Abstract
The neuroprotective properties of ginsenosides have been found to
reverse the neurological damage caused by oxidation in many
neurodegenerative diseases. However, the distribution of ginsenosides
in different tissues of the main root, which was regarded as the
primary medicinal portion in clinical practice was different, the
specific parts and specific components against neural oxidative damage
were not clear. The present study aims to screen and determine the
potential compounds in different parts of the main root in ginseng.
Comparison of the protective effects in the main root, phloem and xylem
of ginseng on hydrogen peroxide-induced cell death of SH-SY5Y neurons
was investigated. UPLC-Q-Exactive-MS/MS was used to quickly and
comprehensively characterize the chemical compositions of the active
parts. Network pharmacology combined with a molecular docking approach
was employed to virtually screen for disease-related targets and
potential active compounds. By comparing the changes before and after
Content-Effect weighting, the compounds with stronger anti-nerve
oxidative damage activity were screened out more accurately. Finally,
the activity of the selected monomer components was verified. The
results suggested that the phloem of ginseng was the most effective
part. There were 19 effective compounds and 14 core targets, and
enriched signaling pathway and biological functions were predicted.
After Content-Effect weighting, compounds Ginsenosides F1, Ginsenosides
Rf, Ginsenosides Rg[1] and Ginsenosides Rd were screened out as
potential active compounds against neural oxidative damage. The
activity verification study indicated that all four predicted
ginsenosides were effective in protecting SH-SY5Y cells from oxidative
injury. The four compounds can be further investigated as potential
lead compounds for neurodegenerative diseases. This also provides a
combined virtual and practical method for the simple and rapid
screening of active ingredients in natural products.
Keywords: UPLC-Q-Exactive-MS/MS, content-effect weighting, ginseng
1. Introduction
Oxidative damage to nerves has been implicated in the pathogenesis of
various chronic neurodegenerative diseases, such as Alzheimer’s disease
and Parkinson’s disease [[48]1]. Neuropathological features include
oxidative damage, neuronal and synaptic loss [[49]2]. Proteins,
deoxynucleic acids and lipid membranes can be damaged by oxidative
damage caused by reactive oxygen species generated after an oxidative
burst or the presence of excess free transition metals, thereby,
disrupting the cellular function and integrity.
The presence of H[2]O[2] causes lipid peroxidation and DNA damage,
which results in apoptosis in a variety of cell types
[[50]3,[51]4,[52]5]. Recently, therapeutic strategies to prevent or
delay reactive-oxygen-species-induced apoptosis have been proposed as a
viable option to treat these diseases [[53]6,[54]7,[55]8]. Many
synthetic chemicals, such as phenolic compounds have been proven to be
strong radical scavengers; however, they usually have some severe
adverse effects [[56]9]. Therefore, research attention has focused on
searching for natural substances with neuroprotective potential to
protect from neural oxidative damage.
Regarding ginseng, the root and rhizome of Panax ginseng C.A.Mey,
studies have shown wide medical benefits, such as curing central
nervous system disease, endocrine system disease, cardiovascular system
disease and so on [[57]10,[58]11,[59]12,[60]13,[61]14]. Research
reported neuroprotective value for central nervous system disorders and
neuronal diseases attributed to the ginsenosides, which are the main
bioactive compounds in Ginseng [[62]15,[63]16]. Studies reported
neuroprotective value for central nervous system disorders and neuronal
diseases attributed to the ginsenosides, which are the main bioactive
compounds in Ginseng—in total, over 150 ginsenosides have been
identified thus far [[64]17,[65]18,[66]19].
In addition to whole organs, some studies have focused on the
accumulation and histologic distribution patterns of ginsenosides in
different parts of the main root, which is considered as the major
medicinal part. In particular, studies have shown that xylem and phloem
belong to the vascular system and express multiple genes involved in
the synthesis of ginsenosides
[[67]14,[68]20,[69]21,[70]22,[71]23,[72]24]. A ginsenoside was
synthesized in vascular tissue, such as the phloem, and then
transported to the roots’ periderm and thin walls with transporters
acting as defenders against insects and animals [[73]13,[74]25].
In order to study the material basis and action mechanism of compound
compatibility, network pharmacology was widely used. It uses the
network model to express and study on the interaction amongst the
active component, target and disease, whose behaviors were further
elucidated by a molecular docking simulation intended to simulate the
docking of small-molecule ligands and large-molecule proteins, and the
docking results were evaluated using the binding energy. Through
calculation, active compounds and key target proteins for the treatment
of diseases can be screened [[75]26,[76]27,[77]28].
Despite the convenience, this is not without its shortcomings—for
example, each component was treated equally, which is unreasonable, and
it ignores the effect of the content in each component [[78]29].
Specifically, it relies too much on public databases to accurately
reflect the presence and proportion of compounds in medicinal
materials, as the active ingredients in the database are not
necessarily present in the material under investigation, or the
database may not be complete. Thus, it was necessary to verify and
supplement the information of the database through actual measurements
using by UPLC-Q-Exactive-MS/MS or other methods.
Second, most of the studies in network pharmacology were based on
qualitative “component-target-disease” analysis, and using the content
as a key factor can prevent a very low level of ingredients from being
defined as a critical ingredient. Therefore, it was necessary to
introduce the content coefficient, which can more accurately locate the
components with high content to the center of the network and transform
one-dimensional qualitative research into two-dimensional qualitative
and quantitative research.
Moreover, the traditional method only describes the interaction between
the active compound and the target but does not mention the strength of
the interaction and cannot more accurately predict the target with a
stronger effect and its related pathways, which affect the analysis of
its mechanism of action. Similarly, it was necessary to introduce the
effect coefficient, which can more accurately capture the more
important compounds and the targets playing the main role in the
network, which can combine and have a certain intensity.
Some factors of traditional network pharmacology will lead to problems
in the completeness and accuracy of prediction data, and thus there is
an urgent need to design values about the content and effect and
integrate them into the corresponding network algorithm to evaluate the
effects. Inspired by the above applications, an effective strategy was
used on UPLC-Q-Exactive-MS/MS with the Content-Effect weighted method
to screen the active compounds in ginseng for neural oxidative damage.
2. Results
2.1. The Protective Effects of Different Parts of Ginseng on H[2]O[2] Damage
to SH-SY5Y Cells
All the different parts in ginseng showed a certain protective effect
on H[2]O[2] damage to SH-SY5Y cells. Microscopic observation showed
that good cell apposition, adequate extension, good refractive index,
clear medium without impurities and a large number of dividing cells
were visible in the control group. In the model group, the growth state
of the adherent cells was poor and not clarified, while, with the
prolongation of the drug action time, the cells showed a distinct
growth promotion state: the number of adherent cells gradually
increased. MTT experiments showed that, when the concentrations were
25, 50 and 100 μg/mL, there was a clear dose-dependent effect. The
vitality gradually became stronger, and the most significant reduction
in damage was observed in the phloem group ([79]Figure 1).
Figure 1.
[80]Figure 1
[81]Open in a new tab
Effects of the different parts in ginseng on H[2]O[2] damage to SH-SY5Y
cells. The data are presented as the means ±SEM (n = 3). *: p < 0.05
compared with the model group.
2.2. Identification of Compounds in Phloem of the Ginseng by
UPLC-Q-Exactive-MS/MS
The samples of the phloem group were analyzed by UPLC-Q-Exactive-MS/MS,
and the total ion flow diagram (negative ion mode) is shown in
[82]Figure 2. The identification of 19 ginsenoside compounds based on
retention time, molecular ion peaks, related ion fragments and related
literature mass spectrometry data ([83]Table 1).
Figure 2.
[84]Figure 2
[85]Open in a new tab
Total ion flow diagram (negative ion mode) of phloem in ginseng.
Table 1.
Summary of compounds of phloem in ginseng identified by UPLC-Q-Exactive
MS/MS.
No. RT/min [M-H]- [M+HCOO]- MS/MS Fragment Ions Peak Area Compound
1 4.85 - 683.5000 475.3746; 161.0438 7,483,087.36 Ginsenoside F1
2 19.32 851.4600 - - 382,261.81 Unknown
3 20.06 799.0000 - 637.4302; 475.3775; 161.0445 2,462,891.31
Ginsenoside Rg[1]
4 20.19 769.4744 - 637.4314; 475.3788 399,896.95 Notoginsenoside R2
5 21.01 1163.5860 - 1077.5839; 945.5390; 783.4875 521,391.11
Malonyl-ginsenoside Rc
6 21.18 1193.5960 - 945.5067; 783.4598; 621.4125 417,224.70
Malonyl-ginsenoside Rb[1]
7 21.54 - 991.9580 783.4615; 621.4111 3,865,902.28 Ginsenoside Rd
8 23.15 1077.5851 - 945.5088; 621.4125 151,726.43 Ginsenoside Rb[3]
9 24.91 955.0521 - 631.3852; 455.3498; 119.0336 2,640,554.75
Ginsenoside Ro
10 25.17 799.4849 - 475.3657; 221.0609; 101.0247 3,432,721.17
Ginsenoside Rf
11 25.96 716.3397 - 783.4935; 621.4398; 459.3845 1,542,993.88
Gypenoside IX
12 26.35 - 769.3937 769.4711; 637.4299; 475.3777 1,580,772.42
Ginsenoside F3
13 26.98 - 829.7129 621.4223; 459.3791; 161.0381 1,869,503.76
Ginsenoside F2
14 27.48 1107.1880 945.5067; 783.4598; 621.4125 3,839,623.34
Ginsenoside Rb[1]
15 28.27 597.3012 - - 149,999.06 Unknown
16 28.57 - 683.3636 475.3784; 161.0442 275,862.57 20(R)-Ginsenoside
Rh[1]
17 29.29 - 811.6000 221.0579; 161.0381; 101.0186 146,976.50 Ginsenoside
Rk[1]
18 30.23 991.5514 - - 998,851.03 Unknown
19 30.69 - 811.1000 603.4211; 441.3664; 221.0579 1,155,965.76
Ginsenoside Rg[5]
20 31.12 783.4900 - 621.4309; 459.3768; 221.0598 296,078.61
20(S)-Ginsenoside Rg[3]
21 29.79 - 825.4038 783.4897; 621.4352; 459.3832 157,781.60 Ginsenoside
Rs[3]
22 35.46 - 811.2000 619.4207; 457.3665 169,659.90 Ginsenoside Rg[6]
[86]Open in a new tab
2.3. Analysis of the Network Pharmacology
The target information of 19 ginsenosides was collected by TCMSP, a
systematic pharmacological analysis platform for traditional Chinese
medicine, and 439 relevant targets were obtained by removing duplicate
items. The target information was collected and screened using the Gene
Cards database with “neural oxidative damage” and “Oxidative nerve
injury” as the keywords. The disease targets with Score > 1 were
selected, and 6197 targets related to memory enhancement were obtained
by removing duplicates. After mapping, 400 disease effect targets were
obtained.
The 400 effect targets were entered into the STRING database, and the
minimum required interaction score was set to 0.9 to hide the nodes
disconnected from the network to obtain the protein–protein interaction
network. The results of the above analysis were imported into Cytoscape
3.7.2 software in TSV format, and 14 core targets were screened by
(degree > mean× 2) to construct a metabolite-core target network by
Cytoscape ([87]Figure 3).
Figure 3.
[88]Figure 3
[89]Open in a new tab
Protein–protein interaction network.
KEGG pathway enrichment and GO analysis were performed for 14 core
targets using the DAVID database. KEGG pathway enrichment showed 119
signaling pathways, and the top 20 pathways according to p-value are
shown in [90]Figure 4. GO analysis revealed 221 enriched processes,
including 175 biological processes, 24 molecular functions and 22
cellular components. The top 30 ranked according to p-value are shown
in [91]Figure 5.
Figure 4.
[92]Figure 4
[93]Open in a new tab
KEGG pathway enrichment.
Figure 5.
[94]Figure 5
[95]Open in a new tab
GO analysis.
2.4. Analysis of the Molecular Docking
The binding energy results of molecular docking between the 19
identified ginsenosides and the 14 core targets are shown in [96]Figure
6. [97]Figure 7 shows the results of the highest binding energy of
ginsenosides docked with each core target. It is generally believed
that, when the binding energy is less than 0, this indicates that the
component and the target can be bound together. The lower the binding
energy, the stronger the binding ability of the two and the more stable
the conformation formed.
Figure 6.
[98]Figure 6
[99]Open in a new tab
The binding energy of molecular docking between the nineteen identified
ginsenosides and the fourteen core targets.
Figure 7.
[100]Figure 7
[101]Open in a new tab
The highest binding energy of ginsenosides docked with each core
target. (a) AKT1 docking with Ginsenoside F2; the binding energy was
−14.5. (b) EGFR docking with 20(S)-Ginsenoside Rg[3]; the binding
energy was −12.7. (c) GRB2 docking with Ginsenoside Ro; the binding
energy was −8.8. (d) HSP90AA1 docking with Ginsenoside Rf; the binding
energy was −13.8. (e) JUN docking with 20(S)-Ginsenoside Rg[3]; the
binding energy was −10.7. (f) MAPK1 docking with Ginsenoside Rk[1]; the
binding energy was −13.4. (g) MAPK14 docking with Ginsenoside Rk[1];
the binding energy was −13.3. (h) MMP9 docking with Gypenoside IX; the
binding energy was −10.2. (i) PIK3CA docking with Ginsenoside F1; the
binding energy was −12. (j) PTPRC docking with Ginsenoside Ro; the
binding energy was −7.1. (k) SRC docking with Gypenoside IX; the
binding energy was −12.4. (l) STAT3 docking with Ginsenoside F3;the
binding energy was −9.5. (m) VEGFA docking with Ginsenoside Rg[5]; the
binding energy was −8.8. (n) TNF docking with Ginsenoside Rf; the
binding energy was −10.7.
2.5. Analysis of Content-Effect Weighted Method
Based on the content of components and the binding energy of molecular
docking, 19 ginsenosides were weighted. Ginsenoside F1, Ginsenoside Rd,
Ginsenoside Rg[1] and Ginsenoside Rf were screened as important
compounds with functions against neural oxidative damage, and the
results are shown in [102]Table 2.
Table 2.
Weighted values of nineteen ginsenosides using the Content-Effect
weighted method.
NO. Compound Original Degree Original Binding Energy Content
Coefficient Effect Coefficient Weighted Value Original * Order Order
after Weighted Order Changing after Weighted
1 Ginsenoside Rd 10 90.8 26.30 3.25 856.02 1 2 ↓1
2 Ginsenoside Rk[1] 9 100.8 1.00 3.61 32.52 2 13 ↓11
3 Ginsenoside Rg[1] 9 89.2 16.76 3.20 482.69 3 4 ↓1
4 20(R)-Ginsenoside Rh[1] 8 90.7 2.01 3.24 52.35 4 11 ↓7
5 Ginsenoside Rg[5] 8 85.6 7.86 3.07 193.04 5 7 ↓2
6 20(S)-Ginsenoside Rg[3] 8 84.3 1.03 3.02 24.95 6 15 ↓9
7 Ginsenoside Rf 8 79.4 23.36 2.85 531.74 7 3 ↑4
8 Ginsenoside F1 7 75.7 50.91 2.71 966.99 8 1 ↑7
9 Ginsenoside F2 7 71.1 12.72 2.55 226.90 9 6 ↑3
10 Notoginsenoside R2 7 70.6 2.72 2.53 48.19 10 12 ↓2
11 Gypenoside IX 7 64.1 10.50 2.30 169.02 11 8 ↑3
12 Ginsenoside Rg[6] 6 65.1 1.15 2.33 16.16 12 18 ↓6
13 Ginsenoside F3 6 63.7 10.76 2.28 147.34 13 9 ↑4
14 Ginsenoside Rs[3] 5 52.6 1.07 1.89 10.12 14 19 ↓5
15 Ginsenoside Rb[1] 5 50.5 26.12 1.81 236.43 15 5 ↑10
16 Ginsenoside Rb[3] 5 45.8 2.01 2.46 24.78 16 16 -
17 Malonyl-ginsenoside Rb[1] 5 43.4 2.84 1.56 22.08 17 17 -
18 Malonyl-ginsenoside Rc 5 43.3 3.55 1.55 27.49 18 14 ↑4
19 Ginsenoside Ro 3 27.9 17.97 1.00 53.90 19 10 ↑9
[103]Open in a new tab
* The original order was sequenced according to the numerical value of
the original degree with the same, it was sequenced according to the
absolute value of the sum of the original binding energy.
2.6. The Protective Effect of Active Compounds on H[2]O[2]-Induced SH-SY5Y
Cells
The protection of active compounds against H[2]O[2]-induced SH-SY5Y
cells is shown in [104]Figure 8. The results showed that the viability
of H[2]O[2]-induced SH-SY5Y cells significantly decreased in the model
group. Compared with the blank group, the cell viability of
H[2]O[2]-induced SH-SY5Y cells was increased by the intervention of the
five active compounds compared with the model group, and there were
significant differences. Thus, all five active compounds were effective
in protecting H[2]O[2] damage to SH-SY5Y cells with the relative
protective order of Ginsenoside Rf > Ginsenoside F1 > Ginsenoside Rk[1]
> Ginsenoside Rg[1] > Ginsenoside Rd. (p < 0.05 or p < 0.01).
Figure 8.
[105]Figure 8
[106]Open in a new tab
Effects of five active compounds in SH-SY5Y cells induced by H[2]O[2].
The data are presented as the means ± SEM (n = 3) *: p < 0.05 compared
with the model group, **: p < 0.01 compared with the model group.
3. Discussion
Traditional network pharmacology was used to predict the compounds of
ginseng phloem with protective effect on neural oxidative damage,
including Ginsenoside Rd, Ginsenoside Rk[1], Ginsenoside Rg[1] (with
the high original degree value). However, this might not be accurate,
because it treated each component equally, ignoring the impact of the
content. The content coefficient and the effect coefficient were
introduced by the weighting method to establish the relationship
between the content and the effect, and thus the active ingredient
could be predicted more accurately.
The order of activity strength predicted by Content-Effect weighted
network pharmacology was Ginsenoside F1, Ginsenoside Rd, Ginsenoside Rf
and Ginsenoside Rg[1]. This ranking was clearly different from the
unweighted ones; thus, it was necessary to verify the prediction
through monomeric activity experiments, in which the top four weighted
compounds and the second top unweighted compound Ginsenoside Rk[1]
(with the original degree value 9) were used. The first and the third
top unweighed compounds, Ginsenoside Rd (with the original degree value
10) and Ginsenoside Rg[1] (with the original degree value 9), were
excluded because they were already included in the top four weighed
counterparts.
The results showed that all the above five compounds were protective
against neural oxidative damage, which was consistent with the
conclusions reported in the literature, in which Ginsenoside Rf and
Ginsenoside F1 were more prominent. In the results of the unweighted
network pharmacology, Ginsenoside Rf and Ginsenoside F1 ranked seventh
and eighth; however, the weighted prediction ranked them third and
first. Therefore, this order was significantly improved, which is
consistent with the results of the monomer activity validation.
The relative contents of these two compounds were 10.11% and 22.04%,
Ginsenoside F1 was the ginsenoside with the highest content in the
phloem of ginseng; thus, it was reasonable to be one of the strongest
neuroprotective activity compounds. Similarly, Ginsenoside Rk[1] had
its predicted activity ranked second in the unweighted network
pharmacology; however, its content was only 0.43%, located in the
lowest content of the 19 compounds. The monomer activity was not
particularly significant strong in the five ginsenosides; therefore, it
was reasonable to predict that its activity ranked 15th after
weighting.
All the above results showed that Content-Effect weighted network
pharmacology transformed the traditional 1D (one-dimensional)
qualitative parameter into 2D qualitative and quantitative parameters,
thereby, improving the accuracy of the data with reasonable, effective
and necessary changes. This could more accurately predict the activity
of compounds compared to traditional network pharmacology. Based on the
above research, an activity screening method was established, which was
beneficial to the activity screening of other natural products.
4. Materials and Methods
4.1. Materials and Chemicals
Fresh roots of 5-year-old ginseng were collected from Fusong county,
Jilin province, China. The samples were taxonomically identified by
Changchun University of Chinese Medicine, and a voucher specimen (No.
202105) was deposited at the laboratory of Jilin Ginseng Academy,
Changchun University of Chinese Medicine, Changchun, China.
UPLC grade acetonitrile was obtained from Tedia Company Inc.,
(Fairfield, OH, USA). Purified water was made by a water purifier
(Global Water Solution Ltd., Randolph, MA, USA). Other reagents and
chemicals of analytical grade, including methanol, ethanol, n-butanol,
trichloromethane, DMSO, Na[2]HPO[4]·12H[2]O and NaH[2]PO[4]·2H[2]O,
were purchased from Beijing Chemical Works, (Beijing, China). Phosphate
buffer (PBS, 0.1M, pH 7.6), containing 0.05%
3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid sodium salt (TSP) as an
internal standard, was acquired from Cambridge Isotope Laboratories
Inc. (Andover, MA, USA). Deuterium Oxide (D[2]O 99.9% atom% D) was
purchased from Tenglong Weibo Technology Co., Ltd. (Qingdao, China).
The standards Ginsenoside Rg[1], Ginsenoside Rf, Ginsenoside F1,
Ginsenoside RK[1], Ginsenoside Rd were all obtained from Beijing
Science and Technology (Beijing, China), the purity of all standards
was greater than 98%.
4.2. Sample Preparation
The whole fresh plant was thoroughly rinsed with deionized water, the
main root was cut down, and two parts in the cross section of main
root—namely, the xylem and phloem—were peeled off. Then, all the cut
samples were dried in an oven at 50 °C for 72 h and finally smashed
into a powder sieved through a 20-mesh. A 2.0 g pulverized sample was
weighed into a 50 mL centrifuge tube, and 40 mL of water-saturated
n-butanol was added. The sample was sonicated for 30 min, filtered,
evaporated until dryness on a water bath at 70 °C, stored in a
desiccator and filtered with a 0.22 µm filter membrane for later use.
Extracts from three different parts of ginseng, the main root, phloem
and xylem, with five of each kind, for a total of 15 ginseng samples
were prepared according to the above mentioned extraction method for
testing.
4.3. Cell Culture
The human neuroblastoma SH-SY5Y cells were cultured in MEM medium and
50% Ham’s F-12 containing 10 fetal calf serum, 100 U/mL penicillin and
100 U/mL streptomycin in a humid atmosphere of 95% air and 5% CO[2].
SH-SY5Y cells were plated on plates pretreated with DMSO (1/1000) and
various concentrations (1, 10 and 100 μM) of extracts from three
different parts of ginseng for 24 h, the samples were exposed to 150 µM
of H[2]O[2] for 24 h at the same concentrations. A 30% stock solution
of H[2]O[2] was freshly prepared for each experiment to produce
oxidative stress. The control cells were added the same medium without
H[2]O[2] and extract from three different parts of ginseng. A
hemocytometer was used to count and differentiate viable and dead cells
by adding 10% Trypan Blue.
4.4. MTT Assay
SH-SY5Y cells were plated at a density of 1 × 10^4 cells per well in
96-well plates, and the cell viability was determined using the
conventional MTT reduction assay. Briefly, after 24 h exposure to
H[2]O[2], 4 0 μL of MTT (2 mg/mL in PBS) was added to each well, and
the cells were incubated at 37 °C for 4 h. The supernatants were
aspirated carefully, and 100 μL of dimethyl sulfoxide (DMSO) was added
to each well to dissolve the precipitate. The absorbance at 570 nm was
measured with a microplate reader (BIO-RAD Model 3550, Hercules, CA,
USA).
4.5. UPLC-Q-Exactive-MS/MS Conditions
Chromatographic conditions: an Agilent SB-C18 chromatographic column
(4.6 mm × 100 mm, 1.8 microns, P.N. 828975-902, S.N. USWFM02237,
Agilent Technologies, Inc., Palo Alto, CA, USA) was used with the
following gradient elution: mobile phase: 0.1% formic acid water (B)
and acetonitrile (C). The optimal elution conditions were as follows:
0–10 min: 5% C, 10–15 min: 35% C, 15–30 min: 40% C, 30–35 min: 50% C,
35–50 min: 100% C; flow rate: 0.3 mL/min; injection volume: 10 μL; and
column temperature 30 °C. Mass spectrometry conditions: an Agilent 1100
UPLC/MSD Trap mass spectrometer 6320 (Agilent) equipped with an
electrospray ionization source was used in both positive and negative
ion mode. Electrospray ion source (ESI), positive and negative ion mode
detection, drying gas temperature: 350 °C, sheath gas flow rate: 4 ×
10^6 Pa, auxiliary gas flow rate: 1 × 10^6 Pa, auxiliary gas
temperature: 300 °C, scanning mode: Full scan-ddMS2, resolution: 70,000
FWHM and mass scanning range: 150–2000 m/z.
4.6. Network Pharmacology Analysis
4.6.1. Construction of the Compound-Target and Disease-Target Networks
Based on the structure of active components identified by
UPLC-QTOF-MS/MS, Pubchem and Swiss Target Prediction predicted
component targets, a gene card database was used to identify known
targets associated with oxidative nerve injury. Ginsenosides were
derived as neuroprotective effect targets by mapping component targets
to disease targets.
4.6.2. Protein–Protein Interaction and Pathway-Enrichment Analysis
Topological analysis of the component-target network was done using a
network analyzer. The effector targets were placed in the STRING
([107]https://string-db.org/, Version 11.5, accessed on 9 July 2022.)
[[108]30]. In order to obtain core targets, nodes above the mean of 4
degrees were identified as interacting proteins. The PPI network,
ginseng differential metabolite-core target network was visualized and
analyzed using Cytoscape.
The Metascape database was used for functional enrichment analysis, the
core targets were entered into the Metascape database, the species was
selected as H. sapiens, the PValue Cutoff was set to 0.01, and the
remainder were kept as default settings [[109]31,[110]32]. GO contains
biological process (BP), molecular function (MF) and cellular
composition (CC), and GO analysis and KEGG-related pathway analyses
were performed using the Metascape database (p < 0.05) [[111]33].
4.6.3. Correlation Analysis
To examine the correlation between biomarkers of ginseng phloem,
Pearson correlation analysis of chemical markers in TR, LR and ZR was
performed with SPSS 22.0 and visualized using heatmaps.
4.7. Molecular Docking
Small molecule ligands and protein receptors were processed before
docking, and ligands and non-protein molecules were removed from the
protein using PyMol (Version 2.5.1; The PyMOL Molecular Graphics
System, Schrödinger, LLC, New York, NY, USA) and saved in pdb format.
We converted the molecules from mol2 format to pdb format to save them.
We opened all molecule files in AutoDock Tools (Version 1.5.6;
Department of Molecular Biology, The Scripps Research Institute, La
Jolla, CA, USA), hydrogenated and charged the molecules separately and
saved them as pdbqt files. We opened all proteins, hydrogenated,
charged, added the protein type, etc. and saved them in pdbqt format as
well.
We imported the processed small molecule ligands and protein receptor
structures, the Grid Box coordinates and box size were set, and the
calculations were run using the “local search” algorithm with default
parameters. The docking results were evaluated by the binding energy
value. A binding energy value less than 0 indicates that the ligand and
the receptor could bind spontaneously, and a binding energy −5.0 kJ/
mol indicates that the ligand binds well to the receptor. The
conformation with the lowest binding energy was selected and displayed
on a graph using PyMol (Version 2.5.1).
4.8. Establishment of the Weighted Value
Ginsenosides contain a variety of active components, and the relative
contents of different medicinal parts were also different. The
molecular docking binding energy was an important parameter to measure
the binding degree between active substances and target proteins.
In order to better explain the importance of each component content in
the biological effect, the ginsenoside content and molecular docking
binding energy were linked, and the relationship was established by
weighting. The content coefficient, efficiency coefficient and weighted
weight were introduced to measure the importance of ginsenosides in the
anti-neural oxidative damage weighted network, which could be expressed
by the following formula:
[MATH: C=AAmin :MATH]
(1)
where C represents the content coefficient,
[MATH: A :MATH]
represents the peak area of each component, and
[MATH: Amin :MATH]
represents the peak area representing the smallest component in
content.
[MATH: E=∑BiBmax :MATH]
(2)
where
[MATH: B :MATH]
represents the binding energy of a compound to the target protein,
[MATH: Bi :MATH]
represents the binding energy of a compound to different binding target
proteins, and
[MATH: Bmax :MATH]
represents the maximum value of the sum of the binding energy of a
compound with all binding target proteins.
[MATH: Wd=C·E·D :MATH]
(3)
where
[MATH: Wd :MATH]
represents the weighted value, and
[MATH: D :MATH]
represents the degree of binding target proteins.
According to the order of weighted value, active compounds were
selected.
5. Conclusions
In this study, four active compounds with protective effects against
neural oxidative injury were screened as leading compounds for the
treatment of neurodegenerative diseases. A more accurate screening
method was established to facilitate the active screening of natural
products.
Author Contributions
Conceptualization, J.-M.S. (Jia-Ming Sun) and X.-C.G.; methodology,
J.-M.S.; software, N.-X.Z. and Y.S.; validation, D.-D.C., C.-N.L. and
H.L.; formal analysis, Y.-L.W. and K.-Y.Z.; investigation, N.-X.Z.;
resources, H.Z.; data curation, J.-W.L., J.-M.S. (Jia-Ming Shen) and
M.-Y.Z.; writing—original draft preparation, X.-C.G.; writing—review
and editing, J.-M.S. (Jia-Ming Shen) and Y.S.; visualization, J.-W.L.;
supervision, J.-M.S. (Jia-Ming Sun); project administration, H.Z. and
C.-N.L.; funding acquisition, J.-M.S. (Jia-Ming Sun) and X.-C.G. All
authors have read and agreed to the published version of the
manuscript.
Institutional Review Board Statement
The study did not require ethical approval.
Informed Consent Statement
The study did not involve humans.
Data Availability Statement
The study did not report any data.
Conflicts of Interest
The authors declare no conflict of interest.
Sample Availability
Samples of the compounds Ginsenoside Rg[1], Ginsenoside Rf, Ginsenoside
F1, Ginsenoside RK[1] and Ginsenoside Rd are available from the
authors.
Funding Statement
This work was financially supported by the Science and Technology
Development Project of Jilin Province (No. 20200404081YY), the National
Natural Science Foundation of China (No. 31570347), the
Industrialization research project of Jilin Province Education
Department (No. JJKH20210992KJ), the Health and Wellness Innovation
Project of Jilin Province (No. 2018J111) and the Jilin Provincial
Department of Education (No. JJKH20200905KJ), Young Scientist Program
of “Xinglin Scholar Project” of Changchun University of Chinese
Medicine (No. QNKXJ2-2021+ZR17).
Footnotes
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References