The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management
Open accessAbstract
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.
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Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
The rhizosphere microbiome and plant health
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Functional cartography of complex metabolic networks
Guimerà, R., & Nunes Amaral, L. A. (2005) · Nature · doi:10.1038/nature03288 · Cited by 3,983 · cited 3×
“Guimera and Amaral (2005) described an approach to categorize nodes into functional groups based on the modular structure of networks, applying this approach to metabolic networks for different organisms.”
BackgroundInfluentialUsing network analysis to explore co-occurrence patterns in soil microbial communities
Barberán, A., Bates, S. T., Casamayor, E. O., & Fierer, N. (2012) · The ISME Journal · doi:10.1038/ismej.2011.119 · Cited by 3,044
Will a Large Complex System be Stable?
MAY, R. M. (1972) · Nature · doi:10.1038/238413a0 · Cited by 2,869
Anticipating Critical Transitions
Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., van de Koppel, J., van de Leemput, I. A., Levin, S. A., van Nes, E. H., et al. (2012) · Science · doi:10.1126/science.1225244 · Cited by 2,208
Sparse and Compositionally Robust Inference of Microbial Ecological Networks
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Microbial co-occurrence relationships in the human microbiome
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Cited by
Shi, Y., Delgado-Baquerizo, M., Li, Y., Yang, Y., Zhu, Y.-G., Peñuelas, J., & Chu, H. (2020) · Environment International · doi:10.1016/j.envint.2020.105869 · Cited by 308
Pauvert, C., Buée, M., Laval, V., Edel-Hermann, V., Fauchery, L., Gautier, A., Lesur, I., Vallance, J., & Vacher, C. (2019) · Fungal Ecology · doi:10.1016/j.funeco.2019.03.005 · Cited by 253
Shaping the leaf microbiota: plant–microbe–microbe interactions
Chaudhry, V., Runge, P., Sengupta, P., Doehlemann, G., Parker, J. E., & Kemen, E. (2021) · Journal of Experimental Botany · doi:10.1093/jxb/eraa417 · Cited by 253
Fang, G.-Y., Chai, L.-J., Zhong, X.-Z., & Jiang, Y.-J. (2021) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2021.109070 · Cited by 91
Heffner, T., Brami, S. A., Mendes, L. W., Kaupper, T., Hannula, E. S., Poehlein, A., Horn, M. A., & Ho, A. (2023) · ISME Communications · doi:10.1038/s43705-023-00271-3 · Cited by 9
Metadata
- Authors
- Poudel, R., Jumpponen, A., Schlatter, D. C., Paulitz, T. C., Gardener, B. B. M., Kinkel, L. L., & Garrett, K. A.
- Venue
- Phytopathology®
- Publisher
- Scientific Societies
- Published
- 2016-10
- Volume
- 106
- Issue
- 10
- Pages
- 1083-1096
- ISSN
- 0031-949X (ISSN-L)
- 1943-7684
- OpenAlex
- W2477852950
- Cited by
- 372
- References
- 8
- FWCI
- 27.08
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Poudel, R., Jumpponen, A., Schlatter, D. C., Paulitz, T. C., Gardener, B. B. M., Kinkel, L. L., & Garrett, K. A. (2016). Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management. Phytopathology®. https://doi.org/10.1094/phyto-02-16-0058-fi
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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