Literature DB >> 27482625

Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management.

R Poudel1, A Jumpponen1, D C Schlatter1, T C Paulitz1, B B McSpadden Gardener1, L L Kinkel1, K A Garrett1.   

Abstract

Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

Entities:  

Keywords:  Quercus macrocarpa; Triticum aestivum; biocontrol; networks; phytobiome

Mesh:

Substances:

Year:  2016        PMID: 27482625     DOI: 10.1094/PHYTO-02-16-0058-FI

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  56 in total

1.  NetCoMi: network construction and comparison for microbiome data in R.

Authors:  Stefanie Peschel; Christian L Müller; Erika von Mutius; Anne-Laure Boulesteix; Martin Depner
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

2.  Penicillin Trunk Injection Affects Bacterial Community Structure in Citrus Trees.

Authors:  Marina S Ascunce; Keumchul Shin; Jose C Huguet-Tapia; Ravin Poudel; Karen A Garrett; Ariena H C van Bruggen; Erica M Goss
Journal:  Microb Ecol       Date:  2018-12-01       Impact factor: 4.552

3.  Recovery in methanotrophic activity does not reflect on the methane-driven interaction network after peat mining.

Authors:  Thomas Kaupper; Lucas W Mendes; Monica Harnisz; Sascha M B Krause; Marcus A Horn; Adrian Ho
Journal:  Appl Environ Microbiol       Date:  2020-12-18       Impact factor: 4.792

4.  In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana.

Authors:  Claudia Bartoli; Léa Frachon; Matthieu Barret; Mylène Rigal; Carine Huard-Chauveau; Baptiste Mayjonade; Catherine Zanchetta; Olivier Bouchez; Dominique Roby; Sébastien Carrère; Fabrice Roux
Journal:  ISME J       Date:  2018-05-30       Impact factor: 10.302

5.  Rootstocks Shape the Rhizobiome: Rhizosphere and Endosphere Bacterial Communities in the Grafted Tomato System.

Authors:  Ravin Poudel; Ari Jumpponen; Megan M Kennelly; Cary L Rivard; Lorena Gomez-Montano; Karen A Garrett
Journal:  Appl Environ Microbiol       Date:  2019-01-09       Impact factor: 4.792

6.  Changes in Bacterial and Fungal Microbiomes Associated with Tomatoes of Healthy and Infected by Fusarium oxysporum f. sp. lycopersici.

Authors:  Xin Zhou; Jin-Ting Wang; Wei-Hua Wang; Clement Km Tsui; Lei Cai
Journal:  Microb Ecol       Date:  2020-06-25       Impact factor: 4.552

7.  Location, Root Proximity, and Glyphosate-Use History Modulate the Effects of Glyphosate on Fungal Community Networks of Wheat.

Authors:  Daniel C Schlatter; Chuntao Yin; Ian Burke; Scot Hulbert; Timothy Paulitz
Journal:  Microb Ecol       Date:  2017-12-07       Impact factor: 4.552

8.  Influence of resistance breeding in common bean on rhizosphere microbiome composition and function.

Authors:  Lucas William Mendes; Jos M Raaijmakers; Mattias de Hollander; Rodrigo Mendes; Siu Mui Tsai
Journal:  ISME J       Date:  2017-10-13       Impact factor: 10.302

9.  Core Rhizosphere Microbiomes of Dryland Wheat Are Influenced by Location and Land Use History.

Authors:  Daniel C Schlatter; Chuntao Yin; Scot Hulbert; Timothy C Paulitz
Journal:  Appl Environ Microbiol       Date:  2020-02-18       Impact factor: 4.792

10.  Differential Responses of Arbuscular Mycorrhizal Fungal Communities to Long-Term Fertilization in the Wheat Rhizosphere and Root Endosphere.

Authors:  Yuying Ma; Huanchao Zhang; Daozhong Wang; Xisheng Guo; Teng Yang; Xingjia Xiang; Florian Walder; Haiyan Chu
Journal:  Appl Environ Microbiol       Date:  2021-08-11       Impact factor: 4.792

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