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. 2022 Aug;16(8):1932-1943.
doi: 10.1038/s41396-022-01244-5. Epub 2022 Apr 23.

Trophic interactions between predatory protists and pathogen-suppressive bacteria impact plant health

Sai Guo #  1   2 , Chengyuan Tao #  1   2 , Alexandre Jousset  3 , Wu Xiong  1   2   3 , Zhe Wang  1   2 , Zongzhuan Shen  1   2 , Beibei Wang  4 , Zhihui Xu  1   2 , Zhilei Gao  3 , Shanshan Liu  1   2 , Rong Li  5   6 , Yunze Ruan  4 , Qirong Shen  7   8 , George A Kowalchuk  3 , Stefan Geisen  9   10
Affiliations

Trophic interactions between predatory protists and pathogen-suppressive bacteria impact plant health

Sai Guo et al. ISME J. 2022 Aug.

Abstract

Plant health is strongly impacted by beneficial and pathogenic plant microbes, which are themselves structured by resource inputs. Organic fertilizer inputs may thus offer a means of steering soil-borne microbes, thereby affecting plant health. Concurrently, soil microbes are subject to top-down control by predators, particularly protists. However, little is known regarding the impact of microbiome predators on plant health-influencing microbes and the interactive links to plant health. Here, we aimed to decipher the importance of predator-prey interactions in influencing plant health. To achieve this goal, we investigated soil and root-associated microbiomes (bacteria, fungi and protists) over nine years of banana planting under conventional and organic fertilization regimes differing in Fusarium wilt disease incidence. We found that the reduced disease incidence and improved yield associated with organic fertilization could be best explained by higher abundances of protists and pathogen-suppressive bacteria (e.g. Bacillus spp.). The pathogen-suppressive actions of predatory protists and Bacillus spp. were mainly determined by their interactions that increased the relative abundance of secondary metabolite Q genes (e.g. nonribosomal peptide synthetase gene) within the microbiome. In a subsequent microcosm assay, we tested the interactions between predatory protists and pathogen-suppressive Bacillus spp. that showed strong improvements in plant defense. Our study shows how protistan predators stimulate disease-suppressive bacteria in the plant microbiome, ultimately enhancing plant health and yield. Thus, we suggest a new biological model useful for improving sustainable agricultural practices that is based on complex interactions between different domains of life.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Effects and underlying drivers of organic and chemical fertilizer treatments on Fusarium wilt disease incidence and banana yield.
A Disease incidences of banana Fusarium wilt in organic and chemical fertilizer treatments. B Banana yield in organic and chemical fertilizer treatments. C Fusarium oxysporum density in organic and chemical fertilizer treatments in the three compartments. D, E The random forest mean predictor importance (% increase of the MSE) of bacterial, fungal and protistan community composition for banana Fusarium wilt disease incidence (D) and banana yield (E) in the three compartments. In panels A, B and C, asterisks indicate significant differences as defined by the Student’s t test (***p < 0.001). OF Organic fertilizer treatment, CF Chemical fertilizer treatment. In panels D, E, to estimate the importance of microbial predictors, we used the percentage increases in the MSE (mean squared error). Significance levels of each predictor are represented by *p < 0.05 or **p < 0.01.
Fig. 2
Fig. 2. Comparison of protistan and bacterial community composition and correlations of key protistan and bacterial OTU, predatory protists and Fusarium oxysporum density, and predatory protists and Bacillus/ bacteria.
A The effects of different fertilization treatments on the relative abundance of protistan and bacterial OTUs in the rhizosphere. B Correlations between key indicator protistan and bacterial OTUs of different fertilization treatments in the rhizosphere multi-bipartite model of the food web. C, D Correlations between the relative change of the relative abundance of protistan functional groups and the relative change of Fusarium oxysporum density (C), Bacillus density and the ratio of Bacillus density to total bacteria density (D) in the rhizosphere. In panels A, B, the relative abundances of protistan and bacterial OTUs were significantly higher in the organic and chemical fertilizer treatments based on linear discriminant analysis (higher relative abundance in the organic fertilizer treatment: LDA score > 3.0 and p < 0.05; higher relative abundance in the chemical fertilizer treatment: LDA score < −3.0 and p < 0.05). OF Organic fertilizer treatment; CF Chemical fertilizer treatment. Circles are proportional to the average relative abundance of each OTU. RA Relative abundance, relative change = (OF-CF)/CF. In panels C, D, RC = relative change ((OF-CF)/CF); Bacillus/ bacteria = the ratio of Bacillus density to total bacteria density.
Fig. 3
Fig. 3. Microbial functional genes and their potential interactions with pathogen density.
A The random forest mean predictor importance (% increase of the MSE) of the metabolism gene categories for Fusarium oxysporum density and the relative abundance of the metabolism gene categories in different fertilization treatments. B The top 10 most important random forest mean predictors (% increase of the MSE) of the Q gene category of Fusarium oxysporum density and the relative abundance of these genes in different fertilization treatments. C The top 10 microbial origins of metabolism Q genes. D The top 10 microbial origins of COG1020. In panels A, B, asterisks indicate significant differences of relative abundances as defined by the Student’s t test (*indicates p < 0.05, **indicates p < 0.01); for the random forest analysis, to estimate the importance of microbial predictors, we used the percentage increases in the MSE (mean squared error). In panels C, D, bars with different letters indicate significant differences as defined by one-way ANOVA with Tukey’s HSD test (p < 0.05). Asterisks indicate significant differences as defined by the Student’s t test (*indicates p < 0.05, **indicates p < 0.01, ***indicates p < 0.001). OF Organic fertilizer treatment; CF Chemical fertilizer treatment.
Fig. 4
Fig. 4. Pathogen suppression capability of predatory protists and their potential interactions with Bacillus and NRPS gene.
A The effects of different concentrations of predatory protists on Fusarium oxysporum density. B The effects of different concentrations of predatory protists on the ratio of Bacillus density to total bacteria density. C The effects of different concentrations of predatory protists on the abundance of nonribosomal peptide synthetase (NRPS) gene. In panels A–C, bars with different letters indicate significant differences as defined by one-way ANOVA with Tukey’s HSD test (p < 0.05). In the control, no protists were added. Cer (101): Cercomonas lenta strain ECO-P-01 (1.0 × 101 cells g−1 dry soil); Cer (102): Cercomonas lenta strain ECO-P-01 (1.0 × 102 cells g−1 dry soil); Cer (103): Cercomonas lenta strain ECO-P-01 (1.0 × 103 cells g−1 dry soil). Bacillus/ bacteria = the ratio of Bacillus density to total bacteria density.
Fig. 5
Fig. 5. Interactions between the pathogen, the bacterial isolates and the predator in the greenhouse experiment.
In panels A, C, F and G, bars with different letters indicate significant differences as defined by one-way ANOVA with Tukey’s HSD test (p < 0.05). In panels D, E, asterisks indicate significant differences as defined by the Student’s t test (***p < 0.001). Relative change = (X-control)/control, X = Bacillus + Cercomonas lenta, control = Bacillus only. B Bacillus isolate; Cer: Cercomonas lenta strain ECO-P-01. WT_Bac: wild type Bacillus strain. Mut_Bac: mutant Bacillus strain (disrupted in the bacillomycin D pathway) [54]. Predation intensity = (Yc–Yp)/Yc, where Yc is the Bacillus density in the control, and Yp is the Bacillus density in the Bacillus + Cercomonas lenta treatment [51].
Fig. 6
Fig. 6. Conceptual model.
Conceptual model depicting the mechanisms illustrating how selective grazing by protists on rhizosphere bacteria favours pathogen-antagonistic bacteria, ultimately inhibiting plant pathogens.
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