Dissecting Combinational Mechanisms of Herbal Formula from a Transcriptome-based Multi-scale Network Pharmacology Model

Author:

Li Peng12,Jin Tong3,Deng Qing-Qiong4,Chen Ning5,Zhang Hao-Ran2,Zhang Wu-Xia2,Li Yi-Jie5,Meng Zi-Yu5,Xing Lin5,Zhang Yuan-Yuan2,Zhan Ling-Min2,Cheng Cai-Ping2,Zhao Jin-Zhong2,Fu Bang-Ze5,Li Tian-Gang1,Lu Peng6

Affiliation:

1. School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China

2. College of Basic Sciences, Shanxi Agricultural University, Jinzhong, China

3. Fujian People’s Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, China

4. School of Artificial Intelligence, Beijing Normal University, Beijing, China

5. School of Biomedicine, Beijing City University, Beijing, China

6. State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China

Abstract

Objective: Illumination of the integrative effects of herbs in a formula is a bottleneck that limits the development of traditional Chinese medicine (TCM). In the present study, we developed a transcriptome-based multi-scale network pharmacology model to explore the combined effects of different herbs. Materials and Methods: First, we curated gene signatures at different biological scales, from the molecular to higher tissue levels, including tissues, cells, pathological processes, biological processes, pathways, and targets. Second, using the Xiexin Tang (XXT) formula as an example, we collected transcriptomic data in response to the treatment of XXT or its three compositive herbs on Michigan cancer foundation7 cells. Third, we linked each herbal drug to different biological scales by calculating the correlation scores between herb-induced gene expression profiles and gene signatures. Finally, the combined mechanisms of the three constituent herbs in XXT were deciphered by comparing their multi-scale effects with those of the formula. Results: The results showed that although XXT or single herbs regulated a large number of signatures on each biological scale, the phenotypic effects of these herbal drugs are concentrated onto the “Blood” tissue, types of hemocytes, and hemorrhagic injury-related pathological processes. At the molecular level, these herbs consistently regulate processes such as the cell cycle and blood coagulation-related pathways, as well as protein targets related to the immunoinflammatory response and blood coagulation, such as proteinase-activated receptor 2, integrin beta-3, inhibitor of nuclear factor kappa-B kinase subunit beta, and coagulation factor XII. The analysis of the combinational modes demonstrated that different herbs can cooperate by acting on the same objects and/or regulating different objects in related functions, and cooperative behaviors change at different biological scales. Conclusions: Our model can dissect the combined effects of herbal formulae from a multi-scale perspective and should be beneficial for the development and exploitation of TCM.

Publisher

Medknow

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