Abstract
Background
Previous studies have demonstrated that high-density lipoprotein
cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse
cholesterol transport. Several studies have validated the efficacy and
safety of natural products in treating atherosclerosis (AS). However,
the study of raising HDL-C levels through natural products to treat AS
still needs to be explored.
Methods
The gene sets associated with AS were collected and identified by
differential gene analysis and database query. By constructing a
protein–protein interaction (PPI) network, the core submodules in the
network are screened out. At the same time, by calculating node
importance (Nim) in the PPI network of AS disease and combining it with
Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment
analysis, the key target proteins of AS were obtained. Molecular
docking is used to screen out small natural drug molecules with
potential therapeutic effects. By constructing an in vitro foam cell
model, the effects of small molecules on lipid metabolism and key
target expression of foam cells were investigated.
Results
By differential gene analysis, 451 differential genes were obtained,
and a total of 313 disease genes were obtained from 6 kind of
databases, then 758 AS-related genes were obtained. The enrichment
analysis of the KEGG pathway showed that the enhancement of HDL-C level
against AS was related to Lipid and atherosclerosis, Cholesterol
metabolism, Fluid shear stress and atherosclerosis, PPAR signaling
pathway, and other pathways. Then we intersected 31 genes in the core
module of the PPI network, the top 30 genes in Nims, and 32 genes in
the cholesterol metabolism pathway, and finally found 3 genes. After
the above analysis and literature collection, we focused on the
following three related gene targets: APOA1, LIPC, and CETP. Molecular
docking showed that Genistein has a good binding affinity for APOA1,
CETP, and LIPC. In vitro, experiments showed that Genistein can
up-regulated APOA1, LIPC, and CETP levels.
Conclusions
Based on our research, Genistein may have the effects of regulating
HDL-C and anti-atherosclerosis. Its mechanism of action may be related
to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12967-023-04755-7.
Keywords: Atherosclerosis, High-density lipoprotein cholesterol,
Differential gene analysis, PPI network analysis, Molecular docking,
Genistein
Background
Atherosclerosis (AS) is a chronic immunoinflammatory disease caused by
an imbalance in the metabolism of lipids, which leads to long-lasting
damage to the body’s immune system. Atherosclerotic plaque is formed
when cholesterol, fat, calcium, and other substances are deposited on
the inner wall of blood vessels. It narrows blood vessels and reduces
blood flow and velocity, leading to insufficient blood supply to local
tissues [[31]1]. Carotid atherosclerosis is the manifestation of AS in
the carotid arteries and is a common site in the evaluation of clinical
AS [[32]2]. According to the World Health Organization report, AS is
the leading cause of death in both developed and developing countries.
With the rapid growth of the global economy and the widespread
popularity of Western diets, the mortality and prevalence of AS in
contemporary society are rising. In recent years, AS has shown a high
incidence and is the common cause of death [[33]3]. AS is the main
underlying factor in many cardiovascular diseases (CVDs) and remains
the leading cause of death worldwide. Previous studies have
demonstrated that increasing functional high-density lipoprotein (HDL)
levels in people at risk of CVD events may be a feasible therapy to
inhibit AS progression and promote AS regression [[34]4]. The
exploration of emerging therapeutic approaches for AS has been a hot
topic in recent years, such as magnetic nanoparticles, which can be
used for magnetic drug targeting. In recent years, there have been
several articles on the study of the phenomenon of biomineralization
and the production of inorganic magnetic nanoparticles in biological
systems. The green synthesis method used in the synthesis of
nanoparticles is currently the main area of interest for researchers
[[35]5, [36]6].
Many epidemiological studies have identified an inverse correlation
between HDL levels and AS [[37]7, [38]8]. HDL is protective because it
removes excess cholesterol from surrounding tissues by reverse
cholesterol transport (RCT) and transporting it to the liver for bile
excretion [[39]9]. In addition, low high-density lipoprotein
cholesterol (HDL-C) levels increase beta cell dysfunction and aggravate
insulin resistance. Furthermore, long-term high insulin levels may
promote damage to the lining of the artery wall and promote plaque
formation. In addition, insulin resistance is also associated with
other risk factors, such as high blood pressure, high blood lipids, and
obesity, which are also risk factors for AS [[40]10]. Despite this, no
drugs have been found with a definite effect of raising HDL-C,
including chemical drugs and natural products to date.
Natural products are the components or metabolites of living organisms
that have evolved in nature over a long period. For example, flavonoid
compounds present in plants and polysaccharides in plant cell walls
show a variety of pharmacological effects. Therefore, it is seen as a
potential alternative to traditional treatment methods in the future
[[41]11]. In particular, the effective ingredients in Traditional
Chinese Medicine (TCM), which uphold thousands of years of history and
clinical practice, are widely used in the prevention and treatment of
diseases [[42]12]. The efficacy and safety of TCM have been widely
recognized in TCM theory and clinical practice, but to ensure its
rational use, modern TCM research also pays attention to scientific
research and clinical trials on the effective ingredients,
pharmacological effects, and pharmacokinetics of TCM [[43]13]. TCM has
a long history in the treatment of AS and rich experience in clinical
application. AS was documented thousands of years ago in China and was
treated with Chinese herbal medicine [[44]14]. In TCM theory, AS is
often referred to as “MaiBi”, a vascular problem caused by Qi
stagnation, Blood stasis, and/or Phlegm coagulation [[45]15].
Currently, a variety of chemical drugs have been used to treat AS, but
their mechanism of action is not clear [[46]16, [47]17], some of them
have poor efficacy and serious side effects [[48]18]. It is very
important to develop safe and efficient AS treatment drugs. TCM has
potential use in the treatment of AS because of their few toxic and
side effects, strong safety, definite curative effect, and economic
benefits [[49]19].
In recent years, the rise of bioinformatics has undoubtedly provided a
new analysis method for exploring the mechanism of multi-target and
multi-pathway diseases and the pharmacological effects of drugs
[[50]20]. A network pharmacological research method has been proposed
by pharmacological and systems biology research tools to predict the
underlying mechanism of TCM efficacy [[51]21]. It breaks the
traditional thinking mode of “one disease-one target-one drug” and
reveals the etiological mechanism of complex diseases in a multi-angle
and multi-approach way [[52]22]. It can then be used in conjunction
with molecular docking to aid drug discovery. It has emerging uses and
applications, including adverse reaction prediction,
multi-pharmacology, and drug reuse [[53]23].
Although several TCM compounds have been reported to have good
therapeutic effects and mild side effects, it is of great practical
significance to find additional potential anti-AS components from a
large number of common TCM small molecules with clear pharmacological
actions. Given this, we combined bioinformatics, computer-aided drug
design, and analysis of existing literature, to explore important
targets for the treatment of AS, and further screen out effective
components of TCM with potential therapeutic effects. This study
further verified the above conclusions through in vitro experiments,
and the potential natural product was found to improve the level of
HDL-C by regulating multiple targets and pathways, playing an anti-AS
role, and providing powerful methods and technical support for the
treatment of AS by natural products.
The flowchart of the present study is illustrated in Fig. [54]1.
Fig. 1.
[55]Fig. 1
[56]Open in a new tab
Flowchart of the study of potential natural products for the treatment
of AS by elevating HDL-C levels based on bioinformatics analysis
Materials and methods
Therapeutic targets for AS
In the Gene Expression Omnibus (GEO) database, input the
“Atherosclerosis” query and collection of AS related Gene Expression
profile chip data. R (version 4.1.2) was used to perform preprocessing
of the original data, such as standardization, correction, and gene
name annotation. The R language-based limma package [[57]24] (version
3.44.0) was used for differentially expressed genes (DEGs) analysis.
The upregulated and downregulated DEGs in each set of chip data were
screened out when log2|Fold Change| > 1.0 and P-value < 0.05.
To ensure the integrity of the collected AS-related gene set, according
to our previous research experience [[58]25, [59]26], six commonly used
databases were queried and collected, namely: CTD [[60]27], DisGeNET
[[61]28], GeneCards [[62]29], OMIM [[63]30, [64]31], PharmGKB [[65]32],
and TTD [[66]33]. The keyword “Atherosclerosis” was input into 6
databases to query and collect genes associated with AS disease. CTD
database to select the Direct Evidence tag “marker/mechanism”,
“marker/mechanism | therapeutic” and “therapeutic” genes. The gene with
Score_gda > 0.1 was selected in DisGeNET. Genes with a Relevance
score > 5 were selected in GeneCards. OMIM selected genes with a
definite Entrez Gene ID. In PharmGKB and TTD databases, all relevant
genes were included in the AS-related gene set by inputting keywords.
Then, the results of differential gene analysis and screening based on
GEO were combined with the target genes queried from 6 databases to
remove duplicate genes. All the collected target information was
confirmed through the UniProt to form an AS disease-related target gene
collection S[AS]. It should be noted that the deadline for our query
and use of all the above databases is September 2022.
Construction of the protein–protein interaction (PPI) network
All genes obtained above were imported into STRING (version 11.0)
[[67]34] to obtain the PPI network of AS disease. Here, the specific
parameter is set: Organism is “Homo Sapiens”, a combined score > 0.9.
Meanwhile, we used Gephi software (version 0.9.2) to visualize the
network. In addition, the molecular complex detection (MCODE) [[68]35]
tool in Cytoscape software (version 3.7.1) [[69]36] was used to
identify the key modules in the PPI network, where the parameters were
set to default values.
Node importance (Nim) is an important topological property and can be
used to evaluate the influence of nodes among the network. The nodes
whose Nim is larger than the average Nim of all nodes are treated as
critical roles and hub nodes in the network. We carried out
optimization based on literature and used equation [[70]37] to
calculate Nim in AS disease PPI network:
[MATH: Nim(s)=∑s≠v≠t∈V
σvt(s)σvt×<
munder>∑s≠xexp-d(s,x) :MATH]
1
Among them, Nim is the importance value of each node;
[MATH: σvt :MATH]
is the number of shortest paths between node
[MATH: v :MATH]
and nod
[MATH: t :MATH]
;
[MATH: σvt(s) :MATH]
is the number of shortest paths between node
[MATH: v :MATH]
and nod
[MATH: t :MATH]
passing through node
[MATH: s :MATH]
; And
[MATH: d(s,x) :MATH]
is the shortest path distance between nodes
[MATH: s :MATH]
and all connection points in the network. It is achieved by using the
igraph package (version 1.2.6).
Gene ontology (GO) function and Kyoto Encyclopedia of genes and genomes
(KEGG) pathway enrichment analysis
The GO database is a structured standard biological model constructed
by the GO Consortium in 2000. It covers the Biological Process (BP),
Molecular Function (MF), and Cellular Component (CC) of genes [[71]38].
A biological process or pathway is usually performed by a group of
genes working together, rather than by a single gene. The main basis of
enrichment analysis is that if a biological process or pathway is
abnormal in known studies, the genes that function together are most
likely to be selected as the gene set associated with this process or
pathway. In this study, we further used the clusterProfiler (version
3.14.3) [[72]39] toolkit to conduct GO function and KEGG Pathway
enrichment analysis on the target set of potential AS diseases and
screened them according to P-value < 0.05 and Q-value < 0.05. The
gene-pathway network interrelationships are also mapped using
Cytoscape.
Screening of small molecules on key targets and molecular docking
To screen potential natural products, we use molecular docking
techniques to examine the affinity between the receptor and the ligand.
The selected key protein targets met the following conditions: (1) The
pathway closely related to AS disease in the KEGG pathway enrichment
analysis results; (2) The target was a key protein target in the PPI
network; (3) In the Nims calculation results. After selecting potential
key targets for the treatment of AS diseases, the PDB files of
potential key target proteins is downloaded from the PDB database
[[73]40]. PyMOL software (version 1.7.0) pretreats key target proteins
to remove other miscellaneous ions, including water molecules. We
searched the HIT2.0 [[74]41] for potentially active small molecules of
the target protein. The small molecules downloaded from PubChem are
available in two SDF formats, 2D and 3D. 2D SDF files are made use of
OpenBabel software (version2.4.0) to a 3D structure file. The original
3D structure is converted directly to a PDB file. AutoDock Tools
(version 1.5.6) was used to hydrogenate the treated protein ligands,
charge them, transform them, and save them as PDBQT files. The docking
center parameters were determined by referring to the binding site
(region) of the protein receptor and the original ligand. Define the
box size to be 30 × 30 × 30. Use AutoDock Vina (version 1.1.2) software
for semi-flexible molecular docking. The affinity (kcal/mol) between
all small molecules and potential key targets was calculated. The lower
the affinity value, the more stable the interaction between the target
protein and the active ingredient. Finally, small molecules that
potentially treat AS by increasing HDL-C levels were selected according
to the affinity values from low to high.
Cell culture
The mouse macrophage cell line RAW264.7 (Simuwu Bio, Shanghai, China)
was maintained in Dulbecco’s modified Eagle’s medium (DMEM, Gibco,
Newyork, USA) supplemented with 10% FBS. RAW264.7 cells at the
logarithmic growth stage were inoculated into 6-well plates, each well
5 × 10^5. When the cells were fully adherent, they were transformed
into foam cells by incubation for 24 h with DMEM supplemented with 10%
FBS containing oxidized low-density lipoprotein (ox-LDL, Yiyuan
Biotechnologies, Guangzhou, China) (50 µg/mL).
CCK-8 assay
RAW264.7 cells were inoculated into 96-well plates with 4 × 10^3 cells
per well. Add 100 µL (3.125, 6.25, 12.5, 25, 37.5, 50, 75, 100 µmol/L)
Genistein (Yuanye, Shanghai, China) to each well. The control group was
added with DMEM complete medium. After 24 h culture, 10 µL CCK-8
solution was added to each well. The culture was continued in the
incubator for 2 h. The absorbance at 450 nm was determined by an
enzyme-labeled instrument.
Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR)
Total RNA was extracted from cells by RNA-Easy Isolation Reagent
(Vazyme, Nanjing, China). And for real-time quantitative PCR analysis,
cDNA was synthesized using HiScript II Q RT SuperMix for qPCR Kit
(Vazyme, Nanjing, China). We then used SYBR Green dye combined with
APOA1, LIPC, and CETP primer pairs for mRNA quantification. All primers
were purchased from Sangon Biotech (Shanghai, China). The primer
sequence is shown in Additional file [75]1: Table S1. And the relative
mRNA expression was normalized with GAPDH using the ΔΔCt method. 0.1%
MDSO (Adamas, Shanghai, China) was added to the DMEM of the NC group.
In the MD group, 10% FBS containing ox-LDL (50 µg/mL) was added to DMEM
as a culture system. In the treatment group, 10% ox-LDL (50 µg/mL),
FBS, and corresponding small molecules of TCM were added into DMEM, and
the cells were transformed into foam cells after incubation for 24 h.
Statistical analysis
Data from RT-qPCR experiments were plotted and analyzed by GraphPad
Prism 9.0 and SPSS 26.0. Comparisons among multiple groups were
performed using one-way analysis of variance (ANOVA) for normal
distribution and Kruskal–Wallis test for non-normal distribution.
One-way ANOVA was followed by a least significant difference (LSD)
test. Comparisons between the two groups were performed using an
unpaired t-test for normal distribution and Mann–Whitney test for
non-normal distribution. P-value < 0.05 was considered statistically
significant.
Results
AS chip data collection and differential gene analysis
[76]GSE43292 was obtained from the GEO query to obtain the expression
profile data related to AS. The platform of [77]GSE43292 is
[HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene)
version]. There were 32 normal samples and 32 AS samples. After
analysis, 451 differential genes were screened from [78]GSE43292. There
were 221 up-regulated genes and 230 down-regulated genes (Fig. [79]2A,
B). As shown in Fig. [80]2C, 64, 80, 151, 151, 13, and 27 AS targets
were screened from CTD, DisGeNET, GeneCards, OMIM, PharmGKB, and TTD
databases. A total of 313 genes related to AS were obtained after
consolidation. Finally, all genes obtained by the above two methods
(based on differential gene analysis and target database collection)
were combined. A total of 758 target genes related to AS were obtained,
namely the target gene set S[AS] (Fig. [81]2D).
Fig. 2.
[82]Fig. 2
[83]Open in a new tab
AS chip data collection and differential gene analysis. A Heat map of
[84]GSE43292 differential gene analysis results. B Volcanic map of
[85]GSE43292 differential gene analysis results. C Gene distribution in
different drug target databases. D AS target gene collection based on
GEO differential gene analysis and drug target database
PPI network construction and screening of key targets
All 758 AS differential genes were imported into the STRING database.
Therefore, the corresponding PPI network is obtained (Fig. [86]3A).
Through PPI network module analysis, we screened out a core submodule.
They are APOA1, APOA2, APOA4, APOA5, APOB, APOC2, APOC3, APOE, APOH,
APOM, CCL3, CCL4, CETP, CLU, CXCL10, HP, IFNG, IL17A, IL18, LCAT, LDLR,
LIPC, LPA, LPL, LTA, PLA2G7, PLTP, PON1, STAT3, TLR2, and TLR4. These
31 important proteins become a highly interconnected submodule
(Fig. [87]3B).
Fig. 3.
[88]Fig. 3
[89]Open in a new tab
PPI network construction and screening of key targets. A AS disease PPI
network constructed by differential gene analysis and STRING database,
in which dots represent proteins, and the larger the nodal degree value
is. B Top 31 important proteins and their core submodules in the PPI
network obtained based on the MCC algorithm. C Node importance (Nim)
We use formula to calculate Nim in the AS disease PPI network
(Fig. [90]3C). We selected the top 30 nodes for path enrichment.
Fifteen of them were enriched in the Cholesterol metabolism pathway.
GO function annotation and KEGG pathway enrichment analysis results
ClusterProfiler is used to further GO functional and KEGG Pathway
enrichment analysis on 734 differential gene sets of AS, where
P-value < 0.05 and Q-value < 0.05. The GO function annotation results
of the target show that there are 2371 BP functions involved, such as
lipid localization (GO:0010876), lipid transport (GO:0006869),
regulation of lipid localization (GO:1,905,952), cholesterol transport
(GO:0030301), regulation of plasma lipoprotein particle levels
(GO:0097006) (Fig. [91]4A); There are 54 CC functions involved, such as
plasma lipoprotein particle (GO:0034358), lipoprotein particle
(GO:1990777), protein–lipid complex (GO:0032994), high-density
lipoprotein particle (GO:0034364), collagen-containing extracellular
matrix (GO:0062023), among several others (Fig. [92]4B); And there are
158 MF functions involved, such as receptor-ligand activity
(GO:0048018), signaling receptor activator activity (GO:0030546),
cytokine receptor binding (GO:0005126), amide binding (GO:0033218)
(Fig. [93]4C). KEGG pathway enrichment analysis showed that the target
was mainly enriched in 108 related signaling pathways. They are Lipid
and atherosclerosis (hsa05417), Cholesterol metabolism (hsa04979),
Fluid shear stress and atherosclerosis (hsa05418), and PPAR signaling
pathway (hsa03320). The above pathways have been reported to be closely
related to AS disease (Fig. [94]4D). The gene-enrichment pathway
network is shown in Fig. [95]4E.
Fig. 4.
[96]Fig. 4
[97]Open in a new tab
GO function annotation and KEGG pathway enrichment analysis results.
A–C GO functional annotation of AS differential genes (BP, CC and MF).
D KEGG pathway enrichment analysis of AS differential genes. E AS
target-pathway enrichment network, with dots representing protein
targets and squares representing pathways. The line between the protein
target and the pathway indicates that there is an enrichment
relationship between the target protein and a certain pathway
After the intersection of the gene sets obtained by the above three
methods of PPI network module analysis, Nims calculation results, and
KEGG pathway enrichment analysis, we found that three genes (APOA1,
APOB, APOE) simultaneously met the above three conditions. Based on a
literature review, we are particularly concerned about Apolipoprotein
A1 (APOA1) (Fig. [98]5a).
Fig. 5.
[99]Fig. 5
[100]Open in a new tab
Identification of target genes and potential small molecules. A Target
gene collection based on the module, the Nims, and the Cholesterol
metabolism pathway. B ADMET of Genistein
Molecular docking analysis
In this study, we used HIT 2.0 to search for potential TCM small
molecules that interact with a key target protein, APOA1. The results
showed that 6 small molecules met the conditions（Additional file
[101]2: Table S2.). According to the literature research [[102]42,
[103]43], we selected Genistein. ADMET of Genistein was analyzed by
ADMETlab 2.0 [[104]44] and SwissADME [[105]45] respectively, as shown
in Fig. [106]5b. The results showed that the small molecule had good
drug properties and was a good potential therapeutic compound.
In addition to APOA1, we also selected LIPC and CETP, which are related
to AS, from the PPI network module. We found that Genistein’s affinity
values for LIPC, APOA1, and CETP were − 9.5, − 7.7, and − 5.1
(Table [107]1). Under normal circumstances, the binding energy is less
than − 5, which means that it has good binding potential. The docking
modes between Genistein and the three targets are shown in
Fig. [108]6A, C, E. The 2D binding conformations of Genistein and
target proteins were displayed using BIOVIA Discovery Studio
Visualizer, as shown in Fig. [109]6B, D, F.
Table 1.
Molecular docking results of AS with target proteins
MOL Targets Pathway Protein names Uniprot ID PDB ID Affinity (kcal/mol)
Genistein APOA1 Cholesterol metabolism Apolipoprotein A-I [110]P02647
1GW3 − 5.1
Genistein CETP Cholesterol metabolism Cholesteryl ester transfer
protein [111]P11597 4F2A − 7.7
Genistein LIPC Cholesterol metabolism Hepatic triacylglycerol lipase
[112]P11150 AlphaFold − 9.5
[113]Open in a new tab
Fig. 6.
[114]Fig. 6
[115]Open in a new tab
Molecular docking results of Genistein interaction with APOA1, LIPC,
and CETP. A, B Molecular docking conformation of Genistein interaction
with APOA1. C, D Molecular docking conformation of Genistein
interaction with LIPC. E, F Molecular docking conformation of Genistein
interaction with CETP
Verification with in vitro cell culture experiments
Based on the results of molecular docking, Genistein was selected for
in vitro experiments. The results of CCK-8 showed that the level of
cell proliferation was not significantly affected when the
concentration of Genistein was below 12.5 µmol/L (Fig. [116]7A).
According to the safe dose range, we selected the high dose of
Genistein as 10 µmol/L and the low dose as 2.5 µmol/L in the follow-up
experiment.
Fig. 7.
[117]Fig. 7
[118]Open in a new tab
Verification with in vitro cell culture experiments. A Effect of
Genistein on the viability of macrophage
[MATH: (x¯±s) :MATH]
. B The relative APOA1, LIPC, and CETP mRNA expression levels.
*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001
The mRNA expression levels of target proteins in the NC, MD, and
treated groups were measured by RT-qPCR. The RT-qPCR results
(Fig. [119]7B) showed that the mRNA expression levels of APOA1, LIPC,
and CETP in the MD group were lower than those in the NC group. Both
low and high doses of Genistein could increase the relative expression
of APOA1, LIPC, and CETP. Notably, low-dose Genistein therapy reversed
the reduction in mRNA expression of target proteins induced by ox-LDL
intervention and improved cellular fat metabolism. However, the
regulation of CETP by Genistein was not obvious.
Discussion
HDL-C is considered the “good” cholesterol. Pharmacoepidemiological
studies have shown that HDL-C levels are inversely associated with the
incidence of CVD [[120]7, [121]46]. For a long time, the prevention and
treatment of AS by increasing HDL-C has been considered to have broad
prospects. However, the drugs currently screened for raising HDL-C do
not play this role well [[122]47]. For example, niacin can increase
HDL-C levels, but studies have determined that niacin does not reduce
residual cardiovascular risk [[123]48]. CETP blockers have been
eliminated because they have more adverse reactions or are not
beneficial to the end event [[124]49, [125]50]. The PPARα agonist K-877
is in Phase II clinical trials [[126]51]. Based on the above studies,
we believe that actively exploring natural products to improve HDL-C
has important clinical value.
In this study, literatures and resources from open databases related to
HDL-C were reviewed to screen AS targets. Then, PPI network
construction and pathway enrichment analysis were used to explore the
potential mechanism of resistance to AS. The results of KEGG analysis
showed that Lipid and atherosclerosis, Cytokine-cytokine receptor
interaction, Cholesterol metabolism, TNF signaling pathway, and PPAR
signaling pathway are the core regulatory pathways after the
elimination of unrelated findings. Based on the above results, we
attempted to build the connection between increased HDL-C/anti-AS and
lipid metabolism and the resolution of inflammation. We calculated Nim
in the PPI network for AS disease. The above results were sorted out
and combined, and then related literature reading was carried out.
According to the above method, we obtained three key protein genes:
Apolipoprotein A1 (APOA1), Lipase C (LIPC), and Cholesteryl Ester
Transfer Protein (CETP). APOA1 is a necessary component of HDL
particles. It plays a key role in the biosynthesis of HDL, cholesterol
transport, and RCT [[127]52]. It also has anti-inflammatory,
anti-atherogenic, anti-apoptotic, and anti-thrombotic properties. APOA1
helps stabilize vulnerable plaque by removing cholesterol from
atherosclerotic plaque and reducing the damage caused by lipids
[[128]53]. Previous studies have shown that serum APOA1 is positively
correlated with HDL-C in the Chinese population [[129]54]. APOA1
(M148A) mutation may interfere with HDL remodeling. Low-density
lipoprotein cholesterol (LDL-C) levels were reduced in mice with HDL
particles carrying APOA1 (Y192A) [[130]55]. APOA1 and important APOA1
mutations are still being studied and developed for the treatment of
CVD, AS, and other diseases. CETP promotes the transfer of cholesterol
esters from HDL to very low-density lipoproteins and low-density
lipoprotein (LDL). Therefore, CETP inhibitors were developed to
increase HDL-C levels and lower LDL-C levels to prevent CVD.
Unfortunately, recent large clinical trials have proven disappointing
results. However, researchers are still full of confidence in its
potential protective effect against the risk of CVD and diabetes
[[131]56, [132]57]. Liver lipase is a lipolytic enzyme involved in
plasma lipoprotein metabolism, especially HDL metabolism. Studies have
identified common variants of the hepatic lipase gene associated with
HDL cholesterol [[133]58]. According to reports, HDL is mainly
participating in RCT. In addition, HDL also has several new functions
such as inhibiting endothelial inflammation, promoting endothelial
production of NO and prostacyclin, as well as isolating and
transporting amyloid-producing proteins, oxidized lipids, and lipids
derived from exogenous pathogens [[134]59]. In subjects with extremely
high HDL-C levels, rare or common variants of CETP and common variants
of LIPC are often found [[135]60]. This suggests expression levels of
these genes may have an important effect on HDL.
Molecular docking is then performed to screen candidates with high
therapeutic potential. Based on the analysis, a small molecule with
high affinity, Genistein, was selected. Genistein is a
7-hydroxyisoflavone with the molecular formula of C15H10O5 and a
molecular weight of 270.24 g/mol. It belongs to a class of isoflavone
compounds, which mainly come from legumes. It has various biological
activities such as lowering glucose, lowering lipids, antioxidants,
anti-inflammatory, and anti-tumor [[136]61]. It was found that
Genistein may reduce AS via activating PPARγ-LXRα-ABCA1/ABCG1 pathway
to enhance cholesterol effluence [[137]62]. In this study, foam cells
were induced in vitro to simulate the formation of foam cells in AS
plaques. In our study, Genistein significantly up-regulated APOA1 and
LIPC levels in a dose-dependent manner. Although the regulation of CETP
by Genistein is not obvious, this may be because there is no suitable
PCR primer sequence at present. Based on our findings, we infer that
Genistein may be a promising drug candidate in the treatment of AS. It
can effectively regulate and raise therapeutic targets associated with
HDL-C levels.
The advantage of this study is that the targets related to AS are
obtained by integrating multiple databases. Then, the importance of
nodes and pathway enrichment were calculated by the formula, and the
key therapeutic targets of HDL-C for the treatment of AS were finally
obtained through literature collection: APOA1, LIPC, and CETP. The most
suitable potential natural product, Genistein, was found through
molecular docking simulation, and the Genistein and the target were
verified by in vitro experiments. It is an integrated multidisciplinary
approach to establishing a viable anti-AS drug discovery process. To
the present knowledge, no published study has so comprehensively
screened and validated a natural product that can raise HDL-C levels as
an anti-AS therapeutic target. Of course, there are some limitations to
this study. First, we only verified the relationship between possible
targets and Genistein through computer simulations and in vitro
experiments, not in vivo experiments. Second, we did not compare
Genistein with existing treatments, nor did we conduct a combination
intervention to further evaluate its effects on HDL-C and AS. Third,
Genistein is present in a variety of plants, and the best source for
clinical use has not been identified. Therefore, the potential of
Genistein to increase the level of HDL-C, as well as the mechanism and
effect of the treatment of AS need to be further confirmed in vivo and
in vitro. Experiments are also needed to determine the best source of
Genistein.
Although this study gives insights into treating AS by increasing HDL-C
levels, further research and validation are needed. In future studies,
we may consider conducting long-term population-cohort studies of AS to
track exposure, HDL levels, and the progression of associated diseases
in a large number of participants. This helps us to more fully assess
the potential role of HDL-C in disease progression, as well as its
interaction with other biomarkers. In addition, animal experiments are
also a powerful way to verify the association between our found targets
and HDL-C, which can further elucidate the relevant mechanisms
regulating HDL-C by simulating atherosclerosis models. This will
provide us with a more comprehensive and in-depth understanding of the
mechanism of action and potential natural products in the treatment of
AS, and provide an important theoretical basis and a new way for the
development of future treatment strategies and new drugs for AS.
Conclusions
In summary, through bioinformatics combined with molecular docking and
in vitro experimental verification, this study explored the possible
potential small molecules of natural products and their mechanism of
action in the treatment of AS by increasing HDL-C levels. GEO and PPI
network analysis is helpful to identify several key target genes
related to AS and to screen small molecules of TCM for the treatment of
AS using molecular docking techniques. In addition, we confirmed that
small molecules have significant regulatory effects on related protein
genes through in vitro experiments. A series of comprehensive analysis
methods established in this study is expected to provide a way of
thinking and powerful technical support for the treatment of AS and the
development of novel molecules of natural products.
Supplementary Information
[138]12967_2023_4755_MOESM1_ESM.docx^ (11.5KB, docx)
Additional file 1: Table S1. Primer sequences used for qRT-PCR.
[139]12967_2023_4755_MOESM2_ESM.docx^ (12KB, docx)
Additional file 2: Table S2. The six potential TCM small molecules were
searched from HIT 2.0.
Acknowledgements