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      Morel Production Associated with Soil Nitrogen-Fixing and Nitrifying Microorganisms

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      Journal of Fungi
      MDPI AG

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          Abstract

          True morels (Morchella, Pezizales) cultivated in soil are subject to complex influences from soil microbial communities. To explore the characteristics of soil microbial communities on morel cultivation, and evaluate whether these microbes are related to morel production, we collected 23 soil samples from four counties in Sichuan and Yunnan Provinces, China. Based on ITS and 16S rDNA amplicon sequencing, the alpha diversity analysis indicated that the biodiversity of morel cultivation soil showed a downward trend compared with the bare soil. The results also showed that there were no significant differences in soil microbial communities between OC (bare soil) and OO (after one-year suspension of sowing). This means that, after about one year of stopping sowing, the component and structure of soil that once cultivated morel would be restored. In co-occurrence networks, some noteworthy bacterial microbes involved in nitrogen fixation and nitrification have been identified in soils with high morel yields, such as Arthrobacter, Bradyhizobium, Devosia, Pseudarthrobacter, Pseudolabrys, and Nitrospira. In contrast, in soils with low or no morel yield, some pathogenic fungi accounted for a high proportion, including Gibberella, Microidium, Penicillium, Sarocladium, Streptomyces, and Trichoderma. This study provided valuable information for the isolation and culturing of some beneficial microbes for morel cultivation in further study and, potentially, to harness the power of the microbiome to improve morel production and health.

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

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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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              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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                Author and article information

                Contributors
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                Journal
                JFOUCU
                Journal of Fungi
                JoF
                MDPI AG
                2309-608X
                March 2022
                March 14 2022
                : 8
                : 3
                : 299
                Article
                10.3390/jof8030299
                35330300
                dfdb951c-9a82-48c4-a473-b5e867326a2d
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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