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      Metaproteome analysis reveals that syntrophy, competition, and phage-host interaction shape microbial communities in biogas plants

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          Abstract

          Background

          In biogas plants, complex microbial communities produce methane and carbon dioxide by anaerobic digestion of biomass. For the characterization of the microbial functional networks, samples of 11 reactors were analyzed using a high-resolution metaproteomics pipeline.

          Results

          Examined methanogenesis archaeal communities were either mixotrophic or strictly hydrogenotrophic in syntrophy with bacterial acetate oxidizers. Mapping of identified metaproteins with process steps described by the Anaerobic Digestion Model 1 confirmed its main assumptions and also proposed some extensions such as syntrophic acetate oxidation or fermentation of alcohols. Results indicate that the microbial communities were shaped by syntrophy as well as competition and phage-host interactions causing cell lysis. For the families Bacillaceae, Enterobacteriaceae, and Clostridiaceae, the number of phages exceeded up to 20-fold the number of host cells.

          Conclusion

          Phage-induced cell lysis might slow down the conversion of substrates to biogas, though, it could support the growth of auxotrophic microbes by cycling of nutrients.

          Electronic supplementary material

          The online version of this article (10.1186/s40168-019-0673-y) contains supplementary material, which is available to authorized users.

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          Most cited references49

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          TANDEM: matching proteins with tandem mass spectra.

          Tandem mass spectra obtained from fragmenting peptide ions contain some peptide sequence specific information, but often there is not enough information to sequence the original peptide completely. Several proprietary software applications have been developed to attempt to match the spectra with a list of protein sequences that may contain the sequence of the peptide. The application TANDEM was written to provide the proteomics research community with a set of components that can be used to test new methods and algorithms for performing this type of sequence-to-data matching. The source code and binaries for this software are available at http://www.proteome.ca/opensource.html, for Windows, Linux and Macintosh OSX. The source code is made available under the Artistic License, from the authors.
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            CRISPR–Cas9 Structures and Mechanisms

            Many bacterial clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR-associated (Cas) systems employ the dual RNA–guided DNA endonuclease Cas9 to defend against invading phages and conjugative plasmids by introducing site-specific double-stranded breaks in target DNA. Target recognition strictly requires the presence of a short protospacer adjacent motif (PAM) flanking the target site, and subsequent R-loop formation and strand scission are driven by complementary base pairing between the guide RNA and target DNA, Cas9–DNA interactions, and associated conformational changes. The use of CRISPR–Cas9 as an RNA-programmable DNA targeting and editing platform is simplified by a synthetic single-guide RNA (sgRNA) mimicking the natural dual trans-activating CRISPR RNA (tracrRNA)–CRISPR RNA (crRNA) structure. This review aims to provide an in-depth mechanistic and structural understanding of Cas9-mediated RNA-guided DNA targeting and cleavage. Molecular insights from biochemical and structural studies provide a framework for rational engineering aimed at altering catalytic function, guide RNA specificity, and PAM requirements and reducing off-target activity for the development of Cas9-based therapies against genetic diseases.
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              UniRef: comprehensive and non-redundant UniProt reference clusters.

              Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                heyer@mpi-magdeburg.mpg.de
                kay.schallert@ovgu.de
                siewert@mpi-magdeburg.mpg.de
                kohrs@mpi-magdeburg.mpg.de
                Julia.greve@aol.de
                irena.maus@cebitec.uni-bielefeld.de
                jklang@atb-potsdam.de
                mklocke@atb-potsdam.de
                mheiermann@atb-potsdam.de
                mhoffmann@mpi-magdeburg.mpg.de
                puettker@mpi-magdeburg.mpg.de
                magdalena.calusinska@list.lu
                roman.zoun@ovgu.de
                saake@iti.cs.uni-magdeburg.de
                benndorf@mpi-magdeburg.mpg.de
                udo.reichl@mpi-magdeburg.mpg.de
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                27 April 2019
                27 April 2019
                2019
                : 7
                : 69
                Affiliations
                [1 ]ISNI 0000 0001 1018 4307, GRID grid.5807.a, Bioprocess Engineering, , Otto von Guericke University, ; Universitätsplatz 2, 39106 Magdeburg, Germany
                [2 ]ISNI 0000 0004 0491 802X, GRID grid.419517.f, Max Planck Institute for Dynamics of Complex Technical Systems, Bioprocess Engineering, ; Sandtorstraße 1, 39106 Magdeburg, Germany
                [3 ]ISNI 0000 0001 0944 9128, GRID grid.7491.b, Center for Biotechnology (CeBiTec), University Bielefeld, ; Universitätsstraße 27, 33615 Bielefeld, Germany
                [4 ]ISNI 0000 0000 9125 3310, GRID grid.435606.2, Department Bioengineering, , Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), ; Max-Eyth-Allee 100, 14469 Potsdam, Germany
                [5 ]ISNI 0000 0000 9125 3310, GRID grid.435606.2, Department Technology Assessment and Substance Cycles, , Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), ; Max-Eyth-Allee 100, 14469 Potsdam, Germany
                [6 ]GRID grid.423669.c, Environmental Research and Innovation (ERIN), , Luxembourg Institute of Science and Technology, ; 41 rue du Brill, L-4422 Belvaux, Luxembourg
                [7 ]ISNI 0000 0001 1018 4307, GRID grid.5807.a, Otto von Guericke University, Institute for Databases and Software Engineering, ; Universitätsplatz 2, 39106 Magdeburg, Germany
                Article
                673
                10.1186/s40168-019-0673-y
                6486700
                31029164
                8d91606d-bf06-4334-a8aa-bc93327c1f20
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 14 June 2018
                : 26 March 2019
                Funding
                Funded by: German Federal Ministry of Food and Agriculture
                Award ID: 22404115
                Award ID: 22403915
                Funded by: the German Federal Ministry of Food and Agriculture
                Award ID: 22027707
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: 2069/3-1
                Funded by: BMBF de.NBI network
                Award ID: grant no. de-NBI-039
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                metaproteomics,phages,anaerobic digestion,anaerobic digestion model 1,phage-host interactions,microbiomes

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