Limonene was purchased from Aladdin (CAS: 7705-14-8). The bacterial wilt pathogen, Ralstonia solanacearum, is the same strain used in the patent “Active components from marigold roots and their application in the control of Ralstonia solanacearum” (CN202010774068.0), which was applied for by the Hubei Provincial Tobacco Science Research Institute. The strain was acquired from Shanghai Mailier Chemical Technology Co., Ltd.

NA Medium: The composition of the NA medium includes peptone 3 g, yeast extract 1 g, proteose peptone 5 g, and glucose 10 g, with distilled water added to a final volume of 1000 mL. Ralstonia solanacearum was routinely cultured on Nutrient Agar (NA), Nutrient Broth (NB), and semi-solid medium containing 0.35% agar.

The lyophilized Ralstonia solanacearum stored in our laboratory was inoculated into the NB liquid medium for revival. A sterile inoculation loop was used to transfer the revived bacterial suspension and streak it onto a solid NB agar plate. The plate was incubated overnight at 28˚C; single colonies grown on the plate were then inoculated into Polyamide bottles containing liquid NB medium. After cultivation at 28˚C with shaking at 180 r/min for 12 hours, the bacterial suspension was adjusted to a concentration of 1 × 10⁶ to 1 × 10⁹ CFU/mL to facilitate subsequent experiments.

Sterile 96-well plates were placed on a laminar flow bench. In the sample test groups, 100 μL of bacterial suspension and 100 μL of R. solanacearum bacterial inhibitory liquid of different concentrations were added to each well. In the positive control group, 100 μL of bacterial suspension and 100 μL of a 20% ethanol solution were added to each well. The blank control group was supplemented with 200 μL of NB liquid medium. The 96-well plates were sealed with a film, and incubated under conditions of 28˚C for 24 hours. A microplate reader was set to measure the absorbance values at 600 nm in each well at different time intervals. The inhibition rate (IR%) was calculated according to Equation (1).

$IR\%=\frac{r_0-r}{r_0-2.5}$(1)

In the formula, r₀ represents the colony radius in the bacterial inhibitory liquid group, and r represents the colony radius in the control group.

The biofilm formation by R. solanacearum was assessed using a modified crystal violet staining method. Bacterial suspensions at 1 × 10⁸ CFU/mL were inoculated into NB medium with varying concentrations of the inhibitory liquid. After 24 hours of incubation at 37˚C, the wells were washed with PBS, air-dried, fixed with ethanol, and stained with 1% crystal violet for 30 minutes. Excess stain was removed, and the plates were dried. The biofilms were then solubilized with 33% glacial acetic acid, and the OD₄₉₀ was measured using a microplate reader. The experiment was conducted in triplicate to ensure reliability.

Total RNA was extracted from each sample (weighing 120 ± 10 mg) using the Trizol reagent kit (Thermo Fisher Scientific, USA), and the absorbance at 260 and 280 nm was measured with the NanoDrop 2000 (Thermo Scientific, USA). The integrity of each RNA sample was assessed by 1% agarose gel electrophoresis and the Agilent 2200 Tape Station (Agilent Technologies, Germany). Eukaryotic mRNA from each qualified sample was enriched using magnetic oligo-dT beads and randomly fragmented using RNA fragmentation buffer. First-strand cDNA was synthesized using random hexamers, and double-stranded cDNA was purified with AMPure XP beads (Beckman Coulter, USA). RNA sequencing was conducted at the Shanghai Applied Protein Technology Co., Ltd. using the Illumina NovaSeq 6000 sequencing system, following the manufacturer’s instructions (Illumina, USA).

Each sample (weighing 310 ± 10 mg) was frozen in liquid nitrogen and ground into a fine powder using a mortar and pestle. Sixty milligrams of the powder was mixed with 1 mL of a methanol/acetonitrile/water solution (volume ratio 2:2:1) to remove proteins. The mixture was centrifuged at 14,000 g for 15 minutes at 4˚C. The supernatant was then dried using a vacuum centrifuge. For liquid chromatography-mass spectrometry (LC-MS) analysis, the samples were reconstituted in 100 μL of acetonitrile/water (1:1, v/v) solvent.

To monitor the stability and reproducibility of the instrumental analysis, a quality control sample was prepared by pooling 10 μL from each sample and was analyzed alongside the experimental samples. The quality control samples were randomly interspersed and analyzed. The analysis was performed at the Shanghai Applied Protein Technology Co., Ltd. using an Agilent 1290 Infinity 2D-LC system (Agilent Technologies, Germany) coupled with a Triple TOF 6600 hybrid triple-quadrupole/time-of-flight mass spectrometer (AB Sciex, USA).

Measurement data were analyzed using one-way analysis of variance (ANOVA) in SPSS 27.0 software (SPSS, USA), followed by post-hoc comparisons using the least significant difference (LSD) and Duncan’s methods. Results were visualized with Origin 2023 software (Origin Lab, USA). Statistical analysis was conducted at a significance level of p < 0.05, indicating significant differences. Values are presented as mean ± standard deviation. Differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were identified and analyzed using DEGseq software, and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation analysis as well as KEGG enrichment analysis were performed on the DEGs and DAMs.

As shown in Figure 1 , limonene exhibits a strong inhibitory effect on R. solanacearum. When the concentration of limonene exceeds 10 μL/mL, it significantly suppresses the growth of the bacteria, and this inhibitory capacity increases with concentration. There was no significant difference in the OD values between the negative control group (20% ethanol) and the blank control group (p > 0.05), indicating that the growth states of the bacteria in both groups were essentially the same. Thus, the impact of the solvent for limonene (20% ethanol) on the growth of R. solanacearum can be neglected. The minimum inhibitory concentration (MIC) of limonene against R. solanacearum was determined to be 10 μL/mL.

The minimum bactericidal concentration (MBC) test revealed that at a limonene concentration of 20 μL/mL, no growth of R. solanacearum was observed on the agar plates, whereas at a concentration of 10 μL/mL, the bacteria grew well. According to the guidelines in “Chemical Analysis of Antibacterial Drug Residues in Food,” the lowest drug concentration with no bacterial growth is defined as the MBC. Therefore, the MBC of limonene against R. solanacearum is 20 μL/mL ( Table 1 ).

The growth curve measurements showed that when the concentration of limonene was greater than 5 μL/mL, it significantly inhibited the growth of R. solanacearum (p < 0.05). Compared to the control group, the growth rate of the limonene-treated R. solanacearum was markedly delayed, with almost no increase observed. At a concentration of 20 μL/mL, limonene severely inhibited bacterial growth, and there was hardly any growth of R. solanacearum after 6 hours of cultivation ( Figure 1(A) ).

Based on the OD490 values measured by the microplate reader, the comparative analysis of the biofilm formation quantity of the strain indicated that the biomass of all treatment groups decreased from 12 hours to 24 hours and then increased from 24 hours to 36 hours. The control group exhibited a significantly higher level of biofilm formation compared to other treatment groups with varying concentrations. The inhibitory effect of limonene on biofilm formation intensified progressively with the increase in concentration, particularly showing a strong inhibitory effect when the limonene concentration was at 5 and 20 μL/mL (p < 0.05) ( Figure 1(B) ).

Note: “−“ indicates no bacterial growth; “+” indicates few colonies; “++” indicates moderate colonies; “+++” indicates a large number of colonies.

Using the Illumina high-throughput sequencing technology, a total of 2,722 genes were identified. The DESeq2 package was utilized to analyze differentially expressed genes (DEGs). The results indicated that the Limonene treatment significantly impacted gene expression in the leaves. After Limonene treatment, 465 genes were upregulated, and 536 genes were downregulated (p < 0.05 and |Log2(Fold Change)| ≥ 1). After applying the Benjamini-Hochberg correction, these numbers were adjusted to 461 upregulated and 523 downregulated genes ( Figure 2(A) ).

As shown in Figure 2(B) , the DEGs were mapped to the KEGG database to analyze the metabolic pathways of the differential genes. In the comparison between Limonene and Control, 88 pathways were significantly enriched (p < 0.05). After adjusting the p-values to false discovery rate.

(FDR) values using the Benjamini-Hochberg procedure, 26 metabolic pathways remained significantly enriched. In the comparison between Limonene and Control, the top three pathways were Terpenoid backbone biosynthesis, Phosphotransferase system, and Photosynthesis ( Figure 1(B) ).

Through the application of ultra-high-performance liquid chromatography coupled with a quadrupole-time-of-flight mass spectrometer (UHPLC-Q-TOF MS), a total of 5887 signals were acquired from the samples under different ionization modes, with 1167 signals successfully annotated against databases (BMDB/Decode/ HMDB/KEGG/Masslist/mzCloud). Quality control samples demonstrated minimal variation across the experimental process, as evidenced by the overlay comparison of total ion current (TIC) profiles, where the response intensity and retention time of each chromatographic peak were largely overlapping, indicating negligible instrumental error. A Principal Component Analysis (PCA) model was derived from the amplified ion peaks extracted from all experimental samples (with six replicates each for blank control, negative control, and Limonene treatment groups) and quality control samples (four replicates per group) ( Figure 2(A) ). As shown in Figure 3(A) , the PCA results confirmed the stability of the analytical system used in this study, and the robustness and reliability of the collected data. The differences in metabolic profiles reflect the biological variations among the samples.

In the comparison between the Limonene treatment and the Control, 485 differentially accumulated metabolites (DAMs) were identified, with 117 upregulated and 368 downregulated ( Figure 3(B) ). KEGG pathway enrichment revealed that 56 metabolic pathways were enriched in the Limonene treatment compared to the Control, among which 16 pathways exhibited significant changes (p < 0.05). The top five pathways were Amino sugar and nucleotide sugar metabolism, Glycerophospholipid metabolism, Metabolic pathways, Monobactam biosynthesis, and Lysine degradation ( Figure 3(C) ).

Ultimately, integrating the Differential Amplification scores, we identified three metabolic pathways in R. solanacearum that were significantly overexpressed following the Limonene treatment: Amino sugar and nucleotide sugar metabolism (Differential Amplification = 0.33), Purine metabolism (Differential Amplification = 1), and Terpenoid backbone biosynthesis (Differential Amplification = 1) ( Figure 3(D) ).

By assigning both differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) to KEGG pathways (p < 0.05), we aimed to understand the impact of Limonene treatment on R. solanacearum. The results indicate that DEGs and DAMs are concurrently involved in several metabolic pathways, including: Terpenoid backbone biosynthesis, Glycolysis/Gluconeogenesis, Phosphotransferase system (PTS), Inositol phosphate metabolism. These findings demonstrate the multifaceted influence of limonene on the metabolic processes of R. solanacearum, affecting both gene expression and metabolite accumulation. The overlap between DEGs and DAMs in these pathways suggests a coordinated response at the genetic and metabolic levels to limonene treatment.