Skip to content

Advertisement

  • Research
  • Open Access

A network pharmacology approach to explore the mechanisms of Erxian decoction in polycystic ovary syndrome

Chinese Medicine201813:46

https://doi.org/10.1186/s13020-018-0201-1

  • Received: 29 May 2018
  • Accepted: 18 August 2018
  • Published:

Abstract

Background

Polycystic ovary syndrome (PCOS) significantly affects women’s health and well-being. To explore the pharmacological basis of the Erxian decoction (EXD) action in PCOS therapy, a network interaction analysis was conducted at the molecular level.

Methods

The active elements of EXD were identified according to the oral bioavailability and drug-likeness filters from three databases: traditional Chinese medicine system pharmacology analysis platform, TCM@taiwan and TCMID, and their potential targets were also identified. Genes associated with PCOS and established protein–protein interaction networks were mined from the NCBI database. Finally, significant pathways and functions of these networks were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to determine the mechanism of action of EXD.

Results

Seventy active compounds were obtained from 981 ingredients present in the EXD decoction, corresponding to 247 targets. In addition, 262 genes were found to be closely related with PCOS, of which 50 overlapped with EXD and were thus considered therapeutically relevant. Pathway enrichment analysis identified PI3k-Akt, insulin resistance, Toll-like receptor, MAPK and AGE-RAGE from a total of 15 significant pathways in PCOS and its treatment.

Conclusions

EXD can effectively improve the symptoms of PCOS and our systemic pharmacological analysis lays the experimental foundation for further clinical applications of EXD.

Keywords

  • System pharmacology
  • Erxian decoction
  • Polycystic ovary syndrome
  • Pharmacological mechanism
  • Targets

Background

Polycystic ovary syndrome (PCOS) affects 5–20% of all reproductive aged women around the world, and is characterized by hyper-androgenism, infertility, irregular menstrual cycle and polycystic ovarian morphology (PCOM) due to abnormal production of androgens by the ovaries [1]. In addition, metabolic disruptions like hyperinsulinemia and abnormal adipokine secretion from the adipose tissue is also seen [2]. PCOS not only compromises women’s physical and mental health, but also increases the risk of type 2 diabetes mellitus (T2DM), atherosclerosis, cardiovascular disease, endometrial cancer, breast cancer and other long-term complications. Currently, PCOS treatment mostly relies on anti-androgen drugs, insulin sensitizers, and ovulation-promoting drugs [3]. Studies show that PCOS is frequently associated with insulin signaling [4], PI3K-Akt Signaling Pathway [5], FoxO 1 Signaling [6], and non-alcoholic fatty liver disease (NAFLD) [7], but the underlying mechanisms are not clear.

Traditional Chinese medicine (TCM) has been continually practiced since 2000 years. The Erxian decoction (EXD) consists of six herbs: Epimedium brevicornum (Yinyanghuo), Curculigo orchioides (Xianmao), Morinda officinalis (Bajitian), Angelica sinensis (Danggui), Anemarrhena asphodeloides (Zhimu) and Phellodendron chinense (Huangbo), and is used to mitigate menopausal side effects [8], osteoporosis [9], and ovarian failure [10]. One study showed that EXD upregulated estrogen receptor, enhanced ovarian function, reduced serum FSH and LH levels, increased E2 and progesterone levels, decreased malonic dialdehyde (MDA) in ovarian tissues, increased total anti-oxidative capacity (T-AOC), reduced follicular atresia by increasing follicle count, and reduced cell stromal hyperplasia [11]. Another study found that EXD could restore menstrual cycle, regulate hypothalamic-pituitary-ovarian axis function, increase steroid hormone secretion, restore primordial follicle recruitment and superior follicle selection, and improve ovulation rate and ovarian function [12]. Although EXD has been used clinically for gynecological diseases for more than 60 years, its mechanism of action is unclear due to its complex composition. In order to enhance their therapeutic efficacy, it is essential to elucidate the molecular and biological basis of TCM preparations. Systems pharmacology (SP) has recently emerged as a technique to decipher complex pharmacological problems [13]. Recently, the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was developed as a digital repository of traditional medicines. In addition, it can predict pharmacological targets and specific maladies of every dynamic compound, and is a major analytical tool in network pharmacology that helps determine the complex interactions between drugs and targets [14]. Since TCM formulations have multiple targets and complex ingredients, SP can even predict novel compounds based on existing formulations [15]. Network pharmacology has helped elucidate the mechanism of several TCM formulations so far [16, 17]. In this study, we used the SP approach to determine the potential mechanism of EXD action in treating PCOS. We first screened the TCMSP database for active compounds of EXD and identified its targets, followed by mining for disease-related genes, and network analysis of those genes (Fig. 1).
Fig. 1
Fig. 1

A schematic diagram of the systems biology-based methodologies for unraveling the pharmacological basis of EXD action in PCOS

Methods

Identification of active EXD compounds

TCMSP (http://lsp.nwu.edu.cn/tcmsp.php) is a Chinese medicine pharmacology database containing information about the herbs used in TCM, and absorption, distribution, metabolism and excretion (ADME) characteristics of the individual compounds, their targets, related diseases, and pathways. The database search for EXD revealed its constituent herbs (E. brevicornum, C. orchioides, M. officinalis, A. sinensis, A. asphodeloides, P. chinense). In drug research and development, approximately 90% of the novel candidates fail the tests due to unexpected toxicity, poor absorption or bioavailability (in addition to other biopharmaceutical/metabolic issues), or poor efficacy [18]. Although the drug ADME characteristics are highly significant, biological testing of each and every candidate drug is impractical due to the high-costs and time involved. In recent years, the focus has therefore shifted to bioinformatics to determine the pharmacokinetic properties of candidate drugs. In order to maximize the chances of finding the fully active compounds, we set two conditions as the criteria for screening these active compounds—oral bioavailability (OB) and drug-likeness (DL), which are the two most important indicators for evaluating ADME characteristics via bioinformatics.

Orally administered drugs must pass some obstructions, like P-glycoprotein (P-gp) [19] and cytochrome P450 s [20], before reaching their target site. The OB of candidate drugs can be predicted with QSAR modeling using linear [multiple linear regression (MLR) and partial least squares regression (PLS)] and nonlinear [support-vector machine regression (SVR)] methods, and the OBioavail 1.2 program [21]. The compounds with OB ≥ 30% were filtered for further analysis. DL, i.e. similarities with the physiochemical or/and structural properties of existing drugs is used to filter out compounds with undesirable qualities [22]. The Dragon program was used to determine the DL index based on parameters like molecular weight, one-dimensional descriptors (e.g. logP, H-donors and H-acceptors), two-dimensional profiles (e.g. extremity number, worldwide topological charge file), three-dimensional variables (average geometric distance degree and radius of gyration), and total positive and negative charges. The DL index of any new molecule is calculated based on Tanimoto similarity [23] as per the formula:
$$f(A,\;B) = \frac{A \cdot B}{{\left| A \right|^{2} + \left| B \right|^{2} - A \cdot B}}$$
where A represents the descriptor of the new numerator and B represents all the 6511 molecules selected from the Drug Bank database. The average of all descriptors was calculated by Dragon and the compounds with DL index ≥ 0.18 were selected.

Prediction of EXD targets and compound-target network establishment

An essential step following the discovery of active molecules is to identify their molecular targets that trigger the biological effects [24]. Bioinformatics methodologies like chemometrics and chemogenomics are often used to mine and integrate information, in order to identify the molecular targets [25]. The Chinese chemical databases and PubChem were mined for the compounds, and their physicochemical properties and biological targets were determined. The genetic information of the targets were obtained from the UniProt Online Resources (http://www.uniprot.org) [26]. Random Forest and Support Vector Machine (SVM) method and prediction models were used for large scale chemical simulation of the drug targets [27, 28]. A compound-target network refers to a mathematical and computable expression of different associations between TCM formulae and diseases, especially in complex natural frameworks [29]. Target interactions were obtained from the STITCH protein database (http://stitch.embl.de/) [30]. The relationship between the above candidate compounds and potential targets were determined with EXCEL as the input source, and the Cytoscape program was used to form a compound-target visual interaction network (CT network) [31]. The nodes in the network are compounds, proteins, enzymes, and targets, and the relationship between them is represented by the lines between the nodes [32].

PCOS-EXD gene network establishment

Genes related to PCOS were downloaded from the NCBI Gene database (http://www.ncbi.nlm.nih.gov/quality) [33]. The database was searched using the keyword ‘Polycystic ovary syndrome’, which yielded 296 known PCOS-related genes of Homo sapiens. The CT network was then mapped to the PCOS-related gene network to establish a PCOS-drug interaction network based on overlapping genes, to determine the mechanism of EXD action in PCOS. In such a network, a node can represent a herb, a compound, or a gene/protein, and an “edge” is an association between the nodes. The “degree” of a hub is the number of edges associated with it, and the “betweenness” of a hub is the number of closest associations. The nodes whose connectivity was greater than twice the median of all nodes are selected as the hub nodes in the network. The hubs with high centrality are considered the key hubs in a network.

Biological function analysis

Gene Ontology (GO, http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) pathway analysis were used to analyze the primary pharmacological units. GO is a database that functionally annotates genes and proteins into three main terms—cellular components (CC), molecular functions (MF), and biological processes (BP) [34] and pathway analysis reveals the possible biological processes with key hub genes. KEGG is a database for determining the high-level functions and biological relevance of a large set of genes [35]. The molecular action of mechanism of EXD could be elucidated by analyzing the significant GO terms and pathways of the overlapping genes. The ClueGO plugin of Cytoscape was used to integrate the GO terms with KEGG pathways [36].

Results

OB prediction and DL calculation

We obtained 981 active compounds from the initial search of three databases: 649 from TCMSP, 142 from TCM@taiwan, and 455 from TCMID. There were 123 common compounds between the TCMSP and TCMID databases, while TCM@taiwan and TCMID had 142 common compounds amongst them, and 70 compounds were shared across the three databases (Fig. 2). Seventy compounds passed the OB and DL filters and had favorable pharmacokinetic profiles, and a total of 247 targets were identified for these compounds. The pharmacokinetic properties of the compounds and the corresponding number of targets are shown in Table 1.
Fig. 2
Fig. 2

A total of 981 compounds were obtained from three databases, including 649 from TCMSP, 142 from TCM@taiwan and 455 from TCMID, of which 70 compounds were shared across all databases

Table 1

Active compounds properties

Compound

Herb

Chemical

OB

DL

Degree

C01

CR01

ZINC03982454

36.91

0.76

2

C02

CR02

Cycloartenol

38.69

0.78

1

C03

CR03 MO13 AS01 PC14

Beta-sitosterol

36.91

0.75

38

C04

CR04

3,2′,4′,6′-Tetrahydroxy-4,3′-dimethoxy chalcone

52.69

0.28

11

C05

CR05

Curculigoside B_qt

83.36

0.19

5

C06

CR06 AS02 AR07 PC10

Stigmasterol

43.83

0.76

31

C07

EH01

24-Epicampesterol

37.58

0.71

2

C08

EH02

Linoleyl acetate

42.1

0.2

4

C09

EH03 PC22

Poriferast-5-en-3beta-ol

36.91

0.75

2

C10

EH04

DFV

32.76

0.18

11

C11

EH05

Chryseriol

35.85

0.27

18

C12

EH06 MO12

Sitosterol

36.91

0.75

3

C13

EH07 AR02

Kaempferol

41.88

0.24

63

C14

EH08

Olivil

62.23

0.41

4

C15

EH09 AR03

Anhydroicaritin

45.41

0.44

37

C16

EH10

C-Homoerythrinan,1,6-didehydro-3,15,16-trimethoxy-,(3.beta.)-

39.14

0.49

38

C17

EH11

Yinyanghuo A

56.96

0.77

9

C18

EH12

Yinyanghuo C

45.67

0.5

11

C19

EH13

Yinyanghuo E

51.63

0.55

11

C20

EH14

6-Hydroxy-11,12-dimethoxy-2,2-dimethyl-1,8-dioxo-2,3,4,8-tetrahydro-1H-isochromeno[3,4-h]isoquinolin-2-ium

60.64

0.66

6

C21

EH15

8-(3-Methylbut-2-enyl)-2-phenyl-chromone

48.54

0.25

30

C22

EH16

Anhydroicaritin-3-O-alpha-L-rhamnoside

41.58

0.61

1

C23

EH17

1,2-bis(4-Hydroxy-3-methoxyphenyl)propan-1,3-diol

52.31

0.22

11

C24

EH18

Icariin

41.58

0.61

1

C25

EH19

Icariside A7

31.91

0.86

3

C26

EH20

Luteolin

36.16

0.25

57

C27

EH21 PC15

Magnograndiolide

63.71

0.19

4

C28

EH22 PC19

Quercetin

46.43

0.28

153

C29

MO01

Ethyl oleate (NF)

32.4

0.19

1

C30

MO02

Alizarin-2-methylether

32.81

0.21

13

C31

MO03

1-Hydroxy-6-hydroxymethylanthracenequinone

81.77

0.21

12

C32

MO04

(2R,3S)-(+)-3′,5-Dihydroxy-4,7-dimethoxydihydroflavonol

77.24

0.33

9

C33

MO05

1,6-Dihydroxy-5-methoxy-2-(methoxymethyl)-9,10-anthraquinone

104.54

0.34

12

C34

MO06

Americanin A

46.71

0.35

11

C35

MO07

2-Hydroxy-1,8-dimethoxy-7-methoxymethylanthracenequinone

112.3

0.37

11

C36

MO08

2-Hydroxy-1,5-dimethoxy-6-(methoxymethyl)-9,10-anthraquinone

95.85

0.37

14

C37

MO09

1,5,7-Trihydroxy-6-methoxy-2-methoxymethylanthracenequinone

80.42

0.38

10

C38

MO10

Diop

43.59

0.39

3

C39

MO11

3beta,20(R),5-alkenyl-stigmastol

36.91

0.75

1

C40

MO14

Isoprincepin

49.12

0.77

2

C41

MO15

3beta-24S(R)-butyl-5-alkenyl-cholestol

35.35

0.82

1

C42

MO16

Ohioensin-A

38.13

0.76

3

C43

AR01

Asperglaucide

58.02

0.52

5

C44

AR04

Anemarsaponin F_qt

60.06

0.79

1

C45

AR05

Hippeastrine

51.65

0.62

11

C46

AR06

Timosaponin B III_qt

35.26

0.87

2

C47

AR08

Icariin I

41.58

0.61

1

C48

AR09

(Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl)ethyl]acrylamide

118.35

0.26

8

C49

AR10

Diosgenin

80.88

0.81

16

C50

AR11

Coumaroyltyramine

112.9

0.2

10

C51

PC01

Berberine

36.86

0.78

17

C52

PC02

Coptisine

30.67

0.86

9

C53

PC03

Phellavin_qt

35.86

0.44

3

C54

PC04

Delta 7-stigmastenol

37.42

0.75

1

C55

PC05

Phellopterin

40.19

0.28

12

C56

PC06

Dehydrotanshinone II A

43.76

0.4

21

C57

PC07

Rutaecarpine

40.3

0.6

18

C58

PC08

Skimmianin

40.14

0.2

5

C59

PC09

Chelerythrine

34.18

0.78

6

C60

PC11

Worenine

45.83

0.87

7

C61

PC12

Cavidine

35.64

0.81

28

C62

PC13

Hericenone H

39

0.63

1

C63

PC16

Palmatine

64.6

0.65

19

C64

PC17

Fumarine

59.26

0.83

28

C65

PC18

Isocorypalmine

35.77

0.59

36

C66

PC20

Phellamurin_qt

56.6

0.39

10

C67

PC21

(S)-Canadine

53.83

0.77

32

C68

PC23

Berberrubine

35.74

0.73

13

C69

PC24

Campesterol

37.58

0.71

1

C70

PC25

Thalifendine

44.41

0.73

14

Network analysis

To determine the relationship between the active compounds of EXD with their putative targets, a compound-target (CT) network was first built (Fig. 3). In such a network, nodes with degree greater than twice the median are considered key nodes; accordingly, 71 hub targets and 13 central compounds were obtained. The protein–protein interaction (PPI) network was constructed using the 247 drug targets, which revealed 238 nodes and 3880 edges. The network radius, diameter and characteristic path length were 3, 5 and 2.2, respectively. The greatest degree was of AKT1 (125), followed by JUN (121), TP53 (118), FOS (113), and EGFR (101). A total of 58 central targets were obtained that had values greater than twice the median (Fig. 4). In addition, the potential targets associated with PCOS were retrieved from the NCBI Gene database, and a network of related genes was built using STITCH, which showed 262 nodes and 3428 edges (Fig. 5). We mapped the drug interaction network to the PCOS-related gene interactions network to obtain the drug-PCOS interaction network (Fig. 6). Pink nodes represent genes associated with PCOS, blue nodes represent EXD targets, green nodes represent co-acting genes, and the edges between nodes represent inter-nodal relationships. A total of 50 targets were identified which are likely the key drug targets in PCOS.
Fig. 3
Fig. 3

C-T network, The multi-colored circles represent different herbs and squares represent the targets. (red for Curculigo, dark pink for Epimedium, yellow for Morinda, light pink for Angelica, dark blue for Anemarrhena, and green for Cork). The blue squares represent the targets of each compound. One target can have multiple compounds and vice versa

Fig. 4
Fig. 4

PPI-target interaction network. Nodes represent targets, proteins, and genes of EXD. The edges represent the links between nodes. There are 238 nodes and 3880 edges in this network

Fig. 5
Fig. 5

A PPI interaction network of PCOS-related genes. Dots represent genes associated with PCOS, and edges represent interactions between genes. There are 262 nodes and 3428 edges in this network

Fig. 6
Fig. 6

PCOS-related drug targets. Pink nodes represent genes associated with PCOS, blue nodes represent EXD targets, green nodes represent co-acting genes, and the edges represent inter-nodal relationships

Biological functional analysis

Biological functions of the PCOS specific drug targets were annotated to clarify the mechanism of action of EXD in PCOS. GO enrichment analysis was performed on the 50 targets using ClueGO, and the top five BP terms were extrinsic apoptotic signaling pathway, positive regulation of reactive oxygen species metabolic process, protein kinase B signaling, positive regulation of sequence-specific DNA binding transcription factor activity, and response to corticosteroids. The top five MF terms were growth factor receptor binding, cytokine activity, ion channel regulator activity, nitric-oxide synthase regulator activity and steroid binding, while the main CC terms were plasma membrane raft, nuclear transcription factor complex, caveola RNA polymerase II transcription factor complex, platelet alpha granule and platelet alpha granule lumen (Fig. 7). The significant KEGG pathways were AGE-RAGE in diabetic complications, fluid shear stress and atherosclerosis, PI3K-Akt, MAPK and FoxO, among a total of 15 pathways (Fig. 8).
Fig. 7
Fig. 7

GO functional analysis. a Biological processes terms were extrinsic apoptotic signaling pathway, positive regulation of reactive oxygen species metabolic process, protein kinase B signaling, positive regulation of sequence-specific DNA binding transcription factor activity, and response to corticosteroids etc. b Cell component terms included plasma membrane raft, nuclear transcription factor complex, caveola RNA polymerase II transcription factor complex, platelet alpha granule, platelet alpha granule lumen etc. c Molecular function terms were growth factor receptor binding, cytokine activity, ion channel regulator activity, nitric-oxide synthase regulator activity, steroid binding etc. d Significant KEGG pathways were AGE-RAGE in diabetic complications, fluid shear stress, atherosclerosis, PI3K-Akt, MAPK, and FoxO signaling pathway etc

Fig. 8
Fig. 8

Analysis of Biological Function of KEGG Pathways (Pathway-Target network). The green points represent the key targets of PCOS relevant to EXD action, and the pink points represent the path of action related to the targets

Discussion

Traditional Chinese medicine (TCM) consists of complex formulations which have hitherto been difficult to characterize, thus limiting their widespread clinical use. In this study, we combined systems pharmacology (SP), pharmacokinetics (PK) and bioinformatics to identify the individual compounds of the EXD formula and their specific PCOS-related targets. EXD is a formulation of six herbs with known ameliorative effects in gynecopathy.

After screening EXD for OB and DL, 70 compounds were obtained, of which the most effective were C28 (quercetin, 153 targets) and C13 (kaempferol, 63 targets). Two constituent herbs of EXD contain these compounds. Quercetin, a polyphenol derived from many plants species, is known for its anti-carcinogenic, anti-inflammatory and antiviral activities, in addition to its active roles in platelet aggregation, lipid peroxidation and capillary permeability [37], cellular uptake, and free radical quenching [38]. Rezvan et al. found that oral quercetin supplementation enhanced AMPK levels to improve the metabolic features of PCOS in a randomized placebo-controlled double-blind trial [39]. Wang et al. showed that quercetin inhibited the Toll-like receptor/NF-kB signaling pathway and improved the inflammatory microenvironment of the ovarian tissue in a PCOS rat model [40]. Kaempferol, a dietary flavonoid, has antioxidant, anti-inflammatory, anti-apoptotic, anticancer, estrogenic, and anti-estrogenic activities [41]. It regulates the MAPK pathway to protect against IR injury by attenuating inflammation and apoptosis [42]. Nevertheless, since each herb of EXD contains multiple compounds, they act on multiple targets via several mechanisms.

The drug target network and the PCOS network had 50 overlapping genes, of which 37 were enriched in 15 pathways, and are likely the key genes involved in PCOS treatment. The significant pathways involving the candidate compounds targeting PCOS can be classified as prototypes, direct and indirect pathways depending on their functions. Based on the network analysis, we obtained three proteins of interest—AKT1, IL6 and INSR. In the hyper-androgenic PCOS patients, high levels of AKT1 have been associated with GCs dysfunction [43]. In addition, IL6, IL1B1 and TNF are associated with increased susceptibility to PCOS [44], and INSR plays a role in compensatory hyperinsulinemia [45]. EXD can regulate the expression of these genes via the AGE-RAGE, PI3K-Akt, and MAPK signaling pathways. The PI3K-Akt signaling pathway [46], non-alcoholic fatty liver disease (NAFLD) [47], MAPK signaling pathway [48], FoxO signaling pathway [6], insulin resistance [4], apoptosis, and Toll-like receptor signaling pathway [46] are strongly correlated with the occurrence and development of PCOS. Therefore, they are mechanistically important for PCOS and may play a role in its treatment as well. Studies show that one pathway contains multiple targets, and each target can act on the multiple pathways, thereby creating an intricate network.

Conclusions

Systems pharmacology and genomics were combined to identify the PCOS relevant targets of the EXD formula. Our findings indicate that 6 of the constituent herbs in EXD act synergistically on certain putative PCOS targets. This study presents a high throughput and economic method of identifying drug targets, and may have significant clinical utility.

Abbreviations

PCOS: 

polycystic ovary syndrome

PCOM: 

polycystic ovarian morphology

EXD: 

Erxian decoction

TCMSP: 

traditional Chinese medicine system pharmacology analysis platform

GO: 

Biological Network Gene Ontology tools

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

TCM: 

traditional Chinese medicine

CC: 

cellular components

MF: 

molecular functions

BP: 

biological processes

FSH: 

follicle stimulating hormone

LH: 

luteinizing hormone

E2: 

estradiol

P: 

progesterone

MDA: 

malonic dialdehyde

T-AOC: 

total anti-oxidative capacity

ADME: 

absorption, distribution, metabolism, excretion

OB: 

oral bioavailability

DL: 

drug-likeness

NAFLD: 

non-alcoholic fatty liver disease

Declarations

Authors’ contributions

LLH, and AH conceived and designed the study. ZHY and DB collected the data. LLH, AH, GXF and ZZ performed the data analysis, LLH and AH wrote the manuscript. All authors are responsible for reviewing data. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

The datasets generated and/or analysed during the current study are available in the TCMSP repository, http://lsp.nwu.edu.cn/tcmsp.php.

TCMID, http://www.megabionet.org/tcmid/.

TCM@taiwan, http://tcm.cmu.edu.tw/.

NCBI-GENE, https://www.ncbi.nlm.nih.gov/gene/?term=.

KEGG, https://www.kegg.jp/.

Gene Ontology, http://www.geneontology.org/.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

Project supported by the Natural Science Foundation of Liaoning province, China (Grant No. 20170540373).

Project supported by the Jinzhou Foundation for Science and Technology, China (Grant No. 16B1G35).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Gynecological Ward, The Third Affiliated Hospital, Jinzhou Medical University, Jinzhou, China
(2)
Liaoning Provincial Key Laboratory of Follicle Development and Reproductive Health (Office of Science and Technology), Jinzhou, China
(3)
Library Department, Jinzhou Medical University, Jinzhou, China
(4)
Department of Gynecological Ward, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China

References

  1. Azziz R, Carmina E, Chen Z, Dunaif A, Laven JS, Legro RS, Lizneva D, Natterson-Horowtiz B, Teede HJ, Yildiz BO. Polycystic ovary syndrome. Nat Rev Dis Primers. 2016;2:16057.View ArticlePubMedGoogle Scholar
  2. Utiger RD. Insulin and the polycystic ovary syndrome. N Engl J Med. 1996;335(9):657–8.View ArticlePubMedGoogle Scholar
  3. Ouyang X. Drug treatment of clinical application development of polycystic ovary syndrome. J Mod Med Health Care. 2017;33(11):1667–9 (in Chinese).Google Scholar
  4. Wang F, Wang SB, Zhang ZH, Lin QQ, Liu YP, Xiao YJ, Xiao KJ, Wang Z. Defective insulin signaling and the protective effects of dimethyldiguanide during follicular development in the ovaries of polycystic ovary syndrome. Mol Med Rep. 2017;16(6):8164–70.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Li TT, Mo H, Chen WF. Role of the PI3K-Akt signaling pathway in the pathogenesis of polycystic ovary syndrome. Reprod Sci. 2017;24(5):646–55.View ArticlePubMedGoogle Scholar
  6. Li N, Wang XY, Wang XJ, Yu HN, Lin L, Sun CM, Liu P, Chu YL, Hou JQ. Upregulation of FoxO 1 signaling mediates the proinflammatory cytokine upregulation in the macrophage from polycystic ovary syndrome patients. Clin Lab. 2017;63(2):301–11.PubMedGoogle Scholar
  7. Anzai A, Marcondes RR, Gonçalves TH, Carvalho KC, Simões MJ, Garcia N, Soares JM Jr, Padmanabhan V, Baracat EC, Silva IDCG, Maciel GAR. Impaired branched-chain amino acid metabolism may underlie the nonalcoholic fatty liver disease-like pathology of neonatal testosterone-treated female rats. Sci Rep. 2017;7(1):13167.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Sze SC, Cheung HP, Ng TB, Zhang ZJ, Wong KL, Wong HK, Hu YM, Yow CM, Tong Y. Effects of Erxian decoction, a Chinese medicinal formulation, on serum lipid profile in a rat model of menopause. Chin Med. 2011;6(1):40.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Sze SC, Ip CW, Ng TB, Zhang KY, Zhang ZJ, Cheung HP, Cheng CL, Tong Y. Compatibility of multiple herbal components in Erxian Decoction, a Chinese medicinal formula, for treating osteoporosis. Eur J Integr Med. 2012;4(2):187–96.View ArticleGoogle Scholar
  10. Wang FY, Xie C, Cheng WW, Han W, Lai DM. Establishment of mouse model of cyclophosphamide-induced ovarian failure. J Shanghai Jiaotong Univ. 2011;31(10):1398–402.Google Scholar
  11. Ai H, Zhang HY, Zhang YL, Xue XO. Effect of Er Xian decoction on ovary function after injury of cisplatin in mic. Tianjin J Tradit Chin Med. 2013;30(5):498–500 (in Chinese).Google Scholar
  12. Mao XL, Bao WQ, Zhao PW, Niu JZ, Zhao FJ, Tao SY. Influence of Erxian Decoction on ovarian reserve of rats suffered from premature ovarian failure. Chin J Tradit Chin Med. 2017;32(2):771–3 (in Chinese).Google Scholar
  13. Berger SI, Iyengar R. Network analyses in systems pharmacology. Bioinformatics. 2009;25(19):2466–72.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Ru JL, Li P, Wang JN, Zhou W, Li BH, Huang C, Li PD, Guo ZH, Tao WY, Yang YF, Xu X, Li Y, Wang YH, Yang L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6(1):13.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Li S, Zhang B, Jiang D, Wei Y, Zhang N. Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae. BMC Bioinform. 2010;11(S11):1–12.Google Scholar
  16. Wang S, Tong Y, Ng TB, Lao L, Lam JK, Zhang KY, Zhang ZJ, et al. Network pharmacological identification of active compounds and potential actions of Erxian decoction in alleviating menopause-related symptoms. Chin Med. 2015;10:19.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Wu L, Wang Y, Li Z, Zhang B, Cheng Y, Fan X. Identifying roles of “Jun-Chen-Zuo-Shi” component herbs of QiShenYiQi formula in treating acute myocardial ischemia by network pharmacology. Chin Med. 2014;9(1):24.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Baertschi SW, Olsen BA, Alsante KM, Ree RA. Stress testing: relation to the development timeline. Boca Raton: CRC Press; 2011.Google Scholar
  19. Fromm MF. P-glycoprotein: a defense mechanism limiting oral bioavailability and CNS accumulation of drugs. Int J Clin Pharmacol Ther. 2000;38:69–74.View ArticlePubMedGoogle Scholar
  20. Van Waterschoot RA, Lagas JS, Wagenaar E, Wagenaar E, van der Kruijssen CM, van Herwaarden AE, et al. Absence of both cytochrome P450 3A and P-glycoprotein dramatically increases docetaxel oral bioavailability and risk of intestinal toxicity. Cancer Res. 2009;69(23):8996–9002.View ArticlePubMedGoogle Scholar
  21. Xu X, Zhang W, Huang C, Li Y, Yu H, Wang Y, Duan J, Ling Y. A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci. 2012;13(6):6964–82.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Jadhav PB, Yadav AR, Gore MG. Concept of drug likeness in pharmaceutical research. Int J Pharm Biol Sci. 2015;6:142–54.Google Scholar
  23. Ma C, Wang L, Xie XQ. GPU accelerated chemical similarity calculation for compound library comparison. J Chem Inf Model. 2011;51(7):1521–7.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Schenone M, Dančík V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol. 2013;9(4):232–40.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Yue SJ, Liu J, Feng WW, Zhang FL, Chen JX, Xin LT, Peng C, Guan HS, Wang CY, Yan D. System pharmacology-based dissection of the synergistic mechanism of Huangqi and Huanglian for diabetes mellitus. Front Pharmacol. 2017;8:694.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Apweiler R, UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2018;35(Database issue):2699.Google Scholar
  27. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43(6):1947–58.View ArticlePubMedGoogle Scholar
  28. Tong S, Koller D. Support vector machine active learning with applications to text classification. J Mach Learn Res. 2002;11(2):45–66.Google Scholar
  29. Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 2013;11(2):110–20.View ArticlePubMedGoogle Scholar
  30. Kuhn M, Szklarczyk D, Franceschini A, von Mering C, Jensen LJ, Bork P. STITCH 3: zooming in on protein–chemical interactions. Nucleic Acids Res. 2012;40(Database issue):876–80.View ArticleGoogle Scholar
  31. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–2.View ArticlePubMedGoogle Scholar
  33. Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 2005;33(S1):54–8.Google Scholar
  34. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, The Gene Ontology Consortium, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;1(1):27–30.Google Scholar
  36. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z, Galon J. ClueGO: a Cytoscape plug-into decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–3.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Li Y, Yao J, Han C, Yang J, Chaudhry MT, Wang S, Liu H, Yin Y. Quercetin, inflammation and immunity. Nutrients. 2016;8(3):167.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Roubalová L, Purchartová K, Papoušková B, Vacek J, Křen V, Ulrichová J, Vrba J. Sulfation modulates the cell uptake, antiradical activity and biological effects of flavonoids in vitro: an examination of quercetin, isoquercitrin and taxifolin. Bioorg Med Chem. 2015;23(17):5402–9.View ArticlePubMedGoogle Scholar
  39. Rezvan N, Moini A, Gorgani-Firuzjaee S, Hosseinzadeh-Attar MJ. Oral quercetin supplementation enhances adiponectin receptor transcript expression in polycystic ovary syndrome patients: a randomized placebo-controlled double-blind clinical trial. Cell J. 2018;19(4):627–33.PubMedGoogle Scholar
  40. Wang ZZ, Zhai DX, Zhang DY, Bai LL, Yao RP, Yu J, et al. Quercetin decreases insulin resistance in a polycystic ovary syndrome rat model by improving inflammatory microenvironment. Reprod Sci. 2017;24(5):682–90.View ArticlePubMedGoogle Scholar
  41. Calderónmontaño JM, Burgosmorón E, Pérezguerrero C, López-Lázaro M. A review on the dietary flavonoid kaempferol. Mini Rev Med Chem. 2011;11(4):298–344.View ArticleGoogle Scholar
  42. Suchal K, Malik S, Gamad N, Malhotra RK, Goyal SN, Chaudhary U, et al. Kaempferol attenuates myocardial ischemic injury via inhibition of mapk signaling pathway in experimental model of myocardial ischemia-reperfusion injury. Oxid Med Cell Longev. 2016;2016(9):1–10.View ArticleGoogle Scholar
  43. Nekoonam S, Naji M, Nashtaei MS, Mortezaee K, Koruji M, Safdarian L, Amidi F. Expression of AKT1 along with AKT2 in granulosa-lutein cells of hyperandrogenic PCOS patients. Arch Gynecol Obstet. 2017;295(4):1041–50.View ArticlePubMedGoogle Scholar
  44. de Alencar JB, Alves HV, Elpidio LN, Visentainer JE, Sell AM. Polymorphisms of cytokine genes and polycystic ovary syndrome: a review. Metab Syndr Relat Disord. 2016;14(10):468–74.View ArticlePubMedGoogle Scholar
  45. Chiofalo B, Laganà AS, Palmara V, Granese R, Corrado G, Mancini E, Vitale SG, Ban Frangež H, Vrtačnik-Bokal E, Triolo O. Fasting as possible complementary approach for polycystic ovary syndrome: Hope or hype? Med Hypotheses. 2017;105:1–3.View ArticlePubMedGoogle Scholar
  46. Zhang HY, Zhang YF, Han YK, Xue FX, Zhao XH, Zhang XL. Activation and significance of the PI3K/Akt pathway in endometrium with polycystic ovary syndrome patients. Zhonghua Fu Chan Ke Za Zhi. 2012;47(1):19–23.PubMedGoogle Scholar
  47. Kelley CE, Brown AJ, Diehl AM, Setji TL. Review of nonalcoholic fatty liver disease in women with polycystic ovary syndrome. World J Gastroenterol. 2014;20(39):14172–84.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Makker A, Goel MM, Das V, Agarwal A. PI3K-Akt-mTOR and MAPK signaling pathways in polycystic ovarian syndrome, uterine leiomyomas and endometriosis: an update. Gynecol Endocrinol. 2012;28(3):175–81.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s) 2018

Advertisement