SPPLEMENTARY MATERIAL Network Pharmacology Uncovers Anti-cancer Activity of Vibsane-Type Diterpenes from Viburnum odoratissimum

Vibsane-type diterpenes, the characteristic compounds of Viburnum odoratissimum , exhibited significant cytotoxicity in many cancer cells. To search for the potential target of vibsane-type diterpenes on lung cancer, we combined methods of network pharmacology prediction and experimental verification. 80 active ingredients, 23 potential targets and 39 related pathways were analysed through constructing the compound-target network and target-pathway network, and the potential target (EGFR) and key pathway (PI3K/Akt) were identified. Vibsanol C, an isolated vibsane-type diterpene with excellent cytotoxicity against lung cancer cells was chosen for further confirmation. Molecular docking study and drug affinity responsive target stability (DARTS) approach further indicated that EGFR is a direct target of Vibsanol C. Moreover, mechanistic studies revealed Vibsanol C might affect PI3K/Akt pathway by Western blot analysis. In conclusion, this study successfully predicted and confirmed the potential target of Vibsane-type diterpenes on lung cancer.


Table S3
The parameters of pathways.

Chemicals and reagents
The dried leaves of V. odoratissimum were obtained from Xishuangbanna Tropical Botanical Garden (Yunnan, China) in 2014 and were identified by Professor Pan Bo (Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences).
The air-dried leaves of V. odoratissimum (7.3 Kg) were flash extracted third with 75% EtOH. The ethanol extract was concentrated and loaded onto a D101 macroporous resin column using an EtOH/H 2 O gradient. And then separated by vacuum liquid chromatography, CHP20 CC and sephadex LH-20 column chromatography, the separation was subjected to the Silica gel column, preparative HPLC and semi-preparative HPLC to yield Vibsanol C (5 mg). The reagent was dissolved in dimethylsulfoxide (DMSO) at a stock concentration of 50 mM.

Database construction
Based on the vibsane-type diterpenes retrieved through literature, a database consisting of 80 natural products was established (Table S1), which was based on the reported vibsane-type diterpenes from the family V. odoratissimum. ChemBioDraw (http://www.cambridgesoft.com/) was used to make 3D chemical structural formulas of the ingredients, which were saved in "mol2" format.
Up to 23 protein targets (Table S2) related to lung cancer were obtained. The candidate proteins were data-mined from literatures and public database sources, including PubMed (www.PubMed.org), Therapeutic Targets Database (TTD; http://bidd.nus.edu.sg/group/ttd/). The X-ray or NMR structures of the proteins for docking were downloaded from the RCSB Protein Data Bank (http://www.pdb.org/) by the following criteria: (a) the source organism is human; (b) the structure should contain an original ligand to define the active site for docking; (c) the resolution of the structure of the protein-ligand complex is below 3 Å. At the same time, the protein structures were prepared with Sybyl-X (version 2.0, TRIPOS Inc.), including addition of hydrogen atoms as well as removal of co-crystalled ligands and water molecules from the complexes.

Molecular docking
Molecular docking, which plays an important role in the rational drug design, is frequently used to predict the binding sites and binding pose(s) of drug candidates to their targets and also to estimate the binding affinity of the molecules(Kitchen et al.

2004
). Surflex-Dock plug-in included in the Sybyl-X (version 2.0, TRIPOS Inc.) was used to perform molecular docking. The binding ability was evaluated using a scoring function analysis and the higher docking score represent better binding ability. For docking studies with Surflex-Dock, the ligand binding site protomol was generated using the ligand option in which the X-ray co-crystalled ligand pose was specified as references. The visualization of intermolecular forces between candidate compound and potential target was preformed on Discovery Studio 4.5 program.

Network construction and analysis
To facilitate scientific interpretation of complex relationships among the compounds, targets and pathways, the C-T network and T-P network were generated by Cytoscape 3.2.1 (http://www.cytoscape.org/), which is an open source software project for analysis and visualization of biological networks (Sheng et al. 2014).
The interaction between molecules and target proteins (docking scores greater than 6.0) were chosen to generate a C-T network in which nodes represent molecules or target proteins. For each of these 23 compounds, the top 10 targets with degree higher than 30 were selected to as the potential targets.
All experiments were performed on logarithmically growing cells.

Target identification using drug affinity responsive target stability
A549 cells (1×10 6 /mL, 2 mL) were seeded into dishes and allowed to grow for 24

Western blot analysis
The total cellular samples were harvested and lysed in RIPA buffer [250 mM Tris-HCl (pH 6.8), 4% SDS, 10% glycerol, 0.006% bromophenol blue, 2% β-mercaptoethanol, 50 mM sodium fluoride, and 5 mM sodium orthovanadate] and boiled for 10 min at 100 o C. Equal amount of protein (30 μg) were separated on a 10% SDS-PAGE gel and transferred to nitrocellulose membranes. The membranes were blocked with 5% BSA and probed with a primary antibody followed by the corresponding secondary antibody. Immunoreactive bands were visualized with a chemiluminescence kit (ThermoFisher, Waltham, USA) followed by incubation with HRP-conjugated secondary antibodies. The densities of protein bands were calculated by the ImageJ software (National Institutes of Health, Wayne Rasband, USA).

Statistical analysis
All results were calculated as the mean ± SD for at least three independent experiments. Experimental data were analysed by one-way or two-way ANOVAs using GraphPad Prism version 6.0 (GraphPad Software, San Diego, CA, USA).
Statistical significance was considered at P < 0.05. Figure S1. Process overview.      The denomination and structures of the Vibsane-type diterpenes Signaling pathways regulating pluripotency of stem cells 1 Table S3 The parameters of pathways.