Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae

Abstract

Background: Traditional Chinese Medicine (TCM) is characterized by the wide use of herbal formulae, which are capable of systematically treating diseases determined by interactions among various herbs. However, the combination rule of TCM herbal formulae remains a mystery due to the lack of appropriate methods.

Methods: From a network perspective, we established a method called Distance-based Mutual Information Model (DMIM) to identify useful relationships among herbs in numerous herbal formulae. DMIM combines mutual information entropy and "between-herb-distance" to score herb interactions and construct herb network. To evaluate the efficacy of the DMIM-extracted herb network, we conducted in vitro assays to measure the activities of strongly connected herbs and herb pairs. Moreover, using the networked Liu-wei-di-huang (LWDH) formula as an example, we proposed a novel concept of "co-module" across herb-biomolecule-disease multilayer networks to explore the potential combination mechanism of herbal formulae.

Results: DMIM, when used for retrieving herb pairs, achieves a good balance among the herb's frequency, independence, and distance in herbal formulae. A herb network constructed by DMIM from 3865 Collaterals-related herbal formulae can not only nicely recover traditionally-defined herb pairs and formulae, but also generate novel anti-angiogenic herb ingredients (e.g. Vitexicarpin with IC50=3.2 μM, and Timosaponin A-III with IC50=3.4 μM) as well as herb pairs with synergistic or antagonistic effects. Based on gene and phenotype information associated with both LWDH herbs and LWDH-treated diseases, we found that LWDH-treated diseases show high phenotype similarity and identified certain "co-modules" enriched in cancer pathways and neuro-endocrine-immune pathways, which may be responsible for the action of treating different diseases by the same LWDH formula.

Conclusions: DMIM is a powerful method to identify the combination rule of herbal formulae and lead to new discoveries. We also provide the first evidence that the co-module across multilayer networks may underlie the combination mechanism of herbal formulae and demonstrate the potential of network biology approaches in the studies of TCM.

Figure 1

DMIM-extracted herb network from 3865 herbal formula.This herb network is constructed from the top 100 herb pairs extracted by DMIM. Herbs with different natural properties and six classical herbal formulae are presented in the network. Data about only two interacted herbs are not shown.

Figure 2

Angiogenic activities of major ingredients in DMIM-extracted herbs. A. The extended hub module in DMIM-extracted herb network. Each node corresponds to a herb colored according to their natural properties. The size of each node is proportional to the number of herbs connecting to it. A solid line links a herb pair while the width of the lines is proportional to the DMIM score. B. Experimental results of the herb ingredients in the module. The pro- or anti-angiogenic effects of each herb were delineated by pro- or anti-angiogenic screening model respectively.

Figure 3

Combination effects in DMIM-extracted herb pairs.According to the HSC model, the dose matrix indicates the combined response of TMP in combination with three other compounds at six different doses. The color of the gird denotes the level of cell growth stimulation or inhibition. The growth percentage and inhibition percentage were calculated by the pro- or anti-angiogenic screening model respectively (A, C and E). B, D and F show the calculated excess growth or inhibition percentage over the HSC additivity model. The percentage above or below zero denotes the combination with synergism or antagonism, respectively.

Figure 4

The co-module underlying Liu-wei-di-huang formula and diseases. For the herb module, two herbs from the Liu-wei-di-huang are linked if they have common responsive genes. For the disease module, two diseases are linked if they have common disease genes. The width of the solid lines is scaled with the number of common herb or disease genes. All herb genes and disease genes are mapped to the protein-protein interaction network. A biomolecular module as a common network target and associated with both the herb module and the disease module is extracted with dashed lines.