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The modulatory properties of Li-Ru-Kang treatment on hyperplasia of mammary glands using an integrated approach

Frontiers in Pharmacology (2018) 9(JUN)
DOI: 10.3389/fphar.2018.00651
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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 PKCa. 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.

Figures

  • FIGURE 1 | 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 | 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×).
  • FIGURE 3 | Principal component analysis (PCA) score plot of the control, model and LRK (2.4 g/kg) groups. (A) ESI+ model. (B) ESI- model.
  • FIGURE 4 | 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).
  • TABLE 1 | Identified metabolites of the serum from different groups.
  • TABLE 2 | Results of integrating enrichment analysis of biomarkers with MetaboAnalyst 3.0.
  • FIGURE 5 | (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.
  • FIGURE 6 | 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.

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Wei, S., Qian, L., Niu, M., Liu, H., Yang, Y., Wang, Y., … Zhao, Y. (2018). The modulatory properties of Li-Ru-Kang treatment on hyperplasia of mammary glands using an integrated approach. Frontiers in Pharmacology, 9(JUN). https://doi.org/10.3389/fphar.2018.00651

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