Abstract
Soil-borne plant diseases are increasingly causing devastating losses in agricultural production. The development of a more refined model for disease prediction can aid in reducing crop losses through the use of preventative control measures or soil fallowing for a planting season. The emergence of high-throughput DNA sequencing technology has provided unprecedented insight into the microbial composition of diseased versus healthy soils. However, a single independent case study rarely yields a general conclusion predictive of the disease in a particular soil. Here, we attempt to account for the differences among various studies and plant varieties using a machine-learning approach based on 24 independent bacterial data sets comprising 758 samples and 22 independent fungal data sets comprising 279 samples of healthy or Fusarium wilt-diseased soils from eight different countries. We found that soil bacterial and fungal communities were both clearly separated between diseased and healthy soil samples that originated from six crops across nine countries or regions. Alpha diversity was consistently greater in the fungal community of healthy soils. While diseased soil microbiomes harbored higher abundances of Xanthomonadaceae, Bacillaceae, Gibberella, and Fusarium oxysporum, the healthy soil microbiome contained more Streptomyces Mirabilis, Bradyrhizobiaceae, Comamonadaceae, Mortierella, and nonpathogenic fungi of Fusarium. Furthermore, a random forest method identified 45 bacterial OTUs and 40 fungal OTUs that categorized the health status of the soil with an accuracy >80%. We conclude that these models can be applied to predict the potential for occurrence of F. oxysporum wilt by revealing key biological indicators and features common to the wilt-diseased soil microbiome.
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Data availability
The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Raw sequence data obtained in this study have been deposited in Genome Sequence Archive in the BIG Data Center, Chinese Academy of Sciences under accession codes CRA002340. All scripts for computational analysis and corresponding raw data are available at https://github.com/taowenmicro/Wen-etal-200214-paper-code.
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Acknowledgements
We thank Yongxin Liu (institute of genetics and developmental biology, Chinese Academy of Sciences) for the comments on this manuscript, and the WeChat subscription ID “meta-Genome” and “Micro-Bioinformatics and microflora” for the analysis methods. This study was financially supported by Natural Science Foundation of Jiangsu Province (BK20170724), Natural Science Foundation of China (31902107), Special Fund for Agro-scientific Research in the Public Interest: integrated management technology of crop wilt disease (No. 201503110), the Innovative Research Team Development Plan of the Ministry of Education of China (Grant No. IRT_17R56), and the Fundamental Research Funds for the Central Universities (Grant No. KYT201802, KYXK2020010, and KJQN202017). JY was supported by National Postdoctoral Program for Innovative Talents (BX201600075).
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JY and TW conducted all experiments, conceived the study, and wrote the paper; HZ and MZ collected sequencing data; QS conceived the study, supervised the study, and wrote the paper; CRP and LST provided critical comments on the study, and helped write the paper.
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Yuan, J., Wen, T., Zhang, H. et al. Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt. ISME J 14, 2936–2950 (2020). https://doi.org/10.1038/s41396-020-0720-5
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DOI: https://doi.org/10.1038/s41396-020-0720-5
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