Mixture of Bacillus Amyloliquefaciens and Bacillus Pumilus Modulates Community Structures of Rice Rhizosphere Soil to Suppress Rice Seedling Blight

doi:10.1016/j.rsci.2024.09.001

Abstract

Abstract:

Rice seedling blight, caused by various fungi, including Fusarium oxysporum, poses a severe threat to rice production. As awareness grows regarding the environmental and safety hazards associated with the application of fungicides for managing rice seedling blight, there has been a shift in focus towards biological control agents. In this study, we isolated biocontrol bacteria from paddy fields that significantly inhibited the growth of F. oxysporum in vitro and identified the strains as Bacillus amyloliquefaciens T40 and Bacillus pumilus T208. Additionally, our findings indicated that the combined application of these Bacillus strains in soil was more effective in reducing the incidence of rice seedling blight than their individual use. Analysis of 16S and internal transcribed spacer rRNA gene sequencing data revealed that the mixture of the T40 and T208 strains exhibited the lowest average clustering coefficients, which were negatively correlated with the biomass of F. oxysporum-inoculated rice seedlings. Furthermore, this mixture led to higher stochastic assembly (average |βNTI| < 2) and reduced selection pressures on rice rhizosphere bacteria compared with individual strain applications. The mixture of the T40 and T208 strains also significantly increased the expression of defense-related genes. In conclusion, the mixture of the T40 and T208 strains effectively modulates microbial community structures, enhances microbial network stability, and boosts the resistance against rice seedling blight. Our study supports the development and utilization of biological resources for crop protection.

Key words: application strategy, disease control, disease resistance, microbial community structure, microbial community assembly process, Oryza sativa

Jiang Nan, Qiu Jiehua, Tian Dagang, Shi Huanbin, Liu Zhiquan, Wen Hui, Xie Shuwei, Chen Huizhe, Wu Meng, Kou Yanjun. Mixture of Bacillus Amyloliquefaciens and Bacillus Pumilus Modulates Community Structures of Rice Rhizosphere Soil to Suppress Rice Seedling Blight[J]. Rice Science, 2025, 32(1): 118-130.

Figures/Tables 8

Fig. 1. Isolation and identification of pathogenic strain RPI 2201. A, Rice nursery tray with seedling blight. B, Rice plants with and without seedling blight. The position within the circle is the area that has been locally magnified. C, Colony morphology of RPI2201 strain. D, Photograph of RPI2201’s spores. E, Rice seedlings inoculated with RPI2201 strain. F, Phylogenetic analysis of RPI2201 strain based on the sequencing of PCR products (primers ITS1 and ITS2 are listed in Table S1).

Fig. 2. Combination of two different Bacillus strains better suppresses rice seedling blight than applying them individually. A, Phenotype of rice seedlings in the presence of Fusarium oxysporum with applying T40 and T208 strains individually or in combination. B‒E, Disease incidence of seedling blight (B), seedling emergence rate (C), plant height (D), and rice seedling biomass (E) under Bacillus strain treatments. Different lowercase letters above the bars indicate significant differences at the 0.05 probability level according to the Duncan’s test (n = 8).

Fig. 3. Microbial community compositions in rhizosphere soil under different treatments. A, Relative abundance of Fusarium spp. treated with the T40 and T208 strains individually or in combination. Different lowercase letters above the bars indicate significant differences among the treatments according to the Duncan’s test (P < 0.05, n = 8). B and C, Stacked bar charts showing the relative abundance of various bacterial (B) and fungal (C) phyla communities. D and E, Ternary plots of bacterial and fungal amplicon sequence variants (ASVs). Each circle represents one ASV, and the size indicates its relative abundance. Circles marked NS indicate ASVs that did not significantly enrich in any of the three treatments.

Fig. 4. Combination of T40 and T208 strains did not significantly change alpha diversities of microbial communities but significantly altered microbial community structures. A and B, Alpha-diversity of bacterial communities in rhizosphere soil of rice, expressed as richness (Chao1 index, A) and evenness (Shannon index, B). C and D, Alpha-diversity of fungal communities in rhizosphere soil of rice, expressed as richness (Chao1 index, C) and evenness (Shannon index, D). E and F, Beta-diversities of bacterial (E) and fungal (F) communities between different treatments. Different lowercase letters above the bars indicate significant differences among the treatments according to the Duncan’s test (P < 0.05, n = 8).

Table 1. Application of different Bacillus strains, either alone or in combination, significantly changed microbial community structures as determined by principal coordinates analysis based on Bray- Curtis dissimilarity.

Treatment comparison		P value
Treatment comparison		Bacterium	Fungus
	Control vs T40	0.017	< 0.01
	Control vs T208	< 0.01	< 0.01
	Control vs T40-T208	< 0.01	0.012
	T40 vs T208	0.014	< 0.01
	T40 vs T40-T208	0.013	0.033
	T208 vs T40-T208	< 0.01	0.028

Table 2. Combined use of different Bacillus strains significantly improved microbial network stability based on topological features of microbial subnetworks, including bacterial and fungal communities.

Treatment	Edge	Node	Degree	Diameter	Density	Modularity	Clustering coefficient	P	P/N
Control	1 254 a	364 b	6.46 a	19.4 b	0.015 a	0.65 c	0.366 a	1 204 a	21.8 a
T40	1 372 a	457 a	5.76 a	21.5 ab	0.013 ab	0.66 c	0.314 b	1 099 a	4.5 b
T208	756 b	446 a	3.31 b	23.1 a	0.008 c	0.89 a	0.326 b	474 b	1.7 b
T40-T208	886 b	422 a	4.00 b	20.1 ab	0.009 bc	0.82 b	0.311 b	687 b	3.6 b

Fig. 5. Combining different Bacillus strains enhances microbial network stability. Co-occurrence networks of microbial communities, including both bacterial and fungal species in the samples. A connection represents a significant (P < 0.01) correlation between two amplicon sequence variants. The size of each node indicates the number of connections (i.e., degree), and the thickness of each connection (i.e., edge) between two nodes represents the value of the Spearman correlation coefficient. A blue edge indicates a positive correlation, while a red edge indicates a negative correlation.

Fig. 6. Spearman’s correlation analysis between rice seedling biomass and major factors that explored by a random forest classification approach. 1, Rice seedling biomass; 2, Disease incidence; 3, β-nearest taxon index; 4, Fungal Chao1 index; 5, Fungal Shannon index; 6, Average degree; 7, Density; 8, Modularity; 9, Average clustering coefficient; 10, Positive correlation within the co-occurrence networks; 11, The ratio of positive to negative correlations in the co-occurrence networks; 12, ITS32; 13, ITS193. Asterisks (*) represent significant differences (Student’s t-test, *, P < 0.05; **, P < 0.01; ***, P < 0.001).

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