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      Bacterial Diversity and Community Structure in Korean Ginseng Field Soil Are Shifted by Cultivation Time

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          Abstract

          Traditional molecular methods have been used to examine bacterial communities in ginseng-cultivated soil samples in a time-dependent manner. Despite these efforts, our understanding of the bacterial community is still inadequate. Therefore, in this study, a high-throughput sequencing approach was employed to investigate bacterial diversity in various ginseng field soil samples over cultivation times of 2, 4, and 6 years in the first and second rounds of cultivation. We used non-cultivated soil samples to perform a comparative study. Moreover, this study assessed changes in the bacterial community associated with soil depth and the health state of the ginseng. Bacterial richness decreased through years of cultivation. This study detected differences in relative abundance of bacterial populations between the first and second rounds of cultivation, years of cultivation, and health states of ginseng. These bacterial populations were mainly distributed in the classes Acidobacteria, Alphaproteobacteria, Deltaproteobacteria, Gammaproteobacteria, and Sphingobacteria. In addition, we found that pH, available phosphorus, and exchangeable Ca + seemed to have high correlations with bacterial class in ginseng cultivated soil.

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          Pyrosequencing enumerates and contrasts soil microbial diversity.

          Estimates of the number of species of bacteria per gram of soil vary between 2000 and 8.3 million (Gans et al., 2005; Schloss and Handelsman, 2006). The highest estimate suggests that the number may be so large as to be impractical to test by amplification and sequencing of the highly conserved 16S rRNA gene from soil DNA (Gans et al., 2005). Here we present the use of high throughput DNA pyrosequencing and statistical inference to assess bacterial diversity in four soils across a large transect of the western hemisphere. The number of bacterial 16S rRNA sequences obtained from each site varied from 26,140 to 53,533. The most abundant bacterial groups in all four soils were the Bacteroidetes, Betaproteobacteria and Alphaproteobacteria. Using three estimators of diversity, the maximum number of unique sequences (operational taxonomic units roughly corresponding to the species level) never exceeded 52,000 in these soils at the lowest level of dissimilarity. Furthermore, the bacterial diversity of the forest soil was phylum rich compared to the agricultural soils, which are species rich but phylum poor. The forest site also showed far less diversity of the Archaea with only 0.009% of all sequences from that site being from this group as opposed to 4%-12% of the sequences from the three agricultural sites. This work is the most comprehensive examination to date of bacterial diversity in soil and suggests that agricultural management of soil may significantly influence the diversity of bacteria and archaea.
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            Beyond the Venn diagram: the hunt for a core microbiome.

            Discovering a core microbiome is important for understanding the stable, consistent components across complex microbial assemblages. A core is typically defined as the suite of members shared among microbial consortia from similar habitats, and is represented by the overlapping areas of circles in Venn diagrams, in which each circle contains the membership of the sample or habitats being compared. Ecological insight into core microbiomes can be enriched by 'omics approaches that assess gene expression, thereby extending the concept of the core beyond taxonomically defined membership to community function and behaviour. Parameters defined by traditional ecology theory, such as composition, phylogeny, persistence and connectivity, will also create a more complex portrait of the core microbiome and advance understanding of the role of key microorganisms and functions within and across ecosystems. © 2011 Society for Applied Microbiology and Blackwell Publishing Ltd.
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              Fast UniFrac: Facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data

              Next-generation sequencing techniques, and PhyloChip, have made simultaneous phylogenetic analyses of hundreds of microbial communities possible. Insight into community structure has been limited by the inability to integrate and visualize such vast datasets. Fast UniFrac overcomes these issues, allowing integration of larger numbers of sequences and samples into a single analysis. Its new array-based implementation offers orders of magnitude improvements over the original version. New 3D visualization of principal coordinates analysis (PCoA) results, with the option to view multiple coordinate axes simultaneously, provides a powerful way to quickly identify patterns that relate vast numbers of microbial communities. We demonstrate the potential of Fast UniFrac using examples from three data types: Sanger-sequencing studies of diverse free-living and animal-associated bacterial assemblages and from the gut of obese humans as they diet, pyrosequencing data integrated from studies of the human hand and gut, and PhyloChip data from a study of citrus pathogens. We show that a Fast UniFrac analysis using a reference tree recaptures patterns that could not be detected without considering phylogenetic relationships and that Fast UniFrac, coupled with BLAST-based sequence assignment, can be used to quickly analyze pyrosequencing runs containing hundreds of thousands of sequences, revealing patterns relating human and gut samples. Finally, we show that the application of Fast UniFrac to PhyloChip data could identify well-defined subcategories associated with infection. Together, these case studies point the way towards a broad range of applications and demonstrate some of the new features of Fast UniFrac.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                17 May 2016
                2016
                : 11
                : 5
                : e0155055
                Affiliations
                [1 ]Korean Ginseng Center and Ginseng Genetic Resource Bank, Kyung-Hee University, Yongin-si, Gyeonggi-do, Republic of Korea
                [2 ]Plant Molecular Biology and Biotechnology Research Center, Gyeongsang National University, JinJu-si, Gyeongsangnam-do, Republic of Korea
                [3 ]Graduation of Biotechnology, Kyung-Hee University, Yongin-si, Gyeonggi-do, Republic of Korea
                Chengdu Institute of Biology, CHINA
                Author notes

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

                Conceived and designed the experiments: YJK SS. Performed the experiments: NLN YJK. Analyzed the data: NLN YJK. Contributed reagents/materials/analysis tools: NLN YJK VAH JPK CHK DCY SS. Wrote the paper: NLN YJK SS. Reanalyzed the raw data to response for the minor revision and gave the authors valuable comments: SS.

                Article
                PONE-D-15-06035
                10.1371/journal.pone.0155055
                4871511
                27187071
                6f811fa0-5d2a-40e0-a908-2d795800812b
                © 2016 Nguyen 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.

                History
                : 6 March 2015
                : 24 April 2016
                Page count
                Figures: 11, Tables: 2, Pages: 27
                Funding
                Funded by: the Next-Generation BioGreen 21 Program
                Award ID: PJ0120342016
                Award Recipient :
                This research was supported by a grant #313038-03-2-S13020 from the Institute of Planning and Evaluation for Technology (IPET) in Food, Agriculture, Forestry and Fisheries, Republic of Korea and also supported by grants #01116602 and #PJ0120342016 from the Next-Generation BioGreen 21 Program, Systems & Synthetic Agrobiotech Center (SSAC), Rural Development Administration, Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Agriculture
                Agricultural Soil Science
                Ecology and Environmental Sciences
                Soil Science
                Agricultural Soil Science
                Biology and Life Sciences
                Organisms
                Bacteria
                Earth Sciences
                Geography
                Physical Geography
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Sequence Databases
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Sequence Analysis
                Sequence Databases
                Research and Analysis Methods
                Molecular Biology Techniques
                Sequencing Techniques
                Sequence Analysis
                Sequence Databases
                Biology and Life Sciences
                Plant Science
                Plant Pathology
                Plant Pathogens
                Plant Bacterial Pathogens
                Physical Sciences
                Chemistry
                Environmental Chemistry
                Soil Chemistry
                Ecology and Environmental Sciences
                Environmental Chemistry
                Soil Chemistry
                Ecology and Environmental Sciences
                Soil Science
                Soil Chemistry
                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
                Actinobacteria
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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