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BY 4.0 license Open Access Published by De Gruyter Open Access February 13, 2023

Characterization of effective constituents in Acanthopanax senticosus fruit for blood deficiency syndrome based on the chinmedomics strategy

  • Chunlei Wan , Xijun Wang EMAIL logo , Hongda Liu , Qingyu Zhang , Guangli Yan , Zhineng Li , Heng Fang and Hui Sun EMAIL logo
From the journal Open Chemistry

Abstract

The fruit of Acanthopanax senticosus (Rupr. and Maxim.) has been newly developed for the treatment of blood deficiency syndrome clinically, but the effective constituents are still unclear, restricting its quality control and the new medicinal development based on it. This study elucidated the efficacy of A. senticosus fruit (ASF) for treating blood deficiency syndrome and accurately characterize the constituents. Chinmedomics strategy was used to identify the metabolic biomarkers of the model and the overall effect of ASF was evaluated based on the biomarker when it showed intervention effects for blood deficiency syndrome. ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to analyze the components in the blood absorbed from A. senticosus fruit, and the components highly relevant to the biomarker are regarded as potential effective constituents for blood deficiency syndrome. Twenty-two of the 28 urine metabolites of blood deficiency syndrome were significantly regulated by A. senticosus fruit, 97 compounds included 20 prototype components, and 77 metabolites were found in vivo under the acting condition. The highly relevant constituents were isofraxidin, eleutheroside B, eleutheroside B1, eleutheroside E, and caffeic acid, which might be the effective constituents of A. senticosus fruit. It is a promising new medicinal resource that can be used for treating blood deficiency syndrome.

1 Introduction

Acanthopanax senticosus (Rupr. and Maxim.; Acanthopanax senticosus (AS)) Harms belong to the Araliaceae family and is a shrub widely disseminated in northeastern China, Japan, South Korea, and other northeastern Asian countries. In traditional Chinese medicine (TCM), the roots and stems of AS have medicinal applications and are used to nourish blood and qi, fortify the spleen, tonify the kidney, and tranquilize the mind. Some properties of AS have been extensively used in the treatment of anemia, inflammatory diseases, and rheumatoid arthritis, demonstrating positive outcomes in clinical approaches. The European Medicines Agency (EMA) has already recognized the use of AS-based prescriptions for patients with debilitating symptoms such as fatigue and weakness [1,2,3].

Due to the high amount of AS roots and stems used in clinical and industrial applications, these wild resources are now considered endangered. On the other hand, cultivated AS has been underutilized. To overcome the negative environmental effects of the constant use of AS roots as medicinal resources, studies have been conducted on the medicinal properties of the fruit of A. senticosus (ASF). The pharmacological community has been paying attention to the potential of ASF to replace some functions of traditional medicinal parts of AS. Many clinical trials conducted in the past decades have proved that ASF can enrich blood properties [3], promote better digestion [4], act as an anti-tumor substance [5,6], bring antioxidant effects [6], improve immune regulation [7], reduce the risk of cardiovascular diseases [8], and treat diabetes [9] and many other pharmacological activities. The identification and pharmacological activities of active ingredients in ASF have also been studied, eleutheroside B, eleutheroside E in the rhizomes in the rhizomes of traditional medicinal parts [10], coumarins isofraxidin, and phenolic acid ingredients caffeic acid and flavonoid ingredients, caffeic acid, chlorpyrine, and quercetin also exist in ASF [3], so there is no significant difference in its activity and rhizomes.

On top of all these health-related advantages, ASF grows and falls off naturally, so it does not affect the sustainable development of natural resources [3,11]. Although ASF has a good blood-enriching effect, the expression of the biological functions of this fruit and its curative effect on the treatment of blood deficiency syndrome are still unclear. ASF-related active constituents and bioactive mechanisms that are beneficial in the treatment of this condition are also yet to be verified.

Blood deficiency syndrome is a common clinical syndrome in TCM, mostly caused by insufficient blood production or excessive blood loss due to blood dysfunction. Its main clinical manifestations are pale face, dizziness, numbness in hands and feet, palpitations and dreaminess. Some TCM theories refer to it as a condition caused by insufficient blood biochemistry or source, resulting in blood loss and hypofunction, which is similar to anemia [12].

In this study, an anemia model was induced by intraperitoneal injection of CTX; CTX is a commonly used clinical anti-tumor drug that can result in bone marrow suppression, thereby destroying hematopoietic function and causing anemia [13]. Some hematological parameters such as body weight, peripheral hemogram, organ indexes, and histopathology were tested, but this kind of data only reflects the local, apparent, and exogenous changes, neglecting the conditions of the whole body and other endogenous factors.

Chinmedomics combines metabolomics with the serum pharmacochemistry of TCM to create a systematic approach, which can identify biomarkers of symptoms, assess the effectiveness of TCM, and discover its active ingredient and its effective constituents [14,15]. Based on the chinmedomics strategy, a large number of TCM classical prescription interpretation practices have been reported. For example, the biomarkers and potential pathogenic mechanism of YangHuang syndrome were elucidated, among which the key target UDP-glucuronosyltransferase 1A1 was discovered, and the scoparone, geniposid, and other ingredients in Yinchenhao Tang were considered to be effective constituents for treating the Yanghuang syndrome [16,17,18]. Other related studies showed that the biomarkers in Alzheimer’s disease and the effective constituents ginsenoside Rf and ginsenoside F1 of Kaixin San could also be considered for the treatment of patients fighting against Alzheimer’s [19]. Recently, in a study conducted by Liu et al., the biomarkers in ShenYin deficiency were revealed, and the effective constituents magnoflorine and jatrorrhizine in the ZhibaiDihuang pill were discovered [20].

In this study, we introduced chinmedomics strategy coupled with blood deficiency syndrome model rats constructed by intraperitoneal injection of CTX to accurately evaluate the clinical efficacy of ASF and further understand the effective constituents of ASF. The outcomes of this study will provide basic evidence for the development of new medicinal approaches using parts of AS.

2 Materials and methods

2.1 Chemicals and instrument

Chromatographic grade acetonitrile and methanol were purchased from Thermo Fisher Scientific (Waltham, MA, USA) and analytical grade formic acid and phosphoric acid were purchased from Shanghai Aladdin Biochemical Technology Corporation (Shanghai, China). Distilled water was purchased from Guangzhou Watson’s Food & Beverage Co., Ltd. (Guangzhou, China). Cyclophosphamide (CTX) was purchased from Jiangsu Hengrui Pharmaceutical Corporation (Lianyungang, Jiangsu, China). ASF used in this study was collected from Xinbei village, Hailin town, Hailin city, and Heilongjiang province in China, after being identified by Prof. Xi-jun Wang of the Pharmacognosy Department, Heilongjiang University of Chinese Medicine.

Ultra-high-performance liquid chromatography quadrupole electrostatic field orbital trap linear ion trap mass spectrometry (Thermo Fisher, CA, USA) was employed with an electrospray ion source in the positive and negative ion mode to the overall quantitation of the mass of constituents and metabolites. The peripheral hemogram of the rats in each group was measured using an XE-5000 automatic blood cell analyzer (Sysmex Corporation, Japan).

2.2 Animal handling and drug administration

Male Sprague-Dawley rats (180 ± 20 g) were provided by Changsheng Biotechnology Co., Ltd. (Liaoning) (Batch No. 2019100). The rats were bred and fed in specific pathogen-free environment; the ambient temperature was controlled at 25 ± 1°C, the humidity was controlled at 50 ± 5%, and the light/dark cycle was alternately kept in cycles of 12 h. In this experiment, Sprague Dawley rats were divided into 4 groups, control group, model group, ASF group, and FEJ group, with 10 rats in each group. Referring to the modeling method of the blood deficiency syndrome model, the dose conversion of rats was carried out according to the body surface area method. The rats in the model group were given CTX (28 mg/kg) by intraperitoneal injection (i.p.) for 4 days. The control group received the same volume of normal saline (i.p.).

One hundred grams of ASF was immersed with 10 volumes of distilled water for 60 min and then decocted for 60 min. The above steps were repeated two times, and then, the filtrate was combined. The filtrate was concentrated and dried at 60°C. According to the clinically administered dose of AS in humans, it was converted into the administration dose of rats using the body surface area method, and the final dose of ASF was 648 mg/kg/day (ASF water extract).

After establishing the model, ASF groups were administered for 8 days from the fifth day. The model and control groups were given an equal volume of distilled water during this period. All experimental procedures were approved by the Animal Care and Ethics Committee of Heilongjiang University of Chinese Medicine and were performed in accordance with the Declaration of Helsinki.

2.3 Collection and preparation of samples

Urine samples were collected at two time points: the last day (4th day) of modeling and the last day (8th day) of administration after the establishment of the model. Each rat was separately placed in a metabolism cages at 8:00 pm and urine samples were obtained at 8:00 am next day, urine was centrifuged at 13,000 rpm for 15 min at 4°C, the supernatant was transferred into centrifugal tube and mix the same volume of ultrapure water, the sample was for UPLC-MS analysis.

Blood sample: on the 9th day, blood sample of all rats was collected from the hepatic portal vein; 2 mL was used for peripheral hemogram analysis. The remaining 2 mL of blood was centrifuged at 4°C, 4,000 rpm for 10 min, and the supernatant was frozen at −80°C for serum pharmacochemistry analysis. One milliliter of rat serum in each group was introduced into the activated HLB SPE column. The column was washed with 1 mL of purified water and then eluted with 1 mL of 72/800 methanol. 1 mL of water is used to elute macromolecular magazines such as proteins in blood samples, while 1 mL of methanol is used to elute exogenous components from blood samples for detection, and methanol eluent is retained. The methanol eluate was collected and blown dry with nitrogen at 40°C, and the residue was reconstituted with 100 µL of 50% methanol and centrifuged at 4°C, 13,000 rpm for 10 min, the sample was for UPLC-MS analysis.

ASF sample in vitro: sample preparation before ASF administration (to characterize in vitro components), weighted approximately 0.1 g ASF powders and add 10 mL of 80% methanol, ultrasonically treated it for 30 minutes (power 250 W, frequency 40 kHz), after centrifugation, the supernatant was passed through a 0.22 um membrane filter for UPLC-MS analysis.

2.4 Peripheral hemogram assay, organ index, and pathological examination

The blood of rats in each group was used for peripheral hemogram analysis, including white blood cells (WBCs), red blood cells (RBCs), hemoglobin (HGB), platelets (PLT), lymphocyte percentage (LY%), and monocyte percentage by using a blood analyzer. The thymus and spleen of rats were harvested, and the immune organs were used to calculate the organ index. Then, the visceral indices were calculated as follows:

Thymus index = Thymus mass/Body mass ,

Spleen index = Spleen mass/Body mass .

The experimental data were statistically analyzed by Graphpad Prism 8.3.0 software. The data passed through one-way ANOVA to compare the differences between multiple groups and p < 0.05 considered to be significantly different.

The spleen and thymus organs of the rats in each group were fixed, washed with distilled water, eluted with ethanol gradient, treated with xylene, embedded in paraffin, placed in a −20 freezer, cooled, placed in a paraffin microtome, sectioned at 4 µm, and stained with hematoxylin and eosin (H&E), and histopathological changes in the thymus and spleen were observed under an optical microscope (Eclipse E100).

2.5 Metabonomics analysis and data preprocessing

Chromatographic conditions were as follows: A Waters ACQUIT UPLC HSS column T3 (2.1 mm × 100 mm, 1.8 µm); mobile phase: A: 0.1% formic acid acetonitrile and B: 0.1% formic acid water; gradient elution procedure; column temperature: 35°C; sample bin temperature: 10°C; flow rate: 0.4 mL/min; and injection volume: 2 µL. The conditions for mobile phase elution are shown in Table 1.

Table 1

Gradient elution conditions of urine metabolic chromatography

Time (min) Flow rate (mL/min) Mobile phase A (%) Mobile phase B (%) Gradient curve
Initial 0.4 1 99 5
1 0.4 1 99 5
1.5 0.4 15 85 5
6 0.4 35 65 5
6.5 0.4 99 1 5
7 0.4 99 1 5
7.5 0.4 5 99 5
8.0 0.4 5 99 5

Mass spectrometry conditions were as follows: positive ion scanning mode: electrostatic ion source (ESI), capillary voltage 3,500 V, sheath gas 40Arb, auxiliary gas 15Arb, backflush gas 0Arb, ion transfer tube temperature 350°C, spray temperature 350°C, standard rate 120 K, mass scanning range m/z = 50–1,200, and scanning mode Full Scan (Level 1) and Acequire X (Level 1 to Level 2); negative ion scanning mode: ESI, capillary voltage 2,800 V, sheath gas 40Arb, auxiliary gas 15Arb, backflush gas 0Arb, mass scanning range m/z = 50–1,200, and scanning mode Full Scan (Level 1), Acequire X (Level 1 to Level 2).

Samples were collected by the Xcalibur workstation from Thermo Fisher Scientific (Waltham, Ma, USA), so as to obtain UPLC-MS raw data, which were then imported to Compound Discoverer 3.3 for chromatographic peak extraction, peak alignment, and normalization. The preprocessed data were imported into EZinfo 3.0 software (Waters, USA) for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) and combined with intergroup p-value (p < 0.05) and fold change value >1. The above-mentioned conditions were used to screen the biomarker markers.

The potential biomarkers Rt_m/z meeting the above criteria were locked, and the secondary fragmentation information of the corresponding ions was accurately collected in an Acequire X mode. With the help of the standard library mzCloud provided by Thermo Fisher Scientific, and using references from databases such as HMDB and KEGG, the chemical structure of the marker was finally determined based on the obtained precursor ion and fragment ion information. The urine samples of the control and model groups on the 4th day were used for marker screening, and the urine samples of all groups on the 8th day were used for the pharmacodynamics study of ASF.

2.6 Constituent analysis of ASF in vitro and in vivo

Chromatographic conditions were as follows: waters ACQUIT UPLC HSS column T3 (2.1 mm × 100 mm, 1.8 µm); mobile phase: A: 0.1% formic acid acetonitrile and B: 0.1% formic acid water; gradient elution procedure; column temperature: 35°C; sample bin temperature: 10°C; flow rate: 0.4 mL/min; and injection volume: 2 µL. The conditions for mobile phase elution are shown in Table 2.

Table 2

Gradient elution conditions of constituent analysis in vitro of ASF

Time (min) Flow rate (mL/min) Acetonitrile with 0.1% formic acid (%) Water with 0.1% formic acid (%) Curve
Initial 0.4 1 99 5
1 0.4 1 99 5
30 0.4 99 1 5
30.5 0.4 99 1 5
31 0.4 1 99 5
32 0.4 1 99 5

Mass spectrometry conditions were as follows: positive ion scanning mode: ESI, capillary voltage 3,200 V, sheath gas 30Arb, auxiliary gas 5 Arb, backflush gas 0Arb, ion transfer tube temperature 350°C, spray temperature 350°C. standard rate 120 K, mass scanning range m/z = 50–2,000, and scanning mode Full Scan (Level 1) and Acequire X (Level 1 to Level 2); negative ion scanning mode: ESI, capillary voltage 2,500 V, sheath gas 30Arb, auxiliary gas 5 Arb, backflush gas 0Arb, mass scanning range m/z = 50–2,000, and scanning mode: Full Scan (Level 1) and Acequire X (Level 1 to Level 2).

2.7 Effective component analysis of ASF in treating blood deficiency syndrome

Chinmedomics strategy was applied to better understand the effective constituents in TCM in treating the symptoms of individuals suffering from blood deficiency syndrome. PCMS software was used to analyze the correlation between different urine biomarkers and ASF blood components after setting appropriate, relevant parameters and selecting components highly related to blood deficiency syndrome from a large number of ASF blood components.

2.8 Exploring the mechanism of ASF in the treatment of blood deficiency syndrome based on the network pharmacology

The active components found in ASF were collected through the Pharmmapper (http://www.lilab-ecust.cn/pharmmapper/) and Way2drug (http://www.way2drug.com/PASSOnline/) databases for their potential targets. Known targets closely related to “blood deficiency syndrome and anemia” were collected using the Genecards database (https://www.genecards.org/). Then, these two batches of data were integrated to intersect the component prediction targets and disease-related targets. The protein database UniProt (http://www.uniprot.org/) was used to enter the target name and selected the species to “human,” and the search results were corrected to the official name and crossover genes were found. Cytoscape3.2.1 software (https://cytoscape.org/) was used to correlate the collected ASF potential components, predicted targets, and in vivo targets of blood deficiency syndrome, so a network of ASF components-predicted and targets-disease targets could be constructed. ASF potential components, target proteins, and related disease names constitute the nodes of the network, and functional connections constitute the edges of the network [21].

To explore the role of target proteins at the system level, the relevant target proteins were uploaded to the online STRING online processing platform (https://string-db.org), and the ones with a score higher than 0.9 were selected according to the system confidence score. The data were used to build a PPI network and the TSV. format file generated by the PPI network into Cytoscape for Network Analyzer was imported, selecting degree >50 as the hub gene, and using the R toolkit to draw a bar graph for the proteins in the PPI network. To explain the role of target proteins in gene function and signaling pathways, the David database (https://david.ncifcrf.gov/) was used to perform GO functional annotation enrichment analysis on target proteins in the PPI network.

Combining the results of ASF’s regulation of blood deficiency syndrome metabolomics profile and the results of network pharmacology, the key metabolic enzymes and mechanisms of ASF’s regulation of blood deficiency syndrome were discovered and determined through pathway analysis and retrospective of the regulation process of biomarkers. The key metabolic enzymes and receptor proteins obtained from the above process were verified by molecular docking of computer simulation.

Next, molecular docking technology was used to verify the binding state of the active ingredient to the target. Autodock tools 1.5.6 and Pymol software were used to modify the structures of proteins and components. At this stage, the 5′-nucleotidase protein acts as the receptor, and the potential active ingredients act as the ligands. The 3D structure of the receptor protein was downloaded from the PDB database. The protein was dehydrated and hydrogenated using Pymol software, and the compounds and target eggs were converted into *pdbqt format using the AutoDock software. Finally, the binding energy was calculated by Lamarck genetic algorithm, and the binding activity of the components to the target was evaluated for further verification.

3 Results

3.1 Bodyweight variety and organ indexes analysis

Figure 1a shows the influence of ASF on weight in rats with blood deficiency syndrome. The body weight of rats in the model group increased slowly and was significantly lower than that in the control group. It was observed that ASF groups could significantly inhibit the above symptoms. Detection of hematological indexes was helpful in judging the occurrence and recovery of blood deficiency syndrome rats. As shown in Figure 1b, compared with the control group, both the thymus and spleen showed obvious atrophy, and there were significant differences in the thymus index and spleen index (p < 0.01). The results show that ASF has a certain regulatory effect on the symptoms of blood deficiency syndrome.

Figure 1 
                  Evaluation index of ASF treatment of blood deficiency syndrome. (a) Trends of rats weight. (b) Viscera index of rats in each group. (c) Peripheral hemogram of rats in each group (ASF, A. senticosus fruit group; FEJ, Fufang E’jiao Jiang group; compared with the control group, *p < 0.05, **p < 0.01; compared with the model group, #p < 0.05, ##p < 0.01).
Figure 1

Evaluation index of ASF treatment of blood deficiency syndrome. (a) Trends of rats weight. (b) Viscera index of rats in each group. (c) Peripheral hemogram of rats in each group (ASF, A. senticosus fruit group; FEJ, Fufang E’jiao Jiang group; compared with the control group, *p < 0.05, **p < 0.01; compared with the model group, #p < 0.05, ##p < 0.01).

3.2 The influence of ASF on hemogram in rats and pathological changes with blood deficiency syndrome

As shown in Figure 1c, the RBC, HGB, HCT, PLT, WBC, and LY in the model group were significantly lower than those in the control group. The results showed that the blood deficiency syndrome model was successfully established. After administration of ASF, WBC and LY significantly increased (p < 0.01), and RBC, HGB, HCT, and PLT were also significantly increased (p < 0.05). The ASF group can significantly inhibit the above symptoms. Detection of hematological parameters was contributed to observe the recovery of anemia rats. RBC, HGB, HCT, PLT, WBC, and LY were significant indexes of peripheral hemogram examination. These indicators can directly reflect the progress of anemia and were the golden indicator to measure the success of anemia model construction.

The H&E staining of the thymus is shown in Figure 2a. The thymic tissue control group showed obvious thymic lobule structure, clear cortical-medulla demarcation (black arrows), abundant small lymphocytes in the cortex, normal morphological structure, and mainly large lymphocytes in the medulla, while the thymus model group showed obvious atrophy, the number of lymphocytes was markedly reduced with massive connective tissue hyperplasia (red arrows) with massive macrophage infiltration (yellow arrows). Compared with the model group, the number of lymphocytes increased significantly after the administration of ASF, and the infiltration of macrophages was weakened.

Figure 2 
                  Effects of ASF on the thymus tissues and spleen tissues in the H&E-stained histopathological images: 200×. (a) Thymus histopathological staining image (black arrows: cortical-medulla demarcation, red arrows: connective tissue hyperplasia, yellow arrows: macrophage infiltration). (b) Spleen histopathological staining image (black arrows: splenic nodules, yellow arrows: germinal center, green arrows: splenic nodules). (c) Femoral tissues staining image (black arrows: hematopoietic cells, yellow arrows: severe bleeding, red arrows: tissue).
Figure 2

Effects of ASF on the thymus tissues and spleen tissues in the H&E-stained histopathological images: 200×. (a) Thymus histopathological staining image (black arrows: cortical-medulla demarcation, red arrows: connective tissue hyperplasia, yellow arrows: macrophage infiltration). (b) Spleen histopathological staining image (black arrows: splenic nodules, yellow arrows: germinal center, green arrows: splenic nodules). (c) Femoral tissues staining image (black arrows: hematopoietic cells, yellow arrows: severe bleeding, red arrows: tissue).

The H&E staining of the spleen is shown in Figure 2b. As shown, a large number of splenic nodules are spotted around the thickened marginal zone (black arrow), and the germinal center can be seen locally (yellow arrow). Splenic nodules were significantly reduced in number and volume (green arrows). Moreover, compared with the model group, the number of spleen nodules increased significantly after the administration of ASF. The above results show that ASF has a certain improvement effect on the symptoms of blood deficiency syndrome.

The H&E staining of the spleen is shown in Figure 2c. The number of bone beams is abundant, the arrangement of the arrangement, the structure of the osteoclastous morphology is normal, the number of blood cells in the bone marrow cavity is abundant, and no obvious abnormalities are seen. The number of hematopoietic cells in the bone marrow cavity is reduced (black arrow), accompanied by severe bleeding (yellow arrow) and a small number of connective tissue proliferation (red arrow). Compared with the model group, the pathological status of the ASF administration of bone marrow has improved significantly.

3.3 Analysis of urine metabolic profile and identification of differential biomarkers in rats with blood deficiency syndrome

Under the premise of successful model replication, urine samples were collected and analyzed by UPLC-MS (Figure S1), EZinfo software was used for multivariate analysis. The PCA results showed that the control group and the model group could be clearly distinguished, it was also observed that the metabolic profiles of the groups had changed significantly (Figure 3a and b). To further distinguish the characteristics of each group, OPLS-DA was used to obtain variable weight values (VIP) to select preliminary differential metabolic biomarkers, as shown in Figure 3c and d, and draw S-plots (Figure 3e and f) reflecting each component contribution to metabolic profile changes. Finally, 28 biomarkers for the treatment of blood deficiency syndrome were characterized. The identification information of the marker (compared with the split process of the secondary split fragment of the marker standard) was shown in Table 3 and Figure S2. With the help of the standard library mzCloud provided by Thermo Fisher Scientific, the chemical structure of the marker was finally determined based on the obtained precursor ion and fragment ion information.

Figure 3 
                  Metabolic profile characterization and multivariate. (a) PCA score plots for the control and model groups in a positive mode. (b) PCA score plots for the control and model groups in a negative mode. (c) OPLS-DA score plots for the control and model groups in a positive mode. (d) OPLS-DA score plots for the control and model groups in a negative mode. (e) Metabolite biomarkers in the S-plot between the control and model groups in a positive mode. (f) Metabolite biomarkers in the S-plot between the control and model groups in a negative mode.
Figure 3

Metabolic profile characterization and multivariate. (a) PCA score plots for the control and model groups in a positive mode. (b) PCA score plots for the control and model groups in a negative mode. (c) OPLS-DA score plots for the control and model groups in a positive mode. (d) OPLS-DA score plots for the control and model groups in a negative mode. (e) Metabolite biomarkers in the S-plot between the control and model groups in a positive mode. (f) Metabolite biomarkers in the S-plot between the control and model groups in a negative mode.

Table 3

Detailed information on biomarkers tentatively identified by urine metabolomics

No. Rt (min) Metabolite name Molecular formula Ion form m/z calculated Error (ppm) MS/MS fragmentation (m/z) HMDB Trend
1 0.903 l-Aspartic acid C4H7NO4 [M−H] 133.03751 0.87957 115.00350 114.01949 HMDB0000191
88.04008 71.01358
2 0.922 d-Ribose-1-phosphate C5H11O8P [M−H] 230.01915 0.06728 211.00114 138.97993 HMDB0001489
96.96938 78.95882
3 0.931 Gluconic acid C6H10O6 [M−H] 196.0583 −0.48147 159.02985 141.01930 HMDB0000625
129.01929 117.01926
111.00877 114.01949
88.04008 71.01358
4 0.952 d-2-Hydroxyglutaric acid C5H8O5 [M−H] 148.03717 0.37466 129.01909 103.03974 HMDB0000606
101.02412 85.02921
5 1.084 Glycolic acid C2H4O3 [M−H] 76.01604 −0.41712 72.99300 67.11357 HMDB0000115
6 1.112 Orotidine C10H12N2O8 [M−H] 288.05937 −0.10383 111.01991 HMDB0000788
7 1.161 d-Malic acid C4H6O5 [M−H] 134.02152 0.62488 115.00349 89.02416 HMDB0031518
87.00851 72.99286
71.01363 59.01361
89.02416[M−H–C3H5O3]
87.00851[M−H–C3H3O3]
72.99286[M−H–C2HO3]
71.01363[M−H–C3H2O2]
59.01361[M−H–C2HO2]
8 1.318 2-Oxoglutaric acid C5H6O5 [M−H] 146.02152 0.29574 101.02405 73.02922 HMDB0000208
57.03433
9 1.332 Orotic acid C5H4N2O4 [M−H] 156.01711 0.52823 111.01982 HMDB0000226
10 1.391 Cytidine C9H13N3 O5 [M−H] 243.08549 −0.10960 112.93710 HMDB0000123
11 1.426 l-Lactic Acid C3H6O3 [M−H] 90.03169 0.34227 87.00854 72.99290 HMDB0000190
71.01366 59.01370
12 1.483 Anthranilic acid C7H7NO2 [M−H] 137.04768 0.65656 92.05040 HMDB0001123
13 1.557 Acamprosate C5H11NO4S [M−H] 181.04088 0.50764 116.07161 80.96500 HMDB0014797
79.95717
14 1.559 Uric acid C5H4N4O3 [M−H] 168.02834 0.19531 124.01526 97.00432 HMDB0000289
96.02032 69.0093
15 1.561 Citric acid C6H8O7 [M−H] 192.027 0.19630 173.00912 154.99849 HMDB0000094
147.02979 130.99838
129.01933 111.00865
101.02417 87.00852
85.02927 67.01873
16 1.652 4-Oxoproline C5H7NO3 [M−H] 129.04259 0.63894 84.04256 82.03025 METPA0228
17 1.696 Citraconic acid C5H6O4 [M−H] 130.02661 0.51973 85.02930 HMDB0000634
18 1.803 Xanthine C5H4N4O2 [M−H] 152.03343 0.56079 108.02029 HMDB0000292
19 1.901 Methylmalonic acid C4H6O4 [M−H] 118.02661 2.06297 73.02923 55.01871 HMDB0000202
20 2.422 Glyceric acid C3H6O4 [M−H] 106.02661 −1.02720 75.00853 59.01364 HMDB0000139
21 2.687 Guanosine C10H13N5O5 [M−H] 283.09167 −0.42297 150.04201 133.01559 HMDB0000133
126.03076 108.02020
107.03622
22 2.959 Syringic acid C9H10O5 [M−H] 198.05282 0.04784 182.02187 166.99850 HMDB0002085
153.05551 138.03215
123.00854 95.01372
23 3.063 Folic acid C19H19N7 O6 [M−H] 441.13968 −0.09755 396.14224 267.09930 HMDB0000121
128.03513 85.02881
24 3.109 5-Hydroxyindole acetic acid C10H9NO3 [M−H] 191.05824 −0.09561 175.02711 146.06097 HMDB0000763
144.04510 131.03734
25 3.307 Tyrosol C8H10O2 [M−H] 138.06808 0.72282 135.04514 119.05028 HMDB0004284
107.05013 93.03479
81.03432
26 3.591 Gentisic acid C7H6O4 [M−H] 154.02661 0.32710 127.07621 125.06041 HMDB0000152
109.06568 71.05014
59.01378
27 4.543 Glycine C2H5NO2 [M−H] 75.03203 0.01277 74.02447 55.32548 HMDB0000123
28 4.707 N-Acetyl-l-leucine C8H15NO3 [M−H] 173.10519 0.64623 130.08717 128.10799 HMDB0011756

3.4 Effect of evaluation of ASF on blood deficiency syndrome

The PCA score chart shows that the urine metabolic spectrum of the blood deficiency model rats has significantly changed after the treatment with ASF. The biomarkers’ relative measurements before and after ASF treatment. The control and model groups are still separated, and the ASF group has a tendency to be close to the control group and far from the model group (Figure 4b and c). The biomarkers’ relative measurements before and after ASF treatment are represented in Figure 4a. MetaboAnalyst 5.0 and the KEGG website were used to explore the relatively disordered pathways of the call backed metabolites of the ASF group (Figure 5a). To clarify the disorder state affecting blood deficiency syndrome from the perspective of metabolism, we will summarize and refine the main markers affecting blood deficiency syndrome and related metabolic pathways. The topological characteristics of the related metabolic pathways of 28 urine metabolic markers identified in Table 3 were analyzed by Metpa database and several advanced path analysis programs that could measure their impact value. Our impact value that affects the metabolic pathway of blood deficiency syndrome is greater than 0.1 as potential target pathway: alanine, aspartate and glutamate metabolism; glycine, serine, and threonine metabolism; glyoxylate and dicarboxylate metabolism; citrate cycle (TCA cycle); pyrimidine metabolism; glycerolipid metabolism; glutathione metabolism; and pentose phosphate pathway. Secondarily, to analyze the main metabolites and pathway changes in the rats with blood deficiency after the ASF was given more intuitively, the analysis network between the relevant metabolic pathways involved in the key biomarkers is shown in Figure 5b.

Figure 4 
                  Regulation ettects of ASF on potential urine biomarkers in blood deficiency rats. (a) Normalized abundance values of ASF between different groups of potential urinary biomarkers in blood-deficiency rats. Results are expressed as mean ± SD, n = 10. *p < 0.05, **p < 0.01 vs control group. #p < 0.05, ##p < 0.01 vs model group. C represents control group, M represents model group, ASF represents A. senticosus fruit group, FEJ, Fufang E’jiao Jiang group. (b) PCA score of metabolic profile of rats in each group in a positive ion mode. (c) PCA score of metabolic profile of rats in each group in a positive ion mode and in a negative ion mode.
Figure 4

Regulation ettects of ASF on potential urine biomarkers in blood deficiency rats. (a) Normalized abundance values of ASF between different groups of potential urinary biomarkers in blood-deficiency rats. Results are expressed as mean ± SD, n = 10. *p < 0.05, **p < 0.01 vs control group. #p < 0.05, ##p < 0.01 vs model group. C represents control group, M represents model group, ASF represents A. senticosus fruit group, FEJ, Fufang E’jiao Jiang group. (b) PCA score of metabolic profile of rats in each group in a positive ion mode. (c) PCA score of metabolic profile of rats in each group in a positive ion mode and in a negative ion mode.

Figure 5 
                  General diagram of main metabolic pathways of ASF in treating the rat model of blood deficiency syndrome. (a) Main metabolic pathways of potential biomarkers. 1: alanine, aspartate and glutamate metabolism; 2: glycine, serine and threonine metabolism; 3: glyoxylate and dicarboxylate metabolism; 4: TCA cycle; 5: pyrimidine metabolism; 6: glycerolipid metabolism; 7: glutathione metabolism; and 8: pentose phosphate pathway. (b) Metabolite profile after administrated ASF the urine metabolites relative-disordered pathway.
Figure 5

General diagram of main metabolic pathways of ASF in treating the rat model of blood deficiency syndrome. (a) Main metabolic pathways of potential biomarkers. 1: alanine, aspartate and glutamate metabolism; 2: glycine, serine and threonine metabolism; 3: glyoxylate and dicarboxylate metabolism; 4: TCA cycle; 5: pyrimidine metabolism; 6: glycerolipid metabolism; 7: glutathione metabolism; and 8: pentose phosphate pathway. (b) Metabolite profile after administrated ASF the urine metabolites relative-disordered pathway.

3.5 ASF constituent analysis in vitro and in vivo

The TIC spectra of ASF samples in vitro ESI+ and ESI under AcequireX mode were collected by UHPLC-Q-Orbitrap-LIT (Figure S3). The mass spectrometry data obtained by UPLC/MS were imported into Compound Discoverer 3.3 for processing. Characterization was conducted by matching the standard database Advanced Mass Spectral Database mzCloud, mzVault and Chemspider chemical database. Finally, a total of 135 in vitro compounds were tentatively identified (Table 4).

Table 4

Analysis of components of ASF in vitro

No Rt (min) Molecular weight Compound name Formula Positive PPM Negative PPM MS2
1 0.617 192.06339 d-(−)-Quinic acid C7H12O6 0.0000713729580752442
2 0.628 194.04265 β-d-Glucopyranuronic acid C6H10O7 0.000216806075911791
3 0.633 104.01096 Malonic acid C3H4O4 0.0000458494204735871
4 0.639 118.02661 Methylmalonic acid C4H6O4 0.000136845176328393
5 0.663 86.03678 Diacetyl C4H6O2 0.0000222361899346879
6 0.672 72.02113 Acrylic acid C3H4O2 0.0000372543388778013
7 0.687 116.01096 Fumaric acid C4H4O4 0.0000957560479974973
8 0.69 174.05282 3,4,5-Trihydroxycyclohex-1-ene-1-carboxylic acid C7H10O5 0.000103457225179682
9 0.701 112.01604 2-Furoic acid C5H4O3 0.000135931638510556
10 1.045 117.07898 5-Aminovaleric acid C5H11NO2 0.0000602628205825795
11 1.135 152.06847 d-(+)-Arabitol C5H12O5 0.0000273450523877727
12 1.532 188.03209 3-Butene-1,2,3-tricarboxylicacid C7H8O6 0.0000613003665819178
13 1.572 104.04734 3-Hydroxybutyric acid C4H8O3 0.0000269789146756239
14 1.589 132.04226 Ethylmalonic acid C5H8O4 0.0000825816856035999
15 1.766 104.04734 4-Hydroxybutyricacid(GHB) C4H8O3 0.0000256717842006537
16 2.182 146.05791 Adipic acid C6H10O4 0.000119504578577789
17 2.521 206.04265 3-Hydroxy-3-(methoxycarbonyl)pentanedioic acid C7H10O7 −5.77623916342418 × 10−6
18 2.715 132.04226 Glutaric acid C5H8O4 0.0000806990416606368
19 2.782 146.05791 2-Methylglutaric acid C6H10O4 0.0000880349837473204
20 3.039 148.07356 Mevalonic acid C6H12O4 0.0000825816856320216
21 3.481 132.04226 Methylsuccinic acid C5H8O4 0.0000832043189404885
22 3.83 168.04226 6-Methoxysalicylic acid C8H8O4 0.000152185780677883
23 4.033 154.02661 Gentisic acid C7H6O4 0.0000872379914085286
24 4.254 354.09508 Neochlorogenic acid C16H18O9 0.0000881231496805412
25 4.776 180.04226 Caffeic acid C9H8O4 0.000150398482958281
26 4.878 473.1659 Folinic acid C20H23N7O7 0.000314791194625741
27 4.913 136.05243 2-Methylbenzoic acid C8H8O2 0.0000764212448700619
28 5.154 138.03169 3-Hydroxybenzoic acid C7H6O3 0.000101501083889843
29 5.284 145.05276 4-Indolecarbaldehyde C9H7NO 0.000101798812806919
30 5.463 290.07904 Catechin C15H14O6 −0.0000880155955087503
31 5.495 354.09508 Chlorogenic acid C16H18O9 −0.000143582136104214
32 5.803 189.10011 2-Aminooctanedioic acid C8H15NO4 0.000107079214529904
33 5.923 168.04226 2,4,6-Trihydroxyacetophenone C8H8O4 0.000141968487696431
34 6.052 160.07356 3-Methyladipic acid C7H12O4 0.0000412166991168306
35 6.298 159.08954 N-Isovalerylglycine C7H13NO3 0.0000502969902242967
36 6.375 194.05791 Ferulic acid C10H10O4 −0.000012347244933153
37 6.441 132.07864 6-Hydroxycaproic acid C6H12O3 0.000134655427302732
38 6.621 388.20972 Dihydroroseoside C19H32O8 0.0000173578727640233
39 6.682 154.02661 3,5-Dihydroxybenzoic acid C7H6O4 0.0000780412000267461
40 6.711 173.10519 2-(Acetylamino)hexanoic acid C8H15NO3 0.000111113721203537
41 6.881 516.12678 4,5-Dicaffeoylquinicacid C25H24O12 0.00058371952059133
42 6.934 108.05751 4-Methylphenol C7H8O 0.0000334693546335529
43 7.106 160.07356 3,3-Dimethylglutaric acid C7H12O4 0.00002199954445814
44 7.225 358.12638 3-[2-(β-d-Glucopyranosyloxy)-4-methoxyphenyl]propanoic acid C16H22O9 −0.00018516239794053
45 .317 152.04734 Vanillin C8H8O3 0.0000631217693580766
46 .632 610.15338 Rutin C27H30O16 0.000700471437994565
47 .691 174.08921 Suberic acid C8H14O4 0.0000929626514789561
48 .824 175.06333 Indole-3-acetic acid C10H9NO2 0.0000768679656744098
49 .151 288.06339 Eriodictyol C15H12O6 −0.000146912958996381
50 .166 464.09548 Quercetin-3β-d-glucoside C21H20O12 0.000264689809114316
51 .241 116.08373 Hexanoic acid C6H12O2 0.000066604073168719
52 8.243 478.07474 Miquelianin C21H18O13 0.000262746446253459
53 8.251 205.07389 Indole-3-lactic acid C11H11NO3 −0.0000820576061641987
54 8.252 302.04265 Quercetin C15H10O7 −0.000135691909008528
55 8.333 448.10056 Cynaroside C21H20O11 0.00022299379895685
56 8.403 222.05282 Isofraxidin C11H10O5 0.02
57 8.525 150.05282 d-(−)-Lyxose C5H10O5 0.0000436373682646263
58 8.573 442.1839 Lusitanicoside C21H30O10 0.000295145840993882
59 8.63 150.05282 d-(+)-Xylose C5H10O5 0.0000410186814292501
60 8.712 448.10056 Astragalin C21H20O11 0.000210417421442344
61 8.821 432.10565 Apigetrin C21H20O10 0.000408938045666218
62 8.981 450.11621 Astilbin C21H22O11 0.000204164772924287
63 9.11 272.06847 Naringenin C15H12O5 0.0000142099602271628
64 9.232 418.09 Juglalin C20H18O10 0.000130868261294381
65 9.247 188.10486 Azelaic acid C9H16O4 0.0000281009327522952
66 9.43 230.15181 Dodecanedioic acid C12H22O4 −0.0000675814641510897
67 9.531 288.06339 Maesopsin C15H12O6 −0.0000951389567944716
68 10.459 274.08412 Phloretin C15H14O5 −0.0000960098334417125
69 10.497 202.12051 3-tert-Butyladipic acid C10H18O4 0.000114371783666911
70 11.117 258.11168 N-(2,3-Dihydro-1H-inden-2-yl)-1-methyl-4-nitro-1H-imidazol-5-amine C13H14N4O2 −18.0119944946248
71 11.225 242.05791 [1,1′-Biphenyl]-2,2′-dicarboxylic acid C14H10O4 −0.0000645529068776796
72 11.322 325.22531 10-Nitrolinoleate C18H31NO4 −18.0107096820331
73 11.991 328.22497 Corchorifatty acid F C18H32O5 −0.000165869641023164
74 12.26 150.10447 2-tert-Butylphenol C10H14O 0.0000678329162724367
75 12.387 302.07904 3′,5,7-Trihydroxy-4′-methoxyflavanone C16H14O6 −0.0000684317502646081
76 12.783 316.0583 3-Methoxy-5,7,3′,4′-tetrahydroxy-flavone C16H12O7 −0.0000942971071822285
77 13.567 258.18311 Tetradecanedioic acid C14H26O4 −0.000102003001359208
78 13.734 222.08921 Monobutyl phthalate C12H14O4 0.0000531652127619964
79 14.167 210.12559 Jasmonic acid C12H18O3 0.0000365041183556514
80 14.334 194.09429 Butylparaben C11H14O3 3.30456151687031 × 10−6
81 14.414 166.09938 Perillic acid C10H14O2 0.0000905301124305424
82 14.588 504.34509 Arjungenin C30H48O6 0.000420181025333477
83 15.863 222.16198 2,5-di-tert-Butylhydroquinone C14H22O2 0.0000534064366206621
84 17.693 314.24571 (+/−)9,10-Dihydroxy-12Z-octadecenoic acid C18H34O4 −0.000174739124588541
85 18.322 206.16707 2,6-di-tert-Butylphenol C14H22O −0.0000203626833013004
86 18.473 324.26645 Labdanolic acid C20H36O3 −0.000165707837993523
87 18.821 292.20384 12-oxoPhytodienoic acid C18H28O3 −0.0000541993261435891
88 18.944 236.17763 3,5-di-tert-Butyl-4-hydroxybenzylalcohol C15H24O2 −0.000054510826743126
89 19.591 266.15518 Dodecylsulfate C12H26O4S −0.000106754440707846
90 19.928 278.22458 Oleicacidalkyne C18H30O2 −0.000152087394212685
91 20.286 244.20384 (R)-3-Hydroxymyristic acid C14H28O3 −0.000111365393962615
92 21.251 272.23514 16-Hydroxyhexadecanoic acid C16H32O3 −0.0000609756394851502
93 21.26 244.20384 2-Hydroxymyristic acid C14H28O3 −0.0000391905671790482
94 23.08 332.23514 17α-Hydroxypregnenolone C21H32O3 −0.000167365334505121
95 23.618 308.27153 11(Z),14(Z)-Eicosadienoicacid C20H36O2 −0.000152642416367144
96 23.906 341.29299 N-Acetylsphingosine C20H39NO3 −0.000170901248054633
97 24.048 294.18648 Myristylsulfate C14H30O4S −0.000118059798410286
98 24.207 282.25588 Trans-Petroselinic acid C18H34O2 −8.40480993247184 × 10−6
99 24.408 326.19157 4-Dodecylbenzenesulfonic acid C18H30O3S −0.000206860495040928
100 24.645 456.36035 Oleanolic acid C30H48O3 0.000239142127554715
101 25.228 310.28718 11(E)-Eicosenoic acid C20H38O2 −0.0000357289986254727
102 26.94 256.24023 Palmitic acid C16H32O2 −0.0000743504740512435
103 0.939 126.03169 Pyrogallol C6H6O3 0.579270523
104 1.056 123.03203 Nicotinic acid C6H5NO2 0.308993012
105 1.825 174.05282 Shikimic acid C7H10O5 0.753128248
106 3.521 138.03169 Salicylic acid C7H6O3 −8.57451709634915
107 3.949 219.11067 Calcium pantothenate C9H17NO5 0.201290851
108 4.726 270.05282 Apigenin C15H10O5 0.028443723
109 5.699 290.07904 (+)-Catechin hydrate C15H14O6 −0.313011542
110 5.8 580.14282 Leucoside C26H28O15 1.397324089
111 5.889 306.07395 (−)-Gallocatechin C15H14O7 −0.538290672
112 5.896 192.04226 Scopoletin C10H8O4 0.643728033
113 5.947 578.14243 Procyanidin B1 C30H26O12 1.053478587
114 6.139 178.02661 5,7-Dihydroxychromone C9H6O4 0.426844477
115 6.503 192.06339 Quinic acid C7H12O6 0.459843679
116 6.741 224.06847 Sinapic acid C11H12O5 0.042785734
117 7.068 682.24728 Pinoresinoldiglucoside C32H42O16 0.376013561
118 7.132 154.02661 Protocatechuic acid C7H6O4 0.364352836
119 7.29 712.25785 (−)Syringaresnol-4-O-β-d-apiofuranosyl-(1→2)-β-d-glucopyranoside C33H44O17 0.415694433
120 7.37 304.0583 Taxifolin C15H12O7 0.79471129
121 7.66 164.04734 p-Coumaric acid C9H8O3 0.147316343
122 7506 742.26841 Eleutheroside E C34H46O18 1.1487103
123 .881 516.12678 1,3-Dicaffeoylquinic acid C25H24O12 0.284136931
124 9.096 178.06299 Ferulaldehyde C10H10O3 0.421759657
125 9.557 520.19446 Pinoresinol4-O-glucoside C26H32O11 0.828699479
126 9.596 162.03169 7-Hydroxycoumarin C9H6O3 0.168054024
127 10.054 354.09508 1-Caffeoylquinicacid C16H18O9 0.423721906
128 11.344 208.07356 Ethylcaffeate C11H12O4 0.167771727
129 12.488 286.04774 Kaempferol C15H10O6 0.489076484
130 15.864 294.18311 6-Gingerol C17H26O4 0.137004218
131 21.276 280.24023 Linoleic acid C18H32O2 0.00003654604
132 25.963 339.3501 Docosanamide C22H45NO 1.977428048
133 5.34 208.07356 Methyl4-hydroxy-3-methoxycinnamate C11H12O4 0.41273
134 8.947 212.06847 Propylgallate C10H12O5 3.4284246
135 9.328 206.09429 Isoeugenolacetate C12H14O3 1.447236

Referring to the chromatographic and mass spectrometry conditions under the above method, the TIC spectra of ASF after entering the blood in ESI+ and ESI− were collected after we carried out an extensive tentative identification of blood constituents in the state of markedly effective ASF treatment of blood deficiency syndrome (Figure S4). In addition, we utilized Compound Discoverer 3.3 and a standard database to identify prototype constituents and metabolites in blood. Finally, a total of 20 in vivo compounds were tentatively identified as ASF prototype constituents (Table 5 and Figure S5) and 77 metabolites were recognized (Table 6).

Table 5

Analysis of constituents in blood deficiency syndrome rat serum after the oral administration of ASF

No. Rt (min) Compound name Molecular formula Ion form m/z calculated m/z theoretical Error (ppm/Da) MS/MS fragmentation (m/z)
1 8.327 Isofraxidin C11H10O5 [M−H]+ 222.05280 222.05282 0.12 107.04888 135.11729
162.03104 190.02611
96.96938 78.95882
2 14.922 Palmitic acid C16H32O2 [M−H]+ 273.26682 273.2668 18.02659 Da 88.07557 102.09120
212.23744 230.24817
3 6.139 Caffeic acid C7H6O4 [M−H] 180.04228 180.04226 0.12 89.03905 134.03658
4 6.501 Protocatechuic acid C6H12O3 [M−H] 154.02660 154.02661 0.08 109.02905
5 6.502 6-Hydroxycaproic acid C8H15NO3 [M−H] 132.07871 132.07864 0.51 85.06518 113.06017
129.05469
6 6.717 2-(Acetylamino)hexanoic acid C10H9NO2 [M−H] 173.10526 173.10519 0.36 128.04982 130.06532
144.04437
7 6.766 Indole-3-acetic acid C9H7NO [M−H] 175.06337 175.06333 0.23 128.04982 130.06532
144.04437
8 8.638 4-Indolecarbaldehyde C7H8O [M−H] 145.05277 145.05276 0.08 115.04188 143.03645
9 7.078 4-Methylphenol C8H14O4 [M−H] 108.05758 108.05751 0.6 106.04153
10 7.728 Suberic acid C8H8O2 [M−H] 174.08924 174.08921 0.18 109.06497 111.08076
11 7.849 2-Methylbenzoic acid C7H6O3 [M−H] 136.05246 136.05243 0.26 88.04445
12 9.088 Salicylic acid C9H16O4 [M−H] 138.03176 138.03169 0.47 65.03897 93.03383
13 9.277 Azelaic acid C10H18O4 [M−H] 188.10488 188.10486 0.12 123.08075 125.09641
143.10703
14 10.84 3-tert-Butyladipic acid C14H30O4S [M−H] 202.12061 202.12051 0.49 139.11198 183.10143
15 12.036 Myristyl sulfate C14H26O4 [M−H] 294.18322 294.18648 11.07 96.96890
16 13.004 Tetradecanedioic acid C14H26O4 [M−H] 258.18315 258.18311 0.16 239.16359
17 14.326 Butylparaben C14H22O2 [M−H] 194.09431 194.09429 0.06 82.29434
18 15.867 2,5-Di-tert-butylhydroquinone C18H34O4 [M−H] 222.16200 222.16198 0.09 148.05188 164.08336
19 18.868 Dodecyl sulfate C12H18O3 [M−H] 266.15523 266.15518 0.17 79.95675 96.95934
20 19.245 Jasmonic acid C16H32O2 [M−H] 210.12572 210.12559 0.58 96.95930
Table 6

Analysis of the metabolites of constituents in blood deficiency syndrome rat serum after the oral administration of ASF

No Rt (min) Parent compound name Transformations Molecular formula Ion form Composition change m/z calculated m/z theoretical Error (ppm) MS2 fragment picture
1 11.002 Caffeic acid Dehydration, nitro reduction, glycine conjugation C11H11NO2 [M+H]+1 –(O2) + (C2H3N) 189.07897 190.08625 −0.05
2 5.475 Caffeic acid Ornitine conjugation C14H18N2O5 [M+H]+1 +(C5H10N2O) 294.12157 295.12884 −0.02
3 6.109 Caffeic acid Nitro reduction, reduction, ornitine conjugation C14H22N2O3 [M+H]+1 –(O) + (C5H14N2) 266.16303 267.17031 −0.03
4 17.43 Caffeic acid Desaturation, nitro reduction, nitro reduction C9H10 [M+H]+1 –(O4) + (H2) 118.07818 119.08546 −0.57
5 4.617 Caffeic acid Dehydration, dehydration, nitro reduction C9H6 [M+H]+1 –(H2O4) 114.04689 115.05417 −0.52
6 8.379 Caffeic acid Oxidation, methylation C10H10O5 [M−H]−1 +(CH2O) 210.05283 209.04555 0.03
7 8.045 Caffeic acid Nitro reduction, reduction, acetylation C11H14O3 [M−H]−1 –(O) + (C2H6) 194.09427 193.087 –0.11
8 6.913 Caffeic acid Dehydration, desaturation, ornitine conjugation C14H14N2O4 [M−H]−1 +(C5H6N2) 274.09546 273.08819 0.39
9 5.507 Caffeic acid Reduction, glucoside conjugation C15H20O9 [M−H]−1 +(C6H12O5) 344.11092 343.10364 0.54
10 5.922 Caffeic acid Sulfation C9H8O7S [M−H]−1 +(O3S) 259.99908 258.9918 0.03
11 5.568 Caffeic acid Glucuronide conjugation C15H16O10 [M−H]−1 +(C6H8O6) 356.07451 355.06723 0.46
12 3.913 Chlorogenic acid Nitro reduction, glycine conjugation C9H17NO5 [M+H]+1 –(C7HO4) + (N) 219.11068 220.11796 0.01
13 11.354 Eleutheroside B1 Hydration, reduction, glucoside conjugation C23H34O16 [M+H]+1 +(C6H14O6) 566.18715 567.19442 4.35
14 15.433 Eleutheroside B1 Desaturation, stearyl conjugation C29H42O6 [M+H]+1 –(O4) + (C12H22) 486.29771 487.30499 −0.88
15 22.69 Eleutheroside B1 Nitro reduction, stearyl conjugation C29H46O4 [M+H]+1 –(O6) + (C12H26) 458.33931 459.34658 −0.66
16 4.906 Eleutheroside B1 Nitro reduction, reduction C6H14O4 [M+H]+1 –(C11H6O6) 150.08907 151.09635 −0.9
17 6.134 Eleutheroside B1 Hydration, reduction C17H26O12 [M+H]+1 +(H6O2) 422.14278 423.15006 0.85
18 5.608 Eleutheroside B1 Nitro reduction, oxidation C17H22O10 [M−H]−1 Reduction 386.12165 385.11437 0.92
19 4.044 Eleutheroside B1 Reduction C17H22O11 [M−H]−1 +(H2O) 402.11653 401.10925 0.79
20 8.502 Eleutheroside B1 Nitro reduction C17H22O9 [M−H]−1 –(O) + (H2) 370.12665 369.11937 0.71
21 5.569 Eleutheroside B1 Dehydration, nitro reduction C6H10O3 [M−H]−1 –(C11H10O7) 130.06314 129.05586 1.13
22 7.321 Eleutheroside B1 Nitro reduction, reduction C17H26O9 [M−H]−1 –(O) + (H6) 374.15796 373.15068 0.74
23 7.647 Eleutheroside B1 Nitro reduction, reduction C16H22O8 [M−H]−1 –(CO2) + (H2) 342.13163 341.12435 0.46
24 5.57 Eleutheroside E Dehydration, nitro reduction C6H10O3 [M−H]−1 –(C28H36O15) 130.06314 129.05586 1.13
25 0.982 Eleutheroside E Desaturation, sulfation C6H8O9S [M−H]−1 –(C28H38O9) + (S) 255.98891 254.98163 0.03
26 12.746 Hyperoside Desaturation, nitro reduction, nitro reduction C21H22O8 [M−H]−1 –(O4) + (H2) 402.13175 401.12447 0.7
27 7.239 Hyperoside Hydration, nitro reduction, acetylation C23H26O12 [M−H]−1 +(C2H6) 494.14282 493.13555 0.8
28 9.211 Hyperoside Desaturation, nitro reduction C21H20O11 [M−H]−1 –(O) 448.10094 447.09366 0.84
29 20.29 Isofraxidin Nitro reduction, palmitoyl conjugation C27H42O4 [M+H]+1 –(O) + (C16H32) 430.30804 431.31532 −0.62
30 15.597 Isofraxidin Desaturation, desaturation, stearyl conjugation C29H40O6 [M+Na]+1 +(C18H30O) 484.28223 507.27145 −0.53
31 14.819 Isofraxidin Hydration, reduction, stearyl conjugation C29H48O7 [M−H]−1 +(C18H38O2) 508.34033 507.33305 0.63
32 8.358 Isofraxidin Nitro reduction C11H14O4 [M−H]−1 –(O) + (H4) 210.08922 209.08194 0.03
33 9.553 Isofraxidin Nitro reduction, oxidation C11H14O5 [M−H]−1 +(H4) 226.08423 225.07695 0.46
34 11.926 Isofraxidin Nitro reduction, nitro reduction C11H16O2 [M−H]−1 –(O3) + (H6) 180.11502 179.10775 −0.03
35 7.23 Isofraxidin Reduction, acetylation C13H16O7 [M−H]−1 +(C2H6O2) 284.08973 283.08245 0.44
36 5.437 Isofraxidin Desaturation, glucoside conjugation C17H20O11 [M−H]−1 +(C6H10O6) 400.10088 399.0936 0.78
37 6.511 Isofraxidin Nitro reduction, glucuronide conjugation C17H22O10 [M−H]−1 +(C6H12O5) 386.12159 385.11431 0.75
38 15.599 Isofraxidin Desaturation, palmitoyl conjugation C27H40O7 [M−H]−1 +(C16H30O2) 476.27536 475.26808 −4.29
39 19.089 Isofraxidin Nitro reduction, palmitoyl conjugation C27H44O5 [M−H]−1 +(C16H34) 448.31924 447.31196 0.82
40 15.198 Isofraxidin Hydration, palmitoyl conjugation C27H44O8 [M−H]−1 +(C16H34O3) 496.304 495.29673 0.77
41 15.428 Isofraxidin Desaturation, stearyl conjugation C29H44O7 [M−H]−1 +(C18H34O2) 504.30913 503.30185 0.84
42 15.679 Isofraxidin Stearyl conjugation C29H46O7 [M−H]−1 +(C18H36O2) 506.32479 505.31751 0.86
43 17.996 Isofraxidin Nitro reduction, stearyl conjugation C29H48O5 [M−H]−1 +(C18H38) 476.35054 475.34326 0.76
44 6.168 Isofraxidin Nitro reduction, oxidation C10H10O4 [M−H]−1 –(CO) 194.0579 193.05062 –0.04
45 6.441 Isofraxidin Hydration, nitro reduction C10H12O4 [M−H]−1 –(CO) + (H2) 196.07355 195.06627 −0.07
46 5.849 Protocatechuic acid Dehydration, desaturation, nitro reduction C7H4O [M+H]+1 –(H2O3) 104.0262 105.03348 −0.12
47 5.85 Protocatechuic acid Desaturation, nitro reduction, glycine conjugation C9H9NO3 [M+H]+1 –(O) + (C2H3N) 179.05821 180.06548 −0.2
48 2.955 Protocatechuic acid Glucuronide conjugation C13H14O10 [M−H]−1 +(C6H8O6) 330.0588 329.05152 0.31
49 3.9 Protocatechuic acid Sulfation C7H6O7S [M−H]−1 +(O3S) 233.98344 232.97617 0.09
50 10.353 Protocatechuic acid Dehydration, reduction, methylation C8H8O3 [M−H]−1 –(O) + (CH2) 152.04733 151.04005 −0.11
51 8.425 Protocatechuic acid Nitro reduction, acetylation C9H10O3 [M−H]−1 –(O) + (C2H4) 166.063 165.05573 0.06
52 7.485 Protocatechuic acid Dehydration, nitro reduction, acetylation C9H8O2 [M−H]−1 –(O2) + (C2H2) 148.05239 147.04511 −0.27
53 10.488 Quercitrin Nitro reduction, oxidation C21H22O10 [M−H]−1 –(O) + (H2) 434.12166 433.11438 0.84
54 5.675 Quercitrin Oxidation, methylation C7H12O6 [M−H]−1 –(C14H8O5) 192.0634 191.05613 0.08
55 9.597 Quercitrin Nitro reduction, acetylation C8H14O4 [M−H]−1 –(C13H6O7) 174.08928 173.082 0.4
56 4.914 Rutin Nitro reduction, glutamine conjugation C11H20N2O5 [M+H]+1 –(C16H10O11) + (N2) 260.13716 261.14444 −0.22
57 9.045 Rutin Dehydration, nitro reduction, palmitoyl conjugation C43H60O14 [M−H]−1 –(O2) + (C16H30) 800.40003 799.39275 2.16
58 4.324 Rutin Dehydration, reduction C6H10O4 [M−H]−1 –(C21H20O12) 146.05792 145.05064 0.09
59 6.493 Rutin Nitro reduction C6H12O3 [M−H]−1 –(C21H18O13) 132.07871 131.07144 0.52
60 4.608 Rutin Dehydration, acetylation C8H12O5 [M−H]−1 –(C19H18O11) 188.06844 187.06117 −0.15
61 8.514 Rutin Nitro reduction, acetylation C8H16O4 [M−H]−1 –(C19H14O12) 176.10486 175.09758 0
62 8.996 Rutin Reduction C21H20O12 [M−H]−1 –(C6H10O4) 464.09591 463.08863 0.93
63 9.494 Rutin Reduction, methylation C22H22O12 [M−H]−1 –(C5H8O4) 478.11151 477.10424 0.81
64 8.219 Rutin Glucuronide conjugation C21H18O13 [M−H]−1 –(C6H12O3) 478.07516 477.06788 0.87
65 5.419 Syringin Hydration C17H26O10 [M+Na]+1 +(H2O) 390.15201 413.14123 −1.51
66 13.364 Syringin Hydration, reduction, methylation C18H30O10 [M+Na]+1 +(CH6O) 406.18361 429.17283 −0.71
67 6.676 Syringin Hydration C16H24O10 [M+H]+1 –(C) + (O) 376.13818 377.14546 3.28
68 15.151 Syringin Dehydration, nitro reduction, nitro reduction C17H26O4 [M−H]−1 –(O5) + (H2) 294.18326 293.17598 0.5
69 7.733 Syringin Desaturation, oxidation, methylation C18H24O10 [M−H]−1 +(CO) 400.13724 399.12996 0.74
70 6.304 Syringin Oxidation, methylation C18H26O10 [M−H]−1 +(CH2O) 402.15288 401.1456 0.71
71 6.416 Syringin Nitro reduction, oxidation, acetylation C19H28O9 [M−H]−1 +(C2H4) 400.17363 399.16635 0.74
72 8.502 Syringin Desaturation C17H22O9 [M−H]−1 –(H2) 370.12665 369.11937 0.71
73 6.374 Syringin Hydration, acetylation C19H28O12 [M−H]−1 +(C2H4O3) 448.15844 447.15116 0.81
74 6.42 Syringin Glucuronide conjugation C17H22O10 [M−H]−1 –(H2) + (O) 386.12161 385.11433 0.81
75 8.569 Syringin Desaturation, nitro reduction C16H22O7 [M−H]−1 –(CH2O2) 326.13672 325.12944 0.5
76 11.478 Syringin Nitro reduction, reduction C16H26O7 [M−H]−1 –(CO2) + (H2) 330.16801 329.16073 0.48
77 8.002 Syringin Hydration, nitro reduction C16H26O8 [M−H]−1 –(CO) + (H2) 346.16296 345.15568 0.56

3.6 Correlation analysis of blood components and biomarkers

The Pearson correlation analysis method was used to analyze the correlation between ASF blood components and urine biomarkers, so as to screen out the most relevant components in ASF for the treatment effect of blood deficiency syndrome, that is, potential effective constituents. According to the results of the Pearson’s correlation, the correlation analysis of the prototype constituents and the metabolites of the prototype constituents with the markers is shown in Figure 6a and b. There were two prototype constituents (C1: isofraxidin and C2: caffeic acid) and nine metabolites derived from the prototype constituents (C1, C9: caffeic acid, C13, C19, C23: eleutheroside, B1, C24: eleutheroside E, C40: isofraxidin, C68, C77: eleutheroside B). These were considered potential effective constituents for ASF to improve the blood deficiency syndrome. The Pearson correlation analysis method was used to analyze the correlation between ASF blood components and urine biomarkers, so as to screen out the most relevant components in ASF for the treatment effect of blood deficiency syndrome, that is, potential effective components. This visualization of the correlation between components and metabolites can help us reveal the mechanism of ASF regulating blood deficiency syndrome (Figure 7).

Figure 6 
                  Correlation analysis between urine biomarkers and chemical constituents in the ASF group. (a) Correlation analysis between ASF prototype components and urine markers. (b) Correlation analysis between metabolites and urine markers of ASF prototype components.
Figure 6

Correlation analysis between urine biomarkers and chemical constituents in the ASF group. (a) Correlation analysis between ASF prototype components and urine markers. (b) Correlation analysis between metabolites and urine markers of ASF prototype components.

Figure 7 
                  The diagram for the connection of potentially effective constituents-biomarkers-related pathways.
Figure 7

The diagram for the connection of potentially effective constituents-biomarkers-related pathways.

3.7 Exploring the mechanism of ASF in the treatment of blood deficiency syndrome based on network pharmacology

According to Pharmmapper and Way2drug database search, there were 3663 potential target proteins corresponding to the five potential components isofraxidin, caffeic acid, eleutheroside B, eleutheroside B1, and eleutheroside E; we used these data to sort out the main components and targets related to blood deficiency syndrome or anemia and the results are organized in Table 7. The ASF potential components, targets, and related disease data were imported into Cytoscape network visualization software to construct a component target-related disease network. The results are shown in Figure 8a.

Table 7

Information on the action targets of potential components of ASF

Component Target protein Gene
Isofraxidin 4-Hydroxyphenylpyruvate dioxygenase HPD
Caffeic acid Adenine phosphoribosyltransferase APRT
Caffeic acid Adenosyl homocysteinase Ahcy
Eleutheroside E Androgen receptor AR
Eleutheroside E Annexin A1 Anxa1
Eleutheroside B Annexin A5 Anxa5
Eleutheroside B1 Annexin A8 Anxa8
Isofraxidin Arachidonate 15-lipoxygenase ALOX15
Eleutheroside B Argininosuccinate lyase ASL
Eleutheroside B1 Arylsulfatase A ARSA
Eleutheroside B Arylsulfatase B ARSB
Eleutheroside E Beta-2-microglobulin B2M
Isofraxidin Carbonic anhydrase 14 Ca14
Eleutheroside B Catalase catB
Isofraxidin Choline O-acetyltransferase ChAT
Isofraxidin Citrate synthase CS
Eleutheroside E Coagulation factor XIII A chain F13A1
Isofraxidin Dihydrofolate reductase DHFR
Eleutheroside B Dipeptidyl peptidase 4 DPP4
Eleutheroside E Fibrinogen alpha chain FGA
Eleutheroside B Gamma-glutamyl hydrolase GGH
Eleutheroside B1 Gelsolin GSN
Eleutheroside B Glucose-6-phosphate isomerase GPI
Isofraxidin Glutamate decarboxylase 2 GAD2
Caffeic acid Glutathione synthetase GSS
Eleutheroside B1 Glyceraldehyde-3-phosphate dehydrogenase GAPDHS
Caffeic acid Glycerol kinase GK
Eleutheroside E Granzyme B GZMB
Eleutheroside B Granzyme K GZMK
Eleutheroside E Heme oxygenase 1 HMOX1
Eleutheroside B Insulin INS
Isofraxidin Lactotransferrin LTF
Eleutheroside E Mitogen-activated protein kinase 9 MAPK9
Isofraxidin Nuclear receptor coactivator 3 NCOA3
Eleutheroside E Nuclear receptor subfamily 1 group I member 3 NR1I3
Isofraxidin Ornithine aminotransferase OAT
Caffeic acid Pantothenate kinase 1 Pank1
Eleutheroside B1 Phosphoglycerate mutase 1 GPM1
Eleutheroside B Proliferating cell nuclear antigen PCNA
Caffeic acid Pyridoxal kinase PK
Eleutheroside E Rap guanine nucleotide exchange factor 4 RAPGEF4
Caffeic acid Replication factor C subunit 1 RFC1
Eleutheroside B1 Rho guanine nucleotide exchange factor 12 ARHGEF12
Eleutheroside B Signal transducer and activator of transcription 5 A STAT5A
Eleutheroside B Spermidine synthase SPE3
Eleutheroside E Sulfite oxidase SUOX
Eleutheroside B1 Thiopurine S-methyltransferase TPMT
Caffeic acid Three prime repair exonuclease 1 TREX1
Eleutheroside E Transient receptor potential cation channel subfamily V member 6 TRPV6
Eleutheroside E Transketolase TKTB
Eleutheroside B Uroporphyrinogen decarboxylase UROD
Eleutheroside E Vascular endothelial growth factor A VEGFA
Eleutheroside B Vascular endothelial growth factor B VEGFB
Eleutheroside B1 von Willebrand factor VWF
Caffeic acid 5′-Nucleotidase inhibitor NT5E
Eleutheroside B 5′-Nucleotidase inhibitor NT5E
Eleutheroside B1 5′-Nucleotidase inhibitor NT5E
Isofraxidin 5′-Nucleotidase inhibitor NT5E
Eleutheroside E Hemoglobin subunit beta HBB
Figure 8 
                  Network pharmacological analysis of potentially effective components of ASF in treating blood deficiency syndrome. (a) ASF potential component-target network diagram (green represents blood components, yellow represents target, and red represents disease). (b) Protein–protein interactions identified in ASF by STRING software. (c) Results of target-pathway enrichment analysis of potential components of ASF. (d) Simulation diagram of molecular docking between caffeic acid and receptor 5′-nucleotidase.
Figure 8

Network pharmacological analysis of potentially effective components of ASF in treating blood deficiency syndrome. (a) ASF potential component-target network diagram (green represents blood components, yellow represents target, and red represents disease). (b) Protein–protein interactions identified in ASF by STRING software. (c) Results of target-pathway enrichment analysis of potential components of ASF. (d) Simulation diagram of molecular docking between caffeic acid and receptor 5′-nucleotidase.

In this network, there were 62 nodes and 118 edges. Green nodes represented potential components of ASF, yellow nodes represented drug targets, and red represented diseases. Each edge represents the interaction relationship between the active components of TCM and the drug target. The ASF potential components, targets, and related disease data were imported into Cytoscape network visualization software to construct a component-target-related disease network.

To better understand the mechanism of action of ASF, the PPI network of ASF targets was constructed using STRING software. The results are shown in Figure 8b. The PPI network contains 55 protein nodes and 130 interaction relationships (connections). The bar graph of the top 30 key protein nodes with scores is listed according to the degree value of the network topology analysis node. Degree nodes ≥90 include 4-hydroxyphenylpyruvate dioxygenase, adenine phosphoribosyltransferase, adenosyl homocysteinase, androgen receptor, arachidone 15 lipoxygenase, argininosuccinate cleavage enzymes, arylsulfate A, arylsulfate B, β-2-microglobulin, carbonic anhydrase 14, catalan, choline O-acetyltransferase, citrate synthase, factor XIII A chain, dihydrofolate reductase, dipeptidyl peptidase 4, alpha chain fibrinogen, γ-glutamyl hydrolase, glucose-6-phosphate isomerase, glutamate decarboxylase 2, and glutathione synthase.

Using the GO enrichment analysis function of the DAVID platform, the functions of 56 proteins involved in the PPI network of ASF targets were annotated and analyzed. In the GO annotation analysis, 173 GO entries were identified, including 137 entries related to biological process (BP), involving cellular secretion, secretion, regulated exocytosis, metabolic process, vesicle-mediated transport, and other metabolism processes such as small molecule, organic nitrogen compound, organic matter, cell metabolism process, and nitrogen compound. It was also considered response to inorganic substances, cell activation, activation of bone marrow cells involved in immune response, leukocyte-mediated immunity, leukocyte activation, response to cadmium ions, organic acid metabolism, response to metal ions, granulation, response to chemicals, establishment of cellular localization, biomass regulation, response to injury, oxoacid metabolism, wound healing, response to stress, carboxylic acid metabolism process, primary metabolic process, response to stimuli, biosynthesis process, immune response, body fluid level regulation, response to reactive oxygen species, immune system process, cellular localization, and organic biosynthesis.

When it comes to molecular function (MF), 13 entries were identified, including catalytic activity, same protein binding, protein homodimerization activity, binding, ion binding, sulfur compound binding, protein dimerization activity, anion binding, vitamin binding, protein binding, VEGF receptor 1 binding, small molecule binding, and MutLalpha complex binding. The 23 entries related to cell composition (CC) were secretory granule cavity, extracellular exosome, extracellular space, vesicle, extracellular region, secretory granule, secretory vesicle, cytoplasmic plasma sol, cytoplasm, cytoplasmic vesicles, PLT alpha granule lumen, intracellular organelle lumen, tertiary granule lumen, ficolin-1 rich granule lumen, specific granule lumen, intracellular, endomembrane system, membrane-bound organelles, blood microparticles, organelles, extracellular matrix, and collagen-containing extracellular matrix. The top 10 of the three parts BP, MF, and CC of the GO analysis were screened out (30 in total), and the results are shown in Table 8.

Table 8

Gene ontology enrichment results of A. senticosus protein interaction network

Term ID Term description Observed gene count Background gene count Strength False discovery rate
GO:0034774 Secretory granule lumen 16 324 1.24 1.27 × 10−12
GO:0070062 Extracellular exosome 29 2,099 0.69 7.85 × 10−12
GO:0005615 Extracellular space 34 3,195 0.58 1.07 × 10−11
GO:0031982 Vesicle 36 3,879 0.52 5.69 × 10−11
GO:0005576 Extracellular region 36 4,166 0.49 4.66 × 10−10
GO:0030141 Secretory granule 17 845 0.85 1.74 × 10−8
GO:0099503 Secretory vesicle 18 1,010 0.8 2.69 × 10−8
GO:0005829 Cytosol 36 5,193 0.39 2.69 × 10−7
GO:0005737 Cytoplasm 51 11,428 0.2 1.90 × 10−6
GO:0031410 Cytoplasmic vesicle 23 2,386 0.54 4.43 × 10−6
GO:0032940 Secretion by cell 22 979 0.9 8.33 × 10−11
GO:0046903 Secretion 23 1,097 0.87 8.33 × 10−11
GO:0045055 Regulated exocytosis 19 697 0.99 9.97 × 10−11
GO:0008152 Metabolic process 48 8,298 0.31 1.43 × 10−8
GO:0016192 Vesicle-mediated transport 24 1,805 0.67 4.20 × 10−8
GO:0044281 Small molecule metabolic process 23 1,684 0.69 6.83 × 10−8
GO:1901564 Organonitrogen compound metabolic process 38 5,244 0.41 1.15 × 10−7
GO:0071704 Organic substance metabolic process 45 7,755 0.31 2.44 × 10−7
GO:0044237 Cellular metabolic process 44 7,513 0.32 4.33 × 10−7
GO:0006807 Nitrogen compound metabolic process 41 6,852 0.33 2.97 × 10−6
GO:0003824 Catalytic activity 39 5,486 0.4 1.83 × 10−7
GO:0042802 Identical protein binding 20 1,896 0.57 0.00016
GO:0042803 Protein homodimerization activity 11 673 0.76 0.0027
GO:0005488 Binding 50 12,516 0.15 0.0033
GO:0043167 Ion binding 34 6,188 0.29 0.0033
GO:1901681 Sulfur compound binding 7 262 0.98 0.0053
GO:0046983 Protein dimerization activity 12 1,037 0.61 0.0123
GO:0043168 Anion binding 20 2,805 0.4 0.0173
GO:0019842 Vitamin binding 5 142 1.1 0.0204
GO:0005515 Protein binding 34 7,026 0.24 0.0248

The targets of the five potential components in ASF were put into the DAVID database for KEGG metabolic pathway enrichment analysis so the respective regulated signaling pathways could be obtained. Then, Omicshare was used to visualize the GO and KEGG enrichment analysis results, as shown in Figure 8c. The top 20 GO analysis items are pyrimidine metabolism and glyoxylate and dicarboxylic acid metabolism, AGE-RAGE signaling pathway in diabetic complications, ferroptosis, type I diabetes, glutathione metabolism, pentose phosphate pathway, complement and coagulation cascade, Rap1 signaling pathway, Ras signaling pathway, HIF-1 signaling pathway, MAPK signaling pathway, folate synthesis, relaxin signaling pathway, fluid shear stress and atherosclerosis, antifolate, DNA replication, C acid, aspartate and glutamate metabolism, and PI3K–Akt signaling pathway.

The key role of ASF in the treatment of blood deficiency was identified based on the results of this ASF metabolomics study on blood deficiency rats and the results of network pharmacology used to predict target protein components. The two parts of data were integrated and focused on the same metabolic process. The mechanism is the hemolysis of erythrocytes caused by abnormal nucleotide metabolism that is related to the deficiency of its key enzyme 5′-nucleotidase. At the same time, these four potential active components have significant molecular interactions with 5′-nucleotidase. Hence, it is inferred that ASF may regulate cytidine through the direct regulation of 5′-nucleotidase by the above four components.

In addition to the above four components, the predicted in vivo target of eleutheroside E is HGB subunit beta (HGB subunit beta), which is the main inducing mechanism of sickle cell anemia. Sickle cell anemias are a group of inherited RBC disorders. When suffering from sickle cell anemia, the HGB in the patient’s body will be abnormal, and HGB is a kind of protein existing in RBCs. Its main function is to transport oxygen to various parts of the body. Sickle cell anemia will make RBCs. The HGB in the RBC becomes a hard rod, thereby turning the disc-shaped RBCs into a crescent or sickle shape. Sickle cells are inflexible and do not change shape easily, and most of them rupture when they pass through blood vessels. In this case, it is difficult for the body to make enough new cells to replace the lost ones. When individuals do not produce enough RBCs they become anemic and one of the most common symptoms of this condition is extreme exhaustion and lack of energy. Sickle cells can also stick to the walls of blood vessels, causing blockages that slow or stop blood flow. And once that happens, the oxygen will be unable to reach nearby tissues. Lack of oxygen can cause sudden, severe pain that is often referred to as pain crisis. The above conditions may happen due to variants in the HGB subunit beta gene and inheritance in an autosomal recessive inheritance pattern.

By combining the metabolomics and network pharmacology integrated analysis results with downloaded ASF 5 potential species from Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and RCSB (https://www.rcsb.org/) websites, the components and corresponding target protein component files and the sdf files of the ligand components were converted into mol2 files through ChemBio3D Ultra 14.0. These files were transferred into the AutoDockTools-1.5.6 software for computer simulation molecular docking calculation. Taking caffeic acid as an example in Figure 8d, the calculation results of the software showed that the molecular binding energy between the two is −7.58 kcal/mol (less than −5), and the theory confirms that the binding energy is firm. Hence, it is inferred that caffeic acid may control the activity of 5′-nucleotidase through direct regulation of 5′-nucleotidase and finally achieve the purpose of intervening in anemia. The binding of other components to receptors can be seen in Table 9. As shown, eleutheroside B1 has the weakest binding ability to 5′-nucleotidase.

Table 9

Molecular binding energy of potential components of A. senticosus combined with receptors (unit kcal/mol)

Receptor Component
Caffeic acid Eleutheroside B Eleutheroside B1 Isofraxidin Eleutheroside E
5′-Nucleotidase inhibitor −7.58 −11.65 −4.58 −13.58
Hemoglobin subunit beta −6.25

4 Discussion

AS is currently recommended by the EMA for the treatment of debilitating symptoms such as fatigue and weakness. ASF can also increase the body’s leukocytes and has immunomodulatory effects, both AS and ASF. Activity components glycosides, phenylpropanoids, and organic acids exist not only in AS but also in ASF.

Chinmedomics is a research strategy that integrates the serum medicinal chemistry and metabolomics technology of TCM. On the premise that the prescription and syndrome are corresponding and effective, the discovery is related to clinical efficacy, reflects the compatibility of prescriptions, and is derived from the research strategy that constitutes the material basis of drug efficacy [22]. Metabolomics technology takes the small-molecule metabolite group of the body as the object to explore the research advantages of exogenous substances on the body and to find the effect biomarkers. Biomarkers are parameters to accurately evaluate the overall effect of prescriptions; integrated research is adopted, and serum medicinal chemistry methods are used to clarify the effective components of the drug in the effective state. This method is a practical way to evaluate the effectiveness of TCM based on the compatibility of prescriptions and the corresponding conditions of prescriptions and syndromes to discover the material basis of pharmacodynamics and can also be used to identify the quality markers of TCMs.

In TCM, blood deficiency syndrome can be manifested in two basic levels, one associated with the reduction of substances and a dysfunctional one, which reflects the pathological state of systemic weakness of the body. Furthermore, according to the physiological process of blood metaplasia in TCM, the causes of blood deficiency syndrome can be divided into two major categories: excessive blood wasting and insufficient blood production. Traditional Chinese medicine believes that blood deficiency syndrome is manifested as the reduction in blood substances in blood vessels or the disorder of hematopoietic function, which leads to low peripheral hemogram indicators. This interpretation is very similar to the way modern medicine defines anemia [23].

CTX is an alkylating agent that can cause bone marrow suppression and destroy immune cells, thus causing anemia and immunodeficiency and reducing peripheral blood, which negatively affects the generation of RBC, WBC, and PLT to produce the blood loss and immunosuppression in the body [13,24,25]. In this study, the rat model of blood deficiency syndrome induced by CTX was used, and the evaluation index and internal and external environment of the model were more in line with blood deficiency syndrome, which was suitable for the study of the efficacy of TCM [12].

The thymus and spleen are important immune organs in the human body, closely related to hematopoietic function [26]. The CTX-induced blood deficiency model constructed for this study could significantly shrink these organs in rats, which is an impactful finding that can help interpret the potential of ASF. Furthermore, our study showed that the organ index was significantly improved with ASF, indicating that it has properties that can improve the symptoms caused by blood deficiency symbol syndrome.

In this experiment, comprehensive metabolomic analysis by UPLC-MS and focused urine metabolites was performed to evaluate the replication and evaluation of blood deficiency syndrome models. We explored the core biomarkers of blood deficiency syndrome induced by intraperitoneal injection of cyclophosphamide and elaborated that the metabolites of the disorder were mainly involved in pyrimidine metabolism, and energy metabolism, glyceride metabolism, and amino acid metabolism.

4.1 Pyrimidine metabolism

Other studies in the field have previously determined that the lesions of 5′-nucleotidase in erythrocyte nucleotide metabolism enzymes can lead to hemolytic anemia [27,28], Furthermore, pyrimidine 5′-nucleotidase (pvrimidine-5′-nucleotidase, P5′N, EC 3.1.3.5) deficiency not only has the highest incidence among nucleotide metabolizing enzymes, but also among all erythrocytoses leading to hemolysis.

Erythrocyte nucleotide metabolism enzymes are mainly found in the pyrimidine metabolism and purine metabolism pathways, so they are considered key enzymes of these two metabolic pathways. In the CTX-induced blood deficiency model, the activity of the erythrocyte nucleotide-metabolizing enzyme 5′-nucleotidase in the pyrimidine metabolism pathway was reduced by the alkylating agent, causing erythrocyte damage and leading to erythrocyte hemolytic anemia. According to the results of the metabolic data analysis, the content of cytidine in the urine metabolism of blood deficiency model rats was significantly reduced, which caused abnormalities in their pyrimidine metabolism. It was concluded that the activity of the upstream key enzyme 5′-nucleotidase was reduced, thereby causing hemolytic anemia (Figure 9). Therefore, it is speculated that the key target of blood deficiency syndrome established in this experiment is erythrocyte metabolic enzyme 5′-nucleotidase.

Figure 9 
                  The process of generating cytidine from the substrate cytidine under the action of key enzymes in pyrimidine metabolism. (a) Pyrimidine metabolism pathway. (b) 5′-Nucleotidase in pyrimidine metabolism regulates the process of CMP metabolism to cytidine.
Figure 9

The process of generating cytidine from the substrate cytidine under the action of key enzymes in pyrimidine metabolism. (a) Pyrimidine metabolism pathway. (b) 5′-Nucleotidase in pyrimidine metabolism regulates the process of CMP metabolism to cytidine.

However, none of the results obtained can be used to explain whether cyclophosphamide causes hemolytic anemia. We know that the generation of hemolytic anemia is caused by the lack of 5′-nucleotidase, a nucleotide metabolizing enzyme of erythrocytes. Through experimental data, the content of cytidine, the lower product of the enzyme, is reduced. So, it is speculated that the upstream key enzyme 5′-nucleotidase is deficient, thereby causing hemolytic anemia. These results point out that cyclophosphamide-induced blood deficiency syndrome can significantly reduce the activity of erythrocyte nucleotide-metabolizing enzyme 5′-nucleotidase, causing RBC damage and hemolytic anemia.

4.2 Energy metabolism

Lactic acid plays a significant role in the body’s energy supply system. It is generated by lactate dehydrogenase catalyzed by pyruvate under hypoxic conditions and it is the final metabolite of the glycolysis energy supply system, responsible for maintaining the balance of the normal energy metabolism in the body [29,30]. Our experiments showed that the relative content of lactic acid in the blood deficiency syndrome model group decreased, indicating that the anaerobic oxidation function of the body was damaged, and the glycolysis process of the rat body was weakened. These results show that the animal model of blood deficiency syndrome caused by cyclophosphamide has anaerobic conditions.

Citric acid and malic acid are intermediate products of the TCA cycle and are closely related to energy metabolism and glycolysis pathways. It is also the main source of energy in the human body [31,32]. In our experiments, the relative content of citric acid in the blood deficiency model group decreased, while the relative content of malic acid increased, indicating that the tricarboxylic acid cycle and the energy metabolism in mitochondria were affected, suggesting energy metabolism imbalance and anaerobic oxidation damage. In the ASF group, the above metabolites were recalled to varying degrees. Therefore, it can be speculated that ASF can relieve blood deficiency syndrome by improving the tricarboxylic acid cycle and energy metabolism. The mechanism of blood deficiency may also be related to impaired anaerobic oxidation, disturbance of energy and fatty acid metabolism, and disturbance of glycolysis and gluconeogenesis.

4.3 Glyceride metabolism

Through the analysis of metabolites in the urine of blood deficiency model rats, we found that glyceric acid is a differential metabolite of blood deficiency, which is mainly involved in the glyceride metabolism pathway. Compared with the control group, the content of glyceric acid in the urine of the model group was significantly decreased (p < 0.05). Some previous studies have found that after the occurrence of blood deficiency, the content of triacylglycerol, glycerol, and glyceric acid in the body tends to decrease. Glycerol and fatty acids are the final products of triglycerides.

Blood deficiency due to glycolysis and gluconeogenesis disorders, blood sugar drops. To supplement the body’s energy, lipid metabolism is compensatory and glycerol is strengthened. The levels were significantly elevated, resulting in elevated glyceric acid. However, after the administration of ASF group, blood glucose level increased, lipid metabolism returned to normal level, and glycerol content gradually weakened. It can be seen from the results that ASF achieves the blood-enriching effect by affecting the metabolism of glycerides.

Studies have shown that these potential active ingredients also have the effect of nourishing blood and treating blood deficiency syndrome, which also verified the results of this trial. ASF can resist fatigue by reducing NK activity and increasing corticosterone. It is speculated that eleutheroside E and eleutheroside B play the main role [33]. Isofraxidin belongs to hydroxy coumarins and has various pharmacological activities. As the main active ingredient in ASF, the protective effect of isofraxidin extracted from the mixed decoction of A. senticosus–Ligustrum lucidum combination on chemotherapy-induced myelosuppression is significant. The content of isofraxidin in the decoction is increased; it is speculated that isofraxidin has a protective effect on bone marrow suppression, thereby relieving the blood deficiency syndrome [2]. Caffeic acid is a phenolic acid compound, and its pharmacological mechanism can constrict local blood vessels, increase the average PLT volume and PLT distribution width, the content of dense bodies, and enzyme activity, enhance PLT aggregation function, shorten coagulation and bleeding time, and stimulate megakaryocytes. Therefore, it has been widely used to promote hematopoietic recovery, treat and prevent leukopenia and thrombocytopenia [34].

5 Conclusions

In this study, it has been shown that ASF had a proved blood-replenishing effect on blood deficiency syndrome. The effective components of ASF for the treatment of blood deficiency syndrome had been identified as isofraxidin, eleutheroside B, eleutheroside B1, and caffeic acid, which can act on the 5′-nucleotidase target and inhibit its activity; ASF and its effective constituents suppressed the aggregation of CMP, reduced the activity of pyrimidine nucleotide monophosphate kinase, thus inhibited hemolysis of RBCs, and played a role in nourishing blood. The medicinal parts of AS roots and stems replenished blood by promoting lymphocyte proliferation and inhibiting the changes in bone marrow-nucleated cell cycle in varying degrees through the effective components isofraxidin. Compared with the traditional medicinal parts of AS, the ASF had a better blood-replenishing effect, and the picking of fruits did not affect the growth of AS plants, making AS medicinal resources protected and utilized. ASF can replace the root and stem of AS in treating blood deficiency syndrome at a certain extent, which provides a foundation for the development of new medicinal sources of AS, the development of new medicinal parts of AS not only avoids the waste of resources but also relieves the endangered state of the medicinal sources of AS.


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Acknowledgments

The authors are thankful to the Science and Technology of the People´s Republic of China (2018YFC1706103), for funding this research work.

  1. Funding information: This work was supported by the Science and Technology of the People´s Republic of China (2018YFC1706103).

  2. Author contributions: Chunlei Wan: data curation, visualization, writing-original draft, investigation. Xijun Wang: investigation, visualization, writing review & editing. Hongda Liu: writing-original draft, investigation. Qingyu Zhang: investigation. Guangli Yan: investigation. Zhineng Li: investigation. Heng Fang: investigation. Hui Sun: funding acquisition, supervision, investigation, visualization, writing review & editing.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Ethical approval: The research related to animal use has been complied with all the relevant national regulations and institutional policies for the care and use of animals, and has been approved by the animal protection and ethics committee of Heilongjiang University of Chinese Medicine.

  5. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-10-25
Revised: 2023-01-02
Accepted: 2023-01-16
Published Online: 2023-02-13

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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