Optimization of ultrasound enzyme-assisted trehalose lipid extraction of Coptis alkaloids and evaluation of its anti-inflammatory effects

Abstract

Coptidis Rhizoma (CR), a traditional Chinese medicine, has extensive pharmacological activity because it is rich in isoquinoline alkaloids. Nonetheless, the limited solubility of these alkaloids presents a significant challenge, hindering the full realization of CR’s therapeutic potential through conventional extraction methods. To address this issue and enhance the solubility of the target compounds while optimizing extraction efficiency, this study employed an environmentally sustainable extraction technique, Ultrasound-Enzyme-Trehalose Lipid (UET), to synergistically extract five key alkaloids from CR. Based on the results of single-factor experiments, Box-Behnken design (BBD) was employed to optimize the selected model variables. The optimal extraction conditions were determined: extraction pH of 4.3, extraction temperature of 40 ℃, solid–liquid ratio of 1:27 g/mL, and ultrasonication time of 48 min. UET extracts were compared with other extracts, and it was proved that UET extracts had higher alkaloid extraction rate, comprehensive evaluation value (CEV) and lower energy consumption and CO₂ emission. Then scanning electron microscopy (SEM), dynamic light scattering (DLS) and transmission electron microscopy (TEM) were used to explore the extraction mechanism of UET extraction. In addition, UET extraction has more excellent anti-inflammatory activity. The establishment of UET method of CR provides a method reference for green and efficient extraction of alkaloid components from natural drugs.

1. Introduction

Coptidis Rhizoma (CR), the dried rhizome of Coptis chinensis Franch., Coptis deltoidea C. Y. Cheng et Hsiao or Coptis teeta Wall., is extensively distributed across regions such as Yunnan, Sichuan, Shanxi, Guizhou and other areas in China. In addition, it has a small amount of cultivation in the Hokuriku region of Japan, and distribution in India, Myanmar, Western Europe, and North America slightly [1,2]. It possesses therapeutic properties, including the ability to clear heat and dampness, as well as to purge fire and detoxify [3]. Due to its abundant content of various protoberberine alkaloids, such as berberine (BER), epiberberine (EPI), coptisine (COP), palmatine (PAL), and jateorrhizine (JAT), CR has emerged as a model plant for investigating the pharmacological activities of benzyl isoquinoline alkaloids [[4], [5], [6]]. Furthermore, the Pharmacopoeia of the People's Republic of China has designated the first four alkaloids as index components for the quantification of CR content, the Japanese Pharmacopoeia also used berberine content to assess CR quality. It can be seen that alkaloid components play a crucial role in many effective components of CR. Regrettably, most CR alkaloids have the disadvantages of poor solubility and low bioavailability, resulting in low extraction efficiency of CR alkaloids at present, which cannot give full play to their efficacy. In order to improve the release efficiency and solubility of the target ingredient and optimize the pharmacological efficacy of CR alkaloids, efficient extraction methods are imperative.

Traditional methods for extracting CR alkaloids, such as the acid water extraction method, lipophilic organic solvent extraction method and the reflux extraction method [[7], [8], [9]], are characterized by their simplicity and cost-effectiveness. However, these techniques suffer from several drawbacks, including low extraction efficiency, prolonged extraction times, limited extraction of components, reliance on organic solvents, and associated environmental pollution. In light of the growing emphasis on sustainable development and the conservation of ecological resources, the principles of “green chemistry” have gained significant importance in the realms of pharmaceutical research, development, and manufacturing [10,11]. In 2012, Chema proposed the concept of “green extraction of natural products”, which advocates for the utilization of alternative solvents and renewable natural resources to achieve safe and high-quality extraction processes [11]. Consequently, the efficient, economical, and sustainable extraction of active ingredients from the complex matrices of and natural medicine presents a significant challenge.

In recent years, advancements in modern extraction and separation technologies have demonstrated distinct advantages, such as two-phase extraction [12], enzyme assisted extraction [13,14], ultrasonic assisted extraction [15] and deep eutectic solvents extraction[16,17] and so on. Among them, enzyme technology works by facilitating the degradation of plant cell walls. It possesses the characteristics of simple and efficient [18]. In addition, ultrasonic extraction employs mechanisms such as vibrational cavitation and mechanical grinding to enhance the mutual penetration between the solvent and achieving the purpose of improving the extraction yield of active components. Additionally, ultrasonic extraction can effectively decrease the usage of organic solvents and energy, making it an environmentally sustainable extraction method [19]. Therefore, the ultrasonic-assisted enzyme extraction method has the potential to be applied to the extraction of CR multicomponent.

Concurrently, in order to further improve the solubility of CR alkaloids, surfactants have come into our view because of their unique structural properties [20,21]. Trehalose lipid is a type of biosurfactant, which can effectively reduce surface and interfacial tension, making it an efficient stain remover, emulsifier, and diffuser [22,23]. Compared to conventional chemical surfactants, biosurfactant offers the benefits of low toxicity, high stability, acid and salt resistance, biodegradability, and environmental sustainability. In aqueous solutions, biosurfactants can form micelles, which increase the contact area between the liquid and solute, thereby enhancing the interaction between solute and solvent molecules. This process facilitates solute dissolution, improves the solubility of active plant ingredients, and may serve as an alternative to organic solvents in extraction processes, thereby promoting environmentally friendly extraction methodologies [[24], [25], [26]]. Similarly, trehalose lipid shows promise as a novel approach for the green extraction of CR alkaloids, which possess low solubility.

To enhance extraction efficiency, the Box-Behnken Design (BBD) was employed to optimize model variables for achieving the optimal CEV. BBD represents an advancement over the response surface Central Composite Design (CCD). By appropriate selecting factor level combinations and minimizing experimental trials, a mathematical model is developed to elucidate the relationship between response variables and influencing factors. This approach not only determines the impact of multiple factors on response variables but also reduces the number of experiments and associated costs. Building upon the aforementioned context, this study introduces an innovative approach by integrating Ultrasound-Enzyme-Trehalose lipid (UET) technology to isolate JAT, EPI, COP, PAL, and BER from CR. The primary objective of this research is to improve the extraction efficiency of CR alkaloids while simultaneously reducing energy consumption and carbon dioxide emissions during the extraction process. This study aims to offer a reference methodology for the sustainable and efficient extraction of alkaloid constituents from natural pharmaceuticals, thereby enhancing the efficacy of traditional medicines.

2. Materials and methods

2.1. Materials and reagents

CR was collected from the Chinese medicine market in Harbin, Heilongjiang Province, and identified by Associate Professor Yanyan Liu of the College of Animal Medicine, Northeast Agricultural University. After the CR were ground and sieved for 80 mesh screens, the CR powder needed for the experiment was obtained. Dry at 60 ℃ and store in refrigerator at 4 ℃ for later use. Cellulase (Cel; 200 U/mg), pectinase (Pec; 30 U/mg), hemicellulose (Hem; ≥5 U/mg), xylanase (Xyl; ≥100 U/mg), papain (Pap; ≥3 U/mg), citric acid, Na₂HPO₄, NaH₂PO₄, lipopolysaccharide (LPS) and hematoxylin were purchased from Beijing Solarbio Technology Co., Ltd. The biosurfactant trehalose lipid was obtained from Tongchuan Boliante Chemical Co., Ltd. The standards of JAT, EPI, COP, PAL and BER were bought from Chengdu Phytolabelling Chemical Pure Biotechnology Co., Ltd. A range of kits used in this test were gained from Shanghai Biyuntian Biotechnology Co., Ltd.

2.2. Optimization of CR extraction process

2.2.1. Single-factor experiment

Based on the UET extraction method outlined above, the UET extraction process was implemented in this experiment, as depicted in Fig. 1. Precisely weighed 1.0 g of CR powder was placed in a 50 mL EP tube and added in different ratios (1:10, 1:15, 1:20, 1:25, 1:30) to disodium phosphate-citrate buffer and adjusted the pH with NaOH or HCl. Subsequently, a specific ratio of cellulase and pectinase was added, and the mixture was subjected to a water bath at 45 ℃ for varying durations (20, 30, 40, 50, 60 min) followed by incubated at 100 ℃ for 5 min to inactivate the enzymes. Afterwards, different concentrations (1, 2, 4, 6, 8 mg/mL) of trehalose lipid was added and sonicated. The temperatures used for sonication were 30, 35, 40, 45 and 50 ℃. In addition, the corresponding sonication times were 20, 30, 40, 50 and 60 min at 300, 400, 500, 600 and 700 W. Finally, the mixtures were centrifuged for 5 min at 5000 rpm, and the volume of the systemic supernatant was recorded; then, the yields of the five alkaloids were determined. The CEV in the final extraction was employed as an indicator to establish the most favorable one-factor extraction conditions.

Fig. 1.

The schematic of the apparatus of UET.

2.2.2. Response surface methodology

Based on the findings of the unifactorial research, four factors namely extraction pH (X₁), extraction temperature (X₂), material-liquid ratio (X₃), and ultrasonic time (X₄) were selected to further optimize the respective variables using a Box-Behnken design (BBD), as well as CEV was utilized as an indicator to evaluate the impact of the four variables on the extraction efficiency of CR. Three parallel tests were performed in each group. The data were analyzed using Design Expert.V8.0.6.1 and the accuracy and fit of the model was assessed by analysis of variance (ANOVA). The optimum extraction process conditions for CR were determined based on the above results.

2.2.3. Calculation of comprehensive evaluation value (CEV)

Entropy weight method is an objective valuation method that depends on the discreteness of the data itself [27,28]. The weight of an indicator is determined according to the influence of the relative change degree of the indicator on the whole system. For an indicator, entropy value can be used to judge the discreteness degree of an indicator. Accordingly, the information carried by entropy can be utilized for determining the weight of each index, and the weight of each index can be assessed based on its variation degree, thus laying a foundation for the comprehensive assessment of multiple indicators. The steps of entropy weight method are as follows:

If there are m items to be evaluated and n evaluation indicators in the system, the original data matrix is formed:

$$\begin{array}{c}\text{X}={\left({\text{x}}_{\text{ij}}\right)}_{\text{m}·\text{n}}\end{array}$$ (1)

where x_ij (i = 1, 2, …, m; j = 1, 2, …, n) is the original data corresponding to the j-th component of the i-th sample.

• (1)Standardized processing of indicators:

$$\begin{array}{c}{\text{r}}_{\text{ij}}=\frac{{\text{x}}_{\text{j}}-{\text{x}}_{\text{min}}}{{\text{x}}_{\text{max}}-{\text{x}}_{\text{min}}}\end{array}$$ (2)

where r_ij is the standardized value of the j-th component in the i sample; x_j is the j-th index value, x_max is the maximum value of the j-th index, and x_min is the minimum value of the j-th index.

• (2)Calculation of the proportion of the index value of the i-th item in the j-th index (p_ij):

$$\begin{array}{c}{\text{p}}_{\text{ij}}=\frac{{\text{r}}_{\text{ij}}}{{\mathrm{\Sigma }}_{\mathbb{i}\text{= 1}}^{\text{m}}{\text{r}}_{\text{ij}}}\left(\text{0}\le {\text{r}}_{\text{ij}}\le \text{1}\right)\end{array}$$ (3)

• (3)Calculation of entropy weight of the j-th index (e_j):

$$\begin{array}{c}{\mathrm{e}}_{\mathrm{j}}=-k{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{P}}_{\mathrm{i}\mathrm{j}}\bullet \ln {\mathrm{P}}_{\mathrm{i}\mathrm{j}},k=\frac{1}{\ln \mathrm{m}}\end{array}$$ (4)

• (4)Calculation of information utility value of the j-th index (d_j):

$$\begin{array}{c}{\text{d}}_{\text{j}}=\text{1}-{\text{e}}_{\text{j}}\end{array}$$ (5)

• (5)Calculation of the weight of the j-th index (w_j):

$$\begin{array}{c}{\text{w}}_{\text{j}}=\frac{{\text{d}}_{\text{j}}}{{\mathrm{\Sigma }}_{\text{j = 1}}^{\text{n}}{\text{d}}_{\text{j}}}\end{array}$$ (6)

• (6)Calculation of comprehensive evaluation value of indicators (CEV):

$$\begin{array}{c}\text{C}\text{E}\text{V}={\sum }_{\text{j = 1}}^{\text{m}}{\text{x}}_{\text{ij}}{\text{w}}_{\text{j}}\end{array}$$ (7)

2.3. High-performance liquid chromatography (HPLC) analysis

The content of all the five alkaloids in the extracts was analyzed using HPLC as explained previously with slight modifications [29]. The separation was carried out utilizing a C₁₈ reversed-phase column (250 mm·4.6 mm, 5 μm), and the detection wavelength was set at 345 nm, the column temperature was set at 30 ℃, and the running time was 40 min. The mobile phase was composed of 0.05 mol/L NaH₂PO₄ (A) and acetonitrile (B) in a ratio of 50:50 (v/v). The mobile phase was operated at a rate of 0.6 mL/min, and the injection volume was 10 μL. Three portions of each sample were collected. The alkaloid content was calculated by internal standard method, and the peak area was represented by the regression equation as shown in Table S1. The HPLC chromatograms of the five alkaloids including JAT (20.64 min), EPI (22.20 min), COP (24.42 min), PAL (28.93 min) and BER (32.08 min) are shown in Fig. 2A. Furthermore, the applicability of HPLC for quantifying the five alkaloids was assessed based on the linearity of standard curves, reproducibility, and recovery rates (as indicated in Tables S1 and S2).

Fig. 2.

(A) HPLC chromatographic diagram of CR (a) extracts by UET and (b) standards (JAT, EPI, COP, PAL and BER). The effects of different mixed enzyme ratio (B), Extraction pH (C), Extraction temperature (D), Ultrasound time (E), Ultrasound power, (F) Solid-liquid ratio (G) and trehalose lipid concentration (H) on the extraction yields and CEV. *** Most significant (p < 0.001). ns non-significant (p > 0.05).

2.4. Comparison different extraction methods

The present study compared the extraction effect of UET with ultrasound-assisted trehalose lipid extraction (UT), enzyme-assisted trehalose lipid extraction (ET) and reverse flow extraction (RE). The extraction procedures of UET, UT and ET were carried out according to their optimum extraction conditions. The conditions of RE process were as follows: 50 mL distilled water was added to 5 g CR and placed in a water bath for 2 h in a reflux extraction device, and then the volume of the system supernatant was recorded.

2.4.1. Extraction efficiency and CEV evaluation

The concentration of five alkaloids in extracts was calculated according to the regression equation and the content of five alkaloids per gram of raw drug dosage was calculated. Then the CEV was evaluated by entropy weight method.

2.4.2. Extraction time, energy consumption and CO₂ emissions analysis

In this study, the total UET time includes enzymatic digestion time, inactivation time and sonication time; the total RE time is the reflux extraction time. Otherwise, the energy consumption and CO₂ emissions of different extraction methods were calculated by the formulae to evaluate the extraction process [30,31]. The formula for calculating energy consumption is as follows:

$${\text{E}}_{\text{Consumed}}=\text{P}\bullet \text{t}$$ (8)

Where E_Consumed (kWh) denotes the energy consumed, P (kW) represents the power, and t (h) denotes the time required for the extraction process.

The formula for calculating CO₂ emissions is as follows:

$${\text{E}}_{{\text{co}}_{\text{2}}}={\text{E}}_{\text{Consumed}}·\text{800}$$ (9)

Where ${\mathrm{E}}_{{\mathrm{c}\mathrm{o}}_2}$ (kg) represents carbon dioxide emissions.

2.5. Exploration of UET extraction mechanism

2.5.1. Scanning electron microscope (SEM) analysis

SEM (SU8010, Hitachi, Ltd) was used to determine the average size of the dispersed particles. The morphology of the CR powders, including size, surface properties, and shape, was observed before and after extraction, as well as after treatment with different extraction methods. The powders were dried and subsequently subjected to analysis using SEM at 5000 × magnification.

2.5.2. Dynamic light scattering (DLS) analysis

DLS was used to determine the change of particle size of CR extraction before and after adding trehalose lipid. Briefly, 1 mL of the extraction was diluted with distilled water and placed in a laser particle analyzer (Malvern Panalytical, U.K.) to measure the particle size of the extraction. Measurements were performed at 25 ℃.

2.5.3. Transmission electron microscope (TEM) analysis

The formation of biosurfactant micelles was verified by TEM. After the extract before and after adding trehalose lipid was diluted to an appropriate concentration, 10 μL drops were added to the carbon-coated copper mesh. After adsorption for 20 min, the excess liquid was absorbed and dried at room temperature. Then, TEM (H-7650, Hitachi, Ltd) was used to analyze the grid of loaded samples, and the shape, size and morphology of the internal structure of the extract system were studied at 100 kV.

2.6. In vitro anti-inflammatory activity

2.6.1. Cell culture and treatment strategy

In this study, cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 12.5 % fetal bovine serum, 37 ℃ humid air incubator, CO₂ concentration of 5 %, and 5 ∼ 12 generations of morphologically normal and well-grown cells were used for the experiments [32].

Macrophages were inoculated in 96-well plates at a density of 1 · 10⁴ cells/well for 12 h and stimulated with LPS (1 μg/mL) for 24 h, except for the blank group. Subsequently, cells with different treatments were continued to be incubated in the presence of LPS for 24 h. UET and RE treatment groups were given 250 μg/mL, 125 μg/mL, and 62.5 μg/mL of UET and RE extracts, respectively. Herbal monomers were given JAT (2.5 μg/mL), EPI (2.9 μg/mL), COP (3.8 μg/mL), PAL (4.6 μg/mL), BER (16.7 μg/mL), and a mixture of the monomers. Furthermore, the dosage of monomers administered was converted according to the results of the content in the 250 μg/mL of UET extracts. Meanwhile, 1 μM dexamethasone (DEX) was given as a positive control. Finally, the levels of NO, ROS, Ca²⁺, IL-6, IL-1β and TNF-α were determined.

2.6.2. Cell viability assay

Cells (1 · 10⁴ cells/well) were inoculated into 96-well plates with 6 parallel wells per group. Then CCK-8 (10 μL/well) solution was added to the plates and incubated for 1 h. The OD value at 450 nm was measured using a microplate reader.

2.6.3. NO production measurement

The concentration of NO in the culture medium was assessed using the Griess assay as a measure of NO production. Briefly, cells were inoculated into 96-well plates at a density of 1 · 10⁴ cells/well with six replicate wells per group. The cells were treated with 1 μg/mL LPS for 24 h, followed by pretreatment under various conditions. After 24 h of culture, the supernatant was harvested and subjected to the Griess reaction according to the NO Measurement Kit protocol, as well as the OD value at 540 nm was measured using a microplate reader.

2.6.4. ROS production measurement

Cells were inoculated using 96-well plates (1 · 10⁴ cells/well) with 6 parallel wells per group. After treatment with LPS, the cells were pretreated with various conditions, then collected and mixed with 2′,7′-dichlorofluorescein diacetate (DCFH-DA; 10 μM). Subsequently, they were incubated for 40 min at 37 ℃ in the dark. Finally, the cells were rinsed and the emitted fluorescence was measured using a multimode zymography at 485 nm excitation and 530 nm emission.

2.6.5. Intracellular Ca²⁺ level analysis

Cells (5 · 10⁵ cells/well) were inoculated using 6-well plates with three parallel wells per group. Following pretreatment with LPS and various conditions, the cells were harvested and washed followed by the addition of Fluo-3 AM at a concentration of 0.5 μM and incubated for 45 min at 37 ℃ in the absence of light [33]. Finally, cells were rinsed and visualized using an inverted fluorescence microscope. The fluorescence intensity was quantified using ImageJ software for analysis.

2.6.6. Inflammatory cytokines secretion assay

Cells (5 · 10⁵ cells/well) were inoculated using 6-well plates with three replicate wells per group. Following pretreatment with LPS under various conditions, the supernatants were collected and the levels of IL-6, IL-1β, and TNF-α were quantified according to the ELISA kit protocol, with absorbance determined by enzyme marker at 540 nm.

2.6.7. Quantitative RT-PCR

RNA was extracted from the cells using the RNAprep Pure Cell/Bacteria Kit (Tiangen Co., Ltd.). The concentration and purity of total RNA were determined. Then, cDNA synthesis was performed using a reverse transcription kit (Tiangen Biotech Co., Ltd.). qRT-PCR was conducted with PowerUp™ SYBR® Green Master Mix (ABI) on a CFX 96 Touch Real-Time PCR System (bio rad, USA). qRT-PCR was programmed as follows: 1 cycle at 95 ℃ for 10 s, followed by denaturation at 95 ℃ for 5 s over a total of 40 cycles, annealing at 55 ℃ for 15 s, and extension at 72 ℃ for 30 s. The primer sequences designed for gene amplification can be found in Table S3. The mRNA expression levels of IL-6, IL-1β and TNF-α were quantified by 2^-ΔΔCT method.

2.6.8. Cell morphology observation

After treatment according to the aforementioned method, the cells were subjected to three washes with PBS and subsequently fixed in 10 % formaldehyde PBS for 1 h [34]. Following this, the cells underwent two washes with distilled water before being stained with hematoxylin solution at room temperature for 5 min. Finally, a single wash with distilled water was performed prior to observation of cell morphology under an inverted fluorescence microscope.

2.7. Statistical analysis

All experimental data shown represent or were calculated from at least 3 independent experiments. Regression analysis and response surface optimization were performed using Design Expert 8.0.6.1 software. Data were analyzed and significance analyzed using GraphPad Prism 8 software. p < 0.05 indicates significant differences.

3. Results and discussion

3.1. Single-factor experiment

3.1.1. Effect of enzyme composition and ratio

The plant cell walls are natural barriers to plant bioactive compounds [35]. Nonetheless, specific enzymes possess the capability to degrade the cell wall, facilitating the release of these bioactive compounds from the cytoplasm through highly specific and efficient catalytic mechanisms [36]. In this study, we systematically screened different types and ratios of enzymes employed during the extraction process to optimize the release of bioactive substances. As illustrated in Fig. S1 A, the pectinase-treated group showed a higher CEV 15.37 compared to the unenzymed treated group and was not significantly different from the cellulase-treated group. Given that the synergistic action of pectinase and cellulase enhances the disruption of the plant cell wall, a combination of these two enzymes was selected for further analysis. The optimal amounts of pectinase and cellulase were determined individually, as depicted in Fig. S1 C and D, to inform the subsequent screening of their combined ratios. This approach ultimately yielded the highest CEV of 17.51 (Fig. 2B; Pec 360 U/mg binding Cel 3000 U/mg). Notably, cellulase and pectinase come from environmentally friendly sources and can be made from fermentation of plant stalks such as corn and rice straw or agricultural wastes such as wheat bran and orange peels [37]. Additionally, these enzymes can be obtained from tobacco wastewater and coconut shells [38]. This method of production aligns with the objectives of green and sustainable development.

3.1.2. Effects of pH and temperature

The rate of enzymatic reactions serves as a crucial indicator of enzyme activity and is regulated by various factors, including enzyme concentration, pH value, and temperature. Previous research has demonstrated that the activities of pectinase and cellulase remain stable within a pH range of 4.0 to 6.0 and at temperatures between 30 ℃ and 50 ℃ [39]. Consequently, this study aimed to optimize pH and temperature conditions for extraction. As depicted in Fig. 2C, the CEV achieves its maximum value at a pH of 4.5. This phenomenon can attribute to the observation that enzyme activity increases with rising pH levels, thereby facilitating more effective binding of the enzyme to the substrate under optimal pH conditions. However, when the pH value is further elevated to 6.0, a gradual decline in CEV is observed, suggesting a concomitant decrease in extraction efficiency. This observation may be primarily attributed to the fact that when the pH value surpasses the optimal range for enzymatic hydrolysis, there is a reduction in enzyme activity [40]. Additionally, the degree of dissociation of essential amino acid residues at the active site of the enzyme is also affected, thereby interfering with the normal substrate binding process [41]. Consequently, based on the results obtained from the single-factor experiments, a pH of 4.5 was selected as the optimal extraction pH value.

Additionally, Fig. 2D illustrated the impact of extraction temperature on the efficiency of the enzymatic reaction. The data indicate that enzyme activity exhibits a positive correlation with temperature within the range of 30 ℃ to 40 ℃, reaching its maximum value at 40 ℃. This phenomenon can be attributed to the enhanced catalytic activity of the enzyme as a result of the increased temperature. However, when the temperature increases to the range of 40 ℃ to 50 ℃, enzyme activity begins to decline. This decline is primarily attributed to the denaturation of the protein enzyme at elevated temperatures, resulting in a reduction of its catalytic efficiency. Beyond a critical temperature threshold, thermal denaturation becomes predominant, causing a further decrease in enzyme activity with continued temperature elevation [42,43]. Considering this, 40 ℃ was identified as the optimal temperature for single-factor enzyme extraction.

3.1.3. Effects of ultrasonic time and power

As illustrated in Fig. 2E, during the ultrasonic treatment period of 20 min to 50 min, the concentration of extracted volume (CEV) increased with prolonged ultrasonic exposure, reaching a peak value of 19.22 at 50 min. This enhancement is attributed to the ultrasonic waves, which leverages the cavitation effect generated by ultrasonic wave. Specifically, cavitation bubbles induced by the energy of the sound waves rapidly expand and ultimately collapse when the internal negative pressure surpasses the surface tension of the medium. This collapse results in the formation of strong shear forces and turbulence within in the cavitation region, accompanied by localized surges in temperature and pressure. This phenomenon can significantly disrupt cellular structures and facilitate the dissolution of cellular components into the solvent, thereby accelerating the release of active ingredients [44,45]. However, CEV decreased within the 50 min to 60 min range, potentially due to the irreversible damage to certain chemical bonds in the medicinal materials caused by prolonged ultrasonic treatment, thereby reducing the component content. Fig. 2F further demonstrated that CEV was dependent on ultrasonic power. As ultrasonic power increased, CEV exhibited a corresponding upward trend, a phenomenon attributed to the cavitation effect of ultrasound. In summary, this study identified that an ultrasonic treatment duration of 50 min and a power setting of 700 W constituted the optimal single-factor extraction conditions.

3.1.4. Effects of solid–liquid ratio

As illustrated in Fig. 2G, the impact of varying solid–liquid ratios on CEV exhibited significant differences. The CEV attained its maximum value at a solid–liquid ratio of 1:25 g/mL. This phenomenon can be attributed to the enhanced interaction between the extractant and CR with increasing solid–liquid ratios, which facilitates the diffusion rate of active components into the solvent, thereby augmenting extraction efficiency [46]. Concurrently, the quantity of solute dissolved in the solvent increased proportionally with the amount of solvent used. However, when the solid–liquid ratio was further increased to 1:30 g/mL, a diminishing trend in CEV was observed. This phenomenon may be attributed to the near-complete extraction of alkaloids, whereby additional solvent results in a decreased alkaloid concentration per unit mass, thereby leading to inefficient use of solvent and energy. Consequently, considering both the extraction efficiency and cost-effectiveness, a solid–liquid ratio of 1:25 g/mL was identified as the optimal condition for this study.

3.1.5. Effects of trehalose lipid concentration

The biosurfactant trehalose lipid is an amphiphilic molecule characterized by a hydrophilic head and a hydrophobic tail. The critical micelle concentration of trehalose lipid has been reported to be 250 mg/L [47]. Upon reaching or surpassing this concentration, trehalose lipid molecules self-assemble into micellar structures, wherein the hydrophilic heads form the exterior surface and the hydrophobic tails constitute the core. This micellization markedly enhances the solubility of sparingly soluble or insoluble compounds in aqueous environments [48]. Fig. 2H illustrates the variation in CEV across different concentrations of trehalose lipid. The results indicated that within the concentration range of 1 to 6 mg/mL of trehalose lipid, CEV proportionally with the concentration, reaching a maximum value of 15.78 at 6 mg/mL. However, further increases in concentration led to a scenario where some surface-active molecules may not effectively interact with the aqueous phase, thereby diminishing their functional efficacy and potentially leading to resource wastage. It is noteworthy that while the CEV values at trehalose lipid concentrations of 4 mg/mL and 6 mg/mL did not exhibit statistically significant differences, the 6 mg/mL treatment group achieved a marginally higher CEV. Therefore, selecting 6 mg/mL as the optimal single-factor extraction condition for trehalose lipid was deemed reasonable.

3.2. RSM optimization

3.2.1. Fitting the response surface models

The response surface optimization was conducted utilizing Design Expert software based on the Box-Behnken design, with CEV as the evaluation index. Following a single-factor analysis, the four factors enumerated in Table 1 were selected as independent variables for subsequent optimization. The results from the 27 experiments were then analyzed using multiple regression, as presented in Table 2. The extraction rates achieved were 8.06 ∼ 9.96 mg/g for JAT, 9.15 ∼ 11.99 mg/g for EPI, 11.99 ∼ 16.09 mg/g for COP, and 15.01 ∼ 18.36 mg/g for PAL. The berberine extraction rate ranged from 56.35 to 67.14 mg/g, while the CEV values varied between 18.71 and 22.83. The association between the dependent variable and the independent variables can be effectively characterized by a second-order polynomial equation as follows:

Table 1.

BBD design and the values of CEV.

Run	X1	X2	X3	X4	JAT (mg/g)	EPI (mg/g)	COP (mg/g)	PAL (mg/g)	BER (mg/g)	CEV
1	4.0(−1)	35(−1)	25(0)	50(0)	9.75	11.99	11.99	16.75	62.32	20.84
2	5.0(1)	35(−1)	25(0)	50(0)	9.16	11.21	13.35	16.79	62.03	20.86
3	4.0(−1)	45(1)	25(0)	50(0)	9.19	11.44	15.37	17.43	61.13	21.35
4	5.0(1)	45(1)	25(0)	50(0)	8.06	9.85	13.22	16.47	58.96	19.73
5	4.5(0)	40(0)	20(−1)	40(−1)	8.47	10.98	14.76	17.50	61.95	21.08
6	4.5(0)	40(0)	30(1)	40(−1)	9.47	11.18	16.09	17.74	63.29	22.00
7	4.5(0)	40(0)	20(−1)	60(1)	8.23	10.13	13.92	16.81	59.72	20.18
8	4.5(0)	40(0)	30(1)	60(1)	9.23	11.23	14.95	17.76	62.62	21.53
9	4.0(−1)	40(0)	25(0)	40(−1)	9.32	11.58	15.26	17.54	63.96	21.90
10	5.0(1)	40(0)	25(0)	40(−1)	9.28	11.35	14.17	17.28	62.12	21.21
11	4.0(−1)	40(0)	25(0)	60(1)	9.59	11.98	14.18	17.33	62.63	21.49
12	5.0(1)	40(0)	25(0)	60(1)	8.63	10.45	12.73	16.32	58.92	19.83
13	4.5(0)	35(−1)	20(−1)	50(0)	9.25	10.66	13.73	16.82	61.34	20.79
14	4.5(0)	45(1)	20(−1)	50(0)	8.46	10.53	13.13	16.38	60.42	20.17
15	4.5(0)	35(−1)	30(1)	50(0)	9.05	11.59	14.66	17.27	63.45	21.54
16	4.5(0)	45(1)	30(1)	50(0)	8.99	11.38	14.75	17.02	61.99	21.22
17	4.0(−1)	40(0)	20(−1)	50(0)	9.02	11.47	14.98	17.43	62.61	21.47
18	5.0(1)	40(0)	20(−1)	50(0)	8.99	11.24	14.15	17.21	61.91	21.06
19	4.0(−1)	40(0)	30(1)	50(0)	9.21	11.77	15.48	17.99	64.60	22.12
20	5.0(1)	40(0)	30(1)	50(0)	9.63	11.49	14.17	18.25	62.04	21.45
21	4.5(0)	35(−1)	25(0)	40(−1)	9.09	11.34	14.27	17.33	62.12	21.19
22	4.5(0)	45(1)	25(0)	40(−1)	9.09	10.68	13.85	16.74	61.48	20.79
23	4.5(0)	35(−1)	25(0)	60(1)	8.88	10.93	13.76	16.89	61.56	20.78
24	4.5(0)	45(1)	25(0)	60(1)	8.15	9.15	12.09	15.01	56.35	18.71
25	4.5(0)	40(0)	25(0)	50(0)	9.88	11.72	15.59	18.36	67.14	22.83
26	4.5(0)	40(0)	25(0)	50(0)	9.67	11.65	15.29	17.96	65.75	22.38
27	4.5(0)	40(0)	25(0)	50(0)	9.96	11.88	15.46	18.21	67.07	22.81

Table 2.

ANOVA for response surface model.

Source	Sum of squares	df	Mean square	F value	p-value	Significant a
Model	20.97	14	1.50	14.95	<0.0001	***
X1	2.11	1	2.11	21.04	0.0006	***
X2	1.35	1	1.35	13.50	0.0032	**
X3	2.18	1	2.18	21.71	0.0006	***
X4	2.66	1	2.66	26.54	0.0002	***
X1X2	0.67	1	0.67	6.71	0.0236	*
X1X3	0.017	1	0.017	0.17	0.6886	−
X1X4	0.24	1	0.24	2.35	0.1514	−
X2X3	0.022	1	0.022	0.22	0.6441	−
X2X4	0.70	1	0.70	6.96	0.0217	*
X3X4	0.046	1	0.046	0.46	0.5099	−
X12	2.21	1	2.21	22.03	0.0005	***
X22	9.16	1	9.16	91.44	<0.0001	***
X32	1.23	1	1.23	12.30	0.0043	**
X42	5.03	1	5.03	50.16	<0.0001	***
Residual	1.20	12	0.10
Lack of fit	1.07	10	0.11	1.66	0.4337	−
Pure error	0.13	2	0.065
Cor total	22.17	26
R2	0.9458
Adjusted R2	0.8825
Predicted R2	0.7080
C.V.%	1.50

Note: a ***, p < 0.001; **, p < 0.01; *, p < 0.05; −, p > 0.05.

Y = 22.67–0.42X₁ − 0.34X₂ + 0.43X₃ − 0.47X₄ − 0.41X₁X₂ − 0.065X₁X₃ − 0.24X₁X₄ + 0.075X₂X₃ − 0.42X₂X₄ + 0.11X₃X₄ − 0.64X₁² − 1.31X₂² − 0.48X₃² − 0.97X₄².

Additionally, the reliability of the second-order polynomial model was confirmed by an ANOVA test, as depicted in Table 2, the model exhibited an R² value of 0.9458, an adjusted R² value of 0.8825, and a total correlation value of 22.17, all of which indicate a robust correspondence between the observed and predicted values. Finally, the normal probability plot of residuals, the residuals vs. predicted values plot, and the predicted vs. actual values plot were presented in Fig. S2 (A-C), illustrating the distribution and fit of the model.

3.2.2. Optimization of UET process

To visualize the interaction among the four variables affecting the CEV response, three-dimensional response surfaces (Fig. 3 A-F) and contour maps (Fig. 3 a-f) were generated based on the quadratic equation. These visualizations were employed to examine the interactions between experimental factors and their impact on CEV [49]. The data unequivocally demonstrate a significant interaction between X₁ and X₂, as well as between X₄ and X₂. In addition, Fig. S2 (D-I) could also prove the interaction between various factors. This indicated that the interaction between X₁ and X₂ exerts a substantial influence on CEV, potentially linked to enzyme activity [50]. Similarly, the interaction between X₄ and X₂ markedly affects CEV, underscoring the importance of ultrasound as a critical model variable in the extraction process. This finding was in line with the outcomes of the model fitting ANOVA as presented in Table 2.

Fig. 3.

The 3D surface plots (A-F) and contour plots (a-f) showing interactions of any two variables on CEV.

According to the BBD, the optimal conditions for UET extraction were determined to be: X₁ = 4.3, X₂ = 40 ℃, X₃ = 1:27 g/mL, X₄ = 48 min, and the predicted CEV = 22.85. As shown in Table 3, the average CEV obtained under these optimal conditions was 22.60, closely aligning with the predicted value, and the RSD was less than 1.16 %. This demonstrates the reliability of the established model.

Table 3.

Validation results of the best extraction conditions.

Extraction yield (mg/g)					CEV	RSD (%)	CEV mean value	CEV predicted value
JAT	EPI	COP	PAL	BER
10.10	11.72	15.16	18.36	67.15	22.78	1.16	22.60	22.85
9.81	11.46	14.92	17.96	65.76	22.30
9.96	11.63	15.26	18.21	67.08	22.72

3.3. Comparison of different extraction methods

3.3.1. Comparison of alkaloids yield and CEV

In order to evaluate the effect of UET extraction, four extraction methods were evaluated in combination with alkaloids yield and CEV. As demonstrated in Table 4 and Fig. 4A, compared with other single extraction methods, UET obtained the best extraction rates of five alkaloids 10.1, 11.72, 15.16, 18.36, 67.15 mg/g and the highest CEV 22.78. The study revealed that ultrasonic vibration cavitation plays a pivotal role [16]. Additionally, the addition of enzymes can destroy the plant cell wall, thereby enhancing the release of intracellular bioactive compounds. Concurrently, the introduction of biosurfactants can decrease the surface tension of the solution, and enhancing the solvent's wetting and permeability properties with respect to the material. The micelles formed by surfactants in aqueous solutions can alter the solubility of these components through surface adsorption or the formation of molecular aggregates, thereby facilitating solubilization and improving the extraction efficiency of active ingredients [51]. In general, RE is a prevalent technique for the extraction of active constituents in traditional Chinese medicine. However, when compared to RE method, UET has demonstrated several advantages. Specifically, UET not only enhances the extraction efficiency of five alkaloids and achieves a higher CEV, but also eliminates the need for organic solvents, instead utilizing biodegradable biosurfactants that microorganisms can decompose into carbon dioxide and water [52]. This is consistent with the concept of sustainable and green development. Consequently, UET extraction presents a promising new technology for the green and efficient extraction of active components from natural plants.

Table 4.

Comparison results of different extraction methods.

Extraction method	Yield (mg/g)					CEV	Extraction time (h)	Energy consumption (kWh)	CO2 emission (kg)
	JAT	EPI	COP	PAL	BER
UT	6.83	9.45	12.12	14.67	52.37	18.82	1.33	1.08	0.86
ET	6.76	9.51	11.79	14.65	52.10	18.69	1.33	1.33	1.06
UET	10.10	11.72	15.16	18.36	67.15	22.78	1.33	1.08	0.86
RE	9.45	6.44	9.60	9.21	38.62	13.65	2.00	2.00	1.60

Fig. 4.

Evaluation of different extraction methods based on (A) the yields of extraction and CEV as well as (B) Energy consumption (kWh), Carbon emission (kg) and total extraction time (h).

3.3.2. Energy saving and carbon reduction

In alignment with the objectives of the United Nations Sustainable Development Agenda, the study investigated the impact of four extraction methods on electricity consumption and CO₂ emissions. As illustrated in Fig. 4B, the UET extraction method demonstrated a lower power consumption (1.08 kWh) and significantly reduced CO₂ emissions (0.865 kg) compared to the RE treatment group. This reduction is attributed to the UET method's integration of ultrasonic, enzyme, and trehalose lipid actions on CR samples, which facilitated the efficient extraction of target components within a relatively short duration (1.33 h). However, under identical conditions, the extraction rate of target components in UT treatment group was found to be significantly lower than that in the UET treatment group. In conclusion, a comparative analysis of all extraction methods indicates that UET is an efficient extraction technique, characterized by higher yield, relatively low power consumption, reduced CO₂ emissions, and shorter extraction times.

3.4. SEM analysis

The impact of various extraction methods on the yield of bioactive components of CR were investigated using SEM. The morphological changes on the surface of CR powder subjected to various extraction methods were observed in this study (Fig. 5A). The surface morphology of the original CR powder was observed to be smooth and intact. In comparison to the untreated sample, the CR powder subjected to RE treatment (Fig. 5B) exhibited a significant increase in surface folds. Fig. 5C illustrates the morphological characteristics of the CR powder treated solely with trehalose lipid, indicating that trehalose lipid alone does not alter the surface morphology of the CR powder. Consequently, the effects of UT, ET and UET treatment groups on CR powder were investigated in this study. Fig. 5D illustrates that the CR powder subjected to UT exhibited significant mechanical damage, accompanied by a noticeable presence of tissue debris. This phenomenon is directly attributable to the vibrational cavitation induced by ultrasonic waves. Conversely, Fig. 5E demonstrates that the CR powder treated with ET displayed varying degrees of crimping. The observed effect is likely a result of enzymatic degradation of the plant cell wall, leading to its rupture and deformation, thereby facilitating the increased release of active components. Obviously, the UET treatment group depicted in Fig. 5F exhibited the most pronounced surface damage and a greater accumulation of tissue debris, indicating that the synergistic interaction between ultrasound and enzyme significantly enhances the degradation of the plant cell wall. This interaction concurrently increases the contact area between the enzyme and substrate, thereby improving the extraction efficiency of target components and CEV, and reducing the extraction time to a certain extent. These findings are consistent with the experimental results presented in Table 4. The above results indicate that UET is indeed an effective extraction method for CR alkaloids.

Fig. 5.

5000× SEM of CR powder (A) and different extracts by (B) RE, (C) Trehalose lipid, (D) UT (E) ET and (F) UET.

3.5. DLS analysis

Trehalose lipid is capable of forming micelles in aqueous solutions when the critical micelle concentration (CMC) is exceeded. The size range of these micelles significantly impacts their properties and potential applications [53]. Firstly, a reduction in particle size leads to an increase in the specific surface area, thereby enhancing solubilization capabilities [54]. Secondly, the reduced particle size facilitates the formation of a stable colloidal system in solution. The uniformity and stability of this colloidal system can be assessed using the polydispersity index (PDI). According to the international unified standard for the normal range of PDI, generally speaking, the PDI value between 0.05 and 0.7 belongs to the normal range [55]. Within this range, a smaller PDI indicates a more uniform distribution of particles [56]. Therefore, dynamic light scattering was employed to observe and compare the particle size changes of CR extracts before and after the addition of trehalose lipid. As shown in Fig. 6(A and B) and detailed in Table S4, the particle size distribution of CR extracts without trehalose lipid was broad, with an average particle size of 610.8 nm and a PDI of 0.563. This indicated that the extraction solution of CR without trehalose lipid was unstable and the system dispersion was not uniform. Conversely, upon the incorporation of trehalose lipid to form micelles, the average particle size of the CR extraction was measured at 218.5 nm, with PDI of 0.208. This suggests that the solution exhibited a consistent particle size and distribution, leading to improved stability.

Fig. 6.

DLS particle size analysis of extracts before (A) and after (B) treatment by trehalose lipid. 10.0 k× TEM images of extracts before (C) and after (D) treatment by trehalose lipid.

3.6. TEM analysis

To further substantiate that trehalose lipid aqueous solution can form micelles and thereby increase the solubility of active components in CR, the internal structure of UET extraction of CR was observed by TEM in this experiment, and the formation of trehalose lipid micelles was verified. As shown in Fig. 6(C and D), the extracts of CR without t trehalose lipid exhibited an irregular and chaotic distribution in both size and shape. However, upon the addition of trehalose lipid, the solution demonstrated a circular structure with regular shape and uniform distribution. This observation indicates that the incorporation of trehalose lipid facilitates a more homogeneous dispersion of the internal components within the aqueous extract, thereby confirming the formation of a micellar system. These findings are in line with the results derived from DLS analysis. Compared to chemical surfactants, trehalose lipid exhibits several advantages, including low toxicity, high stability, resistance to acids and salts, biodegradability, and environmental friendliness. Its aqueous solution can form micelles, thereby enhancing increase the solubility of active plant ingredients and serving as a substitute for organic solvents in extraction processes, thus promoting environmentally sustainable extraction methods.

3.7. In vitro anti-inflammatory activities

3.7.1. Cytotoxicity of CR and RE extracts

Cytotoxicity testing is a crucial parameter for assessing the biosafety of extracts. Consequently, in vitro cytotoxicity assays of CR and RE extracts were performed using RAW264.7 cells [57]. Fig. 7(A and a) presents the OD values of RAW264.7 cells subjected to varying concentrations of UET and RE extracts. The data indicate that treatment with 500 μg/mL of both UET and RE extracts led to a notable decrease in OD values when compared to the control group. In contrast, the OD values for treatment groups with 62.5 to 250 μg/mL of UET and RE extracts did not show significant differences from the control group, maintaining a range between 1.0 and 1.2, which is consistent with the expected range. Fig. 7B integrated the experimental data and confirmed that UET extraction and RE extraction have no cytotoxicity at the concentration of 62.5 ∼ 250 μg/mL, and the cell survival rate is above 90 %. These concentrations are therefore suitable for subsequent experiments.

Fig. 7.

Heat map of the CCK-8 results of UET (A) and RE (a) extracts, where numbers represent OD values. (B) Cytotoxic analysis of UET and RE extracts on RAW264.7 cells. The NO levels of RAW264.7 cells after treatment with UET, RE extracts (C) and different monomers (c). Intracellular ROS production after treatment of UET, RE extracts (D) and different monomers (d). The data represent the mean ± S.D. (n = 3). Different lowercase letters indicate significant differences (p < 0.05) in multiple range analysis among the groups.

3.7.2. NO production measurement

NO serves as a key of inflammation. In this study, LPS-induced RAW264.7 macrophages were used as a model to determine the effects of UET and RE extracts on the production of RAW264.7 cells [58]. The standard curve of NO content in the test was Y = 0.005573X + 0.05009 (R² = 0.9993). The concentration of NO in the supernatant was then calculated based on this standard curve. Results depicted in Fig. 7C, compared with the blank group, the LPS treatment group exhibited a significant increase in NO production in RAW264.7 cells (p < 0.01), and DEX, as a positive drug, reduced the production of NO to some extent (p < 0.01). Furthermore, UET and RE extracts demonstrated a dose-dependent reduction in NO production, with UET extract exhibiting a significantly greater efficacy in reducing NO levels than RE extract at equivalent concentrations (p < 0.01). Fig. 7c illustrates the effects of five alkaloid monomers on NO production in RAW264.7 cells. The results indicate that berberine exhibited the significant inhibitory effect on NO production, suggesting that berberine may be the primary component responsible for the anti-inflammatory effects. Furthermore, the treatment group receiving the mixture of the five alkaloids monomers demonstrated a substantial reduction in NO production, surpassing the efficacy of individual monomer treatments (p < 0.01). This mixture achieved comparable results to the high-dose UET extract treatment group, indicating that the five alkaloids extracted in this experiment constitute an effective component group with anti-inflammatory properties.

3.7.3. ROS production measurement

Inflammation represents a protective response in multicellular organisms aimed at localizing and eliminating harmful irritants while facilitating the restoration of damaged tissues. Recent evidence has accumulated to indicate that ROS are increasingly recognized as playing a pivotal role in the onset, advancement, and resolution of inflammatory reactions [59]. Activated macrophages are capable of synthesizing ROS, thereby contributing to inflammation [60]. In this experiment, the DCFH-DA method was employed to investigate the impact of UET extract derived from CR on the levels of ROS in RAW264.7 cells induced by LPS. As shown in Fig. 7D, ROS levels were significantly elevated following LPS induction compared to the control group (p < 0.01). Conversely, treatment with UET extract resulted in a dose-dependent reduction in ROS levels with statistically significant differences observed when compared to LPS treatment group (p < 0.05 or p < 0.01). Furthermore, the ability of UET extraction to reduce ROS level was markedly superior to that of RE extraction at the same concentration (p < 0.01). Additionally, Fig. 7d demonstrates that the monomer mixture treatment group exhibited greater ROS inhibition and enhanced anti-inflammatory effects compared to the monomer treatment group. Moreover, high dose UET extraction and monomer mixture can inhibit ROS level to DEX group level.

3.7.4. Intracellular Ca²⁺ level analysis

Intracellular Ca²⁺ is recognized as a critical regulatory element within cells, and fluctuations in its concentration are closely associated with the initiation of inflammatory responses [61]. Upon cellular stimulation, there is a transient increase in intracellular Ca²⁺ concentration, which subsequently enhances the secretion of inflammatory mediators and promotes cellular activation [[62], [63], [64]]. Fluo-3/AM, a fluorescent calcium chelating agent with high affinity for Ca²⁺, can permeate the cell membrane, specifically bind to intracellular Ca²⁺, and emit strong fluorescence, thereby facilitating the monitoring of intracellular Ca²⁺ dynamics [65]. Accordingly, the effect of UET extraction on Ca²⁺ level in RAW264.7 cells was detected by inverted fluorescence microscopy in this study. Fig. 8A illustrates the intracellular Ca²⁺ fluorescence of RAW264.7 cells across various treatment groups. The fluorescence intensity was quantified using Image J software, as shown in Fig. 8(B and b). Relative to the control group, LPS treatment markedly elevated fluorescence intensity and Ca²⁺ levels (p < 0.01). Conversely, the UET extraction treatment group demonstrated a dose-dependent reduction in intracellular Ca²⁺ levels, with a statistically significant difference observed compared to the LPS group (p < 0.05 or p < 0.01). In addition, the high-dose UET extraction treatment group and the monomer mixture treatment group showed better effects than the DEX group in reducing intracellular Ca²⁺ levels.

Fig. 8.

(A) Intracellular Ca²⁺ levels analysis of different treatment groups. Scale bar, 50 μm. (B-b) Measurement of Ca²⁺ fluorescence intensity quantified by Image J. The data represent the mean ± S.D. (n = 3). Different lowercase letters indicate significant differences (p < 0.05) in multiple range analysis among the groups.

3.7.5. Determination of inflammatory factors and mRNA expression

Macrophages, as pivotal cells mediating the production of inflammatory factors, play a crucial role in the inflammatory response. Upon stimulation by LPS, macrophages exhibit enhanced activity, resulting in the production and secretion of a significant quantity of inflammatory factors [66]. Among these, IL-6, IL-1β and TNF-α are the most representative cytokines associated with the inflammatory response [67,68]. Therefore, the anti-inflammatory effect of UET extract was assessed by quantifying the levels of IL-6, IL-1β and TNF-α, as well as the expression of their corresponding mRNAs, before and after the administration of LPS-induced RAW264.7 cells. As shown in Fig. 9 (A-a and B-b and C-c), LPS treatment significantly elevated the pro-inflammatory cytokines IL-6, IL-1β and TNF-α contents compared to the control group (p < 0.01). Conversely, DEX administration was observed to inhibit the production of IL-6, IL-1β and TNF-α to a certain extent and down-regulate the expression of their corresponding mRNAs (Fig. 9 D-d and E-e and F-f). Additionally, UET extract demonstrated a dose-dependent suppression of secretion of pro-inflammatory cytokines IL-6, IL-1β, and TNF-α, along with a down- regulation of the expression of their corresponding mRNAs. Furthermore, in comparison to the monomer treatment group, the monomer mixture exhibited a more pronounced inhibitory effect on the pro-inflammatory cytokines. The corresponding mRNA expression levels mirrored these findings, showing no significant difference when compared to the high-dose UET extraction treatment group. Notably, berberine demonstrated exceptional anti-inflammatory activity, likely attributable to its extensive pharmacological properties[69,70]. Compared with the traditional RE extraction treatment group, the UET extraction treatment group, administered at the same concentration range (62.5 ∼ 250 μg/mL), demonstrated a more pronounced inhibition. This indicates that UET extraction possesses superior anti-inflammatory activity, highlighting its potential therapeutic advantage.

Fig. 9.

The levels of LPS-induced inflammatory cytokines (A-a) IL-6, (B-b) IL-1β, (C-c) TNF-α and the mRNA expression levels of LPS-induced inflammatory cytokines (D-d) IL-6, (E-e) IL-1β, (F-f) TNF-α in RAW264.7 cells were detected. The data represent the mean ± S.D. (n = 3). Different lowercase letters indicate significant differences (p < 0.05) in multiple range analysis among the groups.

3.7.6. Morphological observation of macrophage RAW264.7

To further assess the anti-inflammatory activity of CR extraction, the effects of UET and RE extraction on cell morphology were observed using inverted fluorescence microscopy. As depicted in Fig. 10, the untreated cells exhibited a normal round shape with well-defined boundaries. However, after 24 h of LPS induction, the cells appeared pseudopodia and adopted a flattened, pancake-like morphology, indicating a compromised state [71]. Following DEX treatment, a reduction in cellular pseudopodia was observed. However, treatment with varying concentrations (62.5 ∼ 250 μg/mL) of UET extract, as well as with the corresponding monomer and monomer mixture, resulted in differential degrees of cellular recovery. Notably, the UET treatment group demonstrated a near-complete restoration to normal cell morphology, although pseudopodia were still present at lower doses. In the monomer treatment group, a modest recovery of cells was observed, comparable to that seen in the DEX group. Among the treatments, berberine exhibited the most pronounced effect on restoring cell morphology, which can be attributed to its potent anti-inflammatory properties. Furthermore, the monomer mixture significantly enhanced cell morphology restoration, surpassing the effects observed in the DEX group. Notably, the RE treatment group did not demonstrate a substantial restoration of cell morphology, with only high-dose RE extraction showing a limited capacity to restore cell morphology.

Fig. 10.

Morphological changes of RAW264.7 cells were imaged by inverted fluorescence microscope (original magnification × 400). Scale bar, 20 μm. (n = 3). The High, Mid and Low doses of UET and RE were 62.5, 125 and 250 μg/mL respectively.

4. Conclusion

Based on the purpose of green and multi-component extraction of CR alkaloids, the UET method was innovatively proposed in this experiment, and the primary extraction conditions optimized through a BBD informed by single-factor experimental results. Compared with the traditional RE method, the extraction yields of JAT, EPI, COP, PAL and BER obtained by UET reached 10.1, 11.72, 15.16, 18.36 and 67.15 mg/g, respectively. Meanwhile, the energy consumption, CO₂ emission and extraction time of UET extraction were reduced by 46 %, 46.25 % and 33.5 %, respectively. Furthermore, SEM analysis revealed that the concurrent application of ultrasound and enzymes exerted a synergistic effect on the disruption of CR powder, facilitating a greater dissolution of the active components. DLS and TEM results confirmed the formation of micelles due to the unique amphiphilic molecular structure of trehalose lipid, which subsequently enhanced the solubility of the active compound and improved extraction efficiency. Furthermore, UET extract significantly inhibited the production of NO, ROS and Ca²⁺, the secretion of inflammatory factors IL-6, IL-1β and TNF-α and the expression of corresponding mRNA in RAW 264.7 cells induced by lipopolysaccharide. This indicates a potent anti-inflammatory activity. In conclusion, the novel UET method demonstrates significant potential to supplant traditional CR active ingredient extraction techniques. Moreover, the UET method presents an environmentally sustainable approach, marked by diminished energy consumption and CO₂ emissions, alongside the complete elimination of organic solvent use. Consequently, this innovative and eco-friendly extraction methodology serves as a benchmark for the efficient extraction of alkaloid components in natural medicines and has the potential to revolutionize the extraction processes of various plant constituents.

CRediT authorship contribution statement

Huiwen Wang: Writing – original draft, Data curation. Haixin Peng: Supervision. Wenwen Ding: Validation. Zhiyun Zhang: Supervision. Yadan Zheng: Formal analysis. Sikai Chen: Formal analysis. Xueying Chen: Resources. Qianwei Qu: Writing – review & editing. Yanyan Liu: Writing – review & editing. Yanhua Li: Funding acquisition, Conceptualization.

Funding

This work was supported by National Key Research and Development Program of China (Grant No. 2023YFD1800903-2), China Agriculture Research System of MOF and MARA (Grant No. CARS-35), and National Nature Science Foundation of China (Grant No. 32072908).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article: