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      Comparative metagenomics reveals the microbial diversity and metabolic potentials in the sediments and surrounding seawaters of Qinhuangdao mariculture area

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          Abstract

          Qinhuangdao coastal area is an important mariculture area in North China. Microbial communities play an important role in driving biogeochemical cycle and energy flow. It is necessary to identify the microbial communities and their functions in the coastal mariculture area of Qinhuangdao. In this study, the microbial community compositions and their metabolic potentials in the sediments and their surrounding seawaters of Qinhuangdao mariculture area were uncovered by the 16S rRNA gene amplicon sequencing and metagenomic shotgun sequencing approaches. The results of amplicon sequencing showed that Gammaproteobacteria and Alphaproteobacteria were predominant classes. Our datasets showed a clear shift in microbial taxonomic groups and the metabolic pathways in the sediments and surrounding seawaters. Metagenomic analysis showed that purine metabolism, ABC transporters, and pyrimidine metabolism were the most abundant pathways. Genes related to two-component system, TCA cycle and nitrogen metabolism exhibited higher abundance in sediments compared with those in seawaters. The presence of cadmium-resistant genes and ABC transporters suggested the ability of microorganisms to resist the toxicity of cadmium. In summary, this study provides comprehensive and significant differential signatures in the microbial community and metabolic pathways in Qinhuangdao mariculture area, and can develop effective microbial indicators to monitor mariculture area in the future.

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          Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

          The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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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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              Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time.

              The growing need for baseline data against which efforts to reduce the rate of biodiversity loss can be judged highlights the importance of long-term datasets, some of which are as old as ecology itself. We review methods of evaluating change in biodiversity at the community level using these datasets, and contrast whole-community approaches with those that combine information from different species and habitats. As all communities experience temporal turnover, one of the biggest challenges is distinguishing change that can be attributed to external factors, such as anthropogenic activities, from underlying natural change. We also discuss methodological issues, such as false alerts and modifications in design, of which users of these data sets need to be aware. Copyright © 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Investigation
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                4 June 2020
                2020
                : 15
                : 6
                : e0234128
                Affiliations
                [001]State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
                Guangzhou University, CHINA
                Author notes

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

                Author information
                http://orcid.org/0000-0002-3843-4133
                Article
                PONE-D-20-07771
                10.1371/journal.pone.0234128
                7272022
                32497143
                e5eecc83-e735-412d-a4f0-cd73b87a176a
                © 2020 Wang 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
                : 18 March 2020
                : 19 May 2020
                Page count
                Figures: 6, Tables: 0, Pages: 16
                Funding
                Funded by: the Fundamental Research Funds for Central Public Welfare Scientific Research Institutes of China
                Award ID: 2019YSKY-007
                Award Recipient :
                This work was supported by the Fundamental Research Funds for Central Public Welfare Scientific Research Institutes of China [grant number 2019YSKY-007](SW).
                Categories
                Research Article
                Earth Sciences
                Geology
                Petrology
                Sediment
                Earth Sciences
                Geology
                Sedimentary Geology
                Sediment
                Ecology and Environmental Sciences
                Aquatic Environments
                Marine Environments
                Sea Water
                Earth Sciences
                Marine and Aquatic Sciences
                Aquatic Environments
                Marine Environments
                Sea Water
                Biology and Life Sciences
                Genetics
                Genomics
                Metagenomics
                Biology and Life Sciences
                Agriculture
                Aquaculture
                Mariculture
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Nitrogen Metabolism
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolic Pathways
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Purine Metabolism
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Non-coding RNA
                Ribosomal RNA
                Biology and life sciences
                Biochemistry
                Ribosomes
                Ribosomal RNA
                Biology and life sciences
                Cell biology
                Cellular structures and organelles
                Ribosomes
                Ribosomal RNA
                Custom metadata
                Metagenomic datasets are available online at NCBI, BioSample accessions: SAMN13744940, SAMN13744941, SAMN13744942, SAMN13744943 for Seawater1 (S1), Sediment1 (S1S), Seawater2 (S2), and Sediment2 (S2S) samples respectively. The 16S rRNA datasets are available online at NCBI, BioSample accessions: SAMN13752121, SAMN13752122, SAMN13752123, SAMN13752124 for (Seawater sample1) S1, (Sediment sample1) S1S, (Seawater sample2) S2, and (Sediment sample2) S2S samples respectively.

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                Uncategorized

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