Bioactive ingredients obtained from Cortex Fraxini impair interactions between FAS and GPI

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Original article

Bioactive ingredients obtained from Cortex Fraxini impair interactions between FAS and GPI Songtao Wua, Li Tongd, Bo Liua,b,c, Zhongzhu Aia,b,c, Zongchao Honga, Pengtao Youa,b,c, Hezhen Wua,b,c,∗, Yanfang Yanga,b,c,∗∗ a

Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, 430065, China Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Wuhan, 430065, China c Collaborative Innovation Center of Traditional Chinese Medicine of New Products for Geriatrics Hubei Province, Wuhan, 430065, China d Department of Biochemistry and Molecular Biology, Beijing Normal University, Beijing Key Laboratory, Beijing, 100875, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Cortex fraxini Proteomics Microscale thermophoresis FAS inhibitor FAS/GPI pathway

The high expression of fatty acid synthase (FAS) in tumor cells is consistent with their elevated requirement for fatty acids for cell membrane synthesis and energy supply to support their almost unlimited proliferation. The expression levels of FAS in tumor cells are related to their proliferation, invasion, and metastasis. This study investigated the possible bioactive ingredients (fraxin, esculetin, scopolin et al.) of Cortex Fraxini and their effects on the interaction between specific proteins. We used microscale thermophoresis (MST) to show that our target protein, FAS (screened by combining transcriptome and network pharmacology), bound to the active compounds in Cortex Fraxini. It was found that FAS bound strongly to Glucose-6-phosphate isomerase (GPI), and that scopolin could affect this interaction by proteomics and MST. The results of this study demonstrate that the active compounds in Cortex Fraxini could play an anti-tumor role by binding to FAS and inhibiting the interactions between FAS and GPI to affect glucose and lipid metabolism, and that the protein pathway is a potential novel target for tumor treatment.

1. Introduction FAS is a key enzyme which catalyzes endogenous fatty acid synthesis in vivo. It is located within the cytoplasm of cells and is involved in the synthesis of palmitic acid, stearic acid, and myristic acid, with acetyl-CoA and malonyl CoA used as raw materials and NADPH as a reducing agent. In normal cells, exogenous fatty acids are mainly consumed and since almost no endogenous fatty acids need to be synthesized, the expression of FAS is low. The high expression of FAS in tumor cells is consistent with their elevated requirement for fatty acids for cell membrane synthesis and energy supply to support their almost unlimited proliferation [1], as the quantity that can be obtained from the blood circulation is limited. FAS expression levels are related to the tumor cells proliferation, invasion, and metastasis [2]. FAS is a preferred target for cancer because citric acid lyase and acetyl-CoA carboxylase have important roles in other cellular reactions and inhibition of these enzymes may have an adverse effect on normal, healthy cells. FAS consists of two identical subunits connected head-to-tail to form the catalytic center which includes 7 functional domains; acetyl

∗

transferase (ACP), malonyltransferase (MAT), beta-ketoalidyl synthase (KS), beta-ketoalidyl reductase (KR), beta-hydroxyl dehydrase (DH), allyl reductase (ER), and thioesterase (TE). The MAT region is involved in the substrate loading reaction, the beta-ketone ester synthesis region is involved in the synthesis reaction, and the TE region is involved in the carbon chain termination reaction [3]. FAS inhibitors can inhibit tumor cell proliferation and induce tumor cell apoptosis, in a dose-time response. Orlistat is a FAS inhibitor which can inhibit the growth of transplanted tumors in mice [4]. In human SKBR3 colon tumor cells, amentoflavone can inhibit the expression of HER2/neu oncogene by inhibiting FAS and reducing the viability of tumor cells and inducing apoptosis [5]. Inhibiting FAS activity can induce apoptosis in tumor cells without affecting normal cells, thus providing a novel target pathway for the treatment and prevention of tumors. GPI is a multifunctional protein which catalyzes the reversible reaction between glucose-6-phosphate and fructose-6-phosphate in glycolysis and can also catalyze the exchange of the alpha and beta isomers of glucose 6-phosphate [6]. This reversible reaction directly affects glycolysis and gluconeogenesis and indirectly affects sugar metabolism.

Corresponding author. Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, 430065, China. Corresponding author. Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, 430065, China. E-mail addresses: [email protected] (H. Wu), [email protected] (Y. Yang).

∗∗

https://doi.org/10.1016/j.freeradbiomed.2019.11.022 Received 15 August 2019; Received in revised form 1 November 2019; Accepted 17 November 2019 0891-5849/ © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

Please cite this article as: Songtao Wu, et al., Free Radical Biology and Medicine, https://doi.org/10.1016/j.freeradbiomed.2019.11.022

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Table 1 HPLC-DAD-ESI-MS data and identification of the binding compounds. Peek

RT (retention time)/min

MW

Molecular Formula

Fragment ions

1 2 3 4 5 6 7 8 9 10 11 12

13.6 22.6 23.1 35.3 8.5 36.9 39.7 43.8 45.5 52.8 45.8 48.9

340.1 178.0 370.1 192.0 354.1 222.0 222.1 162.0 176.1 524.2 726.2 540.2

C15H16O9 C9H6O4 C16H18O10 C10H8O4 C16H18O9 C11H10O5 C11H10O5 C9H6O3 C9H6O3 C24H28O13 C32H38O19 C25H32O13

339.1, 177.0, 369.1, 191.1, 355.1, 221.0, 206.1, 163.0, 177.1, 523.1, 725.2, 539.2,

177.0, 149.0, 207.0, 176.0, 324.1, 206.0, 191.0, 145.0, 158.9, 361.1, 563.1, 377.1,

149.0 133.0, 192.1 148.0, 293.1, 191.0, 163.0 135.0, 145.0, 291.1, 493.1, 307.1,

Identification results

105.1, 89.0 120.0, 104.0 192.0, 178.0, 151.0 163.0, 135.1, 107 117.0, 135.0, 259.1, 461.1, 275.1,

98.9, 89.1 117.1, 84.1 139.0 339.1 139.0

Esculin Esculetin Fraxin Isoscpoletin Scopolin Isofraxidin 6-Hydroxy-7,8-dimethoxy coumarin 7-hydroxycoumarin 4-Methylumbelliferone Ligustroflavone Escuside Oleuropein

purchased from Abcam (Cam-bridge, UK). FAS and GPI monoclonal antibody was purchased from Cell Signaling Technology (Boston, USA).

Yu et al. [7]found that GPI could regulate matrix metalloproteinase-3 (MMP-3) in tumor cells. Many diseases, such as acute hepatitis, malignant tumors, and acute myocardial infarction, can also significantly increase the GPI activity in the serum of patients, and some studies have found that it is positively correlated with the course of disease. This means that the study of GPI has potential significance for the diagnosis and detection of many diseases [8,9]. Inside cells, GPI is the key glycolysis enzyme, but outside cells its activity has been associated with immune activity, as indicated by its effects on cytokines and cell growth factor activity. It can also induce differentiation in myeloid stem cells, turning them into plasma cells, and plays a role in a variety of extracellular process [10,11]. GPI is also a product of tumor cells and has the same function as nerve interleukin (NLK), maturation factor (MF), and autocrine motor factor (AMF). GPI can promote cell proliferation and metastasis [11–13]. Cortex Fraxini is a widely-distributed and a rich resource in China and pharmacological studies on it have principally focused on its antibacterial, anti-inflammatory, and anti-tumor activities. In this study, we demonstrated that active compounds in Cortex Fraxini could bind to FAS and block the binding between FAS and GPI to treat the tumor. At the same time, we tried to find the binding region of active ingredients and FAS.

2.2. LC-MS analysis The 0.22 μm filtration membrane was used to produce a stock sample solution which was analyzed by HPLC-DAD-ESI-MS. Chromatography was performed with a Hanbon LichrospherTM high performance liquid chromatography C18 column (4.6–250 mm, 5 μm) at 25 °C. HPLC column was eluted with mobile phase A (methanol) and mobile phase B (0.05% (v/v) formic acid aqueous solution), at a gradient program: 0–5min: 5–20% A; 5–20 min: 20% A; 20–40 min: 20–40% A; 40–75 min: 40–95% A; 75–85 min: 95% A. It's 10 μL per milliliter per minute. The detection wavelength is 190–690 nm.The Settings of mass spectrometry are as follows; turbine spray temperature: 550 °C, source voltage: 5.5kv for positive ion mode and −4.5kv for negative ion mode. M/Z varies from 50 to 1600. Compounds were identified by their precise mass, mass/mass ion fragment patterns and retention time in liquid chromatography. 2.3. RNA extraction Total RNA was extracted from HepG2 cells with or without TRIzol® Reagent according to the manufacturer's instructions (Invitrogen) and genomic DNA was removed with DNase I (TaKara). RNA quality was determined using a 2100 Bioanalyser (Agilent) and quantified with an ND-2000 (NanoDrop Technologies). Only high-quality RNA samples (OD260/280 = 1.8–2.2, OD260/230 ≥ 2.0, RIN≥6.5, 28 S:18 S ≥ 1.0, > 10 μg) were used to construct sequencing libraries.

2. Materials and methods 2.1. Screening of binding compounds and reagents HepG2 cells were cultured in Dulbecco's modified eagle medium (DMEM) (Gibco, Waltham, MA) with 10% FBS (Sijiqing Co., Ltd, Shanghai, China) in a 37 °C incubator (Thermo Scientific series II water jacket) in an environment containing 5% CO2. Total proteins were extracted from cells using RIPA buffer with protease/phosphatase inhibitors (Beyotime Biotechnology, China). 0.11 g/mL methanol extract of Cortex Fraxini (10.52 g of Cortex Fraxini were obtained from the Key Laboratory for Traditional Chinese Medicine Resources and Chemistry of Hubei Province) was dissolved to 11 mg/mL by 0.5 M potassium phosphate buffer (pH 7); 12 μL of total protein, 60 μL of methanol extract and 168 μL of 0.5 M potassium phosphate buffer (pH 7) were mixed and incubated at 37 °C for 60 min. After incubation, the mixture was filtered for 30min using an ultrafiltration centrifugal tube (Millipore) at 6350 g and 4 °C, and then washed twice with potassium phosphate buffer to remove unbound compounds. The protein complex which was retained on the membrane of the ultrafiltration device was then transferred to a new centrifuge tube [14]. Reference compounds came from the National Institutes for Food and Drug Control (China). LC-MS grade acetonitrile (ACN) was purchased from Thermo-Fisher (Pittsburgh, Pennsylvania, USA). Deionized water was produced using Milli-Q purification devices (Millipore, Bedford, MA, USA). TruSeq™ RNA sample preparation Kit was purchased from Illumina (San Diego, CA). FAS, GPI and GPDH were

2.4. Illumina Hiseq X Ten sequencing An RNA-seq transcriptome library was prepared according to the TruSeq™ RNA sample preparation Kit using 5 μg of total RNA. Within a short time, mRNA was isolated by oligo(dT) beads using the polyA selection method and fragment buffer was used to fragment the RNA. Double-stranded cDNA was synthesized using a SuperScript doublestranded cDNA synthesis kit (Invitrogen, CA) with random hexamer primers (Illumina). The synthesized cDNA was then end-repaired, phosphorylated and underwent ‘A’ base addition according to Illumina's library construction protocol. Libraries were size selected for cDNA target fragments of 200–300 bp on 2% Low Range Ultra Agarose followed by PCR amplification using Phusion DNA polymerase (NEB) for 15 PCR cycles. After quantification by TBS380, a paired-end RNA-seq sequencing library was used to sequence with the Illumina HiSeq X Ten (2 × 150bp read length). 2.5. Differential expression analysis and functional enrichment To identify differential expression genes (DEGs) between two 2

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(caption on next page)

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Fig. 1. Differential expression of cancer-related genes following administration of methanol extract. (A) A volcanic map of the differences in gene expression. The xcoordinate is the multiple change value of gene expression difference between two samples, and the y-coordinate is the statistical test value of gene expression difference (p-value). Red dots are significantly up-regulated genes, green dots significantly down-regulated genes, and black dots represent non-significantly differentially expressed genes. (B) KEGG enrichment analysis of cancer-related genes. The vertical axis represents the pathway name, and the horizontal axis represents the Rich factor (Sample number/Background number). The larger the Rich factor, the greater the degree of enrichment. The size of the dots indicates the number of genes in this pathway. (C) GO enriched string diagrams. The differentially expressed genes correspond to significantly enriched GO terms, genes are on the left, arranged in the order of log2FC from large to small. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2. Top 10 components of the KEGG pathway and GO enrichment analyses. (A) KEGG Pathways. (B) Biological Process. (C) Cell Components. (D) Molecular function.

2.7. Construction of the network

different samples, the expression level of each transcript was calculated by the number of fragments per kilobase of exon per million mapped reads (FRKM) method. RSEM (http://deweylab.biostat.wisc.edu/rsem/ ) [15] was used to quantify gene abundances. The R statistical package software EdgeR (Empirical analysis of Digital Gene Expression in R, http://www.bioconductor.org/packages/2.12/bioc/html/edgeR.html) [16] was used to assess differential expression. Functional-enrichment analysis using GO and KEGG was conducted to identify which DEGs were significantly enriched in GO terms, and metabolic pathways at Bonferroni-corrected P-value ≤0.05, compared with the whole-transcriptome background. GO functional enrichment and KEGG pathway analysis were carried out through Goatools (https://github.com/ tanghaibao/Goatools) and KOBAS (http://kobas.cbi.pku.edu.cn/home. do) [17].

Cytoscape 3.6.1 software was used to construct an active compound/target gene/pathway network. Compounds and target genes were inputted as nodes and if a connection existed between two nodes, an edge was added to show the connection. The network was analyzed using the “network analysis” function, including the high degree targets in the protein interaction network. From the results of KEGG analysis, key targets of pathways with the greatest numbers of counts were selected for analysis. The proteins suitable for target analysis were obtained by comparing the literature and database.

2.8. Screening of active compounds for inhibition of HepG2 cell proliferation 2.6. Differentially expressed target gene prediction and analysis Cultured HepG2 cells were seeded into 96-well plates at 2 × 104/ mL and the total volume in each well was 100 μL. For each compound, three experimental groups were compared: Culture medium only, HepG2 cells without drug, HepG2 cells with various concentrations of drug. According to the physical and physiological properties of each compound and the relevant literature, each test substance (Esculin, 4methylumbelliferone, esculetin, fraxin, isoscopoletin, isofraxidin, scopolin and 7-hydroxycoumarin) was added at a specified concentration to each well. After incubation for 4 h at 37 °C with 5% CO2, the supernatant was discarded. 100 μL of CCK-8 was added to each well and the absorbance at 450 nm measured using a plate reader (Thermo Scientific, Waltham, MA). Cell survival rate was estimated using SPSS 19.0 statistical software.

According to the results of the transcriptome and LC-MS experiments, a network pharmacology study was conducted. PharmMapper (http://lilab.ecust.edu.cn/pharmmapper/index.php) is a freely available web tool for identifying potential targets using a pharmacophore mapping method. MalaCards (http://www.malacards.org) and GeneCards (https://www.genecards.org) were used for potential target screening of LC-MS results. All potential target genes were collected and uploaded to the DAVID database (https://david.ncifcrf.gov/summary. jsp). Gene Ontology (GO; http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (http://www. genome.jp/kegg/ko.html) were used to study the function and signaling pathways of target genes, as well as pathway enrichment [18,19].

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Fig. 3. Network diagram of active components/target genes/enrichment pathways constructed by Cytoscape.

2.9. Docking prediction

Table 2 Degree of 12 compounds analyzed by Cytoscape. Compound Names

Degree

4-methylumbelliferone Esculetin Isoscopoletin Isofraxidin Scopolin Fraxin Esculin 7-hydroxycoumarin Oleuropein Escuside Ligustroflavone 6-Hydroxy-7,8-dimethoxy coumarin

23 19 16 14 12 11 10 10 8 8 7 7

Virtual docking was performed on FAS using Autodock software to study the interaction between the screened active compounds and FAS protein. The 3D ligand file was derived from PubChem and the 3D structure of FAS was obtained from the Protein Data Bank (PDB:1YET). Prior to docking tests, nonpolar hydrogen atoms were merged and Gasteiger charges added using Dock tools. The three-dimensional grid box was created using an AutoGrid algorithm (part of the Dock package) to evaluate the binding energy on the coordinates of the macromolecule. The grid diagram of the complete ligand docking target location was calculated by creating a three-dimensional grid box with a 60 Å grid size (x, y, z) and spacing of 0.375 Å. Dock was then used to calculate the binding free energy of a given compound's conformation in the macromolecular structure [20–23].

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Fig. 4. (A–I) Effect of compounds on the activity of HepG2 cells proliferation. Data are mean ± SD. *p < 0.05, **p < 0.01, n = 3.

values calculated, as well as error estimates, using the curve fitting algorithm built into the NT 1.5.41 analysis software.

2.10. Detection of active compounds binding to protein All compounds were analyzed with the concentration of FAS (1.89 μM) that had been labeled using the Monolith NTTM Protein Labeling Kit RED-NHS (Cat Nr: L001) before instrumental analysis. The LED power was set at 20%. Analysis was performed using a Monolith Nano Temper (NT) 115 and its accessories. Molecular interactions were measured using standard-treated 4 μL volume glass capillary tubes (Nano Temper Technologies GmbH, Munich, Germany). Fluorescence intensities were determined using MST measurements, values were fitted, and then Kd

2.11. Binding region detection The influence of antibody on interactions between small molecules and FAS was investigated by adding antibody to the buffer in the MST experiments. This interaction was verified by ELISA. A mixture of proteins and compounds of different concentrations (0–200 μM) was added to each well of a 96-well plate, then washed three times with wash buffer. Chromogenic agents A and B were added sequentially to 6

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methylumbelliferone, esculetin, and other compounds acted as binding ligands (Table 1).

Table 3 Dock binding free energies (ΔGb) and bonds of the docked inhibitors against FAS. PDB code

2PX6

Inhibitors

Gb (kcal/ mol)

4-methylumbelliferone

−5.212

Esculin

−5.316

Fraxin

−5.196

Scopolin

−5.739

Esculetin

−5.385

Isoscopoletin

−5.254

7-Hydroxycoumarin

−5.479

Isofraxidin

−5.141

Bonds between groups of compounds and amino acids of FAS

3.2. Differential expression of cancer-related genes following aqueous extract treatment

Groups of comp.

amino acid

Bonds name

O O Sixmember ring Benzene ring OH OH OH O O O O OH OH O OH OH O O Benzene ring O O O O Benzene ring O O Benzene ring

SER2308 ILE2250 TYR2343 TYR2343

H-bond H-bond H-bond Pi-Pi stacking

GLU2431 GLU2431 GLU2431 SER2308 ILE2250 SER2308 GLU2251 GLU2251 GLU2251 SER2308 GLU2251 GLU2251 SER2308 ILE2250 TYR2343

H-bond H-bond H-bond H-bond H-bond H-bond H-bond H-bond H-bond H-bond H-bond H-bond H-bond H-bond Pi-Pi stacking H-bond H-bond H-bond H-bond Pi-Pi stacking H-bond H-bond Pi-Pi stacking

The KEGG analysis classified these extract-induced DEG into different biological process categories. The top 20 functional annotation clusters include; glycolysis/gluconeogenesis, fatty acid degradation, glycerolipid metabolism, and fructose and mannose metabolism. Most of these clusters belong to glucose and lipid metabolism processes, and it is through these processes that Cortex Fraxini may exert its anticancer effect. The GO analysis showed that the DEG were significantly enriched in acetyl-CoA biosynthesis and ethanol metabolism processes (Fig. 1).

ILE2250 SER2308 SER2308 ILE2250 TYR2343 SER2308 ILE2250 TYR2343

3.3. Target gene prediction and analysis Whole potential target genes were synthesized and uploaded to the DAVID database for KEGG pathway annotation and GO enrichment. The threshold value was set at p ≤ 0.05 to analyze pathways or gene functions with higher counts. The top 10 pathways were graphed using GraphPad Prism 6 (Fig. 2). KEGG pathway annotation showed that 35 of the 37 potential target genes were enriched (92.1%), involving 34 pathways, among which 32 were significantly correlated with the target genes (P ≤ 0.05). The following pathways have the highest involvement of target genes: Metabolic pathways (25, 65.8%), Glycolysis/Gluconeogenesis (23, 60.5%), Pyruvate metabolism (12, 31.6%), Carbon metabolism (9, 23.7%) and Fatty acid degradation (5, 13.2%). GO enrichment analysis showed that the number of genes involved in the CC (Cell Components), MF (Molecular Function) and BP (Biological Process) targets was 38 (100%). CC enrichment indicated involvement of target genes in the following areas: cytosol (21, 55.3%), extracellular exosome (19, 50.0%) and cytoplasm (17, 44.7%). MF enrichment mostly involved target genes in the following interactions: protein binding (25, 65.8%), ATP binding (11, 28.9%) and oxidoreductase activity (8, 21.1%). BP enrichment mostly involved target genes in the following processes: oxidation-reduction process (8, 21.1%), canonical glycolysis (7, 18.4%) and glycolytic process (7, 18.4%).

each well, followed by stop solution. Absorbance were measured at 450 nm against a control (570 nm). 2.12. Effects of scopolin on protein-protein interaction

3.4. Construction of the network

MST experiments were performed to detect the interactions between proteins with a higher degree in the network. FAS at a concentration of 338 nM was used with a gradient of 16 concentrations of unlabeled GPI and adding 1 mM scopolin to the buffer at the same time for another group. Each sample was loaded into a standard capillary and measured using the Monolith NT.115. The reaction curve was automatically analyzed using the Nano Temper system, and the molecular binding Kd was automatically calculated. Then, we collected HepG2 cell lysate to incubate with anti-GPI antibody, normal IgG or anti-GPI antibody added 1 mM scopolin overnight at 4 °C. Next, protein A/G beads were added to this mixture and incubated with shaking at 4 °C for 4 h. The beads were washed 3 times with Co-IP buffer, and then eluted with 2 × SDS loading buffer. Finally, mass spectrometry is used to detect protein differences among groups.

Based on the results of transcriptome and LC-MS experiments, target genes in the top 10 pathways and compounds were selected to construct an active compound/target gene/pathway network (Fig. 3). When Cortex Fraxini demonstrated an anti-cancer effect, the network map showed the synergistic effect of different compounds on multiple targets. Using a network analysis tool to analyze the network, higher degree compounds are associated with a greater number of genes (Table 2). When Cortex Fraxini demonstrates an anti-cancer effect, proteins corresponding to genes with a higher degree are thought to play an important role in central correlation. The highest degree protein was FAS, at the intersection of all cancer-related pathways. The results were compared with those of the KEGG pathway analysis, combined with literature research. FAS and the top 10 compounds were selected as targets for binding validation experiments.

3. Results 3.5. Screening of active compounds 3.1. Qualitative analysis Esculin, 4-methylumbelliferone, esculetin, fraxin, isoscopoletin, isofraxidin, scopolin, and 7-hydroxycoumarin, all had a significant effect on HepG2 cell activity at a concentration of 200 μM. Isofraxidin at concentrations between 0 and 200 μM exhibited no significant effect on HepG2 activity (Fig. 4).

After appropriate chromatographic pretreatment, samples were analyzed by LC-MS. By comparing relative retention time, UV absorption spectra, and the published literature [24], mass spectrometry fragmentation of the chromatographic peaks indicated that Esculin, 47

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Fig. 5. Molecular interaction of FAS by NT. 115 analysis. MST time traces of compounds at 16 different concentrations, (A) 4-methylumbelliferone (5.15 × 10−5 to 3.38 mM), (B) esculetin (7.48 × 10−6 to 0.245 mM), (C) isoscopoletin (1.68 × 10−4 to 5.5 mM), (D) isofraxidin (4.93 × 10−4 to 16.2 mM), (E) scopolin (6.51 × 10−5 to 2.13 mM), (F) fraxin (8.1 × 10−5 to 2.66 mM), (G) Esculin (4.52 × 10−4 to 14.8 mM), (H) 7-hydroxycoumarin (7.63 × 10−5 to 2.5 mM). MST signal measured 30s after heating turned on.

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Fig. 6. (A–D) Effects of antibody-FAS on interactions between FAS and four compounds were analyzed by NT. 115. SD-TEST is qualified. MST time traces of 16 different concentrations as in Fig. 5, with a fixed antibody concentration (1/10000). MST signal measured 30s after heating turned on. (E–F) HepG2 lysates incubated with scopolin and isoscopoletin respectively at indicated concentrations, then analyzed by ELISA. Results show dose effects of scopolin and isoscopoletin on the interaction between FAS and its antibody. Data are mean ± SD. *p < 0.05, **p < 0.01, n = 3.

3.6. Molecular interaction of inhibitors against FAS

3.7. Protein binding to small molecules

Virtual docking was performed on FAS using Dock software to study the interactions between it and the active compounds. The following binding free energies were determined; Esculin (−5.316), 4-methylumbelliferone (−5.212), esculetin (−5.385), fraxin (−5.196), isoscopoletin (−5.254), isofraxidin (−5.141), scopolin (−5.739), and 7hydroxycoumarin (−5.479) (Table 3). Based on the ability to bind free energy, binding pose analysis was used to select ligands for further exploration. ILE 2250, SER 2308, and TYR 2343 were considered active site residues. It was found that 7-OH plays an important role in the binding, but the results revealed that position 6- was also required.

The MST assay characterized the molecular interactions between inhibitors and FAS. The dissociation constant was calculated by the difference in normalized fluorescence of the bound and unbound state. All values were multiplied by a factor of 1000 to produce relative change in fluorescence per thousand. The Kd values for Esculin, 4methylumbelliferone, 7-hydroxycoumarin, esculetin, fraxin, isoscopoletin, scopolin, and isofraxidin are shown in Fig. 5, the specific concentrations of the compounds are shown in the legend. 3.8. Active ingredient binding region analysis MST analysis indicated that the antibody affected the binding of 9

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Fig. 7. (A–B) Interactions between FAS and GPI analyzed by NT. 115. SD-TEST is qualified. MST time traces of GPI at 16 different concentrations, (A) GPI (1.23 × 10−5 to 0.403 μM) (B) GPI (1.23 × 10−5 to 0.403 μM) and 1 mM scopolin with a fixed concentration of FAS (0.338 μM). MST signal measured 30s after heating turned on. (C–D) Log2 (FC) analysis to compare the absolute value of change among group means. If this number exceeds a given count (threshold = 1), the variable will be reported as significant. (C) Top 50 different proteins after administration of scopolin. (D) Top 10 different proteins after administration of scopolin. (E) PPI network of different proteins obtained from STRING database and constructed by Cytoscape. (F) Cancer-related signaling pathways associated with different proteins after administration of scopolin.

3.9. Scopolin blocks the binding between FAS and GPI with highest degrees

scopolin and isoscopoletin to FAS, while the two compounds with the strongest Kd did not. An ELISA experiment was used to confirm that it caused a change in the binding of the protein to its antibody by incubation with scopolin and isoscopoletin at different concentrations (Fig. 6). Monoclonal antibodies were produced by immunizing animals with a synthetic peptide around GLY46 corresponding to the sequence of human FAS.

Using network pharmacology, we selected the two proteins with the highest degree. We further detected the binding between proteins and effects of scopolin on this binding through MST experiments (Fig. 7) and calculated the dissociation constant (Kd) based on the association curve. The results hint that the proteins bind well to each other and that FAS and GPI underwent conformational change and scopolin blocks the 10

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LYS518, and their replacement or alteration may alter catalytic and/or protein stability. 6 - phosphoric acid - gluconate, and glyceraldehyde - 3 - phosphoric acid can inhibit GPI's activity. According to the proteomics results, GO enrichment analysis found targets that are located in the cytosol, cytoplasm, nucleus, and other cell compartments. At the molecular level, the targets were involved in binding to proteins, ATP and they were related to protein folding and negative regulation of apoptosis. It suggested that the drug may treat cancer by binding to FAS and other proteins and affect their activity and interactions. The effect of scopolin on antibody-protein interactions attracted our attention. In comparison to fraxin, the special C-7 substituent group on scopolin plays a key role in docking. A glycoside is present at position 7, which may be crucial for inducing a conformational change of FAS. It is possible that this binding interaction would result in a dramatic change in FAS conformation, causing GLY46 to be unable to bind to the antibody, as previously shown. In cell signaling networks, the involvement of FAS-GPI pathways has not demonstrated any anti-cancer effects however the two proteins are essential for controlling proliferation, differentiation, and survival. According to proteomics and MST results, the binding of FAS to GPI decreased following addition of 1 mM scopolin. Based on our results, we found the pathway for FAS and GPI for the first time and verified the inhibitory effect of scopolin on them. Natural coumarin and its derivatives have extensive anti-cancer activity and low toxicity, however, identification of their targets remains challenging. Thus, these active compounds which were found in this study may be an effective and relatively safe base for the design and development of FAS-related antineoplastic drugs.

binding of two proteins. Then, Co-IP further verified the results of MST experiments. It was established that FAS and GPI had significant differences. All proteins were linked around them and enriched pathways of significantly different proteins focused on metabolism and cancer, suggesting that drugs may treat cancer by affecting cancer cell metabolism. 4. Discussion In this study, we have demonstrated that the active compounds of Cortex Fraxini act as ligands for FAS, as identified by biochemical strategies and biophysical techniques. FAS consists of two identical subunits, which are connected head to tail to form the catalytic center, which includes 7 functional domains. FAS inhibitors can inhibit tumor cell proliferation and induce apoptosis, with a dose-time effect. Orlistat is a commercially available weight-loss drug that inhibits FAS activity, irreversibly inhibits the TE functional domain of FAS, and reduces energy intake. FAS inhibitors can competitively bind to this site instead of ATP, thus blocking FAS-ATP binding. Effective inhibition of the protein that plays an important role in signal transduction and anti-apoptosis of tumor cells will, therefore, induce apoptosis. MST is a powerful technique for measuring biomolecular interactions based on thermophoresis—the movement of molecules over a temperature gradient. This technique is highly sensitive and allows precise quantification of molecular interactions [25–28]. The MST results suggest that the selected compounds have potential molecular interactions with FAS. According to Seidel et al. [29], the fitted curve may be either S-shaped or its mirror. The standard symbol for MST amplitude (change in normalized fluorescence) depends on the chemistry of the compound that is titrated, its binding site, and the conformational change resulting from binding. Compounds that have a positive slope have a strong conformational change during complex formation. It is possible that these interactions play an important role in conformational change. We speculate that the underlying mechanisms of the anti-cancer properties involve impairing the conformational changes required for FAS activation. Molecular docking analysis showed that these compounds could enter the thioesterase domain of FAS. We used a monoclonal antibody that specifically binds to FAS to detect whether these active compounds could interfere with this process. The results showed that scopolin could block FAS-antibody binding. Since monoclonal antibodies are produced by immunizing animals with a synthetic peptide around GLY46 corresponding to the sequence of human FAS, we found that scopolin and similar compounds should have consistent docking results. The binding sites were all located in the thioesterase domain. The result of scopolin suggested that binding may induce a conformational change in the domain. As shown in Fig. 5, 7-hydroxycoumarin did not bind at all in the MST experiments, indicating that the basic parent nucleus of coumarin and 7-OH do not play independent roles in compounds binding FAS. In contrast, with 4-methylumbelliferone (which utilizes a similar binding mechanism but with higher Kd and binding energy) position 4-CH3 may be the group that is key to its activity while with esculetin, the –OH in position 6 seems to be the key group and interacts with position 7-OH in docking, which may explain the difference in Kd values. For esculetin and isoscopoletin, 7-OH plays an important role in binding, and previous studies have shown that it needs cooperation with position 6-OH. With regards to fraxin, esculetin, and isofraxidin, steric hindrance may explain the difference in Kds and binding energies and it is for probably the same reason that the binding strength of aglycogenic compounds are smaller than non-aglycogenic compounds. GPI is a product of tumor cells which has the same functions as nerve interleukin (NLK), maturation factor (MF), and autocrine motor factor (AMF), and can promote cell proliferation and metastasis. Jeffery [30] has studied the structure of GPI by X ray diffraction and found that GPI forms cognate dimers, the active site residues are His388 and

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