Abstract
Ischemic stroke (IS) is an acute neurological injury that occurs when a
vessel supplying blood to the brain is obstructed, which is a leading
cause of death and disability. Salvia miltiorrhiza has been used in the
treatment of cardiovascular and cerebrovascular diseases for over
thousands of years due to its effect activating blood circulation and
dissipating blood stasis. However, the herbal preparation is chemically
complex and the diversity of potential targets makes difficult to
determine its mechanism of action. To gain insight into its mechanism
of action, we analyzed “Salvianolic acid for injection” (SAFI), a
traditional Chinese herbal medicine with anti-IS effects, using
computational systems pharmacology. The potential targets of SAFI,
obtained from literature mining and database searches, were compared
with IS-associated genes, giving 38 common genes that were related with
pathways involved in inflammatory response. This suggests that SAFI
might function as an anti-inflammatory agent. Two genes associated with
inflammation (PTGS1 and PTGS2), which were inhibited by SAFI, were
preliminarily validated in vitro. The results showed that SAFI
inhibited PTGS1 and PTGS2 activity in a dose-dependent manner and
inhibited the production of prostaglandin E2 induced by
lipopolysaccharide in RAW264.7 macrophages and BV-2 microglia. This
approach reveals the possible pharmacological mechanism of SAFI acting
on IS, and also provides a feasible way to elucidate the mechanism of
traditional Chinese medicine (TCM).
Keywords: salvianolic acid for injection, computational systems
pharmacology, ischemic stroke, PTGS1, PTGS2
Introduction
Traditional Chinese medicine (TCM) has played a significant role in
treatment of a large variety of diseases for thousands of years and is
still widely used nowadays. TCM uses prescriptions which usually
consist of different components, such as plants, animals or minerals,
depending on the disease to treat. However, due to the presence of a
large variety of chemical components in prescriptions, it is difficult
to identify their active component, its mechanism of action and to
establish clinical parameters in order to test their therapeutic
efficacy. In addition, many prescriptions have more than one single
active component, and these may act synergistically to produce
therapeutic benefit. These impediments have long delayed the
modernization of TCM ([39]Zhang et al., 2019).
In the last years, the development of network pharmacology has made
possible the exploration of mechanisms of action at the molecular level
of many herbal preparations used in TCM. This has generated a new
research paradigm for translating TCM from an experience-based medicine
to an evidence-based medicine system ([40]Li and Zhang, 2013). In
addition, the combination of virtual screening technology,
computational, experimental, and clinical research, are contributing to
a new direction in the TCM network pharmacology research of the future
([41]Jiao et al., 2021; [42]Wang et al., 2021). However, most of
current network propagation algorithms ignore the direction and mode
between interacting proteins. As a consequence, they poorly reflect the
effect of a drug on an entire network, requiring further improvement
for their use in TCM network pharmacology ([43]Zhao et al., 2019).
Ischemic stroke (IS) is an acute neurological injury caused by the
narrowing or occlusion of the blood supply arteries of the brain, which
is a major leading cause of death worldwide and the number one cause
for acquired long-term disability, resulting in a global annual
economic burden ([44]Herpich and Rincon, 2020). Thrombolysis with
thrombolytic agents, such as alteplase and urokinase, is generally the
first choice for the treatment of ischemic stroke. However, due to the
narrow therapeutic time window for thrombolysis treatment, a high
number of patients miss the best time for treatment.
Salvianolic acid for injection (SAFI) is a TCM preparation, approved by
the Chinese Food and Drug Administration since 2011, which is extracted
from Salvia miltiorrhiza and undergoes further separation and
purification steps using standard chemical procedures. Its main
water-soluble components include salvianolic acid B, rosmarinic acid,
lithospermic acid, salvianolic acid D, and salvianolic acid Y ([45]Jing
et al., 2013; [46]Jing-yao et al., 2015; [47]Lei et al., 2015; [48]Li
et al., 2016; [49]Li et al., 2018). SAFI has anti-inflammatory,
anti-oxidative stress, and anti-platelet aggregation effects. Clinical
trials have shown the beneficial effects for ischemic stroke with no
significant adverse effects on liver and renal function, and no
significant risk of bleeding, indicating a good safety profile
([50]Tang et al., 2015; [51]Zhuang et al., 2017; [52]Yang, 2020).
In order to investigate the molecular mechanism of SAFI, we first
collected information on its major components through literature
mining, and compared their similarities with those of approved IS
therapeutic drugs (including anticoagulant, antiplatelet,
neuroprotective, and lipid lowering drugs). Subsequently, SAFI targets,
collected from extensive literature mining, and IS drugs targets were
enriched for biological process analysis. Predicted SAFI inhibition and
activation targets were extracted by signed random walk with restart
(SRWR) method, and were enriched for biological process and signaling
pathways. Potential important targets were found via comparison with
disease genes of ischemic stroke. Differences and similarities between
SAFI and the approved IS therapeutic drugs were also compared by
signaling pathways enrichment analysis. In addition, the potential
important targets of SAFI treatment for IS were validated by QSAR
(quantitative structure-activity relationship) model. Finally, the key
targets were validated experimentally using enzymatic and cell-based
assays.
Materials and Methods
The whole workflow is illustrated in [53]Figure 1.
FIGURE 1.
[54]FIGURE 1
[55]Open in a new tab
Workflow illustrating the steps used in the elucidation of SAFI
mechanism of action in IS treatment. The workflow includes data
collection, data analysis and experimental validations.
Collection of Information on SAFI Main Components
In order to collect information on SAFI main components, “salvianolic
acid for injection” was used as the key word to obtain Chinese-language
literature through CNKI ([56]https://www.cnki.net/) and
English-language literature through PubMed
([57]https://pubmed.ncbi.nlm.nih.gov/). Then, the components contained
in SAFI were collated manually and standardized through PubChem
database ([58]https://pubchem.ncbi.nlm.nih.gov/).
SAFI’s Targets Obtained From the Literature and Database
In order to obtain SAFI target information, the term “salvianolic acid
for injection” was used to obtain related literature through CNKI and
PubMed databases.
The potential targets of five important SAFI components-salvianolic
acid B, rosmarinic acid, lithospermic acid, salvianolic acid D, and
salvianolic acid Y, were collected using HERB database ([59]Fang et
al., 2020).
Collection of IS-Associated Genes, Anti-IS Drugs, and Their Targets
IS-associated genes were collected from the DisGeNET database
([60]Piero et al., 2019) by searching the term “Ischemic stroke”.
Differentially expressed genes in IS were obtained from [61]GSE22255
dataset (downloaded from Gene Expression Omnibus (GEO),
[62]https://www.ncbi.nlm.nih.gov/geo/).
Anti-IS drugs collection were referred to “Chinese guidelines for
diagnosis and treatment of acute ischemic stroke 2018” ([63]Neurology
and Society, 2018), and their targets were collected from the Drugbank
database ([64]Wishart et al., 2017).
Correlation of Drug Targets and Disease Genes
To evaluate the efficacy of a drug on disease, the correlation of the
drug targets and the genes associated with the disease from the
perspective of network propagation was used ([65]Yang et al., 2020).
Briefly, the drug targets and disease genes were used as seed genes to
run Random Walk with Restart (RWR) algorithm ([66]Köhler et al., 2008)
in the background network STRING, which was performed in R package dnet
(version 1.1.7) with 0.75 of the restart probability. In this way, the
influence score vector of the two sets of seed nodes on all nodes in
the background network was obtained. Pearson correlation coefficient of
the two score vectors (Cor) was then calculated, and Z-score was used
to evaluate the significance of the correlation using the formula
[MATH: z−score=Cor−E(Co<
/mi>r)δ
(Cor) :MATH]
where E(Cor) and δ(Cor) are the mean and standard deviation of the
Pearson correlation coefficients between the influence score vector of
drug targets and those of 1000 groups of random contrast disease genes,
each of which contained the same number of randomly selected proteins
as the disease seed nodes.
Drug Similarity Evaluation and Hierarchical Clustering Based on Chemical
Structure, Comprehensive Targets and Cellular Function Fingerprints
The similarity of two drugs was evaluated based mainly on chemical
structure, comprehensive targets or cellular function fingerprints.
Drug clusters with similar features were obtained using hierarchical
clustering.
The chemical structure similarity was measured based on the Morgan
fingerprint using RDKit in python. Molecular fingerprints encode
molecular structure in a series of binary digits (bits) that represent
the presence or absence of particular substructures in the molecule.
Comparing fingerprints enable us to determine the similarity between
two molecules. Once SMILES strings are converted to scalar
fingerprints, the Tanimoto coefficient was used as the similarity score
to measure the absolute similarity between two molecules([67]Hakime et
al., 2016).
The target similarity between two drugs was measured based on
comprehensive targets of compounds. Two drugs acting on same targets
could be considered to have the same effect. For multi-target drugs,
drugs whose targets are very close in the PPI network show similar
effects ([68]Barabási et al., 2011). Here, the network proximity index
proposed by Barabasi (Joerg [69]Menche et al., 2015) was used to
explore the similarity between two drugs. Briefly, the network
proximity of drug target module A and drug-target module B is defined
using the separation measure as follows:
[MATH:
SAB=dAB−dAA+<
/mo>dBB
2 :MATH]
which compares the mean shortest distance within the interactome of
each targets module, ⟨d [AA ]⟩ and ⟨d [BB ]⟩, to the mean shortest
distance ⟨d [AB ]⟩ between targets module A and B. The smaller the S
[AB ], the closer the topological distance between the two drugs, that
is, the more similar the functions of the two drugs. BNet was used as
the background human PPI network.
The cellular function fingerprint similarity of two drugs was measured
based on a “compound-target-cellular function” heterogeneous network
using the PathSim method (Yizhou [70]Sun et al., 2011). As described in
the literature ([71]Fg et al., 2020), in the “compound-target-cellular
function” heterogeneous network, the metapath “compound-target-cellular
function target-compound” of two drugs was considered to describe the
linkage between two drugs. In this instance, cellular function
fingerprints of compounds were described using Gene Ontology (GO)
biological processes term of targets of compounds. Under the metapath
framework, PathSim was developed to find peer objects in the network
and to measure the similarity of peer objects based on metapaths. The
“compound-target-cellular function” heterogeneous network consists of
drug-comprehensive target interactions and target-cellular function
relations from the Gene Ontology database ([72]The Gene Ontology,
2017).
Given a set of N compounds to be clustered, an N × N similarity matrix
was generated. Finally, the similarity matrix was used to perform
hierarchical cluster, which was executed by the R package hClust.
Simulating the Propagation of the Impact of a Drug by Signed Random Walk With
Restart (SRWR) and Evaluation Measure for the Impact of SAFI on the Network
The SRWR algorithm, which was proposed in the research of social
networks for measuring trust and distrust in signed social networks
([73]Jung et al., 2016), was used here to measure how the activation or
inhibition of a seed node corresponding to a drug target, leading to
the activation or inhibition of other nodes in the human signaling
network.
As described in the literature ([74]Zhao et al., 2019), suppose a
signed random walker starts at one of the seed nodes s and walks on a
signed network. The sign of the walker is either positive or negative,
meaning that it exerts activation or inhibition to a node,
respectively. At each step, the walker either moves to a randomly
chosen neighbor u of the current node v or it jumps back to its
starting seed node s and restarts its random walking. When the random
walker goes through a negative edge, it changes its sign from positive
to negative, or vice versa. Otherwise, it keeps its sign. Once the
walker jumps back to its starting node, it regains its original sign.
The SRWR can simulate the process that the activation or inhibition of
a seed node propagates to other nodes in a signaling network.
The hypergeometric cumulative distribution was applied to
quantitatively measure whether a gene set associated with IS
(validation gene set) was more enriched with the genes significantly
inhibited by SAFI (identified by SRWR) than would be expected by
chance. We used IS-associated disease genes collected from DisGeNET as
distinct validation gene sets. The detailed methods to define the
p-value have been previously described ([75]Zhao et al., 2019).
Enrichment Analysis
For the purpose of exploring the biological function of genes
correlated with IS, GO, Kyoto Encyclopedia of Gene and Genomes (KEGG)
and tissue enrichment analysis were performed using DAVID. Significance
of each term was assessed with a p-value, and a term with p-value <0.05
was considered being significant.
Structure-Activity Relationship Between the Main Components of SAFI and
IS-Associated Genes
The dataset of key common genes in IS and SAFI was obtained from the
ChEMBL database ([76]Anna et al., 2012), and processed in the Knime
workflow. IC[50] values in Schrödinger’s Ligprep module were converted
to predicted IC[50] values (PIC[50]). The QSAR model based on
traditional methods was generated for key common genes through
Schrodinger’s AutoQSAR module.
AutoQSAR is an automated machine learning application for building,
validating, and generating quantitative structure-activity relationship
(QSAR) models. The process of descriptor generation, feature selection,
and creation of a large number of QSAR models has been automated into a
single workflow in AutoQSAR ([77]Dixon et al., 2016). These models are
built using various machine learning methods (Bayes, KPLS, MLR, PCR,
PLS, and RP), and each model is scored using a novel method,
considering R^2 and Q^2 respectively ([78]de Oliveira and Katekawa,
2018). AutoQSAR randomly selects a combination of training set and test
set, and allocates data to the learning set according to the same
pattern. Then, it calculates physicochemical and topological
descriptors and 2D fingerprints (linear, dendritic, radial or
MOLPRINT2D) through the Canvas module. These methods are used in
combination with machine learning methods and can be used to build
various predictive models.
Target Validation in vitro
To evaluate the influence on PTGS1 and PTGS2 by SAFI and obtain IC[50]
values, Pharmaron (Beijing) was commissioned to perform in vitro PTGS1
and PTGS2 enzymatic assays. The detailed information about the assays,
such as the reagents, instruments, assay procedure, data analysis, and
calculation is presented in [79]Supplementary Methods.
In order to validate the influence of SAFI on PTGS2 activity, RAW264.7
macrophages were plated in 96-well plate and pretreated with different
concentrations of SAFI for 1 h. Then cells were stimulated by
lipopolysaccharide (LPS, 0.5 μg/ml) for 18 h to induce PTGS2
expression. Similarly, BV-2 microglia were treated by LPS(0.1 μg/ml)
and different concentrations of SAFI for 24 h to induce PTGS2
expression. The prostaglandin E2 content in supernatants, generated by
cyclooxygenase 2 (COX2, encoded by PTGS2) in the conversion of
arachidonic acid, was assayed with enzyme linked immunosorbent assay
according to the protocol of manufacturer(JINGMEI II, Lot 202107).
Statistical Analysis
Values are represented as mean ± SD. Statistical significance was
determined by One-way ANOVA in GraphPad Prism 8. p-value <0.05 was
considered statistically significant.
Results
Identification of SAFI Important Targets in the Treatment of IS
In order to identify the important targets of SAFI in the treatment of
IS, we first extracted its components through literature mining. It was
found that salvianolic acid B, rosmarinic acid, lithospermic acid,
salvianolic acid D, and salvianolic acid Y were major chemical
components of SAFI, and four constituents present in the rat blood
including salvianolic acid B, rosmarinic acid, lithospermic acid and
salvianolic acid D were determined as effective Quality Markers of
SAFI, identified by LC-MS/MS ([80]Li et al., 2018) ([81]Figure 2A;
[82]Table 1, [83]Supplementary Figure S1). Subsequently, the targets of
SAFI were collected from both literature mining and database searches.
As a result, a total of 79 targets were collected from both CNKI and
NCBI databases after searching with the keyword “SAFI”
([84]Supplementary Table S1). Potential targets of SAFI’ main
components were obtained from HERB database, and after manual removal
of duplicates, 227 genes were obtained ([85]Supplementary Table S2).
The relationship between components and targets is shown in [86]Figure
2B. Importantly, salvianolic acid B and rosmarinic acid possessed the
most targets, whereas no targets were obtained from salvianolic acid Y
after literature mining or database searches.
FIGURE 2.
[87]FIGURE 2
[88]Open in a new tab
Potential targets of SAFI in the treatment of IS. (A) The main
components and contents in SAFI collected from literature mining. It
has to be noted that mannitol is used as pharmaceutic adjuvant in SAFI.
(B) Potential targets of main components in SAFI. Pink boxes represent
salvianolic acid B, rosmarinic acid, lithopermic acid, and salvianolic
acid D, and green rounded boxes represent the corresponding targets of
different components of SAFI collected from HERB database. (C) Overlaps
between IS-associated genes from DisGeNET database, targets of SAFI
collected from literature mining and targets of main components of SAFI
(salvianolic acid B, rosmarinic acid, lithopermic acid, and salvianolic
acid D) collected from literature mining and HERB database. (D,E)
Functional annotations (biological process, D, and pathways, E) of 38
common genes obtained from overlaps by enrichment analysis.
TABLE 1.
Detailed information on main SAFI components.
Component PubChem CID InChI key Canonical SMILES
Salvianolic acid B 11629084 SNKFFCBZYFGCQN-PDVBOLEISA-N
C1=CC(=C(C=C1CC(C(=O)O)OC(=O)C=CC2=C3C(C(OC3=C(C=C2)O)C4=CC(=C(C=C4)O)O
)C(=O)OC(CC5=CC(=C(C=C5)O)O)C(=O)O)O)O
Salvianolic acid D 75412558 KFCMFABBVSIHTB-WUTVXBCWSA-N
C1=CC(=C(C=C1CC(C(=O)O)OC(=O)C=CC2=C(C(=C(C=C2)O)O)CC(=O)O)O)O
Salvianolic acid Y 97182154 SNKFFCBZYFGCQN-DUMGGQTMSA-N
C1=CC(=C(C=C1CC(C(=O)O)OC(=O)C=CC2=C3C(C(OC3=C(C=C2)O)C4=CC(=C(C=C4)O)O
)C(=O)OC(CC5=CC(=C(C=C5)O)O)C(=O)O)O)O
Rosmarinic acid 5281792 DOUMFZQKYFQNTF-WUTVXBCWSA-N
C1=CC(=C(C=C1CC(C(=O)O)OC(=O)C=CC2=CC(=C(C=C2)O)O)O)O
Lithospermic acid 6441498 UJZQBMQZMKFSRV-RGKBJLTCSA-N
C1=CC(=C(C=C1CC(C(=O)O)OC(=O)C=CC2=C3C(C(OC3=C(C=C2)O)=CC(=C(C=C4)O)O)C
(=O)O)O)O
[89]Open in a new tab
We also collected the IS-associated disease genes from DisGeNET
database, and obtained a total of 1159 disease genes ([90]Supplementary
Table S3). [91]Figure 2C indicate the overlaps of SAFI and its main
components and IS-associated genes. Intersection analysis highlighted
38 common genes ([92]Supplementary Table S4) between IS, SAFI’s
components and SAFI, representing 3.28% of IS genes, 48.1% of SAFI
putative targets and 16.74% of main components datasets. The PTGS2 gene
was targeted by three main components of SAFI, salvianolic acid B,
rosmarinic acid, and salvianolic acid D, suggesting an important role
in the treatment of IS. Functional annotation of the 38 common genes
was performed by enrichment analysis. As showed in [93]Figure 2D, top
GO terms were enriched in the BP category like negative regulation of
apoptotic process and inflammatory response. Annotated pathways
indicated that inflammatory associated pathways such as TNF signaling
pathway, HIF signaling pathway, and NF-kappa B signaling pathway were
significantly enriched in KEGG pathways ([94]Figure 2E). In fact,
apoptosis and inflammatory response are key characteristics of IS
progression ([95]Datta et al., 2020; [96]Maida et al., 2020). In
summary, we identified the putative target genes of SAFI correlated
with IS and their association with inflammation.
Drug Similarity Based on Structure, Function and Targets
In order to investigate whether SAFI has a similar mechanism of action
with current IS marketed drugs, we compared them using an unsupervised
clustering evaluation method based on similarity. The “Chinese
guidelines for diagnosis and treatment of acute ischemic stroke 2018”
(neurology and society, 2018) recommends 16 drugs to treat IS,
representing five different mechanisms of action, including
antiplatelet (tirofiban, clopidogrel, ticagrelor, aspirin),
anticoagulant (heparin, argatroban), antilipemic (lovastatin,
pitavastatin, fluvastatin, rosuvastatin, pravastatin, simvastatin),
antihypertensive (labetalol, nicardipine) and neoroprotectant
(citicoline, edaravone). Next, the main compounds in SAFI were compared
with the recommended anti-IS drugs based on similarity of chemical
structure, functions and targets. The results showed that the main
compounds of SAFI were not clustered with the five types of anti-IS
drugs based on chemical structure ([97]Figure 3A), indicating that the
main compounds of SAFI do not share a similar structure with the
recommended anti-IS drugs. However, hierarchical clustering of
compounds showed that rosmarinic acid and salvianolic acid B were
clustered with aspirin both based on functions and targets ([98]Figure
3B,C), and salvianolic acid B was also clustered with simvastatin based
on functions ([99]Figure 3B). This indicates that SAFI may share
similar effects with aspirin and simvastatin.
FIGURE 3.
[100]FIGURE 3
[101]Open in a new tab
Drug similarity based on structures, functions and targets. Salvianolic
acid B, rosmarinic acid, lithopermic acid, salvianolic acid Y,
salvianolic acid D were compared with 16 recommended anti-IS drugs
based on chemical structures (A), functions (B) and targets (C). In (A)
and (B) panels, all compounds were divided into 6 subclusters with
different background colors based on hierarchical clustering. Different
types of approved drugs against IS are marked with different colors. In
panel (C), the depth of color indicates the network proximity (S[ AB ])
of the two drugs. The closer the color is getting to red, the smaller
the S[ AB ], and thus, the closer the topological distance between the
two drugs, that is, the more similar the targets of the two drugs.
Evaluation of Correlations Between SAFI and IS-Associated Genes
In order to evaluate and quantify the correlations between SAFI and
IS-associated genes collected from DisGeNET, a parameter named Z-score
was applied. Z-score, which provides a quantitative measure for the
significance of the correlation between two genes, were set as greater
than 3. Z-score indicated that the targets of both SAFI and its main
components have a close link with IS-associated genes, especially
salvianolic acid B (Z-score = 20.994) and rosmarinic acid (Z-score =
19.023), with a total of more than 200 IS-associated genes ([102]Table
2).
TABLE 2.
Correlation of targets for SAFI and its main components with
IS-associated genes.
Drug Targets[103] ^a Genes[104] ^b Original_coef[105] ^c
Random_coef[106] ^d Z score
SAFI 79 1159 0.207 0.005 13.862
Salvianolic acid B 167 1159 0.322 0.014 20.994
Rosmarinic acid 108 1159 0.276 0.011 19.023
Lithospermic acid 15 1159 0.106 0.002 7.760
Salvianolic acid D 11 1159 0.084 0.003 6.071
[107]Open in a new tab
^a
Number of drug targets.
^b
Number of IS-associated disease genes.
^c
Correlation coefficient between drug targets and IS-associated disease
genes.
^d
Correlation coefficient of random extraction.
Prediction and Identification of Genes Significantly Inhibited by SAFI
Through SRWR Algorithm
Although it is known which targets obtained from literature mining are
inhibited or activated by SAFI, the directionality of action of targets
obtained from databases is unknown. The inhibition or activation of a
target by a drug propagates to the other nodes in the human signaling
network, and this can be measured by SRWR algorithm ([108]Zhao et al.,
2019), helping in the assessment on the potential impact of the drug.
To construct the human signaling network, 7118 genes were used, and
4317 were used as the giant strongly connected component (GSC) in the
background of the human signaling network. IS-associated genes,
IS-GEO-UP genes and IS-GEO-down genes were used as the validation sets
([109]Supplementary Tables S3, S5, S6). The method has been previously
described ([110]Zhao et al., 2019). We defined IS-associated genes
significantly inhibited by SAFI as those with negative activation
scores and with absolute values that ranked within the top 10% of all
the nodes obtained with SRWR algorithm. In this way, the top 362
inhibited genes were identified. [111]Table 3 shows the number of genes
in common between the top inhibited genes by each drug and the three
validation gene datasets. In most cases, the p-values were much smaller
than 0.05, indicating the statistically significant enrichment of
inhibited genes in the validation sets. The p-values in IS-GEO-UP
dataset (the upregulated genes in [112]GSE22255 dataset,
[113]Supplementary Table S5) were similar to those in IS disease genes
(from DisGeNET), suggesting that SAFI may work as an inhibitor of
IS-associated disease genes. It is worth mentioning that PTGS1 is in
the top 10% inhibited by SAFI via SRWR algorithm, so it is also
considered one of the most important targets of SAFI.
TABLE 3.
Overlap numbers of top inhibited genes by each group of drugs with the
three validation gene sets.
Drugs Targets Proportion in GSC IS_disease_genes[114] ^a IS_GEO_UP[115]
^b IS_GEO_DOWN[116] ^c
Genes p-value Genes p-value Genes p-value
SAFI Targets + − 71/78(91%) 122 0 17 0.001 7 0.357
Urokinase (PLG PLAUR) + 2/2(100%) 6 0.256 0 NA 1 0.394
Aspirin (PTGS1 PTGS2)- 2/2(100%) 101 1.274E-10 11 0.196 2 0.990
Heparin (SERPINC1 F10)- 2/2(100%) 121 0 17 0.003 5 0.778
Nicardipine (CACNA1C CACNB2 CACNA2D1 CACNA1D)- 4/4(100%) 76 0.001 11
0.189 5 0.772
Simvastatin (ITGAL HDAC2)- 2/3(67%) 87 3.886E-06 14 0.032 7 0.455
Salvianolic acid B Targets + − 138/167(83%) 114 0 15 0.006 5 0.689
[117]Open in a new tab
^a
IS-disease genes set collected from DisGeNET.
^b
IS-GEO-UP set upregulated genes in [118]GSE22255.
^c
IS-GEO-DOWN set downregulated genes in [119]GSE22255.
To investigate whether the top inhibited genes by SAFI were expressed
in IS target tissues, tissue enrichment analysis was conducted using
DAVID. As shown in [120]Table 4, a large number of the inhibited genes
are expressed in whole brain and blood in the category of
GNF_U133A_QUARTILE and UP_TISSUE, respectively (p-value < 0.05). These
results suggest that SAFI could inhibit target tissue proteins to exert
its anti-IS effects.
TABLE 4.
Tissue enrichment analysis of the top inhibited genes by SAFI via SRWR
algorirthm.
Category Term Count Percent (%) p-value Benjamini
GNF_U133A_QUARTILE Whole Brain_3rd 188 52.4 2.7E-22 2.1E-20
GNF_U133A_QUARTILE Globuspallidus_3rd 117 32.6 3.7E-14 1.4E-12
GNF_U133A_QUARTILE Medulla Oblongata_3rd 161 44.8 9.6E-14 2.5E-12
GNF_U133A_QUARTILE LymphomaburkittsDaudi_3rd 107 29.8 4.9E-11 9.5E-10
GNF_U133A_QUARTILE Trachea_3rd 106 29.5 1.5E-7 2.4E-6
UP_TISSUE Blood 43 12.0 2.9E-10 6.0E-8
UP_TISSUE Spleen 47 13.1 8.6E-10 6.0E-8
UP_TISSUE Platelet 36 10.0 9.1E-10 6.0E-8
UP_TISSUE T-cell 27 7.5 1.1E-9 6.0E-8
UP_TISSUE Leukocyte 15 4.2 1.3E-6 6.0E-5
UP_TISSUE Pancreas 44 12.3 2.6E-6 9.9E-5
UP_TISSUE Placenta 99 27.6 1.8E-5 5.7E-4
UP_TISSUE Neutrophil 6 1.7 3.4E-5 9.1E-4
[121]Open in a new tab
For the 362 top inhibited genes predicted by SRWR algorithm, DAVID
Functional Annotation Clustering tool was used to conduct the
functional annotation from GO and KEGG analysis. The results showed
that the top inhibited genes are related with processes such as
inflammatory response, platelet activation and cell adhesion, which are
all anti-IS associated process. KEGG enrichment analysis indicated that
the top inhibited genes are involved in PI3K-Akt, TNF, NF-kappa B, and
HIF signaling pathways, among others, which are associated with
inflammatory response ([122]Du et al., 2020; [123]Minghua et al.,
2021); [124]Supplementary Figure S2). These results indicate that SAFI
significantly inhibits a large number of genes associated with IS and
inflammation, further validating its effect on IS.
Prediction of Binding Activity Between SAFI and Identified Targets
Based on the intersection analysis and SRWR results, PTGS1 and PTGS2
show big potential as important genes regulated by SAFI. We predicted
the potential binding abilities between SAFI/main components and
PTGS1/2 using the QSAR model. The PTGS1/2 dataset was downloaded from
the ChEMBL database and processed in the Knime workflow. We prepared
molecules with IC[50] values in Schrödinger’s Ligprep module, and
converted IC[50] values to PIC[50] values. The QSAR model based on
traditional methods was generated for PTGS1/2 through Schrodinger’s
AutoQSAR module. R^2 (correlation coefficient) and Q^2 (cross
validation coefficient) represent the availability of the model (the
better model the closer to 1). By default, the dataset was divided into
a 75% training set (PTGS1: 1217 molecules, PTGS2: 2634 molecules) and a
25% test set (PTGS1: 406 molecules, PTGS2: 879 molecules). We used two
models with Q^2 > 0.5 in top10 to predict the activity of the compound
on the target. The model score of PTGS1/2 with predictive ability is
shown in [125]Table 5, and the scatter plot in [126]Figure 4 shows the
performance of the model in predicting the experimental binding
affinity of the learning set. We also validated the model with positive
molecules, and the results are shown in [127]Table 6. The prediction
results showed that our model can identify molecules that are active to
the corresponding target, and the difference in activity is no more
than an order of magnitude. The activity values of SAFI predicted by
the QSAR model are shown in [128]Table 7. The results indicated that
salvianolic acid B, lithopermic acid, salvianolic acid Y, and
salvianolic acid D may possess inhibitory effect on PTGS1 and PTGS2
like two anti-IS drugs, aspirin and NS-398.
TABLE 5.
Performance index of QSAR model of PTGS1/2.
Model name Model code R^2 Q^2
PTGS1 kpls_radial_36 0.7720 0.5850
kpls_linear_36 0.8867 0.5532
PTGS2 kpls_linear_46 0.6367 0.5193
kpls_radial_46 0.6633 0.5135
[129]Open in a new tab
FIGURE 4.
[130]FIGURE 4
[131]Open in a new tab
Scatter plots illustrating the performance of the QSAR model in
predicting the experimental binding affinity of the learning set. (A)
Model code: kpls_radial_36, generated by KPLS fitting radical features,
using the 36th split of the learning set into test and training sets;
(B) Model code: kpls_linear_36, generated by KPLS fitting linear
features, using the 36th split of the learning set into test and
training sets; (C) Model code: kpls_linear_46, generated by KPLS
fitting linear features, using the 46th split of the learning set into
test and training sets; (D) Model code: kpls_radial_46, generated by
KPLS fitting radial features, using the 46th split of the learning set
into test and training sets.
TABLE 6.
The PIC50 value of the positive compound predicted by the QSAR model of
PTGS1/2.
Drug PTGS1 PTGS2 PIC[50] (μM) Actual activity value Actual IC[50] (μM)
of drugs
Aspirin 3.787 — 163.305 4.557 27.750
SC-560 8.098 — 0.008 8.046 0.009
Celecoxib — 7.017 0.096 7.398 0.04
Valdecoxib — 6.552 0.281 8.301 0.005
NS-398 — 5.242 5.729 5.420 3.800
[132]Open in a new tab
TABLE 7.
The QSAR model of PTGS1/2 predicts the PIC[50] value of the compound.
Component PTGS1 PIC[50] (μM) PTGS2 PIC[50] (μM)
Salvianolic acid B 6.079 0.834 5.490 3.236
Rosmarinic acid 5.946 1.132 4.743 18.071
Lithospermic acid 6.079 0.834 5.404 3.945
Salvianolic acid D 6.568 0.270 5.501 3.155
Salvianolic acid Y 6.079 0.834 5.490 3.236
[133]Open in a new tab
Validation of SAFI Targets in vitro
Arachidonic acid metabolism plays an important role in acute ischemic
syndromes affecting the coronary or cerebrovascular territory, whereas,
cyclooxygenase, including PTGS1 and PTGS2, is the key enzyme of the
arachidonic acid metabolism ([134]Santovito et al., 2009;
[135]Cipollone et al., 2010). Our computational prediction results show
that SAFI has an inhibitory effect on these two enzymes. To validate
the effect of SAFI on PTGS1 and PTGS2, we determined how their
enzymatic activity was modulated. The enzymatic assay showed that SAFI
had a strong inhibition on PTGS1 and PTGS2, with IC[50] of 0.04 μg/ml
([136]Figure 5A) and 0.03 μg/ml ([137]Figure 5B) respectively. The
inhibition curves of positive controls SC-560 and celecoxib on PTGS1
and PTGS2 can be seen in [138]Supplementary Figure S3. In order to
validate the anti-inflammation effect of SAFI, we determined the
content of prostaglandin E2 induced by LPS in RAW264.7 macrophages and
BV-2 microglia, respectively. As shown in [139]Figure 5C, SAFI, at a
concentration of 250 μg/ml, had a significant inhibition on the
production of prostaglandin E2 both in the RAW264.7 macrophages and
BV-2 microglia. These indicate that SAFI inhibits the production of
prostaglandin E2 in a cell-based assay, and these offer a mode of
action for its anti-inflammation effect.
FIGURE 5.
FIGURE 5
[140]Open in a new tab
SAFI inhibits PTGS1 and PTGS2. In vitro inhibitory of effect of SAFI on
PTGS1 (A) and PTGS2 (B) enzymatic activities. IC[50] values obtained
are indicated on top of histograms. (C) SAFI inhibits the synthesis of
prostaglandin E2 in RAW264.7 macrophages. Cells were pre-treated with
increasing concentrations of SAFI for 1 h and then stimulated with LPS
for 18 h to induce prostaglandin E2 expression.(D) SAFI inhibits the
synthesis of prostaglandin E2 in BV-2 microglia. Cells were treated
with LPS and different concentrations of SAFI for 24 h to induce
prostaglandin E2 expression. Blank indicates cells treated without SAFI
or LPS, and Model indicates cells treated with LPS alone. Data
represent the average ±SEM of three independent replicates.
Discussion
Mechanism of Action of SAFI in IS
Intravenous administration of tissue plasminogen activator and
endovascular treatment are currently used to recanalize the blood flow
in acute IS. However, reperfusion leads to a highly harmful reactive
oxygen species production, generating oxidative stress, which is
responsible for most of the ischemia-reperfusion injury and brain
tissue damage ([141]Sofía et al., 2020). The positive effect of SAFI on
ischemia reperfusion injury has been widely reported in the literature
([142]Tang et al., 2015; [143]Wang et al., 2017; [144]Huang et al.,
2019), although its targets and mechanism of action remain to be
discovered.
In this study, we have systemically analyzed the mechanism of action of
SAFI on IS using the network pharmacology combined computational
prediction and experimental validation. We identified 38 genes shared
by datasets of SAFI targets, SAFI main components targets and
IS-associated genes. GO and KEGG enrichment analysis of this common
shared gene set indicates their association with inflammation. Two
genes associated with inflammation (PTGS1 and PTGS2) were preliminarily
validated in vitro. SAFI inhibited PTGS1 and PTGS2 activity in a
dose-dependent manner and inhibited the production of prostaglandin E2
induced by LPS in RAW264.7 macrophages and BV-2 microglia. Cytokines,
like tumor necrosis factor (TNF), modulate tissue injury in
experimental stroke and affect infarct evolution, making them good
targets for potential future stroke therapy ([145]Lam Be Rtsen et al.,
2012). Nuclear factor kappa B (NF-κB) is a key regulator of the
inflammatory process, regulating expression of proinflammatory and
proapoptotic genes in ischemia-reperfusion injury of the brain and
representing a therapeutic target of IS ([146]Howell and Bidwell,
2020). HIF-1α and Notch-1 engage pro-inflammatory and apoptotic
signaling pathways, thus promoting neuronal cell death in IS
([147]Cheng et al., 2014). SAFI and its main component, Salvianolic
acid B, have been reported to inhibit IL-1β, IL-6 NF-κB, TNF, and HIF-1
([148]Ming-chao et al., 2016; [149]Wang et al., 2016; [150]Wang et al.,
2017; [151]Zhuang et al., 2017; [152]Fan et al., 2018; [153]Zhao et
al., 2020). Interestingly, as one of the main components, no potential
targets of salvianolic acid Y were obtained from HERB database and
literature mining. The only report related to salvianolic acid Y was
about its antioxidant effect ([154]Jun et al., 2015). In view of the
similar structure with salvianolic acid B, the potential
anti-inflammatory activity of salvianolic acid Y deserves to be mined
in the future. QASR model prediction analysis indicates that SAFI has
the potential to bind to PTGS1 and PTGS2, with binding as good as the
positive drugs, aspirin and NS-398. Drug similarity analysis also
indicates that SAFI and aspirin may share a common set of targets. This
highlights once more that the effect of SAFI on IS may be due to its
anti-inflammatory effect by inhibiting the activity of PTGS1 and/or
PTGS2. Aspirin has been frequently prescribed to prevent cardiovascular
disease due to its analgesic, anti-pyretic, anti-inflammatory and
antithrombotic qualities. However, its side effects, such as internal
bleeding and gastrointestinal damage, should also be noted when
long-term medication is used ([155]Sostres and Lanas, 2011; [156]Fiala
and Pasic, 2020). SAFI has been widely used to treat IS clinically in
China, mostly for mild to moderate cases, with no gastrointestinal side
effects. Although SAFI has been reported to inhibit the expression
level of PTGS2, but not PTGS1, the relationship between SAFI, IS and
PTGS1/2 was not confirmed ([157]Wang et al., 2017). Therefore the
interaction between SAFI and PTGS1/PTGS2 in IS are still not clear in
the current literature. Overall, our network pharmacology approach
indicates that the beneficial effect of SAFI on IS is likely due to its
anti-inflammatory properties via direct inhibition of PTGS1 and PTGS2.
It should be noted that PTGS1 and PTGS2 are involved in inflammation
not only via the arachidonic acid (AA) metabolic pathway, but also
through the maintenance of cerebral blood flow, synaptic plasticity,
and cerebrovascular regulation ([158]Niwa et al., 2000; [159]Chen and
Bazan, 2005; [160]Jayaraj et al., 2020). Due to the advantages of
multiple components, multiple targets and multiple pathways, other
potential effects of SAFI in the treatment of IS should be further
explored.
SAFI as a Case Study for the Use of the Computational Systems Pharmacology to
Elucidate Mechanisms of TCM
In this study, we have used network pharmacology combined with
computational prediction to elucidate the molecular mechanisms of SAFI
acting on IS, and demonstrated the feasibility of the approach in
investigating its mode of action. A similar approach can be used for a
variety of herbal medicine used in TCM for a variety of diseases, thus
contributing to its modernization.
The emergence of TCM databases such as HERB, TCMSP and others makes
relatively easy to obtain the chemical components and corresponding
targets of TCM, saving a lot of time and cost of experiments ([161]Ru
et al., 2014; [162]Fang et al., 2020). However, the chemical components
in the prescription and their pharmacological characteristics, such as
their absorption, have not been fully evaluated in many of them, which
may lead to some bias when using enrichment analyses.
Propagation-like algorithms, such as SRWR, have been used in the
identification of proteins or genes influenced by drugs or diseases,
and can simulate the spread of active compound-induced activation or
inhibition to a group of targets on a particular signaling
network([163]Zhao et al., 2019). Using this algorithm, we have
identified the top 10% genes inhibited by SAFI, and enriched them by
pathways and biological process analysis. The results illustrate the
mechanism by which SAFI likely acts when used to treat IS: inhibition
of a group of proteins associated with inflammation. However, the human
signaling network may not include some unknown protein interactions
with orientation and/or patterns, and this may lead to some loss of
information in subsequent identification analysis of inhibited and
activated genes. Given that, more studies using high-throughput data
such as RNA sequencing, single-cell sequencing and other computational
models to elucidate the mechanisms of SAFI treating IS are warranted,
which may provide us more rich and accurate information on
IS([164]Olsen and Baryawno, 2018; [165]Peng et al., 2021; [166]Zheng et
al., 2022).
QSAR has been applied for decades in the development of relationships
between physicochemical properties of chemical substances and their
biological activities in order to obtain a reliable statistical model
for prediction of the activities of new chemical entities ([167]Verma
et al., 2010). Since the study of QSAR is based on a series of
assumptions using known compounds, this methodology does not consider
the possible interactions of complex components in SAFI, or other TCMs,
and there may be deviations between predicted activities and actual
conditions.
In summary, in this study we deduce the mode of action of SAFI on IS
from the levels of the components, targets, pathways, network, and
activity prediction. SAFI putative targets are significantly enriched
in several pathways associated with inflammation, which is critical in
IS. PTGS1 and PTGS2 were found in a dataset of genes shared between
SAFI and its components’ targets and IS-associated genes, and their
binding activities were further predicted by QSAR. The effect of SAFI
on the enzymatic activities of PTGS1 and PTGS2 confirmed the data
deduced from network pharmacology combined with computational
prediction. Due to the limitations of network pharmacology and the
complicated pathogenesis progress of IS, more extensive and in-depth
research is still needed to corroborate these data to elucidate other
action mechanisms of SAFI in the treatment of IS.
Data Availability Statement
The original contributions presented in the study are included in the
article/[168]Supplementary Material, further inquiries can be directed
to the corresponding author.
Author Contributions
XL and KG conducted and completed the data collection and manuscript
writing. KG and RZ conducted and completed data analysis. EY revised
the manuscript. HS and WW provided some ideas and support. YH
contributed to the systematic search and study selection. All authors
contributed to the article and approved the submitted version.
Conflict of Interest
XL, KG, WW, and YH are employed by Cloudphar Pharmaceuticals Co., Ltd.
HS is employed by the company Cloudphar Pharmaceuticals Co., Ltd. and
Tasly Pharmaceuticals Co., Ltd.
The remaining authors declare that the research were conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
The reviewer SC declared a shared affiliation, with no collaboration,
with one of the authors, RZ, to the handling editor at the time of the
review.
Publisher’s Note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at:
[169]https://www.frontiersin.org/articles/10.3389/fphar.2022.894427/ful
l#supplementary-material
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References