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      Enrichment of beneficial cucumber rhizosphere microbes mediated by organic acid secretion

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          Abstract

          Resistant cultivars have played important roles in controlling Fusarium wilt disease, but the roles of rhizosphere interactions among different levels of resistant cultivars are still unknown. Here, two phenotypes of cucumber, one resistant and one with increased susceptibility to Fusarium oxysporum f.sp. cucumerinum (Foc), were grown in the soil and hydroponically, and then 16S rRNA gene sequencing and nontargeted metabolomics techniques were used to investigate rhizosphere microflora and root exudate profiles. Relatively high microbial community evenness for the Foc-susceptible cultivar was detected, and the relative abundances of Comamonadaceae and Xanthomonadaceae were higher for the Foc-susceptible cultivar than for the other cultivar. FishTaco analysis revealed that specific functional traits, such as protein synthesis and secretion, bacterial chemotaxis, and small organic acid metabolism pathways, were significantly upregulated in the rhizobacterial community of the Foc-susceptible cultivar. A machine-learning approach in conjunction with FishTaco plus metabolic pathway analysis revealed that four organic acids (citric acid, pyruvate acid, succinic acid, and fumarate) were released at higher abundance by the Foc-susceptible cultivar compared with the resistant cultivar, which may be responsible for the recruitment of Comamonadaceae, a potential beneficial microbial group. Further validation demonstrated that Comamonadaceae can be “cultured” by these organic acids. Together, compared with the resistant cultivar, the susceptible cucumber tends to assemble beneficial microbes by secreting more organic acids.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

            Abstract We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.
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              Robust methods for differential abundance analysis in marker gene surveys

              We introduce a novel methodology for differential abundance analysis in sparse high-throughput marker gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for under-sampling: a common feature of large-scale marker gene studies. We show, using simulated data and several published microbiota datasets, that metagenomeSeq outperforms the tools currently used in this field.
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                Author and article information

                Contributors
                junyuan@njau.edu.cn
                Journal
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2662-6810
                2052-7276
                1 October 2020
                1 October 2020
                2020
                : 7
                : 154
                Affiliations
                [1 ]GRID grid.27871.3b, ISNI 0000 0000 9750 7019, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Key Laboratory of Plant Immunity, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving Fertilizers, Nanjing Agricultural University, ; 210095 Nanjing, China
                [2 ]GRID grid.135769.f, ISNI 0000 0001 0561 6611, Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, ; 510640 Guangdong, China
                Author information
                http://orcid.org/0000-0002-5662-9620
                Article
                380
                10.1038/s41438-020-00380-3
                7527982
                33082961
                b834c1aa-7647-426e-9496-76fe9f64fd07
                © The Author(s) 2020

                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
                : 2 March 2020
                : 5 July 2020
                : 10 July 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004608, Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation);
                Award ID: BK20170724
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31902107
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                secondary metabolism,soil microbiology
                secondary metabolism, soil microbiology

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