Probiotic Diversity Enhances Rhizosphere Microbiome Function and Plant Disease Suppression

Abstract

Bacterial communities associated with plant roots play an important role in the suppression of soil-borne pathogens, and multispecies probiotic consortia may enhance disease suppression efficacy. Here we introduced defined Pseudomonas species consortia into naturally complex microbial communities and measured the importance of Pseudomonas community diversity for their survival and the suppression of the bacterial plant pathogen Ralstonia solanacearum in the tomato rhizosphere microbiome. The survival of introduced Pseudomonas consortia increased with increasing diversity. Further, high Pseudomonas diversity reduced pathogen density in the rhizosphere and decreased the disease incidence due to both intensified resource competition and interference with the pathogen. These results provide novel mechanistic insights into elevated pathogen suppression by diverse probiotic consortia in naturally diverse plant rhizospheres. Ecologically based community assembly rules could thus play a key role in engineering functionally reliable microbiome applications.

Importance: The increasing demand for food supply requires more-efficient control of plant diseases. The use of probiotics, i.e., naturally occurring bacterial antagonists and competitors that suppress pathogens, has recently reemerged as a promising alternative to agrochemical use. It is, however, still unclear how many and which strains we should choose for constructing effective probiotic consortia. Here we present a general ecological framework for assembling effective probiotic communities based on in vitro characterization of community functioning. Specifically, we show that increasing the diversity of probiotic consortia enhances community survival in the naturally diverse rhizosphere microbiome, leading to increased pathogen suppression via intensified resource competition and interference with the pathogen. We propose that these ecological guidelines can be put to the test in microbiome engineering more widely in the future.

FIG 1

Characterization of biodiversity-ecosystem functioning relationships in vitro. (A) Pseudomonas community niche breadth was defined as the number of carbon sources used by at least one of the members of Pseudomonas community (detailed information on resources can be found in Table S4). (B) Pseudomonas community niche overlap with the pathogen was defined as similarity in resource consumption between the resident community and the pathogen. (C) Antibacterial activity of Pseudomonas community was determined as a reduction in pathogen density in the presence of Pseudomonas bacterial supernatants; all supernatants were derived from monocultures and mixed together in testing the synergistic effects.

FIG 2

Characterization of biodiversity-ecosystem functioning relationships in vivo. (A) The dynamics of bacterial wilt disease incidence in Pseudomonas communities at different richness levels and at different points in time. (B) Pathogen density dynamics as affected by Pseudomonas communities with different richness levels. (C) Pseudomonas density dynamics in communities with different richness levels. Panel columns denote results at 5 days, 15 days, 25 days, and 35 days post-pathogen inoculation (dpi). The red dashed lines show the baseline for control treatments. In panels A and B, red dotted lines denote disease incidence and pathogen density in the absence of Pseudomonas bacteria; in panel C, red dashed lines denote Pseudomonas-specific phlD gene density in natural soil in the absence of introduced Pseudomonas bacteria.

FIG 3

Structural equation models testing the mechanistic links between Pseudomonas community richness and pathogen density (A) and disease incidence (B) 35 days after pathogen inoculation. (A) Direct and indirect (corresponding to Pseudomonas community niche breadth and Pseudomonas community toxicity, respectively) richness effects on pathogen density. (B) Disease incidence data were explained only by a direct richness effect. Blue circles in both panels denote the proportion of the total variance explained. Blue arrows indicate negative relationships, and red arrows indicate positive relationships; double-headed, dashed arrows indicate undirected correlations between different variables (no hypothesis tested); and gray arrows indicate nonsignificant relationships between different variables. Arrow widths indicate the relative sizes of the effects, and the numbers beside the arrows show standardized correlation coefficients (relative effect sizes of nonsignificant correlations are not shown).