Synergistic co-evolution of rhizosphere bacteria in response to acidification amelioration strategies: impacts on the alleviation of tobacco wilt and underlying mechanisms

Abstract

Soil acidification represents a severe threat to tobacco cultivation regions in South China, exacerbating bacterial wilt caused by Ralstonia solanacearum. The comprehension of the underlying mechanisms that facilitate the restoration of rhizosphere microbial communities in "healthy soils" is imperative for ecologically managing tobacco bacterial wilt. This study focuses on acidified tobacco soils that have been subjected to continuous cultivation for 20 years. The experimental treatments included lime (L), biochar (B), and a combination of lime and biochar (L+B), in addition to a control group (CK). Utilizing rhizosphere biology and niche theory, we assessed disease suppression effects, changes in soil properties, and the co-evolution of the rhizosphere bacterial community. Each treatment significantly reduced tobacco bacterial wilt by 16.67% to 20.14% compared to the control group (CK) (p < 0.05) and increased yield by 7.86% to 27.46% (p < 0.05). The biochar treatment (B) proved to be the most effective, followed by the lime-biochar combination (L+B). The key factors controlling wilt were identified through random forest regression analysis as an increase in soil pH and exchangeable bases, along with a decrease in exchangeable acidity. However, lime treatment alone led to an increase in soil bulk density and a decrease in available nutrients, whereas both biochar and lime-biochar treatments significantly improved these parameters (p < 0.05). No significant correlation was found between the abundance of Ralstonia and wilt incidence. Nonetheless, all treatments significantly expanded the ecological niche breadth and average variation degree (AVD), enhanced positive interactions and cohesion within the community, and intensified negative interactions involving Ralstonia. This study suggests that optimizing community niches and enhancing pathogen antagonism are key mechanisms for mitigating tobacco wilt in acidified soils. It recommends using lime-biochar mixtures as soil amendments due to their potential ecological and economic benefits. This study offers valuable insights for disease control strategies and presents a novel perspective for research on Solanaceous crops.

Keywords: acidification amelioration; bacterial wilt; rhizosphere bacteria; synergistic co-evolution; tobacco.

Figure 1

Incidence of bacterial wilt (A) and economic traits (B) of tobacco plants in each treatment (2022). CK, control without soil amendment; L, lime treatment; B, biochar treatment; L + B, lime and biochar mixture treatment. Data depicts means ± SD of three biological replicates. Significant differences between treatments (p < 0.05) are illustrated by different lowercase letters.

Figure 2

Random Forest regression model predicts the correlation between soil physicochemical properties and the incidence percentage of tobacco bacterial wilt (A) as well as the disease index (B). The precision importance measures were calculated for each tree in a random forest and averaged over the entire forest (5,000 trees). The percentage increase in the mean squared error (MSE) of the variables was used to estimate the importance of these predictors. “*”at 0.05 level (two-tailed), the correlation was significant; “**” at level 0.01 (two-tailed), the correlation was significant. CK, control without soil amendment; L, lime treatment; B, biochar treatment; L + B, lime and biochar mixture treatment.

Figure 3

Alterations in the relative abundance of dominant bacterial phyla (A) and genera (B) in the rhizosphere of tobacco, changes in the relative abundance of Ralstonia, and its Pearson correlation analysis with tobacco bacterial wilt incidence (C). Both dominant phyla and genera were selected based on the top 10 in relative abundance. CK, control without soil amendment; L, lime treatment; B, biochar treatment; L + B, lime and biochar mixture treatment. Data depicts means ± SD of three biological replicates. Significant differences between treatments (p < 0.05) are illustrated by different lowercase letters.

Figure 4

Mechanisms of rhizosphere bacterial community assembly, calculated using a phylogenetic null model C-score (A) and the relative contributions of different assembly processes to the community assembly mechanisms (B), community niche breadth (C), and AVD (D). C-score metric using null models. The values of observed C-score (C-score_obs) > simulated C-score (C-score_sim) indicate non-random co-occurrence patterns. Standardized effect size <−2 and >2 represent aggregation and segregation, respectively. AVD, average variation degree; CK, control without soil amendment; L, lime treatment; B, biochar treatment; L + B, lime and biochar mixture treatment. Data depicts means ± SD of three biological replicates. Significant differences between treatments (p < 0.05) are illustrated by different lowercase letters.

Figure 5

Co-occurrence network analysis (A) and community cohesion (B) of rhizosphere bacterial communities under each treatments. Each node represents a distinct bacterial genus, with node size corresponding to its degree, nodes are colored based on their phylum in the co-occurrence network. CK, control without soil amendment; L, lime treatment; B, biochar treatment; L + B, lime and biochar mixture treatment. Data depicts means ± SD of three biological replicates. Significant differences between treatments (p < 0.05) are illustrated by different lowercase letters.

Figure 6

In the co-occurrence network analysis of rhizosphere bacterial communities, the interactions between Ralstonia and other bacterial genera are investigated. CK, control without soil amendment; L, lime treatment; B, biochar treatment; L + B, lime and biochar mixture treatment.

Figure 7

Correlations between mechanisms of community assembly and co-occurrence patterns with the incidence of bacterial wilt disease. “*” at 0.05 level (two-tailed), the correlation was significant; “**” at level 0.01 (two-tailed), the correlation was significant. AVD: average variation degree. CK, control without soil amendment; L, lime treatment; B, biochar treatment; L + B, lime and biochar mixture treatment.