Dataset Information

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

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.

SUBMITTER: Yuan J

PROVIDER: S-EPMC7784920 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Disease-suppressive soils are soils in which specific soil-borne plant pathogens cause only limited disease although the pathogen and susceptible host plants are both present. Suppressiveness is in most cases of microbial origin. We conducted a comparative metabarcoding analysis of the taxonomic diversity of fungal and bacterial communities from suppressive and non-suppressive (conducive) soils as regards Fusarium wilts sampled from the Châteaurenard region (France). Bioassays based on Fusarium wilt of flax confirmed that disease incidence was significantly lower in the suppressive soil than in the conducive soil. Furthermore, we succeeded in partly transferring Fusarium wilt-suppressiveness to the conducive soil by mixing 10% (w/w) of the suppressive soil into the conducive soil. Fungal diversity differed significantly between the suppressive and conducive soils. Among dominant fungal operational taxonomic units (OTUs) affiliated to known genera, 17 OTUs were detected exclusively in the suppressive soil. These OTUs were assigned to the Acremonium, Chaetomium, Cladosporium, Clonostachys, Fusarium, Ceratobasidium, Mortierella, Penicillium, Scytalidium, and Verticillium genera. Additionally, the relative abundance of specific members of the bacterial community was significantly higher in the suppressive and mixed soils than in the conducive soil. OTUs found more abundant in Fusarium wilt-suppressive soils were affiliated to the bacterial genera Adhaeribacter, Massilia, Microvirga, Rhizobium, Rhizobacter, Arthrobacter, Amycolatopsis, Rubrobacter, Paenibacillus, Stenotrophomonas, and Geobacter. Several of the fungal and bacterial genera detected exclusively or more abundantly in the Fusarium wilt-suppressive soil included genera known for their activity against F. oxysporum. Overall, this study supports the potential role of known fungal and bacterial genera in Fusarium wilt suppressive soils from Châteaurenard and pinpoints new bacterial and fungal genera for their putative role in Fusarium wilt suppressiveness.

Project description:BackgroundThe development of suppressive soils is a promising strategy to protect plants against soil-borne diseases in a sustainable and viable manner. The use of crop rotation and the incorporation of plant residues into the soil are known to alleviate the stress imposed by soil pathogens through dynamics changes in soil biological and physicochemical properties. However, relatively little is known about the extent to which specific soil amendments of plant residues trigger the development of plant-protective microbiomes. Here, we investigated how the incorporation of pineapple residues in soils highly infested with the banana Fusarium wilt disease alleviates the pathogen pressure via changes in soil microbiomes.ResultsThe addition of above- and below-ground pineapple residues in highly infested soils significantly reduced the number of pathogens in the soil, thus resulting in a lower disease incidence. The development of suppressive soils was mostly related to trackable changes in specific fungal taxa affiliated with Aspergillus fumigatus and Fusarium solani, both of which displayed inhibitory effects against the pathogen. These antagonistic effects were further validated using an in vitro assay in which the pathogen control was related to growth inhibition via directly secreted antimicrobial substances and indirect interspecific competition for nutrients. The disease suppressive potential of these fungal strains was later validated using microbial inoculation in a well-controlled pot experiment.ConclusionsThese results mechanistically demonstrated how the incorporation of specific plant residues into the soil induces trackable changes in the soil microbiome with direct implications for disease suppression. The incorporation of pineapple residues in the soil alleviated the pathogen pressure by increasing the relative abundance of antagonistic fungal taxa causing a negative effect on pathogen growth and disease incidence. Taken together, this study provides a successful example of how specific agricultural management strategies can be used to manipulate the soil microbiome towards the development of suppressive soils against economically important soil-borne diseases. Video Abstract.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets