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      Global diversity and biogeography of DNA viral communities in activated sludge systems

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          Abstract

          Background

          Activated sludge (AS) systems in wastewater treatment plants (WWTPs) harbor enormous viruses that regulate microbial metabolism and nutrient cycling, significantly influencing the stability of AS systems. However, our knowledge about the diversity of viral taxonomic groups and functional traits in global AS systems is still limited. To address this gap, we investigated the global diversity and biogeography of DNA viral communities in AS systems using 85,114 viral operational taxonomic units (vOTUs) recovered from 144 AS samples collected across 54 WWTPs from 13 different countries.

          Results

          AS viral communities and their functional traits exhibited distance-decay relationship (DDR) at the global scale and latitudinal diversity gradient (LDG) from equator to mid-latitude. Furthermore, it was observed that AS viral community and functional gene structures were largely driven by the geographic factors and wastewater types, of which the geographic factors were more important. Carrying and disseminating auxiliary metabolic genes (AMGs) associated with the degradation of polysaccharides, sulfate reduction, denitrification, and organic phosphoester hydrolysis, as well as the lysis of crucial functional microbes that govern biogeochemical cycles were two major ways by which viruses could regulate AS functions. It was worth noting that our study revealed a high abundance of antibiotic resistance genes (ARGs) in viral genomes, suggesting that viruses were key reservoirs of ARGs in AS systems.

          Conclusions

          Our results demonstrated the highly diverse taxonomic groups and functional traits of viruses in AS systems. Viral lysis of host microbes and virus-mediated HGT can regulate the biogeochemical and nutrient cycles, thus affecting the performance of AS systems. These findings provide important insights into the viral diversity, function, and ecology in AS systems on a global scale.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40168-023-01672-1.

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          Most cited references80

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

              Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                fxysnd@126.com
                linwei.wu@pku.edu.cn
                chm_liq@ujn.edu.cn
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                21 October 2023
                21 October 2023
                2023
                : 11
                : 234
                Affiliations
                [1 ]School of Biological Science and Technology, University of Jinan, ( https://ror.org/02mjz6f26) Jinan, Shandong Province China
                [2 ]Artificial Intelligence Institute, University of Jinan, ( https://ror.org/02mjz6f26) Jinan, Shandong Province China
                [3 ]Institute of Marine Science and Technology, Shandong University, ( https://ror.org/0207yh398) Qingdao, Shandong Province China
                [4 ]State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, ( https://ror.org/0207yh398) Qingdao, Shandong Province China
                [5 ]Marine College, Shandong University, ( https://ror.org/0207yh398) Weihai, Shandong Province China
                [6 ]Institute of Ecology, Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, ( https://ror.org/02v51f717) Beijing, China
                Article
                1672
                10.1186/s40168-023-01672-1
                10589946
                37865788
                eea50d49-40f1-4e74-b61a-c73409880b55
                © BioMed Central Ltd., part of Springer Nature 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 12 May 2023
                : 21 September 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31600148
                Award ID: 32070111
                Award Recipient :
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                © BioMed Central Ltd., part of Springer Nature 2023

                activated sludge,viral community,biogeography,auxiliary metabolic genes,virus-microbe interaction

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