Roles and Preliminary Mechanism of Tobacco cis-Abienol in Inducing Tomato Resistance against Bacterial Wilt

Abstract

Bacterial wilt negatively impacts the yield and quality of tomatoes. cis-Abienol, a labdane diterpenoid abundantly produced in the trichome secretion of Nicotiana spp., can induce bacterial wilt resistance in plants; however, study on its practical application and acting mechanism is very limited. This study established the application conditions of cis-abienol for inducing tomato bacterial wilt resistance by pot-inoculation experiments and investigated the underlying mechanism by determining the physio-biochemical indexes and transcriptomic changes. The results showed that applying cis-abienol to the roots was the most effective approach for inducing tomato bacterial wilt resistance. The optimal concentration was 60 μg/mL, and 2-3 consecutive applications with 3-6 days intervals were sufficient to induce the bacterial wilt resistance of tomato plants. cis-Abienol could enhance the antioxidant enzyme activity and stimulate the defensive signal transduction in tomato roots, leading to the upregulation of genes involved in the mitogen-activated protein kinase cascade. It also upregulated the expression of JAZ genes and increased the content of jasmonic acid (JA) and salicylic acid (SA), which control the expression of flavonoid biosynthetic genes and the content of phytoalexins in tomato roots. cis-Abienol-induced resistance mainly depends on the JA signalling pathway, and the SA signalling pathway is also involved in this process. This study established the feasibility of applying the plant-derived terpenoid cis-abienol to induce plant bacterial wilt resistance, which is of great value for developing eco-friendly bactericides.

Keywords: bacterial wilt; cis-abienol; functional mechanism; induced resistance.

Figure 1

Effect of different concentrations of cis-abienol on the growth of R. solanacearum in vitro.

Figure 2

Response of tomato to bacterial wilt after cis-abienol treatment on different organs. (A) The effects of cis-abienol treatment on tomato bacterial wilt resistance. Values are represented as a percentage of plants that acquired resistance after treatment. The method used to calculate the control efficiency is described in Section 4.4.1. Bars indicate mean ± standard deviation (SD) (n = 4). Different lowercase letters denote statistical significance determined via Duncan’s multiple range test (p < 0.05). (B) Representative images of tomato plants after the cis-abienol treatment.

Figure 3

Induction of tomato bacterial wilt resistance by cis-abienol. (A) Induction of tomato bacterial wilt resistance with different concentrations of cis-abienol. ZSM represents positive control zhongshengmycin (50 μg/mL). The effectiveness of cis-abienol is represented as the percentage of tomato plants that acquired resistance after cis-abienol treatment Bars indicate mean ± SD (n = 4). Different lowercase letters denote statistical significance as determined via Duncan’s multiple range test (p < 0.05). (B) Representative images of tomato seedlings after the cis-abienol treatment.

Figure 4

Effect of different intervals of cis-abienol (60 μg/mL) application on the resistance of tomato seedlings to bacterial wilt. The control efficiency is represented as the percentage of tomato seedlings that acquired resistance after cis-abienol treatment. (A) Bars indicate the mean ± SD (n = 4). Different lowercase letters denote statistical significance as determined via Duncan’s multiple range test (p < 0.05). (B) Representative images of tomato seedlings after the cis-abienol treatment.

Figure 5

Effect of different numbers of cis-abienol (60 μg/mL) treatments on tomato resistance to bacterial wilt. The relative control efficiency is represented as the percentage of tomato seedlings that acquired resistance. (A) Bars indicate the mean ± SD (n = 4). Different lowercase letters denote statistical significance as determined via Duncan’s multiple range test (p < 0.05). (B) Representative images of tomato seedlings after the cis-abienol treatment.

Figure 6

Representative images of tomato seedlings after the cis-abienol treatment.

Figure 7

Effects of cis-abienol on the activities of CAT (A), SOD (B), POD (C), PPO (D), and PAL (E) in tomato roots. Bars indicate the mean ± SD (n = 4. Different lowercase letters denote statistical significance as determined via Duncan’s multiple range test (p < 0.05).

Figure 8

Effect of cis-abienol on the lignin (A) and flavonoid (B) content in tomato root. Bars indicate the mean ± SD (n = 4). Different lowercase letters denote statistical significance as determined via Duncan’s multiple range test (p < 0.05).

Figure 9

Effect of cis-abienol on SA (A) and JA (B) content in tomato root. Bars indicate the mean ± SD (n = 4). Different lowercase letters denote statistical significance as determined via Duncan’s multiple range test (p < 0.05).

Figure 10

Volcano plot (A) showing gene expression levels determined using high-throughput RNA sequencing. Dotted lines represent the p-value and fold-change classification thresholds for differential expression. Red (“up”) and green (“down”) dots represent DEGs with increased and decreased expression, respectively. Genes indicated by blue dots (“no”) were not classified as differentially expressed genes. Heatmap (B) of all DEGs. Colour indicates the level of relative content of each DEG, from green (low) to red (high). CK indicates the control treatment with water, and T indicates the cis-abienol (60 μg/mL) treatment.

Figure 11

GO (A) and KEGG (B) enrichment analysis of DEGs and relative expression levels of selected DEGs determined by qRT-PCR (C). GeneRatio indicates the ratio of DEGs mapped to a certain pathway over the total number of genes mapped to the same pathway. Results from the GO pathway enrichment analysis for DEGs; the 30 most enriched pathways are listed (left). Results from the KEGG pathway enrichment analysis for DEGs; the 20 most-enriched pathways are listed. Bubble colour indicates the padj. Bubble size corresponds to the gene number enriched in the pathway. In (C), 10 defence-related DEGs were selected and determined using qRT-PCR. The bars indicate the mean ± SD (n = 3). Correlation using a linear model (D) between expression measurements obtained by qRT-PCR and RNA-seq. Different lowercase letters in subfigures (C,D) denote statistical significance as determined via Duncan’s multiple range test (p < 0.05).