Abstract
Fufang Danshen (FFDS), a Chinese medicine formula widely used in the
clinic, has proven therapeutic effects on pain relief. However, the
mechanisms of these effects have not been elucidated. Here, we
performed a systematic analysis to discover the mechanisms of FFDS in
attenuating pain to gain a better understanding of FFDS in the
treatment of other diseases accompanied by pain. Relevance analysis
showed that Salvia miltiorrhizae was the best studied herb in FFDS.
Most compounds in FFDS have good bioavailability, and we collected 223
targets for 35 compounds in FFDS. These targets were significantly
enriched in many pathways related to pain and can be classified as
signal transduction, endocrine system, nervous system and lipid
metabolism. We compared Salvia miltiorrhizae and Panax notoginseng and
found that they can significantly affect different pathways. Moreover,
ten pain disease proteins and 45 therapeutic targets can be directly
targeted by FFDS. All 45 therapeutic targets have direct or indirect
connections with pain disease proteins. Forty-six pain disease proteins
can be indirectly affected by FFDS, especially through heat shock
cognate 71 kDa protein (HSPA8) and transcription factor AP-1 (JUN). A
total of 109 targets of FFDS were identified as significant targets.
Introduction
Pain, a major symptom related to cancer, inflammation and other
diseases, has been defined as an unpleasant sensory and emotional
experience associated with actual or potential tissue damage or
described in terms of such damage by the International Association for
the Study of Pain (IASP)^[26]1. Pain is not always good for us. In a
normal state, pain may help us avoid injury, but in a pathological
state, it evolves from a symptom indicating tissue damage to a disease
itself ^[27]2. The mechanisms accounting for pain have not yet been
fully elucidated. The discovery of neurons and their roles in
pain^[28]3 invalidated many theories related to pain. Currently, the
specificity or labeled line theory and gate control theory are the most
controversial topics. According to different characteristics, pain can
be classified into various types, such as acute pain, chronic pain,
inflammatory pain, and neuropathic pain. Clinical pain is a serious
public health issue. As the primary drugs, opioids and nonsteroidal
anti-inflammatory drugs (NSAIDS) are the most widely used in the
treatment of pain. However, these drugs have many severe adverse
effects that have often been observed in a large number of patients.
Common side effects of opioids include constipation, nausea, vomiting,
respiratory depression, and urinary retention. Common side effects of
NASIDS include injury to the gastrointestinal tract, liver and kidney
dysfunction, and hematological system damage. All of these factors have
necessitated the development of alternative analgesics^[29]4. Although
many new analgesics have been introduced to the clinic for the
treatment of pain in the past decades, we cannot deny the lack of real
breakthrough drugs in clinical pain control^[30]5. Improvements have
been made in our understanding of pain mechanisms, and many therapeutic
targets and disease proteins related to pain have been found^[31]6.
However, there may be more undiscovered successful drugs for treating
various types of pain.
Traditional Chinese medicine (TCM) is an important part of world
medicine. Despite its unknown molecular mechanisms, the therapeutic
effects of TCM on curing diseases are recognized by hundreds and
thousands of people. TCM is an important complementary and alternative
medicine accepted by 183 countries and regions worldwide. The most
famous theory of TCM is the balance-regulation theory, which emphasizes
the integrity of the human body as well as the interaction between
human individuals and their environment^[32]7. Scientists have studied
herbal medicines and found that more than 800 types of TCM are
effective in relieving pain^[33]8. Fufang Danshen (FFDS), recorded in
Chinese Pharmacopoeia (2015), has been used clinically to treat
coronary arteriosclerosis, angina pectoris, hyperlipemia and
Alzheimer’s disease in China and is also available as a dietary
supplement or drug in other countries^[34]9. FFDS comprises three
herbs, including Salvia miltiorrhizae (Danshen) as a Jun drug (monarch
drug), Panax notoginseng (Sanqi) as a Chen drug (ministerial drug), and
Borneolum (Bing Pian) as Zuo and Shi drugs (adjuvant drug and messenger
drug). In TCM, herbal formulas are organized based on the rule of
“Jun-Chen-Zuo-Shi” to synergize therapeutic effects and integrally
minimize adverse effects^[35]10,[36]11. Many studies have proven that
FFDS has many biological functions, including relieving pain, promoting
blood circulation, improving reduced blood flow, reducing blood lipids,
protecting blood vessels and myocardium, and improving heart
function^[37]12–[38]14. Herbs in this formula were recently found to
affect other diseases, such as cancer, and osteoporosis^[39]15,[40]16.
Although many studies have demonstrated the significant therapeutic
effects of FFDS on attenuating neuropathic pain, cancer pain,
osteoarthritis pain, migraine and angina pectoris, few studies have
been conducted to uncover the mechanisms. As pain is a common, severe
symptom related to diseases that FFDS can cure, uncovering the
mechanisms of this formula in treating pain will provide a better
understanding of FFDS in the treatment of those diseases.
Network pharmacology is an approach to drug design that encompasses
systems biology, network analysis, connectivity, redundancy and
pleiotropy^[41]17. Network pharmacology is recognized as a new strategy
and powerful tool for the exploration of drug targets and the
identification of potentially active ingredients in TCM
research^[42]18,[43]19. In the present study, we conducted a
bioinformatics investigation to elucidate the multilevel mechanisms of
FFDS in attenuating pain. This investigation includes the following
main steps: (1) assess the relevance of FFDS and its herbs with pain;
(2) drug-likeness and bioavailability analysis of compounds in FFDS;
(3) collection of targets for FFDS; and (4) analysis of the potential
pharmacological mechanisms of FFDS in the treatment of pain. Compared
with other TCM network pharmacology approaches, such as Li’s^[44]20,
our approach focused on uncovering potential pathways and processes
that a herbal formula affects to cure a disease. Similar steps include
preparing data (compounds in a formula, target collection or prediction
for a formula, disease proteins and therapeutic targets or proteins
involved in disease-related pathways, and protein-protein interaction
(PPI) data), analyzing networks and discovering the potential pathways
or processes that the formula affects to cure a specific disease. Most
TCM network pharmacology studies or informatics investigations focus on
uncovering the mechanisms of action of an entire formula and ignore the
discovery of active compounds in the formula (with the exception of
Li^[45]21, who has introduced a novel method to predict active
compounds in a formula), which will hinder the modernization of TCM. In
our investigation, we used the “drug-target” principle and “essential
protein” theory, which suggests that “a highly connected protein is
more important” to uncover potentially active compounds for the
treatment of pain. Our results successfully uncovered some active
compounds that have experimentally proven antinociceptive effects. In
addition, we analyzed disease proteins and therapeutic targets
separately since they are different concepts, while most studies
analyze them as a whole. Disease proteins are products of disease
genes, which are the main factors that can cause diseases, but
therapeutic targets are important factors that can cure diseases. We
also evaluated therapeutic targets based on their degrees in the
drug-target network, and we suggested that therapeutic targets with
higher degrees were more likely or more efficient therapeutic targets
for a specific disease. In addition, we discussed the relevance of FFDS
and its herbs with pain based on text mining. The discussion covers the
extent to which FFDS and its herbs have been associated with pain and
which herb is more efficient for the treatment of pain. Based on these
TCM network pharmacology approaches, we will have a clearer, further
understanding of the pharmacological mechanisms of FFDS in attenuating
pain.
Results
FFDS, its herbs and pain
As shown in Table [46]1, Salvia miltiorrhizae (43 papers) is the best
studied herb in FFDS due to its importance in TCM, followed by Panax
notoginseng (13 papers). Pain is a major, common symptom related to
various diseases; thus, the volume of research on pain is extensive
(599207). The number of papers relating FFDS or its herbs to pain is
very small, and the P-value for FFDS and Panax notoginseng was
nonsignificant, which may be because western medicine is more efficient
and convenient than TCM in the treatment of pain. Additionally, TCM is
preferred for use in complex diseases, such as cancer and Alzheimer’s
disease. The study of TCM in the treatment of pain can provide a better
understanding of its efficacy in the treatment of other diseases
accompanied by pain and aid in the development of new analgesics from
TCM.
Table 1.
Relevance between FFDS, its herbs and pain.
Herb Total Relevant to diseases Relevant to pain Ratio P-value
Fufang Danshen 38 15 1 6.67% 0.2858
Salvia miltiorrhizae 2296 1105 43 3.89% 0.0049
Panax notoginseng 1031 410 13 3.17% 0.1291
Borneolum 25 16 3 18.75% 0.0004
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The ratio is the volume of papers relevant to pain/the volume of papers
relevant to diseases.
Drug-likeness and bioavailability analysis for compounds in FFDS
Ultimately, we obtained 123 compounds for Salvia miltiorrhizae
(Danshen), 78 compounds for Panax notoginseng (Sanqi), and 20 compounds
for Borneolum (Bing Pian). Dauricine and gamma-Sitosterol are shared by
Salvia miltiorrhizae and Panax notoginseng; Bata-Caryophyllene and
Elemicin are shared by Panax notoginseng and Borneolum. By contrast,
the other compounds belong to only one of the three herbs. In total,
217 compounds were obtained for FFDS. The detailed information is
listed in Table [48]S1. The statistics for the six main properties of
all compounds in FFDS are shown in Table [49]2. As shown, the mean and
median values of properties of compounds in FFDS conform to “the rule
of five”, and most (173/217) of the ingredients in FFDS conform to
drug-likeness rules. A bioavailability score was also obtained to
evaluate the bioavailability of ingredients in FFDS. The result showed
that 75.6% of ingredients have good bioavailability. Conforming to the
predictions, the poor bioavailability of ginsenosides and salvianolic
acids has been proven.
Table 2.
Statistics of the six main properties of compounds in FFDS.
Descriptors Max value Min value Mean value Median value
TPSA 447.21 0 105.52 67.51
Num. H-bond acceptors 27 0 6.28 4
Num. H-bond donors 18 0 3.29 1
Num. rotatable bonds 21 0 4.82 2
MLOGP 6.92 −6.15 1.46 1.48
Molecular weight (MW) 1269.46 44.1 402.74 311.33
[50]Open in a new tab
FFDS targets and their functions
A total of 223 targets were retained for 35 compounds, with 99 targets
belonging to Salvia miltiorrhizae, 143 targets to Panax notoginseng and
17 targets to Borneolum. Among the 35 compounds, 18 compounds belong to
Salvia miltiorrhizae, 17 belong to Panax notoginseng, and 2 belong to
Borneolum. Based on these two perspectives, we concluded that the rule
of “Jun-Chen-Zuo-Shi” is reasonable to organize herbs in a formula.
Detailed information is available in Table [51]S2. Salvia miltiorrhizae
and Panax notoginseng shared 24 targets; Salvia miltiorrhizae and
Borneolum shared 7 targets; Panax notoginseng and Borneolum shared 8
targets; and cytochrome P450 3A4 (CYP3A4), high mobility group protein
B1 (HMGB1), and caspase-3 (CASP3) belong to the three herbs.
Seventy-one targets belong to only Salvia miltiorrhizae, and these
targets were significantly enriched in steroid hormone biosynthesis,
HIF-1 signaling pathway, Jak-STAT signaling pathway and PI3K-Akt
signaling pathway. These four pathways have relationships with pain and
may constitute the mechanisms of Salvia miltiorrhizae in the treatment
of pain. For example, steroids can affect the nervous system and are of
particular interest in the modulation of pain^[52]22,[53]23; molecules
targeting the Jak-STAT signaling cascade are successful even though the
specific contribution of this pathway in the modulation of pain is
unknown^[54]24. Additionally, Jak-STAT and PI3K-Akt are important
microglial intracellular signaling cascades that are essential for
neuropathic pain development and maintenance^[55]25. A total of 114
targets belong to only Panax notoginseng, and they were significantly
enriched in 13 pathways related to pain, including arachidonic acid
metabolism, adipocytokine signaling pathway, tumor necrosis factor
(TNF) signaling pathway, serotonergic synapse, inflammatory mediator
regulation of TRP channels, Toll-like receptor signaling pathway,
linoleic acid metabolism, oxytocin signaling pathway, NF-kappa B
signaling pathway, MAPK signaling pathway, GnRH signaling pathway, mTOR
signaling pathway and vascular endothelial growth factor (VEGF)
signaling pathway, which were ranked by P-values. Leptin, an
adipocytokine, plays an important role in nociceptive behavior induced
by nerve injury^[56]26. Modulators of the arachidonic acid cascade have
been the focus of research on treatments for inflammation and pain for
several decades, and the design and development of multitarget
inhibitors of this pathway that exhibit improved efficacy and less
undesired side effects is a new paradigm^[57]27. The targets belonging
to only Salvia miltiorrhizae or Panax notoginseng were also involved in
pathways significantly enriched by targets belonging to the opposite
herb only. For example, the targets of Panax notoginseng were involved
in the HIF-1 signaling pathway and PI3K-Akt signaling pathway, which
were significantly enriched by targets of Salvia miltiorrhizae.
Additionally, targets of Salvia miltiorrhizae were involved in the TNF
signaling pathway and NF-kappa B signaling pathway, which were
significantly enriched by targets of Panax notoginseng. Five targets
belong to only Borneolum, while 12 targets are shared with Salvia
miltiorrhizae or Panax notoginseng. Five unique targets, including
UDP-glucuronosyltransferase 2B10 (UGT2B10), UDP-glucuronosyltransferase
2B11 (UGT2B11), Nuclear factor erythroid 2-related factor 2 (NFE2L2),
NAD(P)H dehydrogenase [quinone] 1 (NQO1) and Prostacyclin receptor
(PTGIR), may explain why Borneolum is necessary for the formula.
UGT2B10, UGT2B11 and NQO1 play important roles in the elimination of
toxic materials. NFE2L2 is a transcription activator that binds to
antioxidant response elements (AREs) in the promoter regions of target
genes and is important for the coordinated upregulation of genes in
response to oxidative stress. FFDS has further potential to exert
therapeutic effects through targets with at least two herbs or
compounds; thus, we retained 51 targets with at least two herbs or
compounds in Table [58]S3. Thirteen targets can interact with at least
three compounds, and CASP3 is targeted by the most compounds (7),
followed by CYP3A4 (4). Of note, Salvia miltiorrhizae and Panax
notoginseng possess the most targets and most of the same targets,
indicating that they are major herbs in FFDS. However, Borneolum is
also essential for its unique targets.
Subsequently, we performed enrichment analysis for 223 FFDS targets,
and pathways with P-value <= 0.01 and Fold Enrichment >= 1.5 were
retained to find their relationships with pain. A total of 26 pathways
may have relationships with pain. Among the 26 pathways, fourteen
pathways with Bonferroni <= 0.01 were retained in Table [59]3. The
other 12 pathways include the ErbB signaling pathway, prolactin
signaling pathway, oxytocin signaling pathway, insulin signaling
pathway, sphingolipid signaling pathway, neurotrophin signaling
pathway, GnRH signaling pathway, cholinergic synapse, Jak-STAT
signaling pathway, inflammatory mediator regulation of TRP channels,
estrogen signaling pathway and AMPK signaling pathway. These pathways
were also significantly enriched by targets with at least two compounds
except for the adipocytokine signaling pathway, NF-kappa B signaling
pathway, ErbB signaling pathway, GnRH signaling pathway, Jak-STAT
signaling pathway, inflammatory mediator regulation of TRP channels and
AMPK signaling pathway. Among the 26 pathways, 16 pathways were also
enriched by 24 targets shared by Salvia miltiorrhizae and Panax
notoginseng, including the TNF signaling pathway, VEGF signaling
pathway, apoptosis, HIF-1 signaling pathway, toll-like receptor
signaling pathway, neurotrophin signaling pathway, sphingolipid
signaling pathway, insulin signaling pathway, PI3K-Akt signaling
pathway, prolactin signaling pathway, MAPK signaling pathway, estrogen
signaling pathway, cholinergic synapse, serotonergic synapse, mTOR
signaling pathway and ErbB signaling pathway. These 16 pathways may
constitute the same major functions of Salvia miltiorrhizae and Panax
notoginseng in the treatment of pain. Inhibition of tumor necrosis
factor-alpha (TNF-α) can reduce inflammation and pain^[60]28. Toll-like
receptors are now recognized to contribute to the chronic pain
process^[61]29. Moreover, myocyte apoptosis was detected as a possible
promoter of pain and motor dysfunction in neuropathic rats^[62]30.
These 26 pathways can be classified into the following categories:
environmental information processing, organismal systems, metabolism
and cellular processes. Pathways in signal transduction (11) account
for the most pathways. Pain is transmitted by neurons and nerve
conduction. The nerve conduction of pain can be classified into four
cascades, including pain sensing of the nociceptive receptor, pain
transmission of the primary afferent fiber, dorsal horn of the spinal
cord, spinal cord-fasciculus thalamicus and other ascending tracts,
including pain integration in the cortical and limbic systems,
descending control and pain modulation of neurotransmitters. Therefore,
there is no doubt that many pathways in signal transduction are
involved in pain signal transduction, and these pathways represent
therapeutic targets in the treatment of pain. Pathways in the endocrine
system (6) account for the second highest number of pathways. Many
painful conditions appear to be induced, reduced, and in some cases,
modulated by hormones; additionally, knowledge of the role of the
endocrine system in chronic pain mechanisms is slowly increasing in
experimental and clinical studies^[63]31. Pathways in the nervous
system (3) and lipid metabolism (3) account for the third highest
number of pathways. Pain is a nervous system disease. Specialized
pro-resolving lipid mediators have a function in pain^[64]32. Analgesic
lipid mediators include enzyme pathways, such as endogenous agonists of
cannabinoid receptors (endocannabinoids), lipid-amide agonists of
peroxisome proliferator-activated receptor-α, and products of oxidative
metabolism of polyunsaturated fatty acids via cytochrome P450. These
lipid messengers are produced and act at different stages of the
response to tissue injury and may be part of a peripheral gating
mechanism that regulates the access of nociceptive information to the
spinal cord and brain^[65]33.
Table 3.
Fourteen pathways related to pain are significantly enriched by 223
targets of FFDS.
KEGG pathway Count P-Value Fold Enrichment Bonferroni Class
TNF signaling pathway 23 2.56E-14 8.15 6.13E-12 Environmental
information processing; signal transduction
Arachidonic acid metabolism 16 3.91E-11 9.69 9.34E-09 Metabolism; lipid
metabolism
HIF-1 signaling pathway 18 5.19E-10 6.90 1.24E-07 Environmental
information processing; signal transduction
Toll-like receptor signaling pathway 18 1.84E-09 6.38 4.40E-07
Organismal systems; immune system
Adipocytokine signaling pathway 15 2.68E-09 8.05 6.42E-07 Organismal
systems; endocrine system
Linoleic acid metabolism 10 3.34E-08 12.95 7.98E-06 Metabolism; lipid
metabolism
Serotonergic synapse 16 1.83E-07 5.41 4.38E-05 Organismal systems;
nervous system
PI3K-Akt signaling pathway 28 3.12E-07 3.05 7.42E-05 Environmental
information processing; signal transduction
Apoptosis 12 5.22E-07 7.27 1.25E-04 Cellular processes; cell growth and
death
Steroid hormone biosynthesis 11 2.30E-06 7.12 5.49E-04 Metabolism;
lipid metabolism
NF-kappa B signaling pathway 13 2.60E-06 5.61 6.21E-04 Environmental
information processing; signal transduction
VEGF signaling pathway 11 3.71E-06 6.77 8.86E-04 Environmental
information processing; signal transduction
mTOR signaling pathway 10 1.83E-05 6.47 4.36E-03 Environmental
information processing; signal transduction
MAPK signaling pathway 20 4.03E-05 2.95 9.58E-03 Environmental
information processing; signal transduction
[66]Open in a new tab
The relationships of herbs, compounds and FFDS targets with pain disease
proteins
We built the FFDS targets-other human proteins PPI network and pain
disease proteins-other human proteins PPI network. We used 141 pain
disease proteins (Table [67]S4) and 223 FFDS targets to identify
FFDS-related pain disease proteins. Pain disease proteins that can be
directly targeted by FFDS were treated as FFDS primary pain disease
proteins. Pain disease proteins that can directly interact with FFDS
targets but cannot be directly targeted by FFDS were treated as FFDS
secondary pain disease proteins. FFDS primary and secondary pain
disease proteins can be defined as FFDS-related pain disease proteins.
Fifty-six FFDS-related pain disease proteins (Table [68]S5) were
identified, including 10 primary pain disease proteins and 46 secondary
pain disease proteins. The primary pain disease proteins include
catechol O-methyltransferase (COMT), endothelin-1 (EDN1), interleukin-2
(IL2), RAC-alpha serine/threonine-protein kinase (AKT1), neuronal
acetylcholine receptor subunit alpha-7 (CHRNA7), prostaglandin G/H
synthase 2 (PTGS2), serum albumin (ALB), protachykinin-1 (TAC1),
oxytocin-neurophysin 1 (OXT) and TNF. Five primary pain disease
proteins have direct interactions with the pain disease protein set,
including AKT1, ALB, PTGS2, TNF and COMT, and the minimum path lengths
are 2 between any of the other primary pain disease proteins and the
pain disease protein set except for IL2 (3). Two proteins may have no
direct interaction, but they may have indirect connections through
other human proteins called intermediators, which form several paths to
connect these proteins. The minimum path length can show whether a
protein has close interaction or distant interaction with a protein set
(without that protein). AKT1 and ALB can interact with most pain
disease proteins. Among the 46 secondary pain disease proteins, 27
proteins have direct interactions with the pain disease protein set,
and the minimum path lengths between any of the other 17 secondary pain
disease proteins and the pain disease protein set (except for the
proteins themselves) are 2, and the minimum path lengths are 3 and 4
for the remaining two secondary pain disease proteins. Among the 27
secondary pain disease proteins that have direct interactions with the
pain disease protein set, high affinity nerve growth factor receptor
(NTRK1) can interact with the most pain disease proteins (14) directly,
followed by transitional endoplasmic reticulum ATPase (VCP) (11),
heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) (9) and
RNA-binding protein FUS (FUS) (8). These results suggest that all 56
FFDS-related pain disease proteins have direct or indirect connections
with the pain disease protein set except for themselves.
Six primary pain disease proteins belong to Panax notoginseng, 3 belong
to Salvia miltiorrhizae, and AKT1 is shared by Panax notoginseng and
Salvia miltiorrhizae. AKT1 can be targeted by three compounds, while
COMT, EDN1 and IL2 can be targeted by two compounds in Salvia
miltiorrhizae. PTGS2 can be targeted by two compounds in Panax
notoginseng, and CHRNA7, ALB, TAC1, OXT and TNF can be targeted by one
compound in Panax notoginseng. AKT1 is involved in the PI3K-Akt
signaling pathway. IL2 is a cytokine; cytokines play an important role
in pain through different mechanisms in several sites of pain
transmission pathways^[69]34. PTGS2 inhibitors have been widely used to
treat inflammation and pain^[70]35,[71]36. As shown in Fig. [72]1, we
built a network for the 56 FFDS-related pain disease proteins and their
interactive FFDS targets. AKT1 is a FFDS primary pain disease protein,
and it interacts with the most FFDS targets (19), followed by TNF (5).
Among the 46 secondary pain disease proteins, NTRK1 is associated with
most FFDS targets (23), followed by VCP (19), HNRNPA1 (16) and
sequestosome-1 (SQSTM1) (14). NTRK1 and VCP can be indirectly targeted
by 13 compounds in the three herbs, HNRNPA1 can be indirectly targeted
by 12 compounds in three herbs, and SQSTM1 can be targeted by 11
compounds in three herbs; all of these proteins are associated with the
highest number of compounds. Nerve growth factor (NGF) is a
neurotrophic factor that acts as a mediator of pain, and NTRK1 encodes
a receptor tyrosine kinase for NGF^[73]37. In addition, heat shock
cognate 71 kDa protein (HSPA8) and transcription factor AP-1 (JUN) are
FFDS targets instead of pain disease proteins, but they are associated
with most pain disease proteins. Thus, HSPA8 and AP-1 may be factors
that contribute to the therapeutic effects of FFDS in the treatment of
pain. HSPA8 is implicated in a wide variety of cellular processes,
including the protection of the proteome from stress, folding and
transport of newly synthesized polypeptides, activation of proteolysis
of misfolded proteins and the formation and dissociation of protein
complexes. JUN directly interacts with specific target DNA sequences to
regulate gene expression.
Figure 1.
[74]Figure 1
[75]Open in a new tab
The network for 56 FFDS-related pain disease proteins and their
interactive FFDS targets. Green nodes represent FFDS targets, blue
nodes represent pain disease proteins, and yellow nodes represent FFDS
targets/pain disease proteins.
Salvia miltiorrhizae possesses 36 pain disease proteins, and four of
these proteins can be directly targeted by it. Panax notoginseng
possesses 49 pain disease proteins, and seven of these proteins can be
directly targeted by it. Borneolum possesses 12 pain disease proteins,
and none of them can be directly targeted by it. Twenty-eight compounds
in FFDS have direct or indirect interactions with pain disease
proteins. Eicosatetraenoic acid in Salvia miltiorrhizae can directly
target the most pain disease proteins (5) and is directly or indirectly
related to the most pain disease proteins (29). Polysaccharide, ursolic
acid, hexadecanoic acid, baicalin, rosmarinic acid and oleanolic acid
are directly or indirectly related to at least ten pain disease
proteins. We retrieved the literature in PubMed and found that 13
compounds in FFDS have antinociceptive effects. These compounds include
ursolic acid, baicalin, rosmarinic acid, ferulic acid, caffeic acid,
tanshinone IIA, protocatechuic acid, tetramethylpyrazine and
gamma-sitosterol in Salvia miltiorrhizae; polysaccharide, ginsenoside
Rg1 and ginsenoside Rg3 in Panax notoginseng; and oleanolic acid in
Borneolum. For example, ursolic acid has anti-inflammatory,
antioxidant, and antinociceptive effects in different animal
models^[76]38. Polysaccharide pretreatment significantly reduced the
number of writhes and licking time but did not increase the latency
time of responses, demonstrating its antinociceptive
effects^[77]39,[78]40. Orally administered oleanolic acid showed an
antinociceptive effect in a dose-dependent manner as measured in the
acetic acid-induced writhing test^[79]41. We used a scoring system to
assess herbs and compounds in FFDS that affect pain disease proteins
and found that 13 compounds (ursolic acid:3.65, polysaccharide:3.13,
baicalin:2.17, oleanolic acid:1.78, rosmarinic acid:1.33, ferulic
acid:1.08, caffeic acid:1.04, tanshinone IIA:0.92, protocatechuic
acid:0.76, tetramethylpyrazine:0.73, gamma-Sitosterol:0.52, ginsenoside
Rg3:0.28 and ginsenoside Rg1:0.22. P <= 0.01) with known
antinociceptive effects had scores. Additionally, 15 other compounds
(eicosatetraenoic acid:4.90, hexadecanoic acid:3.21, ginsenoside
Rd:1.96, ginsenoside Rg2:1.60, tanshinone I:1.07, ginsenoside Re:0.79,
pentadecanoic acid: 0.71, cryptotanshinone:0.63, magnesium
lithospermate B:0.44, ginsenoside Rh1:0.29, dauricine:0.17,
miltirone:0.17, isoferulic acid:0.16, Danshensu:0.12 and asiatic
acid:0.03; P <= 0.01 except for Dauricine and miltirone (P = 0.029)) in
FFDS had scores. The maximum score, mean score and minimum score of the
13 known compounds are 3.65, 1.36 and 0.22, respectively, and the
maximum score, mean score and minimum score of the 15 unknown compounds
are 4.90, 1.08 and 0.03, respectively. We compared unknown compounds
with known compounds by using t-tests and found no significant
difference in the score values between them (P = 0.57), indicating that
these unknown compounds are of equal importance in the treatment of
pain. Six known compounds could directly target pain disease proteins.
At least two targets (including pain disease proteins) of each of the
13 known compounds have direct associations with pain disease proteins.
The percentages (number of targets directly associated with pain
disease proteins (including pain disease proteins)/all targets of a
compound) of 10 known compounds exceed 0.5. Additionally, the scores of
11 known compounds exceed 0.5. To improve prediction accuracy, we
required satisfying at least two of the following conditions: directly
targeting a pain disease protein, at least 2 targets with direct
associations with pain disease proteins (including pain disease
proteins), percentage >= 0.5 and score >= 0.5 to determine that a
compound has effects in the treatment of pain. Indeed, all known
compounds can be determined to have effects in treating pain by using
this method. This method has been proven reliable in screening active
compounds from herbal formulas. Using this method, eicosatetraenoic
acid, hexadecanoic acid, ginsenoside Rd, ginsenoside Rg2, tanshinone I,
ginsenoside Re, pentadecanoic acid, cryptotanshinone, magnesium
lithospermate B, isoferulic acid, danshensu and asiatic acid can be
determined to have effects in treating pain. These compounds and the 13
known compounds may be the main active ingredients of FFDS in treating
pain. Relevant information about these compounds and the 13 known
compounds is listed in Table [80]4. However, scores always show the
comprehensive relevance of compounds to pain. The scores of
eicosatetraenoic acid, hexadecanoic acid, ginsenoside Rd, ginsenoside
Rg2, tanshinone I, ginsenoside Re and pentadecanoic acid in the 15
unknown compounds exceed 0.7, and these compounds have more potential
as drugs for pain. In fact, the scores of 7/15 unknown compounds exceed
0.7, while the scores of 10/13 known compounds exceed 0.7.
Table 4.
Relevance information about herbs and compounds (determined to have
effects in treating pain) with regard to pain.
Herb/compound n k Number of directly targeted pain disease proteins
Percentage (k/n) Sum score Max score P-Value Type
Salvia miltiorrhizae (herb) 99 58 4 0.586 14.97 0.54 0.00 known
Ursolic acid 16 13 1 0.812 3.65 0.54 0.00 known
Baicalin 23 13 0 0.565 2.17 0.42 9.55E-15 known
Rosmarinic acid 7 6 1 0.857 1.33 0.37 4.23E-09 known
Ferulic acid 16 7 0 0.438 1.08 0.36 1.64E-07 known
Tanshinone I 7 5 0 0.714 1.07 0.45 4.25E-07 unknown
Caffeic acid 20 5 1 0.250 1.04 0.40 2.28E-04 known
Tanshinone IIA 11 8 1 0.727 0.93 0.22 8.05E-11 known
Protocatechuic acid 4 3 1 0.750 0.76 0.43 9.75E-05 known
Tetramethylpyrazine 3 2 0 0.667 0.73 0.49 2.51E-3 known
Cryptotanshinone 4 3 1 0.750 0.63 0.35 9.75E-05 unknown
Gamma-Sitosterol 4 4 0 1.000 0.52 0.15 7.28E-07 known
Magnesium lithospermate B 5 3 1 0.600 0.44 0.19 2.39E-04 unknown
Isoferulic acid 3 2 0 0.667 0.16 0.12 2.51E-03 unknown
Danshensu 2 2 0 1.000 0.12 0.06 8.54E-04 unknown
Panax notoginseng (herb) 143 43 7 0.301 17.78 0.67 0.00 known
Eicosanetetraenoic acid 39 16 5 0.410 4.90 0.67 5.00E-15 unknown
Hexadecanoic acid 48 15 2 0.313 3.21 0.63 4.15E-12 unknown
Polysaccharide 14 14 0 1.000 3.13 0.53 0.00 known
Ginsenoside Rd 3 3 1 1.000 1.96 0.62 2.49E-05 unknown
Ginsenoside Rg2 3 3 0 1.000 1.60 0.36 2.49E-05 unknown
Ginsenoside Re 4 4 0 1.000 0.79 0.39 7.28E-07 unknown
Pentadecanoic acid 2 2 0 1.000 0.71 0.33 8.54E-04 unknown
Gamma-Sitosterol 4 4 0 1.000 0.52 0.15 7.28E-07 known
Ginsenoside Rg3 4 3 0 0.750 0.28 0.13 9.75E-05 known
Ginsenoside Rg1 5 2 1 0.400 0.22 0.14 8.05E-03 known
Dauricine 1 1 0 1.000 0.17 0.17 2.92E-02 unknown
Borneolum (herb) 17 11 0 0.647 1.80 0.37 1.38E-13 known
Oleanolic acid 14 9 0 0.643 1.78 0.37 2.70E-11 known
Asiatic acid 3 2 0 0.667 0.03 0.03 2.51E-03 unknown
[81]Open in a new tab
Lines 2, 17 and 29 are herbal items. Compounds below an herb belong to
this herb. The “Materials and methods” section contains definitions for
n and k.
Overlap analysis between FFDS targets and drug targets with indications for
pain
To explore similarities between FFDS and known pain drugs, we collected
known drugs in treating pain as well as their affected targets in
humans from the DrugBank and TTD databases. These drug targets can be
used as therapeutic targets. Subsequently, we built a drug-target
network to explore frequently affected therapeutic targets. If a
therapeutic target is affected by more drugs in treating the same
disease, this target has more potential as an efficient approach for
treating the disease. After we analyzed the networks (Table [82]S6),
the Mu-type opioid receptor (OPRM1) was targeted by the most drugs
(55), and PTGS2 was targeted by the most approved drugs (14),
indicating that they are efficient therapeutic approaches for pain
treatment. PTGS2 can be directly targeted by FFDS. Prostaglandin G/H
synthase 1 (PTGS1) is associated with 22 drugs, including 8 approved
drugs, and can be directly targeted by FFDS.
In total, 45 therapeutic targets are affected by both FFDS and known
drugs. All therapeutic targets have direct or indirect connections with
the pain disease protein set. Six therapeutic targets are also pain
disease proteins, including PTGS2, CHRNA7, COMT, TNF, ALB and OXT,
while 24 therapeutic targets are not pain disease proteins but can
directly interact with the pain disease proteins. For the remaining 15
therapeutic targets, the minimum path lengths between each target and
pain disease proteins are 2. As shown in Fig. [83]2, we built a network
to show the direct connections between the 45 therapeutic targets and
pain disease proteins. For the six therapeutic targets/pain disease
proteins, ALB can interact with 4 other pain disease proteins, TNF can
interact with 2 other pain disease proteins, and PTGS2 and COMT can
interact with 1 other pain disease protein. By contrast, CHRNA7 and OXT
have no direct interactions with other pain disease proteins. Vascular
cell adhesion protein 1 (VCAM1), which is not a pain disease protein,
can directly interact with the most pain disease proteins (6), followed
by NF-kappa B inhibitor alpha (NFKBIA) (5), mitogen-activated protein
kinase 3 (MAPK3) (5) and inhibitor of nuclear factor kappa B kinase
subunit beta (IKBKB) (4). Seven therapeutic targets associated with at
least four known drugs are listed in Table [84]5. According to the
enrichment results, the 45 therapeutic targets were significantly
associated with 13 pain-related pathways, including the TNF signaling
pathway, NF-kappa B signaling pathway, apoptosis, HIF-1 signaling
pathway, Toll-like receptor signaling pathway, serotonergic synapse,
VEGF signaling pathway, arachidonic acid metabolism, adipocytokine
signaling pathway, cholinergic synapse, mTOR signaling pathway,
sphingolipid signaling pathway and neurotrophin signaling pathway. All
of these pathways could be found in the 26 pathways enriched by all
FFDS targets. When we compared those pathways enriched by the 45
therapeutic targets and pathways enriched by all FFDS targets, we found
that some of the 45 therapeutic targets and other FFDS targets were
involved in the same pathways.
Figure 2.
Figure 2
[85]Open in a new tab
The direct interactions between 45 common therapeutic targets and pain
disease proteins. Green nodes represent therapeutic targets, blue nodes
represent pain disease proteins, and yellow nodes represent therapeutic
targets/pain disease proteins.
Table 5.
The 7 common targets associated with the most drugs known for the
treatment of pain.
Gene Protein Compound Type
PTGS2 Prostaglandin G/H synthase 2 Hexadecanoic acid, eicosatetraenoic
acid successful
PTGS1 Prostaglandin G/H synthase 1 Eicosatetraenoic acid successful
HTR3A 5-hydroxytryptamine receptor 3A Ginsenoside Rg3 successful
NR3C1 Glucocorticoid receptor Ginsenoside Re investigational
CHRNA7 Neuronal acetylcholine receptor subunit alpha-7 Ginsenoside Rg1
investigational
TOP2A DNA topoisomerase 2-alpha Ursolic acid, oleanolic acid
investigational
ALOX5 Arachidonate 5-lipoxygenase Baicalin, caffeic acid,
eicosatetraenoic acid investigational
[86]Open in a new tab
Evaluation of FFDS targets and analysis of significant targets
Highly connected proteins in PPIs can be defined as significant
proteins. To identify the significant targets of FFDS, we constructed a
FFDS targets-pain disease proteins-other human proteins PPI network to
evaluate FFDS targets. We calculated the degrees of FFDS targets, and
109 FFDS targets (Table [87]S7) with higher degrees than the median
value of the degrees of all FFDS targets in the network can be defined
as significant targets of FFDS. The degrees of JUN, Myc proto-oncogene
protein (MYC), VCAM1, HSPA8, Catenin beta-1 (CTNNB1) and AKT1 are the
highest at 1474, 892, 656, 428, 368 and 314, respectively. VCAM1 and
AKT1 are therapeutic targets of drugs with indications for pain and
pain disease protein, respectively. Five significant targets (AKT1,
ALB, TNF, COMT, and PTGS2) are pain disease proteins, and ALB, TNF,
COMT and PTGS2 are also therapeutic targets. There are 25 therapeutic
targets except for those four therapeutic targets/pain disease
proteins. As shown in Fig. [88]3, a highly connected subnetwork was
extracted from the FFDS targets-pain disease proteins-other human
proteins PPI network. The highly connected subnetwork can be defined as
a core of FFDS. The highly connected subnetwork includes 29 pain
disease proteins or therapeutic targets of drugs with indications for
pain. Thus, FFDS may significantly affect pain through this network.
Figure 3.
Figure 3
[89]Open in a new tab
A highly connected subnetwork in the FFDS targets-pain disease
proteins-other human proteins network. Triangle nodes correspond to
FFDS targets or other human proteins, circle nodes are pain disease
proteins, diamond nodes are therapeutic targets, rectangle nodes
represent therapeutic targets/pain disease proteins, yellow nodes can
be directly targeted by FFDS, and blue nodes can be indirectly affected
by FFDS.
Then, we performed pathway enrichment analysis, and 109 significant
targets were significantly associated with 28 pathways related to pain.
The TNF signaling pathway, NF-kappa B signaling pathway, apoptosis,
HIF-1 signaling pathway, Toll-like receptor signaling pathway,
serotonergic synapse, VEGF signaling pathway, adipocytokine signaling
pathway, cholinergic synapse, mTOR signaling pathway, sphingolipid
signaling pathway and neurotrophin signaling pathway were also enriched
by all FFDS targets and 45 therapeutic targets. This finding indicates
that the significant therapeutic targets are involved in those
pathways. The PI3K-Akt signaling pathway, MAPK signaling pathway, ErbB
signaling pathway, prolactin signaling pathway, oxytocin signaling
pathway, insulin signaling pathway, GnRH signaling pathway, estrogen
signaling pathway, AMPK signaling pathway and inflammatory mediator
regulation of TRP channels were also enriched by all FFDS targets.
Other pathways, including the cAMP signaling pathway, retrograde
endocannabinoid signaling, Wnt signaling pathway, dopaminergic synapse
and Rap1 signaling pathway, can be classified into environmental
information processing (signal transduction) and organismal systems
(nervous system). VEGF is a selective endothelial cell mitogen that
promotes angiogenesis and increases blood vessel permeability.
Inhibiting VEGF provides effective pain relief^[90]42. Endocannabinoids
modulate neuronal, glial and endothelial cell function and have
neuromodulatory, anti-excitotoxic, anti-inflammatory and vasodilatory
effects. Endocannabinoids behave as analgesics in acute nociception and
clinical pain models, such as inflammation and painful
neuropathy^[91]43,[92]44.
Discussion
Pain is a severe symptom related to many diseases as well as a disease
itself that affects thousands of people. As quality of life has
improved and the need for painless treatment is increasing, studies on
pain are attracting more attention than ever. Though many new
analgesics have been introduced to the clinic, pain remains an issue.
Additionally, alternative analgesics are required because of the side
effects of currently available analgesics. Chinese herbal formulas are
well known for their advantages in curing complex diseases with few
side effects. FFDS is a Chinese herbal formula that has significant
therapeutic effects on attenuating pain. FFDS has been widely used to
treat various diseases. One common symptom among these diseases, such
Alzheimer’s disease, is pain; thus, determining the mechanisms of FFDS
in attenuating pain will aid in the development of many new therapeutic
approaches for patients in pain and provide better understanding of
pain in the treatment of other diseases.
In the present study, we conducted a bioinformatics investigation to
uncover the potential pharmacological mechanisms of FFDS in the
treatment of pain. A large-scale text mining revealed that three herbs
in FFDS have effects on pain, and some active compounds in each herb
have antinociceptive effects. New drugs that may have analgesic effects
were discovered based on drug-target principles, including tanshinone I
in Salvia miltiorrhizae, eicosatetraenoic acid, hexadecanoic acid,
ginsenoside Rg2 and ginsenoside Rd in Panax notoginseng. The pathways
constituting the foundational mechanisms of the effects of FFDS on pain
have been enriched by 223 targets of FFDS, and they can be classified
into signal transduction, endocrine system, lipid metabolism and
nervous system. Salvia miltiorrhizae and Panax notoginseng can
significantly affect different pathways related to pain, but they also
contribute to the effects of the opposite herb in a corresponding
pathway, such as the HIF-1 signaling pathway, PI3K-Akt signaling
pathway, TNF signaling pathway and NF-kappa B signaling pathway.
Pain disease proteins and therapeutic targets of drugs with indications
for pain are directly or indirectly affected by FFDS, and these
proteins may be the main factors that FFDS targets to treat pain. Most
pain disease proteins and therapeutic targets are highly connected in
the PPI network and can be defined as significant proteins. By
comparing their locations in KEGG pathways, we found that most of these
proteins were located at the beginning of these pathways or at
important points within the pathways. Targeting these proteins can
affect an entire pathway and has significant therapeutic effects. Other
targets of FFDS can affect many pain disease proteins, which cannot be
ignored, such as HSPA8 and JUN. Of course, a single drug affects only a
few therapeutic targets. However, a formula can affect more targets due
to its complex composition. Except for those disease proteins and
therapeutic targets, other targets of FFDS were also involved in the
same pathways as those disease proteins and therapeutic targets. These
“unnecessary targets” were located in unimportant locations or could be
indirectly affected by the disease proteins and therapeutic targets.
“Unnecessary targets” may bridge several points to enhance the
functions of FFDS or they may be therapeutic targets that have not yet
been discovered. Moreover, we speculate that for a pathway related to a
disease, affecting only a small part of the pathway is necessary to
cure this disease; thus, these “unnecessary targets” are affected by
FFDS only to reverse its side effects on the human body. Some
“unnecessary targets” may be harmful to the human body; therefore, the
formula should be optimized.
Based on the published literature, we can conclude that FFDS has
therapeutic effects in the treatment of neuropathic pain, cancer pain
and arthralgia. In terms of neuropathic pain, three disease proteins of
neuropathic pain can be directly targeted by FFDS, including
TNF^[93]45, PTGS1^[94]46 and PTGS2^[95]47; twenty therapeutic targets
of drugs with indications including neuropathic pain can be directly
affected by FFDS, such as PTGS2, PTGS1 and 5-hydroxytryptamine receptor
3A (HTR3A), which have all been associated with at least four drugs
known for the treatment of neuropathic pain. Additionally, fourteen
pathways that have relationships with neuropathic pain therapy can be
significantly enriched by targets of FFDS, for example, the TNF
signaling pathway^[96]45, Toll-like receptor signaling pathway^[97]48,
PI3K-Akt signaling pathway^[98]49 and apoptosis^[99]50. No disease
proteins for cancer pain can be directly targeted by FFDS, while one
therapeutic target (HTR3A) can be directly targeted by FFDS, and two
pathways (HIF-1 signaling pathway^[100]51 and T cell receptor signaling
pathway)^[101]52 that have relationships with cancer pain therapy are
significantly enriched by FFDS targets. FFDS has proven anticancer
properties; in our investigation, many cancer-related proteins and
pathways could be affected by FFDS. Thus, many indirect factors should
be affected by FFDS to attenuate cancer pain, including proteins that
can interact with cancer pain-related proteins. Seven disease proteins
for arthralgia can be directly targeted by FFDS, including macrophage
migration inhibitory factor (MIF), interleukin-6 (IL6), 72 kDa type IV
collagenase (MMP2), Toll-like receptor 4 (TLR4), connective tissue
growth factor (CTGF), cartilage oligomeric matrix protein (COMP) and
interleukin-10 (IL10). Arthrogenic alphaviruses can cause debilitating
illnesses characterized by arthritis and arthralgia, and evidence
suggests that both MIF and CD74 play a critical role in mediating
alphaviral disease^[102]53. In the chronic phase, the level of IL-6 has
been associated with persistent arthralgia, providing a possible
explanation for the etiology of arthralgia that plagues CHIKV-infected
patients^[103]54. No therapeutic targets of drugs with indications
including arthralgia can be targeted by FFDS. Two pathways (the
Toll-like receptor signaling pathway^[104]55 and osteoclast
differentiation)^[105]56 that have relationships with arthralgia can be
significantly enriched by FFDS targets. Based on our results, we can
conclude that FFDS can treat the aforementioned diseases through a
target network, not simply one or two targets. Due to space
limitations, we cannot discuss many mechanisms of action of FFDS in the
treatment of these diseases. Therefore, we suggest that more
investigators should use TCM network pharmacology approaches to unveil
these mechanisms. In conclusion, this paper has fully elucidated the
potential mechanisms of the effects of FFDS on pain from a systemic
perspective based on targets of FFDS and network analysis. Moreover,
this study will provide basic theories for the usage of this formula
and its herbs.
Materials and Methods
FFDS, its herbs and pain
We referred to an article and used a computational method to assess the
relevance between FFDS, its herbs and pain. The more papers relating an
herb to pain, the more efficient this herb will be in treating pain. A
parameter was used to balance bias and further assess the relevance
between FFDS, its herbs and pain. The parameter is the ratio of pain
herb-related papers and disease herb-related papers. The P-value was
calculated by Eq. ([106]1) and used to examine the error.
[MATH:
P=1−∑i=0k−1(Ki)(N−Kn−i
)(Nn) :MATH]
1
where N is the total number of papers in PubMed (27 million), K is the
number of papers related to pain (599207, using “Pain” as a keyword), n
is the number of papers about FFDS or its herbs and diseases (using
synonyms for FFDS or its herbs and disease as keywords), and k is the
number of papers about FFDS or its herbs and pain (using synonyms for
FFDS or its herbs and pain as keywords). We used synonyms of an item
(such as Salvia miltiorrhizae, its synonyms are Salvia miltiorrhizae
and Danshen, which are wildly used in papers) to search PubMed and
download Pubmed IDs, then we removed duplicate IDs and calculated the
total number of the searched papers.
Data preparation
Herbal compounds in FFDS were collected and extracted from TCM
database@TW^[107]57 (http://tcm.cmu.edu.tw/zh-tw/index.php, updated
2013 July) and PubMed literature. Therapeutic targets and their
associated drugs were collected from DrugBank^[108]58
(https://www.drugbank.ca/, version 5.0) and Therapeutic Target
Database^[109]59 (http://bidd.nus.edu.sg/BIDD-Databases/TTD/TTD.asp,
last updated on Sep 10, 2015). Pain disease proteins were collected
from the Therapeutic Target Database and DisGeNET^[110]60 (with
score >= 0.2, http://www.disgenet.org, version 5.0). We retained only
the drugs (their therapeutic targets) and disease proteins that clearly
belonged to “Pain”. PPI data were obtained from Mentha^[111]61
(http://mentha.uniroma2.it/). The known targets of compounds in FFDS
were collected from STITCH^[112]62 (http://stitch.embl.de/, version
5.0) with a higher interaction score (confidence >= 0.7).
Drug-likeness and bioavailability analysis
Compounds with drug-likeness properties and high bioavailability are
more likely to be drugs. To explore the drug-likeness and
bioavailability of the compounds in FFDS, we used SwissADME
(http://www.swissadme.ch/)^[113]63,[114]64 as a tool to calculate the
drug-likeness and bioavailability of each compound. The drug-likeness
rules include Lipinski’s rule, Ghose’s rule, Veber’s rule, Egan’s rule
and Muegge’s rule.
Statistical analysis to assess the probabilities of compounds and herbs in
FFDS in the treatment of pain
Uncovering active compounds in Chinese herbal formulas for specific
diseases can facilitate the modernization of TCM. Here, we built a new
computational method to simply assess compounds and herbs in FFDS in
treating pain. First, we constructed the pain disease proteins-other
human proteins PPI network to evaluate the significance of pain disease
proteins for pain. Increased significance of a protein in the network
indicates that this protein is more essential for pain^[115]65.
According to the centrality of a protein in the PPI network, which has
a strong correlation with the essentiality of a protein, the protein
will be given a weight value Si to show its relative significance.
First, we calculated the degrees of pain disease proteins in the pain
disease proteins-other human proteins PPI network. Then, we calculated
log10 values of degrees. Finally, we divided log10 values by the
maximum log10 value for normalization. We used those normalized values
as the weight values Si of each pain disease protein. As degrees 1 and
2 are similarly important and log10 of 1 is 0, we changed the weight
values of pain disease proteins with degrees 1 or 2 to 0.4 (log10 of 3
is 0.4771) divided by the maximum log10 value. In truth, the degrees of
only two pain disease proteins used here were 1 or 2. The probability
scores of PPIs were obtained from the Mentha database. The product of
the weight value of a pain disease protein and the maximum probability
of a specified compound directly or indirectly interacting with that
protein will be considered the computational relevance score of the
compound with pain through this pain disease protein. The sum of the
relevance scores of a compound with regard to all its directly or
indirectly interactive pain disease proteins will be treated as the
computational relevance score of the compound with pain through all its
related pain disease proteins. Ultimately, the sum of relevance scores
of all compounds in one herb was treated as the computational relevance
score of the herb with pain through all its related pain disease
proteins. The score could not represent the real therapeutic effects,
but it may show the probabilities and relative significance of herbs
and their compounds in the treatment of diseases. Similar to Eq.
([116]1), we also used P-values to examine the possibility of finding
that a certain number of proteins were targets of a compound or an herb
and had direct associations with pain disease proteins (including pain
disease proteins themselves) in at least k proteins by chance. Here, N
is the total number of human proteins in UniProt (162191), and K is the
number of proteins that have direct associations with pain disease
proteins (including pain disease proteins) (4739). n is the number of
targets of a compound or an herb, and k is the number of proteins that
were targets of a compound or an herb and had direct associations with
pain disease proteins (including pain disease proteins).
Network construction and analysis
Many types of networks were built and analyzed to explore the
pharmacological mechanisms of the effects of FFDS on pain. The degree
was calculated to assess the significance of a protein in a network by
NetworkAnalyzer in Cytoscape 3.2. The centrality-lethality rule shows
that a highly connected protein is more important to an organism than a
poorly connected protein^[117]65,[118]66. All networks were visualized
and analyzed by Cytoscape 3.2.
Pathway enrichment and analysis
Database for Annotation, Visualization, and Integrated
Discovery^[119]67 (DAVID, https://david.ncifcrf.gov/, version 6.8) was
used as a tool for KEGG pathway enrichment by using default settings.
Related pathways are available in the KEGG pathway database^[120]68
(KEGG: Kyoto Encyclopedia of Genes and Genomes, http://www.kegg.jp/,
release 81.0).
Supplementary information
[121]Table S1^ (68.6KB, xlsx)
[122]Table S2^ (61.4KB, xlsx)
[123]Table S3^ (51.8KB, xlsx)
[124]Table S4^ (53.3KB, xlsx)
[125]Table S5^ (51.6KB, xlsx)
[126]Table S6^ (62.4KB, xlsx)
[127]Table S7^ (54.3KB, xlsx)
Acknowledgements