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      Bacillus velezensis stimulates resident rhizosphere Pseudomonas stutzeri for plant health through metabolic interactions

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          Abstract

          Trophic interactions play a central role in driving microbial community assembly and function. In gut or soil ecosystems, successful inoculants are always facilitated by efficient colonization; however, the metabolite exchanges between inoculants and resident bacteria are rarely studied, particularly in the rhizosphere. Here, we used bioinformatic, genetic, transcriptomic, and metabonomic analyses to uncover syntrophic cooperation between inoculant ( Bacillus velezensis SQR9) and plant-beneficial indigenous Pseudomonas stutzeri in the cucumber rhizosphere. We found that the synergistic interaction of these two species is highly environmental dependent, the emergence of syntrophic cooperation was only evident in a static nutrient-rich niche, such as pellicle biofilm in addition to the rhizosphere. Our results identified branched-chain amino acids (BCAAs) biosynthesis pathways are involved in syntrophic cooperation. Genome-scale metabolic modeling and metabolic profiling also demonstrated metabolic facilitation among the bacterial strains. In addition, biofilm matrix components from Bacillus were essential for the interaction. Importantly, the two-species consortium promoted plant growth and helped plants alleviate salt stress. In summary, we propose a mechanism in which synergic interactions between a biocontrol bacterium and a partner species promote plant health.

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          Is Open Access

          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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            Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

            The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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              Prokka: rapid prokaryotic genome annotation.

              T Seemann (2014)
              The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. Prokka is implemented in Perl and is freely available under an open source GPLv2 license from http://vicbioinformatics.com/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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                Author and article information

                Contributors
                xzh2068@njau.edu.cn
                rfzhang@njau.edu.cn
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                30 September 2021
                : 1-14
                Affiliations
                [1 ]GRID grid.27871.3b, ISNI 0000 0000 9750 7019, Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers, The Key Laboratory of Plant Immunity, , Nanjing Agricultural University, ; Nanjing, Jiangsu People’s Republic of China
                [2 ]GRID grid.5170.3, ISNI 0000 0001 2181 8870, Bacterial Interactions and Evolution Group, DTU Bioengineering, , Technical University of Denmark, ; Kongens Lyngby, Denmark
                [3 ]GRID grid.5170.3, ISNI 0000 0001 2181 8870, Bacterial Ecophysiology and Biotechnology Group, DTU Bioengineering, , Technical University of Denmark, ; Kongens Lyngby, Denmark
                [4 ]GRID grid.8954.0, ISNI 0000 0001 0721 6013, Biotechnical Faculty, , University of Ljubljana, ; Ljubljana, Slovenia
                [5 ]GRID grid.5170.3, ISNI 0000 0001 2181 8870, Present Address: Quantitative Modelling of Cell Metabolism Group, The Novo Nordisk Foundation Center for Biosustainability, , Technical University of Denmark, ; Kongens Lyngby, Denmark
                Author information
                http://orcid.org/0000-0002-3987-8836
                http://orcid.org/0000-0001-6929-3671
                http://orcid.org/0000-0003-0905-5705
                http://orcid.org/0000-0003-4136-986X
                http://orcid.org/0000-0002-5662-9620
                http://orcid.org/0000-0002-3334-4286
                http://orcid.org/0000-0002-4465-1636
                Article
                1125
                10.1038/s41396-021-01125-3
                8483172
                34593997
                9d5cc6b6-865f-4ee7-affa-7ae43edae14e
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 2 June 2021
                : 16 September 2021
                : 20 September 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 32072675
                Award ID: 32072665
                Award Recipient :
                Funded by: National Nature Science Foundation of China (31972512, 32072675 and 32072665), the Agricultural Science and Technology Innovation Program of CAAS (CAAS-ZDRW202009), the Fundamental Research Funds for the Central Universities (KYXK202009). XS was supported by a Chinese Scholarship Council fellowship. ÁTK, MLS and AD were supported by the Danish National Research Foundation (DNRF137) for the Center for Microbial Secondary Metabolites. AD was supported by Slovenian Research Agency (N1-0177). Biofilm related work in the group of ÁTK is supported by a DTU Alliance Strategic Partnership PhD fellowship. Funding from Novo Nordisk Foundation (grant NNFOC0055625) for the infrastructure “Imaging microbial language in biocontrol (IMLiB)” is acknowledged.
                Funded by: FundRef https://doi.org/10.13039/501100001732, Danmarks Grundforskningsfond (Danish National Research Foundation);
                Award ID: DNRF137
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100009708, Novo Nordisk Fonden (Novo Nordisk Foundation);
                Award ID: NNFOC0055625
                Award Recipient :
                Funded by: National Nature Science Foundation of China 31972512
                Categories
                Article

                Microbiology & Virology
                microbial ecology,bacteria,biofilms,microbial communities,soil microbiology

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