Systematic exploration of therapeutic effects and key mechanisms of Panax ginseng using network-based approaches

Abstract

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms.

Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood.

Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model.

Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms.

Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

Keywords: Network-based approach; Panax ginseng; Therapeutic effects.

Graphical abstract

Fig. 1

Integrated workflow for investigating novel indication of P. ginseng and their potential mechanisms. The workflow includes three main steps: A. compiling P. ginseng ingredients, their protein targets, and known indications; B. evaluating the performance of network-based methods to on recapulating therapeutic effects of P. ginseng; and C. performing case studies to validate the predicted indications and mechanisms of P. ginseng.

Fig. 2

Selection process for flavonoids evaluated in this study and their chemical distribution. A. The flowchart of selecting the compounds of Panax ginseng. B. Distribution of compounds in Panax ginseng across different chemical superclasses and classes obtained from ClassyFire. The inner circle and outer circle represent the proportions of superclass and classes, respectively.

Fig. 3

A compound-target network for Panax ginseng and its property. A. Statistics of experimentally target information for Panax ginseng. $N_D$, $N_T$, and $N_{DTI}$ represents the number of drugs, targets, and drug-target interactions, respectively. B. Distribution of the number of protein targets of ginseng ingredients. C. A representative compound-target network for Panax ginseng. For efficient visualization, a subnetwork between 66 targets interacting with 3 or more of the protein targets and their ginseng ingredients was visualized. Circles and diamonds denote protein targets and compounds, respectively. Edges denote experimentally validated interactions between them. D. Top (n = 15) enriched pathways (left) and gene ontology terms (Biological Process, right) among all protein targets of Panax ginseng. The x-axis represents the statistical significance associated with each term.

Fig. 4

Performance Comparison of Network-Based Prediction Methods for Recapitulating Ginseng Component-Disease Associations. A. Performance curve between all ginseng ingredients-disease pairs. B. Performance distribution of predicting the therapeutic effects of ginseng ingredients, evaluated based on both disease levels (n = 78, left penal) and ingredient levels (n = 14, right penal). The figure presents a comparison of the performance of three network-based prediction methods—protein overlap, network proximity, and multiscale interactome—in recapitulating known associations and unknown associations between ginseng ingredients and diseases. Each data point is color-coded based on the method used, the dashed line indicates the chance level performance. MSI: Multiscale interactome; AUC: Area under the curve.

Fig. 5

Potential mechanisms of ginseng ingredients for breast neoplasms (A) and liver cirrhosis (B). Each network was constructed by inducing a subgraph consisting of the top 20 nodes in the diffusion profiles of the selected ginseng ingredients and disease. These nodes correspond to the proteins and biological functions most affected by ginseng ingredients or disease.

Fig. 6

Prediction and validation of a novel ginseng ingredient candidate for liver cirrhosis. A. Effects of ginsenoside Rc and Rh1 on TGF-β1-induced HSCs activation. Rc and Rh1 suppressed TGF-β1-induced expression of the fibrotic marker protein α-SMA. B. Effects of Rc and Rh1 on TGF-β1/Smad pathway in HSCs. Phosphorylation levels of Smad 2 and 3 were analyzed by immunoblotting with specific antibodies. Western blot performed on the lysates of serum starved LX-2 cells that were treated with Rc and Rh1 in the presence or absence of TGF-β1 (5 ng/ml) for an additional 24 h. C. MTT assay for cancidate ginseng ingredients. AGS cells were treated with β-Elemene (25, 50 and 100 μg/ml) and Estragole (2.5, 5 and 10 μM) for 4, 8 and 24 h. Data represents the mean ± S.E.M. from three separate experiments. *P < 0.05, **P < 0.01 vs vehicle-treated control;^#P < 0.05 vs TGF-β1 treated group.