Biotic and abiotic responses to soilborne pathogens and environmental predictors of soil health

Abstract

Soilborne pathogens affect agricultural productivity and soil functions, yet the specific impacts of pathogen infections on the soil and plant root-associated microbiomes, the key determinants of plant health, remain unclear. Filling this knowledge gap is required to understanding microbial ecological responses, and to developing biological tools to manage and predict the prevalence and severity of soilborne diseases, which can improve soil health, and plant yield. We hypothesized that soilborne pathogens impact the diversity, function, and ecological interactions in soil microbiomes and form a pathobiome. To test the hypothesis, we conducted field sampling in 35 cotton fields in Australia and collected a total of 560 soil samples, which included samples from the rhizosphere (the area in close contact with the roots) and bulk soils. We aimed to investigate how soil microbiomes and key soil properties respond to Verticillium wilt (VW) of cotton, which is caused by the soilborne pathogen Verticillium dahliae. We found that the presence of the pathogen altered the microbiome structure in both bulk and rhizosphere soils. Notably, healthy soils exhibited more complex microbial networks than diseased soils. Furthermore, a putative pathobiome consisting of various microbial taxa that could influence pathogen infection was identified. Specific pathobiome taxa, such as Gibellulopsis spp., displayed a positive association with V. dahliae. In contrast, known biocontrol agents (BCAs) such as Bacillus spp., Fusarium spp., and Talaromyces spp., were negatively correlated with pathogen abundance. After isolation, pathobiome BCA members demonstrated strong biocontrol activity against V. dahliae in vitro, indicating their potential for enhancing resistance to pathogen invasion in agricultural soils. Moreover, shotgun sequencing analysis revealed a gene encoding beta-glucosidase as a putative indicator of soil health. Structural Equation Modelling showed that VW disease incidence is significantly influenced by several factors, including the relative abundance of V. dahliae, the pathobiome, and various abiotic factors such as precipitation, soil moisture, and pH. Taken together, our findings offer novel field evidence for the key biotic and abiotic drivers of a soilborne pathogen. This knowledge could facilitate the development of biological tools to control soilborne diseases and predict soil health and disease incidence, thus providing innovative strategies for sustainable agricultural disease management.