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      Repeated introduction of micropollutants enhances microbial succession despite stable degradation patterns

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          Abstract

          The increasing-volume release of micropollutants into natural surface waters has raised great concern due to their environmental accumulation. Persisting micropollutants can impact multiple generations of organisms, but their microbially-mediated degradation and their influence on community assembly remain understudied. Here, freshwater microbes were treated with several common micropollutants, alone or in combination, and then transferred every 5 days to fresh medium containing the same micropollutants to mimic the repeated exposure of microbes. Metabarcoding of 16S rRNA gene makers was chosen to study the succession of bacterial assemblages following micropollutant exposure. The removal rates of micropollutants were then measured to assess degradation capacity of the associated communities. The degradation of micropollutants did not accelerate over time but altered the microbial community composition. Community assembly was dominated by stochastic processes during early exposure, via random community changes and emergence of seedbanks, and deterministic processes later in the exposure, via advanced community succession. Early exposure stages were characterized by the presence of sensitive microorganisms such as Actinobacteria and Planctomycetes, which were then replaced by more tolerant bacteria such as Bacteroidetes and Gammaproteobacteria. Our findings have important implication for ecological feedback between microbe-micropollutants under anthropogenic climate change scenarios.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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              QIIME allows analysis of high-throughput community sequencing data.

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                Author and article information

                Contributors
                ayhu@iue.ac.cn
                Journal
                ISME Commun
                ISME Commun
                ISME Communications
                Nature Publishing Group UK (London )
                2730-6151
                2730-6151
                7 June 2022
                7 June 2022
                2022
                : 2
                : 48
                Affiliations
                [1 ]GRID grid.10548.38, ISNI 0000 0004 1936 9377, Department of Ecology, Environment and Plant Sciences, , Stockholm University, ; 106 91 Stockholm, Sweden
                [2 ]GRID grid.9227.e, ISNI 0000000119573309, CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, , Chinese Academy of Sciences, ; Xiamen, 361021 China
                [3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [4 ]GRID grid.7492.8, ISNI 0000 0004 0492 3830, Department of Environmental Microbiology, , UFZ, Helmholtz Centre for Environmental Research, ; Leipzig, Germany
                [5 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, Institute of Marine Microbes and Ecospheres, State Key Laboratory of Marine Environmental Science, , Xiamen University, ; Xiamen, China
                [6 ]GRID grid.9227.e, ISNI 0000000119573309, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, , Chinese Academy of Sciences, ; Nanjing, 210008 China
                [7 ]GRID grid.19188.39, ISNI 0000 0004 0546 0241, Graduate Institute of Environmental Engineering, , National Taiwan University, ; Taipei, 106 Taiwan
                Author information
                http://orcid.org/0000-0002-3280-1166
                http://orcid.org/0000-0002-4210-2668
                http://orcid.org/0000-0001-7039-7136
                http://orcid.org/0000-0002-0774-1608
                http://orcid.org/0000-0001-6226-2741
                Article
                129
                10.1038/s43705-022-00129-0
                9723708
                f3a1ddca-0ae4-49dd-a2e2-69c49d185dce
                © The Author(s) 2022

                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
                : 28 January 2022
                : 4 May 2022
                : 9 May 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31870475
                Award ID: U1805244
                Award Recipient :
                Funded by: the International Partnership Program of Chinese Academy of Sciences (132C35KYSB20200012), the CAS Youth Interdisciplinary Team (JCTD-2021-13), and the Youth Innovation Project of Xiamen (3502Z20206093)
                Categories
                Article
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                © The Author(s) 2022

                microbial ecology,pollution remediation
                microbial ecology, pollution remediation

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