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      Microbiomes of gall-inducing copepod crustaceans from the corals Stylophora pistillata (Scleractinia) and Gorgonia ventalina (Alcyonacea)

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          Abstract

          Corals harbor complex and diverse microbial communities that strongly impact host fitness and resistance to diseases, but these microbes themselves can be influenced by stresses, like those caused by the presence of macroscopic symbionts. In addition to directly influencing the host, symbionts may transmit pathogenic microbial communities. We analyzed two coral gall-forming copepod systems by using 16S rRNA gene metagenomic sequencing: (1) the sea fan Gorgonia ventalina with copepods of the genus Sphaerippe from the Caribbean and (2) the scleractinian coral Stylophora pistillata with copepods of the genus Spaniomolgus from the Saudi Arabian part of the Red Sea. We show that bacterial communities in these two systems were substantially different with Actinobacteria, Alphaproteobacteria, and Betaproteobacteria more prevalent in samples from Gorgonia ventalina, and Gammaproteobacteria in Stylophora pistillata. In Stylophora pistillata, normal coral microbiomes were enriched with the common coral symbiont Endozoicomonas and some unclassified bacteria, while copepod and gall-tissue microbiomes were highly enriched with the family ME2 ( Oceanospirillales) or Rhodobacteraceae. In Gorgonia ventalina, no bacterial group had significantly different prevalence in the normal coral tissues, copepods, and injured tissues. The total microbiome composition of polyps injured by copepods was different. Contrary to our expectations, the microbial community composition of the injured gall tissues was not directly affected by the microbiome of the gall-forming symbiont copepods.

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          DECIPHER, a search-based approach to chimera identification for 16S rRNA sequences.

          DECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchime's performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (<0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (http://DECIPHER.cee.wisc.edu).
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            The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance

            Microbes are well-recognized members of the coral holobiont. However, little is known about the short-term dynamics of mucus-associated microbial communities under natural conditions and after disturbances, and how these dynamics relate to the host's health. Here we examined the natural variability of prokaryotic communities (based on 16S ribosomal RNA gene amplicon sequencing) associating with the surface mucus layer (SML) of Porites astreoides, a species exhibiting cyclical mucus aging and shedding. Shifts in the prokaryotic community composition during mucus aging led to the prevalence of opportunistic and potentially pathogenic bacteria (Verrucomicrobiaceae and Vibrionaceae) in aged mucus and to a twofold increase in prokaryotic abundance. After the release of aged mucus sheets, the community reverted to its original state, dominated by Endozoicimonaceae and Oxalobacteraceae. Furthermore, we followed the fate of the coral holobiont upon depletion of its natural mucus microbiome through antibiotics treatment. After re-introduction to the reef, healthy-looking microbe-depleted corals started exhibiting clear signs of bleaching and necrosis. Recovery versus mortality of the P. astreoides holobiont was related to the degree of change in abundance distribution of the mucus microbiome. We conclude that the natural prokaryotic community inhabiting the coral SML contributes to coral health and that cyclical mucus shedding has a key role in coral microbiome dynamics.
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              Microbial disease and the coral holobiont.

              Tropical coral reefs harbour a reservoir of enormous biodiversity that is increasingly threatened by direct human activities and indirect global climate shifts. Emerging coral diseases are one serious threat implicated in extensive reef deterioration through disruption of the integrity of the coral holobiont - a complex symbiosis between the coral animal, endobiotic alga and an array of microorganisms. In this article, we review our current understanding of the role of microorganisms in coral health and disease, and highlight the pressing interdisciplinary research priorities required to elucidate the mechanisms of disease. We advocate an approach that applies knowledge gained from experiences in human and veterinary medicine, integrated into multidisciplinary studies that investigate the interactions between host, agent and environment of a given coral disease. These approaches include robust and precise disease diagnosis, standardised ecological methods and application of rapidly developing DNA, RNA and protein technologies, alongside established histological, microbial ecology and ecological expertise. Such approaches will allow a better understanding of the causes of coral mortality and coral reef declines and help assess potential management options to mitigate their effects in the longer term.
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                Author and article information

                Contributors
                ivanenko@mail.bio.msu.ru
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 August 2018
                1 August 2018
                2018
                : 8
                : 11563
                Affiliations
                [1 ]ISNI 0000 0004 0619 6198, GRID grid.435025.5, Kharkevich Institute for Information Transmission Problems RAS, ; B. Karetny per. 19, Moscow, 127051 Russia
                [2 ]ISNI 0000 0004 0404 8765, GRID grid.433823.d, Vavilov Institute of General Genetics RAS, ; Gubkina str. 3, Moscow, 119333 Russia
                [3 ]ISNI 0000 0004 0555 3608, GRID grid.454320.4, Center for Data-Intensive Biomedicine and Biotechnology, , Skolkovo Institute of Science and Technology, ; Nobel str. 1, Moscow, 121205 Russia
                [4 ]ISNI 0000 0001 2342 9668, GRID grid.14476.30, Faculty of Bioengineering and Bioinformatics, , Lomonosov Moscow State University, ; Moscow, 119992 Russia
                [5 ]ISNI 0000 0001 1926 5090, GRID grid.45672.32, Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), ; Thuwal, 23955 Saudi Arabia
                [6 ]ISNI 0000 0001 2159 802X, GRID grid.425948.6, Naturalis Biodiversity Center, ; Leiden, 2332 AA The Netherlands
                [7 ]ISNI 0000 0001 0681 808X, GRID grid.483628.3, National Research Council, Institute of Ecosystem Study, ; Verbania, 28922 Italy
                [8 ]ISNI 0000 0004 0578 2005, GRID grid.410682.9, Faculty of Computer Science, , Higher School of Economics, ; Kochnovsky pr. 3, Moscow, 125319 Russia
                [9 ]ISNI 0000 0001 2342 9668, GRID grid.14476.30, Department of Invertebrate Zoology, Biological Faculty, , Lomonosov Moscow State University, ; Moscow, 119992 Russia
                Author information
                http://orcid.org/0000-0003-2463-2742
                http://orcid.org/0000-0003-1255-0491
                Article
                29953
                10.1038/s41598-018-29953-y
                6070567
                30069039
                9d246298-f94a-4d86-8be8-1bcc36391279
                © The Author(s) 2018

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 February 2018
                : 18 July 2018
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