Abstract
Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.
Highlights
The key group of effective components (KGEC) in a formula of traditional Chinese medicine (TCM) refer to the pharmacologically active components that are closely related to the positive response to the therapy of the diseases
The effective proteins were used to select the KGEC based on contribution index (CI) module and the KGEC was used to infer the underlying molecular mechanism of Lang Chuang Wan (LCW) in treating systemic lupus erythematosus (SLE)
Eight out of top 30 genes with the highest weight in the gene regulatory network enriched in the common SLE pathways, including TNF (Geng et al, 2014), HLA-DRB1 (Shimane et al, 2013), IFNG (Leng et al, 2016), CD40LG (Wu et al, 2016), IL10 (Liu P. et al, 2013), FCGR3A (Kyogoku et al, 2013), FCGR2A (Bentham et al, 2015) and TRIM21 (Kyriakidis et al, 2014). These genes are enriched in cytokine-cytokine receptor interaction, T cell receptor signaling pathway and Th17 cell differentiation, which are closely related to SLE (Figure 3). These results indicate that the weight gene regulatory network and weight genes can reflect the pathogenesis of SLE, which provides a reliable reference for the step to construct the optimization space
Summary
The key group of effective components (KGEC) in a formula of traditional Chinese medicine (TCM) refer to the pharmacologically active components that are closely related to the positive response to the therapy of the diseases. Selecting the KGEC in the formula of TCM is an important direction in the reduction of non-active components and analysis of the treatment mechanism of formula. There are several formula optimization models based on network pharmacology have been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug or components, and ignore the optimized functional space, which couldrepresent effective relationships between drug targets and pathogenetic genes (Wang et al, 2018; Xie et al, 2018; Duan et al, 2019). It is desirable to design a module to detect the KGEC and infer the potential mechanisms of formula on complex disease based on chemical properties analysis, targets prediction, and construction of functional optimization space
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