Comparative analysis of biological activities and phytochemical constituents between Pegaeophyton scapiflorum and Solms-laubachia eurycarpa revealed by UPLC-Q-Orbitrap HRMS-based metabolomics

2.3.1. Sample preparation

Accurately, 0.5 g of the powdered underground part samples of both DHJ and KGCF were weighed and added to separate stoppered conical flasks. Subsequently, 10 mL of methanol was added to each flask, ensuring the powder was fully immersed. The contents were thoroughly mixed, the initial weight was recorded, and the flasks were sonicated for 30 min at a power of 240 W and a frequency of 40 Hz. After sonication, the samples were allowed to cool to room temperature. The flasks were reweighed, and any weight loss was replenished with methanol. The mixtures were rigorously mixed to achieve homogeneity. Finally, the supernatants were filtered through 0.22-μm microporous membranes, and the filtrates were collected as the prepared samples for subsequent analysis.

2.3.2. UPLC-Q-Orbitrap HRMS

Metabolite analysis was performed using a Vanquish UPLC system coupled to a Q Exactive Orbitrap high-resolution MS (Thermo Fisher Scientific, Waltham, USA). Instrumentation and data acquisition were performed by Xcalibur 4.1 software. The samples underwent analysis and separation on an Accucore TM C18 column (3 mm×100 mm, 2.6 μm). A binary mobile phase system consisting of 0.1% formic acid in water (A) and acetonitrile (B) was utilized for gradient elution. This gradient elution protocol significantly enhanced the separation efficiency of the analytes. The elution conditions were carried out according to the references [34], and some optimizations were made. The gradient elution conditions were as follows: 0-3 min, 93%-80% A; 3-6 min, 80%-77% A; 6-8 min, 77%-73% A; 8-10 min, 73%-61% A; 10-12 min, 61%-58% A; 12-14 min, 58%-48% A; 14-18 min, 48%-0% A; 18-22 min, 0%- 98% A; 22-23 min, 98%-93% A; 23-28 min, 93%-93% A). A flow rate of 0.3 mL/min was applied, the column was maintained at a temperature of 30°C, and the injection volume was set at 5 μL.

Mass spectrometry analysis was performed on an HMRS high-resolution mass spectrometer with the ionization conditions optimized to: 3.5 KV spray voltage in positive mode and 3.0 KV in negative mode, 320°C capillary temperature, 300°C heater temperature, 40 arb sheath gas, and 15 arb auxiliary gas flow rate. Full MS-dd MS2 (positive-negative switching) scanning was used, with a full scanning mode scanning range of m/z 100-1500 with a resolution of 35,000, and MS/MS with data-dependent scanning with a resolution of 17,500; the hybrid normalized collision energies were set to 20, 40, and 60 V.

2.3.3. Method validation

A representative quality control (QC) sample was prepared by pooling 100 μL of each methanol extract from all DHJ and KGCF samples, followed by thorough mixing. To maintain system balance and stability, QC samples were inserted for analysis after every three test samples, ensuring consistent and reliable analytical performance throughout the experiment.

2.3.4. Identification of compounds

Xcalibur software (Thermo Fisher Scientific) is used to collect and process raw data files for LC-MS and gas chromatography-mass spectrometry (GC-MS) analyses, and it provides a wide range of features for mass accuracy assessment, metabolite identification, and proteomics analysis.

These raw files were imported into Compound Discover 3.3 software (Thermo Scientific), and the files were cross-referenced with the mass spectrometry information from the online database mzCloud, These raw files were imported into Compound Discover 3.3 software (Thermo Scientific) and compared with mass spectral information in the online database mzCloud for initial screening (mzCloud Best Match ≥ 85 and Area(Max) ≥ 1 × 10⁵). The MS1 fragments were first compared using Xcalibur 4.1 software, with absolute ppm values in the range of 0 ∼ 10 as the initial identification criteria. Subsequently, we checked the secondary fragment ion information in databases such as PubChem, Massbank, Webbook, and Human Metabolome database based on retention time. The unknown metabolites were identified based on the agreement between MS1 and MS2 spectra.

2.4. Evaluation of biological activity

Experimental studies were conducted using in vitro cellular assays, using aqueous extracts of DHJ and KGCF. Thereafter, a comparative investigation was performed to assess and contrast the anti-inflammatory and antioxidant bioactivities of these two herbs.

2.4.1. Cell culture

Mouse monocyte RAW264.7 macrophage cells were procured from the Cell Bank of the Chinese Academy of Sciences. The RAW264.7 cells were inoculated into high-sugar Dulbecco’s Modified Eagle Medium (DMEM, sourced from Wuhan Servicebio) supplemented with 10% fetal bovine serum (FBS, Zhejiang Tianhang) and 1% penicillin/streptomycin solution (Cytiva). The culture dishes were then placed in a cell culture incubator (Model HCP-168, Qingdao Haier) and maintained at 37°C, with 5% CO₂ and 70-80% humidity. The cells were cultured until they reached a confluence of 80-90%, after which they were passaged at a ratio of 1:3 and cultured to the logarithmic growth phase for subsequent experiments.

2.4.2. Drug preparation

The underground section of DHJ and KGCF herbs were taken, crushed into coarse powder, added with 10 times the amount of pure water, subjected to heated reflux extraction for 2 h each time for two extractions, filtered. The filtrates were combined, concentrated, and dried in a freeze-dryer [12].

2.4.3. Cytotoxicity

RAW264.7 macrophages cultured to the logarithmic phase were inoculated into 96-well plates at a density of 1×10⁴ cells per well, with a volume of 100 μL per well. The plates were then incubated in an incubator at 37°C with 5% CO₂ for 24 h. After discarding the old medium, the plates were treated with gradient concentrations of aqueous extracts of DHJ and KGCF and subsequently returned to the incubator for an additional 24 h of incubation. The normal control group (NC), which received only medium intervention, was established, and each group was run in triplicate. Next, 10 μL of Cell Counting Kit-8 (CCK-8) solution (5 mg/mL) was added to each well. The plates were then placed in an incubator maintained at 37°C with 5% CO₂ and saturated humidity for 1 h of static incubation. Afterward, the absorbance of each well was measured at 450 nm using a microplate reader (ReadMax 1500; Shanghai Flash Spectrum), with blank wells serving as the reference.

2.4.4. Anti-inflammatory effect

Based on the results of the CCK-8 cell viability assay, a model of LPS-induced inflammation in RAW264.7 cells was constructed. Cell grouping and modeling were then carried out for the experiment. The experimental groups were set as follows: the normal control group (NC group, with only medium intervention), the LPS group (containing 1 μg/mL LPS without any test substance treatment), the dexamethasone group (DEX group, co-treated with 0.5 μg/mL DEX and 1 μg/mL LPS), and the gradient concentration groups of DHJ and KGCF aqueous extracts (co-treated with varying concentrations. RAW264.7 macrophages were inoculated with 1.5×10⁵ cells/well in 24-well plates and incubated for 24 h. After discarding the culture medium, drug intervention was performed according to the above grouping method, and the cells were placed in a saturated humidity, 5% CO₂ incubator at 37°C to continue the incubation for 24 h. After incubation, the cell supernatant was centrifuged for 10 min at 2000 r/min, and the supernatant was collected and used for the subsequent experiments.

The concentration of inflammatory mediator NO was determined according to the Griess kit (Beyotime) operating method, the absorbance was read at 540 nm by enzyme marker, the nitrite content was calculated by the standard curve of NaNO₂ solution solubilized in RAW264.7 cell complete medium, and the blank control was calculated by the intervention of RAW264.7 cell complete medium only. The enzyme-linked immunosorbent assay (ELISA) kits (Meilian) were used to determine the levels of inflammatory factors (IL-6, IL-1β, and TNF-α) in the supernatant of RAW264.7 cells after LPS stimulation as well as pharmacologic intervention.

2.4.5. Antioxidant effect

Further comparison of the anti-inflammatory activities of DHJ and KGCF was made by measuring the content of reactive oxygen species (ROS) in LPS-stimulated RAW264.7 cells. After the 6-well plate was placed in the incubator for 24 h, the culture medium was aspirated and blown repeatedly using serum-free culture medium or 0.01 Mannitol-Phosphate Buffered Saline (MPBS), and the bottom of the well plate (bottom of the bottle) was observed with the naked eye to turn from semi-transparent (cell monolayers connected into sheets) to transparent, and the cell layer was almost completely blown into PBS. The cell suspension was collected in its entirety into a centrifuge tube. It was washed twice with serum-free culture medium or 0.01 MPBS and centrifuged at 1000 rpm for 5 min at room temperature. The supernatant was removed, and the cell sediment was left for the assay. Fluorescence detection was performed according to the instructions in the ROS detection kit (Suzhou Geruisi).

3.3.1. PCA

Samples were analyzed by multivariate statistical PCA. According to the graph PCA score chart (Figure 3a), with a contribution of 68.8% for principal component 1 and 12.3% for principal component 2, this PCA model fits well. At the same time, the samples all lie within the 95% confidence interval, and DHJ and KGCF are clearly clustered into two categories that are largely effectively differentiated. It shows that there is a significant difference in the composition between the two herb groups of DHJ and KGCF, the data of which need to be further analyzed and studied.

3.3.2. OPLS-DA

To further clarify the differences between the two herbs, differences between groups were analyzed quickly and accurately by zooming in on the differences between groups while narrowing down the differences between groups and within groups, and filtering out information not relevant to classification [36]. Therefore, the supervised OPLS-DA model was chosen for the analysis. According to the OPLS-DA score plot (Figure 3b), samples were all within the 95% confidence interval, indicating that the model has a good predictive ability and the data are basically simulated within. The results showed that the DHJ and KGCF samples were distributed on the left and right sides of the confidence interval, with a clear differentiation effect, indicating that the compositional differences between the two samples were very significant and allowed for differential metabolite finding. Fractions 1 and 2 explained 24.2% and 40.5% of the variation, respectively. R²Y equals 0.899 and Q² equals 0.81 (Figure 3c). Its Q² indicates the predictive power of the model, and R²Y indicates the explanatory power of the model on the Y-axis, which represents the stability of the model. The more reliable and stable the model is, the closer their values are to 1. To check for overfitting of the model, the model was evaluated by a permutation test (n=100) (Figure 3d). Its results show that Q² is equal to -0.22, the intercept of the Q² regression line is negative, and all Q²s are consistently lower than the original blue Q² points from left to right, indicating that the model is not overfitted. The above data further indicate that the model is valid and well-predicted and can be used for the identification of chemical compositional differences between DHJ and KGCF and further screening of differential metabolites.

The species correlation heat map has been shown in Figure 4(a). Their results showed that the intergroup correlation between the two herbs was low, and the degree of their intragroup correlation was strong. Combined with HCA (Figure 4b), 12 batches of herbs were clustered into two categories. This situation further confirms this categorization trend. In summary, there are differences in the chemical composition of DHJ and KGCF.

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Figure 4.

(a) Species correlation heat map of DHJ and KGCF herbs in negative ion mode. (b) Identification of DHJ and KGCF herbs by HCA in negative ion mode. (c) In negative ion mode, the top 15 substances are ranked according to VIP. (d) Volcano plot.

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3.3.3. Differential metabolite analysis

A supervised pattern recognition method, OPLS-DA, was used to determine the VIP value, P value, and FC value to determine the differential markers between the two herbs, DHJ and KGCF. VIP values, P-values, and FC-values are commonly used to supervise the analysis of the contribution of evaluation variables in the OPLS-DA model. Variables with VIP > 1, FC > 2, and P-value <0.05 were generally considered to reflect the metabolic characteristics of the study population and were statistically significant [37]. According to the variable importance projection, VIP>1 can get the VIP score plot (Figure 4c) and T-test plot of the substance to find the potential metabolic differentiators. According to FC > 2 and P-value less than 0.05, the volcano plot (Figure 4d) of the substance can be obtained. In this study, 38 differentials were obtained from the identified substances, of which 33 were up-regulated and eight were down-regulated. Among the 41 differential metabolic constituents obtained from the screening, the metabolic constituents with large multiplicative differences included flavonoids, organic acids, amino acids, and phenolic acid constituents. Substances with high content of DHJ metabolic components were mainly substances such as hypericin, 3-Indoxyl sulphate, Azelaic acid, and Linolenic acid, while substances with high content of KGCF metabolic components were substances such as Quercetin, Chlorogenic acid, 5,7,3’,4’- Tetrahydroxy-4-phenyl coumarin 5-O-apiosyl-(1->6)-glucoside, and other substances. The peak areas corresponding to the higher relative contents of the differentiated components among the groups were selected to represent their relative contents, and the relative contents of the differentiated components among the different groups were compared macroscopically (Figure 5).

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Figure 5.

Box plots of the characteristic distribution of substances with relatively high content in the herbs DHJ and KGCF.

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Flavonoids are a class of natural phytochemicals. They are widely distributed in plants, participate in various biological processes, and mainly consist of polyphenolic structures [38]. Structurally, flavonoids are classified into various subtypes by C6-C3-C6 carbon skeleton substituents. Widely applied in nutrition, pharmaceuticals, and cosmetics, they are key functional biomolecules. Flavonoids show potent anti-inflammatory, antioxidant, antifungal, and other bioactivities, and are used to treat Alzheimer’s, atherosclerosis, cancer, etc. Hispidulin, also known as 6-methoxy-4′,5,7-trihydroxyflavone or rough hairy ragweedin, is a naturally occurring flavonoid compound widely found in plants [39]. It is a monomethoxy and trihydroxy flavonoid compound. Several experimental articles have demonstrated, through in vivo and in vitro studies, that hypericin has a variety of beneficial biological effects [40,41]. These compounds exhibit antioxidant, anti-osteoporotic, antiepileptic, anti-platelet aggregation, anti-inflammatory, and antimicrobial properties. They play crucial roles in treating various diseases, including epilepsy, neurological disorders, fungal infections, oxidative stress, osteoporosis, and asthma. Studies have demonstrated the anti-inflammatory and antioxidant effects of hypericin [42]. Hispidulin suppresses LPS-induced nitric oxide generation and reduces the expression of inflammatory mediators such as iNOS, TNF-α, and IL-1β in Raw264.7 and HT29 cells [43]. Hispidulin also has some antioxidant effects, which can be achieved by increasing the level of antioxidant enzymes and inhibiting the respiratory chain [44,45]. Quercetin is a naturally occurring flavonoid commonly found in a variety of fruits, vegetables, and plants [46]. It is generally recognized for its potent anti-apoptotic, anti-cancer, and anti-inflammatory properties, as well as its ability to modulate mitochondrial biogenesis [47]. Quercetin is a member of the flavonoid flavonol subclass and is widely used as an anti-inflammatory, antioxidant, lipid peroxidant, and anticancer agent in the pharmaceutical and nutraceutical fields. 5,7,3’,4’-Tetrahydroxy-4-phenylcoumarin-5-O-apiosyl-(1→6)-glucoside belongs to the coumarin glycoside analogs, all of which have a benzo-α-pyrone parent nucleus with an aromatic ring structure. In contrast, simple coumarin analogs have significant anti-inflammatory activity. Coumarins are a class of compounds containing a C6-C3 backbone derived from tyrosine and possessing different biological activities, such as anti-inflammatory, antiviral, antibacterial, anticoagulant, anticancer, anti-asthmatic, anti-osteoporosis, and neuroprotective.

Azelaic acid is a natural, non-toxic, saturated nine-carbon dicarboxylic acid, also known as azelaic acid. Originally thought to be a secondary metabolite of Malassezia fungal infections [48], azelaic acid has mechanisms of action such as antibacterial, anti-inflammatory, regulation of skin keratinization, inhibition of oil secretion, and inhibition of abnormal cell proliferation. It can be used as a cosmetic or medicine to treat skin diseases such as acne, rosacea, melasma, etc [49]. Of course, Azelaic acid scavenges ROS and inhibits the production and action of oxygen-free radicals, exerting an anti-inflammatory effect [50]. In vitro, azelaic acid inhibits the hydroxylation of aromatic compounds (including lorans) and the peroxidation of arachidonic acid induced by reactive oxygen groups. Omega-3 polyunsaturated fatty acids are essential fatty acids important for human health. Especially α-linolenic acid, it is a precursor to eicosapentaenoic acid and docosahexaenoic acid [51]. Both substances play crucial roles in brain development, cardiovascular health, and the inflammatory response. In rodent studies, a diet rich in α-linolenic acid protects cardiovascular function through anti-inflammatory and antioxidant mechanisms, demonstrating its anti-inflammatory, antioxidant, and neuroprotective properties [52]. Chlorogenic acid is a condensed phenolic acid produced by esterification of the hydroxyl group of quinic acid and the carboxyl group of caffeic acid, which is a natural antioxidant [53]. Chlorogenic acid reduces oxidative stress by decreasing ROS levels and enhancing antioxidant enzyme activities. It also exhibits diverse biological functions, including anti-inflammatory, antibacterial, lipid-lowering, blood sugar-regulating, anti-fatigue, anti-tumor, and skin-improving effects, earning it the nickname “plant gold” [54].

Based on the above results, to further refine and identify the differential metabolites of the two herbs, 20 substances were selected from the 41 differential metabolites obtained in the previous screening for the second-round modeling. New chemometric analyses, such as OPLS-DA and HCA, were applied to analyze the differential metabolites of the two herbs.

From the OPLS-DA score plot (Figure 6a), within the 95% confidence interval, all samples are distributed on either side of the interval, indicating clear sample differentiation. With R²Y = 0.911 and Q² = 0.9, the model demonstrates good quality. The combination of the correlation heat map (Figure 6b,c) and the HCA clustering diagram (Figure 6d) shows that all samples are grouped into two distinct clusters. These results indicate that the top 20 ranked differential substances obtained from the screening can effectively distinguish between DHJ and KGCF.

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Figure 6.

Chemometric discrimination of potential markers with top 20 FC values. (a) The plot of OPLS-DA scores for 20 differential metabolites. (b) Heatmap of top 20 features. (c) Spearman rank correlation coefficients heatmap for the top 20 FC. (d) HCA analysis of the top 20 FC values.

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3.4.1. Cytotoxicity

The cytotoxicity of DHJ and KGCF aqueous extracts (0-2500 μg/mL) on RAW64.7 cells was assessed using CCK-8. As shown in Figure 7(a), the cell viability of RAW264.7 cells treated with 0-2000 μg/mL DHJ and KGCF aqueous extracts was greater than 75%. It was shown that the administered doses of the aqueous extracts of both herbs were non-toxic to RAW264.7 cells within the range of 0-2000 μg/mL. Therefore, two aqueous extracts of the herb at 250, 500, and 1000 μg/mL were selected for subsequent experimental concentrations, representing low, medium, and high dose groups, respectively.

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Figure 7.

Evaluation of biological activities of DHJ and KGCF. (a) Drug toxicity test. Cytotoxicity assays for DHJ and KGCF, respectively. (b) Measurement of the inflammatory mediator nitric oxide. (c) Measurement of inflammatory factors IL-6, IL-1β, and TNF-α. (d) Determination of ROS levels. (e,f) IC50 analysis of NO and ROS on RAW264.7 cells. Data are presented as Mean±SEM; compared with the control group, # p<0.05, statistically significant difference; compared with the model group, *p<0.05, **p<0.01, ***p<0.001statistically significant difference.

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3.4.2. Analysis of anti-inflammatory effects

Activated macrophages produce nitric oxide and pro-inflammatory cytokines, including the inflammatory factors IL-6, IL-1β, and TNF-α, which contribute to an enhanced inflammatory response [55]. Elevated levels of NO are found during inflammation, and macrophages are a major source of NO secretion and synthesis. Immediately after secretion, NO is degraded to nitrate and nitrite, both of which are used to track the amount of NO produced by the cell [56]. NO is recognized as an important second messenger in inflammation that promotes cytokine release from lymphocytes and macrophages [57].

To evaluate the anti-inflammatory effects of the aqueous extracts of DHJ and KGCF, we determined the LPS-induced release of NO and the levels of the inflammatory factors IL-6, IL-β, and TNF-α in RAW264.7 cells as a means of efficiently assessing the anti-inflammatory effects of the drugs. The results have been shown in Figure 7. According to the results of the Griess experiment (Figure 7b), it can be found that the NO content of the model group was significantly increased compared to the normal group, and the nitric oxide content of its drug group was decreased by the intervention of DHJ and KGCF aqueous extracts.

According to the experimental results of the ELISA kit (Figure 7c), it is known that, compared with the NC, after LPS stimulation, the secretion of three cytokines, IL-6, IL-β, and TNF-α, was significantly increased by RAW264.7 cells in the model group. After the intervention in the drug group, the content of the three cytokines decreased in the drug group, and the secretion of the three cytokines, IL-6, IL-β, and TNF-α, gradually decreased with the increase in the concentration of the administered drug in a dose-dependent manner. In summary, the aqueous extracts of DHJ and KGCF inhibited the secretion of nitric oxide and three cytokines, suggesting that DHJ and KGCF have some anti-inflammatory effects.

3.4.3. Analysis of antioxidant effects

Macrophages produce ROS in response to microbial and inflammatory stimuli, and excess ROS can further promote the release of inflammatory mediators such as IL-β and TNF-α by inflammatory cells [58]. In this experiment, the anti-inflammatory effects of the two drugs were further evaluated by determining the level of intracellular ROS in RAW264.7 cells stimulated by the LPS modeling drug. The experimental results showed that the ROS level (Figure 7d) was significantly elevated in the model group compared with the normal group; under the intervention of drugs, the ROS level was significantly decreased in the middle-dose and high-dose groups in a dose-dependent manner; the positive group could significantly ameliorate the LPS-induced ROS elevation with obvious effects.

The mean half-inhibitory concentrations of the anti-inflammatory and antioxidant activities (Figures 7e, f) of the aqueous extracts of both herbs were analyzed. The aqueous extract of DHJ inhibited the inflammatory mediators NO and ROS in RAW264.7 cells significantly less than KGCF. Preliminary evidence suggests that the aqueous extract of DHJ has stronger anti-inflammatory and antioxidant effects than KGCF.