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Synergistic mechanism of Bacillus subtilis Czk1 combined with propiconazole and tebuconazole mixtures against Pyrrhoderma noxium

Abstract

Background

Brown root rot disease is one of the devastating diseases in the rubber production process. It is not easy to be detected in the early stage of the disease. Our early research revealed that the rubber tree brown root rot fungus Pyrrhoderma noxium Pn006 was effectively inhibited by Bacillus subtilis Czk1 and 25% propiconazole–tebuconazole, and that the two agents might work in synergy. Therefore, in this investigation, we used non-targeted metabolomic method to evaluate the synergistic mechanism of B. subtilis Czk1 and 25% propiconazole–tebuconazole on Pyrrhoderma noxium Pn006.

Results

Metabolomics analysis identified 708 unique metabolic markers, including mainly lipids and lipid-like molecules, organic acids, alcohols, ketones, alkaloids and their derivatives, nucleotides and their analogues, benzene ring compounds and amino acids and their derivatives. Further screening identified 105 key metabolic markers that could be potential biomarkers to reveal the mechanism of biocontrol bacteria and chemical fungicides combination synergy. Three key metabolic pathways were found in pathway enrichment, including linolenic acid metabolism, benzoic acid degradation, and valine, leucine and isoleucine degradation.

Conclusions

The mechanism might be related to the influence of the energy supply and cell functional integrity of pathogenic fungal cells.

Critical relevance statement A metabolomic method was utilized to investigate the synergistic effects of B. subtilis Czk1 and 25% propiconazole–tebuconazole use on Pyrrhoderma noxium Pn006.

Graphical Abstract

Key points

  1. 1.

    Optimal combination ratio of B. subtilis Czk1 and 25% propiconazole–tebuconazole and its mechanism of action.

  2. 2.

    Mechanism of potentiation of B. subtilis Czk1 and 25% propiconazole–tebuconazole associations identified by metabolites.

  3. 3.

    Provides a reference for pesticide reduction and efficiency technologies.

Introduction

Root  rot disease is a serious kind of disease on rubber trees, commonly occurring in rubber plantation areas around the world, and can be caused by a variety of fungal infections [1]. The disease is spread mainly through root contact, causing root rot and wilting of plants [2]. There are seven types of root rot diseases found in China: red root rot disease, brown root rot disease, white root rot disease, purple root rot disease, black root rot disease, stink root rot disease and ustulina root rot disease. Among them, Pyrrhoderma noxium (Corner) L.W. Zhou & Y.C. Dai (former name: Phellinus noxius (Corner) G.H. Cunn. [3]) is second only to red root rot disease in China, with an incidence of more than 2%, and more than 10% in severe forest segments [4]. However, its lethality rate is much higher than that of other root rot diseases in China, which is one of the devastating diseases in the rubber production process [5]. More than 200 different host plant species are at risk from Pyrrhoderma noxium, which can remain in the soil for more than 10 years and spread when healthy plant tree roots come contact touch with the pathogen or germ-carrying leftovers [2, 6]. One of the pests in Chinese quarantine is Pyrrhoderma noxium [3]. The most effective control method for this disease is still chemical control. Brown root rot disease has the characteristics of fast spreading and stealthy, so it is not easy to detect in the early stage of the disease. In addition, although chemical control is direct and effective, long term and single use is easy to cause a decline in the effectiveness of prevention and soil ecological insecurity in rubber forests [7, 8].

25% propiconazole–tebuconazole (Genkang) is a triazole fungicides. Triazole fungicides are broad-spectrum fungicides that are widely used to control diseases caused by ascomycetes, deuteromycotina and basidiomycetes [9]. By interacting with the C-14-α-demethylase of cytochrome P450 of pathogenic fungi, the biosynthesis of ergosterol of fungi is inhibited and the integrity of cell membranes is damaged to control the disease [10]. Tebuconazole is one of the most widely sold pesticides in the world, and widely used for controlling many plant fungus diseases [11]. Propiconazole is used to restrain the fungal growth on vegetables, fruits and cereals and it is widely used in crop fields as a foliar spray [12]. But if a single chemical fungicide is used repeatedly, harmful fungus will develop a resistance to it [13]. The approach of using bio-chemical synergistic control of plant diseases can enable the complementary benefits of biological and chemical control, which can reduce the application of chemical pesticides [14, 15], but also slow down the development of pathogen resistance and environmental pollution [16], and promote the colonization of biological control bacteria and stability of biocontrol effect [17, 18], and may also produce synergistic effect [19, 20]. In particular, with the ongoing research on synergistic biological and chemical control, many bacterial biocontrol agents have been developed and identified as safer and more sustainable control measures than synthetic pesticides [21, 22]. In recent years, in the fight against soil-borne plant diseases, many positive results have been seen [23, 24].

Bacillus is a biotrophic bacterium that plays an important role in the control of plant pathogens. They can not only protect plants from pathogens but also promote plant growth through repopulation, secretion of compounds and provision of nutrients [25]. Bacillus subtilis is a bacterium in the genus Bacillus, which is widely distributed in nature, harmless to humans and animals, and has efficient and broad-spectrum antifungal activity [26]. Bacillus subtilis can colonize on the root surface of host plants and in host plants. It can also secrete antimicrobial substances that inhibit the growth of plant pathogens and induce systemic resistance in plants, which will prevent disease infestation [22, 27]. However, using biocontrol bacteria alone to control plant diseases, frequently has the drawbacks of poor colonization potential, unstable effects and sluggish effects. The combined application of biocontrol bacteria and chemical fungicides can not only stabilize the control effect of biocontrol bacteria but also reduce the use of chemical fungicides [28]. The combination of biocontrol agents (BACs) and chemical fungicide tebuconazole to control okra powdery mildew, and the control effect was significantly more effective than the chemical fungicide alone [29]. In addition to having potent inhibitory and controlling effects on tobacco root rot, Bacillus velezensis isolated from soil can also encourage the growth and root development of tobacco plants [30]. More successfully than carbendazim alone, two biologically released bacteria, B. cepacia c91 and B.megaterium c96, respectively, attenuated tomato green blight and root rot [31]. The screening of antagonistic microorganisms [32], as well as the identification and analysis of inhibitory chemicals [5, 33, 34], are the main areas of current study in rubber tree root rot disease biocontrol technology. Li et al. measured the antagonistic effects of 9 strains of Trichoderma on the rubber tree pathogen responsible for brown root rot and ultimately screened out 3 strains of Trichoderma strains with strong antagonistic effects [35]. Zahari et al. extracted the antifungal chemical from Catharanthus roseus, to investigate the antifungal compound of inhibitory effect on Rigidoporus microporus, Ganoderma philippi, and Phellinus noxius. The results showed that the extract was effective against Ganoderma philippii and Rigidoporus microporus but not effective against Phellinus noxius [36]. He et al. identified the lipopeptides produced by Bacillus subtilis Czk1. The bacterial isolate exhibits strong antagonistic activity against Ganoderma pseudoferreum, Phellinus noxius, Helicobasidium compactum, Rigidoporus lignosus, Sphaerostilbe repens, and Colletotrichum gloeosporioides [37]. There are few reports on the mechanism of the synergistic effect of bacterial and fungicide compounding [15].

The study of all small molecule compounds of quantitative, qualitative, and dynamic changes in organisms, organs, tissues, or cells throughout a certain amount of time or in a given environment is known as metabolomics. It identifies the functional characteristics and processes that take place when organisms are stimulated by examining the altering patterns of metabolic conditions in living things. It is crucial to the study of the interactions between plants and microbes, transgenic breeding, and the validation of gene function. Physiological and biochemical analyses of the mechanisms are possible [38]. Steroidal compounds and fatty acid derivatives were found to be more abundant in infected roots when comparing the metabolites of oil palm roots with and without Ganoderma boninense infection, indicating that these metabolites are crucial for pathogen defense [39]. It is hypothesized that metabolites like sugars, organic acids, and amino acid derivatives may be connected to the metabolic level defense response of soybean against soybean to P. sojae based on the changes of these DAMs in response to P. sojae infection in different lines and at different time points, as well as the differences in the contents of these differentially accumulated metabolites between the resistant line and the susceptible line [40]. The Epichlo endophytes strains NEA12 and NEA23 exhibit strong antifungal activity. The discovery that NEA12 and NEA23 create antifungal compounds in a symbiotic relationship was supported by metabolite analysis of the endophytes and bioactivity study of the metabolites [41]. On barley that had received a Gibberella zeae inoculation, a metabolic analysis was carried out. Three metabolic pathways linked to susceptibility were found, metabolites greatly slowed down the mycelial development of G. zeae in vitro [42].

Although prior research has demonstrated that the combination of Bacillus subtilis Czk1 and a chemical fungicide known as Genkang has strong inhibition activity and synergistic effects against red root rot and brown root rot pathogens of rubber trees, studies on its mechanism of action have not been clarified [43]. Therefore, in this study, non-target metabolomics technology based on Liquid chromatography–tandem mass spectrometry (LC–MS/MS) was utilized to investigate the target and mechanism of exogenous substances, search for endogenous differential metabolite changes of Czk1 under the combined stress of 25% propiconazole–tebuconazole (Genkang), and provide new target sites for the design of new drugs molecular biosynthesis.

Materials and methods

Materials and chemicals

The biocontrol bacterium B. subtilis Czk1(GenBank: GQ395245.1) and the pathogen Pyrrhoderma noxium Pn006 (GenBank: KC255249.1) were both isolated, identified and preserved by the Tropical Food and Specialty Crop Diseases Research Group, Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences. 25% propiconazole–tebuconazole (Genkang, microemulsion, 5% propiconazole and 20% tebuconazole) microemulsion produced by Guangdong Dafeng Plant Protection Technology Co., Ltd. The mass spectrometer (QTOF/MS-6545, Aglient, Germany), ultra-high performance liquid chromatography (1290 Infinity LC, Aglient, Germany), vortex mixer (MIX-200, Shanghai, China), automatic sample rapid grinder (Tissuelyser-64, Shanghai, China) and acetonitrile (chromatographic purity) were purchased from Aglient Technologies, Shanghai Jingxin Industrial Development Co., and Eppendorf AG, Germany, respectively.

Strain (cell) cultivation

The single colony of Czk1 was cultured in LB liquid medium (LB) and activated at 37 ℃ and 180 r/min for 12 h to obtain a seed solution. The seed solution was cultured in a fresh LB at the inoculation ratio of 5%, shaken, and fermented at 28 ℃ and 180 r/min for 72 h, and the fermentation solution was centrifuged at 4 ℃ and 12,000 r/min for 15 min. The supernatant was sterilized by a filter with a 0.22 μm pore size to obtain a sterile culture filtrate, which was stored at 4 ℃ for later use Pn006 was cultured on PDA medium (PDA) at 28 ℃ for 3 d, and Pn006 mycelium block was prepared with a hole punch with a diameter of 5 mm.

Inhibitory effect of different concentrations of Czk1 on Pn006

The culture filtrate was added to the PDA at different concentrations to make the final concentrations 10, 20, 40, 60, and 80 μL/mL, respectively. Then Pn006 was incubated in PDA dishes containing each concentration of culture filtrate, and the same amounts of LB were used as the control for constant temperature oscillation culture at 28 ℃. When the radius of the control mycelium reached 2/3 of the culture dish radius, the colony diameter was observed and recorded. ANOVA and regression analysis were performed using SPSS, and the EC50 of Czk1 culture filtrate to Pn006 was calculated.

Inhibitory effect of Genkang combined with Czk1 on Pn006

The effective inhibitory medium concentration solution was made using the EC50 value of 0.0522 g/mL for Genkang determined by Xie [43] and the EC50 value of Czk1 culture filtrate determined. Then the culture filtrate of Czk1 without concentration was combined with different concentrations of Genkang drug solution. The compounding agents with the volume ratio V (Czk1 culture filtrate): V (Genkang) of 0:10 (T1), 1:9 (T2), 2:8 (T3), 3:7 (T4), 4:6 (T5), 5:5 (T6), 6:4 (T7), 7:3 (T8), 8:2 (T9), 9:1 (T10) and 10:0 (T11) was prepared. Depending on the compounding agents' inhibitory rates, calculating combined virulence and evaluating the synergistic effects. The increase ratio (IR) was assessed using the Horsfall method [44], where IR < 1 indicates antagonism, IR = 1 indicates an additive effect, and IR > 1 indicates a synergistic effect.

Effect of Genkang combined with Czk1 on metabolites of Pn006

Sample preparation. The Czk1 culture filtrate was mixed with Genkang and added to PDA to make its concentration the same as screened for their highest synergistic ratio combination. In a petri dish containing the compound and the two single agents, a layer of cellophane was placed flat, and a Pn006 mycelial block was placed in the center of the cellophane. The control was a solution made up of an equal mixture of LB and sterile water. When the mycelium grows all over the petri dish, collect the mycelium sample, wash and absorb the water, then use liquid nitrogen to freeze for 15 min. They were separated into four groups: the compound treatment group (CG group), the Czk1 culture filtrate single agent treatment group (C group), the Genkang single agent treatment group (G group), and the blank control group (CK group), and stored at − 80 °C.

Sample pretreatment. A sample of 50 ± 2 mg was weighed into a 2 mL centrifuge tube and 500 μL of 70% methanolic water (containing 1 μg/mL of 2-chlorophenylalanine) pre-chilled at − 20 °C was added. Homogenate the mixture at 30 Hz for 2 min. After homogenization, shake the mixture for 5 min and incubate it on ice for 15 min. Centrifuge the mixture at 12,000 rpm at 4 ℃ for 10 min and suck supernatant 400 μL into another centrifuge tube. Add 500 μL of ethyl acetate/methanol (V, 1:3) into the original centrifuge tube, oscillate the mixture for 5 min, and incubate it on ice for 15 min. Then centrifuge it at 12,000 rpm at 4 ℃ for 10 min and take 400 μL of supernatant. Merge the two supernatants and concentrate them. Then add 100 μL of 70% methanol water into the dried product and perform transonic treatment for 3 min. Finally centrifuge it at 12,000 rpm at 4 ℃ for 3 min, and suck 60 μL of supernatant for LC–MS/MS analysis.

Chromatography–mass spectrometry acquisition conditions. All samples were acquired by the LC–MS/MS system following machine orders. The analytical conditions were as follows, UPLC: column, Waters ACQUITY UPLC HSS T3 C18 (1.8 µm, 2.1 mm*100 mm); column temperature, 40 ℃; flow rate, 0.4 mL/min; injection volume, 2 μL; solvent system, water (0.1% formic acid): acetonitrile (0.1% formic acid); gradient program, 95:5 V/V at 0 min, 10:90 V/V at 11.0 min, 10:90 V/V at 12.0 min, 95:5 V/V at 12.1 min, 95:5 V/V at 14.0 min.

Data processing and analysis

The original data file obtained by LC–MS/MS analysis is firstly converted into mzML format by Proteo Wizard software. Peak extraction, alignment, and retention time correction are performed by the XCMS program. The “SVR” method was used to correct the peak area. Filter the peaks with a deletion rate > 50% in each group of samples. After that, metabolic identification information was obtained by searching the laboratory self-built database and integrating the public database and MetDNA. Finally, statistical analysis was carried out by the R program. Statistical analysis includes univariate analysis and multivariate analysis. Univariate statistical analysis includes Student’s t-test and variance multiple analysis. Multivariate statistical analysis includes principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA).

Effects of metabolites on pathogenic fungus

Based on the results of the metabolome data, 27 key differential metabolites were selected to test their effects on pathogenic fungus in vitro. The 27 differential metabolites are shown in Table 1.

Table 1 Differential metabolites for testing

Inhibitory effect of 27 key metabolites on Pn006. The 27 compounds were prepared as 5 mg/mL solutions. Using the Oxford Cup method, four Oxford Cups were placed at an intermediate distance from each PDA plate, 100 μL of the solution was added to each Oxford Cup separately, and a Pn006 mycelium block was accessed in the center of the PDA plate. Three replicates of each treatment and incubated at a constant temperature at 28 °C for 72 h. The test results were observed and noted.

Indoor toxicity determination of active metabolites on Pn006. To further understand the effect of the differential metabolites on the pathogenic fungus, the mycelial growth rate method was used. The three differential metabolites with better inhibitory effect on Pn006 were configured into different concentration gradients and added to the PDA to make drug-containing PDA plates. Six replicates of each treatment and incubated at 28 ℃ for 72 h. The concentration gradients of the three different metabolites are shown in Table 2.

Table 2 Concentration gradients of the test differential metabolites

Results

Effect of Czk1 on the antagonistic activity of Pn006

The effects of different concentrations of Czk1 culture filtrate on the antifungal activity of Pn006 are shown in Table 3. The Czk1 has good antifungal activity against Pn006. With the increase in the concentration of Czk1 filtrate, the antifungal activity of Czk1 was enhanced gradually. When the concentration of Czk1 culture filtrate was 80 μL/mL, the inhibitory rate was 79.54% (Table 3). By employing the logarithm of the concentration as the horizontal coordinate and the odds value of the inhibition rate as the vertical coordinate in a regression analysis carried out with SPSS software, the regression equation for the inhibition of Pn006 by Czk1 was derived: y = 0.019x + 4.308. The EC50 of Czk1 against Pn006 was determined to be  36.42 μL/mL using the regression equation.

Table 3 Effect of different concentrations of Czk1 on the antibacterial activity of Pn006

Effect of Czk1 combined with Genkang on the antagonistic activity of Pn006

The effect of Czk1 combined with Genkang on the antagonistic activity of Pn006 is shown in Table 4. When different volume ratios of VCzk1 culture filtrate combined with VGenkang were 1:9 (T2), 2:8 (T3), 3:7 (T4), 4:6 (T5), 5:5 (T6), 6:4 (T7), 7:3 (T8), 8:2 (T9), 9:1 (T10), the toxicity ratio IR was all greater than 1. The results showed that all of the above nine ratios of mycobacterial drug combinations had synergistic inhibitory effects on Pn006. The strongest synergistic effect was observed with the T3 combination (VCzk1: VGenkang = 2:8), with a toxicity ratio of 1.49. Therefore, the T3 combination was used as the dosing concentration for the metabolomics study.

Table 4 Toxicity ratios of different combinations of Czk1 and Genkang against Pn006

Effect of Genkang combined with Czk1 on Pn006 metabolites

Principal component analysis. Before performing difference analysis, principal component analysis (PCA) was first performed on the grouped samples for difference comparison, and the degrees of variation among the grouped samples and the samples within the group were observed. PCA model showed that in the positive and negative electron spray ionization (ESI+ and ESI−) (Additional file 1: Fig S1), there was a significant segregation trend between the blank control and compound groups (CK-CG), indicating that there was a difference in the overall metabolism of these groups. The OPLS-DA model was established by combining orthogonal signal correction and partial least squares discriminant analysis (Fig. 1). The OPLS-DA model showed that CK-CG groups were completely separated, which indicated that there were significant differences in metabolic characteristics between CK-CG groups. In both ESI+ and ESI−, the prediction parameters Q2 of the OPLS-DA model are 0.982, which indicates that the OPLS-DA model is excellent and reliable.

Fig. 1
figure 1

Outline of the grouped OPLS-DA model. a–c OPLS-DA model in positive ion mode; d–f OPLS-DA model in negative ion mode

Screening and identification of differential metabolites. Based on the OPLS-DA results, the metabolites that differed between groups could be initially screened from the obtained Variable Importance in Projection (VIP) of the multivariate analysis OPLS-DA model, and the screening criterion was set as VIP ≥ 1. Further screening for differential metabolites was combined with a t-test of P < 0.05 for univariate analysis and FC ≥ 2 or FC ≤ 0.5 for Fold Change (FC) values of differences. The volcano diagram (Fig. 2) shows the degree of variability of metabolites in positive and negative ion modes. A total of 2519 differential metabolites were screened, including 2109 in the positive ion mode and 410 in the negative ion mode (Table 5). Figure 2 shows the relationship between the different metabolites in each group as a Venn diagram. Through comparative analysis, positive ion mode metabolites from the CK-CG group total 594, with the majority of them being lipids and lipid-like molecules such as organic acids, alcohols, ketones, alkaloids and their derivatives, nucleotides and their analogues, benzene ring compounds, amino acids and their derivatives; In the negative ion mode, there are 114 different metabolites, most of which are lipids and lipid-like compounds, organic acids, sugars, amino acids and their derivatives, nucleotide analogues, and alkaloids. Further screening of 708 differential metabolites in the CG-CK group revealed 105 identified as key metabolites (Additional file 2: Table S1 and Table 6), which may be potential biomarkers to reveal the synergistic effect of Czk1 combination with Genkang.

Fig. 2
figure 2

Differential metabolite Wein diagram. a positive ion mode; b negative ion mode

Table 5 Number of differential metabolites in ESI+ and ESI−
Table 6 Unique differences in metabolic pathways information of the compounding agent

Metabolic pathway analysis. Based on the results of differential metabolites, metabolic pathway enrichment was performed using KEGG (Kyoto Encyclopedia of Genes and Genomes) (Fig. 3). In positive ion mode, the significantly differential metabolic pathway enriched in the CG-CK group (Fig. 3a) was linolenic acid metabolism. In the negative ion mode, the CG-CK group (Fig. 3b) was enriched for benzoic acid degradation, dioxin degradation, and valine, leucine and isoleucine degradation. After the screening, the metabolic pathways unique to the compound were the linolenic acid metabolic pathway, valine, leucine and isoleucine degradation pathways, and benzoic acid degradation pathway (Table 6).

Fig. 3
figure 3

Differential metabolite KEGG pathway enrichment. a ESI+; b: ESI−

In vitro inhibition of Pn006 by differential metabolites

Oxford Cup method primary screening. The differences in inhibitory effects that the 27 distinct metabolites had on Pn006, with eugenol, methyl jasmonate, and 6-dimethylaminopurine displaying the strong inhibitory effects (Fig. 4). The three chemicals mentioned above were, therefore, chosen for additional validation.

Fig. 4
figure 4

Inhibition effect of different metabolites on Pn006

Indoor toxicity determination. The regression equation of the toxicity of each metabolite on Pn006 was calculated based on the results of the indoor toxicity assay (Table 7). Eugenol, methyl jasmonate, and 6-dimethylaminopurine all demonstrated strong inhibitory effects on Pn006, with eugenol exhibiting the best inhibitory effect with an EC50 value of 27.87 μg/mL.

Table 7 Indoor toxicity measurements of three differential metabolites against Pn006

Discussion

Pesticides can leave considerable residues in agricultural soils and crops when they are used extensively. The half-life of tebuconazole in the soil varies from a few weeks to a year and mainly depends on the quality of the soil and the initial residual concentration of tebuconazole [45]. Tebuconazole also harms soil ecology, suppresses soil fungi, and modifies soil microbial diversity [46]. Tebuconazole degraded slowly during the four repeated treatments, with the half-life initially increased and then decreased, according to research on the impact of tebuconazole on the soil by Han et al. [47]. Additionally, the variety of the soil microbial biomass and bacterial community diversity were both significantly decreased. Tebuconazole stimulated the proliferation of organotrophic bacteria and inhibited that of actinobacteria. It was to be a strong inhibitor of urease and catalase activities, while in turn, it enhanced the activities of dehydrogenases, acid phosphatase, alkaline phosphatase, and arylsulfatase [48]. However, there have also been reports that tebuconazole when administered at prescribed doses did not significantly disturb the biological homeostasis of soil and did not diminish its fertility. It also stimulates the proliferation of organotrophic bacteria and fungi, and also the activities of soil enzymes responsible for phosphorus, sulfur, and carbon metabolism [49]. Therefore, the effect of tebuconazole on soil is related to the dose at which it is applied. Fungi were found to be more responsive to the fungicide propiconazole than bacteria when its impact on soil microorganisms was examined. And while it does not affect basal respiration, it dramatically inhibits substrate-induced respiration [50]. The urease and phosphatase activities at lower propiconazole concentrations up. The microbial growth and urease and phosphatase activities were relentlessly reduced at higher propiconazole concentrations [51]. Therefore, similar to tebuconazole, the effects of propiconazole on soil enzyme activity and microorganisms are related to its concentration.

Bacillus can promote plant growth and improve soil activity. Grapes treated with Bacillus velezensis strain GUMT319 displayed a notable increase in production and a higher concentration of soil microorganisms [52]. Application of microbial inoculants (containing effective strains of Bacillus megaterium and Bacillus mucilaginous) significantly increased chili pepper yield, inter-root soil fast-acting phosphorus and fast-acting potassium content. It did not significantly affect the diversity and composition of the soil bacterial community but increased the relative abundance of the bacterial genus Flavobacterium, responsible for promoting root development throughout the growth phase [53]. In organic maize pots, inoculants resulted in higher biomasses of bacteria, fungi, and actinomycetes compared to the control. Microbial inoculation may cause tremendous changes in the number and composition of the taxonomic groups [54]. These changes may influence plants and soil and thereby induce unpredictable feedback reactions [55]. Through qualitative or quantitative analysis of the levels of endogenous metabolites in organisms in a certain period, metabolomics has a certain guiding significance in exploring the relationship between metabolites and physiopathological changes, as well as the mechanism of drug therapy [56]. Endogenous cellular metabolites can be precisely detected and identified, revealing their metabolic routes and processes. This information can lead to new insights into the development, prevention, and treatment of diseases. Nuclear magnetic resonance (NMR), gas-mass spectrometry (GC–MS), liquid-mass spectrometry (LC–MS), and capillary electrophoresis (CE) are currently used assays in metabolomics. Among these, metabolomics by LC–MS is widely used due to its straightforward sample pre-treatment and comprehensive metabolite database information [57].

Cellular metabolomics studies are used to explain the mechanism of action by comparing the differences in concentrations of each metabolite between multiple groups and identifying key metabolites associated with the development and treatment of diseases or disorders. The experimental results showed that all nine combinations of the extracellular metabolites of Czk1 and the chemical fungicide Genkang were effective against the brown root rot disease fungus of the rubber tree (Pn006), with a volume ratio of Czk1 and Genkang of 2:8 having the highest efficacy. The method of action of the complex was discovered to be different from that of the single doses of the biocontrol fungus Czk1 and Genkang when Pn006 was studied by LC–MS/MS for differential changes in metabolites under stress by a strong synergistic mycorrhizal drug combination. The complexes changed the levels of several metabolites in the cells of Pn006. A total of 105 important differential metabolites were screened, and the complexes had new inhibitory effects. Disorders of lipid peroxidation, energy metabolism, auxin biosynthesis, and nucleotide metabolism in cells may be linked to these important metabolites.

Coenzyme A is a cofactor in intracellular metabolism and is widely distributed in all areas of life, including eukaryotes, bacteria and archaea [58]. It is involved in sugar, lipid, protein and energy metabolism and is produced through pyruvate oxidation and secondary metabolites of the tricarboxylic acid cycle [59]. Pantothenic acid is an important precursor for the synthesis of coenzyme A [60], which can be synthesized by enzymatic reactions in both plants and microorganisms [15, 61]. Pathogenic fungi of pantothenic acid content considerably increased following treatment with a compounding agent, indicating that these agents stimulate pantothenic acid production, which in turn controls the synthesis of coenzyme A and, in turn, indirectly affects cellular lipid and energy metabolism. Additionally, the buildup of nicotinamide mononucleotide (NMN), a significant synthetic precursor of intracellular coenzyme I (NAD+), which is essential for cellular antioxidation, energy metabolism, and genomic stability, was also noted [62]. The level of NMN was significantly increased in the CK-CG group compared to the control, rising 3.35 times, and NMN conversion was inhibited. The mechanism for the manufacture of NAD+ may have been hampered, which would have had an impact on the rate of lipid peroxidation and cell energy metabolism.

The polyunsaturated fatty acid alpha-linolenic acid is extremely sensitive to oxidative processes and strongly correlates with lipid peroxidation in cells [63]. In this study, there was a 7.81-fold increase in the content of alpha-linolenic acid in the compound that appeared to be very significantly upregulated, indicating that the metabolic activity of alpha-linolenic acid was significantly inhibited and that alpha-linolenic acid could not be properly metabolized into docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), causing problems with cellular lipid metabolism. To some extent, the increased linolenic acid concentration raises the level of lipid peroxidation in cells. It has been demonstrated that too much linolenic acid makes cells more susceptible to lipid peroxidation [64]. Furthermore, it was discovered that the jasmonic acid content of pathogenic fungi decreased after treatment with compounding agents, possibly due to the down-regulation of the linolenic acid metabolic pathway. This result implies that the synthesis mechanism of jasmonic acid in fungi is comparable to the synthesis pathway in plants, which utilizes linolenic acid as a synthetic substrate, as also documented in the investigations of Dong and Sun [65, 66].

Leucine and isoleucine levels were 3.32 and 5.54 times greater than the control in this study, indicating that the degradation mechanisms for valine, leucine, and isoleucine were dramatically downregulated. Leucine and isoleucine are branched-chain amino acids that serve as precursors to acetyl coenzyme A, an essential component of energy metabolism. By inhibiting either amino acid of degradation pathway, acetyl coenzyme A levels were significantly reduced, which interfered with the TCA cycle and the production of cellular energy [67, 68]. Pyruvate is produced in the cytoplasm by the glycolytic pathway and is present in eukaryotic cells and many prokaryotic cells [69] and is an important metabolite in the tricarboxylic acid cycle, participating in aerobic and anaerobic respiration of cells and is essential for maintaining normal cellular energy metabolism [70]. In this study, we found a 2.47-fold increase in pyruvate content in pathogenic bacteria (Pn006) cells treated with the compounding agent, but no change in lactate content was found, indicating no significant change in anaerobic respiration in which pyruvate is involved. The compounded drug reduced succinate in the group it was administered to by a factor of 0.39, indicating that the pathogen cells of the TCA cycle was disrupted, impairing pyruvate catabolism and leading to a large buildup of pyruvate. Succinate is a crucial substrate for the TCA cycle. This supports the conclusions reached by Chen et al. [71]. The accumulation of pyruvate was associated with a large consumption of sugars in pathogenic fungi. Trehalose, lactose, honey disaccharide, cottonseed sugar, and sucrose showed varying degrees of down-regulation, reflecting that pathogenic fungi undergo a large amount of energy metabolism to maintain normal cellular life activities after stress by compounding agents, which is similar to the findings of Lv et al. [72].

Additionally, because of the slow TCA cycle, pyruvate is not properly oxidized and broken down, which encourages the content of pyruvate to rise. Eugenol, methyl jasmonate, and 6-dimethylaminopurine were also discovered to have substantial inhibitory efficacy against pathogenic fungi when the inhibitory effect of 27 important differently metabolized chemicals was tested. Eugenol is a phenolic molecule of the phenylpropanoid group that exhibits antioxidant, fungicidal, and antiviral effects. It also affects cellular respiration and photosynthesis by compromising the integrity and permeability of cell membranes [73,74,75]. By interfering with the function of related enzymes and altering synthetic pathways, it can also prevent the growth of fungi [76]. Eugenol treatment caused the cytoplasmic walls of rice Rhizoctonia solani to separate, vesicles and mitochondria to shrink or dissolve, the synthesis of ergosterol to be blocked, and changes to the amino acid and glucose transport across the cell membrane [77]. Methyl jasmonate is a linoleic acid-derived cyclopentanone compound, and treatment of B. dothidea with methyl jasmonate inhibits its respiratory metabolism through the tricarboxylic acid cycle and suppresses ATP, DNA, and protein synthesis [78]. 6-Dimethylaminopurine is a serine/threonine kinase inhibitor, and Bhandari found that the metabolite of Streptomyces 6-dimethylaminopurine was found to have a good inhibitory effect after molecular docking [79]. Fungi can produce a wide range of metabolites, including hazardous natural compounds [80]. The study of metabolomes still faces many obstacles and limitations, and the existence of metabolites generated from fungi makes it more difficult to conduct metabolomic research on plant-pathogen interactions. This makes the interpretation of the obtained metabolites more difficult, and the establishment of a comprehensive metabolite database of pathogenic fungi is expected to help overcome this obstacle [81].

In summary, this study demonstrated that the combination of Czk1 and Genkang significantly inhibited the cell viability of Pn006 from a metabolomic perspective, and together they exerted a synergistic effect. The mechanism of action may involve interfering with the manufacture of coenzymes and energy metabolism in pathogenic fungi cells to inhibit and potentiate the growth of the fungi. In this study, only a preliminary investigation of the mechanism of inhibition and synergism of Czk1 in combination with the chemical fungicide (Genkang) was carried out. To fully reflect the synergistic mechanism of the Czk1 in combination with Genkang, multi-omics analysis and further validation with genome, transcriptome, and proteome are required in the future.

Conclusion

The chemical fungicide Genkang, in combination with Czk1, significantly inhibited and strong synergistic on Pn006, with the strongest synergistic effect when the combined volume ratio Czk1: Genkang = 2:8 and the increase ratio was 1.49. The LC–MS/MSbased non-target metabolomics research revealed that the fungicide-bacterial combination of 105 important metabolites was screened from a total of 708 distinct differential metabolites. The metabolism of linolenic acid and the degradation of valine, leucine, and isoleucine were the unique differential metabolic pathways of the combination. Specific synergies mechanisms are as follows. The increase in the content of α-linolenic acid in Pn006 cells increased the sensitivity of Pn006 to oxidative reactions and the degree of cellular lipid peroxidation increased. The activities of antioxidant enzymes POD and SOD in Pn006 cells were reduced, which severely weakened the ability of Pn006 to cope with oxidative stress, and further led to the gradual accumulation of free radicals in Pn006 cells, thus accelerating cellular senescence and death. By interfering with the content of succinic acid, it slows down the process of Pn006 energy, thus reducing the ability of Pn006 to cope with various stresses. Destroying the cell membrane structure, prompting the leakage of cell contents, leading to the disruption of the ion and water balance inside and outside the cell, further destroying the balance of protoplasts, and interfering with the normal physiological functions of the cell. Chemical fungicides enhance the ability of cellulase secretion by biocontrol bacteria so that biocontrol bacteria can destroy the cell wall of Pn006 more effectively and weaken the competitive ability of Pn006, which is conducive to the colonization of biocontrol bacteria as well as exerting the other bacterial inhibitory ability of biocontrol bacteria.

Availability of data and materials

The data used in this study are completely provided during submission.

Abbreviations

Genkang:

25% Propiconazole–tebuconazole

LC–MS/MS:

Liquid chromatography–tandem mass spectrometry

Czk1:

Bacillus subtilis Czk1

Pn006:

Pyrrhoderma noxium Pn006

IR:

Increase ratio

PCA:

Principal component analysis

ESI+:

Positive electron spray ionization

ESI−:

Negative electron spray ionization

CK-CG:

Compound groups

CK-C:

Bacillus subtilis Czk1 groups

CK-G:

Genkang groups

VIP:

Importance in Projection

FC:

Fold Change

LC–MS:

Liquid Chromatograph with Mass Spectrometry

NMN:

Nicotinamide mononucleotide

NAD+:

Coenzyme I

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Acknowledgements

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Funding

Supported by Hainan Provincial Natural Science Foundation of China, (Grant No. 320RC692, No. 322QN360); The specific research fund of The Innovation Platform for Academicians of Hainan Province (Grant No. SQ2020PTZ0011); The Earmarked Fund for China Agriculture Research System (Grant No. CARS-33-BC1).

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ZG, JY and HW experimented and did data analysis. YL, YL, WW, RL, ST, CH and KY supervised the project. JY, YL and CH designed the study. ZG, JY, HW and CH participated in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Chun-ping He or Ke-xian Yi.

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Supplementary Information

Additional file 1: Fig. S1.

Grouped PCA analysis model. a-c: PCA model in positive ion mode; d-f: PCA model in negative ion mode.

Additional file 2: Table S1.

Changes in key metabolites in the CK-CG group (positive ion mode).

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Gu, Zw., Yin, Jh., Wu, H. et al. Synergistic mechanism of Bacillus subtilis Czk1 combined with propiconazole and tebuconazole mixtures against Pyrrhoderma noxium. Chem. Biol. Technol. Agric. 10, 113 (2023). https://doi.org/10.1186/s40538-023-00487-4

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