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. 2024 Mar 28;14:7421. doi: 10.1038/s41598-024-58078-8

Comparative analysis and evaluation of wild and cultivated Radix Fici Simplicissimae using an UHPLC-Q-Orbitrap mass spectrometry-based metabolomics approach

Kai-Xin Guo 1, Yan-Fang Li 1, Hui Tang 1, Hao-Yang Wei 1, Wei Zeng 1, Xiao-Cui Yang 2, Yan Luo 1,✉, Xue-Hong Ke 1,3,✉
PMCID: PMC10978936  PMID: 38548824

Abstract

Radix Fici Simplicissimae (RFS) is widely studied, and is in demand for its value in medicines and food products, with increased scientific focus on its cultivation and breeding. We used ultra-high-performance liquid chromatography quadrupole-orbitrap mass spectrometry-based metabolomics to elucidate the similarities and differences in phytochemical compositions of wild Radix Fici Simplicissimae (WRFS) and cultivated Radix Fici Simplicissimae (CRFS). Untargeted metabolomic analysis was performed with multivariate statistical analysis and heat maps to identify the differences. Eighty one compounds were identified from WRFS and CRFS samples. Principal component analysis and orthogonal partial least squares discrimination analysis indicated that mass spectrometry could effectively distinguish WRFS from CRFS. Among these, 17 potential biomarkers with high metabolic contents could distinguish between the two varieties, including seven phenylpropanoids, three flavonoids, one flavonol, one alkaloid, one glycoside, and four organic acids. Notably, psoralen, apigenin, and bergapten, essential metabolites that play a substantial pharmacological role in RFS, are upregulated in WRFS. WRFS and CRFS are rich in phytochemicals and are similar in terms of the compounds they contain. These findings highlight the effects of different growth environments and drug varieties on secondary metabolite compositions and provide support for targeted breeding for improved CRFS varieties.

Keywords: Discrimination, Metabolomics, Wild Radix Fici Simplicissimae, Cultivated Radix Fici Simplicissimae, UPLC-Q-Orbitrap HRMS

Subject terms: Mass spectrometry, Metabolomics

Introduction

Radix Fici Simplicissimae (RFS), the dry root of Ficus hirta Vahl., often called Guangdong ginseng or Wuzhaolong, is a widely distributed mulberry plant, occurring in Guangdong, Fujian, and Guangxi, in China1,2. RFS is a common medicine used by minority nationalities in the Lingnan region, especially the Yao and Zhuang nationalities3. It was first recorded in the Natural Preparation of Raw Herbs, and its application has beneficial impacts on the spleen and lungs, qi and dampness, muscles, and collateral circulation. Some applications of RFS include spleen deficiency and edema, insufficient food and abdominal distension, limb fatigue and weakness, lung deficiency and phlegm asthma, belching, night sweats, rheumatism and pain, postpartum non-lactation, and bruising4–6.

RFS was included in the 1977 edition of the Chinese Pharmacopoeia, and is a primary medicinal material used in Gongyanping tablets in the Chinese Pharmacopoeia7. It is also included in the Quality Standard of Yao Medicinal Materials in the Guangxi Zhuang Autonomous Region (Volume I) (2013 edition)8. Recently, with increasing attention being paid by the state to develop minority medicines, Wild Radix Fici Simplicissimae (WRFS) has been widely studied and frequently applied as a genuine Yao medicine. The value of RFS in medicine and food products is also increasingly researched and medically applied. RFS resources are primarily wild, but intensive land use and mining have reduced the wild RFS resources despite high market demand, while RFS cultivation has increased9. Cai et al.10 and Huang et al.11 assessed psoralen content as the quality standard of RFS and found great differences in the quality of RFS produced in different regions of Guangdong Province. However, research is lacking on WRFS and Cultivated Radix Fici Simplicissimae (CRFS) using technologies such as mass spectrometry combined with chemical pattern recognition, limiting standard development of RFS formulations.

Among several primary metabolomics research technologies, nuclear magnetic resonance (NMR) and chromatography coupled with tandem mass spectrometry (MS) are the most used. MS is used to identify metabolites using rapid, sensitive, and selective qualitative and quantitative methods, and combined with effective sample pretreatment and chromatographic separation, has high sensitivity and specificity. Liquid chromatography–mass spectrometry (LC–MS) uses high-throughput MS screening technology combined with metabolite identification, elucidates relevant biomarkers, and effectively analyzes the product components. Metabolomics combined with stoichiometry is used to monitor changes in the chemical components of traditional Chinese medicines from different sources12,13, growth sites14, and processing methods15,16, and for quality control (QC) of traditional Chinese medicines17–19.

In the early stage, we established the RFS fingerprint and the detection method of main components by HPLC20. In the present study, we used ultra-high-performance liquid chromatography quadrupole-orbitrap mass spectrometry-based metabolomics to elucidate the similarities and differences in phytochemical compositions of WRFS and CRFS. We aimed to analyze the effects of different growth environments and drug varieties on secondary metabolites and provide insights for targeted breeding of improved CRFS varieties.

Materials and methods

Plant materials

Qingyuan City is in the mountainous area of northern Guangdong Province and is a central residential area of the Yao nationality in China. In this study, 29 batches of RFS were harvested. Seventeen batches of WRFS samples were collected from Qingyuan City (Guangdong Province, China), and twelve CRFS samples were collected from hospitals and pharmacies (Table 1). Professor Yuan Xiaohong of Guangdong Provincial Hospital of Traditional Chinese Medicine identified all the medicinal materials. Among them, WRFS were provided by Qingyuan Traditional Chinese Medicine Hospital in May 2022, and CRFS were provided by Guangzhou First Affiliated Hospital of Traditional Chinese Medicine in June 2022. All samples were collected with the approvals from the respective authorities. The phenotypes of RFS are shown in Fig. 1.

Table 1.

WRFS and CRFS samples from Guangdong province, China.

Species Sample no. Source Collection time or batch number
WRFS 1 Lianshan County, Qingyuan City, Guangdong Province, China October 2020
WRFS 2 Lianshan County, Qingyuan City, Guangdong Province, China October 2020
WRFS 3 Lianshan County, Qingyuan City, Guangdong Province, China October 2020
WRFS 4 Yingde County, Qingyuan City, Guangdong Province, China October 2020
WRFS 5 Yingde County, Qingyuan City, Guangdong Province, China October 2020
WRFS 6 Yingde County, Qingyuan City, Guangdong Province, China October 2020
WRFS 7 Yingde County, Qingyuan City, Guangdong Province, China December 2020
WRFS 8 Yingde County, Qingyuan City, Guangdong Province, China December 2020
WRFS 9 Yingde County, Qingyuan City, Guangdong Province, China December 2020
WRFS 10 Liannan County, Qingyuan City, Guangdong Province, China January 2020
WRFS 11 Liannan County, Qingyuan City, Guangdong Province, China January 2020
WRFS 12 Liannan County, Qingyuan City, Guangdong Province, China April 2020
WRFS 13 Qingcheng County, Qingyuan City, Guangdong Province, China April 2020
WRFS 14 Qingcheng County, Qingyuan City, Guangdong Province, China May 2020
WRFS 15 Qingcheng County, Qingyuan City, Guangdong Province, China May 2020
WRFS 16 Qingcheng County, Qingyuan City, Guangdong Province, China December 2020
WRFS 17 Qingcheng County, Qingyuan City, Guangdong Province, China December 2020
CRFS 18 Heyuan Jinyuan Green Life Co., Ltd. Jinlusheng Traditional Chinese Medicine Factory 220,101
CRFS 19 Guangdong Tiancheng Traditional Chinese Medicine Slices Co., Ltd 210,801
CRFS 20 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,107,137
CRFS 21 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,109,087
CRFS 22 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,202,045
CRFS 23 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,202,044
CRFS 24 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,108,186
CRFS 25 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,107,049
CRFS 26 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,111,011
CRFS 27 Zhongshan Xianyitang Traditional Chinese Medicine Slices Co., Ltd 2,109,084
CRFS 28 Traditional Chinese Medicine Slice Factory of Guangdong Pharmaceutical Company W2422311
CRFS 29 Traditional Chinese Medicine Slice Factory of Guangdong Pharmaceutical Company W2422312
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WFRS, Wild Radix Fici Simplicissimae; CRFS, Cultivated Radix Fici Simplicissimae.

Figure 1.

Figure 1

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Phenotype of RFS : CRFS (wild Radix Fici Simplicissimae) (A,C) and WRFS (cultivated Radix Fici Simplicissimae) (B,D).

Ethics statement

Collection of Radix Fici Simplicissimae in this research material conforms to and complies with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. In addition, according to the List of National Key Protected Wild Plants issued by the State Forestry and Grassland Bureau of China, Radix Fici Simplicissimae, the experimental material of this study, is not a national key protected wild plant nor an endangered plant species.

Sample preparation and extraction

The samples were ground and sieved (Chinese National Standard Sieve No. 3, R40/3 series) to obtain a homogeneous powder. Then, 0.1 g dried samples were added to a 5 mL volumetric flask, and 5 mL of 50% methanol was added. The mixture was left standing for 60 min and extracted using ultrasound (350 W, 35 kHz) (SK3300LH Ultrasonic Cleaner (Shanghai Kedao Ultrasonic Instrument Co., Ltd.)) for 60 min at 37 °C. Methanol (50%) was added to compensate for the loss in weight. The mixture was centrifuged (13,000 rpm; Thermo Legend Micro17R Centrifuge) for 10 min to obtain a clear solution. Additionally, a reference solution of psoralen and apigenin was prepared using the same method.

To ensure the suitability and stability consistency of MS analysis, a QC sample was prepared by pooling the same volume (10 µL) from every sample. In the entire worklist, one QC sample was inserted into every five test and analysis samples, and six QC injections were given to monitor the repeatability of the analysis. A volume of 3 µL was injected for each sample and QC. Metabolite extraction and detection repeatability were determined by overlapping the total ion flow diagram of MS detection and analysis of different QC samples.

UPLC-Q-Orbitrap HRMS analysis

Liquid chromatography

Ultra-high-performance liquid chromatography quadrupole-orbitrap mass spectrometry (UPLC-Q-Orbitrap HRMS) analysis was performed on a Thermo QExactive mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a UPLC system through an electrospray ionization (ESI) interface. Samples were separated on a Thermo Hypersil Gold VANQUISH C18 (2.1 × 100 mm, 3 μm). The mobile phases were eluent A (0.1% formic acid in water, v/v) and eluent B (acetonitrile, v/v). The elution conditions applied were: 0–5 min, 5% B; 5–12 min, 25–80% B; 12–18 min, 80–99% B; 18–20 min, 99–5% B. The flow rate was 0.2 mL/min and sample injection volume was 3 µL. The column was maintained at 40 °C. Mass spectrometric grade formic acid, chromatographic grade methanol, and acetonitrile were purchased from Merck, Germany; ultrapure water and all other reagents were of analytical grade.

Mass spectrometry

The positive mode conditions were as follows: capillary voltage, 4.00 kV; carrier gas, nitrogen; sheath gas pressure, 3.5 MPa; auxiliary gas pressure, 1.0 MPa; capillary temperature: 320 °C; auxiliary gas heating temperature: 320 °C; primary resolution: 70,000. The negative mode conditions were identical to the positive mode conditions except for the capillary voltage (3.00 kV). The full scan mode was used, and positive and negative ions were detected simultaneously. The scanning range of the positive and negative ion spectra recorded by MS was 80–1200 m/z.

Data analysis

Chemical component identification

For data collection, the samples were detected simultaneously in the first and second scanning modes under positive and negative ions, respectively, using UPLC-Q-Orbitrap HRMS (Thermo Fisher Scientific), and a total ion flow diagram was plotted. According to the pyrolysis spectrum detected in the electrostatic field orbital well analyzer, the accurate relative molecular weight, retention time, and multistage fragment ion information of the compound were obtained using a Compound Discoverer 3.2. The parameters were as follows: for 2D peak detection, 200 was set as the minimum peak area; for 3D peak detection, the peak intensities of low and high energy were set as > 1000 and > 200 counts, respectively; mass error in the range of ± 5 ppm was set for identified compounds; retention time in the range of ± 0.1 min was allowed to match the reference substance21. The predicted fragments generated from the structures were matched and identified against the mzCloud database and ChemSpider. Supporting information was obtained from relevant literature in databases such as PubMed.

Multivariate statistical analysis

The differences between WRFS and CRFS were explored using a metabolomics workflow. Multivariate statistical analysis was performed using SIMCA-P 14.0, and unsupervised principal component analysis (PCA) was used to obtain an initial understanding of the relationships between the data matrices. First, PCA was used to show pattern recognition and maximum variation to obtain an overview and classification. Second, the metabolite differences between different varieties of RFS and culture methods were detected using orthogonal projections to latent structures discriminant analysis (OPLS-DA) monitoring. OPLS-DA in ESI+ and ESI− modes was performed to obtain the maximum separation between the CRFS and WRFS groups and to explore the potential biochemical markers contributing to the differences. S-plots were created to visualize the OPLS-DA predictive component loading to facilitate model interpretation. The corresponding variable importance for projection (VIP) was calculated in the OPLS-DA model, and VIP values were used to screen the different components. Metabolites with a VIP value of > 1 and a p-value of < 0.05 were considered potential markers. A heatmap was generated from these biochemical markers to visualize the variations in differential metabolites in the different groups, and metabolites with significant statistical differences among the classes were used to generate a heatmap in MetaboAnalyst4.0 (www.metaboanalyst.ca)22. We annotated the obtained differential metabolites using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and identified the corresponding pathways.

Results and discussion

Stability of the UPLC–MS/MS system

QC samples were used to evaluate the stability of the UPLC–MS/MS system. The curve overlaps between the metabolite detection and total ion current were high. The relative standard deviations (RSD) of the areas of all peaks were calculated, and the screening rates of the characteristic RSD < 30% in the positive and negative modes were 98.54% and 98.33%, respectively. These results suggest a high stability of the UPLC–MS/MS system throughout the experiment.

Identity assignment and compound confirmation

Differential analysis in chemical composition between CRFS and WRFS

The chemical profiles of WRFS and CRFS were analyzed using UPLC-Q-Orbitrap HRMS. A total of eighty one compounds were identified or tentatively characterized in ESI+ and ESI− modes from WRFS and CRFS. Representative base peak intensity (BPI) chromatograms of the WRFS and CRFS are shown in Fig. 2.

Figure 2.

Figure 2

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Base peak intensity (BPI) chromatograms of WRFS and CRFS in ESI+ and ESI− modes: (A) WRFS in ESI+ mode; (B) CRFS in ESI+ mode; (C) WRFS in ESI− mode; (D) CRFS in ESI− mode; (E) Blank control in ESI+ mode (50% methanol solution); (F) Blank control in ESI− mode (50% methanol solution).

The similarity between the two BPI chromatograms was relatively high. Using Compound Discoverer 3.2 (Table 2), eighty one compounds were characterized from CRFS and WRFS, which were equivalent to [M + H]+ and [M−H]− ions and were unambiguously or tentatively identified through a match with accurate molecular weights within a mass accuracy of < 5 ppm. Both types of RFS extracts were rich in compounds with various structural patterns, including flavonoids, coumarins, alkaloids, glycosides, organic acids, and organic acid esters. In addition, the ion chromatograms and mass spectra of psoralen and apigenin standards were compared, as shown in Fig. 3; the secondary fragment peaks were consistent with those of the corresponding compounds in Table 2, indicating the accuracy of compound identification by CD 3.2 software.

Table 2.

UPLC-Q-Orbitrap HRMS-based determination of chemical composition of CRFS and WRFS cultivated in different ways.

No RT (min) Tentative identification of compound Formula Observed neutral mass (Da) Observed m/z Error (ppm) Adducts Main fragments via MS/MS
1 0.616 D-( +)-Proline C5H9NO2 115.06313 116.07060 − 1.74  + H 116.07036; 70.06546
2 0.707 Cytosine C4H5N3O 111.04325 112.05054 − 0.11  + H 112.05058
3 0.725 Betaine C5H11NO2 117.0789 118.08626 − 0.68  + H 118.08641
4 0.729 Trigonelline C7H7NO2 137.04732 138.05495 − 2.64  + H 138.05447
5 0.934 DL-Carnitine C7H15NO3 161.10478 162.11247 − 2.55  + H 162.11206; 103.03913
6 0.994 Guanine C5H5N5O 151.04907 152.05669 − 2.26  + H 152.05637
7 1.039 Nicotinic acid C6H5NO2 123.03186 124.03930 − 1.33  + H 124.03917
8 1.21 Hordenine C10H15NO 165.11495 166.12264 − 2.48  + H 166.12209; 121.06458
9 1.495 5-Hydroxymethyl-2-furaldehyde C6H6O3 126.03152 127.03897 − 1.34  + H 127.03873; 109.02834; 81.03376
10 1.506 l-Isoleucine C6H13NO2 131.09436 132.10191 − 2.04  + H 132.10146; 86.09663
11 1.676 2-(3-methoxyphenyl)acetamide C9H11NO2 165.07861 166.08626 − 2.26  + H 166.12196; 121.06451
12 2.092 l-Phenylalanine C9H11NO2 165.07859 166.08626 − 2.33  + H 166.08554; 120.08066
13 2.218 Pyrogallol C6H6O3 126.03149 127.03897 − 1.6  + H 127.03868; 109.02834; 81.03374
14 4.124 (1r,3R,4 s,5S)-4-{[(2E)-3-(3,4-dihydroxyphenyl)prop-2-enoyl]oxy}-1,3,5-trihydroxycyclohexane-1-carboxylic acid C16H18O9 354.09409 355.10236 − 2.8  + H 355.10049; 163.03877
15 4.71 Scopoletin C10H8O4 192.04188 193.04954 − 1.99  + H 193.04912; 178.02559
16 4.926 7-Hydroxycoumarine C9H6O3 162.03126 163.03897 − 2.67  + H 163.03860; 119.02550
17 5.225 Vitexin C21H20O10 432.1045 433.11292 − 2.66  + H 433.11154; 313.06964; 283.05920
18 5.331 Orientin C21H20O11 448.09911 449.10784 − 3.23  + H 449.10388; 329.06363; 299.05377
19 5.591 Vanillin C8H8O3 152.04697 153.05462 − 2.43  + H 153.05408; 125.05941; 111.04398; 93.03362
20 5.953 1,5-Anhydro-1-[5,7-dihydroxy-3-(4-hydroxyphenyl)-4-oxo-4H-chromen-8-yl]hexitol C21H20O10 432.10426 433.11292 − 3.21  + H 433.10980; 313.06921; 283.05884; 337.06882
21 7.673 7,8-Dihydroxy-4-methyl coumarin C10H8O4 192.04184 193.04954 − 2.19  + H 193.04921; 175.03865
22 7.871 Luteolin C15H10O6 286.0469 287.05501 − 2.92  + H 287.05423
23 7.942 (2S)-2-(2-hydroxypropan-2-yl)-2H,3H,7H-furo[3,2-g]chromen-7-one C14H14O4 246.08845 247.09649 − 3.07  + H 247.09590; 229.08540; 175.03862
24 8.049 Eriodictyol C15H12O6 288.06238 289.07066 − 3.5  + H 289.06949; 163.03838; 153.01772
25 8.101 Naringenin C15H12O5 272.06761 273.07575 − 3.16  + H 273.07535; 153.01807; 147.04388
26 8.289 Psoralen C11H6O3 186.03114 187.03897 − 2.98  + H 187.03816; 143.04860; 131.04869
27 8.418 Apigenin C15H10O5 270.05202 271.06010 − 2.96  + H 271.05933
28 8.559 Apocynin C9H10O3 166.06277 167.07027 − 1.32  + H 167.06989
29 8.98 Diosmetin C16H12O6 300.06233 301.07066 − 3.52  + H 301.06989; 286.04636
30 9.716 Bergapten C12H8O4 216.04168 217.04954 − 2.69  + H 217.04901; 202.02550
31 10.198 Chrysin C15H10O4 254.05736 255.06519 − 2.16  + H 255.06454
32 10.201 Trioxsalen C14H12O3 228.0781 229.08592 − 2.41  + H 229.08533
33 11.24 Benzophenone C13H10O 182.0727 183.08044 − 2.57  + H 183.07977; 105.03344
34 11.884 Psoralidin C20H16O5 336.0985 337.10705 − 3.79  + H 337.10547
35 12.321 3,5-di-tert-Butyl-4-hydroxybenzaldehyde C15H22O2 234.16121 235.16926 − 3.27  + H 235.16846; 179.10611
36 12.338 5-hydroxy-2-(4-hydroxyphenyl)-8,8-dimethyl-4H,8H-pyrano[3,2-g]chromen-4-one C20H16O5 336.09858 337.10705 − 3.56  + H 337.10587; 283.05927
37 12.529 Cryptotanshinone C19H20O3 296.14038 297.14852 − 2.92  + H 297.14764; 251.14246
38 0.675 D-(-)-Mannitol C6H14O6 182.07827 181.07176 − 4.21 –H 181.07089; 101.02327; 89.02323; 71.01263
39 0.785 Gluconic acid C6H12O7 196.05783 195.05103 − 2.42 –H 195.05052; 129.01845; 75.00767
40 0.803 D-(-)-Quinic acid C7H12O6 192.06259 191.05611 − 4.16 –H 191.05530; 85.02831
41 0.846 d-Glucose 6-phosphate C6H13O9P 260.02925 259.02244 − 1.8 –H 259.02206; 96.96844; 78.95784
42 1.078 α,α-Trehalose C12H22O11 342.11581 341.10894 − 1.16 –H 341.10867; 179.05527; 119.03397; 113.02333; 101.02331; 89.02327; 71.01267; 59.01268
43 1.401 Citric acid C6H8O7 192.02631 191.01973 − 3.62 –H 191.01921; 111.00783; 87.00772; 85.02847
44 2.082 3-Hydroxy-3-(methoxycarbonyl)pentanedioic acid C7H10O7 206.04198 205.03538 − 3.25 –H 205.03505; 111.00767; 87.00760
45 2.621 3-[3-(beta-d-Glucopyranosyloxy)-2-hydroxyphenyl]propanoic acid C15H20O9 344.11042 343.10346 − 0.91 –H 343.10345; 181.04984; 163.03908; 137.05981
46 3.307 Quercetin C15H10O7 302.04235 301.03538 − 0.99 –H 301.03494; 151.00244
47 3.64 Caffeic acid C9H8O4 180.04157 179.03498 − 3.81 –H 179.03464; 135.04442
48 4.159 Catechin C15H14O6 290.07881 289.07176 − 0.8 –H 289.07126; 245.08139
49 4.25 Chlorogenic acid C16H18O9 354.09497 353.08781 − 0.33 –H 353.08823; 191.05576
50 4.397 2-(Acetylamino)hexanoic acid C8H15NO3 173.10442 172.09792 − 4.45 –H 172.09688; 130.08630
51 4.601 Fraxetin C10H8O5 208.03666 207.0299 − 2.49 –H 207.02928; 192.00574
52 4.76 4-Methylumbelliferone C10H8O3 176.04658 175.04007 − 4.32 –H 175.03926
53 4.783 (1ξ)-1,5-Anhydro-1-[2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4-oxo-4H-chromen-8-yl]-d-galactitol C21H20O11 448.10026 447.09329 − 0.66 –H 447.09293; 357.06134; 327.05075; 299.05521; 298.04642; 297.04022
54 5.027 (9R,10R)-10-hydroxy-8,8-dimethyl-9-{[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-2H,8H,9H,10H-pyrano[2,3-h]chromen-2-one C20H24O10 424.1365 423.12967 − 1.06 –H 423.12885; 179.05565; 89.02407; 71.01264; 59.01350
55 5.387 2-(acetylamino)-3-(1H-indol-3-yl)propanoic acid C13H14N2O3 246.10008 245.09317 − 1.48 –H 245.09259; 203.08188; 116.03413; 116.04924; 98.02341; 74.02351; 70.02863; 58.02859
56 5.445 Cnidioside A C17H20O9 368.11046 367.10346 − 0.75 –H 367.10306; 205.04997; 161.05986
57 5.516 3-[4-(beta-d-Glucopyranosyloxy)-6-methoxy-1-benzofuran-5-yl]propanoic acid C18H22O10 398.1212 397.11402 − 0.25 –H 397.11465; 235.06100; 191.07083; 176.04729
58 5.521 Suberic acid C8H14O4 174.08848 173.08193 − 4.21 –H 173.08125; 111.08056
59 6.128 Isovanillic acid C8H8O4 168.04145 167.03498 − 4.84 –H 167.03418; 152.01070
60 6.259 2,4,6-Trihydroxy-2-(4-hydroxybenzyl)-1-benzofuran-3(2H)-one C15H12O6 288.06329 287.05611 − 0.36 –H 287.05649; 259.06131; 125.02356
61 6.904 Azelaic acid C9H16O4 188.10412 187.09758 − 3.92 –H 187.09683; 125.09612
62 7.425 Diplosal acetate C16H12O6 300.06303 299.05611 − 1.19 –H 137.02335
63 7.839 Ferulic acid C10H10O4 194.05724 193.05063 − 3.47 –H 193.04990
64 8.196 Genistein C15H10O5 270.0527 269.04555 − 0.44 –H 269.04538
65 8.546 Corchorifatty acid F C18H32O5 328.22484 327.2177 − 0.42 –H 327.21732
66 9.094 Dodecanedioic acid C12H22O4 230.15147 229.14453 − 1.46 –H 229.14404; 230.414699; 211.13297
67 9.233 Mycophenolic acid C17H20O6 320.1259 319.11871 − 0.28 –H 319.11868; 191.03410
68 9.318 Hispidulin C16H12O6 300.06313 299.05611 − 0.87 –H 299.05582; 284.03232
69 9.792 (15Z)-9,12,13-Trihydroxy-15-octadecenoic acid C18H34O5 330.24045 329.23335 − 0.52 –H 329.23294; 171.10173
70 10.085 Taurochenodeoxycholic acid C26H45NO6S 499.29649 498.28948 − 0.54 –H 498.28958
71 10.247 Monobutyl phthalate C12H14O4 222.08874 221.08193 − 2.12 –H 221.08136; 177.09140; 149.09613; 134.03627; 121.02839; 71.04902; 69.03339
72 10.749 Chrysin C15H10O4 254.05765 253.05063 − 1 –H 253.05031
73 11.286 Asiatic acid C30H48O5 488.34964 487.3429 − 1.09 –H 487.34369
74 12.666 N-(3-Chloro-4-morpholinophenyl)-6-oxo-1,4,5,6-tetrahydro-3-pyridazinecarboxamide C15H17ClN4O3 336.09964 335.09164 2.15 –H 335.09204
75 12.871 16-Hydroxyhexadecanoic acid C16H32O3 272.23496 271.22787 − 0.7 –H 271.22763
76 13.021 Oleic acid alkyne C18H30O2 278.22431 277.2173 − 0.99 –H 277.21695
77 13.05 Dodecyl sulfate C12H26O4S 266.15496 265.1479 − 0.84 –H 265.14767; 96.95897
78 13.303 (R)-3-Hydroxy myristic acid C14H28O3 244.20354 243.19657 − 1.26 –H 243.19693; 59.01270
79 14.709 Ursolic acid C30H48O3 456.35989 455.35307 − 0.99 –H 455.35260
80 15.503 Myristyl sulfate C14H30O4S 294.18635 293.1792 − 0.43 –H 293.17935; 96.95897
81 15.738 Linoleic acid C18H32O2 280.2402 279.23295 − 0.13 –H 279.23276
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Figure 3.

Figure 3

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Ion chromatogram of spsoralen in ESI+ mode (A); Ion chromatogram of apigenin in ESI+ mode (B); Mass spectra of psoralen (C) and apigenin (D).

PCA is an important method for the dimensionality reduction of data and an unsupervised multivariate statistical pattern recognition method, and may be used to highlight specific samples from all data. The PCA score plots of WRFS and CRFS showed substantial aggregation separation (Fig. 4A,B). To evaluate the differences in RFS between different cultivation methods and to understand the variables responsible for sample separation, we determined the importance of the variables in the OPLS-DA scoring charts, S-charts, permutation tests, and projection values. OPLS-DA differs from PCA because it is a supervised discriminant analysis method with superior classification and prediction capabilities. OPLS-DA uses partial least squares regression to establish a relationship model between metabolite expression and sample categories to predict the sample categories. Therefore, the OPLS-DA method was used to determine the differences between WRFS and CRFS components. The WRFS samples were separated from CRFS samples in the OPLS-DA score plot (Fig. 4C,D), suggesting differences in biochemistry between WRFS and CRFS.

Figure 4.

Figure 4

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Principal component analysis (PCA) of WRFS and CRFS in ESI+ (A) and ESI− (B) mode. OPLS-DA score plot with multivariate statistical analysis WRFS and CRFS in ESI+ (C) and ESI− (D) mode. Cross-validation plot of OPLS-DA model with 200 permutation tests in ESI+ (E) and ESI− (F) mode. OPLS-DA S-plot in ESI+ (G) and ESI− (H) mode. (The red marked points in red of the S-plot graph G and H are potential chemical markers).

The data processed by Compound Discoverer 3.2 was imported into SIMCA-P 14.0 software, and unsupervised PCA was used to evaluate the classification trend and differences between groups. The R2X of the model in positive and negative ion mode was greater than 0.4 (0.492 and 0.522, respectively), indicating that the model was stable and reliable. Two hundred rounds of random permutations were performed to verify the established OPLS-DA model, indicating that the model was reliable (ESI+: R2X = 0.359, R2Y = 0.975 and Q2 = 0.926; ESI−: R2X = 0.333, R2Y = 0.945 and Q2 = 0.892, respectively) (Fig. 4E,F). Variables with VIP values of > 1 and p < 0.05 in the nonparametric test were considered potential biochemical markers between WRFS and CRFS. (Fig. 4G,H).

A heatmap was generated based on these markers to evaluate them systematically and intuitively (Fig. 5), and to show the strength of potential chemical markers between two samples. The close relationship of 17 potential markers is illustrated by combining the identification results as mentioned above. The samples were divided into two categories, WRFS and CRFS, and the results were consistent with those of the PCA. The 17 potential differential metabolites that could be considered as potential chemical markers for WRFS and CRFS were compounds 7, 8, 14, 16, 17, 19, 22, 23, 26, 27, 30, 35, 42, 48, 58, 60, and 79 (Table 3). The color indicates the signal strength of each metabolite; the darker the red, the greater the extent to which the metabolite appears above the average level of the sample, and blue indicates that the metabolite is at a lower level.

Figure 5.

Figure 5

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Heatmap of WRFS and CRFS metabolite content.

Table 3.

Seventeen differential metabolites.

No. Differential metabolite Formula VIP Fold-change Type p-Value
7 Nicotinic acid C6H5NO2 1.404 2.507 Up 1.101E−02
8 Hordenine C10H15NO 4.194 0.205 Down 2.708E−07
14 (1r,3R,4 s,5S)-4-{[(2E)-3-(3,4-dihydroxyphenyl)prop-2-enoyl]oxy}-1,3,5-trihydroxycyclohexane-1-carboxylic acid C16H18O9 1.243 0.109 Down 1.086E−03
16 7-Hydroxycoumarine C9H6O3 1.610 13.176 Up 3.254E−06
17 Vitexin C21H20O10 1.071 0.439 Down 2.384E−02
19 Vanillin C8H8O3 1.383 0.101 Down 1.563E−05
22 Luteolin C15H10O6 1.595 0.109 Down 1.583E−03
23 (2S)-2-(2-hydroxypropan-2-yl)-2H,3H,7H-furo[3,2-g]chromen-7-one C14H14O4 1.117 0.177 Down 3.224E−03
26 Psoralen C11H6O3 22.854 7.535 Up 1.952E−06
27 Apigenin C15H10O5 4.019 4.019 Up 6.356E−03
30 Bergapten C12H8O4 10.046 8.541 Up 7.922E−10
35 3,5-di-tert-Butyl-4-hydroxybenzaldehyde C15H22O2 1.034 0.271 Down 7.997E−09
42 α,α-Trehalose C12H22O11 9.809 0.262 Down 8.921E−05
48 Catechin C15H14O6 2.225 0.128 Down 7.591E−03
58 Suberic acid C8H14O4 1.423 2.035 Up 1.594E−04
60 2,4,6-Trihydroxy-2-(4-hydroxybenzyl)-1-benzofuran-3(2H)-one C15H12O6 5.414 0.042 Down 2.505E−03
79 Ursolic acid C30H48O3 1.114 0.260 Down 1.515E−03
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Up: compared with CRFS, the corresponding metabolite was upregulated in WRFS. Down: compared with CRFS, the corresponding metabolite was downregulated in WRFS.

Kyoto Encyclopedia of Genes and Genomes analysis of differential metabolites

The KEGG database integrates genome, chemistry, and system function information and is a comprehensive dataset of metabolic pathway information23–25. The metabolic pathways are classified into different modules according to their functions, such as glycolysis, carbohydrate, TCA cycle, nucleoside and amino acid, organic compound and enzyme biodegradation, and other comprehensive metabolic pathways. Among the 17 differential metabolites, 14 were annotated to the KEGG database, 11 of which were annotated 29 times to KEGG pathways (Table 4). After removing duplication, 15 KEGG pathways were identified. Phenylpropanoid biosynthesis in the KEGG pathway is an example (Fig. 6).

Table 4.

Categories of 14 differential metabolite-annotated KEGG pathways.

KEGG pathway ID annotation Number Differential metabolite Matching IDs
Biosynthesis of phenylpropanoids map01061 5 Vanillin, apigenin, bergapten;(2S)-2-(2-hydroxypropan-2-yl)-2H,3H,7H-furo[3,2-g]chromen-7-one, psoralen C00755|C01477|C01557|C09276|C09305
Flavonoid biosynthesis map00941 3 Apigenin, vitexin, luteolin C01460|C01477|C01514
Flavone and flavonol biosynthesis map00944 2 Apigenin, luteolin C01477|C01514
Biosynthesis of secondary metabolites map01110 6 Apigenin, bergapten, psoralen, luteolin, nicotinic acid, 7-hydroxycoumarine C00253|C01477|C01514|C01557|C05851|C09305
Nicotinate and nicotinamide metabolism map00760 1 Nicotinic acid C00253
Tropane, piperidine, and pyridine alkaloid biosynthesis map00960 1 Nicotinic acid C00253
Biosynthesis of alkaloids derived from ornithine, lysine, and nicotinic acid map01064 1 Nicotinic acid C00253
2,4-Dichlorobenzoate degradation map00623 1 Vanillin C00755
Starch and sucrose metabolism map00500 1 α,α-Trehalose C01083
Phosphotransferase system (PTS) map02060 1 α,α-Trehalose C01083
Phenylpropanoid biosynthesis map00940 1 7-Hydroxycoumarine C05851
Tyrosine metabolism map00350 1 Hordenine C06199
ABC transporters map02010 1 α,α-Trehalose C01083
Biosynthesis of alkaloids derived from the shikimate pathway map01063 1 Vanillin C00755
Metabolic pathways map01100 3 Apigenin, luteolin, nicotinic acid C00253|C01477|C01514
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ID Annotation, ID of KEGG pathway; Number, the number of metabolites that can be annotated to the corresponding KEGG pathways; Matching IDs, Number of compounds in the KEGG pathway.

Figure 6.

Figure 6

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Phenylpropanoid biosynthesis. The compounds marked with red are differential metabolites belonging to phenylpropanes.

Discussion

This study, a metabolomics study based on UPLC-Q-Orbitrap HRMS combined with multivariate statistical analysis revealed substantial differences in the compound compositions of WRFS and CRFS. The results of the identification and analysis of eighty one compounds showed distinct chemical profiles between WRFS and CRFS samples from different cultivation methods. Moreover, the identification results of these compounds in this study are consistent with those of Cheng Jun et al.26,27, which proves that RFS mainly contains phenylpropanoids, flavonoids, coumarins and other substances. Moreover, the chemical composition identification of RFS by Lao et al.28 and Zhao et al.29 showed that Vitexin, Vanillin, Luteolin, Psoralen, Apigenin, Bergapten, Ursolic acid and so on (17 potential differences between CRFS and WRFS) were consistent with our identification results. Using multivariate statistical analysis and a heatmap, WRFS and CRFS showed remarkable discrimination. Many markers exhibited different expression levels between the two samples. Psoralen, bergapten, and apigenin were upregulated in WRFS, and the content of these three active substances was much higher in WRFS than in CRFS. Many researchers have found that Psoralen, bergapten, and apigenin can be used as a quality marker of RFS, and it is the active ingredient with the highest content30,31.

Radix Fici Simplicissimae, one of ten famous medicines in Lingnan region, has been proven to play a role in protecting the liver, relieving inflammation, and having antioxidant and anti-cancer activities32. The ethanol extract of RFS can protect the liver of mice from alcohol-induced liver injury, probably by inducing and regulating downstream antioxidant factors, and also by suppressing the abnormal activation of CYP2E1 protein, reducing oxidative stress, and ultimately reducing the damage to the liver caused by alcohol33. Zhou Tiannong et al.34 found that compared with the control group, the water extract of RFS can significantly inhibit the increase of abdominal capillary diameter and improve the pain threshold of mice. It can also reduce the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in mouse serum, which have good anti-inflammatory, analgesic, and liver protective effects. Deep research on the active components of RFS shows that it mainly consists of phenolic acids, terpenoids, flavonoids, coumarins, and phenolic acids26,35. Many scholars36–39 believe that the active components with a pharmacological effect that can be used as quality markers are psoralen, biflavonoids, and apigenin. Therefore, it should be studied as the main index. Psoralen, biflavonoids and apigenin have anti-tumor40,41, neuroprotective42, anti-inflammatory43, antioxidant and other pharmacological activities, which can be used to treat cancer, insomnia, Alzheimer's disease, rheumatoid arthritis, and aging. Psoralen and biflavonoids can prevent osteoporosis44, while apigenin can enhance immunity and prevent hypertension, arteriosclerosis, and cardiovascular and cerebrovascular diseases45. Because these different metabolites play an important role in health-related effects, these three components are very important for the quality evaluation of RFS. Our results show that the quality of WRFS is better than that of CRFS.

The main metabolic pathways that differ between WRFS and CRFS include primary and secondary metabolite biosynthesis. Psoralen, apigenin, and biflavonoids are annotated in multiple KEGG pathways related to phenylpropanoid biosynthesis, flavonoid biosynthesis, flavone and flavonol biosynthesis, and so on. Phenylpropanoid biosynthesis is an important metabolic process in humans, mainly involving the metabolism of amino acids such as phenylalanine and tyrosine. The process involves participation of various enzymes in catalyzing reactions to convert phenylalanine into other amino acids such as tyrosine. This biochemical process is crucial for the normal functioning of many physiological functions in the human body46. Flavonoids are an important branch of the phenylpropanoid metabolic pathway. The biosynthesis of flavonoids begins with phenylalanine, which is catalyzed by enzymes such as chalcone synthase to produce chalcone. Subsequently, the chalcone isomerizes into flavonoids, which then produces a variety of other flavonoid compounds, such as flavonols, isoflavones, and anthocyanins47. In addition, flavonoid and flavonols are two important components of flavonoids. Therefore, the results of this study provide clues for analyzing these metabolites and their metabolic networks in RFS. The variety and quantity of RFS collected in this study are limited, and its limitation should be attributed to the lack of sufficient sample size to support the research results, which can be expanded for further exploration.

This study showed that WRFS was superior to CRFS in quality, and explained the effects of different growth environments and drug varietie on secondary metabolites, and provides insights for further targeted breeding of improved CRFS varieties.

Conclusion

In this study, a UPLC-Q-Orbitrap HRMS method was established and successfully applied to determine the component profiles of various RFS samples grown under different cultivation methods. Using multivariate statistical analysis and heat maps, WRFS and CRFS were shown to have significant differences. Psoralen, bergapten, and apigenin were significantly upregulated in WRFS compared to CRFS. Due to the important roles of these differential metabolites, our results indicate that the quality of WRFS is superior to that of CRFS, and this strategy will benefit the process of quality evaluation of RFS formulations.

Acknowledgements

We are grateful to the Lingnan Medical Research Center of Guangzhou University of Traditional Chinese Medicine and The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine for providing technical support for the study of traditional Chinese medicine.

Author contributions

Conceptualization, K.X.G. and X.H.K.; methodology, K.X.G. and X.H.K.; software, K.X.G. validation, K.X.G., Y.F.L. and X.H.K.; formal analysis, K.X.G.; investigation, X.C.Y., H.T., H.Y.W. and Y.L.; resources, X.H.K. and X.C.Y.; data curation, K.X.G.; writing—original draft preparation, K.X.G.; writing—review and editing, K.X.G. and X.H.K.; visualization, W.Z. and X.H.K.; supervision, W.Z. and X.H.K.; project administration, and X.H.K. and X.C.Y.; funding acquisition, X.H.K. and X.C.Y. All authors have read and agreed to the published version of the manuscript.Author contributions.

Funding

This research was funded by the National Natural Science Foundation of China, China, Grant Number: 82074099; Guangdong Natural Science Foundation Project, China, Grant Number: 2023A1515011126; Guangdong Provincial Bureau of Traditional Chinese Medicine Research Project, China, Grant Number: 20201404; and, Qingyuan City Science and Technology Plan Project, China, Grant Number: 2021SJXM025.

Data availability

All data is available within the article or supplementary material.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yan Luo, Email: 409182103@qq.com.

Xue-Hong Ke, Email: kexuehong@126.com.

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