes and Background Microbiota from Meat Processing Surfaces

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          ABSTRACT

          Surfaces of food processing premises are exposed to regular cleaning and disinfection (C&D) regimes, using biocides that are highly effective against bacteria growing as planktonic cells. However, bacteria growing in surface-associated communities (biofilms) are typically more tolerant toward C&D than their individual free-cell counterparts, and survival of pathogens such as Listeria monocytogenes may be affected by interspecies interactions within biofilms. In this study, Pseudomonas and Acinetobacter were the most frequently isolated genera surviving on conveyor belts subjected to C&D in meat processing plants. In the laboratory, Pseudomonas, Acinetobacter, and L. monocytogenes dominated the community, both in suspensions and in biofilms formed on conveyor belts, when cultures were inoculated with eleven-genus cocktails of representative bacterial strains from the identified background flora. When biofilms were exposed to daily C&D cycles mimicking treatments used in food industry, the levels of Acinetobacter and Pseudomonas mandelii diminished, and biofilms were instead dominated by Pseudomonas putida (65 to 76%), Pseudomonas fluorescens (11 to 15%) and L. monocytogenes (3 to 11%). The dominance of certain species after daily C&D correlated with high planktonic growth rates at 12°C and tolerance to C&D. In single-species biofilms, L. monocytogenes developed higher tolerance to C&D over time, for both the peracetic acid and quaternary ammonium disinfectants, indicating that a broad-spectrum mechanism was involved. Survival after C&D appeared to be a common property of L. monocytogenes strains, as persistent and sporadic subtypes showed equal survival rates in complex biofilms. Biofilms established preferentially in surface irregularities of conveyor belts, potentially constituting harborage sites for persistent contamination.

          IMPORTANCE In the food industry, efficient production hygiene is a key measure to avoid the accumulation of spoilage bacteria and eliminate pathogens. However, the persistence of bacteria is an enduring problem in food processing environments. This study demonstrated that environmental bacteria can survive foam cleaning and disinfection (C&D) at concentrations used in the industrial environment. The phenomenon was replicated in laboratory experiments. Important characteristics of persisting bacteria were a high growth rate at low temperature, a tolerance to the cleaning agent, and the ability to form biofilms. This study also supports other recent research suggesting that strain-to-strain variation cannot explain why certain subtypes of Listeria monocytogenes persist in food processing environments while others are found only sporadically. The present investigation highlights the failure of regular C&D and a need for research on improved agents that efficiently detach the biofilm matrix.

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

            The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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              QIIME allows analysis of high-throughput community sequencing data.

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                Author and article information

                Journal
                Applied and Environmental Microbiology
                Appl Environ Microbiol
                American Society for Microbiology
                0099-2240
                1098-5336
                September 01 2017
                June 30 2017
                August 17 2017
                September 01 2017
                : 83
                : 17
                Affiliations
                [1 ] Nofima, Norwegian Institute of Food, Fisheries and Aquaculture Research, Ås, Norway
                [2 ] Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
                Article
                10.1128/AEM.01046-17
                5561291
                28667108
                2aa4ed9b-be66-4a78-9616-273fbce335e8
                © 2017
                History

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