Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt

Authors: YUAN, JUN - Nanjing Agricultural University; WEN, TAO - Nanjing Agricultural University; ZHANG, HE - Nanjing Agricultural University; ZHAO, MENGLI - Nanjing Agricultural University; PENTON, RYAN - Arizona State University; Thomashow, Linda; SHEN, QIRONG - Nanjing Agricultural University

Submitted to: The ISME Journal: Multidisciplinary Journal of Microbial Ecology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/9/2020
Publication Date: 7/17/2020
Citation: Yuan, J., Wen, T., Zhang, H., Zhao, M., Penton, R., Thomashow, L.S., Shen, Q. 2020. Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt. ISME Journal. https://doi.org/10.1038/s41396-020-0720-5.
DOI: https://doi.org/10.1038/s41396-020-0720-5

Interpretive Summary: The composition and functional capacity of the soil microbiome directly influences agricultural productivity by shaping critical ecosystem functions such as nutrient cycling and resistance to plant pathogens. Therefore, understanding the relationships between the soil microbiome and soil-borne disease is a current grand challenge in microbial ecology, with applied consequences for agricultural food production worldwide. Because a single study rarely yields a general conclusion concerning a disease in a particular soil, we attempted to account for the differences in Fusarium wilt, one of the most destructive soilborne diseases, among various studies and plant varieties by using a machine-learning approach based on global DNA sequencing data. We found that 45 bacterial taxa and 40 fungal taxa categorized the health status of a soil with an accuracy greater than 80%. We conclude that these models can be utilized to predict the incidence of Fusarium wilt by revealing features common to the wilt-diseased soil microbiome through the identification of key biological indicators.

Technical Abstract: An increasing number of soil-borne plant diseases are causing devastating losses in agricultural production. While, a single independent case study rarely yields a general conclusion concerning a 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 global meta-sequencing data. We found that alpha-diversity was consistently greater in the fungal community of healthy soils. While diseased soil microbiomes harbored higher abundances of Xanthomonadaceae and Fusarium oxysporum, the healthy soil microbiome contained more Streptomyces and non-pathogenic 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 greater than 80%. We conclude that these models can be utilized to predict the incidence of Fusarium wilt by revealing features common to the wilt-diseased soil microbiome through the identification of key biological indicators.