Abstract

   Background: Li-Ru-Kang (LRK) has been used in the treatment of
   hyperplasia of mammary glands (HMG) for several decades and can
   effectively improve clinical symptoms. This study aims to investigate
   the mechanism by which LRK intervenes in HMG based on an integrated
   approach that combines metabolomics and network pharmacology analyses.

   Methods: The effects of LRK on HMG induced by estrogen-progesterone in
   rats were evaluated by analyzing the morphological and pathological
   characteristics of breast tissues. Moreover, UPLC-QTOF/MS was performed
   to explore specific metabolites potentially affecting the pathological
   process of HMG and the effects of LRK. Pathway analysis was conducted
   with a combination of metabolomics and network pharmacology analyses to
   illustrate the pathways and network of LRK-treated HMG.

   Results: Li-Ru-Kang significantly improved the morphological and
   pathological characteristics of breast tissues. Metabolomics analyses
   showed that the therapeutic effect of LRK was mainly associated with
   the regulation of 10 metabolites, including prostaglandin E2,
   phosphatidylcholine, leukotriene B4, and phosphatidylserine. Pathway
   analysis indicated that the metabolites were related to arachidonic
   acid metabolism, glycerophospholipid metabolism and linoleic acid
   metabolism. Moreover, principal component analysis showed that the
   metabolites in the model group were clearly classified, whereas the
   metabolites in the LRK group were between those in the normal and model
   groups but closer to those in the normal group. This finding indicated
   that these metabolites may be responsible for the effects of LRK. The
   therapeutic effect of LRK on HMG was possibly related to the regulation
   of 10 specific metabolites. In addition, we further verified the
   expression of protein kinase C alpha (PKCα), a key target predicted by
   network pharmacology analysis, and showed that LRK could significantly
   improve the expression of PKCα.

   Conclusion: Our study successfully explained the modulatory properties
   of LRK treatment on HMG using metabolomics and network pharmacology
   analyses. This systematic method can provide methodological support for
   further understanding the complex mechanism underlying HMG and possible
   traditional Chinese medicine (TCM) active ingredients for the treatment
   of HMG.

   Keywords: Li-Ru-Kang, hyperplasia of mammary glands, modulatory
   properties, metabolomics, network pharmacology

Introduction

   Hyperplasia of mammary glands is one of the most common breast diseases
   in middle-aged women and accounts for more than 70% of all breast
   disease ([45]Chen et al., 2015). HMG is easily overlooked because of
   its clinical characteristics until the generation of mammary carcinoma.
   ([46]Zhang et al., 2012). The morbidity of HMG has increased due to
   increased work-related stress and competitive career pressures along
   with the fast pace of modern life ([47]Li et al., 2017a). However,
   there is still not a sufficient understanding of the etiology of HMG,
   and pertinent therapeutic strategies are limited. Hormone or endocrine
   therapy is one of the most commonly used methods to mitigate the
   clinical symptoms of HMG. Nevertheless, the side effects also decrease
   the quality of life for patients who receive long-term treatment.
   Surgical treatment as a form of therapy is hardly accepted by most
   patients with recurring symptoms ([48]Henry, 2014). Therefore, finding
   a more appropriate treatment with fewer side effects and more
   therapeutic advantages is the current goal standard for treating HMG.

   Traditional Chinese medicine (TCM) has been practiced in China for
   thousands of years to treat acute and chronic diseases. Its application
   in the prevention and treatment of HMG has garnered increasing
   attention. To explore the mechanism of action and active substances of
   TCM, a variety of constructive technologies have been used over the
   past few decades, such as metabolomics, proteomics, genomic arrays and
   network pharmacology ([49]Xu et al., 2017). However, few researchers
   fully integrate the aforementioned techniques.

   Li-Ru-Kang is composed of Curcumae radix, Prunellae spica, Pseudobulbus
   cremastrae Seu pleiones, Radix bupleuri, licorice, Polygonum
   multiflorum, Crassostrea gigas and Cornu cervi, and has been used for
   the treatment of HMG for several decades as a cipher prescription based
   on the clinical experience of many medical experts. Clinical researches
   showed that the total effective rate of LRK in the treatment of HMG was
   88.0%. Moreover, LRK showed obvious superiority in improving the
   patients’ symptoms and abnormalities of gonadal hormone ([50]Qian et
   al., 2007).

   As a rapidly developing technology, metabolomics can discover the
   pathogenesis of diseases by detecting more than 1,000 molecules in
   various biological fluids, such as urine, saliva, and blood. This
   technology has been successfully applied to the diagnosis and
   identification of various diseases, such as coronary heart disease
   ([51]Li et al., 2017c), early chronic kidney disease ([52]Benito et
   al., 2018) and thyroid cancer ([53]Navas-Carrillo et al., 2017).
   Metabolomics has also been widely applied in the field of TCM ([54]Li
   et al., 2017b; [55]Liu et al., 2017) and provides insight into the
   complex network mechanisms of HMG. In addition, network pharmacology
   has also been successfully applied to the study of TCM molecular
   mechanisms and can provide a deep understanding of the complex
   relationship between TCM components and diseases.

   In this study, we elucidated the bioactive components, potential
   biomarkers and possible mechanisms of LRK in the treatment of HMG using
   metabolomics and network pharmacology.

Materials and Methods

Water Extract of LRK Preparation

   Curcumae radix, Prunellae spica, Pseudobulbus cremastrae Seu pleiones,
   Radix bupleuri, licorice, Polygonum multiflorum, Crassostrea gigas and
   Cornu cervi were purchased from He yanling, Co., Ltd. (Beijing, China).
   The origin and quality of the 8 herbs were identified according to the
   Chinese Pharmacopeia (2015 Edition). Water extract from LRK was
   prepared by extracting the mixture of the 8 herbs (at a ratio of 12:
   12: 9: 10: 30: 10: 9: 6, respectively) twice with water for 1 h. The
   extract was then decanted, filtered and dried under reduced pressure.
   The final ratio of powder to raw herb was 9.2%.

Animals

   Female Sprague-Dawley rats weighing 200 ± 5 g (license number: SCXK-(A)
   2012-0004) were obtained from the laboratory animal center of Military
   Medical Science Academy of the PLA. All studies were performed in
   accordance with the Guiding Principles for the Care and Use of
   Laboratory Animals of China. The animals were housed in the same
   feeding environment (temperature: 25 ± 2°C; humidity: 50 ± 10%) and fed
   based on lighting conditions (12:12 h light: dark cycle) with a
   standard diet and water ad libitum. After acclimatization for 7 days,
   all animals were randomly divided into six groups (n = 6): a normal
   group (A), a model group (B), a tamoxifen-treated HMG group (C), a low
   dose LRK-treated HMG group (D), a medium dose LRK-treated HMG group (E)
   and a high dose LRK-treated HMG group (F). Rats in the B, C, D, E, and
   F groups were intramuscularly injected with estrogen (0.5 mg/kg/d) for
   25 days, followed by progestogen (5 mg/kg/d) for another 5 days to
   induce the experimental HMG model ([56]Wang et al., 2011). Rats in
   groups D, E, and F were given 0.6, 1.2 and 2.4 g/kg LRK orally for 30
   days, respectively. Rats in groups A and B were treated with an equal
   volume of saline. Rats in group C were given oral tamoxifen (4 mg/kg/d)
   for 30 days. All animal studies were approved by the Ethical Committee
   of 302 Military Hospital of China.

Sample Preparation

   After the last treatment, all rats were sacrificed. Blood samples were
   collected and centrifuged at 3000 ×g for 10 min to obtain serum for
   mass spectrometry. The mammary samples were rapidly excised and fixed
   in 10% paraformaldehyde solution for histopathological analysis.
   Paraffin-embedded sections ranging from 4 to 5 μm in thickness were
   stained with haematoxylin-eosin (H & E). The stained sections were
   analyzed with a Nikon microscope (Nikon Instruments Corporation,
   Shanghai, China) and Image-Pro Plus 7200 software.

Sample Handling

   Briefly, 200 μL of serum samples and 600 μL of methanol were mixed
   uniformly and allowed to stand for 20 min at 4°C. The samples were
   centrifuged at 12,000 rpm at 4°C for 10 min to obtain the supernatant
   and then filtered through a syringe filter (0.22 μm) to obtain the
   sample for injection.

Chromatography and Mass Spectrometry

   Chromatography was carried out using an Agilent 1290 series UHPLC
   system. The sample injection volume was 4 μL, and all samples were
   detected at 4°C on a ZORBAX RRHD 300 SB-C18 column (2.1 mm × 100 mm,
   1.8 μm; Agilent, United States). The mobile phases were composed of
   0.1% formic acid in acetonitrile (solvent A) and 0.1% formic acid in
   water (solvent B), and the flow rate was 0.30 mL/min. The gradient was
   set as follows: the first minute: 95% A; 1.0 to 9.0 min: 95–60% A; 9.0
   to 19.0 min: 60–10% A; 19.0 to 21.0 min: 10–0% A; and 21.0 to 25.0 min:
   0% A. After injection of the 10 samples and the QC sample compounded
   with all samples, a blank was injected to guarantee the stability and
   repeatability of the UPLC-QTOF/MS systems.

   Mass spectrometry was performed using an Agilent 6550 Q-TOF/MS
   instrument (Agilent Technologies, Santa Clara, CA, United States) with
   an electrospray ionization source (ESI) in both positive and negative
   ionization mode. The following electrospray source parameters were
   used: the electrospray capillary voltages were 3.0 kV (negative
   ionization mode) and 4.0 kV (positive ionization mode); the gas
   temperature was 200°C (negative ionization mode) and 225°C (positive
   ionization mode); the mass range ranged from m/z 80 to 1000; the gas
   flow was 11 L/min; the nebulizer was 35 pisg in negative ionization
   mode and 45 pisg in positive ionization mode; the sheath gas
   temperature was set to 350°C, and the sheath gas flow was 12 L/min; the
   nozzle voltage was 500 V in both negative and positive ionization
   modes.

Data Extraction and Multivariate Analysis

   MassHunter Profinder software (Agilent, California, United States) was
   used to extract sample data for peak detection and alignment. Full scan
   mode was applied in the mass range of 80–1000 m/z. The initial and
   final retention times were set for data collection. The resultant data
   matrices were normalized using MetaboAnalyst 3.0^[57]1 and then
   introduced to SIMCA-P 13.0 software (Umetrics, Umea, Sweden) for PCA
   and PLSDA analysis. PCA was used as an unsupervised pattern recognition
   approach to reduce the dimension of the UPLC-QTOF/MS data and disclose
   intrinsic clustering of samples. PLS-DA analysis was employed to
   maximize the differences in inter-class discrimination and minimize the
   differences in inter-class discrimination. The variables with VIP >1.5
   and |p(corr)|≥0.58 in the PLS-DA analysis were further evaluated with
   an independent sample t-test.

Biomarkers Identification and Pathway Enrichment Analysis

   The significant variables (p < 0.05 in ANOVA) were selected as
   potential biomarkers for further pathway enrichment analysis. These
   biomarkers were identified by METLIN^[58]2, and the identified compound
   names were resubmitted to MetaboAnalyst 3.0 to analyse their signaling
   pathways.

Identification of Drug Targets and Potential Metabolites and Network
Construction

   To systematically elucidate the complex relationships between potential
   metabolites and their associated targets, we conducted network analysis
   using network pharmacology. The ingredients with an oral
   bioavailability (OB) ≥30% and a drug-likeness (DL) >0.18 from Radix
   bupleuri, licorice, Pseudobulbus cremastrae Seu pleiones, Prunellae
   spica, Polygonum multiflorum and Curcumae radix were put into the TCMSP
   database^[59]3, and their corresponding chemical components were also
   collected from the same database ([60]Huang et al., 2017). The
   ingredients and corresponding targets of Cornu cervi and Crassostrea
   gigas were retrieved from the BATMAN-TCM database^[61]4. The protein
   targets of potential metabolites were collected from the MBROLE 2.0
   database^[62]5 ([63]Lopez-Ibanez et al., 2016). Different protein ID
   types were converted to UniProt IDs. Then, the
   “metabolite-target-chemical components” interactive network was
   established by using protein–protein interaction (PPI) information.
   Cytoscape 2.8.3 (National Institute of General Medical Sciences, United
   States) was applied to visualize and analyse the network.

Immunohistochemistry

   To evaluate the effect of LRK on the expression of PKCα in breast
   tissue of HMG rats, immunohistochemistry was performed as described
   previously ([64]Chen et al., 2014). Breast tissue slides were incubated
   with anti- PKCα (Abcam, Cambridge, MA, United States) for 12 h at 4°C.
   After treatment with HRP-conjugated goat anti-mouse IgG and 50 μL of
   streptavidin-peroxidase solutions for 30 min at RT, the sections were
   stained with DAB and counterstained with hematoxylin. The positive
   areas showed the color of brown yellow.

Statistical Analysis

   Data were expressed as the mean ± SE and analyzed with the SPSS 13.0
   software program (Chicago, United States). The differences between the
   group means were calculated by ANOVA. p < 0.05 was considered
   statistically significant, and p < 0.01 was considered highly
   significant. The results of the three-dimensional matrix containing the
   peak index, sample name, and peak area were introduced into SIMCA-P
   13.0 software for pattern recognition analysis of PCA and PLS-DA.

Results

Therapeutic Effects of LRK on HMG

   The average diameter and height of nipples (Left 2 and Right 2) were
   measured as indices of HMG. As shown in Figure [65]1, rats given
   estrogen and progesterone displayed remarkable increases in the average
   nipple diameter and height. The administration of LRK (2.4 and 1.2
   g/kg) markedly decreased the average nipple diameter and height,
   respectively. Pathological studies provided direct evidence of
   protective effects of LRK on HMG. In Figure [66]2, the mammary tissues
   of the rats in the normal group exhibited normal morphological
   structures with no abnormal features. By contrast, the mammary tissues
   of the rats in the model group displayed more acinus and lobules,
   expanded mammary lumens, and hyperplastic ducts. Conversely, treatment
   with LRK (2.4 and 1.2 g/kg) ameliorated these morphological changes.
   These results preliminarily verified that 2.4 and 1.2 g/kg LRK
   significantly protected the mammary tissue in the rats with HMG induced
   by estrogen and progesterone.

FIGURE 1.

   [67]FIGURE 1
   [68]Open in a new tab

   The average diameter and height of nipples in the HMG rats. (A) The
   average diameter of nipples. (B) The average height of nipples. 1:
   normal group. 2: model group. 3: tamoxifen-treated HMG group (4 mg/kg).
   4: low-dose LRK-treated HMG group (0.6 g/kg). 5: medium-dose
   LRK-treated HMG group (1.2 g/kg). 6: high-dose LRK-treated HMG group
   (2.4 g/kg). Data are expressed as the mean ± SE (n = 6). ^∗p < 0.01
   compared with the normal group; ^##p < 0.01, ^#p < 0.05 compared with
   the model group.

FIGURE 2.

   [69]FIGURE 2
   [70]Open in a new tab

   Effects of LRK on the histopathology of mammary tissues using
   haematoxylin and eosin staining (200× and 400×). (A) Normal group. (B)
   Model group. (C) Tamoxifen-treated HMG group (4 mg/kg). (D) Low-dose
   LRK-treated HMG group (0.6 g/kg). (E) Medium-dose LRK-treated HMG group
   (1.2 g/kg). (F) High dose LRK-treated HMG group (2.4 g/kg). The
   magnified areas (×400) are marked with a black arrow in the
   pathological tissue figures (200×).

Multivariate Statistical Analysis

   Principal component analysis (PCA) and orthogonal partial least
   squares-discriminant analysis (OPLS-DA) are pattern recognition
   approaches frequently used to classify metabolic phenotypes and
   identify different metabolites to evaluate variation among complex data
   sets. Score plots revealed a direct image of observational clusters. A
   distinguished classification between the clustering of the normal and
   model groups and the normal and LRK groups was observed in both the
   positive (Figure [71]3A) and negative modes (Figure [72]3B). Further
   multivariate analysis was needed to explore which metabolites caused
   these differences.

FIGURE 3.

   [73]FIGURE 3
   [74]Open in a new tab

   Principal component analysis (PCA) score plot of the control, model and
   LRK (2.4 g/kg) groups. (A) ESI+ model. (B) ESI- model.

   Orthogonal partial least squares-discriminant analysis was used to
   identify potential biomarkers that were significantly changed between
   the normal, model and LRK groups. The parameters R^2X (cum), R^2Y
   (cum), and Q^2 (cum) were used to provide an estimate of how well the
   model fit the data. We further determined whether the metabolite
   fingerprints in the serum differed between the normal, model, and LRK
   groups by constructing an OPLS-DA model. A distinguished classification
   between the clustering of the normal, model, and LRK groups was
   observed in both the positive (Figure [75]4) and negative models
   (Supplementary Figure [76]S1), suggesting a significant serum
   biochemical perturbation in the model and LRK groups. The R^2X (cum),
   R^2Y (cum), and Q^2 (cum) of OPLS-DA in our positive model were 0.506,
   0.936, 0.79, respectively, using the data from the control and model
   groups and 0.304, 0.87, 0.748, respectively, using the data from the
   model and LRK groups. The R^2X (cum), R^2Y (cum), and Q^2 (cum) of
   OPLS-DA in our negative model were 0.261, 0.908, 0.768, respectively,
   using the data from the control and model groups and 0.233, 0.97,
   0.806, respectively, using the data from the model and LRK groups. The
   corresponding parameter results indicated that models were of good
   quality and provided accurate predictions. Variables further away from
   the origin in the corresponding S-plots were thought to contribute more
   significantly were more thus responsible for the separation between the
   normal and model groups as well as the model and LRK groups in both the
   positive (Figures [77]4B,E) and negative ionization models
   (Supplementary Figure [78]S1). These variables may therefore be
   regarded as potential biomarkers. Permutation tests with 100 iterations
   were performed to validate the model. These tests compared the goodness
   of fit of the original model with the goodness of fit of randomly
   permuted models. As shown in Figures [79]4C,F, the validation plots
   indicated that the original models were valid.

FIGURE 4.

   [80]FIGURE 4
   [81]Open in a new tab

   The OPLS-DA score plots, S-plots and 100-permutation test generated
   from the OPLS-DA data of the normal, model and LRK groups in ESI+ mode.
   OPLS-DA score plots were the pair-wise comparisons between the normal
   and model groups (A) as well as between the model and LRK groups (D).
   S-plots of the OPLS-DA model for the normal and model groups (B) as
   well as for the model and LRK groups (E). The 100-permutation test of
   the OPLS-DA model was for the normal and model groups (C) as well as
   for the model and LRK groups (F).

Identification of Potential Metabolites in HMG Treatment

   Among the 1986 signals detected in the control, model and 2.4 g/kg LRK
   groups, variables that significantly contributed to the clustering and
   discrimination were identified according to a threshold of VIP ≥1.5 and
   |p(corr)|≥ 0.58. Those thresholds were obtained after OPLS-DA
   processing these variables. According to the VIP and |p(corr)|, 366
   variables were selected from the control, model and 2.4 g/kg LRK groups
   as the candidates for fold-changes and ANOVA analyses. The candidates
   that significantly differed among the groups with fold change exceeding
   two were identified as candidate biomarkers for METLIN and
   Metaboanalyst identification. Ten potential biomarkers were summarized
   in Table [82]1 with their corresponding formula, retention time, m/z,
   and differences by group.

Table 1.

   Identified metabolites of the serum from different groups.
   No Metabolites Formula R.T. (min) Mass (m/z) Ratio changes
   (significance)
     __________________________________________________________________

   Control/Model LRK/Model
   1 Sulfolithocholylglycine C[26]H[42]NO[7]S 7.36 536.2628 0.40ˆ**
   0.45ˆ##
   2 Leukotriene B4 C[20]H[32]O[4] 6.57 339.2557 0.23ˆ** 0.16ˆ##
   3 Phosphatidylcholine C[10]H[18]NO[8]PR[2] 6.9 792.5771 1.36ˆ** 1.07ˆ#
   4 Ceramide C[19]H[36]NO[3]R 6.57 678.6891 3.10ˆ** 2.98ˆ##
   5 Prostaglandin E2 C[20]H[32]O[5] 10.99 375.2233 3.50ˆ** 2.72ˆ##
   6 3-Hydroxypicolinic acid C[6]H[5]NO[3] 1.25 173.9994 0.68ˆ** 0.67ˆ##
   7 5-Amino-4-imidazole carboxylate C[4]H[5]N[3]O[2] 1.06 162.006 3.88ˆ**
   3.04ˆ##
   8 Phosphatidylserine C[8]H[12]NO[10]PR[2] 6.96 854.5852 6.43ˆ** 3.23ˆ##
   9 Coenzyme Q9 C[54]H[82]O[4] 6.95 829.5664 0.14ˆ** 0.58ˆ##
   10 Gamma-glutamyl-L-putrescine C[9]H[19]N[3]O[3] 4.73 216.1361 0.20ˆ**
   0.51ˆ##
   [83]Open in a new tab

   ^∗∗P < 0.01 compared with normal group; ^##P < 0.01, ^#P < 0.05
   compared with model group.

Pathway Analysis of HMG Treatment

   Herein, 10 potential metabolites were expressed at significant levels.
   Pathway analysis was performed in detail using Metaboanalyst.
   HMG-associated metabolites were responsible for metabolizing
   arachidonic acid, glycerophospholipid, linoleic acid, sphingolipid,
   alpha-linolenic acid, glycine, serine, threonine, arginine, proline,
   and purine (Table [84]2 and Figure [85]5). The top three metabolic
   pathways were arachidonic acid, glycerophospholipid and linoleic acid.

Table 2.

   Results of integrating enrichment analysis of biomarkers with
   MetaboAnalyst 3.0.
   No Pathway Name Match Status p -log(p) Impact
   1 Arachidonic acid metabolism 3/62 0.0059386 0.0059386 0.0059386
   2 Glycerophospholipid metabolism 2/39 0.023519 0.023519 0.023519
   3 Linoleic acid metabolism 1/15 0.048758 0.089758 0.089758
   4 Sphingolipid metabolism 1/25 0.14536 0.14536 0.14536
   5 alpha-Linolenic acid metabolism 1/29 0.16669 0.16669 0.16669
   6 Glycine, serine and threonine metabolism 1/48 0.26143 0.26143 0.26143
   7 Arginine and proline metabolism 1/77 0.38685 0.94973 0.01185
   8 Purine metabolism 1/92 0.44363 0.81278 0.0162
   [86]Open in a new tab

FIGURE 5.

   [87]FIGURE 5
   [88]Open in a new tab

   (A) Metabolomic Pathway construction of the metabolic pathways involved
   in the effects of LRK on HMG. (B) Signaling networks associated with
   the differentially expressed metabolic pathways. 1: Arachidonic acid
   metabolism. 2: Glycerophospholipid metabolism. 3: Linoleic acid
   metabolism. 4: Sphingolipid metabolism. 5: alpha-linolenic acid
   metabolism. 6: Glycine, serine and threonine metabolism. 7: Arginine
   and proline metabolism. 8: Purine metabolism. The red solid box
   represents the peak area of the LRK/model >1. The blue solid box
   represents the peak area of LRK/model < 1.

“Potential Metabolite-Target-Component” Interactive Network and Analysis

   To reveal the proteins targets and chemical components of LRK
   regulation, the “potential metabolite-target-component” interactive
   network was structured by combing the drug targets and the targets
   associated with potential metabolites. As shown in Figure [89]6, 445
   drug targets, 170 targets associated with potential metabolites, and 3
   potential metabolites, including phosphatidylserine ([90]C02737),
   prostaglandin E2 ([91]C00584) and phosphatidylcholine ([92]C00157),
   participated in the “potential metabolite-target-component” interactive
   network (Figure [93]6A). Five drug targets, including protein kinase C
   alpha type ([94]P17252), prostaglandin E synthase ([95]O14684),
   prostaglandin E2 receptor EP3 subtype ([96]P43115), group IIE secretory
   phospholipase A2 ([97]Q9NZK7) and phospholipase B1 ([98]Q6P1J6),
   directly regulated the 3 potential metabolites (Figure [99]6B). The 5
   drug targets were directly regulated by multiple chemical components,
   including beta-sitosterol, quercetin, p-coumaric acid and naringenin,
   which were collected from 4 herbs, including Curcumae radix, Prunellae
   spica, Radix bupleuri and Pseudobulbus cremastrae Seu pleiones in LRK
   (Figure [100]7).

FIGURE 6.

   [101]FIGURE 6
   [102]Open in a new tab

   The “potential metabolite-target-component” interactive network with
   all target information (A) and key target information (B) participating
   in the treatment of HMG by LRK. The red triangles represent active
   chemical constituents of LRK. The blue dots represent the protein
   targets of drugs. The yellow dots represent potential metabolites. The
   purple dots represent the targets associated with potential
   metabolites.

FIGURE 7.

   [103]FIGURE 7
   [104]Open in a new tab

   The “potential metabolite-target-component-herb” interactive network
   participating in the treatment of HMG by LRK. The red triangles
   represent active chemical constituents of LRK. The blue dots represent
   the protein targets of herbs. The yellow dots represent the potential
   metabolites. The blue rectangles represent herbs.

The Effect of LRK on the Expression of PKCα in Breast Tissue of HMG Rats

   Analysis of the network pharmacology prediction results showed that the
   greatest number of chemical components directly connected to PKCα,
   suggesting that PKCα may play a more important role in the treatment of
   HMG by LRK. Therefore, to verify the authenticity of the network
   pharmacology prediction results, we evaluated the expression of PKCα in
   breast tissue by immunohistochemistry. The results indicated the
   expression of PKCα was significantly increased in the breast tissues of
   rats in the model group, while the different doses of LRK significantly
   reduced the expression of PKCα (Figure [105]8).

FIGURE 8.

   [106]FIGURE 8
   [107]Open in a new tab

   Effects of LRK on the expression of PKCα in breast tissue of HMG rats
   using immunohistochemistry (200× and 400×). (A) (200×) and a (400×) :
   normal group. (B) (200×) and b (400×): model group. (C) (200×) and c
   (400×): low-dose LRK-treated HMG group (0.6 g/kg). (D) (200×) and d
   (400×): medium-dose LRK-treated HMG group (1.2 g/kg). (E) (200×) and e
   (400×): high dose LRK-treated HMG group (2.4 g/kg).

Discussion

   The morbidity rate of HMG is increasing annually and continues to
   affect younger women ([108]Zhang et al., 2008). HMG is classified in
   the “Rupi” category according to TCM theory ([109]Fan et al., 2013).
   While network pharmacology and metabonomics have been applied to TCM
   research ([110]Su et al., 2017; [111]Wang et al., 2017), few studies
   have attempted to combine these two approaches. Metabolomics can
   analyze and detect small molecules in bodily fluids to determine which
   compounds have significant abnormalities and to uncover the mechanism
   of disease development. Network pharmacology can analyze the
   interaction of macromolecule targets corresponding to chemical
   components and diseases and reveal the molecular mechanisms of drug
   treatment. By combining metabolomics and network pharmacology, we were
   able to gain a deeper understanding of the molecular mechanisms
   underlying TCM treatment.

   Li-Ru-Kang is made up of 8 kinds of herbs. It has been effective in
   treating HMG for decades. In this study, we combined metabonomics and
   network pharmacology analyses to evaluate the efficacy, active
   components and possible molecular mechanisms of LRK in the treatment of
   HMG. LRK significantly improved the morphological characteristics of
   nipples and reduced the pathological state of HMG in rats. Furthermore,
   we described the metabolomic feature profile and metabolite interaction
   network of LRK in treating HMG. LRK exhibited protective effects
   against HMG by reversing potential metabolites to normal levels. Ten
   metabolites were significantly regulated by LRK. Seven of these
   metabolites, including prostaglandin E2, leukotriene B4,
   phosphatidylcholine, coenzyme Q9, ceramide, gamma-glutamyl-L-putrescine
   and 5-amino-4-imidazole carboxylate, could interact in different ways.
   The results indicated that the occurrence and development of HMG were
   caused by changes in many aspects of physiologically and pathologically
   related molecules and that most of the changes in the body’s molecules
   were interrelated. LRK regulated these 10 biomarkers to normalize their
   expression levels, indicating that LRK could be used to treat HMG via
   multiple pathways and multiple targets.

   To obtain a deeper understanding of the LRK mechanism for treating HMG
   and the correlation between LRK chemical components and metabolites, we
   further combined the network pharmacology method to establish the
   “potential metabolite-target-component” interactive network. The
   results showed that four chemical components, beta-sitosterol,
   quercetin, p-coumaric acid and naringenin, which come from Curcumae
   radix, Prunellae spica, Radix bupleuri and Pseudobulbus cremastrae Seu
   pleiones, acted directly on multiple targets that are directly related
   to metabolites, including prostaglandin E synthase, protein kinase C
   alpha type, prostaglandin E2 receptor EP3 subtype, group IIE secretory
   phospholipase A2 and phospholipase B1. The results suggested that
   multiple components in LRK played an important role in regulating the
   endogenous physical disturbance of the body, thus indicating the
   advantages of LRK in the clinical treatment of HMG. By investigating
   the pathogenesis of HMG, we found that inflammation and oxidative
   stress mechanisms played a key role in the development of HMG
   ([112]Holloway et al., 2012). At the same time, some drugs act on HMG
   by exerting anti-inflammatory and antioxidant pharmacological effects
   ([113]Das Gupta et al., 2015; [114]Huang et al., 2017). Studies have
   shown that beta-sitosterol ([115]Villasenor et al., 2002; [116]Tan et
   al., 2008), quercetin ([117]Kamaraj et al., 2007), p-coumaric acid
   ([118]Ilavenil et al., 2016) and naringenin ([119]Cavia-Saiz et al.,
   2010; [120]Chen et al., 2013) in LRK have anti-inflammatory and
   antioxidant pharmacological activities. The network pharmacology
   results showed that PKCα was the target directly connected to the four
   chemical components. This target is also the most closely related
   target to inflammation and oxidative stress ([121]Kopach et al., 2013;
   [122]Andersson et al., 2016). Therefore, to verify the accuracy of the
   network pharmacology prediction results, we further examined the
   expression of PKCα in the breast tissues of HMG rats. The expression of
   PKCα was increased significantly in HMG rats, and LRK significantly
   reduced PKCα expression. These results suggested that the network
   pharmacology prediction results were credible. This experiment also
   demonstrated the significance of PKCα in the pathogenesis of HMG and
   LRK treatment.

   This study systematically explored the molecular mechanism of LRK for
   the treatment of HMG by combining metabonomics and network pharmacology
   analyses. In addition, this innovative research method can provide new
   ideas for research on the molecular mechanisms of TCM in the treatment
   of complex diseases.

Author Contributions

   SW and LQ performed the experiments, analyzed the data, and wrote the
   manuscript. MN, HHL, YY, YW, LZ, XZ, and HTL collected and prepared
   samples. RW, KL, and YZ performed the analyses.

Conflict of Interest Statement

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

   Funding. This work was financially supported by Project of the Project
   of Chinese Medicine Education Association (No. 2016SKT-M035), Chinese
   Medicine Education Association (No. 2016SKT-Z003) and Beijing Municipal
   Science & Technology Commission (No. Z171100001717013), and State
   Natural Science Foundation (No. 81573631).
   ^1

   [123]http://www.metaboanalyst.ca/
   ^2

   [124]http://metlin.scripps.edu/
   ^3

   [125]http://lsp.nwu.edu.cn/tcmsp.php
   ^4

   [126]http://bionet.ncpsb.org/batman-tcm/
   ^5

   [127]http://csbg.cnb.csic.es/mbrole2/

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [128]https://www.frontiersin.org/articles/10.3389/fphar.2018.00651/full
   #supplementary-material
   [129]Click here for additional data file.^ (140.6KB, DOCX)

References