Abstract
Background
Chinese medicine usually acts as "multi-ingredients, multi-targets and
multi-pathways" on complex diseases, and these action modes reflect the
coordination and integrity of the treatment process with traditional
Chinese medicine (TCM). System pharmacology is developed based on the
cross-disciplines of directional pharmacology, system biology, and
mathematics, has the characteristics of integrity and synergy in the
treatment process of TCM. Therefore, it is suitable for analyzing the
key ingredients and mechanisms of TCM in treating complex diseases.
Intracerebral Hemorrhage (ICH) is one of the leading causes of death in
China, with the characteristics of high mortality and disability rate.
Bring a significant burden on people and society. An increasing number
of studies have shown that Chinese medicine prescriptions have good
advantages in the treatment of ICH, and Ditan Decoction (DTT) is one of
the commonly used prescriptions in the treatment of ICH. Modern
pharmacological studies have shown that DTT may play a therapeutic role
in treating ICH by inhibiting brain inflammation, abnormal oxidative
stress reaction and reducing neurological damage, but the specific key
ingredients and mechanism are still unclear.
Methods
To solve this problem, we established PPI network based on the latest
pathogenic gene data of ICH, and CT network based on ingredient and
target data of DTT. Subsequently, we established optimization space
based on PPI network and CT network, and constructed a new model for
node importance calculation, and proposed a calculation method for PES
score, thus calculating the functional core ingredients group (FCIG).
These core functional groups may represent DTT therapy for ICH.
Results
Based on the strategy, 44 ingredients were predicted as FCIG, results
showed that 80.44% of the FCIG targets enriched pathways were coincided
with the enriched pathways of pathogenic genes. Both the literature and
molecular docking results confirm the therapeutic effect of FCIG on ICH
via targeting MAPK signaling pathway and PI3K-Akt signaling pathway.
Conclusions
The FCIG obtained by our network pharmacology method can represent the
effect of DTT in treating ICH. These results confirmed that our
strategy of active ingredient group optimization and the mechanism
inference could provide methodological reference for optimization and
secondary development of TCM.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12906-022-03831-7.
Keywords: Traditional chinese medicine (TCM), Intracerebral hemorrhage
(ICH), Important gene network motif, Mechanism, System pharmacology
Introduction
Hypertensive intracerebral hemorrhage (ICH) is one of the main causes
of death and disability among Chinese residents. According to the
statistics, the mortality rate is as high as 35%-52%, and about 80% of
the surviving patients still have disabilities within half a year
[[45]1], which brings great burden to individuals and society. ICH
mostly occurs in the area of perforator arteries. Under the influence
of long-term hypertension, these perforating arteries will have a
series of pathological changes, such as "lipid hyaline degeneration",
hyperplasia of subendothelial fibroblasts, deposition of macrophages,
and replacement of collagen-rich medial smooth muscle cells. These
changes result in decreased vascular compliance and lumen stenosis,
which is prone to cerebral hemorrhage when blood pressure fluctuates
greatly. Modern medicine mainly treats ICH by controlling blood
pressure, clearing hematoma and preventing rebleeding. Some basic
studies in recent years have revealed some key mechanisms for treating
cerebral hemorrhage. Isoliquiritigenin reduces early brain injury
following experimental intracerebral hemorrhage by suppressing ROS-
and/or NF-B-mediated NLRP3 inflammasome activation via the Nrf2
antioxidant pathway [[46]2]. Following ICH, the Striatal P2X7R and
NLRP3 inflammasomes were activated. P2X7R gene silencing inhibited
NLRP3 inflammasome activation and interleukin (IL)-1/IL-18 release,
significantly alleviating brain edema and neurological deficits
[[47]3].
In recent years, many experimental studies have been carried out on
ICH. Statins can improve neurological outcome and promote neurovascular
recovery after ICH [[48]4]. Mesenchymal stromal cells-derived exosomes
effectively improve functional recovery after ICH [[49]5]. However, due
to the limited treatments and effects, there are still great challenges
to the problems such as the disappearance of brain edema, rebleeding
after treatment, and recovery after nerve function injury. Increasing
evidence confirms that the prescriptions of TCM have been widely used
in the treatment of ICH, Ling et al. investigate that Tongfu Xingshen
Decoction can improve clinical symptoms of patients and promote the
recovery of neurological function by reducing serum S100β, IL-6 and
MMP-9 levels [[50]6]. Guo et al. reported that Angong Niuhuang Pill can
alleviate the brain damage caused by ICH from diuresis and dehydration,
reducing intracranial pressure and protecting central nervous system
[[51]7]. Zhou et al. confirmed that DTT can promote the recovery of
cognitive function and improve the clinical efficacy in patients with
ICH [[52]8]. Among these prescriptions, DTT is one of the most high
frequency used in clinic.
The DTT prescription contains 9 herbs: Glycyrrhiza uralensis Fisch. ex
DC.(1.5 g), Zingiber officinale Roscoe (1.5 g), Citrus reticulata
Blanco (2.1 g), Arisaema heterophyllum Blume (2.5 g). Panax ginseng
C.A.Mey.(3 g), Pinellia ternata (Thunb.) Makino(3 g), Poria cocos
(Schw.) Wolf.(3 g), Citrus acida Pers (2.5 g),
Bambusa tuldoides Munro (2.1 g).
The prescriptions of TCM have multiple ingredients, multiple targets
and multiple pathways, which are necessary factors for treating many
complex diseases. As a new subject, network pharmacology is beneficial
to identify the effective ingredients and explore the mechanisms of TCM
in modern pharmacology research. It is helpful to explain the
"combination-effect relationship" and compatibility rule of TCM at a
system level. The effect of TCM on cells and organisms is a complex
biological network, which is inline with the "integrity"
characteristics of systems pharmacology. Thus, system pharmacology is
suitable for studying the underlying rules of TCM prescription.
At present, several formula optimization methods have been proposed.
Most of these methods take the structural similarity of ingredients as
the most important feature but ignore the linkage of pathogenic genes
and drug targets. In this study, systems pharmacology was used to
detect FCIG and clarify the mechanisms of DTT in treating ICH.
We present a reverse optimization model based on the association
between disease genes and ingredient targets is proposed, which
provides space for optimization based on effective proteins. This
method can well determine the optimization space of the target. Second,
reverse searching related ingredients based on the optimized space
provided by effective protein. The results showed that the enrichment
functional pathway of effective proteins could cover 96% of the
enrichment functional pathway of disease genes.
Materials and methods
Construct weighted gene regulatory network of ICH
PPI data derived from Dip [[53]9], HPRD [[54]10], BioGRID [[55]11],
STRING [[56]12], Reactome [[57]13], Mint [[58]14] and Intact [[59]15]
were used to construct comprehensive protein–protein interaction
network (Supplementary Table [60]1). ICH related genes were extracted
from GeneCards. The genes with “Relevance Scores” higher than average
score were kept as high pathogenic gene. These pathogenic genes are
aligned into the PPI network to figure out the weighted gene regulatory
network of ICH. The Cytoscape software was utilized to visualize the
network.
Collect chemical ingredients of DTT
Ingredients of DTT were extracted from three herbal medicine databases:
TCMID and TCMSP. For all ingredients, the OpenBabel toolkit (version
2.4.1) was employed to convert the format of structure to canonical
SMILES. Subsequently, the chemical properties, such as molecular weight
(MW), DL (drug-likeness), Caco-2 permeability (Caco-2) and oral
bioavailability (OB) were retrieved from TCMSP.
Select potential active ingredients of DTT based on ICH models
Three published ADME-related modules, OB, Caco-2 and DL, were used to
select the bio-active ingredients. OB (%F) refers to the relative
amount and rate of absorption of drugs into circulation after oral
administration. High oral bio-availability is one of the key indicators
to determine the therapeutic properties of drugs. Selecting suitable
ingredients with OB ≥ 30% as potential active ingredients for next step
analysis. Caco-2 cell model is a human clonal colon adenocarcinoma
cell, which has similar structure and function as intestinal epithelial
cells, and it contains an enzyme system related to the brush border
epithelium of the small intestine, which can be used to simulate the
intestinal transport of drugs in vivo. The transport rate (nm/s) of
ingredients in Caco-2 cells represents the permeability of intestinal
epithelium. Ingredients with Caco-2 value less than -0.4 have poor
permeability in intestinal epithelial cells, so we choose ingredients
with Caco-2 > -0.4 as candidate ingredients. DL is used to evaluate the
drug-like properties of expected compounds, which is helpful to
optimize pharmacokinetics and drug characteristics, such as solubility
and chemical stability. In this study, the drug-like scores of the
ingredients were more than 0.18 as the selection criteria [[61]16].
After screening by ADME, some ingredients that do not meet the three
screening criteria are also selected, because experiments have proved
that they have high content and high biological activity. These
high-content and high-activity ingredients were merged with ADMET
screened ingredients for subsequent analysis.
Predicting targets of active ingredients
To obtain the targets of active ingredients in DTT, the widely used
prediction tools, HitPick, Swiss Target Prediction and Similarity
Ensemble Approach (SEA) [[62]8] and Swiss Target Prediction were used
to predict the targets. All chemical structures were converted to SMILE
format.
Node importance calculation method
For calculating the importance of each node in the network, we
constructed a Node importance calculation method (BCD), in which, m
represent the number of nodes in the network; h, i and j represent
nodes in the network, V represent the unit of all nodes in the network,
[MATH: σhi :MATH]
represents the number of the shortest path between nodes h and i;
[MATH: σhij :MATH]
is the number of the shortest path passing through node j.
[MATH: Th :MATH]
is the number of edges directly connected to a node h. N stands for
natural number. The method can be described as follows:
[MATH: BDh=Th×(∑h≠
i≠j∈Vσhi(j)σhi) :MATH]
There are m nodes in the network. Combining degree with the center
degree of betweenness, BD can effectively reflect the direct
relationship between nodes and neighboring nodes, as well as the
control function of neighboring nodes in the whole network
[[63]17–[64]19].
After being quantized, BD was sorted from small to large, and was
represented by a new variable Y.
[MATH: Y=Y1,Y2,Y3,⋯,Ym=BDx,BDx+1,
BDx+2,
⋯,BDx+n,x∈1,mandx+n=m :MATH]
The new variable R represented the nodes in the network. Each R
responds to its unique Y.
[MATH: R∈R1,R2,R3,⋯,Rm⇌Y1,Y2,Y3,⋯,Ym :MATH]
BCD represented the important nodes selected form all nodes in the
network. N represented natural number.
[MATH: BCD∈Rr,⋯,
R(m-2),R(m-1),Rm⇌Ym+1/2,⋯,Y(m-2),Y(m-1),Ym,m=2p+1andp∈NRr,⋯,<
mi mathvariant="normal">R(m-2),R(m-1),Rm⇌(Ym/2+Y(m+2)/2)2,⋯,Y(m-2),Y(m-1),Ym,m=2pandp∈N :MATH]
We collected all ingredients of DTT from databases and literature. All
ingredients of DTT were used to screen out potential active ingredients
by using previous reported ADME models. Three public online tools were
employed to identify the targets of the active ingredients. The active
ingredients and their targets were used to construct CT network. The
pathogenic genes were mapped to a comprehensive protein–protein
interaction network to obtain a pathogenic gene regulation network. CT
network and pathogenic gene regulation network were integrated and then
used to find potential effect space (PES) by using novel node
importance calculation model. Finally, the contribution index (CI)
model was employed to optimize the FCIG in the PES, and the key
functional ingredients were obtained, and the molecular mechanism of
DTT on ICH was expounded.
Gene ontology and pathway analysis
Function enrichment analysis is the basic way to understand gene
function. Here, the clusterProfiler of R package were performed to
conduct pathway and GO term enrichment analysis. The p value less than
0.05 were considered to be functionally related pathways and GO terms.
Experimental validation
Materials
Jiangsu Yongjian Biotech Co., Ltd provided 6-shogaol, 6-singerol,
kaempferol, and sitostrol (98% purity by HPLC) (Chengdu, China). Gibco
supplied fetal bovine serum (FBS) and Dulbecco's modified Eagle's
medium (DMEM) (Grand Island, USA). CHI SCIENTIFIC provided the mouse
hippocampal HT-22 cells (Shanghai, China). Mitsubishi Gas Chemical
Company, Inc. provided the hypoxic bags (Japanese). Dojindo
Laboratories supplied the Cell Counting Kit-8 (CCK-8) (Japanese).
Cell Culture and oxygen–glucose deprivation (ogd) treatment
The cells were cultured in DMEM with 10% FBS, and incubated at 37 °C
under 5% CO[2]. Hypoxic bags were used to perform OGD model according
to the methods in the literature [[65]20].
Cell viability assay
HT22 cells (6 × 10^4 cells/well) were seeded in 96-well plates, and
treated with different concentrations of 6-Shogaol, 6-Singerol,
kaempferol, and sitostrol (40, 80, 120, 160 and 200 μM). CCK8 was
superinduced to 96-well plate for 4 h. The plate reader was utilized to
detect the absorbance at 450 nm.
Statistical analysis
The data were all expressed as mean SEM. For multiple comparisons,
one-way ANOVA was used, and the Student's t test was used to compare
the significance of differences between two groups. If the p-value was
less than 0.05, the results were considered statistically significant.
Results
In this study, we designed a new system pharmacology module, which was
used to detect the key active ingredients and clarify the therapeutic
mechanism of DTT in treating ICH. The work flow is illustrated in
(Fig. [66]1) and described as follows: Firstly, all the effective
ingredients of DTT were collected from databases and literature. The
potential active ingredients were selected from DTT and predicted by
three published prediction tools. Then, the weighted gene regulatory
network and the active ingredient target network are used to construct
the PES to determine the effective protein. Selecting key active
ingredients based on CI module by using effective proteins. Finally,
the molecular mechanism of DTT in treating ICH was deduced from the
FCIG.
Fig. 1.
[67]Fig. 1
[68]Open in a new tab
The work scheme of system pharmacology approach
Chemical analysis
Chemical identification is the critical step to clarify the material
basis and action mechanism of compound prescription. In this study, the
information of specific chemical ingredients of DTT Chinese herbal
medicines was obtained from the literature (Table [69]1). The results
showed that the chemical ingredients of herbs and the content of
identified ingredients provided experimental auxiliary chemical space
for searching active ingredients. The chemical constituent analysis
serves as a reference for the screening of active constituents in DTT.
Table 1.
The information on chemical analysis of the herbs from the literature
in DTT
Herb Method Component Concentration Ref
Glycyrrhiza uralensis Fisch. ex DC HPLC Glycyrrhizin 98.01 mg/g Chen et
al., 2009 [[70]21]
Liquiritin 102.63 mg/g
Isoliquritigenin 98.18 mg/g
Zingiber officinale Roscoe HPLC 6-Gingerol 16.62 mg/g Zhang et al.,
2009 [[71]22]
6-Shogaol 4.92 mg/g
Citrus reticulata Blanco HPLC Naringin 24.87 mg/g Liu et al., 2013
[[72]23]
Hesperidin 2.19 mg/g
Arisaema heterophyllum Blume HPLC Guanosine 0.436 mg/g Zhang et al.,
2020 [[73]24]
Adenosine 0.642 mg/g
Chafertoside 0.618 mg/g
Isochafertoside 0.517 mg/g
Panax ginseng C.A.Mey HPLC–MS Rg1 0.205 mg/g Shang et al., 2018
[[74]25]
Re 0.175 mg/g
Rf 0.050 mg/g
Rb1 0.112 mg/g
Rb2 0.033 mg/g
Rd 0.016 mg/g
Pinellia ternata (Thunb.) Makino RP-HPLC Uridine 0.224 mg·g Chen et
al., 2013 [[75]26]
guanosine 0.337 mg·g
adenosine 0.084 mg·g
Poria cocos (Schw.) Wolf HPLC Dehydrotumonic acid 0.34 mg/g Peng et
al., 2017 [[76]27]
dehydrofulic acid 0.29 mg/g
poriatic acid 0.72 mg/g
melinolic acid 0.15 mg/g
Citrus acida Pers HPLC Naringenin 1.91 mg/g Zhan et al., 2015 [[77]28]
Hesperetin 1.37 mg/g
Marmin 1.52 mg/g
6’,7’-Dihydroxy bergamot 2.96 mg/g
Citronella 2.90 mg/g
Orange peel 2.18 mg/g
Bambusa tuldoides Munro HPLC Flavonoids 0.81 mg/mL Li et al., 2017
[[78]29]
[79]Open in a new tab
Select potential active ingredients
Although each TCM compound contains many ingredients, only a few
ingredients have satisfactory pharmacodynamic and pharmacokinetic
properties. In the present work, three ADME-related models, including
OB, Caco-2 and DL, are used to screen active ingredients. After ADME
screening, some ingredients not pass the criterion of three
ADME-related models were kept as active ingredients due to their high
content and high bio-activity. Finally, 181 active ingredients were
captured from 939 active ingredients (Table [80]2).
Table 2.
Components in DTT for further analysis after AD ME screening
Herb MOL_ID molecule_name ob caco2 drug-likeness
Fuling MOL000273
(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentame
thyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6
-methylhept-5-enoic acid 30.93 0.01 0.81
Fuling MOL000275 trametenolic acid 38.71 0.52 0.80
Fuling MOL000276 7,9(11)-dehydropachymic acid 35.11 0.03 0.81
Fuling MOL000279 Cerevisterol 37.96 0.28 0.77
Fuling MOL000280
(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentame
thyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-5
-isopropyl-hex-5-enoic acid 31.07 0.05 0.82
Fuling MOL000282 ergosta-7,22E-dien-3beta-ol 43.51 1.32 0.72
Fuling MOL000283 Ergosterol peroxide 40.36 0.84 0.81
Fuling MOL000285
(2R)-2-[(5R,10S,13R,14R,16R,17R)-16-hydroxy-3-keto-4,4,10,13,14-pentame
thyl-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]-5-iso
propyl-hex-5-enoic acid 38.26 0.12 0.82
Fuling MOL000287 3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid
38.70 0.61 0.81
Fuling MOL000289 pachymic acid 33.63 0.10 0.81
Fuling MOL000290 Poricoic acid A 30.61 -0.14 0.76
Fuling MOL000291 Poricoic acid B 30.52 -0.08 0.75
Fuling MOL000292 poricoic acid C 38.15 0.32 0.75
Fuling MOL000296 hederagenin 36.91 1.32 0.75
Fuling MOL000300 dehydroeburicoic acid 44.17 0.38 0.83
Renshen MOL000358 beta-sitosterol 36.91 1.32 0.75
Renshen MOL000422 kaempferol 41.88 0.26 0.24
Renshen MOL000449 Stigmasterol 43.83 1.44 0.76
Renshen MOL000749 Linoleic 41.90 1.23 0.14
Renshen MOL000787 Fumarine 59.26 0.56 0.83
Renshen MOL001641 METHYL LINOLEATE 41.93 1.44 0.17
Renshen MOL002879 Diop 43.59 0.79 0.39
Renshen MOL003648 Inermin 65.83 0.91 0.54
Renshen MOL004492 Chrysanthemaxanthin 38.72 0.51 0.58
Renshen MOL005308 Aposiopolamine 66.65 0.66 0.22
Renshen MOL005314 Celabenzine 101.88 0.77 0.49
Renshen MOL005317 Deoxyharringtonine 39.27 0.19 0.81
Renshen MOL005320 arachidonate 45.57 1.27 0.20
Renshen MOL005321 Frutinone A 65.90 0.89 0.34
Renshen MOL005348 Ginsenoside-Rh4_qt 31.11 0.50 0.78
Renshen MOL005356 Girinimbin 61.22 1.72 0.31
Renshen MOL005357 Gomisin B 31.99 0.60 0.83
Renshen MOL005360 malkangunin 57.71 0.22 0.63
Renshen MOL005366 Malvic acid 30.99 1.22 0.15
Renshen MOL005376 Panaxadiol 33.09 0.82 0.79
Renshen MOL005384 suchilactone 57.52 0.82 0.56
Renshen MOL005396 cis-Widdrol alpha-epoxide 69.04 1.07 0.15
Renshen MOL005399 alexandrin_qt 36.91 1.30 0.75
Renshen MOL005401 ginsenoside Rg5_qt 39.56 0.88 0.79
Juhong MOL000358 beta-sitosterol 36.91 1.32 0.75
Juhong MOL001040 (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one
42.36 0.38 0.21
Juhong MOL001798 neohesperidin_qt 71.17 0.26 0.27
Juhong MOL001942 isoimperatorin 45.46 0.97 0.23
Juhong MOL004328 naringenin 59.29 0.28 0.21
Juhong MOL005849 didymin 38.55 0.60 0.24
Juhong MOL013331 Isomeranzin 42.78 0.69 0.15
Juhong MOL013332 Pranferol 38.29 0.54 0.25
Juhong MOL013352 Obacunone 43.29 0.01 0.77
Tiannanxing MOL000131 EIC 41.90 1.16 0.14
Tiannanxing MOL000358 beta-sitosterol 36.91 1.32 0.75
Tiannanxing MOL000359 sitosterol 36.91 1.32 0.75
Tiannanxing MOL000432 linolenic acid 45.01 1.21 0.15
Tiannanxing MOL000449 Stigmasterol 43.83 1.44 0.76
Tiannanxing MOL000953 CLR 37.87 1.43 0.68
Tiannanxing MOL001510 24-epicampesterol 37.58 1.43 0.71
Tiannanxing MOL001641 METHYL LINOLEATE 41.93 1.44 0.17
Tiannanxing MOL002203 Exceparl M-OL 31.90 1.39 0.16
Tiannanxing MOL010925 Isooleic acid 33.13 1.15 0.14
Tiannanxing MOL013131 12,15-Octadecadienoic acid, methyl ester 41.93
1.46 0.17
Tiannanxing MOL013144 Methyl (6E,9E)-6,9-octadecadienoate 41.93 1.44
0.17
Tiannanxing MOL013145 Methyl-7,10-octadecadienoate 41.93 1.43 0.17
Tiannanxing MOL013146 8,11,14-Docosatrienoic acid, methyl ester 43.23
1.53 0.30
Tiannanxing MOL013156
[(2R)-2-[[[(2R)-2-(benzoylamino)-3-phenylpropanoyl]amino]methyl]-3-phen
ylpropyl] acetate 38.88 0.35 0.56
Banxia MOL000131 EIC 41.90 1.16 0.14
Banxia MOL000358 beta-sitosterol 36.91 1.32 0.75
Banxia MOL000432 linolenic acid 45.01 1.21 0.15
Banxia MOL000449 Stigmasterol 43.83 1.44 0.76
Banxia MOL000519 coniferin 31.11 0.42 0.32
Banxia MOL000675 oleic acid 33.13 1.17 0.14
Banxia MOL001755 24-Ethylcholest-4-en-3-one 36.08 1.46 0.76
Banxia MOL002495 6-shogaol 31.00 1.07 0.14
Banxia MOL002670 Cavidine 35.64 1.08 0.81
Banxia MOL002714 baicalein 33.52 0.63 0.21
Banxia MOL003578 Cycloartenol 38.69 1.53 0.78
Banxia MOL005030 gondoic acid 30.70 1.20 0.20
Banxia MOL006936 10,13-eicosadienoic 39.99 1.22 0.20
Banxia MOL006937 12,13-epoxy-9-hydroxynonadeca-7,10-dienoic acid 42.15
0.18 0.24
Banxia MOL006944 8-Octadecenoic acid 33.13 1.15 0.14
Banxia MOL006956 cyclo-(leu-tyr) 111.16 0.16 0.15
Banxia MOL006957
(3S,6S)-3-(benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone 46.89 0.41
0.27
Zhishi MOL000006 luteolin 36.16 0.19 0.25
Zhishi MOL000131 EIC 41.90 1.16 0.14
Zhishi MOL001798 neohesperidin_qt 71.17 0.26 0.27
Zhishi MOL001803 Sinensetin 50.56 1.12 0.45
Zhishi MOL001941 Ammidin 34.55 1.13 0.22
Zhishi MOL002914 Eriodyctiol (flavanone) 41.35 0.05 0.24
Zhishi MOL004328 naringenin 59.29 0.28 0.21
Zhishi MOL005100
5,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one 47.74 0.28
0.27
Zhishi MOL005828 nobiletin 61.67 1.05 0.52
Zhishi MOL005849 didymin 38.55 0.60 0.24
Zhishi MOL007879 Tetramethoxyluteolin 43.68 0.96 0.37
Zhishi MOL009053
4-[(2S,3R)-5-[(E)-3-hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydr
obenzofuran-2-yl]-2-methoxy-phenol 50.76 0.03 0.39
Zhishi MOL013277 Isosinensetin 51.15 1.16 0.44
Zhishi MOL013279 5,7,4'-Trimethylapigenin 39.83 1.01 0.30
Zhishi MOL013352 Obacunone 43.29 0.01 0.77
Zhishi MOL013430 Prangenin 43.60 0.80 0.29
Zhishi MOL013433 prangenin hydrate 72.63 0.14 0.29
Zhishi MOL013435 poncimarin 63.62 0.66 0.35
Zhishi MOL013436 isoponcimarin 63.28 0.50 0.31
Zhishi MOL013437 6-Methoxy aurapten 31.24 1.01 0.30
Zhishi MOL013443 isolimonic acid 48.86 0.43 0.18
Zhishi MOL013445 naringenin-4'-glucoside-7-rutinoside_qt 30.61 0.33
0.16
Gancao MOL000098 quercetin 46.43 0.05 0.28
Gancao MOL000211 Mairin 55.38 0.73 0.78
Gancao MOL000239 Jaranol 50.83 0.61 0.29
Gancao MOL000354 isorhamnetin 49.60 0.31 0.31
Gancao MOL000359 sitosterol 36.91 1.32 0.75
Gancao MOL000392 formononetin 69.67 0.78 0.21
Gancao MOL000417 Calycosin 47.75 0.52 0.24
Gancao MOL000422 kaempferol 41.88 0.26 0.24
Gancao MOL000497 licochalcone a 40.79 0.82 0.29
Gancao MOL000500 Vestitol 74.66 0.86 0.21
Gancao MOL001484 Inermine 75.18 0.89 0.54
Gancao MOL001789 isoliquiritigenin 85.32 0.44 0.15
Gancao MOL001792 DFV 32.76 0.51 0.18
Gancao MOL002311 Glycyrol 90.78 0.71 0.67
Gancao MOL002565 Medicarpin 49.22 1.00 0.34
Gancao MOL002844 Pinocembrin 64.72 0.61 0.18
Gancao MOL003656 Lupiwighteone 51.64 0.68 0.37
Gancao MOL003896 7-Methoxy-2-methyl isoflavone 42.56 1.16 0.20
Gancao MOL004328 naringenin 59.29 0.28 0.21
Gancao MOL004805
(2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydr
opyrano[2,3-f]chromen-4-one 31.79 1.00 0.72
Gancao MOL004806 euchrenone 30.29 1.09 0.57
Gancao MOL004808 glyasperin B 65.22 0.47 0.44
Gancao MOL004810 glyasperin F 75.84 0.43 0.54
Gancao MOL004811 Glyasperin C 45.56 0.71 0.40
Gancao MOL004814 Isotrifoliol 31.94 0.53 0.42
Gancao MOL004815
(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one
39.62 0.66 0.35
Gancao MOL004820 kanzonols W 50.48 0.63 0.52
Gancao MOL004824
(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dih
ydrofuro[3,2-g]chromen-7-one 60.25 0.00 0.63
Gancao MOL004827 Semilicoisoflavone B 48.78 0.45 0.55
Gancao MOL004828 Glepidotin A 44.72 0.79 0.35
Gancao MOL004829 Glepidotin B 64.46 0.46 0.34
Gancao MOL004833 Phaseolinisoflavan 32.01 1.01 0.45
Gancao MOL004835 Glypallichalcone 61.60 0.76 0.19
Gancao MOL004836 echinatin 66.58 0.38 0.17
Gancao MOL004838 8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol
58.44 1.00 0.38
Gancao MOL004841 Licochalcone B 76.76 0.47 0.19
Gancao MOL004848 licochalcone G 49.25 0.64 0.32
Gancao MOL004849
3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy
-coumarin 59.62 0.40 0.43
Gancao MOL004855 Licoricone 63.58 0.53 0.47
Gancao MOL004856 Gancaonin A 51.08 0.80 0.40
Gancao MOL004857 Gancaonin B 48.79 0.58 0.45
Gancao MOL004863
3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone
66.37 0.52 0.41
Gancao MOL004864
5,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone 30.49
0.90 0.41
Gancao MOL004866
2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone
44.15 0.48 0.41
Gancao MOL004879 Glycyrin 52.61 0.59 0.47
Gancao MOL004882 Licocoumarone 33.21 0.84 0.36
Gancao MOL004883 Licoisoflavone 41.61 0.37 0.42
Gancao MOL004884 Licoisoflavone B 38.93 0.46 0.55
Gancao MOL004885 licoisoflavanone 52.47 0.39 0.54
Gancao MOL004891 shinpterocarpin 80.30 1.10 0.73
Gancao MOL004898
(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphen
yl)prop-2-en-1-one 46.27 0.41 0.31
Gancao MOL004904 licopyranocoumarin 80.36 0.13 0.65
Gancao MOL004905
3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oi
c acid 34.32 -0.06 0.55
Gancao MOL004907 Glyzaglabrin 61.07 0.34 0.35
Gancao MOL004908 Glabridin 53.25 0.97 0.47
Gancao MOL004910 Glabranin 52.90 0.97 0.31
Gancao MOL004911 Glabrene 46.27 0.99 0.44
Gancao MOL004912 Glabrone 52.51 0.59 0.50
Gancao MOL004913 1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone
48.14 0.48 0.43
Gancao MOL004914
1,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone 62.90 0.40
0.53
Gancao MOL004915 Eurycarpin A 43.28 0.43 0.37
Gancao MOL004935 Sigmoidin-B 34.88 0.42 0.41
Gancao MOL004941 (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one 71.12
0.41 0.18
Gancao MOL004945
(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one
36.57 0.72 0.32
Gancao MOL004948 Isoglycyrol 44.70 0.91 0.84
Gancao MOL004949 Isolicoflavonol 45.17 0.54 0.42
Gancao MOL004957 HMO 38.37 0.79 0.21
Gancao MOL004959 1-Methoxyphaseollidin 69.98 1.01 0.64
Gancao MOL004961 Quercetin der 46.45 0.39 0.33
Gancao MOL004966 3'-Hydroxy-4'-O-Methylglabridin 43.71 1.00 0.57
Gancao MOL004974 3'-Methoxyglabridin 46.16 0.94 0.57
Gancao MOL004978
2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methox
yphenol 36.21 1.12 0.52
Gancao MOL004980 Inflacoumarin A 39.71 0.73 0.33
Gancao MOL004985 icos-5-enoic acid 30.70 1.22 0.20
Gancao MOL004988 Kanzonol F 32.47 1.18 0.89
Gancao MOL004989 6-prenylated eriodictyol 39.22 0.40 0.41
Gancao MOL004990 7,2',4'-trihydroxy-5-methoxy-3-arylcoumarin 83.71 0.24
0.27
Gancao MOL004991 7-Acetoxy-2-methylisoflavone 38.92 0.74 0.26
Gancao MOL004993 8-prenylated eriodictyol 53.79 0.43 0.40
Gancao MOL004996 gadelaidic acid 30.70 1.20 0.20
Gancao MOL005000 Gancaonin G 60.44 0.78 0.39
Gancao MOL005001 Gancaonin H 50.10 0.60 0.78
Gancao MOL005003 Licoagrocarpin 58.81 1.23 0.58
Gancao MOL005007 Glyasperins M 72.67 0.49 0.59
Gancao MOL005008 Glycyrrhiza flavonol A 41.28 -0.09 0.60
Gancao MOL005012 Licoagroisoflavone 57.28 0.71 0.49
Gancao MOL005016 Odoratin 49.95 0.42 0.30
Gancao MOL005017 Phaseol 78.77 0.76 0.58
Gancao MOL005018 Xambioona 54.85 1.09 0.87
Gancao MOL005020 dehydroglyasperins C 53.82 0.68 0.37
Shengjiang MOL000358 beta-sitosterol 36.91 1.32 0.75
Shengjiang MOL002467 6-gingerol 35.64 0.54 0.16
Shengjiang MOL002495 6-shogaol 31.00 1.07 0.14
Shengjiang MOL006129 6-methylgingediacetate2 48.73 0.55 0.32
[81]Open in a new tab
Construct the weighted gene regulatory network of ICH
Constructing and analyzing weighted gene regulatory network is the
basis and key step to understand the pathogenesis of ICH and provide
intervention strategies. We obtained the genes related to ICH from
Genecard database. We selected 2648 genes larger than the average score
as the genes related to ICH. At the same time, we used dispenet for
comparison, and found that the selected genes could cover 88% of the
dispenet library. The comprehensive PPI network, which combined by from
several PPI databases were download from the CMGRN [[82]30] and PTHGRN
[[83]31]. A total of 999 genes were extracted from DisGeNET and OMIM
related to ICH, and mapped to PPI network, and a weighted gene
regulatory ne48.82twork was constructed. The weighted gene regulatory
network contains 999 nodes and 46,432 edges (Fig. [84]2). In the
disease network, the degree indicates the importance of nodes. We
further analyze it by using Network Analyzer software. In PPI network,
the target average degree of different ingredients is 48.82. Among the
top weighted targets, APP [[85]32], NOTCH3 [[86]33], KRIT1, CCM2,
PDCD10, CST3, ENG, TREX1, ATRIP, APOE, etc.
Fig. 2.
[87]Fig. 2
[88]Open in a new tab
The disease weight gene regulatory network of ICH
Common ingredients of Chinese herbal medicines in DTT
It can be seen from Table [89]3 that there are 12 active ingredients
shared by more than two kinds of herbs in DTT. For example,
β-sitosterol is a common ingredient of ginseng, Arisaema cum, orange,
ginger and Pinellia ternate. Studies have found that β-sitosterol has a
strong inhibitory effect on oxygen free radicals and a strong
antioxidant effect on oils and fats [[90]34]. Naringin is shared by
Fructus Aurantii Immaturus, Glycyrrhrizae Radix and Exocarpium Citri
Grandis. Naringin can obviously alleviate permanent nerve injury and
has protective effect. Its mechanism is to down-regulate the expression
of NOD2, RIP2 and MMP-9, up-regulate the expression of claudin-6 and
protect the blood–brain barrier [[91]35]. 6-gingerol is a common
ingredient of Pinellia ternata and Ginger. 6- gingerol may protect PC12
cells against apoptosis induced by Aβ 1–42 through PI3K/Akt/GSK-3β
signaling pathway, and has protective effect on nerve cells [[92]36].
In addition, linolenic acid is shared by Pinellia ternata and Arisaema
cum laude. Experiments show that increasing DHA level in brain can
limit the expression of inflammatory factors after TBI and accelerate
the recovery of nerve function [[93]37].
Table 3.
12 active ingredients shared by more than two kinds of herbs in DTT
ingredients Herbs
6-shogaol Pinellia ternata (Thunb.) Makino; Zingiber officinale Roscoe
β-sitosterol Pinellia ternata (Thunb.) Makino; Zingiber officinale
Roscoe; Citrus reticulata Blanco; Panax ginseng C.A.Mey
didymin Citrus acida Pers; Citrus reticulata Blanco
EIC Pinellia ternata (Thunb.) Makino; Citrus reticulata Blanco;
Arisaema heterophyllum Blume
kaempferol Glycyrrhiza uralensis Fisch. ex DC.; Panax ginseng C.A.Mey
linolenic acid Pinellia ternata (Thunb.) Makino; Arisaema heterophyllum
Blume
methyl linoleate Arisaema heterophyllum Blume; Panax ginseng
C.A.Mey.(Renshen)
naringenin Citrus reticulata Blanco; Citrus reticulata Blanco (Juhong);
Glycyrrhiza uralensis Fisch. ex DC
neohesperidin_qt Citrus acida Pers; Citrus reticulata Blanco
Obacunone Citrus acida Pers; Citrus reticulata Blanco
sitosterol Arisaema heterophyllum Blume; Glycyrrhiza uralensis Fisch.
ex DC
Stigmasterol Pinellia ternata (Thunb.) Makino; Arisaema heterophyllum
Blume; Panax ginseng C.A.Mey
[94]Open in a new tab
Special ingredients of Chinese herbal medicine in DTT
Apart from the common ingredients, most herbs have their specific
ingredients. For example, the main ingredient of ginseng is
ginsenosides. Studies by Shi et al. and others have found that neural
stem cells are activated and proliferated after intracerebral
hemorrhage [[95]38]. Ginsenosides can induce the proliferation of
neural stem cells and improve motor function after intracerebral
hemorrhage. Ferulic acid is one of the main ingredients of Arisaema cum
laude. Ferulic acid reduces the expression of phosphorylated IKK and
the transport of NRF2 and NF-kappab to the nucleus, thus inhibiting the
activities of IL-6 and NF-kappab promoters. These data indicate that
ferulic acid could inhibit inflammatory by mediating IKK/NF-kappab
signaling pathway [[96]39]. Naringin (DTT187) is one of the most
effective ingredients in tangerine peel. Liu Wei et al. found that
naringin pre-intervention can effectively alleviate cerebral
ischemia–reperfusion injury, and the protective effect is closely
related to the decrease of Cx43 expression in astrocytes [[97]40].
Target prediction of active ingredients
In order to deduce the underlying mechanism of DTT in the treatment of
ICH, 181 active ingredients and 1544 target points were employed to
construct the ingredient target network. Among them, several active
ingredients are associated with multiple targets, resulting in 41,650
target relationships between 181 active ingredients and 1544 targets.
The average target number for each ingredient is 47.11. The results
indicate that DTT is a multi-ingredient and multi-target therapy for
ICH. Among these ingredients, the target of MOL000131 (degree = 333) is
the most, followed by MOL000358 (degree = 300), MOL002495
(degree = 246), MOL013156 (degree = 217), MOL000442 (degree = 156) and
MOL000432 (degree = 146). Most of these ingredients are related to
inflammation and oxidation related pathways of DTT. For example,
6-gingerol has anti-inflammatory and antioxidant effects, which can
improve the activities of SOD), glutathione peroxidase and catalase in
organisms, eliminate hydroxyl radicals and superoxide radicals, and
reduce lipid peroxides in tissues [[98]41]. Stigmasterol can
significantly inhibit the increase of ROS induced by Ang ii in A7r5
cells, and increase the activities of SOD and CAT enzymes in A7r5 cells
treated by Ang ii. In addition, stigmasterol can obviously inhibit the
increased iNOS mRNA and protein levels of COX-2 and iNOS induced by
LPS, and has anti-inflammatory effect [[99]42]. The role of other
ingredients in the treatment of ICH has been described in the chapters
of "Shared Ingredients of Chinese Herbal Medicine in DTT" and "Specific
Ingredients of Chinese Herbal Medicine in DTT". These results proved
the important role of these ingredients in ICH and further suggested
that the multi-ingredient role of DTT in treating ICH.
In the ingredient target network, the average target degree of
different targets is 3.86. Among the 20 weighted targets, ABCG2, ABCB1,
ALOX5, ALOX15, ALOX12, etc. Interestingly, most of these targets are
related to oxidation and inflammation, which have been confirmed to be
related to the pathogenesis of ICH, and may indicate the potential
therapeutic mechanism of DTT on ICH. For example, caspase-3 is closely
related to cerebral hemorrhage and is one of the main factors of
neuronal apoptosis. The activation of caspase-3 after intracerebral
hemorrhage may be the mechanism of ischemia–reperfusion injury caused
by secondary cerebral ischemia around hematoma [[100]43]. In addition,
the early release of cytochrome C may be related to hematoma
occupation, ischemia and hypoxia of peripheral neurons caused by brain
edema, energy metabolism disorder, calcium overload, oxidative stress
and the production of a large number of free radicals.The mechanism by
which DTT can block the release of cytochrome C is related to the
mitochondrial permeability transition pore channel and the Bcl-2 family
of proteins. By blocking the release of cytochrome C into the
cytoplasm, the apoptosis of neurons was prevented [[101]44].
These results suggest that DTT can treat ICH synergistically by
regulating inflammation and antioxidant function, which further
confirms the multi-target role of DTT in ICH treatment.
Functional proteins selection and validation based on dtt potential effect
space
We constructed a C-T-P network based on PPI using the weighted
pathogenic gene regulatory network of ICH and the target network of
active ingredients. In this network, it contains 2786 nodes and 45,647
edges. We extracted the relationship between drug targets and
pathogenic genes from C-T-P network and constructed DTT potential
effect space. Node importance is an important factor to be considered
in optimizing network. At present, the methods to describe node
importance mainly include degree, betweenness, closeness to centrality,
shortest path and so on. These methods mainly depend on the nature of a
certain aspect of the network to describe the importance of nodes in
the network. In this study, we designed a new network importance
calculation method, which takes into account the influence and
connectivity of nodes. The comparison results show that the therapeutic
response protein obtained by our node importance calculation method
accounts for 92.57% of the pathogenic gene enrichment pathway in go
function enrichment analysis, which shows that our method has good
accuracy. There are three types of proteins in DTT potential effect
space. The first is the direct interaction between pathogenic genes and
drug targets. We define this category as the basic common targets. The
second category is the interaction of disease-specific targets. The
third category is the interaction of ingredient-specific targets. In
order to determine whether the therapeutic response protein we selected
from DTT PES is optimal, we examined the coverage of the enrichment
pathway of therapeutic response protein, common target,
ingredient-specific target and disease-specific target in the
enrichment pathway of pathogenic genes from the functional level. The
results showed that the coverage rate of therapeutic response protein
was as high as 84.9%, which was 3.1%, 80.6% and 16.8% higher than the
three, indicating that the therapeutic response protein we selected had
better functional coverage (Fig. [102]3A and [103]B).
Fig. 3.
[104]Fig. 3
[105]Open in a new tab
Functional proteins validation. KEGG pathways distribution of response
proteins, common targets, ingredient-specific targets, disease-specific
targets and pathogenic genes (A); the coverage rate of response
proteins, common targets, ingredient-specific targets, and
disease-specific targets enriched pathways compared with pathogenic
gene-enriched pathways of ICH (B)
The contribution coefficient model is constructed to optimize the
effective ingredients and obtain FCIG values, which can be used to
clarify the potential mechanism of DTT in treating ICH. According to
the result of contribution accumulation rate (Fig. [106]4), the
cumulative contribution rate of the first 44 ingredients reaches
90.75%, which is selected as FCIG. Include EIC (MOL000131), 6-shogaol
(MOL002495), [(2r)-2-[[(2r)-2-(benzoylamino)-3-phenylpropyl] amino]
methyl]-3-phenylpropyl] acetate (n = 1) Beta-sitosterol (MOL000358),
kaempferol (MOL000422), methyl linoleate (MOL001641), etc.
Fig. 4.
[107]Fig. 4
[108]Open in a new tab
The contribution accumulation rate of DTT FCIG
There is a high consistency between FCIG and C-T network in the number
of pathogenic genes. In order to evaluate whether the genes in FCIG are
related to ICH, we compared the coverage of pathogenic genes in FCIG
and C-T networks. We collected the published literature and the known
ICH pathogenic genes in the database, which were verified by the
average score of GeneCards greater than 6. It was found that there were
389 pathogenic genes in C-T network and 348 pathogenic genes in FCIG of
DTT (Fig. [109]5A and [110]B). The FCIG of DTT reached 89.5% of C-T
network (Fig. [111]5C). This result shows that our FCIG detection model
can maximize the coincidence degree of pathogenic genes in the formula
CTT network.
Fig. 5.
[112]Fig. 5
[113]Open in a new tab
Venn diagram was used to visualize the overlap number of pathogenic
genes between C-T network targets (A) and FCIG network targets (B), and
the overlap number of C-T network targets overlap pathogenic genes and
FCIG network targets overlap pathogenic genes
FCIG is highly consistent with C-T network in gene function level:
another index to evaluate the importance of FCIG network is determined
by their functional consistency, which can be evaluated by their
enrichment pathway in KEGG. The purpose is to test whether FCIG found
in DTT can represent its complete C-T network. This result shows that
our FCIG detection model can cover C-T network functions to the maximum
extent (Fig. [114]6). There is a high degree of coincidence between
FCIG and C-T network in topology: node degree is a key topological
parameter to characterize network topology, and it is also the most
influential node in the network, so we use it to further determine the
importance of FCIG. A mathematical model was established to evaluate
the importance of network FCIG in DTT. Then the CC value of FCIG in DTT
was calculated by topological parameters. The results show that FCIG
can cover 80.44% of C-T composed of DTT. The results show that the FCIG
in DTT is consistent with the pathway and pathogenic genes, and our
FCIG model can maximize the coverage of network topology and C-T
network composed of DTT.
Fig. 6.
[115]Fig. 6
[116]Open in a new tab
The functional similarity analysis between targets of C-T network and
FCIG
Functional core ingredients selection and validation
GO enrichment analysis was performed using the R software
clusterProfiler package to identify the biological functions of the
main targets with p values < 0.05. To further profile the combined
effects of DTT, all targets that interacted with FCIG in DTT were
enriched by GO enrichment analysis (Fig. [117]7).
Fig. 7.
[118]Fig. 7
[119]Open in a new tab
Go enrichment analysis of the targets of FCIG
DTT regulatory targets were abundantly expressed in biological
processes related to inflammatory response, according to GO
analysis.For example, reducing oxidative stress (GO:0,070,482,
GO:0,036,293, GO:0,061,418) leukocyte activation involved in
inflammatory responses (GO:0,002,758), the production of molecular
mediators involved in inflammatory responses (GO:0,070,498), and
inflammatory responses to antigen stimulation (GO:0,002,220). These
results confirmed that DTT could treat ICH by reducing inflammatory
reaction, reducing oxidative stress and inhibiting apoptosis
(Fig. [120]8).
Fig. 8.
[121]Fig. 8
[122]Open in a new tab
Pathway enrichment analysis of the targets of DTT
Potential mechanisms analysis of DTT in treating ICH
Pathological changes of ICH are related to many factors, among which
oxidative stress and apoptosis are the key factors. When oxidative
stress occurs, a large amount of reactive oxygen species will damage
the blood–brain barrier, mediate demyelination and axonal injury, and
activate various signal pathways to induce and aggravate autoimmune
inflammation and neuronal apoptosis. We found 100 pathways shared by
pathogenic genes, including MAPK signaling pathway (hsa04010), PI3K-Akt
signaling pathway (HSA 04,151) and so on. More and more evidences show
that these pathways and the pathogenesis or therapeutic targets of ICH,
such as MAPK signaling pathway (hsa04010), mainly regulate protein
synthesis, cell proliferation and apoptosis. MAPK plays a positive role
in the initiation and recognition of oxidative stress, which has a
positive correlation with brain injury. Using MAPK-related protein
blockers can play a role in brain protection, and the effect of
combining blockers is better. This suggests that multiple blocking of
oxidative stress-related signaling pathways is one of the research
ideas to reduce brain injury. DTT can play a role in pathological
changes of cerebral hemorrhage by regulating apoptosis and oxidative
stress pathway. In order to deduce the underlying mechanism of DTT on
ICH, a comprehensive signal pathway was constructed at the system
level. In order to figure out the location of DTT target on the
pathway, the first three columns were defined as the upstream and the
others were defined as the downstream of the pathway. We found that
PI3K-Akt signaling pathway (hsa04151) is one of the most important
pathways in the treatment of ICH with DTT (Fig. [123]9).
Fig. 9.
[124]Fig. 9
[125]Open in a new tab
Distribution of ingredients of DTT on the compressed ICH pathway
Experimental validation in vitro
The effects of 6-Shogaol, 6-Singerol, kaempferol, and sitostrol on OGD
model were evaluated. Compared with control group, the cell viability
of OGD groups were significantly reduced. Compared with control group,
the effect of cell viability was significantly decreased by 31.47% in
the hypoxia treated cells (Fig. [126]10). However,6-Singerol
(40 μM、80 μM、120 μM、160 μM and 200 μM) markedly increased the cell
viability level by18.81%、19.98%、21.96%、16.53% and 23.47%.Compared with
control group, the effect of cell viability was significantly decreased
by 36.40% in the hypoxia treated cells. However,6-Singerol (160 μM and
200 μM) markedly increased the cell viability level by 20.96% and
20.44%.Compared with control group, the effect of cell viability was
significantly decreased by 40.61% in the hypoxia treated cells.
However, kaempferol (200 μM) markedly increased the cell viability
level by 29.01%.Compared with control group, the effect of cell
viability was significantly decreased by 35.08% in the hypoxia treated
cells. However, sitostrol (200 μM) markedly increased the cell
viability level by 23.65%.The above results demonstrated that
6-Shogaol, 6-Singerol, kaempferol, and sitostrol possessed protect
effect in hypoxia treated HT22 cells.
Fig. 10.
[127]Fig. 10
[128]Open in a new tab
Effects of 6-Shogaol (A),6-Singerol (B), kaempferol (C), and sitostrol
(D) on cell viabilities. ***p < 0.001 compared with control group.
#p < 0.05, #p < 0.05, ###p < 0.001 compared with the OGD group
Discussion
Reducing non-pharmacological ingredients and improving curative effects
are the main objectives of formula optimization. According to the
theory of Chinese medicine, different herb medicines make up
prescriptions, but whether the herbs or ingredients in prescriptions
are necessary, especially for certain indications, needs to be analyzed
and verified. By optimizing the formula, the medicinal materials or
ingredients with certain efficacy were screened out, which made the
formula more clarified and the efficacy more enhanced. In order to
better capture the clinical efficacy of classical prescriptions,
bioinformatics and systems pharmacology methods were combined to study
the coverage rate of changed targets, and the relevant functional
analysis was made on the coverage of pathogenic genes. The changed
targets responds to the combination changes of different herbs and
different chemical ingredients in each formula. In order to find the
optimal FCIG, the optimization space definition and ingredient reverse
search strategy were applied to evaluate it. At present, how to
optimize and obtain FCIG and deduced underlying mechanisms is the
foundation of TCM research. TCM analysis focus on the holistic view and
regards the body as a whole. Systems pharmacology focuses on analyzing
the action mechanisms of formula from a systematic and holistic
perspective, which accords with the theoretical system of TCM research.
Systems pharmacology emphasizes the multi-target regulation of multiple
signal pathways to promote the synergistic effect of drugs and reduce
toxic and side effects. At present, systems pharmacology has been
widely used in the research of TCM formulas, especially to determine
the molecular mechanism of treating complex diseases with TCM formula.
However, there are few reports on optimization of TCM formulas based on
systems pharmacology. On this basis, a comprehensive optimization
strategy of DTT based on network pharmacology is proposed, and the key
ingredients of DTT in treating ICH are obtained, and the potential
mechanism of these ingredients is analyzed.
Our method has two advantages: 1. In the process of analyzing the
treatment mechanism, network pharmacology has formed a fixed analysis
rule, that is, according to the chemical properties of Chinese medicine
ingredients, through screening, predicting targets and analyzing
potential mechanisms of action. This flow chart solves the molecular
mechanism of some prescriptions for treating complex diseases with TCM,
but there is also a problem that there is no reference in the
optimization space. In this paper, we identified the representative
pathogenic genes of ICH, and these pathogenic genes have been reported
in the literature. For example, APP, NOTCH3 weighted gene regulatory
network of ICH, Zhu Bin et al. found that amyloid precursor protein
(APP) mutation can cause typical pathological changes of AD and
perivascular amyloid deposition. Fu Jiayu et al. showed that NOTCH3
gene mutation may cause cerebral hemorrhage by changing the structure
and function of cerebral small vessels. We present a reverse
optimization model based on the association between disease genes and
ingredient targets is proposed, which provides space for optimization
based on effective proteins. This method can well determine the
optimization space of the target. Second, reverse searching related
ingredients based on the optimized space provided by effective protein.
The results showed that the enrichment functional pathway of effective
protein could cover 96% of the enrichment functional pathway of disease
genes. It is proved that our strategy of selecting effective proteins
to construct the target optimization space is correct. On the basis of
optimization space provided by effective protein, CI model was used to
optimize the contribution degree, and finally 82kgec was optimized. The
target of FCIG is closely related to the pathogenesis and functional
annotation of ICH. This proves the reliability of our optimization
space and CI model again.
At present, network pharmacology provides a powerful tool for exploring
the compatibility and action mechanism of TCM prescriptions. However,
there are some limitations. For example, more main active ingredients
should be considered in animal medical research of DTT. We will verify
the efficacy and mechanism of active ingredients in treating ICH
through in vivo or in vitro experiments. The results show that the
model has good accuracy in screening FCIG in TCM formula and provides
reference for optimization and mechanism deduction of TCM formulas.
Conclusion
A network pharmacology model-based bioinformatics algorithm was
established to obtain functional core ingredients group and decode the
mechanisms of DTT in the treatment of ICH. Compared with other
published work, potential effect space construction strategy based on
novel node importance calculation method and validation strategy and
maximum targeting weight model for mechanism speculation were reported.
In addition, this new systemic pharmacology model closely combines the
representative pathogenic genes of ICH, which can reflect the incidence
of ICH well, and can effectively screen out the core ingredients of DTT
in treating ICH.
Results of the in vitro validation that 6-Shogaol, 6-Singerol,
kaempferol, and sitostrol possessed protect effect in hypoxia treated
HT22 cells demonstrated that the core ingredient group selected by us
based on system pharmacology had significant effect on ICH.
Our research is a computational mining work based on pharmacological
basic data, which provides a feasible scheme to reduce the verification
scale for the experiment, provides methodological reference for
optimization of core ingredients group and interpretation of the
molecular mechanism in the treatment of complex diseases using TCM. Our
model has been proved to be effective in the compound optimization of
DTT for the treatment of ICH. In future studies, we hope to apply this
model to more studies and get better improvements, so as to provide
methodological reference for the treatment of ICH with Chinese
medicine.
Supplementary Information
[129]12906_2022_3831_MOESM1_ESM.xlsx^ (565.9KB, xlsx)
Additional file 1: Supplementary Table 1. Protein-Protein Interaction
Network.
Acknowledgements