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      Black soldier fly larvae ( Hermetia illucens) strengthen the metabolic function of food waste biodegradation by gut microbiome

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          Summary

          Vermicomposting using black soldier fly ( BSF) larvae ( Hermetia illucens) has gradually become a promising biotechnology for waste management, but knowledge about the larvae gut microbiome is sparse. In this study, 16S rRNA sequencing, SourceTracker, and network analysis were leveraged to decipher the influence of larvae gut microbiome on food waste ( FW) biodegradation. The microbial community structure of BSF vermicompost ( BC) changed greatly after larvae inoculation, with a peak colonization traceable to gut bacteria of 66.0%. The relative abundance of 11 out of 21 metabolic function groups in BC were significantly higher than that in natural composting ( NC), such as carbohydrate‐active enzymes. In addition, 36.5% of the functional genes in BC were significantly higher than those in NC. The changes of metabolic functions and functional genes were significantly correlated with the microbial succession. Moreover, the bacteria that proliferated in vermicompost, including Corynebacterium, Vagococcus, and Providencia, had strong metabolic abilities. Systematic and complex interactions between the BSF gut and BC bacteria occurred over time through invasion, altered the microbial community structure, and thus evolved into a new intermediate niche favourable for FW biodegradation. The study highlights BSF gut microbiome as an engine for FW bioconversion, which is conducive to bioproducts regeneration from wastes.

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          Most cited references63

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
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            Cytoscape: software for visualization and analysis of biological networks.

            Substantial progress has been made in the field of "omics" research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Global requirements for such programs are discussed. Utilization of visualization software is exemplified using the widely used Cytoscape tool. Additional information about the use of Cytoscape is provided in the Notes section. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements.
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              Soil microbes drive the classic plant diversity-productivity pattern.

              Ecosystem productivity commonly increases asymptotically with plant species diversity, and determining the mechanisms responsible for this well-known pattern is essential to predict potential changes in ecosystem productivity with ongoing species loss. Previous studies attributed the asymptotic diversity-productivity pattern to plant competition and differential resource use (e.g., niche complementarity). Using an analytical model and a series of experiments, we demonstrate theoretically and empirically that host-specific soil microbes can be major determinants of the diversity-productivity relationship in grasslands. In the presence of soil microbes, plant disease decreased with increasing diversity, and productivity increased nearly 500%, primarily because of the strong effect of density-dependent disease on productivity at low diversity. Correspondingly, disease was higher in plants grown in conspecific-trained soils than heterospecific-trained soils (demonstrating host-specificity), and productivity increased and host-specific disease decreased with increasing community diversity, suggesting that disease was the primary cause of reduced productivity in species-poor treatments. In sterilized, microbe-free soils, the increase in productivity with increasing plant species number was markedly lower than the increase measured in the presence of soil microbes, suggesting that niche complementarity was a weaker determinant of the diversity-productivity relationship. Our results demonstrate that soil microbes play an integral role as determinants of the diversity-productivity relationship.
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                Author and article information

                Contributors
                zhangzhijian@zju.edu.cn
                Journal
                Microb Biotechnol
                Microb Biotechnol
                10.1111/(ISSN)1751-7915
                MBT2
                Microbial Biotechnology
                John Wiley and Sons Inc. (Hoboken )
                1751-7915
                18 March 2019
                May 2019
                : 12
                : 3 ( doiID: 10.1111/mbt2.2019.12.issue-3 )
                : 528-543
                Affiliations
                [ 1 ] College of Environmental and Resource Sciences ZheJiang University HangZhou 310058 China
                [ 2 ] HangZhou GuSheng Biotechnology Co. Ltd HangZhou 311108 China
                [ 3 ] Department of Biological and Agricultural Engineering University of Arkansas Fayetteville AR 72701 USA
                [ 4 ] College of Agriculture and Biotechnology ZheJiang University HangZhou 310058 China
                Author notes
                [*] [* ]For correspondence. E‐mail zhangzhijian@ 123456zju.edu.cn ; Tel. +86 571 8898 2057; Fax + 86 571 8898 1719.
                Author information
                https://orcid.org/0000-0002-4133-9620
                Article
                MBT213393
                10.1111/1751-7915.13393
                6465238
                30884189
                3fd349ca-2cef-4e84-a44e-be050ad42b50
                © 2019 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 July 2018
                : 06 February 2019
                : 13 February 2019
                Page count
                Figures: 8, Tables: 0, Pages: 16, Words: 9606
                Funding
                Funded by: YuHang Initiative for Eco‐Agriculture Development
                Award ID: 2018‐0AG‐045
                Funded by: the National Natural Science Foundation of China
                Award ID: 41673081
                Funded by: ZheJiang Science and Technology Innovation Program of China
                Award ID: 2015C03009
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                mbt213393
                May 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.2.1 mode:remove_FC converted:15.04.2019

                Biotechnology
                Biotechnology

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