Abstract
Background
Flavonoids from plant medicines are supposed to be viable alternatives
for the treatment of type 2 diabetes (T2D) as less toxicity and side
effects. Radix scutellariae (RS) is a widely used traditional medicine
in Asia. It has shown great potential in the research of T2D. However,
the pharmacological actions remain obscured due to the complex chemical
nature of plant medicines.
Methods
In the present study, a systematic method combining ultrafiltration
UPLC-TripleTOF-MS/MS and network pharmacology was developed to screen
α-glucosidase inhibitors from flavonoids of RS, and explore the
underlying mechanism for the treatment of T2D.
Results
The n-butanol part of ethanol extract from RS showed a strong
α-glucosidase inhibition activity (90.55%, IC[50] 0.551 mg/mL) against
positive control acarbose (90.59%, IC[50] 1.079 mg/mL). A total of 32
kinds of flavonoids were identified from the extract, and their
ESI-MS/MS behaviors were elucidated. Thirteen compounds were screened
as α-glucosidase inhibitors, including viscidulin III,
2′,3,5,6′,7-pentahydroxyflavanone, and so on. A compound-target-pathway
(CTP) network was constructed by integrating these α-glucosidase
inhibitors, target proteins, and related pathways. This network
exhibited an uneven distribution and approximate scale-free property.
Chrysin (k = 87), 5,8,2′-trihydroxy-7-methoxyflavone (k = 21) and
wogonin (k = 20) were selected as the main active constituents with
much higher degree values. A protein-protein interaction (PPI) weighted
network was built for target proteins of these α-glucosidase inhibitors
and drug targets of T2D. PPARG (C[d] = 0.165, C[b] = 0.232,
C[c] = 0.401), ACACB (C[d] = 0.155, C[b] = 0.184, C[c] = 0.318), NFKB1
(C[d] = 0.233, C[b] = 0.161, C[c] = 0.431), and PGH2 (C[d] = 0.194,
C[b] = 0.157, C[c] = 0.427) exhibited as key targets with the highest
scores of centrality indices. Furthermore, a core subnetwork was
extracted from the CTP and PPI weighted network. Type II diabetes
mellitus (hsa04930) and PPAR signaling pathway (hsa03320) were
confirmed as the critical pathways.
Conclusions
These results improved current understanding of natural flavonoids on
the treatment of T2D. The combination of ultrafiltration
UPLC-TripleTOF-MS/MS and network pharmacology provides a novel strategy
for the research of plant medicines and complex diseases.
Keywords: Radix scutellariae, Flavonoids, α-Glucosidase inhibitors,
Ultrafiltration, LC-MS, Network pharmacology
Background
Type 2 diabetes (T2D) is one of the most serious chronic metabolic
disorders characterized by persistent hyperglycemia. It accounts for
more than 90% of all diabetes [[35]1], and is directly linked to
pathogenic consequences in the eyes, kidney, and cardiovascular
diseases [[36]2]. Natural products with less side effects have been
used to treat diabetes for thousands of years [[37]3]. Previously,
various natural products were found to exhibit anti-diabetic effects,
such as herbal formulas, fruits, vegetables, spices, or natural
beverages [[38]4]. These remedies are more accessible and affordable
than modern pharmaceuticals [[39]5].
Radix scutellariae (RS) is the dried root of Scutellaria baicalensis
[[40]6]. It is widely used as herbal medicine in Asia for thousands of
years [[41]7]. This medicine has various therapeutic functions,
including antitumor, cardiovascular, neuroprotective and
anti-inflammatory effects [[42]6, [43]8, [44]9]. A growing body of
exciting evidences also indicated an antidiabetic effect of RS. For
instance, water extract of RS showed a reduction of body weight and
blood triglyceride in type 2 diabetic db/db mice [[45]10]. Methanol
extract of RS had strong α-glucosidase inhibition [[46]11]. Another
traditional medicine coptis together with RS demonstrated potent
anti-hyperglycemic effect on diabetic rats [[47]12]. In addition,
ethanolic extract of RS was found to enhance the antidiabetic effect of
metformin, and increase pancreatic insulin content as well as improving
the lipid profile in diabetic Wistar rats [[48]13]. These reports
suggested great potential of RS in the drug discovery of T2D.
The α-glucosidase is an exo-type carbohydrate enzyme that catalyzes the
liberation of α-glucose from the non-reducing end of the carbohydrates.
It locates in the brush border surface membrane of the small intestinal
cells. This enzyme accelerates glucose reabsorption in the intestine
[[49]14]. Inhibition of α-glucosidase could delay the digestion,
absorption of carbohydrates, and suppress postprandial hyperglycemia
[[50]15]. Natural α-glucosidase inhibitors have presented viable
alternatives to the treatment of T2D as fewer toxicity and adverse
effects [[51]16]. More than one hundred herbal medicines have exhibited
great potency in α-glucosidase inhibition and equivalent efficacies to
synthetic drugs in managing diabetes [[52]17].
Numerous compounds of natural origin are considered as models for drug
discovery of T2D, such as flavonoids, polyphenols, terpenoids,
alkaloids, saponins, quinones [[53]18]. Flavonoids are a group of
natural polyphenolic derivatives that widely exist in traditional
medicines [[54]19]. In recent years, considerable portions of natural
flavonoids displayed anti-diabetic effects, including quercetin, rutin,
naringin, baicalein [[55]20–[56]22]. Many flavonoids were also found in
RS, such as baicalein, baicalin, wogonin, wogonoside, and so on
[[57]23]. A part of these compounds were reported to exhibit
α-glucosidase inhibition [[58]22]. Therefore, RS is considered as a
source of natural α-glucosidase inhibitors.
The identification of the pharmacological profile of natural products
is always a challenging task. Ultrafiltration method has attracted much
attention in the screening and analysis of bioactive compounds from
botanical extracts [[59]24]. It has the advantage of high-speed and
high-reliability, which facilitates the separation of ligand-receptor
complexes for unbound compounds [[60]25, [61]26]. For instance, Zhang
et al. established an ultrafiltration LC-MS method for screening and
characterizing thrombin inhibitors from Rhizoma. Wang et al. applied
ultrafiltration LC-MS combined with reverse phase-medium pressure
liquid chromatography for screening and isolation potential
α-glucosidase inhibitors from RS [[62]27]. However, these studies
mainly focused on the screening of bioactive molecules. Further
researches are urgently needed to elucidate the underlying mechanism.
Molecular mechanism of natural products is always difficult and
confused as the complex chemical nature [[63]28, [64]29]. Recently,
network pharmacology approach, also known as system pharmacology, has
emerged as a powerful tool to solve the problem [[65]30, [66]31]. This
methodology holds a significant potential for extracting biological
information from large amounts of chemical data [[67]32], and enables
to predict the target profiles and pharmacological actions of herbal
compounds [[68]33, [69]34]. Chen et al. constructed a multi-parameter
network model on the basis of three important parameters to tentatively
explain the anti-fibrosis mechanism of herbal medicine Sophora
flavescens [[70]35]. Luo et al. used systems pharmacology strategies
for anti-cancer drug discovery based on natural products [[71]36].
Gogoi et al developed a network pharmacology-based virtual screening of
natural products from Clerodendrum species for identification of novel
anti-cancer therapeutics [[72]37]. These studies demonstrated that
network pharmacology approach had real potential in the mechanism
research of natural products [[73]38, [74]39].
In the present study, we developed a systematic method to screen
α-glucosidase inhibitors from plant flavonoids and explore the
underlying mechanism. Ultrafiltration UPLC-TripleTOF-MS/MS was used to
identify flavonoids from the RS extract and screen potential
α-glucosidase inhibitors. Network pharmacology was applied to
investigate the interrelationships between these compounds, related
target proteins and pathways. Several networks were constructed, and a
series of topological characteristics were calculated to determine the
main active constituents, key targets and critical pathways.
Methods
Materials and reagents
Crude Radix scutellariae was purchased from Baoji Medicinal Material
Company (Shaanxi, China). The plant species was authenticated by Prof.
Xiaomei Wang from Shaanxi Key Laboratory of Phytochemistry in Baoji
University of Art and Sciences. Wogonin ([75]HW158604), baicalin
([76]HB158602), wogonoside ([77]HW158601), oroxylin A ([78]HB158728),
chrysin (C110078), skullcapflavone II ([79]HA062620), oroxylin
A^− 7-O-β-D-glucuronopyranoside ([80]HO158605),
baicalein-6-O-β-D-glucuronopyranoside (XB161661), and
chrysin-7-O-β-D-glucuronopyranoside ([81]HA061609) were obtained from
Chenguang Biotech Co. Ltd. (Shaanxi, China) with a purity higher than
98%. The α-Glucosidase (from Saccharomyces cerevisiae) was purchased
from Sigma-Aldrich (St. Louis, MO, USA). Acarbose and
p-nitrophenyl-α-D-glucopyranoside (α-pNPG) were acquired from Aladdin
Industrial Corporation (Shanghai, China). Methanol of HPLC grade was
supplied by Merck (Darmstadt, Germany). Formic acid (HPLC grade) was
purchased from Tedia (Fairfield, OH, USA). Ultrapure water was obtained
using a Milli-Q purification system (Millipore Co., USA). All other
reagents were analytically pure.
Standards and sample preparation
Reference substances were accurately weighed and dissolved in methanol
(5 μg/mL). The solutions were stored at 4 °C until use. Dried Radix
scutellariae powders (100 g) were passed through 100-mesh sieves, then
orderly extracted with n-hexane, chloroform, 70% ethanol by heating
reflux for 2–3 h, three times. Then, the 70% ethanol solution was
leached with n-butanol (saturated by water). The solvents were removed
by evaporation in vacuo, and the extracts were stored at − 20 °C until
required, thawed at room temperature, dissolved in methanol (1 mg/mL).
Finally, the solution was filtered with 0.22 μm Millipore filter
membrane, and used directly for LC-MS.
α-Glucosidase inhibition assay
The α-glucosidase inhibitory activity was evaluated based on the
slightly modified method of the literature [[82]40]. The assay mixture
(160 μL) contained 20 μL of phosphate buffer (0.1 M, pH 7.0) in 96-well
plates, 20 μL of enzyme solution (0.1 U /mL α-glucosidase in phosphate
buffer), 20 μL of sample in phosphate buffer with different
concentrations to be mixed and incubated at 37 °C for 15 min. Then, the
reaction was initiated by adding 20 μL of α-pNPG (2.5 mM in phosphate
buffer). After 15 min at 37 °C, the reaction was stopped by adding
80 μL Na[2]CO[3] (0.2 M) solution. Amount of released PNP
(4-nitrophenol) was quantified by a microplate reader at the absorbance
of 405 nm. The inhibitory rates (%) were calculated as follows:
[MATH:
Inhibition%=1−As−Ac/Ab×100 :MATH]
Where the symbol ‘A[s]’ is the absorbance of the test sample; ‘A[c]’ is
the absorbance of the sample contrast (without enzyme solution); and
‘A[b]’ is the absorbance of the blank (without tested sample). All
reactions were conducted in three replications and acarbose was used as
positive control. The half maximal inhibitory concentration of the test
sample (IC[50]) was calculated using the modified Karber’s method.
Screening of α-glucosidase inhibitors from RS
A 2 μL aliquot of n-butanol part of ethanol extract from RS (50 mg/mL)
was incubated with 8 μL of α-glucosidase (100 μM, dissolved in 10 mM
ammonium acetate buffer, pH 6.86) for 30 min at 37 °C. After
incubation, each sample was filtered through a Vivaspin 2 concentrator
(MWCO 10 kDa, Sartorius, Göttingen, Germany) at 10,000 g for 10 min.
Then, the filter was washed three times with 200 μL ammonium acetate
buffer (PH 6.86) to remove the unbound compounds. The bound ligands
were released by adding 200 μL of methanol/water mixtures (1:1, v/v)
adjusted with acetic acid to pH 3.30, followed by centrifugation at
10,000 g for 15 min. This procedure was repeated three times. A control
experiment in which α-glucosidase omitted was also carried out before
each screening experiment. The released ligands were then analyzed by
LC-MS.
UPLC-TripleTOF-MS/MS analysis
Chromatographic separations were achieved on LC-20AD^XR (Shimadzu,
Tokyo, Japan) coupled with a Shim-pack XR-ODS column (100 mm × 2.0 mm,
2.2 μm, Shimadzu). The mobile phases consisted of eluent A (0.1% formic
acid in water, v/v) and eluent B (0.1% formic acid in methanol, v/v).
The gradient elution program was set as follows: 10 to 48% B from 0 to
8 min, holding for 6 min, 48 to 100% B from 14 to 20 min. After holding
100% B for next 5 min, the ratio was returned to its starting
condition. The injection volume was 20 μL (20 μg/mL) at a flow rate of
0.3 mL/min. The column was maintained at 40 °C. MS analysis was
performed on a TripleTOF 4600 mass analyzer (AB SCIEX, USA) equipped
with electrospray ionization (ESI) source. The instrument was operated
in positive ESI mode with a declustering potential voltage (DP) of
100 V and ionspray voltage of 5.5 KV. The nebulization temperature was
550 °C. GS1, GS2 and curtain gas were maintained at 55, 55 and 30 psi,
respectively. Collision energy was 10 eV for MS and 50 eV for MS/MS. An
automated calibration delivery system (CDS) was applied to regulate the
MS and the MS/MS. The constituents were identified by the comparison
with reference standards, the accurate molecular weights (with a mass
tolerance of ±5 ppm), as well as the MS/MS fragment patterns. The
operations, acquisition, and analysis of data were monitored by Analyst
TF 1.7 (AB SCIEX, Concord, Canada) and PeakView 2.0 (AB SCIEX, Concord,
Canada).
Collection of target proteins and pathways enrichment analysis
Target proteins of the α-glucosidase inhibitors from RS were collected
using SuperPred ([83]http://prediction.charite.de/) and DrugBank
([84]https://www.drugbank.ca/). The target prediction is based on the
similarity distribution among the targets’ ligands. The distributions
are utilized for estimating individual thresholds and probabilities for
a specific target. By means of these individual thresholds and
probabilities, the input compound was screened against a database
containing about 341,000 compounds, 1800 targets and 665,000
compound-target interactions [[85]41]. Information of all the targets
was uniformed by Uniprot ([86]http://www.uniprot.org/). Pathway
analysis was applied to these proteins by DAVID 6.8
([87]https://david.ncifcrf.gov/). The queried species was Homo sapiens.
Raw P-values were adjusted using Benjamini & Hochberg procedure
[[88]42]. Pathways with adjusted P-values less than 0.05 were
considered as significant.
Construction of networks for the α-glucosidase inhibitors from RS
A complex network analysis was performed on the collected data for
further interpretation. First, a compound-target-pathway (CTP) network
was constructed to screen main active components from RS. This network
consisted of numerous nodes and edges. Nodes represented the
α-glucosidase inhibitors from RS, corresponding target proteins and
related pathways, respectively. Edges referred to interactions between
them. If a protein was the hit target of particular inhibitor, or
involved in any pathways, connections were made between these elements.
Subsequently, we collected therapeutic targets of T2D from TTD database
([89]https://db.idrblab.org/ttd/) [[90]43], and integrated with targets
of the α-glucosidase inhibitors into a protein-protein interaction
(PPI) weighted network. This network was designed to evaluate the
closeness of interaction between RS and T2D. Interactions between the
two groups of proteins were calculated by Search Tool for the Retrieval
of Interacting Genes/Proteins (STRING, [91]https://string-db.org/)
[[92]44]. STRING uses a scoring mechanism to give a comprehensive score
to the results obtained by these different methods, including
experimental data, data mined from PubMed abstract text, database data,
and results predicted by bioinformatics methods. A weighted
protein-protein interaction (PPI) network was constructed on the basis
of these data. Nodes indicated the proteins, and that connections
represented interactions between them with scores higher than 0.7.
Moreover, key nodes of the PPI network and their neighbor nodes were
extracted, as well as the directly connected α-glucosidase inhibitors
and related pathways. These elements were reconstructed as a core
subnetwork to explore the underlying pharmaceutical mechanism of RS.
Construction and visualization of all the networks were performed by
Pajek ver. 2.00 (Batagelj and Mrvar, 2009).
Statistical and topological analysis of the network
To interpret the behavior of the α-glucosidase inhibitors from RS and
T2D, several topological parameters of the network were analyzed
(Table [93]1). The degree k[i] is the number of its connections
attached to a given node i. The directly linked nodes are called
neighbors of node i. Mean value of k of all nodes is defined as average
degree ⟨k⟩. Degree distribution is the proportion of randomly selected
nodes with specific number of connections, and denoted as P(k). Average
path length (L) refers to the mean distance between each pairs of
nodes, which measures the overall navigability of a network. The
diameter (D) is the maximum distance between any pair of nodes.
Table 1.
Definitions of the topological parameters used in the network analysis
Statistical characteristic Symbol Equation ^a
Degree k
[MATH: ki=∑j=1
Neij :MATH]
Average degree
[MATH:
<k>=1N∑i=1N
ki :MATH]
Average path length L
[MATH: L=1NN−1∑i≠j
dij :MATH]
Diameter D D = max {d[ij]}
Node strength s
[MATH:
si=∑
j∈Ni
wij :MATH]
Dispersion of weight distribution Y
[MATH:
Yi=∑
j∈Ni
wijsi2 :MATH]
Degree centrality C[d]
[MATH:
Cd=ki
N−1 :MATH]
Betweenness centrality C[b]
[MATH: Cb=∑j<kN∑kNgjkigjk :MATH]
Closeness centrality C[c]
[MATH:
Cc=N−
1∑j=1
Ndij :MATH]
[94]Open in a new tab
^aN is the total number of all nodes in the network; e[ij] is the
numbers of edges from node i to j; d[ij] is the shortest path length
from node i to j; g[jk] is the numbers of geodesics connecting nodes j
and k; N[i] is the neighbor collection of node i; W[ij] is the edge
weight between node i and j
Centrality measures the relative influence of a node within the overall
architecture of a network. In this study, three centrality metrics were
comprehensively evaluated. Each of them focused on specific influence
of a node on other nodes. Degree centrality (C[d]) indicates the
proportion of other nodes adjacent to a node, representing the
immediate influence that the closest nodes produce on the corresponding
vertex. Betweenness centrality (C[b]) refers to the total number of
shortest paths going through a node, which directly reflects the
influence of a node has on the spread of information through the
network. Closeness centrality (C[c]) is the number of other nodes
divided by the sum of distances between one node and the others,
reflecting how close a node is to others. The statistical analysis was
performed with MATLAB 2016a (The MathWorks Inc., Natick, MA, USA).
Results
Identification of flavonoids from Radix Scutellariae
The n-butanol part of ethanol extract from Radix Scutellariae were
analyzed by UPLC-TripleTOF-MS/MS. A total of 32 kinds of flavonoids
were identified within 5 ppm mass tolerance. Nine of them (compound 10,
11, 12, 14, 16, 24, 26, 28, 29) were confirmed by the reference
standards, and the others by fragmentation analysis. The identification
results were also compared with those from previous studies to ensure
the accuracy. Detailed MS data of these compounds are listed in
Table [95]2, and the MS/MS fragmentation patterns of typical flavonoids
from RS are shown in Additional file [96]1.
Table 2.
Characterization of flavonoids from Radix Scutellariae extracts by
UPLC-TripleTOF-MS/MS
No. Compound ^a t[R] (min) Formula Measured m/z ^b Error ^c (ppm) MS^2
(m/z)
1 2′,3,5,6′,7-Pentahydroxyflavanone 5.80 C[15]H[12]O[7] 305.0659 1.00
153.0079, 123.0366, 97.0212
2 5,2′6’-Trihydroxy-7,8-dimethoxyflavone-2′-O-β-D-glucopyranoside 8.69
C[23]H[24]O[12] 493.1336 −0.90 331.0841, 316.0528, 298.0487, 287.0291
3 2′,5,6′,7-Tetrahydroxyflavane 9.33 C[15]H[12]O[6] 289.0709 0.80
169.0200, 153.0076, 147.0345, 134.9982
4 Chrysin-7-O-β-D-glucopyranoside 9.55 C[21]H[20]O[9] 417.1179 −0.30
307.0808, 297.0547, 279.0451, 267.0465
5 Viscidulin III 9.58 C[17]H[14]O[8] 347.0764 0.70 314.0373, 289.0361,
286.0436, 233.0423
6 2′,6′,7-Trihydroxy-5-methoxyflavanone 9.69 C[16]H[14]O[6] 303.0859
−1.40 167.0340, 152.0095, 123.0445, 107.0489
7 Baicalein-7-O-β-D-glucopyranoside 10.79 C[21]H[20]O[10] 433.1133 0.90
271.0555, 253.0444, 197.0546, 169.0102
8 Dihydroxybaicalein 11.00 C[15]H[12]O[5] 273.0760 0.90 169.0112,
123.0070, 103.0529
9 5,7,2′-Trihydroxy-6′-methoxyflavone 11.33 C[16]H[12]O[6] 301.0700
−2.20 250.9845, 241.0439, 153.0148, 139.0000
10 Baicalin * 11.33 C[21]H[18]O[11] 447.0925 0.70 271.0583, 253.0482,
225.0534, 1,690,121
11 Baicalein-6-O-β-D-glucuronopyranoside * 12.57 C[21]H[18]O[11]
447.0926 0.90 327.0583, 271.0590, 253.0515, 184.0556
12 Chrysin-7-O-β-D-glucuronopyranoside * 13.07 C[21]H[18]O[10] 431.0971
−0.40 255.0683, 187.0802, 1,530,556, 103.0534
13 5,6,7-Trihydroxy-8-methoxyflavone-7-O-β-D-glucuronopyranoside 13.14
C[22]H[20]O[12] 477.1027 −0.10 301.0752, 286.0516, 199.0233, 184.0029
14 Oroxylin A-7-O-β-D-glucuronopyranoside * 13.73 C[22]H[20]O[11]
461.1071 −1.60 285.0748, 270.0550, 242.0571, 168.0048
15 5,7,2′-Trihydroxy-6-methoxyflavone-7-O-β-D-glucuronopyranoside 13.87
C[22]H[20]O[12] 477.1029 0.30 301.0735, 286.0499, 183.9976, 157.0433
16 Wogonoside * 14.14 C[22]H[20]O[11] 461.1069 −2.00 285.0748,
270.0550, 242.0571, 149.1111
17 4′-Hydroxywogonin 16.22 C[16]H[12]O[6] 301.0703 −1.20 286.0501,
184.0006, 156.0054, 137.9944
18 6-Methoxynaringenin 16.85 C[16]H[14]O[6] 303.0865 0.60 168.0022,
147.0416, 135.0801, 129.0299
19 5,6,7-Trihydroxy-4′-methoxyflavanone 16.86 C[16]H[14]O[6] 303.0861
−0.70 147.0423, 135.0801, 129.0299, 107.0491
20 2′,5,6′,7-Tetrahydroxy-8-methoxyflavone 16.86 C[16]H[12]O[7]
317.0658 0.70 209.0372, 147.0453, 129.0344, 123.0368
21 5,8,2′-Trihydroxy-6,7-dimethoxyflavone 16.86 C[17]H[14]O[7] 331.0811
−0.40 301.0164, 239.0294, 183.0857, 147.0425
22 Oroxylin A-7-O-β-D-glucuronide methyl ester 17.42 C[23]H[22]O[11]
475.1231 −0.80 285.0784, 271.0634, 253.0525, 225.0554
23 5,8,2′-Trihydroxy-7-methoxyflavone 17.74 C[16]H[12]O[6] 301.0709
0.80 286.0456, 168.0050, 140.0096, 121.0289
24 Skullcapflavone II * 17.91 C[19]H[18]O[8] 375.1077 0.70 345.0560,
327.0460, 227.0541, 212.0289
25 Dihydrooroxylin A 18.22 C[16]H[14]O[5] 287.0916 0.70 272.0570,
183.0258, 168.0043, 140.0092
26 Wogonin * 18.32 C[16]H[12]O[5] 285.0764 2.30 270.2520, 252.0420,
241, 0409, 179.0462
27 5,8-dihydroxy-6,7-dimethoxyflavone 18.43 C[17]H[14]O[6] 315.0867
1.20 285.0382, 257.0436, 197.0589, 182.9920
28 Chrysin * 18.62 C[15]H[10]O[4] 255.0654 0.80 153.0192, 129.0342,
103.0547
29 Oroxylin A * 18.71 C[16]H[12]O[5] 285.0760 0.90 270.0511, 168.0051,
140.0097, 112.0151
30 Tenaxin I 19.18 C[18]H[16]O[7] 345.0971 0.60 315.0528, 297.0426,
272.0299, 197.0095
31 Moslosooflavone 19.43 C[17]H[14]O[5] 299.0911 −1.00 284.0606,
283.0583, 255.0621, 238.0570
32 Oroxylin A-7-O-β-D-glucuronopyranoside butyl ester 19.52
C[26]H[28]O[11] 517.1694 −2.00 285.0532, 270.0317
[97]Open in a new tab
^a Compounds were identified by the comparison with exact mass
(< 5 ppm), reference standards (indicated by an asterisk), as well as
the MS/MS fragmentation patterns (Ref. [[98]7, [99]8,
[100]45–[101]54]); ^b Measured m/z of peak [M + H]^+; ^c Mass accuracy
between the calculated m/z and measured m/z of peak [M + H]^+
The identified flavonoids contained 20 aglycones (compound 1, 3, 5, 6,
8, 9, 17–21, 23–31) and 12 glycosides (compound 2, 4, 7, 10–16, 22,
32). Compound 1, 3, 8, 28 belonged to aglycones, substituted by several
hydroxyl groups. Compound 28 showed [M + H]^+ peak at m/z 255.0654
(Additional file [102]1a). This protonated molecular ion yielded the
product ions at m/z 153.0192, 103.0547 in the MS/MS spectra. The two
ions were attributed to the ^1,3A^+ and ^1,3B^+, indicating the
occurrence of two OH groups in A-ring and none OH in B-ring. It was
consistent with the report of chrysin by Luo et al [[103]7]. Hence,
compound 28 was tentatively identified as chrysin, which was further
confirmed by the standard. Similarly, compound 1, 3, 8 were identified
as 2′,3,5,6′,7-pentahydroxyflavanone, 2′,5,6′,7-tetrahydroxyflavane,
and dihydrobaicalein, respectively [[104]45, [105]46].
Compound 5, 6, 9, 17–21, 23–27, 29–31 belonged to the methoxylated
flavonoid aglycones. They exhibited a characteristic ion (15*n Da) due
to the loss of CH[3] radicals. Protonated molecular ion of compound 26
was observed at m/z 285.0764 (Additional file [106]1b). The sole
flavone aglycone easily gave a prominent ion [M + H-15]^+ at m/z
270.2520, originated from losing one CH[3] (15 Da). It also lost a CHO
(29 Da) from C-ring, and produced the fragment at m/z 241.0494. In
addition, the neutral loss of H[2]O (18 Da) from m/z 270.2520 produced
the ions at m/z 252.0420 and 179.0462. Diagnostic fragment ions
originated from Retro-Diels-Alder (RDA) reaction are often helpful to
the structural determination of A- and B-ring substitution patterns
[[107]45]. Our data showed fragment ions ^1,3A^+ (m/z 168.0076) and
^1,3B^+ (m/z 103.0523), originated from the cleavage of the bond at
position 1/3 of C-ring. It was also annotated by Ma et al. [[108]47],
and that compound 26 was finally identified as wogonin.
Compound 17 showed [M + H]^+ peak at m/z 301.0703 (Additional file
[109]1c). MS/MS spectra of this compound exhibited a methoxylated
flavone characteristic loss of CH[3] (15 Da), resulting in a product
ion at m/z 286.0501. Besides, the parent ion m/z 301.0703 also yielded
the ions at m/z 184.0006, 156.0054, 137.9944, and 119.0452. The product
ion m/z 184.0006 was attributed to the ^1,3A^+, indicating that the
substituent groups of two OH and an OCH[3] were located in A-ring. The
ion m/z 156.0054 was produced by the neutral loss of CO and H[2]O from
the ^1,3A^+. This compound was finally identified as 4′-hydroxywogonin
[[110]46]. Compound 23 (Additional file [111]1d) exhibited a same
characteristic loss of CH[3] (15 Da) at m/z 286.0456. Moreover, other
RDA fragments from the fragment ion, ^1,3A^+ at m/z 168.0050 and
^1,4A^+ at m/z 140.0096, could also be observed. It was identified as
5,8,2′-trihydroxy-7-methoxyflavone, the isomer of compound 17.
Identification of other compounds in this group was also conducted by
comparison with previous reports, including compound 5, 6, 9 [[112]7,
[113]45, [114]48], compound 18–21 [[115]49, [116]50], compound 25
[[117]51], compound 27 [[118]52], compound 30–31 [[119]8].
Compound 2, 4, 7, 10–16, 22, 32 belonged to flavonoid glycosides,
glycosylated in different positions. Neutral loss of glucuronic acid
(176 Da) or glucose (162 Da) is common in flavone glycoside, as the
O-glucosylic bond is easily cleaved to generate aglycone. Reference
standards of compound 10, 11, 12, 14, and 16 showed a fragment ion at
[M + H-176]^+ due to the loss of glucuronic acid. These compounds could
be further distinguished by the fragment of residual aglycone. Compound
14 (Additional file [120]1e) and compound 16 (Additional file [121]1f)
were a pair of isomers. They both went through the loss of glucuronic
acid (176 Da), and produced the aglycone ions wogonin (8-OCH[3]) and
oroxylin A (6-OCH[3]), respectively. Furthermore, they both exhibited a
base peak [M + H-176-CH[3]] at m/z 270.0494. However, these two
compounds could be distinguished by the relative abundances of the
losses of CO and CHO from the parent ion [M + H-176-CH[3]]. In the
MS/MS of compound 16, the relative abundance of ion
[M + H-176-CH[3]-CO] at m/z 242.0598 was lower than
[M + H-176-CH[3]-CHO] at m/z 241.0494, since the loss of CHO could
produce a more stable p-quinoid skeleton. Nevertheless, it was contrary
to compound 14. Finally, compound 14 was identified as oroxylin
A-7-O-β-D-glucuronopyranoside, and compound 16 was wogonoside.
Compound 12 also exhibited the characteristic fragment ion [M + H-176]
at m/z 255.0683. Moreover, other RDA fragments from the aglycone ion of
chrysin (^1,3A[0]^+ at m/z 153.0186 and ^1,3B[0]^+ at m/z 103.0558)
were observed. This compound was identified as
chrysin-7-O-β-D-glucuronopyranoside, which was supported by the report
by Luo et al [[122]7]. Identification of other compounds in this group
was according to previous studies, including compound 2 and 4 [[123]7],
compound 7 [[124]53], compound 13 and 15 [[125]8], compound 22 and 32
[[126]54]. However, these results are only based on LC-MS/MS, which
might be limited by various factors. More reference standards and
analytical tools would be used to check the accuracy of identification
in our next study.
Potential α-glucosidase inhibitors, target proteins and related pathways
An in vitro α-glucosidase inhibition assay was performed on the
n-butanol part of ethanol extract from RS (Additional file [127]2). It
showed higher α-glucosidase inhibitory activity (IC[50] = 0.551 mg/mL)
than the positive control (IC[50] = 1.079 mg/mL). The inhibition rate
reached 90.55% at the concentration of 2.34 mg/mL, whereas that of the
positive control was 90.59% at 15 mg/mL. These data indicated that the
crude extract of RS was much more potent than acarbose on α-glucosidase
inhibition.
Ultrafiltration LC-MS/MS has been widely used to screen bioactive
compounds from natural products [[128]55]. In this study, the complexes
of α-glucosidase and ligands from RS were retained by an
ultrafiltration membrane, whereas the unbound, low molecular weight
compounds were washed away from the chamber. Subsequently, the
remainings were dissociated, and the ligands were identified by
LC-MS/MS. Finally, a total of 13 peaks were detected, yet not presented
in control group, indicating a specific binding to α-glucosidase.
Chemical structures of the trapped ligands are shown in Fig. [129]1,
including wogonin, chrysin, oroxylin A,
5,8,2′-trihydroxy-7-methoxyflavone, and so on. These compounds were
considered as potential α-glucosidase inhibitors, and reorganized as a
chemical ingredients database for the next analysis.
Fig. 1.
[130]Fig. 1
[131]Open in a new tab
Chemical structures of the potential α-glucosidase inhibitors from
flavonoids of Radix Scutellariae extract
Interactions between small molecules and proteins are often highly
valued in biomedical and pharmaceutical sciences [[132]56]. Numerous
target proteins have been used for the treatment of T2D, such as
insulin receptor, peroxisome proliferator activated receptor gamma
(PPARG), and α-glucosidase [[133]57]. In this study, target proteins of
the 13 α-glucosidase inhibitors were collected using web tools. A total
of 117 target proteins were obtained (Additional file [134]3). Some of
them were therapeutic targets of T2D, such as bile acid receptor,
histone deacetylase 1, prostaglandin G/H synthase 2, and so on
[[135]58]. It indicated that these α-glucosidase inhibitors might
affect T2D through multi-targets.
A pathway contains a panel of cascade reactions among various
biomolecules [[136]59]. Pathway analysis demonstrated that the 117
targets of α-glucosidase inhibitors were involved in 86 pathways
(Additional file [137]3), including metabolism of xenobiotics by
cytochrome P450, steroid hormone biosynthesis, insulin resistance,
retinol metabolism, and so on. These data were further analyzed by
network pharmacology.
Compound-target-pathway (CTP) network and main active constituents of the
α-glucosidase inhibitors from RS
Natural products have advantages of multi-components and multi-targets
[[138]60]. Their pharmacological effects are the consequence of a
series of interactional biochemical reactions. On the other hand, T2D
is a chronic degenerative disease involving various genetic and
environmental factors [[139]61]. These elements cause great difficulty
in the related researches. Network methodology provides us a great
opportunity to solve the problem from a systemic perspective. In the
present study, a compound-target-pathway network was first built for
the α-glucosidase inhibitors, target proteins and related pathways
(Fig. [140]2). This network contained 216 nodes (N = 216) and 877
connections (E = 877). The nodes consisted of 13 α-glucosidase
inhibitors (red nodes), 117 target proteins (yellow nodes), and 86
pathways (green nodes). The larger circles denote the nodes with the
most connections, and that gray lines represent connections.
Annotations of these nodes were listed in Additional file [141]3.
Fig. 2.
[142]Fig. 2
[143]Open in a new tab
Compound-target-pathway (CTP) network of the potential α-glucosidase
inhibitors from RS. The network consists of 13 red nodes (potential
α-glucosidase inhibitors), 117 yellow nodes (target proteins), 86 green
nodes (pathways), and 877 connections. The larger circles denote key
nodes with the most connections. Node information is listed in
Additional file [144]4. Gray lines represent connections
Several topological parameters were calculated to describe
characteristics of the network. Degree is the most elementary
characteristic for a node, which tells us how many directly connected
neighbors a node holds. ⟨k⟩ of the CTP network was 8.12, demonstrating
that an average of more than eight neighbors were connected with one
node. On the other hand, degree distribution measures the diversity of
a network. Obviously, a few nodes had numerous neighbors, whereas
others only had a small number of connections. Figure [145]3a shows
that the red and blue nodes had an uneven distribution, whereas green
nodes exhibited approximate scale-free property. The difference of P(k)
for α-glucosidase inhibitors, hit targets and related pathways might be
due to the complexity of natural products. The data suggested that a
few highly connected nodes existed in the CTP network.
Fig. 3.
Fig. 3
[146]Open in a new tab
a Degree distribution of the CTP network. k represents degree values,
and that P(k) indicates degree distribution. b Degree values (k) of all
nodes in the CTP network, ranked in a descending order
As interactions between molecules play a critical role in modulating
the intrinsic biological processes, more attention should be paid to
the highly-connected elements [[147]62]. Within the framework of
network science, the nodes with most connections are generally
considered as hubs [[148]63]. Although hubs are few in number, they are
generally positioned to make strong contributions to global network
function [[149]63]. Disturbances on these hubs would spread rapidly
throughout the entire network. Figure [150]3b shows a descending order
of degree values for all nodes of the CTP network.
Among the α-glucosidase inhibitors, chrysin (k = 87) had the largest k,
followed by 5,8,2′-trihydroxy-7-methoxyflavone (k = 21), and wogonin
(k = 20). The three compounds exhibited much higher degree values than
average (⟨k⟩ = 8.12). Chrysin treatment has been used to improve
diabetes in rats, which could attenuate diabetes-induced impairment
[[151]64]. Wogonin could enhance the intracellular level of
adiponectin, a therapeutic target for insulin resistance, diabetes, and
diabetes-related complications [[152]65]. Although there were few
reports about bioactivity of 5,8,2′-trihydroxy-7-methoxyflavone, the
activities of flavonoids are structure dependent with the hydroxylated
phenolic structure [[153]66], which should be tested in the future.
These compounds have significant impacts on the global network
function, and were considered as main active ingredients. They might
contribute the most to the pharmacological effects of α-glucosidase
inhibitors from RS.
Multi-targets could provide superior therapeutic effect and less side
effect compared to a single action, especially in the treatment of
complex diseases [[154]67]. In the target proteins, mitogen-activated
protein kinase 3 (MAPK3, k = 60), mitogen-activated protein kinase 1
(MAPK1, k = 59) and phosphoinositide-3-kinase regulatory subunit 1
(PIK3R1, k = 49) had much higher degree values than others. The two
MAPKs have been found to be increased in human and rodent adipose
tissue in the diabetic state [[155]68]. PIK3R1 plays a key role in
insulin signaling and diabetes [[156]69]. The three proteins were
interconnected with the most compounds and pathways in the CTP network,
and were also considered as hub nodes.
Normal pathways maintain balance between complex intracellular and
intercellular networks. The most highly connected pathway in the CTP
network was metabolic pathways (hsa01100, k = 29), followed by pathways
in cancer (hsa05200, k = 21), metabolism of xenobiotics by cytochrome
P450 (hsa00980, k = 15) and PI3K-Akt signaling pathway (hsa04151,
k = 15). Hsa01100 is a mega pathway defined in Kyoto Encyclopedia of
Genes and Genomes (KEGG), that encompasses several other pathways
[[157]70], and was excluded to avoid redundant data. Hsa05200 is
related to many diseases. A cross talk is existed between diabetes and
obesity, and that diabetes has been shown to increase cancer risk
[[158]71]. Some drugs appear to reduce cancer incidence and improve
prognosis of patients with diabetes [[159]72]. Hsa00980 takes part in
biotransformation of medicines in vivo [[160]73]. Hsa04151 is required
for insulin-dependent regulation on cellular metabolism, which was
directly associated with T2D [[161]74]. These reports indicated that
the α-glucosidase inhibitors from RS might be work through various
pathways.
Protein-protein interaction (PPI) weighted network and key targets of the
α-glucosidase inhibitors from RS
Many biological systems found in biology contain numerous components,
and the interactions between individual agents cause the emergence of
structures and functions [[162]75]. T2D is a typical polygenic disease
affected by various therapeutic targets [[163]76]. Elucidation of the
interactions between targets of T2D and ligands of α-glucosidase
inhibitors from RS would help to understand the molecular mechanisms.
In this study, a total of 64 targets of T2D were collected, including
34 successful and 30 clinical trial targets. Coincidentally, nine of
them were also the targets of α-glucosidase inhibitors from RS, such as
bile acid receptor (NR1H4), histone deacetylase 1 (HDAC1),
prostaglandin G/H synthase 2 (PGH2), and so on. We analyzed the
functional associations between the two groups of proteins using STRING
database. After preliminary exclusion of isolated nodes, 69 ligands of
α-glucosidase inhibitors from RS and 35 drug-targets of T2D were
preserved. A protein-protein interaction (PPI) weighted network was
then constructed (Fig. [164]4) for the two groups of proteins,
containing 104 nodes and 228 connections.
Fig. 4.
[165]Fig. 4
[166]Open in a new tab
Protein-protein interaction (PPI) weighted network for the ligands of
α-glucosidase inhibitors from RS and targets of T2D, containing 104
nodes and 228 connections. The yellow nodes are the targets of
potential α-glucosidase inhibitors from RS, and that blue nodes
represent therapeutic targets of T2D
In this PPI weighted network, degree k[i] represents the number of
other proteins interacted with node i. Node strength (s[i]) describes
the interactive intensities between the two groups of proteins. This
parameter comprehensively reflects local information of node i by
considering neighbor nodes and edge weights. Correlation between degree
k and the average strength (k) for nodes with specific k values were
investigated. If s(k) ∼ k^β with an exponent β ≠ 1, the edge weight is
correlated with the network topology. Figure [167]5a depicts the
correlation between k and s(k) for the PPI network. The s(k) increased
with k as s(k) ∼ k^β with the exponent β ≈ 0.87, indicating that the
node strengths were closely associated with degrees.
Fig. 5.
Fig. 5
[168]Open in a new tab
a Average strength s(k) as a function of degree k in logarithmic
coordinates. The data points are fitted to a straight line, showing the
relation s(k) ∼ k^β.b Node strengths of the PPI network sorted in
descending order. c Disparity Y(k) in the edge weight decays as a
function of k. The data points are well approximated by the curve
Y(k) = 1/k
Node strengths of the PPI network were then sorted in descending order
(Fig. [169]5b). Larger strengths point to nodes with larger degrees.
Nuclear factor kappa B subunit 1 (NFKB1) had the strongest interactions
(s = 21.26) with the targets of the potential α-glucosidase inhibitors,
followed by phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic
subunit gamma (PK3CG, s = 16.61), acetyl-CoA carboxylase beta (ACACB,
s = 13.32), and insulin precursor (INS, s = 12.96). In the targets of
the potential α-glucosidase inhibitors, PGH2 (s = 17.47) and PPARG
(s = 14.66) interacted with the most nodes of T2D. Moreover, the two
proteins were also successful targets of T2D. These nodes occupied
important positions in the overall organization of PPI network.
System behaviors are dependent largely on the overall structure rather
than the individual parts. Disparity Yi depicts the dispersion of
weight distribution [[170]77]. If the weights of all edges are
approximately equal, Y[i] ∝ 1/k[i]. If one edge weight is important
whereas the others negligible, Y[i] ≈ 1. It is obvious that Yi is
associated with k[i]. Y(k) often attracts more attentions in the
weighted network. It is the average of Y[i] for all nodes with degree
k. If the weight distribution is relatively uniform, Y(k) ∝ 1/k,
otherwise Y(k) ≈ 1. As shown in Fig. [171]5c, the average disparity
Y(k) ∝ 1/k in the PPI network. It demonstrated that the edge weight
distribution for nodes with the same degree k was approximately equal.
Changes in central positions of the network are always more important
than those in marginal or relatively isolated positions [[172]78]. To
determine central nodes of the PPI network, three centrality indices,
degree centrality, betweenness centrality, and closeness centrality
were integrated into a three-dimensional diagram (Fig. [173]6).
Distribution of these values seemed roughly uniform. However, a few
nodes appeared as outliers. PPARG (C[d] = 0.165, C[b] = 0.232,
C[c] = 0.401), ACACB (C[d] = 0.155, C[b] = 0.184, C[c] = 0.318), NFKB1
(C[d] = 0.233, C[b] = 0.161, C[c] = 0.431), and PGH2 (C[d] = 0.194,
C[b] = 0.157, C[c] = 0.427) showed higher centrality scores than other
nodes. A total of 54 neighbors were found to be connected with these
central nodes, accounting for 51.9% of the total target proteins. The
highly connectivity of the four proteins indicated that they could
affect the PPI weighted network greatly.
Fig. 6.
Fig. 6
[174]Open in a new tab
Three-dimensional diagram of degree centrality (C[d]), betweenness
centrality (C[b]) and closeness centrality (C[c]) for the nodes in PPI
network
PPARG and PGH2 are both important targets of the α-glucosidase
inhibitors and T2D. Recent studies have demonstrated the association of
PPARG with T2D. PPARG is a master transcriptional regulator of
adipocyte differentiation. Variants in PPARG with decreased activity in
adipocyte differentiation were found to be associated with increased
risk of T2D [[175]79]. A family-based study of Mexican Americans showed
that variation in PPARG contributes to declining insulin resistance and
concomitant deterioration in β-cell function at risk for T2D [[176]80].
PGH2 generates prostaglandins and causes insulin insensitivity. PGH2
polymorphisms were found to be associated with T2D in Pima Indians
comprising 1000 subjects [[177]81]. Another variant of PGH2 had a
protective role against T2D in two German cohorts [[178]82].
ACACB and NFKB1 are therapeutic targets of T2D, and have a strong
relationship with targets of α-glucosidase inhibitors from RS. ACACB is
a regulator of fatty acid metabolism. It catalyzes the carboxylation of
acetyl-CoA to malonyl-CoA. The problems in fatty acid metabolism can
lead to insulin resistance, which is a precursor for T2D. Polymorphisms
of ACACB are associated with T2D in postmenopausal women and Pakistani
Punjabis [[179]83, [180]84]. NFKB1 encodes a subunit of NF-kappa B. It
is specifically involved in anti-inflammatory effects, and that
inflammation is linked to insulin resistance and diabetes. Two common
NFKB1 variants were found to be involved in T2D in an elderly cohort
[[181]85]. A transcriptome and proteome study demonstrated that NFKB1
were increased in expression in diabetic subjects [[182]86]. These
reports further confirmed the importance of PPARG, PGH2, ACACB, and
NFKB1 to the α-glucosidase inhibitors from RS.
Core subnetwork and critical pathways of the α-glucosidase inhibitors from RS
In order to get further understanding of the key targets, the nodes
connected to PPARG, ACACB, NFKB1, and PGH2 were extracted from PPI
network. A total of 45 targets of the α-glucosidase inhibitors and 13
drug targets of T2D were selected. Pathway analysis indicated that
these proteins were involved in 91 pathways. After querying KEGG
DISEASE database, the type II diabetes mellitus pathway (hsa04930) and
PPAR signaling pathway (hsa03320) showed a direct correlation with T2D.
Therefore, the two processes might play significant roles in the
pharmacological activities of the α-glucosidase inhibitors from RS.
Moreover, we also extracted the α-glucosidase inhibitors connected to
these key targets from CTP network, including chrysin,
5,8,2′-trihydroxy-7-methoxyflavone, and wogonin. All these elements
were reorganized as a core subnetwork (Fig. [183]7), consisted of 63
nodes and 220 connections.
Fig. 7.
Fig. 7
[184]Open in a new tab
Core subnetwork for the potential α-glucosidase inhibitors and type 2
diabetes mellitus, consisted of 29 nodes and 47 connections. The yellow
nodes are the targets of potential α-glucosidase inhibitors from RS,
and that blue nodes indicate therapeutic targets of T2D. Red and green
nodes represent the related α-glucosidase inhibitors and pathways,
respectively
In this core subnetwork, chrysin, 5,8,2′-trihydroxy-7-methoxyflavone,
and seven targets, MAPK1, MAPK3, PIK3R1, protein kinase C delta
(PRKCD), INS, insulin receptor (INSR), solute carrier family 2 member 4
(GLUT4) belonged to hsa04930. As Fig. [185]8a shown, type II diabetes
mellitus contains various kinases. MAPK1 and MAPK3 (also known as ERK2
and ERK1) play an important role in the MAPK/ERK cascade. Diabetogenic
factors have been found to affect insulin signaling through activation
of the ERK signaling pathway [[186]87]. Previous research has revealed
that targeting of the ERK pathway held promise for the treatment of T2D
[[187]88]. In the present study, MAPK1 and MAPK3 were the targets of
chrysin and 5,8,2′-trihydroxy-7-methoxyflavone. In addition, PIK3R1 is
necessary for the insulin-stimulated increase in glucose uptake and
glycogen synthesis in insulin-sensitive tissues. Mutations of PIK3R1
could cause insulin resistance, which is strongly associated with
insulin resistance [[188]69]. Interestingly, chrysin,
5,8,2′-trihydroxy-7-methoxyflavone, MAPK1, MAPK3 and PIK3R1 were also
hub nodes of the CTP network.
Fig. 8.
Fig. 8
[189]Open in a new tab
Critical pathways of the potential α-glucosidase inhibitors from RS. a
Type 2 diabetes mellitus pathway. b PPAR signaling pathway. The yellow
nodes are the targets of potential α-glucosidase inhibitors from RS,
blue nodes indicate therapeutic targets of T2D, and that pink nodes
denote targets belonged to both the two groups. Red nodes represent the
related α-glucosidase inhibitors
Chrysin, 5,8,2′-trihydroxy-7-methoxyflavone, and another six targets,
peroxisome proliferator activated receptor alpha (PPARA), peroxisome
proliferator activated receptor delta (PPARD), PPARG, retinoid X
receptor alpha (RXRA), 3-phosphoinositide dependent protein kinase 1
(PDPK1), stearoyl-CoA desaturase (ACOD) were involved in hsa03320 (Fig.
[190]8b). Accumulating evidence highlighted the role of PPAR signaling
in T2D [[191]89]. PPARs (Peroxisome proliferator-activated receptors)
are nuclear hormone receptors that are activated by fatty acids and
their derivatives, containing three subtypes (PPAR alpha, beta/delta,
and gamma). The three PPAR isoforms all appeared in hsa03320. They were
both the targets of the α-glucosidase inhibitors from RS and T2D.
Moreover, PPARG was the central node of PPI weight network. It could
promote adipocyte differentiation to enhance blood glucose uptake. In
hsa03320 process, chrysin and 5,8,2′-trihydroxy-7-methoxyflavone were
directly connected with the PPARs. This further confirmed the
importance of the two α-glucosidase inhibitors. These data supported
the hypothesis that the α-glucosidase inhibitors from RS might
influence T2D through hsa04930 and hsa03320 processes.
Discussion
Many previous studies have investigated the antidiabetic constituents
of Radix scutellariae. For instance, Cui et al. used a tandem
quadrupole mass spectrometer coupled with enzyme activity analysis to
explore hypoglycemic effect of RS and Coptidis rhizome [[192]90]. This
method was accurate and sensitive enough for quantitative evaluation of
seven major components and six enzymes. The results indicated that
combined extract had stronger effects on T2D through multiple
components against multiple targets. Tahtah et al. applied triple
aldose reductase/α-glucosidase/radical scavenging high-resolution
profiling combined with high-performance liquid
chromatography-high-resolution mass spectrometry-solid-phase
extraction-nuclear magnetic resonance spectroscopy to identify
antidiabetic constituents from RS [[193]91]. Baicalein was screened as
α-glucosidase inhibitor. In another approach, a systematic study on
metabolism and activity evaluation of Radix Scutellaria extract in rat
plasma was conducted, using UHPLC with quadrupole time-of-flight mass
spectrometry [[194]92]. Wogonoside, norwogoin-7-O-Glu acid and
oroxyloside exhibited better binding affinities with α-glucosidase.
These results are partially consistent with those obtained in our
study. While they demonstrate versatility and success of phytochemical
analysis in the identification of novel ligands for therapeutic
targets, these studies are labor-intensive and time consuming
[[195]55]. Moreover, the mechanism analysis of active constituents is
often limited by existing knowledges and experiences. Our study
constructs a network model of compounds, target proteins and pathways
to explore mechanism of α-glucosidase inhibitors identified by
ultrafiltration LC-MS from RS. This approach is more rapidly and
extensive as the application of computational tools as well as systems
biology. The selected main active components, key targets and critical
pathways would provide more information for the interpretation of Radix
Scutellaria and T2D. However, these results are mainly based on
statistical analysis and prediction. Further studies on cell and animal
models are required to give a distinct answer.
Conclusion
This study presents one of the first systematic analyses of
α-glucosidase inhibitors from natural products using ultrafiltration
LC-MS/MS and network pharmacology. Our findings suggested a possible
application of flavonoids from Radix scutellariae in the treatment of
T2D. The n-butanol part of ethanol extract from RS showed strong
α-glucosidase inhibition activity. Thirty-two kinds of flavonoids were
identified from the extract, and 13 of them were screened as
α-glucosidase inhibitors, including viscidulin III, oroxylin A,
2′,3,5,6′,7-pentahydroxyflavanone, and so on. The results were strongly
supported by previous reports about natural flavonoids and T2D [[196]4,
[197]5, [198]14, [199]22, [200]66]. These compounds, together with
their target proteins and related pathways, were integrated into three
complex networks. The underlying mechanism of these natural
α-glucosidase inhibitors were revealed by network analyses. Chrysin,
5,8,2′-trihydroxy-7-methoxyflavone and wogonin were the main active
constituents. PPARG, PGH2, ACACB, and NFKB1 were key targets. The
α-glucosidase inhibitors from RS might influence T2D progression
through the type II diabetes mellitus and PPAR signaling pathways. In
all, the combination of ultrafiltration LC-MS and system pharmacology
would enable to generate novel insight into the research of plant
medicines.
Supplementary information
[201]12906_2020_2871_MOESM1_ESM.pdf^ (15.1KB, pdf)
Additional file 1. Total ions chromatogram (TIC) of the n-butanol part
of ethanol extract from Radix Scutellariae by UPLC-TripleTOF.
[202]12906_2020_2871_MOESM2_ESM.pdf^ (559.3KB, pdf)
Additional file 2. Primary fragmentation pathways of compound 14
(oroxylin A-7-O-β-D-Glucuronopyranoside), 16 (wogonoside), 17
(4′-hydroxywogonin), 23 (5,8,2′-trihydroxy-7-methoxyflavone), 26
(wogonin) and 28 (chrysin).
[203]12906_2020_2871_MOESM3_ESM.docx^ (266.1KB, docx)
Additional file 3. α-glucosidase inhibition curves of crude extract of
Radix scutellariae and acarbose.
[204]12906_2020_2871_MOESM4_ESM.docx^ (47.9KB, docx)
Additional file 4. Information of nodes in the compound-target-pathway
(CTP) network.
[205]12906_2020_2871_MOESM5_ESM.xlsx^ (21.8KB, xlsx)
Additional file 5. Degree values of CTP network, node strength and
centrality indices of PPI network.
Acknowledgments