Changes in bulk soil affect the disease-suppressive rhizosphere microbiome against Fusarium wilt disease

doi:10.15302/J-FASE-2020328

Front. Agr. Sci. Eng.

2020, Vol. 7

Issue (3) : 307-316 https://doi.org/10.15302/J-FASE-2020328

RESEARCH ARTICLE

Changes in bulk soil affect the disease-suppressive rhizosphere microbiome against Fusarium wilt disease

Lin FU^1,², Wu XIONG³, Francisco DINI-ANDREOTE^4,⁵, Beibei WANG⁶, Chengyuan TAO¹, Yunze RUAN⁶, Zongzhuan SHEN¹, Rong LI¹(

), Qirong SHEN¹

¹. Jiangsu Provincial Key Laboratory of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Education Ministry Engineering Center of Resource-Saving Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
². School of Life Sciences, Liaoning University, Shenyang 110036, China
³. Ecology and Biodiversity Group, Department of Biology, Institute of Environmental Biology, Utrecht University, Utrecht, 3584 CH, The Netherlands
⁴. Department of Plant Science, The Pennsylvania State University, University Park, PA 16802, USA
⁵. Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
⁶. Hainan Key Laboratory for Sustainable Utilization of Tropical Bio-Resources, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China

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Abstract

Harnessing disease-suppressive microbiomes constitutes a promising strategy for optimizing plant growth. However, relatively little information is available about the relationship between bulk and rhizosphere soil microbiomes. Here, the assembly of banana bulk soil and rhizosphere microbiomes was investigated in a monoculture system consisting of bio-organic (BIO) and organic management practices. Applying BIO practice in newly reclaimed fields resulted in a high-efficiency biocontrol rate, thus providing a promising strategy for pre-control of Fusarium wilt disease. The soil microbiota was further characterized by MiSeq sequencing and quantitative PCR. The results indicate that disease suppression was mediated by the structure of a suppressive rhizosphere microbiome with respect to distinct community composition, diversity and abundance. Overall microbiome suppressiveness was primarily related to a particular set of enriched bacterial taxa affiliated with Pseudomonas, Terrimonas, Cupriavidus, Gp6, Ohtaekwangia and Duganella. Finally, structural equation modeling was used to show that the changes in bulk soil bacterial community determined its induced rhizosphere bacterial community, which serves as an important and direct factor in restraining the pathogen. Collectively, this study provides an integrative approach to disentangle the biological basis of disease-suppressive microbiomes in the context of agricultural practice and soil management.

Keywords agricultural practice bulk soil disease suppression rhizosphere ecology

Corresponding Author(s): Rong LI

Just Accepted Date: 02 April 2020 Online First Date: 27 April 2020 Issue Date: 28 July 2020

Cite this article:

Lin FU,Wu XIONG,Francisco DINI-ANDREOTE, et al. Changes in bulk soil affect the disease-suppressive rhizosphere microbiome against Fusarium wilt disease[J]. Front. Agr. Sci. Eng. , 2020, 7(3): 307-316.

URL:

https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2020328
https://academic.hep.com.cn/fase/EN/Y2020/V7/I3/307

Fig.1 Banana Fusarium wilt disease incidence (a) and yield in BIO and CF systems (b). P-values were determined by two-tailed Student’s t-test. Error bars are standard errors, n = 3.

Fig.2 Bacterial and fungal abundance (target copy number), richness (S_obs) and diversity (Faith’s PD) index. Box plots show the median (thick colored line), mean (blacked diamond), the first quartile (lower box bound), the third quartile (upper box bound), the range of data values that deviate from the box no more than 1.5 times the height of the box (vertical lines). BIOB, bulk soil from bio-organic system; CFB, bulk soil from organic system; BIOR, rhizosphere soil from bio-organic system; CFR, rhizosphere soil from organic system. P-values over paired columns indicate that the means are significantly different according to the two-tailed Student’s or Welch’s t-test or the Wilcoxon rank sum test (P<0.05).

Fig.3 (a, b) Non-metric multidimensional scaling (NMDS) ordinations and (c, d) multivariate regression tree (MRT) analysis. BIOB, bulk soil from bio-organic system; CFB, bulk soil from organic system; BIOR, rhizosphere soil from bio-organic system; CFR, rhizosphere soil from organic system. The identity and number of soil samples included in the analysis are shown under the tree by symbols with different shapes and colors. Numbers under the crosses of each split indicate percentages of variance explained by the split. The R², error, cross-validation error (CV error), and standard error (SE) of MRT analysis are listed under the trees.

Fig.4 Spearman rank correlations between copy numbers of Fusarium oxysporum and specific bacterial relative abundances in the BIO system.

Fig.5 Structural equation modeling integrating direct and indirect relationships of bulk soil and rhizosphere microbiomes (bacteria and fungi), differentially occurring bacterial OTUs (bOTUs), the abundance of the biocontrol agent NJN-6, and the abundance of Fusarium oxysporum. Arrows indicate the flow of causality. Solid arrows represent statistically significant relationships (P<0.05), and dashed black arrows are nonsignificant relationships. Numbers adjacent to arrows are standardized path coefficients and bootstrap P-values. The arrow width is proportional to the strength of path coefficients. R² denotes the proportion of variance explained. Goodness-of-fit statistics for the model are shown at the bottom

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