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. 2019 Nov 16:2019:8706589.
doi: 10.1155/2019/8706589. eCollection 2019.

A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Paeonia lactiflora Pall. On the Treatment of Alzheimer's Disease

Qiang Zeng  1   2   3 , Longfei Li  4 , Yu Jin  2   3 , Zongzheng Chen  1   2   3 , Lihong Duan  2   3 , Meiqun Cao  2   3 , Min Ma  1 , Zhengzhi Wu  2   3
Affiliations

A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Paeonia lactiflora Pall. On the Treatment of Alzheimer's Disease

Qiang Zeng et al. Evid Based Complement Alternat Med. .

Abstract

Objective: To investigate the potential active compounds and underlying mechanisms of Paeonia lactiflora Pall. (PLP) on the treatment of Alzheimer's disease (AD) based on network pharmacology.

Methods: The active components of PLP were collected from Traditional Chinese Medicine System Pharmacology (TCMSP) database, and their possible target proteins were predicted using TCMSP, SwissTargetPrediction, and STITCH databases. The putative AD-related target proteins were identified from Therapeutic Target Database (TTD), GeneCards, and MalaCards database. The compound-target-disease network interactions were established to obtain the key targets about PLP acting on AD by network topology analysis. Then, the function annotation and signaling pathways of key targets were performed by GO and KEGG enrichment analysis using DAVID tools. Finally, the binding capacity between active ingredients and key targets was validated by molecular docking using SystemsDock tools.

Results: There were 7 active compounds involving in 151 predicted targets identified in PLP. Besides, a total of 160 AD-related targets were identified. Among these targets, 30 shared targets of PLP and AD were acquired. After topological analysis of the PLP potential target-AD target network, 33 key targets that were highly responsible for the therapeutic effects of PLP on AD were obtained. Further GO and KEGG enrichment analysis showed that these key targets were significantly involved in multiple biological processes and pathways which participated in cell apoptosis and inflammatory response and maintained the function of neurons to accomplish the anti-AD activity. The molecular docking analysis verified that the 7 active compounds had definite affinity with the key targets.

Conclusions: The ameliorative effects of PLP on AD were predicted to be associated with regulating neural cell apoptosis, inflammatory response, and neurotrophy via various pathways such as PI3K-Akt signaling pathway, MAPK signaling pathway, and neurotrophin signaling pathway.

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Conflict of interest statement

There are no conflicts of interest to declare.

Figures

Figure 1
Figure 1
(a) Compound-target network of PLP. Red rectangle nodes represent potential active compounds in PLP, while blue rectangle nodes represent potential targets of PLP. (b)–(d) GO enrichment analysis for potential targets of PLP (count number ≥15). (e) KEGG enrichment analysis for potential targets of PLP (count number ≥15).
Figure 2
Figure 2
Disease-target network of AD. Pink ellipse nodes represent AD-related targets, and the size of nodes is proportional to degree centrality by topology analysis.
Figure 3
Figure 3
(a) PLP potential target-AD target network. (b) Network of the 33 key targets. Blue ellipse nodes stand for PLP potential targets, pink ellipse nodes represent AD-related targets, and red ellipse nodes represent the shared targets of PLP and AD. The size of nodes is proportional to degree centrality by topology analysis. (c) GO enrichment analysis for 33 key targets. (d) KEGG enrichment analysis for 33 key targets.
Figure 4
Figure 4
Heat map of binding capacity between the active compounds and key targets by molecular docking. The heat map was depicted based on docking scores.
Figure 5
Figure 5
Representative compounds and potential mechanisms of PLP on AD treatment. The green triangle stands for representative compounds in PLP, and the red rectangle stands for the compound-related targets.
See this image and copyright information in PMC

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