Prediction and Characterisation of the System Effects of Aristolochic Acid: A Novel Joint Network Analysis towards Therapeutic and Toxicological Mechanisms

Abstract

Aristolochic acid (AA) is the major active component of medicinal plants from the Aristolochiaceae family of flowering plants widely utilized for medicinal purposes. However, the molecular mechanisms of AA systems effects remain poorly understood. Here, we employed a joint network analysis that combines network pharmacology, a protein-protein interaction (PPI) database, biological processes analysis and functional annotation analysis to explore system effects. Firstly, we selected 15 protein targets (14 genes) in the PubChem database as the potential target genes and used PPI knowledge to incorporate these genes into an AA-specific gene network that contains 129 genes. Secondly, we performed biological processes analysis for these AA-related targets using ClueGO, some of new targeted genes were randomly selected and experimentally verified by employing the Quantitative Real-Time PCR assay for targeting the systems effects of AA in HK-2 cells with observed dependency of concentration. Thirdly, the pathway-based functional enrichment analysis was manipulated using WebGestalt to identify the mostly significant pathways associated with AA. At last, we built an AA target pathway network of significant pathways to predict the system effects. Taken together, this joint network analysis revealed that the systematic regulatory effects of AA on multidimensional pathways involving both therapeutic action and toxicity.

Figure 1. Schematic illustration of the standard workflow utilized in this study.

This workflow is composed of the following four steps. 1) Retrieve protein targets from the PubChem database and their interaction proteins from the STRING database. 2) Visualise the AA-specific gene network (APN) using Cytoscape v 2.8.2. 3) Validate the genes associated with AA through literature retrieval, network analyses and experimental verification. 4) Engage in functional enrichment analysis of biological processes (BPs) and pathway analysis.

Figure 2. AA-specific protein network.

Green denotes genes that can directly associate with AA. Pink denotes genes that can indirectly associate with AA.

Figure 3. AAI treatment remarkable regulated the gene expressions with a remarkable concentration dependent in HK-2 cells.

AAI-0: HK-2 cells without AAI treatment; AAI-1: HK-2 cells were treated with AAI (10 mM); AAI-2: HK-2 cells were treated with AAI (50 mM); AAI-3: HK-2 cells were treated with AAI (100 mM). Compared to AAS-0 (control group), it was considered as statistical difference while P value is less than 0.05 (*), and it was considered as significantly statistical difference when P value is less than 0.01 (**).

Figure 4. AAI treatment regulated the gene expressions in a variety of manners in HK-2 cells.

AAI-0: HK-2 cells without AAI treatment; AAI-1: HK-2 cells were treated with AAI (10 mM); AAI-2: HK-2 cells were treated with AAI (50 mM); AAI-3: HK-2 cells were treated with AAI (100 mM). Compared to AAS-0 (control group), it was considered as statistical difference while P value is less than 0.05 (*), and it was considered as significantly statistical difference when P value is less than 0.01 (**).

Figure 5. Network groupings based on functionally enriched BP terms.

(A) A functionally grouped network of enriched categories was generated for AA-related targets using GO terms as nodes and linked using ClueGO analysis. Only the most significant terms in the group are labelled. Functionally related groups partially overlap. (B) Functional groups and their corresponding colours.

Figure 6. AA target pathway network mode.

A red square denotes AA, blue circles denote genes, and green triangles denote KEGG pathways.