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      Microbial Diversity and Parasitic Load in Tropical Fish of Different Environmental Conditions

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          Abstract

          In this study we analysed fecal bacterial communities and parasites of three important Indonesian fish species, Epinephelus fuscoguttatus, Epinephelus sexfasciatus and Atule mate. We then compared the biodiversity of bacterial communities and parasites of these three fish species collected in highly polluted Jakarta Bay with those collected in less polluted Indonesian areas of Cilacap ( E. sexfasciatus, A. mate) and Thousand Islands ( E. fuscoguttatus). In addition, E. fuscoguttatus from net cages in an open water mariculture facility was compared with free living E. fuscoguttatus from its surroundings. Both core and shared microbiomes were investigated. Our results reveal that, while the core microbiomes of all three fish species were composed of fairly the same classes of bacteria, the proportions of these bacterial classes strongly varied. The microbial composition of phylogenetically distant fish species, i.e. A. mate and E. sexfasciatus from Jakarta Bay and Cilacap were more closely related than the microbial composition of more phylogentically closer species, i.e. E. fuscoguttatus, E. sexfasciatus from Jakarta Bay, Cilacap and Thousand Islands. In addition, we detected a weak negative correlation between the load of selected bacterial pathogens, i.e. Vibrio sp. and Photobacterium sp. and the number of endoparasites. In the case of Flavobacterium sp. the opposite was observed, i.e. a weak positive correlation. Of the three recorded pathogenic bacterial genera, Vibrio sp. was commonly found in E. fuscoguttatus from mariculture, and lessly in the vicinity of the net cages and rarely in the fishes from the heavily polluted waters from Jakarta Bay. Flavobacterium sp. showed higher counts in mariculture fish and Photobacteria sp. was the most prominent in fish inside and close to the net cages.

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          Most cited references 26

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          Differential expression analysis for sequence count data

          High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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            UCHIME improves sensitivity and speed of chimera detection

            Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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              Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

              A 16S rRNA gene database (http://greengenes.lbl.gov) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.
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                Author and article information

                Affiliations
                [1 ]Cell and Molecular Biology, Leibniz-Institute for Natural Product Research and Infection Biology, Jena, Germany
                [2 ]Friedrich-Schiller-University, Jena, Germany
                [3 ]Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany
                [4 ]Faculty of Veterinary Medicine, Udayana University, Kampus Bukit Jimbaran, Indonesia
                [5 ]Australian Centre for Ancient DNA, University of Adelaide School of Biological Sciences, Adelaide, Australia
                Yeungnam University, REPUBLIC OF KOREA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PH GM HPS HWP ST SK. Performed the experiments: GM ST SK. Analyzed the data: PH ST SK. Contributed reagents/materials/analysis tools: PH ST SK. Wrote the paper: PH MAA HPS HWP ST SK.

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2016
                28 March 2016
                : 11
                : 3
                PONE-D-15-50660
                10.1371/journal.pone.0151594
                4809571
                27018789
                © 2016 Hennersdorf et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                Figures: 4, Tables: 3, Pages: 18
                Product
                Funding
                Financial support was provided through the German Academic Exchange Service (DAAD) (ST) German Federal Ministry for Education and Science within the framework of the joint Indonesian-German research programs SPICE III - MABICO (Science for the Protection of Indonesian Coastal Marine Ecosystems, BMBF Grant Nos. 03F0641B, 03F0641D).
                Categories
                Research Article
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbiome
                Biology and Life Sciences
                Genetics
                Genomics
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Microbiology
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Agriculture
                Aquaculture
                Mariculture
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Fishes
                Marine Fish
                Biology and Life Sciences
                Marine Biology
                Marine Fish
                Earth Sciences
                Marine and Aquatic Sciences
                Marine Biology
                Marine Fish
                Medicine and Health Sciences
                Parasitic Diseases
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Shannon Index
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Shannon Index
                Biology and Life Sciences
                Organisms
                Bacteria
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogenesis
                Host-Pathogen Interactions
                Custom metadata
                We confirm that all relevant data has been included within the paper and Supporting Information files. In the paper, we have mentioned accession numbers that are linked to the storage of RAW data from the microbiome analysis. Raw sequence data are deposited at NCBI’s Short Read Archive under accession number SRP059667.

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