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

The ISME Journal
Jun YuanQirong Shen

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, Bacill...Continue Reading

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Citations

Jul 22, 2021·Environmental Science & Technology·Hongxing JiangGan Zhang
Aug 27, 2021·Applied Microbiology and Biotechnology·Gei-Lin TianWei-Wei Gao
Sep 6, 2021·Environmental Science and Pollution Research International·Huihui DiGuijun Bu
Sep 11, 2021·Proceedings of the National Academy of Sciences of the United States of America·Hannah BüttnerChristian Hertweck
Nov 2, 2021·Frontiers in Veterinary Science·Li XiJinliang Zhang

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Methods Mentioned

BETA
PCR

Software Mentioned

Google Scholar
Trimmomatic
SparCC
QIIME
RF
R package igraph
R
R package ggplot2
FastQC
vegan

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