Abstract
Traditional Chinese medicine (TCM) formulas treat complex diseases
through combined botanical drugs which follow specific compatibility
rules to reduce toxicity and increase efficiency. “Jun, Chen, Zuo and
Shi” is one of most used compatibility rules in the combination of
botanical drugs. However, due to the deficiency of traditional research
methods, the quantified theoretical basis of herbal compatibility
including principles of “Jun, Chen, Zuo and Shi” are still unclear.
Network pharmacology is a new strategy based on system biology and
multi-disciplines, which can systematically and comprehensively observe
the intervention of drugs on disease networks, and is especially
suitable for the research of TCM in the treatment of complex diseases.
In this study, we systematically decoded the “Jun, Chen, Zuo and Shi”
rules of Huanglian Jiedu Decoction (HJD) in the treatment of diseases
for the first time. This interpretation method considered three levels
of data. The data in the first level mainly depicts the characteristics
of each component in single botanical drug of HJD, include the physical
and chemical properties of component, ADME properties and functional
enrichment analysis of component targets. The second level data is the
characterization of component-target-protein (C-T-P) network in the
whole protein-protein interaction (PPI) network, mainly include the
characterization of degree and key communities in C-T-P network. The
third level data is the characterization of intervention propagation
properties of HJD in the treatment of different complex diseases,
mainly include target coverage of pathogenic genes and propagation
coefficient of intervention effect between target proteins and
pathogenic genes. Finally, our method was validated by metabolic data,
which could be used to detect the components absorbed into blood. This
research shows the scientific basis of “Jun-Chen-Zuo-Shi” from a
multi-dimensional perspective, and provides a good methodological
reference for the subsequent interpretation of key components and
speculation mechanism of the formula.
Keywords: system pharmacology, traditional Chinese medicine,
compatibility, Dijkstra model, information graph algorithm
Introduction
After thousands of years of experience in the treatment of diseases,
the effectiveness of traditional Chinese medicine (TCM) is indisputable
([48]Liang et al., 2015). During this process, TCM has also formed
unique medication standards and compatibility theories, such as “Jun
(emperor), Chen (minister), Zuo (adjuvant) and Shi (messenger)”
([49]Wang et al., 2008; [50]Lin and Yuhang, 2009). The basic and common
feature of TCM is that it is consisted in the form of formula by the
guidance of compatibility theory, different botanical drugs are
orchestrated to form a multi-botanical drug combination of TCM ([51]Lv
et al., 2018). The principle of a formula is not just simply to mix
botanical drugs, it is a process to increase efficiency and reduce
toxicity. During this process, traditional compatibility rules will
affect the changes of effective and toxic components of the formula,
thus could reflect the advantages of enhancing synergistic effect and
reducing toxicity of the formula ([52]Liu et al., 2018). In TCM
formula, botanical drugs are divided into “Jun, Chen, Zuo and Shi”
according to the therapeutic characteristics of TCM, which are the
basis of compatibility of TCM formula. “Jun” botanical drugs play
leading roles in the treatment of diseases in the formula. “Chen”
botanical drugs are usually worked as assistants to provide help to Jun
botanical drugs in treating diseases; “Zuo” botanical drugs are used in
combination with Jun and Chen botanical drugs to treat diseases or
inhibit the toxic effects of Jun and Chen botanical drugs; “Shi”
botanical drugs are widely used to coordinate the above botanical drugs
and enhance their functions. It can be seen from this that the “Jun,
Chen, Zuo and Shi” of the botanical drugs in the formula are mainly
distinguished according to the primary and secondary roles of the
medicine in the formula ([53]Yao et al., 2013; [54]Wu et al., 2014).
Although “Jun, Chen, Zuo and Shi” is a widely used principle in the
compatibility of TCM, the action pattern and underlying mechanisms of
the compatibility rule of “Jun, Chen, Zuo and Shi” is still unclear.
How to understand the action pattern and underlying mechanisms of
compatibility rule is the basic and key step to decode the functional
mechanisms of formulas in the treatment of complex diseases and benefit
to secondary development of formulas.
A TCM formula generally contains several botanical drugs, and each
botanical drug contains numbers of chemical components ([55]Jia et al.,
2004). These components are the material basis of the effect and
mechanism of action (MOA) of the TCM formula. Studying the chemical
composition and differences in efficacy of formula compatibility are
helpful to clarify the pharmacological mechanism of TCM formula
([56]Zhang, 2017). The traditional method to study the pharmacological
effects of the single or several components in TCM is cell test or
animal verify. These experiment-based research methods are divorced
from the characteristics of the overall treatment of TCM, not only
unable to tap the effective components of TCM, but also have no ability
to effectively reveal the mechanism of TCM.
Network pharmacology has been widely used in the research of TCM in the
treatment of complex diseases ([57]Gu and Jianfeng, 2017). With the
accumulation of omics data and the progress of network pharmacology
technologies, increasing models are proposed to decode the therapeutic
molecular mechanisms of formulas in treating complex diseases. For
example, Wang et al. employed network pharmacology model combined with
hypergeometric distribution to identify the enriched significant
pathways. These pathways affected by a group of differentially
expressed genes in pathway enrichment analysis, and further be used to
reveal the mechanism of Wuwei-Ganlu-Yaoyu-Keli in treating rheumatoid
arthritis ([58]Wang et al., 2017). Gu et al. established a model for
predicting signal transduction effects and extracting sub-networks by
using EGS for mechanism analysis based on the data of TCM components
and diseases ([59]Gu et al., 2014). However, most models are used to
decode the mechanism of TCM in treating complex diseases via networks
analysis. Few models considered the compatibility rules and formulation
principles of TCM, which are closely related to the effect and toxicity
of drugs. He et al. studied the effect of compatibility of Ginseng
trifolium (L.) Alph. Wood and Aconitum carmichaeli Debeaux on cardiac
toxicity of rats through metabolomics and found that Ginseng trifolium
(L.) Alph. Wood compatibility of Aconitum carmichaeli Debeaux can
reduce its cardiac toxicity and increase its pharmacological effects by
affecting the content of citric acid, glutathione, phosphatidyl choline
and uric acid ([60]He et al., 2015). In the treatment of rheumatoid
arthritis, Zhang et al. observed that total glucosides of paeony
combined with Tripteryginum wilfordii polyglycoside has better
performance in the treatment of RA by increasing efficiency and
reducing toxicity at the clinical applications ([61]Zhang et al.,
2019). How to utilize system pharmacology to analyze the principle of
formulas in TCM at systemic level through mathematical and quantitative
methods could be benefit for truly understanding the mechanisms of
formula in the treatment of diseases.
Huanglian Jiedu Decoction (HJD) is composed of four botanical drugs,
namely, Coptis chinensis Franch (Huanglian), Scutellaria baicalensis
Georgi (Huangqin), Phellodendron amurense Rupr (Huangbo) and Gardenia
jasminoides J. Ellis (Zhizi), with a compatible dosage of 3:2:2:3
([62]Ma et al., 2009). HJD is widely used in diabetes, cardiovascular
and cerebrovascular diseases, inflammation, alzheimer’s disease, etc.
in clinical applications ([63]Wang and Xu, 2000; [64]Xin et al., 2011;
[65]Zhang et al., 2014). It has a wide range of pharmacological
activities such as antibacterial, anti-inflammatory, antioxidant,
neuroprotective, etc ([66]Lv et al., 2017). Previous pharmacological
studies have shown that HJD could effectively control the weight of
diabetic rats and has a good regulating effect on blood lipid and
oxygen radical metabolism ([67]Zhang et al., 2011). It has been
reported that HJD has a strong lipid-regulating effect on type 2
diabetic rats, which can reduce the increase of pancreatic lipase
activity in intestinal tract and inhibit the activity of pancreatic
lipase in vitro ([68]Zhang et al., 2014). In addition, pharmacological
experimental study has found that the inflammatory factors in
cerebrospinalfluid of AD rats demonstrated a callback trend after
treatment with HJD, indicated that HJD can ameliorate the central
inflammatory status of AD rats by regulating the levels of inflammatory
factors ([69]Gu et al., 2018). The above experimental results showed
that HJD possessed obvious beneficial effects in the treatment of DM
and AD.
Four botanical drugs in this formula are used for clearing away heat
and toxic materials for thousands of years. In order to compare the
effects of HJD and its botanical drugs on C. albicans biofilm
formationin in vitro, Wang et al. found that the inhibitory effects of
Huangqin, Huangbo on C. albicans biofilm were close to that of HJD, and
Huanglian was superior to the other agents, Zhizi had no evidently
inhibitory effect. Studies have also shown that HJD has obvious
protective effect on cerebral ischemia injury ([70]Wang et al., 2008).
Wang et al. evaluated the effect of HJD and its botanical drugs on
rabbit platelet aggregation and found that the aggregation inhibition
rate of HJD was higher than that of each single botanical drug, and in
four botanical drugs of HJD the aggregation inhibition rate of
Huanglian was higher than that of the other three botanical drugs
([71]Wang et al., 2014). The above applications of “Jun, Chen, Zuo and
Shi” of HJD show that the relationship among “Jun, Chen, Zuo and Shi”
does exists and has quantitative basis in modern pharmacology and
experimental scientific research. How to detect the action pattern and
underlying mechanisms of “Jun, Chen, Zuo and Shi” at a systematic and
global perspective is the key to understand the mechanisms of formulas
in the treatment of complex diseases in TCM.
In this study, the compatibility rules of “Jun, Chen, Zuo and Shi” of
HJD in the treatment of diseases are systematically interpreted.
Specifically, our new system pharmacology strategy integrated three
levels of data including the characteristics of each component in HJD
single botanical drug, the characterization of component-target-protein
(C-T-P) network in the whole protein-protein interaction (PPI) network
and the characterization of intervention propagation properties of HJD
in the treatment of different complex diseases. Overall, our study
provides a comprehensive systems pharmacology framework to decode the
principles of “Jun-Chen-Zuo-Shi” from multi-level perspective, which
may give some enlightenment for the subsequent interpretation of key
components and hidden mechanism of the formula.
Materials and Methods
Chemical Components Collection
All chemical components of HJD and seven important pharmacological
related descriptors (MW, ALOGP, HDON, HACC, CACO-2, OB (%) and DL) for
each component were collected from Traditional Chinese Medicine Systems
Pharmacology (TCMSP) database ([72]Ru et al., 2014)
([73]http://lsp.nwsuaf.edu.cn/tcmsp.php). The chemical identification
and concentration of in HJD were collected from the previous reports
([74]Yang et al., 2019). All chemical structures were prepared and
converted into canonical SMILES using Open Babel Toolkit (version
2.4.1). The targets of HJD were predicted by using Similarity Ensemble
Approach SEA ([75]Keiser et al., 2007) ([76]http://sea.bkslab.org/),
hitpick ([77]Liu et al., 2013)
([78]http://mips.helmholtz-muenchen.de/proj/hitpick) and Swiss Target
Prediction ([79]David et al., 2014)
([80]http://www.swisstargetprediction.ch/).
Active Components Screening
ADME properties of drugs refer to the Absorption, Distribution,
Metabolism and Elimination, which are the key properties of whether
small molecules of drugs can be used as medicines. It is estimated that
due to low intestinal absorption rate and poor metabolic stability, the
oral bioavailability of drugs is low, and finally about 50% of drugs
fail in clinical trials ([81]Beaumont et al., 2014). Therefore, ADME
predictive screening of drugs is particularly important in drug
discovery.
Oral bioavailability (OB) refers to the percentage of oral dose of
drugs reaching the blood circulation system, which is the most common
pharmacokinetic parameter for drug screening ([82]Chiou, 2001). For
specific oral drugs, due to poor intestinal absorption, drug
metabolism, efflux and other reasons, the number of drugs that
eventually reach the circulatory system is greatly reduced, so the oral
availability of drugs ranges from 0 to 100%. As the initial absorption
rates of intestinal tract and liver are generally about 43 and 44%
respectively, components with OB greater than or equal to 30% are
selected as active components.
Drug-like (DL) refers to a class of components that have the same
functional group or similar physical characteristics as most known
drugs ([83]Han et al., 2011). The drug-like index of a new component is
calculated based on Tanimoto similarity. According to the drug-like
index, molecules with drug-like properties less than 0.14 are
eliminated. Finally, the components screened according to OB and
drug-like index take intersection as the active component ([84]Wang et
al., 2018).
Network Construction
The C-T-P network of HJD were constructed by using Cytoscape wsoftware
(Version 3.7.0) ([85]Lopes et al., 2010). The networks and topological
parameters were analyzed using NetworkAnalyzer, which is a plugin of
Cytoscape ([86]de Jong et al., 2003). The PPI data were derived from
public databases BioGRID, STRING, Dip, HPRD, Intact, Mint and Reactome
([87]Guan et al., 2014).
Detection of Functional Communities Structure in HJD
Community structures in the biomedical network are more important for
annotating the biological means. One index codebook and n community
codebooks were defined to character the movements of random walker
within and between communities respectively. Community codebook x has
one codeword for each node α∈x and one exit codeword. The frequency at
which random walkers visit each node in the community is the codeword
length, k [α∈x], and exits the community,
[MATH: sx↷ :MATH]
. The was used to denote the sum of these frequencies, the total use of
codewords in community x, and K ^x to denote the normalized probability
distribution. Consistent with the above, the index codebook has the
community entries of codewords. The codeword lengths are derived from
the set of frequencies at which the random walker enters each
community. The was used to denote the sum of these frequencies, the
total use of codewords to move into communities, and S to denote the
normalized probability distribution. We want to express average length
of codewords from the index codebook and the community codebooks
weighted by their rates of use. Therefore, the map equation is
[MATH:
L(N)=<
/mo>s↶H(S<
mo>)+∑x=1<
/mn>nkx↻H(kx) :MATH]
(1)
Next, we elaborate on the terms of the map equation in detail and
illustrate it with Hoffman code examples.
L(N) represents the per-step description length for community partition
N. That is, for community partition N of n nodes into n communities,
the lower bound of the average length of the code describing a step of
the random walker.
[MATH: s↶=∑x=1<
/mn>nsx↶ :MATH]
(2)
The index codebook rate is used. The per-step use rate of the index
codebook is given by the total probability that the random walker
enters any of them communities. This variable represents the proportion
of all codes representing community names in the codes. Where is
probability of jumping out of community x.
[MATH:
H(S)=−∑x=1<
/mn>n(s↶/sx
↶)log(sx↶/s↶) :MATH]
(3)
This variable represents the average byte length required to encode
community names. The frequency-weighted average length of codewords in
the index codebook. The entropy of the relative rates to use the motif
codebooks measures the smallest average codeword length that is
theoretically possible. The heights of individual blocks under Index
codebook correspond to the relative rates and the codeword lengths
approximately correspond to the negative logarithm of the rates in base
2.
[MATH: kx↻=∑α∈x<
/mi>kα+sx↷ :MATH]
(4)
This variable represents the coding proportion of all nodes (including
jump-out nodes) belonging to community x in the coding. The rate at
which the community codebook x is used, which is given by the total
probability that any node in the community is visited, plus the
probability that the random walker exits the community and the exit
codeword is used.
[MATH:
H(kx
)=−(s
x↷/kx↻)log(sx↷/kx↻)−∑α∈x<
/mi>(ka/kx↻)log(ka/<
mi>kx↻)
:MATH]
(5)
This variable represents the average byte length required to encode all
nodes in community x. The frequency-weighted average length of
codewords in community codebook x. The entropy of the relative rates at
which the random walker exits community x and visits each node in
community x measures the smallest average codeword length that is
theoretically possible. The heights of individual blocks under
community codebooks correspond to the relative rates and the codeword
lengths approximately correspond to the negative logarithm of the rates
in base 2.
Propagation Coefficient Calculation
The role of drug response in the body is a complex process involving
different proteins or genes. However, these proteins and genes are
regulated by different cellular components, which constitute complex
network relationships in the process of disease occurrence and
development. These network relations could propagate the therapeutic
effects through the network and orchestrate the cascade path of drug
response. At present, most network pharmacological models just focus on
the direct relationships between drugs and targets, and do not consider
the propagation mode and propagation effect of drug intervention. In
this study, we use Dijkstra model to detect the shortest distance from
the direct target to the pathogenic gene based on C-T-P network, and
keep the path with less than or equal to three nodes in the shortest
distance. We believe that the initial node is the direct target of
components, the final node is the pathogenic gene, which is also
defined as the number of reached effector proteins, and the
intermediate node is defined as the mode of propagation. Components
with more modes of propagation and more effector proteins usually have
better intervention effects. Based on the number of reached effector
proteins and the mode of propagation, we calculated the propagation
coefficient of each single botanical drug in HJD.
We defined the C-T-P network as the graph G (V, E), V and E represents
nodes and edges, respectively (u, v) represents edge in E, W[u,v] stand
for the weight of the edge. V is divided into two sets S and T, where
the distance from the targets in S to U has been determined and the
distance from the target to U contained in T has not been determined.
Then, the distance from u to the target x in T is set as d[u,x], which
is defined as the shortest path length from u to the target x in T. The
specific formula is as follows:
* 1) At the beginning, S = {u}, T = V-{u}. For all targets x in t,
if there is a path from u to x, then d[u,x] = w [u,v]; Otherwise,
set d[u,x] = ∞.
* 2) For all targets x in T, find the target T with smallest d[u,x],
i.e.:
* d[u,t] = min{d[u,x]∣x∈T∣}
* d[u,t] is the shortest distance from target t to effector proteins
u. At the same time, target T is also the target closest to u among
all targets in set T. Delete target t from T and merge it into S.
* 3) For all targets x adjacent to t in T, update the values of
d[u,x] with the following formula:
* d[u,x] = min{d[u,x], d[u,t] + w [t,x]}
* 4) Continue the above steps until T is an empty set.
* 5) During calculation of d[u,x] holding paths less than or equal
to 3 nodes as the shortest distance dmin, in a path with three
nodes, the first node mean components targets, the second node
represent propagation modes, the third node represent effector
proteins, the formula for calculating the propagation coefficient
of each single botanical drug is:
[MATH: PC(botanical
drug)=∑indmin/n
∑jmu/m :MATH]
(6)
The propagation coefficient (PC) represents the strength of
intervention ability of a botanical drug. The n represents the number
of propagation modes, the m represents the number of effector proteins.
KEGG Pathway
To analyze the main function of botanical drugs in HJD, the latest
pathway data were obtained from the Kyoto Encyclopedia of Genes and
Genomes (KEGG) database ([88]Draghici et al., 2007) were extracted for
KEGG pathway enrichment analyses. p-values were set at 0.05 as the
cut-off criterion. The results of analysis were annotated by Pathview
([89]Luo and Brouwer, 2013) in the R Bioconductor package
([90]https://www.bioconductor.org/).
Statistical Analysis
To compare the importance of communities in HJD, SPSS22.0 was used for
statistical analysis. One-way analysis of variance followed by a
Dunnett post-hoc test was used to compare more than two groups.
Obtained p-values were corrected by Benjamini-Hochberg false discovery
rate (FDR). Results were considered as statistically significant if the
p-value was <0.05.
Results
“Jun-Chen-Zuo-Shi” is one of the widely used compatibility principles
of TCM in the treatment of diseases. However, there is still lack
researches on the compatibility principle of “Jun-Chen-Zuo-Shi”. In
this study, we systematically analyze the compatibility principle and
mechanism of HJD based on “Jun-Chen-Zuo-Shi” at three levels: the
characteristics of each component in single botanical drug of HJD,
including the physical and chemical properties of component, ADME
properties and functional enrichment analysis of component targets; the
characterization of C-T-P network in the whole PPI network, mainly
including the characterization of degree and key communities in C-T-P
network; the characterization of intervention propagation properties of
HJD in the treatment of different complex diseases, mainly including
target coverage of pathogenic genes and propagation coefficient of
intervention effect between targets and pathogenic genes ([91]Figure
1).
FIGURE 1.
[92]FIGURE 1
[93]Open in a new tab
A schematic diagram of network pharmacology-based strategy to decode
the principles of “Jun-Chen-Zuo-Shi” in TCM.
Comparison of Chemical Properties of Components
Physical and chemical properties of drugs directly affect the activity
of drugs and play important roles in the druggability of TCM. In order
to detect the importance of these physical and chemical properties in
“Jun-Chen-Zuo-Shi”, seven important pharmacologically related
descriptors, MW, ALOGP, HDON, HACC, CACO-2, OB (%) and DL, were
analyzed from four botanical drugs in HJD. Principal component analysis
(PCA) widely used as a pattern recognition method to reflect the most
primitive state of data in an unsupervised state. Here, PCA was applied
to detect the distribution of the above physical and chemical
properties of four botanical drugs in “Jun-Chen-Zuo-Shi”. The results
showed that Huangqin and Huangbo are closest to Huanglian, indicating
that their physical and chemical properties are close to Huanglian.
Zhizi is farthest from Huanglian, which indicates that their physical
and chemical properties are quite different in PCA scatter plot
([94]Figure 2A). To further describe the specific differences among the
four botanical drugs, we have made a detailed analysis of the seven
parameters. As shown in [95]Figures 2B,C, 1) For MW, the average value
of all components in huanglian (342.2) is higher than that of huangqin
(277.7), huangbo (287) and zhizi (297). 2) For bioavailability, the
average OB value (%) of huanglian (36.09) is also higher than that of
huangqin (31.42), huangbo (34.51) and zhizi (29.43). (3) For
permeability, the average Caco-2 value of huanglian (0.4423) is lower
than that of huangqin (0.9371) and huangbo (0.7851). (4) For DL, like
MW and OB, huanglian possessed higher average DL value (0.4165), that
is very different from that of huangqin (0.2301), huangbo (0.3303) and
zhizi (0.258). (5) Compared with the all components of huanglian
(2.551), the ALogP value of huangqin (4.101), huangbo (3.096) and zhizi
(2.964) exhibited siginifically higher average ALogP values, which
indicates the majority components in huangqin, huangbo and zhizi are
hydrotropic, but that in huanglian are hydrophobic. (6) The values of
nHAcc in huanglian (5.5) are all higher than those in others (3.476,
3.743, 4.337, respectively). The above analysis results show that the
chemical properties of the four botanical drugs are obviously
different. Therefore, we can speculate that each botanical drug plays a
different role in the compatibility of this TCM. The Jun botanical drug
huanglian is distinguished based on the compatibility principle of
“Jun-Chen-Zuo-Shi”, and has good performance in most physical and
chemical properties such as OB (%), DL and MW, which indicated that it
may play a leading role at the functional level.
FIGURE 2.
[96]FIGURE 2
[97]Open in a new tab
Analysis seven chemical properties of huanglian (HL), huangqin (HQ),
huangbo (HB) and zhizi (ZZ) in HJD. A, B and C represent the chemical
space, chemical distribution and the value of chemical parameters of
all components visualized by PCA scores plot, polar coordinate petal
diagram and bar chart, respectively.
Chemical Analysis in HJD
Chemical analysis plays an important role in the study of the
substances basis and mechanism of botanical drugs in the formula. The
information on specific chemical identification and concentration of
the botanical drugs in HJD were collected by searching from the
literature ([98]Yang et al., 2019). The detailed information was shown
in [99]Table 1. The results suggest that the chemical components of
botanical drugs and the concentration of identified components provide
an experiment-aided chemical space for the search of active components.
This will provide valuable reference for further analysis.
TABLE 1.
The information on chemical analysis of the botanical drugs from the
literature in HJD.
Formula Method Component Concentration (mg/g) Ref
Huanglian Jiedu decoction (HJD) HPLC Phellodendrine 3.8276 ± 0.1158
([100]Yang et al., 2019)
Heriguard 1.0800 ± 0.0261
Magnoflorine 6.7489 ± 0.0450
Geniposide 72.3830 ± 1.0948
Coptisine 14.0580 ± 0.1631
Epiberberine 8.9056 ± 0.0864
Jatrorrizine 9.4028 ± 0.0966
Berberine 53.0820 ± 0.5443
Palmatine 19.6820 ± 0.1452
Baicalin 18.5770 ± 0.0927
Oroxindin 17.5360 ± 0.2370
Wogonin 2.1689 ± 0.3488
Oroxylin A 0.2618 ± 0.0212
[101]Open in a new tab
Comparison of Active Components
TCM formula contains a large number of chemical molecules, the
traditional methods of exploring the active components in TCM are
mainly based on the separation, purification and structure analysis of
mass spectrometry (MS) and high-performance liquid chromatography
(HPLC). These methods supplied quantitative concentration of
components, however, these experiment-based methods cost a lot of
manpower, material and financial resources to excavate the effective
components in TCM. Therefore, it is particularly important to analyze,
explore and optimize the TCM formula by calculating ADME properties,
which could be helpful to screen the potential active components of
TCM, and further optimize the TCM formula, improve the research and
development of new drugs from TCM. In this study, botanical drug
components were evaluated by using the two representative ADME
parameters, oral bioavailability (OB) and drug-like (DL), to screen the
active components of TCM formula.
Our statistic results show that 26.81% (94) of the components in HJD
meet OB ≥ 30% and DL ≥ 0.14 ([102]Table 2). Specifically, 31.25% of the
components in Huanglian satisfy OB ≥ 30% and DL ≥ 0.14. These
components are regarded as the active components in Huanglian, which
included berberine, coptisine, epiberberine, and palmatine, etc.
Studies have shown that berberine could significantly reduce
hyperglycemia and glycogen content in liver of diabetic mice, increase
the expression of Akt and IRS, and inhibit the expression of GSK-3β
([103]Xie et al., 2011). It has been reported that berberine can reduce
the release of neuroamyloid through PI3K/Akt/GSK3 pathway, decrease the
number of senile plaques in the brain of AD mice model, and play a
therapeutic role in AD ([104]Durairajan et al., 2012). Zhai et al. has
reported that coptisine could improve oxidative renal injury in
diabetic rats, and the potential mechanisms may be associated to
activation of the Nrf2 signaling pathway ([105]Zhai et al., 2019). Jung
et al. found that epiberberine has a strong potential of inhibition and
prevention of AD mainly through ChEs and beta-amyloids pathways, and
additionally through antioxidant capacities ([106]Jung et al., 2009).
Previous pharmacological studies have shown that palmatine treatment
can alleviate the hyperalgesia, allodynia and depressive behaviors of
rats with comorbidity of diabetic neuropathic pain and depression
([107]Shen et al., 2018). 27.27% of the components in Huangqin meet OB
≥ 30% and DL ≥ 0.14, including most common active components such as
coptisine, epiberberine, wogonin, and oroxylin A, etc. [108]Khan et al.
(2016) found that wogonin administration could suppress hyperglycemia,
improve cardiac function, and mitigate cardiac fibrosis in STZ-induced
diabetic mice. 29.28% of the molecules in Huangbo meet OB ≥ 30% and DL
≥ 0.14, a total of 41 components meet the threshold selection criteria,
such as coptisine, berberine, and palmatine, etc. Only 20.41% of the
molecules in Zhizi meet OB ≥ 30% and DL ≥ 0.14, including oleic acid,
kaempferol and mandenol, etc. The above results suggest that the number
of active components retained by Huanglian are the highest compared
with those before screening. This shows that more components in
Huanglian have better OB and DL properties, and indicates that these
components may play a major therapeutic role in the treatment disease.
Huangqin and Huangbo have the second highest proportion of active
components, and have more overlapping components with Huanglian, which
intimates that Huangqin and Huangbo can assist and enhance therapeutic
effect of Huanglian. In HJD, Zhizi is the Zuo botanical drug with the
lowest content of active components, indicating that it may have
auxiliary effect on Jun botanical drugs and/or Chen botanical drugs.
TABLE 2.
Components in HJD for further analysis after ADME screening.
No Component OB (%) DL botanical drug No Component OB (%) DL botanical
drug
HJD1 Quercetin 46.43 0.28 Huanglian Jun HJD58 Magnograndiolide 63.71
0.19 Huangbo Chen
HJD2 Magnograndiolide 63.71 0.19 Huanglian Jun HJD59 Oleic acid 33.13
0.14 Huangbo Chen
HJD3 Palmidin A 35.36 0.65 Huanglian Jun HJD60 Palmidin A 35.36 0.65
Huangbo Chen
HJD4 Corchoroside A_qt 104.95 0.78 Huanglian Jun HJD61 phellamurin_qt
56.6 0.39 Huangbo Chen
HJD5 Obacunone 43.29 0.77 Huanglian Jun HJD62 Poriferast-5-en-3beta-ol
36.91 0.75 Huangbo Chen
HJD6 Palmatine 64.6 0.65 Huanglian Jun HJD63 Kihadalactone A 34.21 0.82
Huangbo Chen
HJD7 Berberine 36.86 0.78 Huanglian Jun HJD64 Phellavin_qt 35.86 0.44
Huangbo Chen
HJD8 Coptisine 30.67 0.86 Huanglian Jun HJD65 Delta 7-stigmastenol
37.42 0.75 Huangbo Chen
HJD9 Fagarine 72.23 0.15 Huanglian Jun HJD66 Phellopterin 40.19 0.28
Huangbo Chen
HJD10 Worenine 45.83 0.87 Huanglian Jun HJD67 Dehydrotanshinone II A
43.76 0.4 Huangbo Chen
HJD11 Berberrubine 35.74 0.73 Huanglian Jun HJD68 Dihydroniloticin
36.43 0.81 Huangbo Chen
HJD12 Epiberberine 43.09 0.78 Huanglian Jun HJD69 Kihadanin A 31.6 0.7
Huangbo Chen
HJD13 (R)-Canadine 55.37 0.77 Huanglian Jun HJD70 Niloticin 41.41 0.82
Huangbo Chen
HJD14 Berlambine 36.68 0.82 Huanglian Jun HJD71 Chelerythrine 34.18
0.78 Huangbo Chen
HJD15 Moupinamide 86.71 0.26 Huanglian Jun HJD72 Candletoxin A 31.81
0.69 Huangbo Chen
HJD16 Ent-Epicatechin 48.96 0.24 Huangqin Chen HJD73 Hericenone H 39
0.63 Huangbo Chen
HJD17 EIC 41.9 0.14 Huangqin Chen HJD74 Hispidone 36.18 0.83 Huangbo
Chen
HJD18 Wogonin 30.68 0.23 Huangqin Chen HJD75 Campesterol 37.58 0.71
Huangbo Chen
HJD19 (2 R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one 55.23 0.2
Huangqin Chen HJD76 Melianone 40.53 0.78 Huangbo Chen
HJD20 Beta-sitosterol 36.91 0.75 Huangqin Chen HJD77 Phellochin 35.41
0.82 Huangbo Chen
HJD21 Sitosterol 36.91 0.75 Huangqin Chen HJD78 Obacunone 43.29 0.77
Huangbo Chen
HJD22 Stigmasterol 43.83 0.76 Huangqin Chen HJD79 Palmatine 64.6 0.65
Huangbo Chen
HJD23 Norwogonin 39.4 0.21 Huangqin Chen HJD80 Fumarine 59.26 0.83
Huangbo Chen
HJD24 5,2′-dihydroxy-6,7,8-trimethoxyflavone 31.71 0.35 Huangqin Chen
HJD81 Isocorypalmine 35.77 0.59 Huangbo Chen
HJD25 Coptisine 30.67 0.86 Huangqin Chen HJD82 Berberine 36.86 0.78
Huangbo Chen
HJD26 Supraene 33.55 0.42 Huangqin Chen HJD83 (S)-Canadine 53.83 0.77
Huangbo Chen
HJD27 Acacetin 34.97 0.24 Huangqin Chen HJD84 Coptisine 30.67 0.86
Huangbo Chen
HJD28 Methyl linolelaidate 41.93 0.17 Huangqin Chen HJD85
N-Methylflindersine 32.36 0.18 Huangbo Chen
HJD29 Baicalein 33.52 0.21 Huangqin Chen HJD86
delta7-dehydrosophoramine 54.45 0.25 Huangbo Chen
HJD30 Diop 43.59 0.39 Huangqin Chen HJD87 Rutaecarpine 40.3 0.6 Huangbo
Chen
HJD31 Epiberberine 43.09 0.78 Huangqin Chen HJD88 Skimmianin 40.14 0.2
Huangbo Chen
HJD32 5,7,2,5-Tetrahydroxy-8,6-dimethoxyflavone 33.82 0.45 Huangqin
Chen HJD89 Fagarine 72.23 0.15 Huangbo Chen
HJD33 Carthamidin 41.15 0.24 Huangqin Chen HJD90 Worenine 45.83 0.87
Huangbo Chen
HJD34 2,6,2′,4′-tetrahydroxy-6′-methoxychaleone 69.04 0.22 Huangqin
Chen HJD91 Cavidine 35.64 0.81 Huangbo Chen
HJD35 Dihydrobaicalin_qt 40.04 0.21 Huangqin Chen HJD92 Berberrubine
35.74 0.73 Huangbo Chen
HJD36 Eriodyctiol (flavanone) 41.35 0.24 Huangqin Chen HJD93 Ptelein
72.44 0.15 Huangbo Chen
HJD37 Salvigenin 49.07 0.33 Huangqin Chen HJD94 Thalifendine 44.41 0.73
Huangbo Chen
HJD38 5,2′,6′-trihydroxy-7,8-dimethoxyflavone 45.05 0.33 Huangqin Chen
HJD95 Quercetin 46.43 0.28 Zhizi Zuo
HJD39 5,7,2′,6′-tetrahydroxyflavone 37.01 0.24 Huangqin Chen HJD96 EIC
41.9 0.14 Zhizi Zuo
HJD40 Dihydrooroxylin A 38.72 0.23 Huangqin Chen HJD97 Beta-sitosterol
36.91 0.75 Zhizi Zuo
HJD41 Skullcapflavone II 69.51 0.44 Huangqin Chen HJD98 Kaempferol
41.88 0.24 Zhizi Zuo
HJD42 Oroxylin a 41.37 0.23 Huangqin Chen HJD99 Stigmasterol 43.83 0.76
Zhizi Zuo
HJD43 Panicolin 76.26 0.29 Huangqin Chen HJD100 Oleic acid 33.13 0.14
Zhizi Zuo
HJD44 5,7,4′-trihydroxy-8-methoxyflavone 36.56 0.27 Huangqin Chen
HJD101 Crocetin 35.3 0.26 Zhizi Zuo
HJD45 NEOBAICALEIN 104.34 0.44 Huangqin Chen HJD102 Mandenol 42 0.19
Zhizi Zuo
HJD46 DIHYDROOROXYLIN 66.06 0.23 Huangqin Chen HJD103 Supraene 33.55
0.42 Zhizi Zuo
HJD47 Moslosooflavone 44.09 0.25 Huangqin Chen HJD104 METHYL LINOLEATE
41.93 0.17 Zhizi Zuo
HJD48 11,13-Eicosadienoic acid, methyl ester 39.28 0.23 Huangqin Chen
HJD105 (4aS,6 aR,6aS,6bR,8 aR,10R,12
aR,14bS)-10-hydroxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,1
0,11,12,13,14 b-tetradecahydropicene-4a-carboxylic acid 32.03 0.76
Zhizi Zuo
HJD49 Linolenic acid methyl ester 46.15 0.17 Huangqin Chen HJD106
Methyl vaccenate 31.9 0.17 Zhizi Zuo
HJD50 5,7,4′-trihydroxy-6-methoxyflavanone 36.63 0.27 Huangqin Chen
HJD107 Ammidin 34.55 0.22 Zhizi Zuo
HJD51 5,7,4′-trihydroxy-8-methoxyflavanone 74.24 0.26 Huangqin Chen
HJD108 Isoimperatorin 45.46 0.23 Zhizi Zuo
HJD52 Rivularin 37.94 0.37 Huangqin Chen HJD109 Exceparl M-OL 31.9 0.16
Zhizi Zuo
HJD53 bis [(2 S)-2-ethylhexyl] benzene-1,2-dicarboxylate 43.59 0.35
Huangqin Chen HJD110 Ethyl oleate (NF) 32.4 0.19 Zhizi Zuo
HJD54 5,8,2′-trihydroxy-7-methoxyflavone 37.01 0.27 Huangqin Chen
HJD111 5-Hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone 51.96
0.41 Zhizi Zuo
HJD55 Quercetin 46.43 0.28 Huangbo Chen HJD112 3-Methylkempferol 60.16
0.26 Zhizi Zuo
HJD56 Beta-sitosterol 36.91 0.75 Huangbo Chen HJD113 GBGB 45.58 0.83
Zhizi Zuo
HJD57 Stigmasterol 43.83 0.76 Huangbo Chen HJD114 Sudan III 84.07 0.59
Zhizi Zuo
[109]Open in a new tab
Coverage Rate Based on Functional Pathway
Most complex diseases are not caused by a single pathological change,
but a series of physiological reactions caused by abnormal pathways due
to disorder of multiple proteins or genes in the cell. In order to
further explore the “Jun-Chen-Zuo-Shi” compatibility principle of HJD
at the potential molecular mechanism level, we evaluated it through the
functional pathway enrichment analysis based on KEGG ([110]Figure 3A).
Previous reports confirm that HJD has significant therapeutic effects
on Alzheimer’s disease (AD), Parkinson’s disease (PD) and diabetes
mellitus (DM) etc. The top 15 enriched pathways were selected for
further analysis. The targets of each botanical drug in HJD was mapped
to the enriched genes involved in these 15 enriched pathways for
enrichment analysis. It was found that 40.54, 33.14, 38.10, and 29.69%
of the targets in the Jun (Huanglian), Chen (Huangqin and Huangbo) and
Zuo (Zhizi) botanical drug were enriched in the top 15 enriched
pathways, respectively ([111]Figure 3B). The above analysis show that
Huanglian has a higher target contribution rate among all the genes
enriched in the top 15 functional pathways, which means that targets of
Huanglian could play primary therapeutic roles in the treatment of
disease, and the target contribution rate of Chen botanical drugs is
slightly lower, which indicates that the relatively low target
utilization rate may play a role in assisting Jun botanical drug in the
treatment process. Zuo botanical drug have the lowest target
contribution rate, indicating that the ability of therapeutic role in
the treatment of diseases is slightly weak, and may play an auxiliary
role in other aspects. The above results once again confirm the major
functional role of the Jun botanical drug Huanglian, the auxiliary
effects of the Chen botanical drugs Huangqin and Huangbo, and the
supplementary effects of the Zuo botanical drug Zhizi in the functional
level of HJD.
FIGURE 3.
[112]FIGURE 3
[113]Open in a new tab
Gene enrichment analysis of all targets from HJD (A). botanical
drug-pathway network of HJD (B). The circle nodes represent botanical
drugs, and the inverted triangle represents the top 15 pathways of HJD.
The size and color of the node represents the importance of the herbal
regulation pathway.
C-T-P Network Construction and Analysis
In the process of treating complex diseases, TCM formula usually acts
in the form of multi-component and multi-target. These components and
targets form the most direct target-protein network, which can reflect
some therapeutic effects but cannot reflect the propagation mode of
this therapeutic effect. More and more evidences show that the
therapeutic effect of drugs on diseases can be propagated through PPI
([114]Andras et al., 2013). Hormozdiari et al. proposed that identified
potential multiple-drug targets in pathogenic PPI networks can help us
to better discover the therapeutic effect of drugs ([115]Hormozdiari et
al., 2010). Chu et al. applied a nonlinear stochastic model and maximum
likelihood parameter estimation to identify the cancer-perturbed PPI
involved in apoptosis and to identify potential molecular targets for
the development of anti-cancer drugs ([116]Chu and Chen, 2008). How to
characterize this propagation effect has not been systematically
reported.
In this study, we first constructed the C-T network of HJD, then
integrated multiple PPI data to construct a comprehensive PPI network.
C-T network and PPI networks were integrated as the C-T-P network. By
analyzing the C-T-P network, comprehensive information can be obtained
and intricate relationships that manage cellular activities can be
revealed. In a network, the number of nodes directly interacting with a
node is called degree. Several reports have confirmed that the greater
the degree, the more biological functions it participates in, and the
stronger its biological importance. Under this concept, we made further
analysis of C-T-P network. Our results show that the targets of Jun
botanical drug Huanglian has the highest average degree 131.75 in C-T-P
network. It indicates that these targets affect more proteins in the
C-T-P network and have the possibility to play more important roles. By
comparative analysis, we found that the degree of huangqin and huanngbo
in C-T-P network are 102.46 and 110.33, respectively. The degree of
targets of Huangqin and Huangbo are relatively smaller, which indicate
that the number of target protein is not as high as that of Jun
botanical drug and may play a supplementary role. Zhizi has the lowest
average degree 101.42, while indicates that the number of targeted
proteins is smallest, and together with Chen botanical drugs to assist
the Jun botanical drug.
Functional Communities Structure Predication and Analysis
In complex life activities such as diseases, development, and drug
intervention, etc, a plurality of genes, proteins, and other
constituent components in cells are involved, and these genes,
proteins, and components form a complex regulation network in cells. In
the process of drug intervention, drugs play an intervention regulatory
role on the complex network by targeting specific proteins. This
intervened regulation and intracellular gene regulation network form a
drug-target-pathway complex network at the molecular level. Further
research found that the neighbors of drug responding genes in the
network tend to be related to the same or similar intervention
responses ([117]Li and Zhan, 2006; [118]Zhang et al., 2014).
Genes with the same drug response are often functionally related and
form biological network communities ([119]Tari et al., 2005). At the
molecular level, the community can be considered as a group of genes,
proteins or metabolites that are functionally related, physically
interact or jointly response to drug. The molecules in these
communities usually jointed together to drive a biological process or
respond to the treatment of drugs ([120]Tripathi et al., 2019). For
drug intervention in complex diseases, single gene analysis cannot
effectively consider the cooperative relationship among genes, and is
difficult to explain its biological mechanism. However, community-based
analysis can identify response gene sets with cooperative
relationships. Revealing the functions of these simple network modules
at the molecular level is the key step for understanding the drug
response regulation mechanism of more complex networks and even for
understanding the mechanism of drug treatment.
In this study, we construct the C-T-P network by integrating C-T
network of HJD and PPI network. This extremely complex C-T-P network
with 12,324 nodes and 84,138 interactions is difficult to clarify drug
MOA, so discovery of functional communities in the C-T-P network is
very important for understanding the organization and function units of
the HJD under the concept of the compatibility principle
“Jun-Chen-Zuo-Shi”. The extraction of these community structures can
reduce the dimension of complex networks and could be considered as the
key factor for further clarify the compatibility principle of HJD’s
“Jun-Chen-Zuo-Shi”, we identify the functional communities in the C-T-P
network based on the information graph algorithm combining random walk
theory and huffman encoding. The algorithm performs to optimize the
discovery of communities in C-T network heuristically by using a
reasonable global metric. The results show that 8 significant
functional communities are found in the C-T-P network ([121]Figure 4A).
In order to determine whether communities found in HJD can represent
their complete C-T network. We evaluate the importance of communities
at the gene functional level based on enrichment pathway analysis. The
analysis results showed that genes enriched pathways of HJD communities
accounts for 93.4% of genes enriched pathways of the full C-T network
in HJD ([122]Figure 4B), which indicated that the enriched pathways of
genes involved in communities of HJD are highly compatible with
enriched pathways of genes in C-T network. Further analysis of the
components identified by these functional communities shows that 93.33,
87.5, 84.62, and 75% of the components in the Jun botanical drug
Huanglian is covered by functional communities, which once again
indicates that Huanglian plays a leading role in the function of C-T-P
network ([123]Figure 5).
FIGURE 4.
[124]FIGURE 4
[125]Open in a new tab
The predicated communities of C-T-P network of HJD (A). Different color
represents different communities. The functional similarity analysis
between C-T-P network and communities in HJD (B).
FIGURE 5.
[126]FIGURE 5
[127]Open in a new tab
Venn diagram was used to visualize the overlap number between C-T-P
network and communities in HJD, the pink represents the C-T-P network,
and the green represents communities.
Coverage Rate Based on Pathogenic Genes
In order to better explore how HJD exerts its therapeutic effect based
on the compatibility principle of “Jun-Chen-Zuo-Shi”, DM and AD were
selected for further evaluation. Both of two diseases have been
reported with significant therapeutic effects of HJD. Pathogenic genes
of both diseases were collected from GeneCards database ([128]Safran et
al., 2010). PPI data of both diseases were extracted from STRING
database ([129]Szklarczyk et al., 2019). The weighted gene reulatory
network of disease was constructed by mapping pathogenic genes to PPI
data, the weight was assigned by using relevance scores in GeneCards
database ([130]Figure 6A,B; [131]Supplementary Table S1). The relevance
score of genes in GeneCards takes into account three aspects: the
frequency of the term in the disease related document would raises the
score, while the frequency of the term in disease related documents
across the site would lower the score, and the size of the subfield
containing the term, if the term appears in a smaller field, such as
gene name, the score would be increased ([132]Stelzer et al., 2016).
This indicates that the higher the relevance score, the more important
that the genes are involved in pathogenesis of the disease. For further
analyzed the “Jun-Chen-Zuo-Shi” compatibility principle of HJD, we
design a pipeline to capture the role of each botanical drug in HJD
based on botanical drug targets and their associated pathogenic genes.
Firstly, we get common gene datasets by overlapping pathogenic genes of
each diseases and component targets of each botanical drug, and then
analyze the average relevance score of the common gene datasets, then
we calculate the possession rate by compare the common gene datasets to
targets genes of each botanical drug in HJD. For DM, 87.91% of the
targets in the Jun botanical drug Huanglian overlap with the pathogenic
genes with an average relevance score of 6.91, 80.67% and 81.41% of the
targets in the Chen botanical drugs Huangqin and Huangbo overlap with
the pathogenic genes with average relevance scores of 5.44 and 6.13,
and 82.93% of the targets in the Zuo botanical drug Zhizi overlap with
the pathogenic genes with an average relevance score of 6.51. For AD,
73.08% of the targets in the Jun botanical drug Huanglian overlap with
the disease genes with an average relevance score of 12.55, 65.83 and
67.80% of the targets in the Chen botanical drugs Huangqin and Huangbo
overlap with the pathogenic genes with average relevance scores of 9.74
and 10.52, and 66.11% of the targets in the Zuo botanical drug Zhizi
overlap with the pathogenic genes with an average relevance score of
11.02 ([133]Figure 6). The above results show that the Jun botanical
drug Huanglian has the highest possession rate and average relevance
score, which indicate that Huanglian acts on as many important targets
as possible in pathogenic genes.
FIGURE 6.
[134]FIGURE 6
[135]Open in a new tab
The disease weight gene regulatory network of diabetes mellitus (A) and
alzheimer’s disease (B). The size and color of the node represents the
relevance score of herbal therapeutic targets. The bar chart represents
the average relevance score (C) and possession rate (D) of overlap of
botanical drug targets and pathogenic genes.
Calculation and Analysis of Propagation Coefficient
The interactions between genes or proteins in cells form complex
biological networks. Molecular interactions in biological networks have
dynamic and spatiotemporal specific features. At present, protein
interaction network and drug regulatory network can only provide static
interaction information. In the function analysis of drug targets and
pathogenetic genes, the dynamic characteristics of molecular
interactions are more significant than static characteristics for
understanding the MOA and propagation features of drug intervention in
the disease networks. In order to analyze and understand the
propagation characteristics of drugs in the disease network more
effectively, this study proposed an important monitor which named as
propagation coefficient to characterize the drug response
network-propagation characteristics by integrating the data of
botanical drug targets, PPI, and pathogenic genes. The propagation
coefficient contains propagation modes and effector proteins, which
could be used to indicate the propagation power of drug. Based on the
novel calculate method, the propagation coefficient of single botanical
drug in HJD is analyzed, and the scientific basis of the compatibility
rule of “Jun-Chen-Zuo-Shi” is revealed from the perspective of
propagation characteristics.
The propagation coefficient value of each botanical drug in HJD is
calculated and showed in [136]Figure 7. According to the calculation
results, for DM and AD, the Huanglian with a propagation coefficient of
72.62 and 67.05, the Huangbo with a propagation coefficient of 63.69
and 59.30, the Huangqin with a propagation coefficient of 59.55 and
55.54, and the Zhizi with a propagation coefficient of 59.10 and 54.87,
respectively. From the above analysis, Huanglian has highest
propagation coefficient both in DM and AD, the propagation coefficient
of Haungqin and Huangbo is lower than Huanglian and Zhizi has the
lowest propagation coefficient, which indicate that Huanglian plays a
major role in disease treatment by spreading the intervention effect at
a more powerful level, and Chen botanical drugs and Zuo botanical drugs
play a role in assisting Huanglian. This once again confirmed the
compatibility rules of “Jun-Chen-Zuo-Shi” in HJD at quantitative level.
FIGURE 7.
[137]FIGURE 7
[138]Open in a new tab
The propagation coefficient of HJD botanical drugs in diabetes mellitus
(DM) and alzheimer’s disease (AD).
Experimental Evaluation
In order to further explore the accuracy and reliability of the above
strategies for analyzing the compatibility rules of HJD
“Jun-Chen-Zuo-Shi,” components absorbed into blood were used to
validate our strategy. In the study of complex components system of
TCM, it is generally believed that the components which can be absorbed
into blood are the active components with therapeutic effect. Analysis
of the components in blood after oral administration of TCM is an
effective and accurate way to study the substance basis of drug effect
of TCM. The absorbed components in rat plasma after oral administration
of HJD were collected from the previous reports ([139]Zuo et al.,
2014). A total of 22 prototype components were obtained, the detailed
information was shown in [140]Supplementary Table S2.
For the components obtained by functional communities structure
prediction and active components screening in the above strategy,
26.67% of the predicted components in the Jun botanical drug of
Huanglian overlap with the components were absorbed into blood; 10.26
and 7.5% of the predicted components in the Chen botanical drugs
Huangqin and Huangbo overlap with the components were absorbed into
blood. There is no overlap between the predicted components and the
components were absorbed into blood in the Zuo botanical drug Zhizi.
The above results indicate that the proportion of components in Jun
botanical drug were absorbed into blood is higher, followed by Chen
botanical drugs, which confirm the accuracy and reliable of our
analysis strategy. Meanwhile, the results once again confirm the
important role of the Jun botanical drug Huanglian through a higher
absorbed into blood rate, the auxiliary effects of the Chen botanical
drugs Huangqin and Huangbo, and the supplementary effects of the Zuo
botanical drug Zhizi.
Discussion
TCM usually exerts its efficacy in the form of formula, which is not
only a simple combination of botanical drugs, but follows reasonable
compatibility principles to treat complex diseases ([141]Sucher, 2013).
The main purpose of these compatibility principles is to enhance
efficacy or reduce toxicity, so that different chemical components in
botanical drugs can promote, coordinate, and restrict each other, thus
ensuring the safety and effectiveness of clinical medication
([142]Sucher, 2013). “Jun-Chen-Zuo-Shi” is one of the most common used
rules in the compatibility principles of TCM ([143]Yao et al., 2013;
[144]Wu et al., 2014). The botanical drugs in a formula can be divided
into “Jun”, “Chen”, “Zuo” and “Shi” botanical drugs according to their
functions. Based on the compatibility principles of “Jun-Chen-Zuo-Shi”,
the function of each botanical drug and its relationship with other
botanical drugs are revealed. For example, Yujinfang is based on the
compatibility rules of “Jun-Chen-Zuo-Shi” in the treatment of
cardiovascular and cerebrovascular diseases ([145]Li et al., 2014). The
“Jun” botanical drug Curcuma wenyujin Y.H.Chen and C. Ling accounts for
the largest proportion of active ingredients and action targets, and
treats diseases by acting on the main targets of cardiovascular and
cerebrovascular diseases. “Chen” botanical drug Gardenia jasminoides J.
Ellis can enhance the effect of Curcuma wenyujin Y.H.Chen and C. Ling.
The “Zuo” and “Shi” botanical drugs can achieve their auxiliary effects
by reducing the toxicity of Curcuma wenyujin Y.H.Chen and C. Ling and
Gardenia jasminoides J. Ellis. Many botanical drugs in TCM have both
unique effects and strong toxicity in clinical application. According
to the needs of clinical treatment, the effectiveness of these
botanical drugs should be utilized as much as possible and the toxic
and side effects should be reduced at the greatest extent. Based on the
compatibility rules of “Jun-Chen-Zuo-Shi”, botanical drugs with
toxicity usually are compatible with other botanical drugs to inhibit
their toxicity and side effects and to play unique curative effects.
This is an important aspect of improving the efficacy of TCM. For
example, Pinellia cordata N.E.Br. in Xiaobanxia decoction is a commonly
used medicine for resolving phlegm and arresting vomiting, but it is
toxic. Compatibility with Zingiber officinale Roscoe can not only
relieve the toxicity of Pinellia cordata N.E.Br. but also enhance the
anti-vomiting effect of Pinellia cordata N.E.Br. to achieve synergistic
effect ([146]Gong-Chang et al., 2015).
Recently, more and more attention has been paid to the practice of
verifying and explaining the compatibility theory of TCM by using
different modern technologies, such as component combination and
computer modeling ([147]Wu et al., 2014). However, most of these
analysis of compatibility rules of “Jun-Chen-Zuo-Shi” in TCM only
focuses on one or several aspects. There is a lack of systematic and
multi-dimensional analysis of the compatibility rules of
“Jun-Chen-Zuo-Shi.” Network pharmacology mainly focus on problems from
the perspective of mutual connection, which is exactly consistent with
the core idea of TCM ([148]Li and Zhang, 2013). Therefore, the
application of network pharmacology in Chinese medicine research has
unique advantages and great development potentiality. However, most of
the current network pharmacology research focuses on the interpretation
of the functional mechanism of formula in the treatment of specific
diseases, and does not interpret the compatibility rules of
“Jun-Chen-Zuo-Shi” at a systematic level.
In this study, the compatibility rules and possible mechanisms of TCM
in treating complex diseases are analyzed through six detail
properties, include the physical and chemical properties of each
component in single botanical drug of HJD, ADME properties and
functional enrichment analysis of component targets, the
characterization of degree and key communities in C-T-P network, the
characterization of intervention propagation properties in the
treatment of different complex diseases. The pharmacological action of
drugs depends on the physical and chemical properties of drugs, which
can reflect ADMET characteristics of drugs in the body and is also a
basic attribute to be considered in interpreting the compatibility
rules of TCM. Complex networks are made up of a large number of nodes,
of which the important nodes are a few special parts that can deeply
influence on the structure and function of the whole network. Node
degree in the topological structure can reflect the importance of
nodes, and is a key topological parameter to characterize the most
influential nodes in the network ([149]Lv et al., 2014). For example,
in order to evaluate the influence of components in ZZW on FD
treatment, Wang et al. constructed a contribution index model based on
the topological parameter degree in the network. By using this
algorithm, they selected key component groups for FD treatment and
clarified possible cooperative mechanisms ([150]Wang et al., 2018).
Modularity is a very important characteristic of complex networks and a
common phenomenon in biological systems ([151]Yang and Leskovec, 2012).
Studying the response network modules of different chemical components
in different botanical drugs is very important to analyze the drug
mechanism. It is also an important way to systematically validate the
rules of “Jun-Chen-Zuo-Shi.” The intervention effect of compounds in
herbal medicine could propagate through PPI ([152]Comola and Prina,
2013). The propagation of this intervention in the network has specific
propagation modes and paths. The propagation coefficient defined based
on the propagation method and path is also different in the principle
of “Jun-Chen-Zuo-Shi.” Enrichment analysis of gene function is a
routine method for gene group function analysis, which is of great
significance for revealing the molecular mechanism of different Chinese
medicines in formula, and is a further explanation for the correlation
between compatibility rules and mechanisms of Chinese medicines based
on “Jun-Chen-Zuo-Shi”. Based on the above-mentioned features, we
designed a new system pharmacology strategy, which can systematically
interpret the compatibility rule of “Jun-Chen-Zuo-Shi” from structure
to function and then to propagation mode at a multi-level perspective.
Taking HJD as an example, we deeply decoded the compatibility rules of
TCM. Jun botanical drugs play a leading role in formula, and exert the
strongest effect in treating diseases through the best chemical
properties, the highest occupancy rate of active components, the
highest topological structure of drug action network, the highest
occupancy rate of functional communities of drug response network and
the highest drug intervention coefficient and potential molecular
pathways of action. The Chen botanical drugs can enhance the
pharmacological effects of Jun botanical drugs and reduce the dosage
required by Jun botanical drugs through slightly lower functional
targets. Zuo and Shi botanical drugs could be improved the
bioavailability and active component of Jun and Chen botanical drugs.
In addition, the C-T-P network proves the multidirectional
pharmacological treatment mechanism of TCM, i.e. multiple components,
multiple targets and multiple therapeutic effects. The prescription
principle of different botanical drugs provides a unique opportunity to
explore multiple therapeutic mechanisms according to the efficacy of
botanical drugs. The combination of different botanical drugs can not
only treat diseases by increasing bioavailability or promoting the
synergistic effect of different botanical drugs, but also reduce the
toxicity of some botanical drugs. The synergistic mechanism and
toxicity-reduced effect embodied by these compatibility rules also
indicate that the botanical drug combination is more effective than the
use of single botanical drug.
Additionally, HJD was widely used in the treatment of AD and PD, thus,
the study of brain tissue distribution of HJD is particularly
important. Passing through the blood-brain barrier (BBB) is crucial for
drugs to enter the central nervous system and play therapeutic roles.
Through the analysis of components absorbed into blood in HJD, we found
the components that can be absorbed into blood in the Jun botanical
drug of Huanglian have strong permeability of BBB (>0.3), including
berberine, palmatine, coptisine, and epiberberine. However, specific
components wogonin and oroxylin a absorbed into blood in the Chen
botanical drug of Huangqin only have moderate permeability of BBB. This
proves once again that Jun botanical drugs are the core of the
formulas, and they play a major role in treating diseases.
In this study, the compatibility rule of “Jun-Chen-Zuo-Shi” of HJD was
in-depth decoded from multi-scale perspective. At the components and
targets level, the Jun botanical drug huanglian plays a leading role at
the functional level and has good performance in most physical and
chemical properties, ADME properties and functional enrichment analysis
of component targets. At the C-T-P interaction level, the leading role
of the Jun botanical drug huanglian is also confirmed by having the
highest average degree in C-T-P network and targets coverage rate of
functional communities. At the intervention propagation level, the Jun
botanical drug Huanglian has highest propagation coefficient both in DM
and AD, this once again confirmed the Jun botanical drug plays a
leading role in a formula for treating diseases. Finally, the results
of experimental validation showed that the proportion of components in
Jun botanical drug were absorbed into blood is higher than Chen and Zuo
botanical drugs, including berberine, palmatine, and coptisine etc. Our
approach confirmed the compatibility rules of “Jun-Chen-Zuo-Shi” in HJD
at multiple quantitative level. This research shows the scientific
basis of “Jun-Chen-Zuo-Shi” from a multi-dimensional perspective, which
providing a good methodological reference for the subsequent
interpretation of key components and speculation mechanism of the
formula.
However, there are still some limitations of this study. This research
is a computational pharmacological work based on pharmacological
experiment data and public data. Pharmacological calculation is the
forerunner and basis of the experiment, which provides a feasible
scheme to reduce the verification scale for the experiment. Evidence
from pharmacological experiments should be added in future research.
Data Availability Statement
The raw data supporting the conclusions of this article will be made
available by the authors, without undue reservation, to any qualified
researcher.
Author Contributions
K-XW and YG contributed equally to this work. D-GG, A-PL, and X-MQ
provided the concept and designed the study. K-W and YG conducted the
analyses. K-XW and YG wrote the manuscript. K-XW, YG, W-XG, X-FY, CW,
L-YF, and X-FG participated in data analysis. X-MQ, G-HD, LG, and A-PL
provided oversight. D-GG and A-PL contributed to revising and
proof-reading the manuscript.
Funding
This study is financially supported by the Startup fund from Southern
Medical University (grant No. G619280010), the Natural Science
Foundation Council of China (grant No. 31501080), Hong Kong Baptist
University Strategic Development Fund (grant No. SDF13-1209-P01,
SDF15-0324-P02(b) and SDF19-0402-P02), the Key Laboratory of Effective
Substances Research and Utilization in TCM of Shanxi Province (No.
201705D111008-21), Hong Kong Baptist University Interdisciplinary
Research Matching Scheme (grant No. RC/IRCs/17-18/04), the General
Research Fund of Hong Kong Research Grants Council (grant No. 12101018,
12100719, 12102518).
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at:
[153]https://www.frontiersin.org/articles/10.3389/fphar.2020.567088/ful
l#supplementary-material
[154]Click here for additional data file.^ (33.5KB, xlsx)
[155]Click here for additional data file.^ (16.5KB, docx)
References