Abstract
The rapidly increasing diabetes mellitus (DM) is becoming a major
global public health issue. Traditional Chinese medicine (TCM) has a
long history of the treatment of DM with good efficacy. Huangqi and
Huanglian are one of the most frequently prescribed herbs for DM, and
the combination of them occurs frequently in antidiabetic formulae.
However, the synergistic mechanism of Huangqi (Radix Astragali) and
Huanglian (Rhizoma Coptidis) has not been clearly elucidated. To
address this problem, a feasible system pharmacology model based on
chemical, pharmacokinetic and pharmacological data was developed via
network construction approach to clarify the synergistic mechanisms of
these two herbs. Forty-three active ingredients of Huangqi (mainly
astragalosides and isoflavonoids) and Huanglian (primarily isoquinoline
alkaloids) possessing favorable pharmacokinetic profiles and biological
activities were selected, interacting with 50 DM-related targets to
provide potential synergistic therapeutic actions. Systematic analysis
of the constructed networks revealed that these targets such as GLUT2,
NOS2, PTP1B, and IGF1R were mainly involved in PI3K-Akt signaling
pathway, insulin resistance, insulin signaling pathway, and HIF-1
signaling pathway, and were mainly located in retina, pancreatic islet,
smooth muscle, immunity-related organ tissues, and whole blood. The
contribution index of every active ingredient also indicated five
compounds, including berberine (BBR), astragaloside IV (AIV),
quercetin, palmatine, and astragalus polysaccharides, as the principal
components of this herb combination. These results successfully
explained the polypharmcological and synergistic mechanisms underlying
the efficiency of Huangqi and Huanglian for the treatment of DM and its
complications.
Keywords: Huangqi, Huanglian, synergistic mechanism, diabetes, system
pharmacology
Introduction
Diabetes mellitus (DM) is a chronic metabolic disorder influenced by
interactions between genetic and environmental factors. The global
prevalence of DM among adults aged 20–79 years was 8.8% in 2015, and
its incidence is increasing rapidly (International Diabetes Federation,
[41]2015). As complementary and alternative medicine, traditional
Chinese medicine (TCM) has been proven to possess satisfactory
effectiveness toward DM and its complications clinically, such as Gegen
Qinlian Decoction (Tong et al., [42]2011) and Huangqi San (Xu et al.,
[43]2015), wherein Huangqi (Radix Astragali, the dried roots of
Astragalus membranaceus (Fisch.) Bunge. var. mongholicus (Bunge.) Hsiao
or A. membranaceus (Fisch.) Bunge, Fabaceae) and Huanglian (Rhizoma
Coptidis, the rhizomes of Coptis chinensis Franch, Ranunculaceae) are
one of the most frequently prescribed herbs (Xie et al., [44]2011).
Specifically, Huangqi is widely used in East Asia to reinforce Qi (Ma
et al., [45]2002) and its use to treat DM, classified as Xiao Ke
syndrome in TCM, has been firstly documented in Shen Nong Ben Cao Jing
(206 BC–24 AD, Western Han Dynasty). Huangqi has been developed into
the intravenous injection (mainly astragalosides) in China to treat DM
with good clinical effects (Nie et al., [46]2014). As a holy herb to
treat Xiao Ke syndrome, Huanglian is frequently used in diabetic care
partially due to its antihyperglycemic, antihyperlipidemic,
antihypertensive, anti-inflammatory, and antioxidant activities (Tong
et al., [47]2011; Pang et al., [48]2015). Based on a previous
statistics, Huanglian has been used as a major ingredient in many
antidiabetic Chinese patent medicines (CPMs) approved by the China Food
and Drug Administration and the majority of them are combined with
Huangqi, such as Jinqi Jiangtang tablets, Xiaokeping tablets,
Tangmaikang capsules, and Shenjing Zhike Wan (Xie et al., [49]2011).
However, although the combination of Huangqi and Huanglian has been
frequently used in antidiabetic formulae and CPMs (Supplementary Figure
[50]S1), we still know little about how the active ingredients in
Huangqi and Huanglian modulate the synergistic network for combating
DM.
System pharmacology is emerging as a holistic and efficient tool to
study the role of TCM due to its capable of describing complex
interactions between drugs and biological systems including the human
body, organs, and diseases from a network perspective (Kloft et al.,
[51]2016; Zhang et al., [52]2016). Combined with pharmacology and
pharmacodynamics, it has been successfully applied to interpret the
synergistic mechanisms of herb combinations at molecular network level
(Zhou et al., [53]2016; Yu et al., [54]2017; Yue et al., [55]2017). In
the present study, we tried to establish the compound-target (C-T),
target-pathway (T-P), and target-organ (T-O) networks by the system
pharmacology model based on chemical, pharmacokinetic and
pharmacological data at the system, organ, and molecular levels
(Supplementary Figure [56]S2), so as to uncover the underlying
synergistic mechanisms of Huangqi and Huanglian for treating DM.
Materials and methods
Chemical ingredients database building
All of the constituent data of Huangqi and Huanglian were retrieved
from TCM Systems Pharmacology Database and Analysis Platform (TcmSP™,
[57]http://ibts.hkbu.edu.hk/LSP/tcmsp.php) (Ru et al., [58]2014), and
then manually supplemented through a wide-scale text-mining method.
Meanwhile, four important pharmacology-related properties were also
obtained from TcmSP™, including MW, CLogP, nHDon, and nHAcc. The
principal component analysis (PCA) of the chemical distribution of
Huangqi and Huanglian was built with the above four properties using
the SIMCAP+ software package (version 11.0, Umetrics). The variances of
PC1, PC2, and PC3 in Figure [59]1 account for 0.71, 0.23, and 0.04,
respectively. The PCA of 34 known drug/drug-like compounds retrieved
from DrugBank ([60]http://www.drugbank.ca/) was performed in the same
process as above (Supplementary Table [61]S1).
Figure 1.
Figure 1
[62]Open in a new tab
The chemical distribution according to principal component analysis.
The red and black circles represent ingredients of Huangqi and
Huanglian, respectively, while the blue circles delineate common
ingredients of Huangqi and Huanglian. The yellow circles stand for
antidiabetic drugs from DrugBank.
Active ingredients screening
The active ingredients from Huangqi and Huanglian were mainly filtered
by integrating oral bioavailability (OB) and drug-likeness (DL). A
robust in silico model OBioavail 1.1 that integrated the metabolism
(P450 3A4) and transport (P-glycoprotein) information was employed to
calculate the OB values of all herbal ingredients (Xu et al.,
[63]2012). Those ingredients with OB ≥ 30% were selected.
Database-dependent DL evaluation approach based on Tanimoto coefficient
(Ma et al., [64]2011) was applied and shown as T(A, B) =
(A×B)/(|A|^2+|B|^2−A×B). In this equation, A represents the molecular
descriptors of herbal compounds, and B displays the average molecular
properties of all compounds in DrugBank. Those ingredients with DL ≥
0.18 were preserved. In this study, the ingredients were adopted as the
candidate compounds for further analysis when they met both of these
criteria. Besides, owing to the profound pharmacological effects and
high contents, those compounds with low OB or DL values were also
selected for further research.
Targets prediction
To identify the corresponding targets of the active ingredients of
Huangqi and Huanglian, several approaches combined with chemometric
method, information integration, and data-mining were implemented.
First of all, the active ingredients were submitted to various servers
viz. DRAR-CPI (Luo et al., [65]2011), Similarity Ensemble Approach
(SEA, [66]http://sea.bkslab.org/) (Keiser et al., [67]2007), STITCH
([68]http://stitch.embl.de/) (Kuhn et al., [69]2012), and PharmMapper
server (Wang et al., [70]2017). All active compounds were also sent to
Herbal Ingredients' Targets database (HIT) (Ye et al., [71]2011),
Therapeutic Targets Database (TTD,
[72]http://bidd.nus.edu.sg/group/ttd/) (Zhu et al., [73]2012),
BindingDB database ([74]http://www.bindingdb.org/bind/index.jsp)
(Gilson et al., [75]2016), DrugBank and Google Scholar to mine C-T,
interactions supported by literature. Then, to better dissect the role
of Huangqi and Huanglian in DM treatment, all targets obtained from the
previous two steps were sent to TTD, Comparative Toxicogenomics
Database (CTD, [76]http://ctdbase.org/), Online Mendelian Inheritance
in Man (OMIM) database ([77]http://www.omim.org/) and PharmGKB
([78]http://www.pharmgkb.org) (Whirl-Carrillo et al., [79]2012) to mine
whether these targets are related to DM. Noteworthy, only the targets
of Homo sapiens were kept for further analysis.
Gene ontology (GO) and target organ location analysis
The Molecule Annotation System 3.0 (MAS 3.0,
[80]http://bioinfo.capitalbio.com/mas3/) and DAVID (The Database for
Annotation, Visualization and Integrated Discovery,
[81]http://david.abcc.ncifcrf.gov) webservers were employed to perform
GO enrichment analysis for the 50 genes targeted by Huangqi and
Huanglian (Huang da et al., [82]2009). The target organ distribution
was determined based on the microarray analyses data of different
tissue types lodged in the BioGPS bank ([83]http://biogps.org) (Wu et
al., [84]2009).
Network construction
Three visualized networks were constructed: (1) Compound-Target network
(C-T network). Active ingredients of Huangqi and Huanglian and their
corresponding targets were employed to generate the C-T network; (2)
Target-Pathway network (T-P network). The pathway information of
targets were extracted from KEGG (Kyoto Encyclopedia of Genes and
Genomes, [85]http://www.kegg.jp), and then a bipartite T-P network
composed of targets and their corresponding putative pathways was
built; (3) T-O network. The potential targets and their tissue types
were applied to T-O network. All visualized networks were constructed
by Cytoscape 3.5.1 ([86]http://www.cytoscape.org/), an open software
package project for visualizing, integrating, modeling and analyzing
the interaction networks (Smoot et al., [87]2011).
Contribution indexes calculation
In order to estimate the contribution of each active ingredient to the
antidiabetic effects of Huangqi and Huanglian, a contribution index
(CI) based on network based efficacy (NE) weighted by literature was
calculated by Equation (1) and equation (2) (Yue et al., [88]2017):
[MATH: NE(j)=∑i=1n
di :MATH]
(1)
[MATH: CI(j)=cj×NE(j)∑i=1m
ci×NE(i)×100
% :MATH]
(2)
Where n is the number of targets associated with ingredient j in the
C-T network; d[i] is the degree of target i associated with ingredient
j in the T-P network; c[i] is the number of DM-related literature of
ingredient i; m is the number of ingredients. For DM-related
literature-mining approach, the following keywords were used for DM
terms: diabetes, hyperglycemia, and insulin resistance and the common
names of active ingredients were also used as search keywords. The
numbers of papers having keywords in the title or abstract published in
1990–2017 were obtained from the PubMed database. If the sum of CIs for
the top N ingredients was more than 85%, these relevant N ingredients
were considered to contribute the most to the antidiabetic effects.
Results
Chemical distribution of huangqi and huanglian
The ingredients in Huangqi and Huanglian were retrieved from TcmSP™ and
published literatures. Since those glycosides in Huangqi and Huanglian
might be deglycosylated in the intestinal tract associated with gut
microbiota, 13 aglycones were also incorporated into the compound
library labeled by _qt. Thus, a total of 171 ingredients were retrieved
for Huangqi (112) and Huanglian (64), including five common
ingredients. The detailed information about these molecules was
provided in Supplementary Table [89]S2.
The PCA was further conducted to give visual illustration in chemical
distribution. The constituents of Huangqi and Huanglian were diverse
and both of them possessed a broad diversity in chemical space (Figure
[90]1), but the majority of them satisfied the Lipinski's rule of five.
Moreover, the large overlap between the ingredients in Huangqi and
Huanglian and 34 known drug/drug-like compounds for DM demonstrated
that many compounds contained in these two herbs had drug potential on
DM. Noticeably, since PC2 was highly associated with the value of
nHDon, those ingredients of Huangqi (red circle) including
astragalosides with PC2 in the range 3~5 possessed larger nHDon values
than the antidiabetic drugs from DrugBank (yellow circle), whereas the
ingredients of Huanglian (black circle) overlap better with the
antidiabetic drugs in Figure [91]1. Thus, it is meaningful to figure
out these active ingredients in Huangqi and Huanglian for DM treatment.
Active ingredients in huangqi and huanglian
Although a single herb or TCM formula usually contains large numbers of
compounds, virtual screening approaches are always of great help to
distinguish those active ingredients. In the present work, two ADME
(absorption, distribution, metabolism, and excretion)-related models,
including OB and DL were employed to screen most of the active
ingredients from Huangqi and Huanglian. A few of active compounds that
do not meet either of these two criteria were also selected in the
cases of high bioactivities and huge amounts. Consequently, a total of
43 active compounds were selected from the 171 compounds of these two
herbs (Table [92]1).
Table 1.
Active ingredients and ADME parameters of Huangqi and Huanglian.
No. Name Structure OB (%) DL Herb
M1[93]^a Berberine graphic file with name fphar-08-00694-i0001.jpg 068
078 C. chinensis
M2[94]^a Columbamine graphic file with name fphar-08-00694-i0002.jpg
2694 059 C. chinensis
M3 Berberrubine graphic file with name fphar-08-00694-i0003.jpg 3574
073 C. chinensis
M4 8-Oxocoptisine graphic file with name fphar-08-00694-i0004.jpg 4683
089 C. chinensis
M10[95]^a Magnoflorine graphic file with name fphar-08-00694-i0005.jpg
2260 055 C. chinensis
M12 Epiberberine graphic file with name fphar-08-00694-i0006.jpg 4309
078 C. chinensis
M13[96]^a Groenlandicine graphic file with name
fphar-08-00694-i0007.jpg 2842 072 C. chinensis
M16[97]^a Phellodendrine graphic file with name
fphar-08-00694-i0008.jpg 250 058 C. chinensis
M18 (R)-Canadine graphic file with name fphar-08-00694-i0009.jpg 5537
077 C. chinensis
M19 Berlambine graphic file with name fphar-08-00694-i0010.jpg 3668 082
C. chinensis
M20[98]^a Jatrorrhizine graphic file with name fphar-08-00694-i0011.jpg
1965 059 C. chinensis
M21 Palmatine graphic file with name fphar-08-00694-i0012.jpg 6460 065
C. chinensis
M23[99]^a Coptisine graphic file with name fphar-08-00694-i0013.jpg 721
086 C. chinensis
M26 Worenine graphic file with name fphar-08-00694-i0014.jpg 45.83 0.87
C. chinensis
M27 Obacunone graphic file with name fphar-08-00694-i0015.jpg 43.29
0.77 C. chinensis
M32[100]^a Ferulic acid graphic file with name fphar-08-00694-i0016.jpg
39.56 0.06 C. chinensis
M33[101]^a Vanillic acid graphic file with name
fphar-08-00694-i0017.jpg 35.47 0.04 C. chinensis
M60 β-Sitosterol graphic file with name fphar-08-00694-i0018.jpg 36.23
0.78 C. chinensis/A. membranaceus
M61 Hederagenin graphic file with name fphar-08-00694-i0019.jpg 36.91
0.75 A. membranaceus
M62[102]^a Lupeol graphic file with name fphar-08-00694-i0020.jpg 12.12
0.78 A. membranaceus
M85[103]^a Soyasaponin I graphic file with name
fphar-08-00694-i0021.jpg 2.06 0.15 A. membranaceus
M92[104]^a Astragaloside I graphic file with name
fphar-08-00694-i0022.jpg 46.79 0.11 A. membranaceus
M94[105]^a Astragaloside II graphic file with name
fphar-08-00694-i0023.jpg 0.79 0.13 A. membranaceus
M96[106]^a Astragaloside III graphic file with name
fphar-08-00694-i0024.jpg 31.83 0.10 A. membranaceus
M98[107]^a Astragaloside IV graphic file with name
fphar-08-00694-i0025.jpg 2.20 0.15 A. membranaceus
M104[108]^a Isoastragaloside I graphic file with name
fphar-08-00694-i0026.jpg 37.80 0.14 A. membranaceus
M109[109]^a Cycloastragenol graphic file with name
fphar-08-00694-i0027.jpg 25.70 0.10 A. membranaceus
M115 Kumatakenin graphic file with name fphar-08-00694-i0028.jpg 50.83
0.29 A. membranaceus
M118 Isorhamnetin graphic file with name fphar-08-00694-i0029.jpg 4960
031 A. membranaceus
M119 3,9-di-O-Methylnissolin graphic file with name
fphar-08-00694-i0030.jpg 5374 048 A. membranaceus
M120 Calycosin graphic file with name fphar-08-00694-i0031.jpg 47.75
0.24 A. membranaceus
M121[110]^a Calycosin 7-O-β-D-glucopyranoside graphic file with name
fphar-08-00694-i0032.jpg 1005 081 A. membranaceus
M122 7-O-Methylisomucronulatol graphic file with name
fphar-08-00694-i0033.jpg 7469 030 A. membranaceus
M123 (6αR, 11αR) 3-Hydroxy-9,10-dimethoxypterocarpan-3-O-β-D-glucoside
graphic file with name fphar-08-00694-i0034.jpg 36.74 0.92 A.
membranaceus
M124 (6αR, 11αR) 3-Hydroxy-9,10-dimethoxypterocarpan graphic file with
name fphar-08-00694-i0035.jpg 64.26 0.42 A. membranaceus
M125 Formononetin graphic file with name fphar-08-00694-i0036.jpg 69.67
0.21 A. membranaceus
M126[111]^a Formononetin-7-O-β-D-glucoside graphic file with name
fphar-08-00694-i0037.jpg 1152 078 A. membranaceus
M127[112]^a Rhamnocitrin-3-O-glucoside graphic file with name
fphar-08-00694-i0038.jpg 2.87 0.76 A. membranaceus
M132 Isomucronulatol graphic file with name fphar-08-00694-i0039.jpg
67.67 0.26 A. membranaceus
M141[113]^a Rutin graphic file with name fphar-08-00694-i0040.jpg 11.70
0.68 C. chinensis/A. membranaceus
M148 Quercetin graphic file with name fphar-08-00694-i0041.jpg 4643 028
C. chinensis/A. membranaceus
M154 Kaempferol graphic file with name fphar-08-00694-i0042.jpg 67.43
0.24 C. chinensis/A. membranaceus
M171[114]^a Astragalus polysaccharides N/A N/A N/A A. membranaceus
[115]Open in a new tab
OB, oral bioavailability; DL, druglikeness; A. membranaceus, Astragalus
membranaceus (Huangqi); C. chinensis, Coptis chinensis (Huanglian).
^a
Compounds with OB < 30% and/or DL < 0.18, yet validated
pharmaceutically.
Active ingredients from huangqi
In Huangqi, only 26 ingredients passed through the strict filtering
criteria, and most of them exhibited potent pharmacological activities.
For examples, calycosin (M120, OB = 47.75%, and DL = 0.24) displayed
therapeutic effects on diabetic complication (Xu et al., [116]2011);
formononetin (M125, OB = 69.67%, and DL = 0.21) showed significant
antihyperglycemic activity (Qiu et al., [117]2017). Other
characteristic isoflavonoids in Huangqi accounted for large contents
and were also preserved. Specifically, calycosin-7-O-β-D-glucoside
(M121, OB = 10.05%, and DL = 0.81), formononetin-7-O-β-D-glucoside
(M126, OB = 11.52%, and DL = 0.78), (6αR, 11αR)
3-hydroxy-9,10-dimethoxypterocarpan-3-O-β-D-glucoside (M123, OB =
36.74%, and DL = 0.92), together with calycosin and formononetin in
Huangqi were measured up to 0.44–1.76 mg/g (Wu et al., [118]2005).
Surprisingly, astragalosides, the main active and characteristic
compounds in Huangqi, exhibited low OB values (Gu et al., [119]2004; Ma
et al., [120]2017). Among them, astragaloside IV (AIV, M98, OB = 2.20%,
and DL = 0.15) has been determined as the quality marker of Huangqi in
Chinese Pharmacopoeia (The State Pharmacopoeia Commission of China,
[121]2015) and exhibited significant hypoglycemic effect (Lv et al.,
[122]2010). Astragaloside II (M94, OB = 0.79%, and DL = 0.13) and
isoastragaloside I (M104, OB = 37.80%, and DL = 0.14) could alleviate
insulin resistance and glucose intolerance by enhancing the expression
of an insulin-sensitizing adiponectin (Xu et al., [123]2009). Besides,
the contents of astragaloside I (M92, OB = 46.79%, and DL = 0.11),
astragaloside II, astragaloside III (M96, OB = 31.83%, and DL = 0.10)
and AIV in Huangqi were 0.78, 0.35, 0.20, and 0.26 mg/g, respectively
(Zu et al., [124]2009). Importantly, astragalus injection (the major
components are astragalosides) has been used in China to treat DM with
good clinical effects (Nie et al., [125]2014). Thus, astragalosides
were also selected for targeting. Astragalus polysaccharides (M171) as
a polysaccharide mixture from Huangqi (accounted for 90.00–288.75 mg/g)
was also preserved because it could ameliorate insulin resistance and
restore glucose homeostasis, in part, via gut microbiota (Zou et al.,
[126]2009; Zhang et al., [127]2013; Liu et al., [128]2014).
Active ingredients from huanglian
By ADME screening, 21 out of 64 ingredients with excellent
pharmacological effects were extracted from Huanglian, and half of them
possessed satisfactory pharmacokinetic profiles. It should be pointed
out that most of the representative isoquinoline alkaloids in Huanglian
showed extremely low OB values (Li et al., [129]2006; Chen et al.,
[130]2011; Bao et al., [131]2015), but they exhibited potent
antidiabetic, anti-inflammatory, and antioxidant activities (Patel and
Mishra, [132]2012; Zhang et al., [133]2012). For instances, berberine
(BBR, M1, OB = 0.68%, and DL = 0.78) had lipid- and glucose-lowing
effects in treatment of DM and obesity, which may be associated with
gut microbiota (Zhang et al., [134]2012); magnoflorine (M10, OB =
22.60%, and DL = 0.55) could exert antioxidant and antiglycemic effects
in vivo (Patel and Mishra, [135]2012). Besides, BBR and coptisine (M23,
OB = 7.21%, and DL = 0.86) together with epiberberine (M12, OB =
43.09%, and DL = 0.78) and palmatine (M21, OB = 64.60%, and DL = 0.65)
have been chosen as the marker components for quality control of
Huanglian in Chinese Pharmacopoeia (The State Pharmacopoeia Commission
of China, [136]2015). Meanwhile, the isoquinoline alkaloids accounted
for large amounts in Huanglian. For example, the contents of BBR,
coptisine, jatrorrhizine (M20, OB = 19.65%, and DL = 0.59), palmatine,
and epiberberine ranged from 58.47 to 71.35, 19.38 to 22.56, 4.96 to
5.33, 15.68 to 20.57, and 11.05 to 12.44 mg/g, respectively, and the
total content of these five alkaloids was 111.33–129.42 mg/g (Ding et
al., [137]2012). In view of the facts mentioned above, isoquinoline
alkaloids were deemed as the active ingredients for further analysis.
Strikingly, ferulic acid (M32, OB = 39.56%, and DL = 0.06) as a minor
constituent in Huanglian exhibited synergistic effect on
antihyperglycemic activity in combination with BBR (Chen et al.,
[138]2012). Vanillic acid (M33, OB = 35.47%, and DL = 0.04) has the
potential to prevent the progression of DM via ameliorating insulin
resistance (Chang et al., [139]2015). It was reasonable to believe that
the above compounds could be listed as potential active ingredients for
Huanglian (Table [140]1).
Noteworthy, besides choline, other four shared compounds were selected
and have beneficial effects on DM. Specifically, β-sitosterol (M60, OB
= 36.23%, and DL = 0.78) exhibited potent antidiabetic and antioxidant
activities (Gupta et al., [141]2011); rutin (M141, OB = 11.70%, and DL
= 0.68) had hypoglycemic effect and may confer protective effects
against diabetic nephropathy (Han et al., [142]2017); quercetin (M148,
OB = 46.43%, and DL = 0.28) presented anti-obesity and antidiabetic
activities (Aguirre et al., [143]2011); additionally, kaempferol (M154,
OB = 67.43%, and DL = 0.24) could exert antidiabetic benefits through
protecting beta-cells against glucotoxicity (Zhang and Liu, [144]2011).
Target proteins of huangqi and huanglian
Searching for the targets of candidate drugs solely by the experimental
approaches is overspending, labor-intensive, and time-consuming. In the
present work, an integrated in silico approach was introduced to
identify the target proteins for the active ingredients of Huangqi and
Huanglian. Predictive models were used including DRAR-CPI, SEA, STITCH
and PharmMapper server, and databases were mined including HIT, TTD,
BindingDB database, DrugBank and Google Scholar. Finally, 50 DM-related
targets were determined, interacting with the selected 43 active
ingredients of this herb combination (Table [145]2). Of note, we have
implemented molecular docking and surface plasmon resonance (SPR) assay
to explore the reliability of the interactions between the active
ingredients and their putative targets. As shown in Supplementary Table
[146]S3, Supplementary Figures [147]S3, [148]S4, it could be concluded
that the predicted targets were convincible to explore the network
interactions of Huangqi and Huanglian.
Table 2.
Target information of Huangqi and Huanglian.
ID Target UniProt ID Gene name
T-01 Estrogen receptor [149]P03372 ESR1
T-02 Estrogen receptor beta [150]Q92731 ESR2
T-03 Peroxisome proliferator-activated receptor alpha [151]Q07869 PPARA
T-04 Peroxisome proliferator activated receptor gamma [152]P37231 PPARG
T-05 Superoxide dismutase [Cu-Zn] [153]P00441 SOD1
T-06 Hepatocyte nuclear factor 4-alpha [154]P41235 HNF4A
T-07 Prostaglandin G/H synthase 2 [155]P35354 PTGS2
T-08 Calmodulin-1 [156]P0DP23 CALM1
T-09 5-Hydroxytryptamine 2C receptor [157]P28335 HTR2C
T-10 Vascular endothelial growth factor A [158]P15692 VEGFA
T-11 Nitric oxide synthase, inducible [159]P35228 NOS2
T-12 Nitric oxide synthase, endothelial [160]P29474 NOS3
T-13 Glucocorticoid receptor [161]P04150 NR3C1
T-14 Interleukin-1 beta [162]P01584 IL1B
T-15 Heme oxygenase 1 [163]P09601 HMOX1
T-16 Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit,
gamma isoform [164]P48736 PIK3CG
T-17 Tumor necrosis factor [165]P01375 TNF
T-18 Glutathione S-transferase Mu 1 [166]P09488 GSTM1
T-19 Acetylcholinesterase [167]P22303 AChE
T-20 Caspase-3 [168]P42574 CASP3
T-21 Caspase-9 [169]P55211 CASP9
T-22 mRNA of protein-tyrosine phosphatase, non-receptor type 1
[170]P18031 PTP1B
T-23 Glucagon-like peptide 1 receptor [171]P43220 GLP1R
T-24 Aldose reductase [172]P15121 AKR1B1
T-25 Solute carrier family 2, facilitated glucose transporter member 2
[173]P11166 GLUT2
T-26 Solute carrier family 2, facilitated glucose transporter member 4
[174]P14672 GLUT4
T-27 Interleukin-2 [175]P60568 IL2
T-28 Interleukin-6 [176]P05231 IL6
T-29 Hepatocyte nuclear factor 1-alpha [177]P20823 HNF1A
T-30 Glucokinase [178]P35557 GCK
T-31 Insulin-degrading enzyme [179]P14735 IDE
T-32 Insulin-like growth factor 1 receptor [180]P08069 IGF1R
T-33 Insulin receptor [181]P06213 INSR
T-34 Lysosomal alpha-glucosidase [182]P10253 GAA
T-35 Phosphatidylinositol 3-kinase regulatory subunit alpha [183]P27986
PIK3R1
T-36 Dipeptidyl peptidase IV [184]P27487 DPP4
T-37 C-C motif chemokine 2 [185]P13500 CCL2
T-38 Glycogen synthase kinase-3 beta [186]P49841 GSK3B
T-39 Glutathione S-transferase Mu 2 [187]P28161 GSTM2
T-40 Mitogen-activated protein kinase 1 [188]P28482 MAPK1
T-41 Mitogen-activated protein kinase 14 [189]Q16539 MAPK14
T-42 Beta-2 adrenergic receptor [190]P07550 ADRB2
T-43 NAD(P)H dehydrogenase [quinone] 1 [191]P15559 NQO1
T-44 78 kDa glucose-regulated protein [192]P11021 HSPA5
T-45 Glycogen phosphorylase [193]P06737 PYGL
T-46 Farnesoid X receptor [194]B6ZGS9 FXR
T-47 5′-AMP-activated protein kinase catalytic subunit alpha-2
[195]P54646 AMPK
T-48 Pancreatic α-amylase [196]P04746 AMY2A
T-49 Cytochrome P450 3A4 [197]P08684 CYP3A4
T-50 Cyclin-dependent kinase 2 [198]P24941 CDK2
[199]Open in a new tab
Target proteins of huangqi
For Huangqi, by target fishing, 26 active ingredients were validated to
bind with 42 DM-related target proteins. For example, AIV may have the
potential to act on 21 targets including NR3C1, VEGFA, NOS2, CASP3,
PYGL, PPARG, and AKR1B1. Actually, AIV has been identified as an
inhibitor of NR3C1, which might contribute to its therapeutic
application in DM (Liu et al., [200]2016). Beyond that, it also has a
strong antagonistic effect on PYGL, suggestive of its contribution to
the control of blood glucose homeostasis (Lv et al., [201]2010).
Analogously, formononetin and calycosin exhibited strong activation on
PPARA and PPARG to correct dyslipidemia and to restore glycemic balance
(Shen et al., [202]2006). Formononetin could also alleviate the retinal
neovascularization of diabetic retinopathy by inhibiting VEGFA (Wu et
al., [203]2016). Cycloastragenol, as the aglycone of astragalosides
could improve hepatic steatosis through activating FXR, thereby
alleviating DM-related hyperglycemia and hyperlipidemia (Gu et al.,
[204]2017). Hederagenin and lupeol interacted with GSTM1 to decrease
the risk of diabetic retinopathy and nephropathy (Datta et al.,
[205]2010; Sun et al., [206]2015). Notably, although it is hardly to
predict the targets of astragalus polysaccharides in silico, some
DM-related targets, such as GAA, PTP1B, and AMPK, have been reported to
be associated with its antidiabetic effect (Zou et al., [207]2009; Zhao
et al., [208]2012; Zhu et al., [209]2014).
Target proteins of huanglian
Forty-three targets were identified for 21 active ingredients of
Huanglian with 318 interactions, such as PIK3CG, PTP1B, AMPK, PIK3R1,
IL1B, HNF4A, and MAPK1. For instance, BBR, epiberberine, magnoflorine,
and coptisine inhibited PTP1B to increase insulin and leptin
activities, thereby possibly exerting antidiabetic activity (Choi et
al., [210]2015). Vanillic acid may interact with four potential targets
including GLUT2, IDE, GLP1R, and GAA to display antidiabetic activity
(Chang et al., [211]2015). It was worthy to mention that some major
targets, such as AKR1B1, ADRB2, HMOX1, and NOS3, were also closely
concerned with the various symptoms of diabetic complications. BBR,
epiberberine, coptisine, and groenlandicine could inhibit AKR1B1 to
alleviate the diabetic complications (Jung et al., [212]2008; Liu et
al., [213]2008). BBR, palmatine and (R)-canadine may interact with
ADRB2 to prevent the progression of obesity and hypertriglyceridaemia
(Ishiyama-Shigemoto et al., [214]1999).
GO enrichment analysis for targets
Analysis of interaction network regulation of 50 targets was performed
using MAS 3.0. As shown in Figure [215]2, biological process (BP,
GO:0008150), molecular function (MF, GO:0003674), and cellular
component (CC, GO:0005575) accounted for 64.82, 24.10, and 11.08%,
respectively. Further, BP, MF, and CC enrichment analysis were
performed by DAVID bioinformatics resources. The top 10 significantly
enriched terms in BP, MF, and CC categories (P < 0.05, P-values were
corrected using the Benjamini-Hochberg procedure) were listed in Figure
[216]2, indicating that Huangqi and Huanglian may regulate glucose
homeostasis, insulin secretion, and nitric oxide biosynthetic process
via enzyme binding, insulin binding, and kinase binding in the cytosol,
caveola, and plasma membrane so as to exert antidiabetic potential. It
is interesting to note that a large number of targets were associated
with a variety of BP terms such as regulation of insulin secretion,
glucose homeostasis, cellular response to insulin stimulus,
lipopolysaccharide-mediated signaling pathway, and positive regulation
of nitric oxide biosynthetic process, which are closely related to the
pathogenesis of DM. KEGG pathway enrichment analysis was also performed
by DAVID bioinformatics resources and displayed in Supplementary Figure
[217]S5. Furthermore, network and functional association of 50 targets
of Huangqi and Huanglian was mapped in Supplementary Figure [218]S6
using GeneMANIA ([219]http://www.genemania.org/).
Figure 2.
[220]Figure 2
[221]Open in a new tab
GO enrichment analysis of the targets of Huangqi and Huanglian.
Biological process (green), molecular function (blue), and cellular
component (red) accounted for 64.82, 24.10, and 11.08%, respectively.
Network analysis to decipher the synergistic mechanisms of huangqi and
huanglian
Compound-target network analysis
To facilitate the visualization and interpretation of the complex
relationships between all active ingredients of Huangqi and Huanglian
and their targets, a bipartite graph of C-T network was constructed
(Figure [222]3A). All active ingredients in these two herbs were
potential multiple-kinase inhibitors or activators. Amongst them, those
ones with high interconnection degrees were responsible for the high
interconnectedness of the C-T network, especially BBR (degree = 33),
lupeol (degree = 22), quercetin (degree = 22), AIV (degree = 21),
epiberberine (degree = 21), calycosin (degree = 21), and (R)-canadine
(degree = 21). Importantly, as shown in the C-T network (Figure
[223]3A), the efficacy of this herb combination not only concentrated
on modulating the crucial targets involving in the glucose and insulin
homeostasis (IGF1R, GAA, IDE, HNF1A, GCK, and DPP4), but also, more
essentially, focused on the regulation of the other proteins mediating
diabetic complications including inflammation, retinopathy, neuropathy,
nephropathy, and abdominal pain (NOS2, AKR1B1, VEGFA, PTGS2, ESR2, and
AChE) to relieve the pathological changes and prolong the efficient
curing process. Additionally, as expected, the majority of the targets
(32) such as GLUT2, NOS2, PTP1B, and IGF1R were synergistically
regulated by different components of Huanglian and Huangqi.
Figure 3.
[224]Figure 3
[225]Open in a new tab
Compound-Target (A) and Target-Pathway (B) networks of Huangqi and
Huanglian. The yellow and light blue nodes are active ingredients and
their potential targets of Huangqi and Huanglian, while the red nodes
represent the pathways.
Target-pathway network analysis
Signaling pathways, as an important component of the system
pharmacology, link receptor-ligand interactions to pharmacodynamics
outputs. All of the targets interacting with the active ingredients
were mapped onto the 30 KEGG pathways and the T-P network was generated
(Figure [226]3B). The PI3K-Akt signaling pathway exhibited the highest
number of target connections (degree = 13), followed by insulin
resistance with 12 targets, insulin signaling pathway and HIF-1
signaling pathway with 11 ones, respectively. These high-degree
pathways have well-established roles in the insulin secretion and
glucose homeostasis (Taniguchi et al., [227]2006). Besides, the VEGF
signaling pathway played an important role in the diabetic retinopathy
involved in multiple targets including PIK3CG, PTGS2, and VEGFA
(Antonetti et al., [228]2012). The activation of mTOR signaling pathway
was an underlying cause of renal hypertrophy at the early stage of DM
(Sakaguchi et al., [229]2006). Interestingly, we also found that the
combination of Huangqi and Huanglian may exert its therapeutic effects
on DM by regulating the pathways related to the adipogenesis and/or
lipolysis in adipocytes, liver, and vascular tissues. As shown in the
compressed pathway (Figure [230]4), Huangqi and Huanglian
synergistically acted on the multiple targets in these high-degree
pathways.
Figure 4.
[231]Figure 4
[232]Open in a new tab
Distribution of partial targets of Huangqi and Huanglian on the
compressed pathway. The orange nodes are potential targets of Huangqi
and Huanglian, while the light blue nodes are relevant targets in the
pathway.
Integrating with the above two networks, a CI of every active
ingredient was proposed based on NE weighted by literature (Figure
[233]5, Supplementary Table [234]S4). Five compounds emerged from the
active ingredients, including BBR, AIV, quercetin, palmatine, and
astragalus polysaccharides. They displayed the most contribution to the
antidiabetic effects of Huangqi and Huanglian with a sum of CIs of
85.01%. Therefore, the above discussion may fully clarify why Huangqi
and Huanglian could produce synergistic and complementary effects.
Figure 5.
Figure 5
[235]Open in a new tab
The CI and accumulative CI of active ingredients in Huangqi and
Huanglian. The sum of CIs for the top five ingredients including M1
(BBR), M98 (AIV), M148 (quercetin), M21 (palmatine), and M171
(astragalus polysaccharides) was more than 85%.
Target-organ network analysis
It may facilitate the development of enhanced detection and treatment
modalities for DM by understanding how the multi-organs respond to
indications on a system level. We compared the expression patterns of
50 targets across different tissues at different levels according to
the BioGPS database. The tissue distribution network of the 50 targets
were mapped and shown in Figure [236]6. Most targets acted on two or
more tissues, suggesting that these tissues are closely correlated.
Specifically, 30 targets contained high mRNA expression in retina,
accounting for 60% of all the targets. There were 29 targets
(accounting for 58% of all the targets) located in the CD33 + myeloid,
suggesting they were potential effective targets for the treatment of
autoimmune DM. In addition, 20 targets were overexpressed in small
intestine and 15 targets in colon. It is evident that patients with DM
have a high incidence of gastrointestinal dysfunction (Abrahamsson,
[237]1995) and gut microbiota disturbance (Kootte et al., [238]2012).
Similarly, 27 targets acted in cardiac myocytes and 22 targets in
heart, consistent with the fact that DM increases coronary heart
disease (CHD) morbidity and mortality and is considered a CHD risk
equivalent (Newman et al., [239]2017). Noticeably, the targets in whole
blood were linked with tissues in almost all the forms, indicating that
whole blood acted as the bridge and glycemic excursion played a vital
role in the pathological processes of these tissues.
Figure 6.
Figure 6
[240]Open in a new tab
Target-Organ network of Huangqi and Huanglian where kelly nodes
represent the targets and light blue nodes represent the organs.
Discussion
According to the TCM practice for thousands of years, DM can be
classified as Xiao Ke (wasting thirst syndrome), which is usually
associated with the deficiency of both Qi (vital energy) and Yin (body
fluids) resulting in the Heat of tissues and blood or urine stasis. The
“Gan” (sweetish taste) and “Ku” (bitter taste) are recognized as the
popular therapeutic flavors to Xiao Ke syndrome (Xia et al.,
[241]2016). Among the commonly used herbs beneficial for DM, Huangqi
(“Gan” flavor) and Huanglian (“Ku” flavor) are given high priority for
selection and their combination has been frequently used in TCM
prescriptions (Zhang et al., [242]2010; Xie et al., [243]2011). In our
work, an integrated system pharmacology approach was successfully
applied to illuminate the molecular synergy of Huangqi and Huanglian on
DM. Forty-three active ingredients and 50 corresponding DM-related
targets were selected and predicted, which were mainly involved in 30
KEGG signaling pathways associated with DM treatment and prophylaxis.
By systematic analysis of the C-T network, the astragalosides and
isoflavonoids of Huangqi may mainly stimulate insulin secretion,
improve insulin resistance and promote glucose utilization, but the
isoquinoline alkaloids of Huanglian could regulate inflammatory
cytokines, promote the utilization of glucose, and improve endocrine
and metabolism so as to achieve the synergistic and complementary
curative effects of Huangqi and Huanglian.
DM is inevitably accompanied with the development of serious
complications, including retinopathy, neuropathy, and nephropathy.
Therefore, in clinical practice, hypoglycemic drugs are always
prescribed with other drugs for complications, such as antibiotics and
antiulcer agents, which may increase the risk of adverse drug events
(Held et al., [244]2017). In our study, this herb combination could not
only regulate the insulin secretion and glucose homeostasis but also
inflammation and immunity. And the targets were mainly located in
retina, pancreatic islet, smooth muscle, immunity-related organ
tissues, and whole blood, which were highly associated with Xiao Ke,
especially San Xiao syndromes. These results are expected to take full
clinical advantage of Huangqi and Huanglian for diabetic complications.
Disruptions in gut microbiota composition and function are increasingly
implicated in the pathogenesis of obesity, insulin resistance, and DM
(Tremaroli and Bäckhed, [245]2012). On the other hand, gut microbiota
plays a crucial role in TCM therapy by complicated interplay with
herb-derived compounds, such as alkaloids, saponins, and
polysaccharides (Xu et al., [246]2017). Therefore, the availability of
these active constituents of Huangqi and Huanglian by gut microbiota
especially under the DM state may be a critical step toward the
emergence of their bioactivities in vivo.
Besides, through SPR assay, we demonstrated that calycosin and
coptisine could bind with GAA, which were consistent with the previous
reports (Zhou et al., [247]2012; Zhao et al., [248]2015). Astragaloside
I and AIV were found to exhibit significant interaction with TNF and
PTP1B, respectively, suggestive of their potential for diabetic
complications. However, instead of AIV, there are very few reports that
focus on the pharmacological actions of other astragalosides.
Jatrorrhizine and palmatine showed a strong interaction with GLP1R;
calycosin and calycosin 7-O-β-D-glucopyranoside were observed to bind
with PPARG and INSR, respectively. Therefore, more experiments are
anticipated to support our intriguing findings.
Author contributions
CW and DY conceived of and proposed the idea. SY and JL designed the
study. SY, JL, and FZ performed the experiments. SY, JL, WF, JC, and DY
participated in data analysis. CW, DY, CP, and HG contributed to
writing, revising and proof-reading the manuscript. All authors read
and approved the final manuscript.
Conflict of interest statement
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.
Acknowledgments