Ultrasonic-assisted DES extraction optimization, characterization, antioxidant and in silico study of polysaccharides from Angelica dahurica Radix

3.3.1 Effect of liquid/solid ratio on ADRP yield

As illustrated in Fig. S2 (supporting information), the extraction rate of ADRP peaked at a liquid–solid ratio of 10:1 mL/g, subsequently declining as the ratio increased. This decrease can be attributed to a reduction in polysaccharides concentration within the solution due to the higher solvent volume, leading to an increased pressure difference in polysaccharides molecular diffusion (Qian et al., 2020). Further escalation of the liquid–solid ratio results in polysaccharides loss, consequently lowering the extraction rate of polysaccharides (Guo et al., 2019). Thus, the optimal liquid–solid ratio of 10:1 mL/g was recommended.

3.3.2 Effect of ultrasonic temperature on ADRP yield

As depicted in Fig. S3 (supporting information), higher temperatures enhance mass transfer efficiency and accelerate extraction speed. The extraction rate of ADRP demonstrate an optimal temperature range. Within this range, higher temperatures increase the yield of polysaccharides extraction. Nevertheless, surpassing this temperature range decreases the polysaccharides yield, potentially caused by structural changes induced by elevated temperatures. This observation aligns with findings from previous studies on polysaccharides extraction (Huo et al., 2017). Consequently, the ideal extraction temperature for polysaccharides in this experiment was determined to be 60 °C.

3.3.3 Effect of extraction time on ADRP yield

The duration of extraction plays a pivotal role in determining the extraction rate, with longer extraction time positively impacting polysaccharides yield. Previous studies have highlighted the advantageous effects of extended extraction durations on polysaccharides production (Lin, 2014). As illustrated in Fig. S4 (supporting information), the polysaccharides extraction rate initially increased from 15 min to 25 min. However, surpassing the 25-min mark led to a decline in extraction rate. This decline could be attributed to potential hydrolysis of some polysaccharides under elevated temperatures and prolonged extraction times (Huo, 2020). Hence, the optimal extraction time was identified as 25 min.

3.3.4 Effect of ultrasonic power on ADRP yield

The extraction yield increased with the ultrasonic power augmentation in the range of 200–400 W, as illustrated in Fig. S5 (supporting information). This phenomenon can be attributed to the mechanical effect of ultra-high pressure, facilitating solvent penetration into the cell material, altering cell structure, and disrupting biological cell tissue. These changes enhance the transfer of the target extract to the solvent within the cell (Zhang et al., 2021). At 400 W, the extraction rate of polysaccharides peaked before declining with higher power settings. This decline is likely a result of increased stress from higher power levels and the continued mechanical effects of ultra-high pressure that further modify cellular structure, impeding the release of polysaccharides by the organism. This finding aligns with prior research indicating that ultrahigh pressure can cause macromolecular denaturation (Liu et al., 2023). Therefore, an ultrasonic power of 400 W emerges as the optimal choice.

3.4.1 Box-Behnken design

L9 (3³) orthogonal design was adopted to further screen the experimental factors. In this work, 3 factors and 3 levels were used for extraction, as shown in Table 2. Each experiment was repeated three times.

To optimize the extraction conditions, ultrasonic time (A), ultrasonic temperature (B), and ultrasonic power (C) were varied to achieve diverse response values of polysaccharides yield. The experimental outcomes are detailed in Table 3 by employing the Box-Behnken central composite design method in Design-Expert 12.

3.4.2 Fitting the response surface model

The Design-Expert 12 software was utilized to analyze the data and determined the optimal results across various parameters. The regression equation for polysaccharides yield was derived through multiple regression analysis, conforming to the quadratic polynomial below:

Y = 8.75＋0.0013 × A＋0.0825 × B + 0.1312 × C–0.0600 × AB–0.2525 × AC＋0.0500 × BC–0.2357 × A²–0.1433 × B²–0.1557 × C².

In the formula, Y is the predicted yield of ADRP, and A, B and C are the parameters of ultrasonic time, temperature and power, respectively.

The statistical significance of the data was determined through regression modeling, and the variance analysis of the response surface data is presented in Table 4.

The analysis in Table 4 reveals an impressive coefficient of determination, with R² = 0.9579, signifying the experimental model’s capability to predict within the range of control variables. Utilizing p-values as a tool to assess the coefficients’ significance, it was evident that smaller p-values correspond to greater coefficient significance. Notably, the primary term (C), quadratic terms (A², B², C²), and the interaction coefficient (AC) exhibited exceptional significance (P < 0.01), while the linear coefficient (B) was also significant (P < 0.05), other term coefficients proved nonsignificant (P > 0.05). Furthermore, the experiment’s low coefficient of variation (CV) underscores its high accuracy and reliability. By evaluating the F values for each factor, it becomes apparent that the order of influence on polysaccharides extraction rate is C > B > A, denoting ultrasonic power as the most influential, followed by ultrasonic temperature and ultrasonic time.

3.4.3 Validation of prediction model

The optimal extraction parameters included an extraction time of 21.409 min, extraction temperature of 59.903°C, extraction power of 434.912 W, and a predicted polysaccharides yield of 8.803 %. To standardize the test conditions, the independent variables were adjusted to an extraction time of 21 min, a liquid–solid ratio of 10:1 mL/g, extraction temperature of 60 °C, and extraction power of 435 W. Through three repetitions of this optimized process, the average polysaccharides extraction rate reached 8.713 %, displaying a deviation of less than 0.09 % from the predicted value. These findings affirm the model’s adequacy for the extraction of ADRP, establishing the success of the response surface optimization experiment.

3.4.4 3D response surface

Design Expert 12 software was utilized to create response surface diagrams and assess various influential factors along with their interactions. The 3D response surface diagram and two-dimensional contour map depict the interdependency of the two independent variables, showcasing the effects on response variables. Specifically, the 3D diagram illustrates how independent variables interact with response variables, while the contour map demonstrates the significance of these interactions. The circular shape in the contour map suggests negligible interaction between variables, whereas an elliptical contour indicates substantial interaction. Fig. 2 and Fig. S6 illustrates the impact of different factor interactions on the extraction rate of ADRP. In cases of interaction between independent variables, an elliptical contour is observed.

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Fig. 2

3D response surface diagram of the two independent variables interdependency: ultrasonic time and ultrasonic temperature (a), ultrasonic time and ultrasonic power (b) and ultrasonic temperature and ultrasonic power (c).

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The results in the Fig. 2 revealed that the yield of polysaccharides extraction rises with increasing ultrasonic time, while ultrasonic temperature follows a pattern of initial increase and subsequent decrease. For the approximate circular contour map of ultrasonic time and temperature, no significant interaction effect on the polysaccharides yield is observed, which is supported by the analysis of variance (AB) where P values above 0.05 confirm this finding. On the other hand, when the ultrasonic temperature (B) was set at 0, Fig. S6 demonstrates the influence of ultrasonic time (A) and power (C) on the extraction rate. The data in Fig. 2 indicates a trend where the polysaccharide extraction initially increases and then decreases with rising ultrasonic time and power. The elliptical contour diagram clearly illustrates a highly significant interaction between ultrasonic time and power, corroborated by a P value below 0.01 in the analysis of variance (AC). In summary, with a fixed ultrasound time, the impact of ultrasound power on the ADRP extraction rate surpasses that of ultrasound time, while the interrelationship between ultrasound power and ultrasound temperature shows minimal significance. Consequently, during ADRP extraction, the hierarchy of influence on ADRP yield is as follows: ultrasound power > ultrasound time > ultrasound temperature.

3.6.1 FT-IR spectroscopy Scan

In the FT-IR spectrum (Fig. S7, supporting information), the polysaccharides powder exhibited a range of 4000–400 cm⁻¹, with distinct absorption bands identified. The broad band observed at 3228 cm⁻¹ corresponds to the O-H stretching vibration, characteristic of polysaccharides, while the signal at 2925 cm⁻¹ indicates C-H stretching vibration (Li et al., 2019). Additionally, peaks at 1623 cm⁻¹ and 1349 cm⁻¹ are attributed to the carbonyl C = O and carbonyl C-O stretching vibrations in uronic acids (Seedevi et al., 2018). The absorption band within 1000–500 cm-¹ is primarily induced by the angular and bending vibrations of alcohols containing hydroxyl and benzene rings (Yi et al., 2022). Moreover, the peaks at 864 cm⁻¹ and 1078 cm⁻¹ signify the α-configuration and α-1,6-linkage (Ming et al., 2014), respectively. These findings establish that the ADRP is a heteropolysaccharide containing uronic acid (Lin et al., 2017).

3.6.2 Monosaccharide composition

After hydrolyzing the polysaccharides acid, the monosaccharide composition was analyzed by liquid chromatography (Fig. 3). The analysis disclosed that the ADRP is mainly composed of fucose, rhamnose, arabinose, galactose, and glucose, with molar ratios of 0.47:1.99:17.61:64.76:15.17.

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Fig. 3

Monosaccharide composition chromatogram: Reference sample profile (A) and sample profile (B). Notes: 1:fucose, 2: rhamnose, 3: arabinose, 4: galactose, 5: glucose.

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3.6.3 Molecular weight

According to the Mark-Houwink Equation, the molecular weights of each component were determined. The number-average molecular weight was 97.422 kDa, while the weight-average molecular weight was measured at 245.678 kDa. Fig. 4 illustrates the absolute molecular weight analysis chart. In the chart, the red line represents the multi-angle laser light scattering signal (LS) measured in volts (V), indicating a direct relationship between the intensity of scattered light and the molecular size and weight of the substance. The blue line shows the differential signal (RI) in Refractive Index Units (RIU), with the response value affected by changes in the effluent’s refractive index post-column, reflecting the type, concentration, and molecular weight of the substance. Lastly, the black line depicts the molecular weight calculated from these combined signals.

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Fig. 4

Polysaccharides molecular weight distribution chart.

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3.8.1 Target prediction

The study predicted interactions between five monosaccharides and 288 target sites: fucose targeted 240 sites, rhamnose 269, arabinose 243, galactose 272, and glucose 259. Additionally, the analysis identified 393 disease-related genes. Interestingly, 23 target sites were found to be shared between the monosaccharides and disease-related genes, as depicted in Fig. 6.

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Fig. 6

Intersection Target Venn Diagram of Monosaccharides and Oxidation-Related Diseases.

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3.8.2 Enrichment analysis

After the analysis, predictions were made for 157 GO entries linked to the 23 common targets. These entries comprised of BP category (108), CC category (16) and MF category (33). Fig.S8 (supporting information) provides a visual representation of the top ten entries in each category, ordered by gene count.

Predictive analysis indicates that ADRP primarily partake in biological pathways such as response to oxidative stress, cellular response to chemical stress, and reactive oxygen species metabolic processes in vivo. The functional localization mainly occurs in the ficolin-1-rich granule lumen, caveolae, plasma membrane rafts, secretory granule lumen, cytoplasmic vesicle lumen, vesicle lumen, membrane rafts, etc., exerting antioxidant, glutathione transferase, peroxidase, NADP binding, heme binding, oxidoreductase activities, acting on peroxide as an acceptor, transferase activities transferring alkyl or aryl (other than methyl) groups, tetrapyrrole binding, oxidoreductase activities acting on paired donors, with incorporation or reduction of molecular oxygen, and MAP kinase activity.

A total of 88 KEGG pathways were predicted for the 23 intersecting target genes. The top ten pathways by gene count include Pathways in cancer, Metabolic pathways, etc. For detailed results, please refer to Fig. S9 (supporting information). Predicting PPI for the 23 intersection targets resulted in 22 nodes, as shown in Fig. 7, where larger nodes indicate higher degree values.

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Fig. 7

The PPI analysis of the intersection targets.

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3.8.3 Molecular docking

Molecular docking of small molecules and proteins is a computational biology method used to study how small molecule compounds interact with proteins. In drug development, this method is widely used to design new drugs or optimize the structures of existing drugs. The AutoDock Vina (v1.1.2) software was utilized to conduct full-atom molecular docking analysis for the calculation of binding energies. Molecular docking analysis was conducted between the genes of the 22 nodes and four monosaccharides (excluding glucose), and the binding energy results are presented in Fig. 8. A trend towards red coloration corresponds to lower binding energy values, suggestive of increased binding stability.

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Fig. 8

The Molecular docking binding energy of monosaccharides and proteins.

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Three sets of molecules exhibiting lower binding energies were chosen for visualization. Please refer to Fig. 9 for visualization details.

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Fig. 9

Molecular docking visualization results: (A) Rha-GSR, (B) Ara-GSTA1 and (C) Rha-GSTA1.

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Glutathione reductase (GSR) plays a crucial role in cellular redox regulation by catalyzing the conversion of oxidized Glutathione (GSH) to its reduced form. This enzymatic process is essential for maintaining the redox balance within cells and ensuring the proper functioning of GSH. GSR serves as a key protein in the restoration and maintenance of GSH activity. GSH is ubiquitously present in the body as an antioxidant, serving as a vital non-protein protector against oxidative stress. It safeguards mitochondrial membranes from oxidative harm and contributes significantly to cytokine production and immune regulation (Lana et al., 2024). GSTA1, a member of the GST-α class, plays a critical role in neutralizing exogenous substances (including heavy metals, pesticides, organic solvents, etc.) that trigger the generation of free radicals in the body. It plays a vital role in cellular detoxification and the removal of diverse physiological compounds and xenobiotics (Grussy et al., 2023). Hence, the robust interaction among Rha-GSR, Ara-GSTA1, and Rha-GSTA1 could represent a pivotal mechanism by which ADRP exerts antioxidative effects within the body and safeguards human health.