Plant Electrophysiological Parameters Represent Leaf Intracellular Water–Nutrient Metabolism and Immunoregulations in Brassica rapa During Plasmodiophora Infection

Abstract

Although Brassica rapa (B. rapa) is vital in agricultural production and vulnerable to the pathogen Plasmodiophora, the intracellular water–nutrient metabolism and immunoregulation of Plasmodiophora infection in B. rapa leaves remain unclear. This study aimed to analyze the responsive mechanisms of Plasmodiophora-infected B. rapa using rapid detection technology. Six soil groups planted with Yangtze No. 5 B. rapa were inoculated with varying Plasmodiophora concentrations (from 0 to 10 × 10⁹ spores/mL). The results showed that at the highest infection concentration (PWB5, 10 × 10⁹ spores/mL) of B. rapa leaves, the plant electrophysiological parameters showed the intracellular water-holding capacity (IWHC), the intracellular water use efficiency (IWUE), and the intracellular water translocation rate (IWTR) declined by 41.99–68.86%. The unit for translocation of nutrients (UNF) increased by 52.83%, whereas the nutrient translocation rate (NTR), the nutrient translocation capacity (NTC), the nutrient active translocation (NAT) value, and the nutrient active translocation capacity (NAC) decreased by 52.40–77.68%. The cellular energy metabolism decreased with worsening Plasmodiophora infection, in which the units for cellular energy metabolism (∆G_E) and cellular energy metabolism (∆G) of the leaves decreased by 44.21% and 78.14% in PWB5, respectively. Typically, based on distribution of B-type dielectric substance transfer percentage (BPn), we found PWB4 (8 × 10⁹ spores/mL) was the maximal immune response concentration, as evidenced by a maximal BPn_R (B-type dielectric substance transfer percentage based on resistance), with increasing lignin and cork deposition to enhance immunity, and a minimum BPn_Xc (B-type dielectric substance transfer percentage based on capacitive reactance), with a decreasing quantity of surface proteins in the B. rapa leaves. This study suggests plant electrophysiological parameters could characterize intracellular water–nutrient metabolism and immunoregulation of B. rapa leaves under various Plasmodiophora infection concentrations, offering a dynamic detection method for agricultural disease management.

1. Introduction

Brassica rapa (B. rapa) is a commonly grown Brassica crop in Asia. The cultivated area in China represents at least 15% of the total vegetable cultivation area of the country, and the cultivation method and nutritional value of B. rapa have long been widely recognized [1,2]. Unfortunately, it can be infected by Plasmodiophora, one of the most severe soil-borne pathogens of Brassica plants, caused by the flagellate fungus Plasmodiophora brassicae, which mainly attacks plant roots [3]. After a severe attack caused by Plasmodiophora, plants exhibit several symptoms, including the expansion and swelling of root structures, impeded development of above-ground parts [4], slowed growth [3], and even death [4]. Therefore, it is crucial to reveal the effects of Plasmodiophora infection on B. rapa.

The previously established methods for assessing plant growth, such as Koch’s law [5], enzyme immunoassays [6], and multispectral imaging [7], have drawbacks, including time consumption, operational difficulty, etc. Additionally, these methods damage plant tissues, making it challenging to quantify the dynamic changes in plant growth. Therefore, a suitable methodology that can analyze the dynamic changes in B. rapa growth during Plasmodiophora infection without causing damage is urgently needed.

Compared with other methods, measuring plant electrical signals is the best technique for performing plant electrophysiological analysis, providing an excellent alternative. The main physiological parameters of plants include capacitance (C), resistance (R), impedance (Z), capacitive reactance (X_C), and inductive reactance (X_L). When plants are affected by metabolic alterations and external stresses, they produce electrical responses, which are changes in the potentials generated by cells and tissues [8]. The cell membrane, which consists of a phospholipid bilayer (PLB) and internal compounds (lipids, proteins, sugars, etc.), is the crucial site for generating plant electrical signals [9]. Typically, the phospholipid bilayer can be divided into electron-dense bands on both the inner and outer membrane sides and a transparent band in the middle. The bilayer structure of the cell membrane is the source of the electrical properties of a cell, and membrane lipids can be viewed as an insulating layer having high electrical resistivity, which induces the cell to store charges [10]. The cell membrane has strict selective permeability to ions, and the electrolyte solution (ES) on both sides has a specific conductive state [11]. The inner and outer sides of the membrane can be modeled as a leaky capacitor (LC). The solution on both sides of the membrane can be viewed as the two polar plates of the capacitor [12]. The cell membrane acts as the dielectric medium of the capacitor [13]. The intracellular ions, ionic groups (IGs), and electric dipoles (EDs) are equivalent to the electrolytes in which plant cell membranes are capacitive [14]. When adverse conditions damage cells, their structure, composition, and ion permeability alter, leading to changes in the electrical properties [15].

Recently, electrophysiology technology has been widely used to analyze the growth of potatoes [14], tomatoes [16], wheat [17], and other crops, as well as in disease prevention. In addition, it is used in the analysis of dynamic changes in plant growth, a process accompanied by the water, nutrient, and cellular metabolism of plant species [16]. All these processes involve charge separation, electron movement, and the transport of dielectric substances, leading to changes in plant electrical signals [17]. Similarly, when plants are subjected to external stimuli, electrical signals provide direct feedback on changes in growth, which are reflected in changes in photosynthesis, water and nutrient uptake, and cell metabolic energy [18]. Since the substance transport capacity depends on the type and amount of surface and bound proteins in the cell membrane [19], the cell membrane structure can maintain the stability of the cellular environment by facilitating the transfer of substances through intrinsic and extrinsic proteins. The plant’s cell membrane chiefly determines its electrical resistance, with capacitance being influenced by the variety and number of extrinsic proteins and inductance being affected by the variety and number of intrinsic proteins [20]. Typically, ∆G and BPn promote the dynamic growth of plant species. Therefore, based on the determined R, Xc, and X_L, three types of B-type dielectric substance transfer percentage—which are BPn_R (the B-type dielectric substance transfer percentage based on resistance), BPn_Xc (the B-type dielectric substance transfer percentage based on capacitive reactance), and BPn_XL (the B-type dielectric substance transfer percentage based on inductive reactance)—need to be determined.

This study aimed to assess the performance of electrophysiological techniques for detecting Plasmodiophora infection in B. rapa by analyzing its growth status, photosynthesis, and electrical signals. It also investigated (1) the electrophysiological responses of B. rapa to Plasmodiophora and (2) the synergistic responses of intracellular water metabolism, nutrient translocation, ATP metabolism, and substance transfer characterization at different levels of Plasmodiophora infection in B. rapa, with the primary purpose of preventing Plasmodiophora infection in agricultural and food production.

2. Materials and Methods

2.1. Plant Selection and Preparation of Conidial Suspensions

In this study, Yangtze No. 5 B. rapa, which is abbreviated as B. rapa in this experiment, sourced from Dezhou City, Shandong Province of China, was chosen for the experiments because of its notable strong disease resistance. Plasmodiophora was selected because it is a prevalent fungal pathogen that commonly infects B. rapa in China. Plasmodiophora was extracted meticulously following the detailed steps a-f outlined in Figure S1 (the process of infection with Plasmodiophora).

2.2. Plasmodiophora Infection Treatment

Figure 1 illustrates the infection of B. rapa with various concentrations of Plasmodiophora. The infected seeds were sown in hole trays containing sterile soil and cultivated under controlled conditions, with temperatures maintained at 25 °C during the day and 10 °C at night. On the 30th day, the seedlings were subjected to treatments with Plasmodiophora at six different concentrations: 0, 2 × 10⁹, 4 × 10⁹, 6 × 10⁹, 8 × 10⁹, and 10 × 10⁹ spores/mL. Following this treatment, samples were harvested after 14 days to assess the impact of fungal infection.

2.3. Biomass Estimation

The root-to-shoot ratio of B. rapa (Equation (1)) under the various treatments was quantified by initially heating the root, stem, and leaf parts of the plants at 108 °C for 30 min, and then drying them at 70 °C.

R/S (%) = Biomass (Root)/Biomass (Shoot) × 100%

(1)

where R represents the root, and S denotes the above-ground parts (shoot) of the plant.

2.4. Measurement of Photosynthesis of Plant Species

The 2nd and 3rd fully expanded leaves of B. rapa were used to measure photosynthesis from 9:00 to 11:00 a.m. using the LI-6400 photosynthesis system (LI-COR, Lincoln, NE, USA). Herein, the net photosynthetic rate (Pn, μmol/m²·s⁻¹), stomatal conductance (Cond, mmol/H₂O m²·s⁻¹), transpiration rate (Tr, mmol H₂O/m²·s⁻¹), and intercellular CO₂ concentration (Ci, μmol CO₂ mol⁻¹·air) were determined. Water use efficiency (WUE) was calculated using Equation (2). The other measured parameters included a temperature of 25 °C, a CO₂ concentration of 400 μmol mol⁻¹ in buffered glass bottles, and a photosynthetically active radiation intensity of 500 μmol/m²·s⁻¹.

WUE (%) = Pn/Tr × 100%

(2)

2.5. Measurement of Electrophysiological Parameters of the Plant

Since photosynthesis in leaves does not directly reflect electrophysiological parameters of the experiment, the second fully expanded leaf of each plant was selected to measure the electrophysiological parameters (Figure 2). The experimental environment was as follows: air relative humidity (75 ± 5)% and daytime/night cycle temperature (30 °C/20 °C). The leaves were placed between two electrode plates (silver electrode, Ag) of a parallel-plate capacitor, which was operated by an LCR-6100 LCR meter (Gwinstek, Taiwan, China) with the frequency (f) set to 3 kHz and the voltage (U) set to 1.5 V. The measurement equipment included a holder, electrode plates, wires, iron blocks, and plastic rods. The electrode plates were embedded in the bracket and the bottom of the plastic rods (Figure S2) and were connected to the LCR meter through wires. The two electrode plates clamped the leaves to be measured. In the parallel mode, C, R, and Z of the plant leaves were determined by adding different numbers of iron blocks of equal mass with a fixed clamping force (1 N, 2 N, 3 N, 5 N, and 7 N). R and Z were measured 15 times for each plant leaf, and we established fitting equations for the electrophysiological parameters.

2.6. Calculation of Plant Electrophysiological Parameters

Considering the metabolic energy of plant cells as a factor, the application of different clamping forces alters the structural morphology of the chloroplasts, thereby affecting the electrical signaling activities within the cells. Based on the Gibbs free energy equation and the Nernst equation, this study constructed a model to describe the variations in R, Z, and C of the plant leaves with clamping force, as shown in Equation (3). The steps for deriving C, R, Z, Xc, and X_L of the plant leaves are detailed in Equations (4)–(8). The variation in plant leaf X_L with clamping force was modeled as Equation (9).

$$\mathrm{F}=\left(\mathrm{M}+\mathrm{m}\right)\mathrm{g}$$

(3)

$$\mathrm{C}={\mathrm{x}}_0+\mathrm{h}\mathrm{F}$$

(4)

$$\mathrm{R}={\mathrm{y}}_0+{\mathrm{k}}_1{\mathrm{e}}^{-{\mathrm{b}}_1\mathrm{F}}$$

(5)

$$\mathrm{Z}={\mathrm{p}}_0+{\mathrm{k}}_2{\mathrm{e}}^{-{\mathrm{b}}_2\mathrm{F}}$$

(6)

$$\mathrm{X}\mathrm{c}={\mathrm{q}}_0+{\mathrm{k}}_3{\mathrm{e}}^{-{\mathrm{b}}_3\mathrm{F}}$$

(7)

$${\displaystyle \frac{1}{-{\mathrm{X}}_{\mathrm{L}}}}={\displaystyle \frac{1}{\mathrm{Z}}}-{\displaystyle \frac{1}{\mathrm{R}}}-{\displaystyle \frac{1}{\mathrm{X}\mathrm{c}}}$$

(8)

$${\mathrm{X}}_{\mathrm{L}}={\mathrm{a}}_0+{\mathrm{k}}_4{\mathrm{e}}^{-{\mathrm{b}}_4\mathrm{F}}$$

(9)

where F is the clamping force exerted by the iron block in newtons (N), M is the mass of the iron block (kg), m is the mass of the plastic rod and the electrode sheet (kg), g is acceleration due to gravity (9.8 N/kg), C is the capacitance of the plant leaves, ${\mathrm{x}}_0$ is a constant, h is a constant, R is the resistance of the plant leaves, ${\mathrm{y}}_0$ is a constant, ${\mathrm{k}}_1$ is a constant, ${\mathrm{b}}_1$ is a constant, e is the base of the natural logarithm, Z is the impedance of the plant leaves, p₀ is a constant, ${\mathrm{k}}_2$ is a constant, ${\mathrm{b}}_2$ is a constant, F is the clamping force, e is the base of the natural logarithm, Xc is a capacitive reactance of the plant leaves, q₀ is the initial capacitive reactance without any clamping force, k₃ is a constant relating exponential decay to the clamping force, b₃ is a decay constant for the capacitive reactance concerning clamping force, X_L is the inductive reactance of the plant leaves, R is the resistance of the plant leaves, Xc is the capacitive reactance of the plant leaves, a₀ is the initial inductive reactance without any clamping force, k₄ is a constant relating exponential decay to the clamping force, and b₄ is a decay constant for the inductive reactance concerning clamping force.

As the nutrient capacity of plant leaves can be calculated when F is 0, we obtain intrinsic resistance (IR), the intrinsic capacitive reactance (IXc), and the intrinsic inductive reactance (IX_L) using Equations (10)–(12), and intrinsic impedance (IZ) and intrinsic capacitance (IC_P) were calculated using Equations (13) and (14).

$$\mathrm{I}\mathrm{R}={\mathrm{y}}_0+{\mathrm{k}}_1$$

(10)

$$\mathrm{I}\mathrm{X}\mathrm{c}={\mathrm{q}}_0+{\mathrm{k}}_3$$

(11)

$$\mathrm{I}{\mathrm{X}}_{\mathrm{L}}={\mathrm{a}}_0+{\mathrm{k}}_4$$

(12)

$${\displaystyle \frac{1}{\mathrm{I}\mathrm{Z}}}={\displaystyle \frac{1}{\mathrm{I}\mathrm{R}}}+{\displaystyle \frac{1}{\mathrm{I}\mathrm{X}\mathrm{c}}}-{\displaystyle \frac{1}{\mathrm{I}{\mathrm{X}}_{\mathrm{L}}}}$$

(13)

$$\mathrm{I}{\mathrm{C}}_{\mathrm{P}}={\displaystyle \frac{1}{2\mathsf{\pi }\mathrm{f}\mathrm{I}{\mathrm{X}}_{\mathrm{L}}}}$$

(14)

where y₀ is the initial or baseline resistance value, k₁ is a constant or coefficient that modifies the initial resistance, q0 is the initial or baseline capacitive reactance value, k3 is a constant or coefficient that modifies the initial capacitive reactance, a0 is the initial or baseline inductive reactance value, k4 is a constant or coefficient that modifies the initial inductive reactance, and f is frequency.

2.7. Estimation of Intracellular Water Metabolism in Plants Based on Electrophysiological Information

Based on the water-holding capacity of cells, which is directly proportional to C3/2, it is possible to characterize the water-holding capacity of the plant leaves using Equation (15) (IWHC). The specific adequate thickness (d) of the plant leaves was calculated using Equations (16) and (17). The relative intracellular water use efficiency (IWUE), intracellular water-holding time (IWHT), and water transfer rate (WRT) of the plant leaves were obtained using Equations (18)–(20).

$$\mathrm{I}\mathrm{W}\mathrm{H}\mathrm{C}=\sqrt{{\mathrm{I}{\mathrm{C}}_{\mathrm{p}}}^3}$$

(15)

$${\mathrm{k}}_0={\displaystyle \frac{{\mathrm{U}}^2}{2\mathrm{d}}}$$

(16)

$$\mathrm{d}={\displaystyle \frac{{\mathrm{U}}^2\mathrm{h}}{2}}$$

(17)

$$\mathrm{I}\mathrm{W}\mathrm{U}\mathrm{E}={\displaystyle \frac{\mathrm{d}}{\mathrm{I}\mathrm{W}\mathrm{H}\mathrm{C}}}$$

(18)

$$\mathrm{I}\mathrm{W}\mathrm{H}\mathrm{T}=\mathrm{C}\times \mathrm{Z}$$

(19)

$$\mathrm{W}\mathrm{R}\mathrm{T}={\displaystyle \frac{\mathrm{I}\mathrm{W}\mathrm{H}\mathrm{C}}{\mathrm{I}\mathrm{W}\mathrm{H}\mathrm{T}}}$$

(20)

2.8. Nutrient Transport Capacity Based on Electrophysiological Information Characterization

Based on the plant electrophysiological information, the unit for nutrient-relative transport (UNF), the nutrient transfer rate (NTR), nutrient transfer capacity (NTC), the unit for nutrient active flow (UAF), and nutrient active transport capacity (NAC) were calculated using Equations (21)–(25).

$$\mathrm{U}\mathrm{N}\mathrm{F}={\displaystyle \frac{\mathrm{R}}{\mathrm{I}\mathrm{X}\mathrm{c}}}+{\displaystyle \frac{\mathrm{R}}{\mathrm{I}{\mathrm{X}}_{\mathrm{L}}}}$$

(21)

$$\mathrm{N}\mathrm{T}\mathrm{R}={\displaystyle \frac{\mathrm{I}\mathrm{W}\mathrm{H}\mathrm{C}}{\mathrm{I}\mathrm{W}\mathrm{H}\mathrm{T}}}$$

(22)

$$\mathrm{N}\mathrm{T}\mathrm{C}=\mathrm{U}\mathrm{N}\mathrm{F}\times \mathrm{N}\mathrm{T}\mathrm{R}$$

(23)

$$\mathrm{U}\mathrm{A}\mathrm{F}={\displaystyle \frac{\mathrm{I}{\mathrm{X}}_{\mathrm{C}}}{{\mathrm{I}\mathrm{X}}_{\mathrm{L}}}}$$

(24)

$$\mathrm{N}\mathrm{A}\mathrm{C}=\mathrm{U}\mathrm{A}\mathrm{F}\times \mathrm{N}\mathrm{T}\mathrm{R}$$

(25)

where R represents resistance, IXc is the current flow through a cell, IX_L is the current flow through a leaf, IWHC is the integrated water-holding capacity, IWHT is the integrated water-holding time, UNF is a nutrient-relative transport unit, NTR is the nutrient transfer rate, UAF is a nutrient active flow unit, and NTR is the nutrient transfer rate.

2.9. The Cellular Metabolic Energy for B. rapa Leaves

Based on C, R, and Z of the plant leaves, cellular metabolic energy can be calculated based on a model constructed with electrophysiological parameters. According to the parameters from Equation (4), the unit for the metabolizable energy of leaf cells (ΔG_R−E) can be obtained as shown in Equation (26). Based on Equation (5), the Z-based metabolic energy per unit of plant leaf cells (ΔG_Z−E) can be obtained using Equation (27). The cellular energy metabolism for R (ΔG_R−E) and Z (ΔG_Z−E) and the average metabolic energy (ΔG) were calculated using Equations (28)–(30).

$$\mathsf{\Delta }{\mathrm{G}}_{\mathrm{R}-\mathrm{E}}={\displaystyle \frac{\mathrm{l}\mathrm{n}{\mathrm{k}}_1-\mathrm{l}\mathrm{n}{\mathrm{y}}_0}{{\mathrm{b}}_1}}$$

(26)

$$\mathsf{\Delta }{\mathrm{G}}_{\mathrm{Z}-\mathrm{E}}={\displaystyle \frac{\mathrm{l}\mathrm{n}{\mathrm{k}}_2-\mathrm{l}\mathrm{n}{\mathrm{p}}_0}{{\mathrm{b}}_2}}$$

(27)

$$\mathsf{\Delta }{\mathrm{G}}_{\mathrm{R}}=\mathsf{\Delta }{\mathrm{G}}_{\mathrm{R}-\mathrm{E}}\mathrm{d}$$

(28)

$$\mathsf{\Delta }{\mathrm{G}}_{\mathrm{Z}}=\mathsf{\Delta }{\mathrm{G}}_{\mathrm{Z}-\mathrm{E}}\mathrm{d}$$

(29)

$$\mathsf{\Delta }\mathrm{G}={\displaystyle \frac{\mathsf{\Delta }{\mathrm{G}}_{\mathrm{R}}+\mathsf{\Delta }{\mathrm{G}}_{\mathrm{Z}}}{2}}$$

(30)

where lnk₂ is a natural logarithm of the rate constant k₂, lnp₀ is the natural logarithm of the initial parameter p₀, b₂ is a coefficient related to energy calculation, ΔG_R is a metabolic energy related to parameter R, ΔG_R−E is energy per unit related to parameter R, d is a dimensionless scaling factor, ΔG_Z is metabolic energy related to parameter Z, ΔG_Z−E is energy per unit related to parameter Z, ΔG is average metabolic energy, ΔG_R is metabolic energy associated with R, and ΔG_Z is metabolic energy related to Z.

2.10. B-Type Dielectric Substance Transfer Percentage of B. rapa Leaves

The B-type dielectric substance transfer percentage is represented by Equations (31)–(34). The percentage values for the R, Xc, and X_L components of the B-type dielectric substance transfer percentage are detailed in Equations (35)–(37). Here, BPn_R, BPn_Xc, and BPn_XL represent the B-type dielectric substance transfer percentage associated with R, Xc, and X_L; Bn_T represents the total transfer number, which is the sum of BPn_R, BPn_Xc, and BPn_XL.

$${\mathrm{B}\mathrm{n}}_{\mathrm{R}}={\mathrm{b}}_1$$

(31)

$${\mathrm{B}\mathrm{n}}_{\mathrm{X}\mathrm{c}}={\mathrm{b}}_3$$

(32)

$${\mathrm{B}\mathrm{n}}_{\mathrm{X}\mathrm{l}}={\mathrm{b}}_4$$

(33)

$${\mathrm{B}\mathrm{n}}_{\mathrm{T}}={\mathrm{B}\mathrm{n}}_{\mathrm{R}}+{\mathrm{B}\mathrm{n}}_{\mathrm{X}\mathrm{c}}+{\mathrm{B}\mathrm{n}}_{\mathrm{X}\mathrm{l}}$$

(34)

$${\mathrm{B}\mathrm{P}\mathrm{n}}_{\mathrm{R}}={\displaystyle \frac{100{\mathrm{B}\mathrm{n}}_{\mathrm{R}}}{{\mathrm{B}\mathrm{n}}_{\mathrm{T}}}}\%$$

(35)

$${\mathrm{B}\mathrm{P}\mathrm{n}}_{\mathrm{X}\mathrm{c}}={\displaystyle \frac{100{\mathrm{B}\mathrm{n}}_{\mathrm{X}\mathrm{c}}}{{\mathrm{B}\mathrm{n}}_{\mathrm{T}}}}\%$$

(36)

$${\mathrm{B}\mathrm{P}\mathrm{n}}_{\mathrm{X}\mathrm{l}}={\displaystyle \frac{100{\mathrm{B}\mathrm{n}}_{\mathrm{X}\mathrm{l}}}{{\mathrm{B}\mathrm{n}}_{\mathrm{T}}}}\%$$

(37)

2.11. Statistical Analysis

The statistical software SPSS 25.0 was employed to analyze variance (ANOVA) and perform Duncan’s multiple range tests to identify significant differences. Additionally, SPSS was utilized to conduct Pearson correlation analysis to determine the relationship between each indicator [10,15]. The experimental results are presented as the mean ± standard deviation (Mean ± SE). The graphs and figures were generated using Origin 2021Pro.

3. Results

3.1. Plant Biomass

Table 1. Biomass of B. rapa infected with different concentrations of Plasmodiophora. The data in the graph are presented as the mean ± standard deviation; n is the number of plants per treatment, n = 5. PWB denotes the infection by Plasmodiophora of B. rapa at different concentrations as follows: CK-0, PWB1-2 × 10⁹, PWB2-4 × 10⁹, PWB3-6 × 10⁹, PWB4-8 × 10⁹, and PWB5-10 × 10⁹ spores/mL. The different lowercase letters a, b, and c in this table denote the significance of differences at p < 0.05.

Treatment	Root (g/Plant)	Shoot (g/Plant)	Total (g/Plant)
CK	3.71 ± 0.17 a	8.79 ± 0.16 a	12.49 ± 0.24 a
PWB1	3.16 ± 0.01 a	8.16 ± 0.03 a	11.30 ± 0.26 a
PWB2	2.28 ± 0.05 b	4.71 ± 0.2 b	6.99 ± 0.25 b
PWB3	2.27 ± 0.1 b	3.69 ± 0.1 c	6.96 ± 0.15 b
PWB4	2.05 ± 0.12 b	3.67 ± 0.1 c	5.71 ± 0.19 c
PWB5	2.2 ± 0.11 b	1.39 ± 0.2 d	3.59 ± 0.12 d

illustrates the biomass of B. rapa after infection with varying concentrations of Plasmodiophora. As the concentration of Plasmodiophora increased, there was a corresponding decrease in the root, shoot, and total biomasses of the plants. Notably, the reduction in biomass was most pronounced in B. rapa treated with PWB5. Specifically, the root biomass decreased by 74.93%, the shoot biomass by 26.38%, and the total biomass by 71.24% compared with the control group (CK). Despite the overall biomass reduction, the root–shoot ratio (R/S) of PWB5-treated B. rapa significantly increased by 50.45% compared with that of the CK.

3.2. Photosynthesis in Plants

Figure 2 illustrates the impact of varying concentrations of Plasmodiophora on the photosynthesis-related parameters in B. rapa. As shown in Figure 2a, the photosynthetic rate (Pn) significantly decreased with increasing Plasmodiophora concentrations. PWB5 exhibited the lowest Pn, approximately 68.87% less than that of CK. This declination indicates a dose-dependent adverse effect of Plasmodiophora on the photosynthetic efficiency of B. rapa. As depicted in Figure 2b, similar to Pn, the level of stomatal conductivity (Cond) decreased as the R. solami concentration increased. The CK group maintained the highest Cond level, whereas PWB5 had the lowest, with a reduction of 78.66%. This indicates impaired stomatal function and gas exchange due to Plasmodiophora infection. Figure 2c shows a substantial decrease in the transpiration rate (Tr) with higher Plasmodiophora levels. PWB5 exhibited the lowest Tr, with a reduction of 66.68% compared with the control. The decreased Tr reflects impaired water movement and possible stomatal closure due to infection. Figure 2d shows that the intercellular CO₂ concentration (Ci) decreases as the Plasmodiophora concentration increases. CK has the highest Ci, while PWB5 has the lowest, with a 38.78% decrease compared to CK. This reduction in Ci suggests restricted CO₂ diffusion into the leaf, impacting photosynthesis. As represented in Figure 2e, water use efficiency (WUE) exhibited the lowest value in the PWB4 treatment, not PWB5. PWB4 showed a 38.32% decrease in WUE compared with that of CK. However, the water usage efficiency (WUE) in PWB5 did not significantly differ from that of CK.

3.3. Fitting Equations for Leaf Electrophysiological Parameters and Clamping Force

Table 2. Fitting equations for electrophysiological parameters and clamping force of B. rapa infected by Plasmodiophora. PWB denotes B. rapa infection experiments at different Plasmodiophora treatment concentrations of CK-0, PWB1-2 × 10⁹, PWB2-4 × 10⁹, PWB3-6 × 10⁹, PWB4-8 × 10⁹, and PWB5-10 × 10⁹ spores/mL. C indicates capacitance, R indicates resistance, Z indicates impedance, Xc indicates capacitive reactance, and X_L indicates inductive reactance. Bonferroni correction was used for all comparisons in Table 2. R² indicates that fitted equations correlate at 0.99, p < 0.01 indicates significance of differences at 0.01, and n = 15 shows number of data fitted in each equation.

Treatment	Capacitance/Clamping Force (C/F)	Resistance/Clamping Force (R/F)	Impedance/clamping force (Z/F)
CK	C = 729.48 + 483.97F	R = 0.0875 + 0.0709e−0.4772F	Z = 0.0559 + 0.0460e−0.5090F
PWB1	C = 483.99 + 280.83F	R = 0.1311 + 0.1214e−0.5151F	Z = 0.0850 + 0.0709e−0.4922F
PWB2	C = 390.16 + 178.93F	R = 0.1932 +0.1698e−0.7741F	Z = 0.1102 + 0.0919e −0.6872F
PWB3	C = 372.02 + 137.29F	R = 0.1889 + 0.1489e−0.6490F	Z = 0.1135 + 0.0865e−0.6183F
PWB4	C = 315.33 + 220.70F	R = 0.3043 + 0.2631e−0.6062F	Z = 0.1558 + 0.1338e−0.5379F
PWB5	C = 159.80 + 102.11F	R = 0.6745 + 0.6210e−0.7561F	Z = 0.2969 + 0.2589e−0.6517F
Treatment	Tolerance/Clamping force (XC/F)	Sense resistance/clamping force (XL/F)	R2/p/n
CK	Xc = 0.0728 + 0.0604e−0.5291F	XL = 0.13.0 + 0.1121e−0.4700F	R2 = 0.99, p < 0.01, n = 15
PWB1	Xc = 0.1103 + 0.0862e−0.5340F	XL = 0.1878 + 0.1555e−0.5071F	R2 = 0.99, p < 0.01, n = 15
PWB2	Xc = 0.1364 + 0.1058e−0.6591F	XL = 0.2800 + 0.2335e−0.7310F	R2 = 0.99, p < 0.01, n = 15
PWB3	Xc = 0.1426 + 0.1050e−0.6146F	XL = 0.2841 + 0.2177e−0.6364F	R2 = 0.99, p < 0.01, n = 15
PWB4	Xc = 0.1731 + 0.1484e−0.5027F	XL = 0.4587 + 0.3966e−0.6007F	R2 = 0.99, p < 0.01, n = 15
PWB5	Xc = 0.3325 + 0.2745e−0.6522F	XL = 0.8771 + 0.7810e−0.7309F	R2 = 0.99, p < 0.01, n = 15

and Figure 3 indicate the fitted equations of the electrophysiological parameters and the clamping force of Plasmodiophora-infected B. rapa. In this study, the C, R, Z, X_C, X_L, and F of B. rapa showed robust significance (R² = 0.99, p < 0.01), proving that the phytoelectric signals could reliably characterize the response of B. rapa to infection with varying levels of Plasmodiophora. The C of the B. rapa leaves showed a trend opposite to that of R, Z, X_C, and X_L, with C showing a significant positive correlation with F. In contrast, R, Z, X_C, and X_L showed a marked negative association with F. C, Z, X, and F were remarkably correlated with F. In contrast, R, Z, X_C, and X_L were negatively associated with F.

3.4. Electrophysiological Information About Plants During Plasmodiophora Infection

Based on the fitting equations of B. rapa electrical signals to the clamping force (F) after infection with varying concentrations of Plasmodiophora, the present study deduced the intrinsic electrophysiological information about the B. rapa leaves when F = 0 (

Table 3. Electrophysiological information inherent in B. rapa at different Plasmodiophora concentrations. Values in table are expressed as mean ± standard deviation, n = 5; n is number of plants per treatment. PWB denotes B. rapa infection experiments at different Plasmodiophora treatment concentrations, which were CK-0, PWB1-2 × 10⁹, PWB2-4 × 10⁹, PWB3-6 × 10⁹, PWB4-8 × 10⁹, and PWB5-10 × 10⁹ spores/mL. IC indicates intrinsic capacitance, IR indicates intrinsic resistance, IZ indicates intrinsic impedance, IXc indicates intrinsic capacitive reactance, and IX_L indicates inductive reactance. Lowercase letters a, b, c, d, and e in table denote significance difference at 0.05 (p < 0.05).

Treatment	ICP/pF	IR/MΩ	IZ/MΩ	IXC/MΩ	IXL/MΩ
CK	729.48 ± 28.48 a	8.75 ± 1.25 c	5.59 ± 0.44 e	7.28 ± 0.29 d	13.70 ± 1.35 d
PWB1	483.99 ± 46.92 b	14.11 ± 1.07 c	8.50 ± 0.18 d	11.03 ± 1.01 c	18.78 ± 4.99 d
PWB2	390.16 ± 28.28 c	19.32 ± 5.18 bc	11.02 ± 0.65 b	13.64 ± 0.95 c	28.00 ± 3.31 c
PWB3	372.02 ± 6.38 cd	18.89 ± 1.64 bc	11.35 ± 0.42 c	14.26 ± 0.24 bc	28.41 ± 1.66 c
PWB4	315.33 ± 61.16 d	30.43 ± 10.83 b	15.58 ± 2.95 a	17.31 ± 3.78 c	45.87 ± 6.01 b
PWB5	159.80 ± 7.48 e	67.45 ± 10.00 a	29.69 ± 0.82 a	33.25 ± 1.57 a	87.71 ± 7.99 a

). The IC_P of B. rapa decreased with increasing Plasmodiophora contents, whereas IR, IZ, IX_C, and IX_L increased. The IC_P of the PWB5 variety was the smallest, being 78.09% lower than that of the CK, whereas the IR, IZ, IX_C, and IX_L values were approximately 4–8 times higher than those of the CK. Specifically, the IR of PWB5 was 67.45 ± 10.00 MΩ, which was significantly higher than the CK value of 8.75 ± 1.25 MΩ. The IZ value for PWB5 was 29.69 ± 0.82 MΩ, whereas the CK had a value of 5.59 ± 0.44 MΩ. The IXC for PWB5 reached 33.25 ± 1.57 MΩ compared to 7.28 ± 0.29 MΩ for the CK. Similarly, the IX_L value of PWB5 was 87.71 ± 7.99 MΩ, markedly higher than the CK’s 13.70 ± 1.35 MΩ. The trend observed indicates a significant electrophysiological shift in B. rapa with increased Plasmodiophora concentration.

3.5. Intracellular Water Metabolism Based on Plant Electrical Signal Quantification in B. rapa

The intracellular water metabolism characteristics of B. rapa were determined using the intrinsic electrophysiological data collected after the plant was infected with various concentrations of Plasmodiophora (

Table 4. Characteristics of intracellular water metabolism in B. rapa at different Plasmodiophora concentrations. Values in table are expressed as mean ± standard deviation, n = 5; n is number of plants per treatment. PWB denotes B. rapa infection experiments at different Plasmodiophora concentrations as follows: CK-0, PWB1-2 × 10⁹, PWB2-4 × 10⁹, PWB3-6 × 10⁹, PWB4-8 × 10⁹, and PWB5-10 × 10⁹ spores/mL. IWHC indicates intracellular water-holding capacity, IWUE indicates intracellular water use efficiency, IWHT indicates intracellular water-holding time, and WRT indicates water rate translocation. Lowercase letters a, b, c, d, and e in table indicate significance difference at 0.05 (p < 0.05).

Treatment	IWHC	IWUE (10−2)	IWHT	WRT
CK	8102.65 ± 211.46 a	6.74 ± 1.11 a	40.67 ± 1.60 b	199.54 ± 12.54 a
PWB1	6160.25 ± 395.00 b	5.01 ± 2.78 ab	41.00 ± 3.08 b	149.96 ± 1.61 b
PWB2	5337.38 ± 256.34 c	3.78 ± 0.23 b	43.13 ± 5.74 ab	124.63 ± 9.92 c
PWB3	5172.47 ± 59.08 cd	2.99 ± 0.80 b	42.22 ± 0.94 ab	122.57 ± 3.86 c
PWB4	4619.06 ± 612.13 d	5.54 ± 1.91 ab	47.92 ± 1.26 a	96.20 ± 10.40 d
PWB5	2944.35 ± 92.05 e	3.91 ± 0.46 ab	47.46 ± 2.95 a	62.13 ± 2.30 e

). This study revealed that the IWHC, IWUE, and WRT of B. rapa decreased, whereas the IWHT increased with increased Plasmodiophora concentrations. The IWHC and the WRT were the lowest in PWB5, where they were 63.66% and 68.86% less than those of the CK, respectively. Specifically, the IWHC decreased from 8102.65 ± 211.46 in the CK to 2944.35 ± 92.05 in PWB5, and the WRT decreased from 199.54 ± 12.54 in the CK to 62.13 ± 2.30 in PWB5. Conversely, the IWHT was the highest in PWB4, showing a 17.83% increase compared with the CK, with the values rising from 40.67 ± 1.60 in the CK to 47.92 ± 1.26 in PWB5. Additionally, IWUE was the lowest in PWB3, showing a 55.64% reduction compared to the CK, with the values decreasing from 6.74 ± 1.11 in the CK to 2.99 ± 0.80 in PWB3. The other treatments also showed significant changes, with varying degrees of decrease in the IWHC, IWUE, and the WRT, whereas the IWHT increased with the concentration of Plasmodiophora.

3.6. Characterization of Nutrient Transport in B. rapa Plant

The nutrient transport characteristics of B. rapa were obtained based on the intrinsic electrophysiological information about B. rapa after infection with different Plasmodiophora concentrations (

Table 5. Characteristics of nutrient transport in B. rapa at different Plasmodiophora concentrations. Values in table are expressed as mean ± standard deviation, n = 5, and n is number of plants in each treatment. PWB denotes infection by Plasmodiophora of B. rapa at different concentrations as follows: CK-0, PWB1-2 × 10⁹, PWB2-4 × 10⁹, PWB3-6 × 10⁹, PWB4-8 × 10⁹, and PWB5-10 × 10⁹ spores/mL. UNF denotes unit for translocation of nutrients. NTR denotes nutrient translocation rate, NTC indicates nutrient translocation capacity, UAF indicates nutrient active flow, and NAC indicates nutrient active translocation capacity. Lowercase letters a, b, c, d, and e in table indicate significant difference between groups (p < 0.05).

Treatment	UNF(10−2)	NTR	NTC	UAF(10−2)	NAC
CK	183.52 ± 15.07 b	199.54 ± 12.54 a	364.95 ± 6.83 a	53.35 ± 3.05 ab	106.71 ± 12.52 a
PWB1	208.21 ± 26,21 ab	149.96 ± 1.61 b	312.11 ± 37.95 ab	62.70 ± 22.82 a	94.21 ± 35.06 a
PWB2	212.14 ± 59.73 ab	124.63 ± 9.92 c	260.45 ± 50.04 bc	49.40 ± 8.63 ab	62.14 ± 15.15 b
PWB3	198.75 ± 11.49 ab	122.57 ± 3.86 c	243.31 ± 6.60 bcd	50.29 ± 2.16 ab	61.70 ± 4.51 b
PWB4	240.68 ± 70.10 ab	96.20 ± 10.40 d	230.62 ± 71.57 cd	37.47 ± 3.10 c	35.83 ± 1.11 bc
PWB5	280.48 ± 42.46 a	62.13 ± 2.30 e	173.70 ± 20.94 d	38.22 ± 5.32 b	23.82 ± 4.21 c

). It was identified that the UNF of B. rapa was significantly enhanced post-infection with worsening Plasmodiophora infection, with the UNF value of PWB5 being the highest at 280.48 ± 42.46 × 10⁻², indicating a 52.83% increase compared with the CK. Conversely, the NTR, NTC, UAF, and NAC values were reduced as the Plasmodiophora concentration increased. Specifically, the NTR of PWB5 decreased to 62.13 ± 2.30, which is a reduction of 68.86% compared to the CK. The NTC of PWB5 fell to 173.70 ± 20.94, representing a 52.40% decrease from the CK value. Similarly, the UAF of PWB5 showed a 28.36% reduction, and the NAC value of PWB5 significantly diminished to 23.82 ± 4.21, showing an 77.68% decrease compared to the CK. These findings highlight the adverse impact of higher Plasmodiophora concentrations on the nutrient transport efficiency in B. rapa.

3.7. Cellular Metabolic Energy for the Leaf of B. rapa in Response to Plasmodiophora

Figure 4 illustrates the unit for metabolic energy (a) and the total metabolic energy (b) for the leaves of B. rapa in response to Plasmodiophora infection. As indicated in Figure 4a, the unit for cell metabolizable energies for R and Z denoted as ∆G_R-E, ∆G_Z-E, and ∆G_E displayed varying trends of increase and decrease. Compared with those in the CK, ∆G_R-E and ∆G_E at PWB1 peaked, showing rises of 14.20% and 13.29%, respectively. Conversely, at PWB3, these values dropped to their lowest, with decreases of 33.11% and 35.22%, respectively. As shown in Figure 4b, ∆G_R, ∆G_Z, and ∆G_E of the B. rapa leaves generally declined with worsening Plasmodiophora infection. Although there were minor increases at PWB2 and PWB4, they remained lower than the CK. The minimum values were observed at PWB5, with reductions of 77.10% and 79.26% for ∆G_R, ∆G_Z, respectively, compared with the CK.

3.8. B-Type Dielectric Substance Transfer Percentage in B. rapa Leaves

Figure 5 illustrates the percentage of B-type dielectric coefficients based on R, Xc, and X_L in the B. rapa leaves, denoted as BPn_R, BPn_Xc, and BPn_XL. The analysis shows that as the degree of Plasmodiophora infection increases, these dielectric coefficients show a noticeable change. Specifically, PWB4 exhibited the highest BPn_R, approximately 21.1% higher than that of the CK. Conversely, the BPn_Xc values for all the treatments were lower than those of the control, with PWB4 showing the lowest BPn_Xc, which was 22.2% lower than that of the CK.

3.9. Correlation Between Growth and Electrophysiological Information About B. rapa During Plasmodiophora Infection

Figure 6 illustrates the correlation between growth, electrophysiological parameters, cellular metabolizable energy, and dielectric substance transfer capacity of B. rapa at varying levels of Plasmodiophora infection. The analysis showed that the growth of B. rapa was significantly positively correlated with several factors, including biomass, Pn, IC_P, IWHC, IWUE, the NTR, NAC, and ΔG. Conversely, growth was significantly negatively correlated with IR, IZ, IXc, IX_L, and the UNF.

4. Discussion

4.1. Electrophysiology Can More Sensitively Reflect Plasmodiophora Infection

In this study, we found Plasmodiophora infection affected the dynamic plant electrical signals of B. rapa. Equations for calculating C, R, Z, X_C, and X_L of the B. rapa leaves were established under a fixed clamping force (Equations (3)–(9)). The correlation coefficient (R²) between C, R, Z, X_C, X_L, and F of the B. rapa leaf blades was 0.99, and the p-values of all the parameters of the fitted equations were <0.0001 (Table 4). These results indicate a significant correlation between the clamping force and the electrical signals (C, R, Z, X_C, and X_L; Figure 5) [21]. Therefore, when F was 0, we calculated the intrinsic electrical signals (IC_P, R, Z, X_C, and X_L) of the B. rapa leaves (Table 3); the changes in growth conditions (Table 1) and photosynthesis (Figure 2a–d) of B. rapa were directly proportional with capacitance, but inversely proportional with resistance, impedance, capacitance, and susceptibility. These findings indicate that the growth of B. rapa was inversely proportional to the degree of Plasmodiophora infection, which could be attributed to a reduction in the biomass and photosynthetic capacity of B. rapa in previous research. When elm trees were infected with Fusarium oxysporum, the electrical resistance of the trunks was substantially elevated, but their growth metabolism was undermined [22]. The electrical resistance of fir trees infected with sooty mold was found to have a significant negative correlation with their growth status [23,24]. The impedance of the root system of apple trees elevated conspicuously with susceptibility to disease [25,26]. The growth of disease-infected apple trees was inhibited, suppressing the electrical signals [27,28].

4.2. Plasmodiophora Infection Changed Intracellular Water and Nutrient Metabolism of B. rapa Leaves

The intracellular water and nutrient metabolism of plant species has been overlooked for a long time. Only 5% of intracellular water is used for physiological processes in plant species [29,30], such as photosynthesis [31], respiration [32], and nutrient uptake, and transport [33,34], but this can be explained using plant electrophysiology technology. In this experiment, we qualified the intracellular water metabolism of B. rapa (Equations (15)–(20)). The IWHC and the WRT were significantly negatively correlated with the Plasmodiophora concentration (Table 4), whereas the IWHT was markedly positively correlated. This finding indicates that B. rapa responded to Plasmodiophora infection by increasing its intracellular water-holding capacity and water translocation rate. It was reported that plants impede water loss and maintain growth by extending the intracellular water-holding time. For example, Orychophragmus violaceus (Ov) under high-cadmium-level stress suppresses growth and metabolism, reducing the loss of water and other materials by decreasing its cellular water-holding capacity and intracellular water-transfer rate, while increasing its intracellular water-holding time [15]. However, in karst soils, Ov adapt to adverse conditions by prolonging the intracellular water-holding time [26], which is consistent with the results of the present study.

Additionally, it has been considered that intracellular nutrient transport is highly dependent on water metabolism in plant species [35,36], so we also qualified the intracellular nutrient metabolism of B. rapa at different Plasmodiophora infection concentrations (Equations (21)–(25)). The Plasmodiophora concentration positively correlated with the UNF, and it was negatively correlated with NTR, NTC, UAF, and NAC of B. rapa (Table 5), suggesting that the nutrient translocation rate, translocation capacity, active flow, and active translocation capacity were reduced. This result indicated this plant species replenish nutrient losses by enhancing nutrient translocation under restricted growth [37,38]. Notably, with worsening Plasmodiophora infection, B. rapa maintained its UNF to reduce the decline in intracellular water content and nutrient metabolism, which shows its adaptive strategy to Plasmodiophora infection.

4.3. Dissociation of Energy Metabolism and Dielectric Substance Transfer in B. rapa During Plasmodiophora Infection

In previous studies, it has been reported that plant immunity closely relates to cell energy metabolism and the B-type dielectric substance transfer percentage [39], cell energy metabolism positively correlates with plant growth [40,41], while the B-type dielectric substance transfer percentage reflects the distributional characteristics of nutrient transporter proteins of plant cells [42,43]. In this study, we found dissociation between cellular metabolism energy and growth of B. rapa during worsening Plasmodiophora infection. In Figure 4a, ΔG_R-E, ΔG_Z-E, and ΔG exhibited the maximum values at PWB2 and the minimum values at PWB5, but the biomass and photosynthesis continuously declined with worsening Plasmodiophora infection. This result is consistent with previous research, showing that in plant species, the photosynthesis and intracellular water and nutrient transfer capacities increased during low-level infection [44,45], but reduced during high-level infection [46,47]. Additionally, we found an interesting result; ∆G was consistent with photosynthesis, but did not consistently decrease (Figure 4b). There was an increasing trend in ∆G of B. rapa at PWB2 and PWB4, suggested a transient increase in cellular energy metabolism rather than a sustained decrease during worsening Plasmodiophora infection, which revealed this plant species prevented its growth due to continuous inhibitions under environmental stress [48].

In this experiment, we define BPn_R as the proportion of extrinsic proteins and BPn_Xc as the proportion of binding proteins. We found dissociation between the B-type dielectric substance transfer percentage and growth of B. rapa during worsening Plasmodiophora infection. As depicted in Figure 5, BPn_R and BPn_Xc of B. rapa exhibited opposite trends, with the BPn_R values being higher than the CK, whereas the BPn_Xc values were lower than the CK. Furthermore, it demonstrated that BPn_R and BPn_Xc of B. rapa did not exhibit linear increases or decreases. Specifically, the BPn_R of PWB4 was the highest, the BPn_Xc was the lowest, and the remaining groups did not show significant differences with the CK, resulting in highest extrinsic protein content and the lowest binding protein content. It has found when the pathogen infected, the higher intracellular nutrient loss of the plants enhanced passive transport, leading to an increase in extrinsic proteins, but a decrease in binding proteins on the cell membrane [49,50]. Consequently, we found PWB4 might be the highest immunological concentration of Plasmodiophora infection in B. rapa.

4.4. Immunological Relevance of Electrophysiological Properties of B. rapa During Plasmodiophora Infection

The correlation results indicate that as Plasmodiophora infection worsened, the growth of B. rapa was inhibited, leading to a decrease in its cellular metabolic energy, as evidenced by a smaller ΔG (Figure 6). Consequently, there were lower values for ΔG, C, the IWHC, the WRT, the NTR, and the NAC. The decreased C of B. rapa reduced the vesicle volume, which is critical for maintaining cellular structure and function. In response to these adverse conditions, B. rapa increased nutrient transfer through the UAF to sustain its material supply. Several immune mechanisms of plant species infected with Plasmodiophora have been shown. On the one hand, plants inhibit nutrient supply by decreasing their energy level. In this study, we found B. rapa cells decreased the metabolizable energy level during worsening Plasmodiophora infection (Figure 4), supporting this result. On the other hand, plants could strengthen the cell wall, such as increasing lignin and cork deposition to enhance immunity [51,52]. We found that the BPn_R of B. rapa cells increased during worsening Plasmodiophora infection, indicating the promotion of extra-membrane transporter proteins, which contributes to the resistance against Plasmodiophora infection [53,54]. Meanwhile, the reduced BPnXc indicated the lower quantity of surface proteins in B. rapa cells, leading to decreased intracellular water metabolism during Plasmodiophor infection (Figure 7). Hence, B. rapa could endure the Plasmodiophora infection by maintaining the total nutrient metabolism and minimizing active nutrient transfer and water metabolic activities to conserve energy and resist the infection.

5. Conclusions

The results obtained from this study have confirmed that plant electrophysiological techniques can effectively be used to analyze B. rapa infected by Plasmodiophora (Figure 7). It has been revealed that the plant electrophysiological approach is well aligned with the growth changes in B. rapa during infection by Plasmodiophora. The degree of Plasmodiophora infection increased inversely with growth of B. rapa, physiological capacitance, intracellular water metabolism, nutrient transfer capacity, and the total cellular metabolic energy, while it positively correlated with electrical resistance and nutrient transfer capacity. Unlike photosynthesis and overall growth, the unit for cell metabolic energy exhibited a nonlinear change, initially increasing, and then decreasing. Specifically, the BPn_R of B. rapa increased at low infection levels. However, at high infection levels, the distribution of B-type dielectric material transfer coefficients balanced to sustain its growth. Typically, based on distribution of the B-type dielectric substance transfer percentages, we found PWB4 was the maximal immune concentration, as evidenced by the maximal BPn_R, with an increasing quantity of extrinsic proteins in the cell membrane, and the minimum BPn_Xc, with a decreasing quantity of intracellular binding proteins in the B. rapa leaves. These findings underscore the potential of plant electrical signals as indicators of Plasmodiophora infection, offering a novel, rapid, non-destructive detection method for agricultural disease management.