- Email: [email protected]
NF-κB signaling in non-small cell lung cancer
European Journal of Pharmacology 855 (2019) 10–19
Contents lists available at ScienceDirect
European Journal of Pharmacology journal homepage: www.elsevier.com/locate/ejphar
Full length article
Schizandrin A enhances the efficacy of gefitinib by suppressing IKKβ/NF-κB signaling in non-small cell lung cancer
T
Haibing Xiana,b,1, Weineng Fengb,1, Jiren Zhanga,∗ a Department of Oncology, Zhujiang Hospital, Southern Medical University/The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510282, Guangdong, China b Department of Head and Neck/Thoracic Medical Oncology, The First People's Hospital of Foshan, Foshan, 528041, Guangdong, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Schizandrin A Gefitinib IKKβ/NF-κB signal NSCLC
The emergence of resistance to EGF receptor (EGFR) inhibitor therapy is a significant challenge for patients with non-small cell lung cancer (NSCLC). During the past few years, a correlation between EGFR TKIs resistance and dysregulation of IKKβ/NF-κB signaling has been increasingly suggested. However, few studies have focused on the effects of combining IKK/NF-κB and EGFR inhibitors to overcome EGFR TKIs resistance. In this study, we discovered that Schizandrin A (Sch A), a lignin compound isolated from Schisandra chinesnesis, could synergize with the EGFR receptor inhibitor Gefitinib to inhibit cell growth, induce cell cycle arrest and apoptosis of HCC827/GR cells. Sch A effectively suppressed the phosphorylation of IKKβ and IκBα, as well as the nuclear translocation of NF-κB p65, and showed high and selective affinity for IKKβ in surface plasmon resonance (SPR) experiments, indicating that Sch A was a selective IKKβ inhibitor. Molecular modeling between IKKβ and Sch A suggested that Sch A formed key hydrophobic interactions with IKKβ, which may contribute to its potent IKKβ inhibitory effect. These findings suggest a novel approach to improve poor clinical outcomes in EGFR TKIs therapy, by combining it with Sch A.
1. Introduction Lung cancer is the most frequently diagnosed cancer and the leading cause of cancer-related deaths worldwide (Bray et al., 2018). More than 85% of cases are currently classified as non-small-cell lung cancer (NSCLC), for which the predicted 5-year survival rate is as low as 15.9% (Chen et al., 2014; Hirsch et al., 2017). Today, numerous NSCLC derived mutations have been identified, including epidermal growth factor receptor (EGFR), Anaplastic Lymphoma Kinase (ALK) and K-Ras mutations (Tan et al., 2016). New small molecule inhibitors which target EGFR or ALK kinase, such as Gefitinib and Crizotinib, have led to remarkable improvements in the clinical outcome (Kazandjian et al., 2016; Solomon et al., 2018). However, the emergence of acquired resistance against targeted therapies greatly impairs their clinical effectiveness (Chong and Janne, 2013; Hrustanovic et al., 2013). Gefitinib (Gefi), an orally bioavailable, reversible, ATP-competitive EGFR tyrosine kinase inhibitor (TKI), was approved by the FDA in 2015 as first-line treatment for patients with metastatic NSCLC harboring activating EGFR mutations (Kazandjian et al., 2016). In EGFR-positive
patients, Gefi significantly prolongs their progression free survival (PFS) and overall survival (OS) when compared with chemotherapy. Unfortunately, the clinical effectiveness of EGFR-targeted therapy has been dramatically limited by the emergence of drug resistance. The appearance of second-site EGFR mutations (T790M, C797S etc.) (Thress et al., 2015; Yun et al., 2008), or activation of alternative pathways (MET and Her2 amplification, STAT3 and ERK reactivation), is regarded as the leading causes of acquired EGFR TKIs resistance (Engelman et al., 2007; Ercan et al., 2012; Lee et al., 2014; Takezawa et al., 2012). Significantly, a large number of recent studies have indicated that crosstalk between the EGFR- and NF-κB- signaling cascades could be another unnoticed key factor involved in the development of EGFR TKI resistance (Bivona et al., 2011; Shostak and Chariot, 2015). Lignans are the major bioactive components of many fruits and possess various pharmacological properties such as anticancer, antioxidant, and anti-inflammatory activities. In the past years, the potential for drug development of dibenzocyclooctadiene lignans that isolated from the fruit of Schisandra chinensis (Turcz.) Baill (SC) attracts extensive research attention worldwide (Szopa et al., 2017).
∗
Corresponding author. Department of Oncology, Zhujiang Hospital, Southern Medical University/The Second School of clinical Medicine, Southern Medical University, Industrial Road No.253, Guangzhou, 510282, Guangdong, China. E-mail address: [email protected] (J. Zhang). 1 These authors have contributed equally to this work. https://doi.org/10.1016/j.ejphar.2019.04.016 Received 12 November 2018; Received in revised form 28 March 2019; Accepted 5 April 2019 Available online 25 April 2019 0014-2999/ © 2019 Elsevier B.V. All rights reserved.
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
exposing parental HCC827 cells to 2 μM Gefi for 2 months followed with one more month of exposure to 4 μM gefitinib using an alternating 5-day drug on and 5-day drug off schedule. The resistant cell population was then routinely cultured with medium containing 1 μM Gefi. The resistance phenotype was preserved even with prolonged culture in Gefi-free culture medium for 6 months, suggesting an irreversible phenotype. We did not isolate pure monoclones. The pooled populations of HCC827/GR cells were used for further studies.
Schisandrin (Schin), schisandrin A (Sch A), schisandrinB (Sch B), schisandrin C (Sch C) and γ-schisandrin (γ-Sch) are the most important bioactive dibenzocyclooctadiene lignans. Although the structure is highly similar, the biological effects of these lignans are quite diverse. The current studies discovered that Sch A and Sch B not only displayed favorable tumor suppressive activity, but also possess great potential on reversing the resistance to antitumor agents. Whereas, the studies of Schin, Sch C and γ-Sch was mainly focused on their neuroprotective and cognitive enhancement properties. Schisandrin A (Sch A), also commonly known as deoxyschizandrin, was reported that as a potent suppressor against the growth of human ovarian cancer and colorectal cancer cell lines. The underlying antitumor mechanisms of Sch A includes inhibition of PI3K/Akt and NF-κB signaling pathway activation (Kong et al., 2018; Lee et al., 2018). In previous studies, researchers discovered that Sch A even could reverse multi-drug resistance (MDR) and identified some of the underlying mechanisms. For example, Zhang et al. showed that the reversal of doxorubicin resistance induced by Sch A may be caused by inhibition of P-gp at the gene and protein levels, an effect mediated by p65 and Stat3 phosphorylation (Zhang et al., 2018). Moreover, Sch A could enhance the chemosensitivity of colon carcinoma cells to 5-fluorouracil by upregulating miR-195 (Kong et al., 2018). Thus, the ability of Sch A to revert chemotherapy resistance in multiple ways has been demonstrated. However, whether Sch A can also counteract acquired resistance to targeted therapy such as EGFR TKI resistance has never been investigated. In this study, the cancer-killing activities of five major dibenzocyclooctadiene ligands of SC (Sch, Sch A, Sch B, Sch C and γ-Sch) were initially tested on HCC827 and HCC827 Gefi-resistant cells (HCC827/ GR). The MTS and flow cytometry data revealed that Sch A markedly increased the sensitivity of HCC827GR cells to Gefi. Furthermore, mechanistic investigation of this effect by Western blot and SPR assays demonstrated that Sch A sensitized resistant NSCLC cells to Gefi treatment by directly interacting with IKKβ to suppress the IKKβ/NF-κB signal. Computer simulations further revealed that Sch A bound to IKKβ mostly through hydrophobic interactions, competing with its natural ligand, ATP.
2.3. Cell viability assay The cell viability was measured by the Cell Titer 96 Aqueous NonRadioactive Cell Proliferation Assay Kit (Promega). 5 × 103 cells were seeded in 96-well plates overnight, then the culture medium was removed and all cells were treated with different concentrations of chemicals for 72 h. The absorbance value of each well was determined by a microplate reader at 490 nm. Each experiment was repeated for three times. 2.4. Clonogenic assay HCC827/GR cells (1000 cells/well) were seeded in 6-well cell culture plate for 24 h, then DMSO, Sch A (20, 40 μM), Gefi (15 μM), and Gefi plus SchA (20 or 40 μM) were added to the culture medium. After 24 h, the culture medium was replaced with fresh culture medium for 14 d. Colonies was removed the medium, washed with PBS twice, fixed with methanol for 15 min, then washed with PBS three times, and finally stained with crystal violet for 15 min. Colonies containing more than 50 cells were counted, and visualized colonies were then photographed. 2.5. Cell apoptosis analysis Cell apoptosis was detected by flow cytometer analysis and Western blotting. For flow cytometer analysis of apoptosis, cells treated as indicated for 24 h were harvested by trypsin, washed twice by PBS, and re-suspended in 100 μl 1 × binding buffer. 5 μl FITC Annexin V and PI (556547, BD Biosciences, USA) was added to the cell suspension and then incubated for 15 min at room temperature. After dilution with 400 μl binding buffer, the samples were analyzed by ACS Calibur flow cytometer (BD). For apoptosis by Western blotting, cleaved caspase 3, cleaved-PARP (poly ADP-ribose polymerase), Bcl-2 and BCl-xL were analyzed.
2. Material and methods 2.1. Cell culture and reagents The NSCLC cell lines HCC827 that possesses activating mutation EGFR and wild-type KRAS were obtained from Shanghai Institute of Biosciences and Cell Resources Center (Chinese Academy of Sciences, Shanghai, China) (Gottlich et al., 2016). The cells were routinely cultured in RPMI-1640 or DMEM (Gibco/BRL life Technologies, Eggenstein, Germany) supplemented with 10% fetal bovine serum FBS (Hyclone, Logan, UT), 1% penicillin/streptomycin solution in a humidified atmosphere of 5% CO2 at 37 °C. The primary antibodies used in this study, including p-IKKβ, IKKβ, p-IκBα, IκBα, P65, Bcl-2, Cleaved-PARP, Cleaved-caspase-3, Cyclin D1, Cdk-4, Histone H3, Actin and GAPDH were all purchased from Cell Signaling Technology (Danvers, MA). Goat anti-rabbit IgG-HRP secondary antibody were obtained from Santa Cruz Biotechnology (Santa Cruz, CA); Human recombinant Tumor Necrosis Factor-α (TNF-α) was purchased from Sigma-Aldrich (Darmstadt, Germany). The full length recombinant IKKα and IKKβ protein was purchased from Abcam (Cambridge, UK), and their purities were both more than 85% assessed by SDS-PAGE; Gefitinib was purchased from Selleck (Shanghai, China), the five dibenzocyclooctadiene lignans were all purchased from Medchem express (Shanghai, China), and the purity of small molecule compound was all over 99%.
2.6. Cell cycle analysis Cells (3 × 105 cells/well) were seeded in 6-well plates and allowed to adhere overnight. The next day, the cells were treated with different compounds as indicated for 24 h. Then the cells were trypsinized, washed, and fixed in 75% ice-cold ethanol at 4 °C overnight. After centrifugation, the pellets were washed with cold PBS, suspended in 500 μl PBS with 50 mg/ml propidium iodide (PI) and incubated at 4 °C for 30 min in the dark. Then cell suspension was subjected to a FACS Calibur instrument (Becton Dickinson FACSCalibor, BD Biosciences, Franklin Lakes, NJ, USA). 2.7. Western blotting After treated as indicated for 24 h, the cells were washed once by PBS. The cell lysates were quantitated by BCA protein assay kit (BioRad Laboratories, Hercules, CA, USA). Equal amounts of proteins were separated by SDS-PAGE and transferred to PVDF membrane. After being blocked with 5% non-fat dry milk in TBST for 1.5 h, membranes were incubated with a 1:1000 dilution of specific primary antibody overnight at 4 °C and the secondary antibody conjugated with horseradish peroxidase (HRP) (1:5000, Santa Cruz, CA) for 2 h, the
2.2. Establishment of acquired Gefi-resistant NSCLC cell line Gefi-resistant HCC827 cell line (HCC827/GR) was established by 11
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
recombinant vector was transformed into Escherichia coli BL21 competent cells (Vazyme Biotech Co., Nanjing, China) and cultured in LB medium in the presence of 50 μg/ml−1 kanamycin. After shaking at 250 rpm for 5 h at 37 °C, the cultures were down-tempered to 18 °C for 1 h before induction with 1 mM IPTG (isopropyl β-D-1-thiogalactopyranoside, Sigma-Aldrich) for 5 h at 25 °C. Cells were collected by rapid centrifugation and supersonic decomposing. The cell lysis solution was purified using HisTrap FF crude columns (GE Healthcare, CT, USA), followed by His-tag cleavage with human thrombin (Sigma-Aldrich, Darmstadt, Germany) overnight at 4 °C, and subsequently purified by anion exchange (GE Healthcare) and gel filtration chromatography. Purified IKKβ mutant was stored in a high salt-containing buffer (20 mM Tris·HCl, pH 8.5, 500 mM NaCl, and 1 mM NaN3) at −80 °C.
immunoreactive bands were visualized with enhanced chemiluminescence (EMD Millipore, Billerica, MA, USA) in Amersham Imager 600 system (GE Healthcare Life Sciences, Shanghai, China). 2.8. Nuclear and cytoplasmic protein extraction The Thermo Scientific™ NE-PER™ Nuclear and Cytoplasmic Extraction Reagent was applied for the extraction of nuclear and cytoplasmic proteins. 3 × 105 cells were seeded in 6-well plates overnight, and incubated with Sch A (20 μM or 40 μM) for 24 h. Cells were harvested with trypsin-EDTA and then centrifuge at 500 g for 5 min. Following the assay instructions provided, the extraction reagents were added to the pelleted cells step by step, then the cytoplasmic and nuclear proteins were collected separately.
2.12. Conventional molecular dynamics (MD) simulation 2.9. Surface Plasmon Resonance (SPR) analysis
The crystal structure of IKKβ was obtained from the Protein Data Bank (PDB) database (PDB code: 4KIK) (Liu et al., 2013). Then, the structure of IKKβ was refined, including delete all water molecules, non-bonded hetero-atoms, and adding missing hydrogen atoms by UCSF Chimera 1.12 program (University of California, San Francisco, CA, USA) (Pettersen et al., 2004). Afterwards, the binding mode of Sch A in the active binding site of IKKβ was predicted by the AutoDock 4.2.6 (The Scripps Research Institute, La Jolla, CA, USA) (Morris et al., 2009). The IKKβ and Sch A were prepared by AutoDockTools 1.5.6 software (The Scripps Research Institute, La Jolla, CA, USA). A grid box size of 60 × 60 × 60 Å dimensions, with a spacing of 0.375 Å between the grid points, was adopted and covered the entire active binding site of IKKβ. Lamarckian Genetic Algorithm (LGA) was used for globe conformational sampling with trials of 200 dockings. Other settings were set as default. The lowest binding energy conformation was considered for further molecular dynamics (MD) simulation analysis.
The IKKβ/Sch A complex constructed by molecular docking was used as the initial structure for the Conventional MD simulations. The partial atomic charges for Sch A was calculated via the restrained electrostatic potential (RESP) method based on HF/6-13G* basis set (Gaussian, Inc.,Wallingford, CT, USA). After that, the IKKβ and Sch A were described by the Amber ff14SB force field and General Amber Force Field 2 (GAFF2) by LEaP modules of Amber 16 program (University of California, San Francisco, CA, USA), respectively. After that, the IKKβ/Sch A complex was solvated in a box of TIP3P water molecules with a 12 Å distance between the surface of complex and the boundary of the water box. In the end, three sodium ions were added to maintain the electroneutrality. Before the productive conventional MD simulations, we performed an equilibration protocol consisting of an initial minimization heating and equilibration by using the pmemd.MPI module in the Amber 16 package. Firstly, a three-step energy minimization strategy was performed. At stage 1, the IKKβ/Sch A complex was restrained so that the water molecules reoriented appropriately in the system. At stage 2, the backbone of the IKKβ was restrained to avoid structural clash in the solvated systems. At stage 3, the all molecules were minimized without any restraint. During each stage, 6000 steps of steepest descent algorithm. 6000 steps of conjugated gradient algorithm were performed. Subsequently, the system was heated up from 0 to 310 K in 200 ps, followed by applying the density procedure at 310 K for 500 ps and equilibration at 310 K for 1 ns in the isothermal isobaric (NPT) ensemble. Eventually, the system was submitted to 200 ns conventional MD simulation in the NPT ensemble. During the productive conventional MD simulations, the temperature and pressure were maintained by the Langevin temperature equilibration scheme and Berendsen barostat, respectively (Berendsen et al., 1984; Loncharich et al., 1992). Particle Mesh Ewald (PME) algorithm and the non-bonded cutoff distance was set as 10.0 Å to calculate the long-range electrostatic interactions and non-bonding interactions. The SHAKE algorithm was utilized to constrain the hydrogen atoms to their equilibrium lengths. Conformational snapshots were recorded every 8 ps for further analysis.
2.11. Mutated recombinant IKKβ protein preparation
2.13. Binding free energy calculation
QuikChange II XL site-directed mutagenesis kit (Agilent Technologies) was used for mutagenesis. Human IKBKB plasmid (pcDNA-Ikkb-FLAG WT) was used as a template to generate mutations of the most key interaction sites between Sch A and IKKβ. The pcDNAIkkb-FLAG WT plasmid was purchased from Addgene (MA, USA); the following mutagenic oligonucleotide primers were used: 5′-AGAGGTT AATACACAAAATTGCTGACCTAGGATATGCCAAGGA-3’ (sense) 5′-TCC TTGGCATATCCTAGGTCAGCAATTTTGTGTATTAACCTCT-3’ (antisense) According to the kit Instruction Manual, the desired mutant plasmid was obtained. Then it was cloned into a pET30a vector containing an Nterminal histidine (His)-tag and thrombin cleavage site. The
Binding free energy and energy decomposition were estimated using molecular mechanics/generalized Born surface area (MM/GBSA) method as implemented in Amber 16 program. All the water molecules and sodium ions were stripped, the Generalized-Boltzmann (GB) model of 2 (igb = 2) was applied (Miller et al., 2012), and 200 snapshots were extracted from the last 40 ns MD trajectory for the binding free energy calculations. The total binding free energy (ΔGbind) was calculated as the following equations:
SPR experiments were performed on a ProteOn XPR36 Protein Interaction Array system (Bio-Rad Laboratories, Hercules, CA, USA). Briefly, IKKβ solution in PBST (5 mM, pH 7.4) at a concentration of 1 mg/ml was diluted to 30 μg/ml with sodium acetate buffer (pH 4.5). The chip was activated with EDC/NHS (10 μl/min for 600 s). Then, IKKβ was loaded (5 μl/min for 400 s) and immobilized covalently. Approximately 8000 RU of IKKβ was immobilized on the chip. Any excess of unbound IKKβ was removed by flowing PBS solution (5 mM, pH 7.4, with 5%, w/v, DMSO). Sch A was prepared as 20–100 μM solution in PBS solution (5 mM, pH 7.4, with 5%, w/v, DMSO), and injected (10 μl/min for 100 s). Five concentrations were injected simultaneously at a flow rate of 30 μM/min for 120 s of association phase, followed with 120 s of dissociation phase at 25 °C. The final graph was obtained by subtracting blank sensorgrams from the duplex or quadruplex sensorgrams. Data were analyzed by ProteOn manager software. 2.10. Construction of the initial structure of IKKβ/Sch A complex
12
ΔGbind = ΔGReceptor + Ligand – (ΔGReceptor + ΔGLigand),
(1)
ΔGbind = ΔEMM + ΔGsol − TΔS,
(2)
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
ΔEMM = ΔEint + ΔE vdW + ΔEelec ,
(3)
ΔGsol = ΔGGB + ΔGSA ,
(4)
ΔGSA = γSASA + β,
(5)
In equation (1), ΔGReceptor+Ligand, ΔGReceptor, and ΔGLigand represent the free energies of complex, receptor and ligand, respectively. In equation (2), ΔEMM, ΔGsol and TS represent the molecular mechanics (MM) interaction, solvation energy, and the conformational entropy at temperature T. In equation (3), the ΔEMM consists of inter-molecular interaction energy (ΔEint), van der Waals energy (ΔEvdW), electrostatic energy (ΔEelec). In equation (4), the solvation free energy is given as the sum of polar (ΔGGB) and nonpolar contributions (ΔGSA). Herein, the solute and solvent dielectric constants were set as 1 and 80, respectively. ΔGSA was estimated by using the solvent accessible surface area (SASA) model (equation (5)) using a water probe radius of 1.4 Å. The surface tension constant (γ), and the nonpolar free energy for the point solute (β) were set as 0.0072 and 0.92, respectively. 2.14. Gaussian accelerated molecular dynamics (GaMD) simulation The equilibrated structure extracted from conventional MD simulation trajectory was selected as the initial structure for the GaMD simulation. GaMD simulation provides the total potential boost, dihedral potential boost, and dual potential boost in order to accelerate the molecular simulations. In this study, the GaMD simulation was implemented using the dual potential boost. The boost parameters were calculated from an initial ∼4 ns NVT conventional MD simulation without boost potential. Then, the GaMD simulation were performed for ∼20 ns, in which the boost potential was updated every 400 ps to reach equilibrium values. Lastly, 300 ns of GaMD simulation was submitted in the NVT ensemble. During the GaMD simulations, the temperature was maintained by the Langevin temperature equilibration scheme (Loncharich et al., 1992). Particle Mesh Ewald (PME) algorithm and the non-bonded cutoff distance was set as 10.0 Å to calculate the long-range electrostatic interactions and non-bonding interactions. The SHAKE algorithm was utilized to constrain the hydrogen atoms to their equilibrium lengths. Conformational snapshots were recorded every 2 ps for further analysis. After GaMD simulation, the cumulant expansion to the second order was applied to compute the free energy map from the projection of the structures extracted from the GaMD simulations on the main components principal component 1 (PC1) and principal component 2 (PC2) by principal component analysis (PCA) via CPPTRAJ module in Amber 16 package (Miao et al., 2014).
Fig. 1. (A) The chemical structures of schisandrin, γ-schisandrin, schisandrin A, schisandrin B and schisandrin C. (B) Antiproliferative effects of Gefitinib (Gefi), schisandrin (Schin), γ-schisandrin (γ-Sch), schisandrin A (Sch A), schisandrin B (Sch B) and schisandrin C (Sch C) against HCC82 and HCC827/GR cells. Cells were treated with the indicated compounds at different concentrations (100, 50, 25, 12.5 and 6.25 μM) for 72 h. Subsequently, cell viability in each group was detected by MTS assay. The data were obtained from 3 independent experiments.
14.62 μM, Fig. 1B), suggesting that the Gefi-resistant cell model was valid. As shown in Fig. 1B, Sch B exhibited the most potent inhibitory effect on Gefi-sensitive HCC827 cells, whereas Sch A showed an optimal suppressive effect in the case of Gefi-resistant HCC827/GR cells. Next, we investigated the effects of Sch A when combined with Gefi. As shown in Fig. 2A, combined Gefi and Sch A treatment decreased the viability of HCC827/GR cells more effectively than Gefi or Sch A alone. Based on the survival curve, the IC50 value for the combined treatment was much lower than for Gefi-treatment alone (2.649 μM versus 14.62 μM). The sensitivity of HCC827/GR cells to Gefi increased approximately 5.5-fold when Sch A was added, compared with treatment with Gefi alone. In addition, colony-forming assays were carried out to evaluate the long-term suppressive effects of combined Gefi and Sch A treatment. After co-incubating with Gefi and Sch A for 24 h, cell colonies were stained with crystal violet and counted on the day 14. Compared with the single compound treatments (Gefi or Sch A), the combined treatment resulted in a much stronger inhibitory effect on cancer cell growth (Fig. 2B), a result which was highly consistent with the previously described MTS assay results. Besides, another important issue is whether SchA acts specifically in resistant models. Therefore, likewise, cell viability assay and colony-forming assays were carried out in gefitinibsensitive cells. The results (Fig. S1) showed that Sch A just faintly influenced the sensitivity of HCC827 to gefitinib.
2.15. Statistical analysis The results are presented as the mean ± standard error (S.D.). The statistics were performed using one-way ANOVA in GraphPad Pro (GraphPad, San Diego, CA, USA). P values < 0.05 were considered statistically significant. All the experiments were repeated a minimum of three times. All of the aforementioned experiments were repeated thrice. 3. Results 3.1. Sch A sensitizes NSCLC cells to Gefi The intriguing research findings of dibenzocyclooctadiene lignans on reversing multidrug resistance aroused our great interest to explore whether it also could impact on targeted therapies resistance. Thus, the anti-proliferative effects of five dibenzocyclooctadiene ligands (Fig. 1A) were tested by means of colorimetric MTS assays and using the Gefisensitive cell line HCC827 and the Gefi-resistant HCC827/GR cell line. The cytotoxic effect of Gefi on HCC827/GR cells was significantly lower than on HCC827 cells (the IC50 value increased from 0.558 μM to 13
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
activity was dose-dependent. To elucidate the mechanisms of Sch A-induced apoptosis-sensitization, we examined the expression levels of the apoptosis-related proteins Cleaved-caspase-3, Cleaved-PARP and Bcl-2 by Western blot. As shown in Fig. 4B, the expression of pro-apoptotic molecules (CleavedPARP and Cleaved-caspase-3) was significantly upregulated, while the anti-apoptotic protein Bcl-2 was downregulated.
3.4. Sch A binds directly to IKKβ and suppresses the IKKβ/NF-κB signaling pathway in HCC827/GR cells The transcription factor NF-κB and its associated regulatory factors IκB kinases (IKKs) are involved in the pathogenesis of a variety of hematologic and solid tumor malignancies (e.g., NSCLC, gastric cancer, breast tumors, and so on). Furthermore, recent studies have indicated that crosstalk between the EGFR- and NF-κB-activating cascades is closely linked to the development of resistance to EGFR inhibitors. Previously, Song et al. showed that Sch A suppressed the activation of the LPS-induced IKKβ-IκB-NF-κB signaling pathway in a dose-dependent manner. Therefore, we speculated that the sensitizing effects of Sch A could involve inhibition of the IKKβ/NF-κB signaling pathway. TNF-α, a well-known NF-κB activator, was used to activate the IKKβ/ NF-κB pathway. The phosphorylation and total protein expression levels of IKKβ and IκB were analyzed by Western blot. IKKβ and IκB are two important upstream modulators of the NF-κB signal transduction cascade. Because IκB phosphorylation is catalyzed by IKK in the canonical NF-κB signaling pathway and occurs downstream of IKK phosphorylation, we measured the phosphorylated levels of IKKβ and IκB at two different time points (0.5 h in the case of p-IKKβ, and 1 h in the case of p-IκB). This approach was described in the previously quoted study (Song et al., 2016). The results showed that Sch A dose-dependently decreased IKKβ and IκB phosphorylation levels following stimulation with TNF-α (10 ng/ml) for 0.5 h and 1 h, respectively (Fig. 5A). As a transcription factor, NF-κB must translocate to the nucleus to regulate target gene transcription. Thus, we also measured the subcellular localization of NF-κB p65 after treatment with Sch A. The levels of NF-κB p65 subunit in the nuclear and cytosolic fractions were quantified by Western blot. As shown in Fig. 5B, Sch A effectively blocked the nuclear translocation of NF-κB. Furthermore, considered the supposed role of NF-κB pathway in determining resistance to gefitinib, the levels of pIKKβ, p-IκBα and NF-κB in gefitinib-resistant cells treated with Sch A, gefitinib, or Sch A plus gefitinib was determined. The result (Fig. 5C and D) demonstrated that treatment of gefitinib enhanced the level of pIKKβ, p-IκBα and NF-κB nuclear translocation, while combined treatment of Sch A and gefitinib effectively downregulated IKKβ/NF-κB signaling activation induced by gefitinib. Based on these results, it indicated that the mechanism of the synergistic effect of Sch A might be through inhibition of IKKβ/NF-κB signal activation. To investigate whether Sch A bound directly to IKKβ, SPR experiments were performed. As shown in Fig. 5E, the SPR response value increased gradually with elevated Sch A concentrations and a low equilibrium dissociation constant (KD) of 37.3 μM was determined. The interaction between Sch B and IKKβ, as well as the binding capacity of Sch A to IKKα, was examined to rule out artificial effects in SPR. As shown in Fig. S2A, Sch B also binds to IKKβ with a slightly higher KD value than Sch A, which was consistent with the results of Zeng et al. that Sch B could inhibit LPS-induced IKKα/β activation and was supposed to bind IKKβ protein (Zeng et al., 2012). To identify whether Sch A selectively binds to IKKβ, the interaction between Sch A and IKKα was also determined. The results (Fig. S2B) demonstrated that the binding of Sch A to IKK alpha was greatly lower than IKKβ, suggesting Sch A might be a potential selective IKKβ inhibitor. Collectively, these results indicate that Sch A bound directly to IKKβ, preventing the phosphorylation of IKKβ and IκB, and the subsequent nuclear translocation of NF-κB.
Fig. 2. Sch A sensitized the anti-growth activity of Gefi against the Gefi-resistance cell line. (A) Effect of combination therapy with Gefi and SchA on viability of HCC827GR cells. Cell viability was determined by MTS assay. (B) Effect of combination therapy with Gefi and SchA on HCC827GR cells clone formation. Cells were exposed to each group for 24 h, and cultured in fresh medium for 14 days. Visualized colonies were photographed. The data were obtained from three independent experiments performed in triplicate, and the representative photos were shown.
3.2. Gefi and Sch A combination induces NSCLC cell cycle arrest To examine whether the sensitizing effect of Sch A involved cell cycle arrest, the proportion of cells in G0/G1, S, and G2/M phases were counted by flow cytometry. The results showed that combined treatment with Gefi and Sch A effectively arrested HCC827/GR cells at the G0/G1 phase, indicating that Sch A significantly elevated the cell cycle arresting capacity of Gefi (Fig. 3A). On the other hand, Western blot results demonstrated that Sch A enhanced the inhibitory effect of Gefi on the expression of cell cycle-related proteins (Cyclin D1, Cdk-4) in a synergistic manner (Fig. 3B). These results suggested that Sch A synergized with Gefi to arrest the cells in the G0/G1 phase by reducing the expression of cell cycle-related proteins. 3.3. Combined Gefi and Sch A treatment induces HCC827/GR cell apoptosis Double staining with annexin V-FITC and PI was performed to determine whether Sch A could enhance the pro-apoptotic effect of Gefi (Crowley et al., 2016). The flow cytometry results showed that when compared with single agent treatment, the early- and late-stage apoptosis rates of HCC827/GR cells were both greatly increased after combined treatment with Gefi and Sch A (Fig. 4A). Moreover, increasing the concentration of Sch A in the combined treatment group promoted cancer cell apoptosis to a greater extent, suggesting that its sensitizing 14
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 3. Combination treatment with Gefi and Sch A induced the cell cycle arrest of HCC827GR cells. (A) HCC827GR cells were treated with Sch A alone (20 or 40 μM), Gefi alone (15 μM) or treated with combination of Sch A (20 or 40 μM) and Gefi (15 μM) for 24 h, and the cell cycle distribution was analyzed by the cytometry (Becton Dickinson FACSCalibor, BD Biosciences, Franklin Lakes, NJ). And the representative histogram of the cell cycle distribution was shown. Data are presented as the mean ± S.D. of three independent experiments conducted in triplicate. “ns” means no significance; *P < 0.05; **P < 0.01. (B) Western blot analysis of cell cycle related protein Cyclin D1 and Cdk-4. Actin was shown as the control of equal loading.
atoms of IKKβ and the heavy atoms of Sch A were analyzed, as shown in Fig. 6A. This analysis indicated that the RMSD values of the backbone atoms of IKKβ showed only small fluctuations after 120 ns and those of Sch A were relative stable during the whole conventional MD simulation. In addition, conformational alignment of the initial structure with the last snapshots of the conventional MD simulation showed that these two structures were very similar, with only minor adjustments (Fig. 6B). These findings indicated that we had determined a relatively
3.5. Analysis of the Sch A binding site by molecular modeling Firstly, molecular docking experiments were conducted to predict the possible interaction between IKKβ and Sch A. 200 ns conventional MD simulations were performed to obtain the equilibrated and stable MD trajectories. To validate whether the studied systems reached equilibrium and to monitor the dynamic stability of the IKKβ/Sch A complex, the root mean square deviations (RMSDs) of all the backbone
Fig. 4. Combination treatment with Gefi and Sch A promoted HCC827GR cells apoptosis. (A) HCC827GR cells were treated with Sch A alone (20 or 40 μM), Gefi alone (15 μM) or treated with combination of Sch A (20 or 40 μM) and Gefi (15 μM) for 24 h, Apoptosis was assessed by Annexin V/propidium iodide (PI) staining. Data are presented as the mean ± S.D. of three independent experiments conducted in triplicate. “ns” means no significance; *P < 0.05; **P < 0.01. (B) The total protein was extracted and the expression of Bcl-2, Bax, Cleaved-caspase-3 and Cleaved-PARP was examined by Western blot. Actin was shown as the control of equal loading. 15
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 5. The effect of Sch A on IKK〈beta〉 pathway, and interaction between Sch A and IKKβ. (A) After HCC827GR cells were treated with TNF-α (10 ng/ml) with or without Sch A (5, 10, 20 and 40 μM) for 0.5 h (p-IKKβ assay) or 1 h (p-IκB assay), the phosphorylated and total IKKβ and IκB proteins were determined by Western blot. All data are shown as the mean ± S.D. from independent experiments performed in triplicate. (B) After HCC827GR cells were treated with TNF-α (10 ng/ml) with or without Sch A (20 and 40 μM) for 2 h, NF-κB p65 levels in the nucleus and cytoplasm were determined by Western blot. (C, D) HCC827GR cells were treated with Gefi alone (15 μM), Sch A alone (40 μM), or treated with combination of Gefi (15 μM) and Sch A (40 μM) for 24 h, the phosphorylated and total IKKβ and IκB proteins (C), and the NF-κB p65 levels in the nucleus and cytoplasm (D) were determined by Western blot. (E) Direct-binding affinity between Sch A and IKKβ was demonstrated by SPR. Kd: disocciation constant, Ka: association constant, KD: equilibrium dissociation constant.
conformational distributions of IKKβ were analyzed by principal component analysis (PCA) and plotted against time (Fig. 7C). As shown in Fig. 7C, the PCA results indicated that the conformational distributions of IKKβ were dynamic during the 0–240 ns simulation, and finally, stabilized during the 240–300 ns simulation. Finally, a free energy map was constructed to further illustrate the relationship between the conformational and energetic changes (Fig. 7D). As shown in Fig. 7D, four energetic deep wells were observed in the free energy map during the simulation time, indicating that the GaMD simulation could sample a much larger conformational space. Additionally, the energetic deep well observed during the simulation period of 240–300 ns suggested that the IKKβ/Sch A complex had achieved stability. Overall, combining the SPR results with the two simulation methods, we concluded that the detailed action mode of Sch A might be directly bind to IKKβ kinase domain through several key hydrophobic interactions. At last, to further verify our speculation, the binding affinity of Sch A and IKKβ mutant was detected by SPR. In the molecular dynamic simulations results, IKKβ Ile165 was identified as the most contributed residue of the interaction between Sch A and IKKβ. Thus, mutated IKKβ (I165A) plasmid was constructed and expressed under the induction of IPTG. After purified by nickel column, gradient imidazole elution and
reasonable binding conformation for our IKKβ/Sch A complex. Thereafter, the last 40 ns of the conventional MD simulation trajectory were used for the energetic analysis. To determine the role of individual residues in the protein-ligand recognition patterns, the binding free energies were decomposed by means of the MM/GBSA method. As illustrated in Fig. 6C and D, the most 10 contributed residues are Ile-165, Leu-21, Val-29, Val-152, Met96, Cys-99, Tyr-98, Gly-102, Gly-24, Val-74. The predominant residues were hydrophobic amino acids, indicating that nonpolar interactions may contribute to the binding of Sch A to IKKβ. To further validate the stability of the predicted conformation determined by conventional MD simulation, we conducted GaMD simulation, which is an enhanced MD simulation method that provides unconstrained enhanced sampling to speed up the conformational sampling process. As shown in Fig. 7A, the RMSD values of the backbone atoms of IKKβ quickly reached a steady state after 70 ns and those of Sch A were also relatively stable during the whole GaMD simulation, agreeing with the results obtained with conventional MD simulation. An overlay of the initial structure (yellow, Fig. 7B) with the last snapshot (light pink, Fig. 7B) showed that they were quite similar, with conformational adjustments within a certain range. Furthermore, the
16
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 6. Structural and energetic analysis of Sch A to the active binding site of IKKβ by conventional MD simulation. (A) RMSD curves for the 200 ns conventional MD simulation; (B) Alignment of the initial structure (yellow) and the last snapshot (green) of IKKβ/Sch A; (C) Most 10 contributed residues between IKKβ and Sch A; (D) Structural analysis of the most 10 contributed key residues of IKKβ to Sch A. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
et al., 2011). Moreover, it has been reported that NF-κB activation resulted in upregulated integrin αvβ3 expression, driving tumor stemness and resistance to EGFR inhibitors by interacting with KRAS (Seguin et al., 2014). All these studies indicated that combined therapy with an EGFR TKI and an inhibitor of NF-κB signaling could enhance the therapeutic effect in some EGFR-TKI-resistant NSCLC tumors. However, the high-toxicity of NF-κB inhibitors prohibits their potential clinical application. In recent years, the discovery of highly effective and relatively nontoxic anticancer compounds derived from natural products has transformed this field into a hotspot of cancer therapy research. Similarly, the search for agents derived from natural products which can effectively reverse tumor resistance is also receiving extensive attention. A good example is resveratrol, a natural polyphenol compound found in a large variety of plant species (in particular, mulberries, peanuts and grapes) which can both reverse the resistance of cancer cells to chemotherapeutic drugs (such as doxorubicin, gemcitabine and pemetrexed), and Gefi-resistance in NSCLC cells (Hu et al., 2014). Therefore, in this study we focused our attention on Sch A, another promising compound derived from a natural product, and investigated whether it could potentially counteract the intractable problem of Gefi resistance. Although there are already some studies on Sch A-mediated reversal of chemotherapeutic resistance (5-FU, doxorubicin), research on the effects of Sch A on Gefi-resistance or to other targeted therapies has not been conducted. In this study, we found that Sch A selectively enhanced the sensitivity to Gefi in resistant cells. Thus, adding Sch A strongly increased the anti-proliferative, cell cycle arrest and proapoptotic effects of Gefi in resistant cells. It is worth noting that the results we obtained with Sch A surpassed even our own initial predictions. The MTS and clone formation assays both demonstrated that Sch A significantly reversed Gefi-resistance. By means of Western blot and SPR assays, we found that Sch A was a potential IKKβ inhibitor. Through determination the influence of IKKβ/NF-κB signaling after Gefi
cleavage of His-tag by thrombin, targeted mutated IKKβ protein was obtained and used for SPR assay. As shown in Fig. 7E, Sch A indeed lost the binding with IKKβ protein. Therefore, this data further verified the direct interaction and the binding site between Sch A and IKKβ. 4. Discussion Gefi, the first anti-EGFR agent to enter clinical development and the first in its class to be approved for clinical use, is a well-tolerated treatment for advanced NSCLC. It is approved as first-line treatment for patients with metastatic NSCLC harboring activating EGFR mutations by US FDA in 2015. Unfortunately, the vast majority of patients with metastatic lung cancer who initially benefit from Gefi eventually develop resistance. During the past few years, different pathways involved in the development of resistance have been described. The EGFR second-site T790M mutation is considered the most common resistance mechanism to EGFR TKIs, and accounts for acquired EGFR TKI resistance in over 50% of NSCLC patients. The second-generation EGFR TKI afatinib and the third-generation anti-EGFR TKIs, such as osimertinib (AZD9291), and rocelitinib (CO-1686), were subsequently developed to covalently bind to EGFR Cys-797, but acquired resistance to these irreversible inhibitors inevitably appeared (Thress et al., 2015). In addition, a bypass signaling pathway (such as MET amplification, HER2 upregulation or constitutive NF-κB activities) was identified in patients with resistance to EGFR TKIs. As a crucial regulator of survival, apoptosis and migration of cancer cells, the IKKβ/NF-κB signaling pathway has not only been linked to the oncogenic potential of the EGFR, but is also involved in the acquisition of EGFR TKIs resistance. By applying an RNA interference-based screening approach, Bivona et al. found that knocking down several components of the NF-κB pathway specifically enhanced cell death induced by EGFR TKIs, and that genetic or pharmacologic inhibition of NF-κB enhanced erlotinib-induced apoptosis in erlotinib-resistant EGFR-mutant lung cancer cells (Bivona 17
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 7. Structural and energetic analysis of Sch A to the active binding site of IKKβ by GaMD simulation. (A) RMSD curves for the 300 ns GaMD simulation; (B) Alignment of the initial structure (yellow) and the last snapshot (green) of IKKβ/Sch A; (C) PCA scatter plot of 150,000 snapshots from GaMD simulations along the first two principal components and plotted against time; (D) Free energy map calculated from the first two principal components. (E) The binding affinity of Sch A with IKKβ I165A mutant was determined by SPR assay. The indicated concentration of Sch A was also set as 20, 40, 60, 80, 100 μM. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
BMS345541 and ML-120B. A distinct structural feature of Sch A is that it has no nitrogen atoms. Thus, it would be difficult for it to form hydrogen bonds with its target. Our molecular docking and simulation analyses showed that IKKβ inhibition by Sch A depended mainly on hydrophobic interactions with the hydrophobic cavity of IKKβ, a result that was highly consistent with the molecular characteristics of Sch A and the IKKβ mutants SPR assay results. Overcoming the low water solubility of Sch A by modifying its structure to improve its druggability has always been one of the most important problems in Sch A research. According to our molecular modeling results, introducing a long-chain hydrophilic moiety in the aromatic ring oriented away from the ATP pocket may effectively increase its water solubility, as well as its inhibitory activity. Moreover, it can be observed that the adjacent Cys-99 residue is located near the octane ring of Sch A, suggesting that adding a Michael receptor at this position may greatly enhance its binding
treatment alone or combination treatment, we further confirmed the synergistic effect of Sch A was mediated by inhibition of IKKβ/NF-κB pathway. Based on the crosstalk that exists between the EGFR and IKKβ/NF-κB signaling cascades and which has been linked to the development of EGFR TKIs resistance, and to their important roles sustaining proliferation and survival, and preventing apoptosis, we believe that combined treatment with Gefi and Sch A may exert a potent lethal effect on Gefi-resistant NSCLC cells. Another important contribution of this study was the utilization of molecular docking and dynamic simulations to analyze the underlying mode of interaction and the key binding site between Sch A and its target IKKβ. Previous studies on the identification of IKKβ inhibitors were mostly based on measurement of their biological activity (IKKβ inhibition) without understanding the mechanisms. This is quite different from the newer and well-known IKKβ inhibitors, such as TPCA-1, 18
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
affinity towards IKKβ.
Ares, L., 2017. Lung cancer: current therapies and new targeted treatments. Lancet 389, 299–311. Hrustanovic, G., Lee, B.J., Bivona, T.G., 2013. Mechanisms of resistance to EGFR targeted therapies. Cancer Biol. Ther. 14, 304–314. Hu, Y., Li, C., Li, H., Li, M., Shu, X., 2014. Resveratrol-mediated reversal of tumor multidrug resistance. Curr. Drug Metabol. 15, 703–710. Kazandjian, D., Blumenthal, G.M., Yuan, W., He, K., Keegan, P., Pazdur, R., 2016. FDA approval of gefitinib for the treatment of patients with metastatic EGFR mutationpositive non-small cell lung cancer. Clin. Cancer Res. 22, 1307–1312. Kong, D., Zhang, D., Chu, X., Wang, J., 2018. Schizandrin A enhances chemosensitivity of colon carcinoma cells to 5-fluorouracil through up-regulation of miR-195. Biomed. Pharmacother. 99, 176–183. Lee, H.J., Zhuang, G., Cao, Y., Du, P., Kim, H.J., Settleman, J., 2014. Drug resistance via feedback activation of Stat3 in oncogene-addicted cancer cells. Cancer Cell 26, 207–221. Lee, K., Ahn, J.H., Lee, K.T., Jang, D.S., Choi, J.H., 2018. Deoxyschizandrin, isolated from Schisandra berries, induces cell cycle arrest in ovarian cancer cells and inhibits the protumoural activation of tumour-associated macrophages. Nutrients 10. Liu, S., Misquitta, Y.R., Olland, A., Johnson, M.A., Kelleher, K.S., Kriz, R., Lin, L.L., Stahl, M., Mosyak, L., 2013. Crystal structure of a human IkappaB kinase beta asymmetric dimer. J. Biol. Chem. 288, 22758–22767. Loncharich, R.J., Brooks, B.R., Pastor, R.W., 1992. Langevin dynamics of peptides: the frictional dependence of isomerization rates of N-acetylalanyl-N'-methylamide. Biopolymers 32, 523–535. Miao, Y.L., Sinko, W., Pierce, L., Bucher, D., Walker, R.C., McCammon, J.A., 2014. Improved reweighting of accelerated molecular dynamics simulations for free energy calculation. J. Chem. Theory Comput. 10, 2677–2689. Miller, B.R., McGee, T.D., Swails, J.M., Homeyer, N., Gohlke, H., Roitberg, A.E., 2012. MMPBSA.py: an efficient program for end-state free energy calculations. J. Chem. Theory Comput. 8, 3314–3321. Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J., 2009. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791. Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C., Ferrin, T.E., 2004. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612. Seguin, L., Kato, S., Franovic, A., Camargo, M.F., Lesperance, J., Elliott, K.C., Yebra, M., Mielgo, A., Lowy, A.M., Husain, H., Cascone, T., Diao, L., Wang, J., Wistuba II, , Heymach, J.V., Lippman, S.M., Desgrosellier, J.S., Anand, S., Weis, S.M., Cheresh, D.A., 2014. An integrin beta(3)-KRAS-RalB complex drives tumour stemness and resistance to EGFR inhibition. Nat. Cell Biol. 16, 457–468. Shostak, K., Chariot, A., 2015. EGFR and NF-kappaB: partners in cancer. Trends Mol. Med. 21, 385–393. Solomon, B.J., Kim, D.W., Wu, Y.L., Nakagawa, K., Mekhail, T., Felip, E., Cappuzzo, F., Paolini, J., Usari, T., Tang, Y., Wilner, K.D., Blackhall, F., Mok, T.S., 2018. Final overall survival analysis from a study comparing first-line Crizotinib versus chemotherapy in ALK-mutation-positive non-small-cell lung cancer. J. Clin. Oncol. 36, 2251–2258. Song, F., Zeng, K., Liao, L., Yu, Q., Tu, P., Wang, X., 2016. Schizandrin a inhibits microglia-mediated neuroninflammation through inhibiting TRAF6-NF-kappab and jak2-stat3 signaling pathways. PLoS One 11, e0149991. Szopa, A., Ekiert, R., Ekiert, H., 2017. Current knowledge of Schisandra chinensis (Turcz.) Baill. (Chinese magnolia vine) as a medicinal plant species: a review on the bioactive components, pharmacological properties, analytical and biotechnological studies. Phytochemistry Rev. 16, 195–218. Takezawa, K., Pirazzoli, V., Arcila, M.E., Nebhan, C.A., Song, X., de Stanchina, E., Ohashi, K., Janjigian, Y.Y., Spitzler, P.J., Melnick, M.A., Riely, G.J., Kris, M.G., Miller, V.A., Ladanyi, M., Politi, K., Pao, W., 2012. HER2 amplification: a potential mechanism of acquired resistance to EGFR inhibition in EGFR-mutant lung cancers that lack the second-site EGFRT790M mutation. Cancer Discov. 2, 922–933. Tan, W.L., Jain, A., Takano, A., Newell, E.W., Iyer, N.G., Lim, W.T., Tan, E.H., Zhai, W., Hillmer, A.M., Tam, W.L., Tan, D.S.W., 2016. Novel therapeutic targets on the horizon for lung cancer. Lancet Oncol. 17, e347–e362. Thress, K.S., Paweletz, C.P., Felip, E., Cho, B.C., Stetson, D., Dougherty, B., Lai, Z., Markovets, A., Vivancos, A., Kuang, Y., Ercan, D., Matthews, S.E., Cantarini, M., Barrett, J.C., Janne, P.A., Oxnard, G.R., 2015. Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M. Nat. Med. 21, 560–562. Yun, C.H., Mengwasser, K.E., Toms, A.V., Woo, M.S., Greulich, H., Wong, K.K., Meyerson, M., Eck, M.J., 2008. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc. Natl. Acad. Sci. U. S. A. 105, 2070–2075. Zeng, K.W., Zhang, T., Fu, H., Liu, G.X., Wang, X.M., 2012. Schisandrin B exerts antineuroinflammatory activity by inhibiting the Toll-like receptor 4-dependent MyD88/ IKK/NF-kappaB signaling pathway in lipopolysaccharide-induced microglia. Eur. J. Pharmacol. 692, 29–37. Zhang, Z.L., Jiang, Q.C., Wang, S.R., 2018. Schisandrin A reverses doxorubicin-resistant human breast cancer cell line by the inhibition of P65 and Stat3 phosphorylation. Breast Canc. 25, 233–242.
5. Conclusion In summary, Gefi and Sch A in combination acted synergistically to inhibit proliferation, arrest the cell cycle and promote apoptosis of Gefiresistant NSCLC cells. Mechanistically, our results demonstrated that Sch A could bind directly to IKKβ, preventing the gefitinib induced phosphorylation of IKKβ and of the downstream molecule IκB, and reducing the nuclear translocation of NF-κB p65. Finally, molecular docking and molecular simulations of the Sch A/IKKβ complex revealed that stable binding between these two molecules depended on hydrophobic interactions. Author contributions H.X. and W.F. designed and performed the research. J.Z., wrote and revised the manuscript; All authors read and revised the final manuscript. Acknowledgments This study was supported by grants from the Special fund project for technology innovation of Foshan City (No. 2014AG10003). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ejphar.2019.04.016. Conflicts of interest The authors declare no conflict of interest. References Berendsen, H.J.C., Postma, J.P.M., Vangunsteren, W.F., Dinola, A., Haak, J.R., 1984. Molecular-dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690. Bivona, T.G., Hieronymus, H., Parker, J., Chang, K., Taron, M., Rosell, R., Moonsamy, P., Dahlman, K., Miller, V.A., Costa, C., Hannon, G., Sawyers, C.L., 2011. FAS and NFkappaB signalling modulate dependence of lung cancers on mutant EGFR. Nature 471, 523–526. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A., 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca - Cancer J. Clin. 68, 394–424. Chen, Z., Fillmore, C.M., Hammerman, P.S., Kim, C.F., Wong, K.K., 2014. Non-small-cell lung cancers: a heterogeneous set of diseases. Nat. Rev. Canc. 14, 535–546. Chong, C.R., Janne, P.A., 2013. The quest to overcome resistance to EGFR-targeted therapies in cancer. Nat. Med. 19, 1389–1400. Crowley, L.C., Marfell, B.J., Scott, A.P., Waterhouse, N.J., 2016. Quantitation of apoptosis and Necrosis by annexin V binding, propidium iodide uptake, and flow cytometry. Cold Spring Harb. Protoc. 11. Engelman, J.A., Zejnullahu, K., Mitsudomi, T., Song, Y., Hyland, C., Park, J.O., Lindeman, N., Gale, C.M., Zhao, X., Christensen, J., Kosaka, T., Holmes, A.J., Rogers, A.M., Cappuzzo, F., Mok, T., Lee, C., Johnson, B.E., Cantley, L.C., Janne, P.A., 2007. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science 316, 1039–1043. Ercan, D., Xu, C., Yanagita, M., Monast, C.S., Pratilas, C.A., Montero, J., Butaney, M., Shimamura, T., Sholl, L., Ivanova, E.V., Tadi, M., Rogers, A., Repellin, C., Capelletti, M., Maertens, O., Goetz, E.M., Letai, A., Garraway, L.A., Lazzara, M.J., Rosen, N., Gray, N.S., Wong, K.K., Janne, P.A., 2012. Reactivation of ERK signaling causes resistance to EGFR kinase inhibitors. Cancer Discov. 2, 934–947. Gottlich, C., Muller, L.C., Kunz, M., Schmitt, F., Walles, H., Walles, T., Dandekar, T., Dandekar, G., Nietzer, S.L., 2016. A combined 3D tissue engineered in vitro/in silico lung tumor model for predicting drug effectiveness in specific mutational backgrounds. JoVE, e53885. Hirsch, F.R., Scagliotti, G.V., Mulshine, J.L., Kwon, R., Curran Jr., W.J., Wu, Y.L., Paz-
19
Contents lists available at ScienceDirect
European Journal of Pharmacology journal homepage: www.elsevier.com/locate/ejphar
Full length article
Schizandrin A enhances the efficacy of gefitinib by suppressing IKKβ/NF-κB signaling in non-small cell lung cancer
T
Haibing Xiana,b,1, Weineng Fengb,1, Jiren Zhanga,∗ a Department of Oncology, Zhujiang Hospital, Southern Medical University/The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510282, Guangdong, China b Department of Head and Neck/Thoracic Medical Oncology, The First People's Hospital of Foshan, Foshan, 528041, Guangdong, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Schizandrin A Gefitinib IKKβ/NF-κB signal NSCLC
The emergence of resistance to EGF receptor (EGFR) inhibitor therapy is a significant challenge for patients with non-small cell lung cancer (NSCLC). During the past few years, a correlation between EGFR TKIs resistance and dysregulation of IKKβ/NF-κB signaling has been increasingly suggested. However, few studies have focused on the effects of combining IKK/NF-κB and EGFR inhibitors to overcome EGFR TKIs resistance. In this study, we discovered that Schizandrin A (Sch A), a lignin compound isolated from Schisandra chinesnesis, could synergize with the EGFR receptor inhibitor Gefitinib to inhibit cell growth, induce cell cycle arrest and apoptosis of HCC827/GR cells. Sch A effectively suppressed the phosphorylation of IKKβ and IκBα, as well as the nuclear translocation of NF-κB p65, and showed high and selective affinity for IKKβ in surface plasmon resonance (SPR) experiments, indicating that Sch A was a selective IKKβ inhibitor. Molecular modeling between IKKβ and Sch A suggested that Sch A formed key hydrophobic interactions with IKKβ, which may contribute to its potent IKKβ inhibitory effect. These findings suggest a novel approach to improve poor clinical outcomes in EGFR TKIs therapy, by combining it with Sch A.
1. Introduction Lung cancer is the most frequently diagnosed cancer and the leading cause of cancer-related deaths worldwide (Bray et al., 2018). More than 85% of cases are currently classified as non-small-cell lung cancer (NSCLC), for which the predicted 5-year survival rate is as low as 15.9% (Chen et al., 2014; Hirsch et al., 2017). Today, numerous NSCLC derived mutations have been identified, including epidermal growth factor receptor (EGFR), Anaplastic Lymphoma Kinase (ALK) and K-Ras mutations (Tan et al., 2016). New small molecule inhibitors which target EGFR or ALK kinase, such as Gefitinib and Crizotinib, have led to remarkable improvements in the clinical outcome (Kazandjian et al., 2016; Solomon et al., 2018). However, the emergence of acquired resistance against targeted therapies greatly impairs their clinical effectiveness (Chong and Janne, 2013; Hrustanovic et al., 2013). Gefitinib (Gefi), an orally bioavailable, reversible, ATP-competitive EGFR tyrosine kinase inhibitor (TKI), was approved by the FDA in 2015 as first-line treatment for patients with metastatic NSCLC harboring activating EGFR mutations (Kazandjian et al., 2016). In EGFR-positive
patients, Gefi significantly prolongs their progression free survival (PFS) and overall survival (OS) when compared with chemotherapy. Unfortunately, the clinical effectiveness of EGFR-targeted therapy has been dramatically limited by the emergence of drug resistance. The appearance of second-site EGFR mutations (T790M, C797S etc.) (Thress et al., 2015; Yun et al., 2008), or activation of alternative pathways (MET and Her2 amplification, STAT3 and ERK reactivation), is regarded as the leading causes of acquired EGFR TKIs resistance (Engelman et al., 2007; Ercan et al., 2012; Lee et al., 2014; Takezawa et al., 2012). Significantly, a large number of recent studies have indicated that crosstalk between the EGFR- and NF-κB- signaling cascades could be another unnoticed key factor involved in the development of EGFR TKI resistance (Bivona et al., 2011; Shostak and Chariot, 2015). Lignans are the major bioactive components of many fruits and possess various pharmacological properties such as anticancer, antioxidant, and anti-inflammatory activities. In the past years, the potential for drug development of dibenzocyclooctadiene lignans that isolated from the fruit of Schisandra chinensis (Turcz.) Baill (SC) attracts extensive research attention worldwide (Szopa et al., 2017).
∗
Corresponding author. Department of Oncology, Zhujiang Hospital, Southern Medical University/The Second School of clinical Medicine, Southern Medical University, Industrial Road No.253, Guangzhou, 510282, Guangdong, China. E-mail address: [email protected] (J. Zhang). 1 These authors have contributed equally to this work. https://doi.org/10.1016/j.ejphar.2019.04.016 Received 12 November 2018; Received in revised form 28 March 2019; Accepted 5 April 2019 Available online 25 April 2019 0014-2999/ © 2019 Elsevier B.V. All rights reserved.
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
exposing parental HCC827 cells to 2 μM Gefi for 2 months followed with one more month of exposure to 4 μM gefitinib using an alternating 5-day drug on and 5-day drug off schedule. The resistant cell population was then routinely cultured with medium containing 1 μM Gefi. The resistance phenotype was preserved even with prolonged culture in Gefi-free culture medium for 6 months, suggesting an irreversible phenotype. We did not isolate pure monoclones. The pooled populations of HCC827/GR cells were used for further studies.
Schisandrin (Schin), schisandrin A (Sch A), schisandrinB (Sch B), schisandrin C (Sch C) and γ-schisandrin (γ-Sch) are the most important bioactive dibenzocyclooctadiene lignans. Although the structure is highly similar, the biological effects of these lignans are quite diverse. The current studies discovered that Sch A and Sch B not only displayed favorable tumor suppressive activity, but also possess great potential on reversing the resistance to antitumor agents. Whereas, the studies of Schin, Sch C and γ-Sch was mainly focused on their neuroprotective and cognitive enhancement properties. Schisandrin A (Sch A), also commonly known as deoxyschizandrin, was reported that as a potent suppressor against the growth of human ovarian cancer and colorectal cancer cell lines. The underlying antitumor mechanisms of Sch A includes inhibition of PI3K/Akt and NF-κB signaling pathway activation (Kong et al., 2018; Lee et al., 2018). In previous studies, researchers discovered that Sch A even could reverse multi-drug resistance (MDR) and identified some of the underlying mechanisms. For example, Zhang et al. showed that the reversal of doxorubicin resistance induced by Sch A may be caused by inhibition of P-gp at the gene and protein levels, an effect mediated by p65 and Stat3 phosphorylation (Zhang et al., 2018). Moreover, Sch A could enhance the chemosensitivity of colon carcinoma cells to 5-fluorouracil by upregulating miR-195 (Kong et al., 2018). Thus, the ability of Sch A to revert chemotherapy resistance in multiple ways has been demonstrated. However, whether Sch A can also counteract acquired resistance to targeted therapy such as EGFR TKI resistance has never been investigated. In this study, the cancer-killing activities of five major dibenzocyclooctadiene ligands of SC (Sch, Sch A, Sch B, Sch C and γ-Sch) were initially tested on HCC827 and HCC827 Gefi-resistant cells (HCC827/ GR). The MTS and flow cytometry data revealed that Sch A markedly increased the sensitivity of HCC827GR cells to Gefi. Furthermore, mechanistic investigation of this effect by Western blot and SPR assays demonstrated that Sch A sensitized resistant NSCLC cells to Gefi treatment by directly interacting with IKKβ to suppress the IKKβ/NF-κB signal. Computer simulations further revealed that Sch A bound to IKKβ mostly through hydrophobic interactions, competing with its natural ligand, ATP.
2.3. Cell viability assay The cell viability was measured by the Cell Titer 96 Aqueous NonRadioactive Cell Proliferation Assay Kit (Promega). 5 × 103 cells were seeded in 96-well plates overnight, then the culture medium was removed and all cells were treated with different concentrations of chemicals for 72 h. The absorbance value of each well was determined by a microplate reader at 490 nm. Each experiment was repeated for three times. 2.4. Clonogenic assay HCC827/GR cells (1000 cells/well) were seeded in 6-well cell culture plate for 24 h, then DMSO, Sch A (20, 40 μM), Gefi (15 μM), and Gefi plus SchA (20 or 40 μM) were added to the culture medium. After 24 h, the culture medium was replaced with fresh culture medium for 14 d. Colonies was removed the medium, washed with PBS twice, fixed with methanol for 15 min, then washed with PBS three times, and finally stained with crystal violet for 15 min. Colonies containing more than 50 cells were counted, and visualized colonies were then photographed. 2.5. Cell apoptosis analysis Cell apoptosis was detected by flow cytometer analysis and Western blotting. For flow cytometer analysis of apoptosis, cells treated as indicated for 24 h were harvested by trypsin, washed twice by PBS, and re-suspended in 100 μl 1 × binding buffer. 5 μl FITC Annexin V and PI (556547, BD Biosciences, USA) was added to the cell suspension and then incubated for 15 min at room temperature. After dilution with 400 μl binding buffer, the samples were analyzed by ACS Calibur flow cytometer (BD). For apoptosis by Western blotting, cleaved caspase 3, cleaved-PARP (poly ADP-ribose polymerase), Bcl-2 and BCl-xL were analyzed.
2. Material and methods 2.1. Cell culture and reagents The NSCLC cell lines HCC827 that possesses activating mutation EGFR and wild-type KRAS were obtained from Shanghai Institute of Biosciences and Cell Resources Center (Chinese Academy of Sciences, Shanghai, China) (Gottlich et al., 2016). The cells were routinely cultured in RPMI-1640 or DMEM (Gibco/BRL life Technologies, Eggenstein, Germany) supplemented with 10% fetal bovine serum FBS (Hyclone, Logan, UT), 1% penicillin/streptomycin solution in a humidified atmosphere of 5% CO2 at 37 °C. The primary antibodies used in this study, including p-IKKβ, IKKβ, p-IκBα, IκBα, P65, Bcl-2, Cleaved-PARP, Cleaved-caspase-3, Cyclin D1, Cdk-4, Histone H3, Actin and GAPDH were all purchased from Cell Signaling Technology (Danvers, MA). Goat anti-rabbit IgG-HRP secondary antibody were obtained from Santa Cruz Biotechnology (Santa Cruz, CA); Human recombinant Tumor Necrosis Factor-α (TNF-α) was purchased from Sigma-Aldrich (Darmstadt, Germany). The full length recombinant IKKα and IKKβ protein was purchased from Abcam (Cambridge, UK), and their purities were both more than 85% assessed by SDS-PAGE; Gefitinib was purchased from Selleck (Shanghai, China), the five dibenzocyclooctadiene lignans were all purchased from Medchem express (Shanghai, China), and the purity of small molecule compound was all over 99%.
2.6. Cell cycle analysis Cells (3 × 105 cells/well) were seeded in 6-well plates and allowed to adhere overnight. The next day, the cells were treated with different compounds as indicated for 24 h. Then the cells were trypsinized, washed, and fixed in 75% ice-cold ethanol at 4 °C overnight. After centrifugation, the pellets were washed with cold PBS, suspended in 500 μl PBS with 50 mg/ml propidium iodide (PI) and incubated at 4 °C for 30 min in the dark. Then cell suspension was subjected to a FACS Calibur instrument (Becton Dickinson FACSCalibor, BD Biosciences, Franklin Lakes, NJ, USA). 2.7. Western blotting After treated as indicated for 24 h, the cells were washed once by PBS. The cell lysates were quantitated by BCA protein assay kit (BioRad Laboratories, Hercules, CA, USA). Equal amounts of proteins were separated by SDS-PAGE and transferred to PVDF membrane. After being blocked with 5% non-fat dry milk in TBST for 1.5 h, membranes were incubated with a 1:1000 dilution of specific primary antibody overnight at 4 °C and the secondary antibody conjugated with horseradish peroxidase (HRP) (1:5000, Santa Cruz, CA) for 2 h, the
2.2. Establishment of acquired Gefi-resistant NSCLC cell line Gefi-resistant HCC827 cell line (HCC827/GR) was established by 11
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
recombinant vector was transformed into Escherichia coli BL21 competent cells (Vazyme Biotech Co., Nanjing, China) and cultured in LB medium in the presence of 50 μg/ml−1 kanamycin. After shaking at 250 rpm for 5 h at 37 °C, the cultures were down-tempered to 18 °C for 1 h before induction with 1 mM IPTG (isopropyl β-D-1-thiogalactopyranoside, Sigma-Aldrich) for 5 h at 25 °C. Cells were collected by rapid centrifugation and supersonic decomposing. The cell lysis solution was purified using HisTrap FF crude columns (GE Healthcare, CT, USA), followed by His-tag cleavage with human thrombin (Sigma-Aldrich, Darmstadt, Germany) overnight at 4 °C, and subsequently purified by anion exchange (GE Healthcare) and gel filtration chromatography. Purified IKKβ mutant was stored in a high salt-containing buffer (20 mM Tris·HCl, pH 8.5, 500 mM NaCl, and 1 mM NaN3) at −80 °C.
immunoreactive bands were visualized with enhanced chemiluminescence (EMD Millipore, Billerica, MA, USA) in Amersham Imager 600 system (GE Healthcare Life Sciences, Shanghai, China). 2.8. Nuclear and cytoplasmic protein extraction The Thermo Scientific™ NE-PER™ Nuclear and Cytoplasmic Extraction Reagent was applied for the extraction of nuclear and cytoplasmic proteins. 3 × 105 cells were seeded in 6-well plates overnight, and incubated with Sch A (20 μM or 40 μM) for 24 h. Cells were harvested with trypsin-EDTA and then centrifuge at 500 g for 5 min. Following the assay instructions provided, the extraction reagents were added to the pelleted cells step by step, then the cytoplasmic and nuclear proteins were collected separately.
2.12. Conventional molecular dynamics (MD) simulation 2.9. Surface Plasmon Resonance (SPR) analysis
The crystal structure of IKKβ was obtained from the Protein Data Bank (PDB) database (PDB code: 4KIK) (Liu et al., 2013). Then, the structure of IKKβ was refined, including delete all water molecules, non-bonded hetero-atoms, and adding missing hydrogen atoms by UCSF Chimera 1.12 program (University of California, San Francisco, CA, USA) (Pettersen et al., 2004). Afterwards, the binding mode of Sch A in the active binding site of IKKβ was predicted by the AutoDock 4.2.6 (The Scripps Research Institute, La Jolla, CA, USA) (Morris et al., 2009). The IKKβ and Sch A were prepared by AutoDockTools 1.5.6 software (The Scripps Research Institute, La Jolla, CA, USA). A grid box size of 60 × 60 × 60 Å dimensions, with a spacing of 0.375 Å between the grid points, was adopted and covered the entire active binding site of IKKβ. Lamarckian Genetic Algorithm (LGA) was used for globe conformational sampling with trials of 200 dockings. Other settings were set as default. The lowest binding energy conformation was considered for further molecular dynamics (MD) simulation analysis.
The IKKβ/Sch A complex constructed by molecular docking was used as the initial structure for the Conventional MD simulations. The partial atomic charges for Sch A was calculated via the restrained electrostatic potential (RESP) method based on HF/6-13G* basis set (Gaussian, Inc.,Wallingford, CT, USA). After that, the IKKβ and Sch A were described by the Amber ff14SB force field and General Amber Force Field 2 (GAFF2) by LEaP modules of Amber 16 program (University of California, San Francisco, CA, USA), respectively. After that, the IKKβ/Sch A complex was solvated in a box of TIP3P water molecules with a 12 Å distance between the surface of complex and the boundary of the water box. In the end, three sodium ions were added to maintain the electroneutrality. Before the productive conventional MD simulations, we performed an equilibration protocol consisting of an initial minimization heating and equilibration by using the pmemd.MPI module in the Amber 16 package. Firstly, a three-step energy minimization strategy was performed. At stage 1, the IKKβ/Sch A complex was restrained so that the water molecules reoriented appropriately in the system. At stage 2, the backbone of the IKKβ was restrained to avoid structural clash in the solvated systems. At stage 3, the all molecules were minimized without any restraint. During each stage, 6000 steps of steepest descent algorithm. 6000 steps of conjugated gradient algorithm were performed. Subsequently, the system was heated up from 0 to 310 K in 200 ps, followed by applying the density procedure at 310 K for 500 ps and equilibration at 310 K for 1 ns in the isothermal isobaric (NPT) ensemble. Eventually, the system was submitted to 200 ns conventional MD simulation in the NPT ensemble. During the productive conventional MD simulations, the temperature and pressure were maintained by the Langevin temperature equilibration scheme and Berendsen barostat, respectively (Berendsen et al., 1984; Loncharich et al., 1992). Particle Mesh Ewald (PME) algorithm and the non-bonded cutoff distance was set as 10.0 Å to calculate the long-range electrostatic interactions and non-bonding interactions. The SHAKE algorithm was utilized to constrain the hydrogen atoms to their equilibrium lengths. Conformational snapshots were recorded every 8 ps for further analysis.
2.11. Mutated recombinant IKKβ protein preparation
2.13. Binding free energy calculation
QuikChange II XL site-directed mutagenesis kit (Agilent Technologies) was used for mutagenesis. Human IKBKB plasmid (pcDNA-Ikkb-FLAG WT) was used as a template to generate mutations of the most key interaction sites between Sch A and IKKβ. The pcDNAIkkb-FLAG WT plasmid was purchased from Addgene (MA, USA); the following mutagenic oligonucleotide primers were used: 5′-AGAGGTT AATACACAAAATTGCTGACCTAGGATATGCCAAGGA-3’ (sense) 5′-TCC TTGGCATATCCTAGGTCAGCAATTTTGTGTATTAACCTCT-3’ (antisense) According to the kit Instruction Manual, the desired mutant plasmid was obtained. Then it was cloned into a pET30a vector containing an Nterminal histidine (His)-tag and thrombin cleavage site. The
Binding free energy and energy decomposition were estimated using molecular mechanics/generalized Born surface area (MM/GBSA) method as implemented in Amber 16 program. All the water molecules and sodium ions were stripped, the Generalized-Boltzmann (GB) model of 2 (igb = 2) was applied (Miller et al., 2012), and 200 snapshots were extracted from the last 40 ns MD trajectory for the binding free energy calculations. The total binding free energy (ΔGbind) was calculated as the following equations:
SPR experiments were performed on a ProteOn XPR36 Protein Interaction Array system (Bio-Rad Laboratories, Hercules, CA, USA). Briefly, IKKβ solution in PBST (5 mM, pH 7.4) at a concentration of 1 mg/ml was diluted to 30 μg/ml with sodium acetate buffer (pH 4.5). The chip was activated with EDC/NHS (10 μl/min for 600 s). Then, IKKβ was loaded (5 μl/min for 400 s) and immobilized covalently. Approximately 8000 RU of IKKβ was immobilized on the chip. Any excess of unbound IKKβ was removed by flowing PBS solution (5 mM, pH 7.4, with 5%, w/v, DMSO). Sch A was prepared as 20–100 μM solution in PBS solution (5 mM, pH 7.4, with 5%, w/v, DMSO), and injected (10 μl/min for 100 s). Five concentrations were injected simultaneously at a flow rate of 30 μM/min for 120 s of association phase, followed with 120 s of dissociation phase at 25 °C. The final graph was obtained by subtracting blank sensorgrams from the duplex or quadruplex sensorgrams. Data were analyzed by ProteOn manager software. 2.10. Construction of the initial structure of IKKβ/Sch A complex
12
ΔGbind = ΔGReceptor + Ligand – (ΔGReceptor + ΔGLigand),
(1)
ΔGbind = ΔEMM + ΔGsol − TΔS,
(2)
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
ΔEMM = ΔEint + ΔE vdW + ΔEelec ,
(3)
ΔGsol = ΔGGB + ΔGSA ,
(4)
ΔGSA = γSASA + β,
(5)
In equation (1), ΔGReceptor+Ligand, ΔGReceptor, and ΔGLigand represent the free energies of complex, receptor and ligand, respectively. In equation (2), ΔEMM, ΔGsol and TS represent the molecular mechanics (MM) interaction, solvation energy, and the conformational entropy at temperature T. In equation (3), the ΔEMM consists of inter-molecular interaction energy (ΔEint), van der Waals energy (ΔEvdW), electrostatic energy (ΔEelec). In equation (4), the solvation free energy is given as the sum of polar (ΔGGB) and nonpolar contributions (ΔGSA). Herein, the solute and solvent dielectric constants were set as 1 and 80, respectively. ΔGSA was estimated by using the solvent accessible surface area (SASA) model (equation (5)) using a water probe radius of 1.4 Å. The surface tension constant (γ), and the nonpolar free energy for the point solute (β) were set as 0.0072 and 0.92, respectively. 2.14. Gaussian accelerated molecular dynamics (GaMD) simulation The equilibrated structure extracted from conventional MD simulation trajectory was selected as the initial structure for the GaMD simulation. GaMD simulation provides the total potential boost, dihedral potential boost, and dual potential boost in order to accelerate the molecular simulations. In this study, the GaMD simulation was implemented using the dual potential boost. The boost parameters were calculated from an initial ∼4 ns NVT conventional MD simulation without boost potential. Then, the GaMD simulation were performed for ∼20 ns, in which the boost potential was updated every 400 ps to reach equilibrium values. Lastly, 300 ns of GaMD simulation was submitted in the NVT ensemble. During the GaMD simulations, the temperature was maintained by the Langevin temperature equilibration scheme (Loncharich et al., 1992). Particle Mesh Ewald (PME) algorithm and the non-bonded cutoff distance was set as 10.0 Å to calculate the long-range electrostatic interactions and non-bonding interactions. The SHAKE algorithm was utilized to constrain the hydrogen atoms to their equilibrium lengths. Conformational snapshots were recorded every 2 ps for further analysis. After GaMD simulation, the cumulant expansion to the second order was applied to compute the free energy map from the projection of the structures extracted from the GaMD simulations on the main components principal component 1 (PC1) and principal component 2 (PC2) by principal component analysis (PCA) via CPPTRAJ module in Amber 16 package (Miao et al., 2014).
Fig. 1. (A) The chemical structures of schisandrin, γ-schisandrin, schisandrin A, schisandrin B and schisandrin C. (B) Antiproliferative effects of Gefitinib (Gefi), schisandrin (Schin), γ-schisandrin (γ-Sch), schisandrin A (Sch A), schisandrin B (Sch B) and schisandrin C (Sch C) against HCC82 and HCC827/GR cells. Cells were treated with the indicated compounds at different concentrations (100, 50, 25, 12.5 and 6.25 μM) for 72 h. Subsequently, cell viability in each group was detected by MTS assay. The data were obtained from 3 independent experiments.
14.62 μM, Fig. 1B), suggesting that the Gefi-resistant cell model was valid. As shown in Fig. 1B, Sch B exhibited the most potent inhibitory effect on Gefi-sensitive HCC827 cells, whereas Sch A showed an optimal suppressive effect in the case of Gefi-resistant HCC827/GR cells. Next, we investigated the effects of Sch A when combined with Gefi. As shown in Fig. 2A, combined Gefi and Sch A treatment decreased the viability of HCC827/GR cells more effectively than Gefi or Sch A alone. Based on the survival curve, the IC50 value for the combined treatment was much lower than for Gefi-treatment alone (2.649 μM versus 14.62 μM). The sensitivity of HCC827/GR cells to Gefi increased approximately 5.5-fold when Sch A was added, compared with treatment with Gefi alone. In addition, colony-forming assays were carried out to evaluate the long-term suppressive effects of combined Gefi and Sch A treatment. After co-incubating with Gefi and Sch A for 24 h, cell colonies were stained with crystal violet and counted on the day 14. Compared with the single compound treatments (Gefi or Sch A), the combined treatment resulted in a much stronger inhibitory effect on cancer cell growth (Fig. 2B), a result which was highly consistent with the previously described MTS assay results. Besides, another important issue is whether SchA acts specifically in resistant models. Therefore, likewise, cell viability assay and colony-forming assays were carried out in gefitinibsensitive cells. The results (Fig. S1) showed that Sch A just faintly influenced the sensitivity of HCC827 to gefitinib.
2.15. Statistical analysis The results are presented as the mean ± standard error (S.D.). The statistics were performed using one-way ANOVA in GraphPad Pro (GraphPad, San Diego, CA, USA). P values < 0.05 were considered statistically significant. All the experiments were repeated a minimum of three times. All of the aforementioned experiments were repeated thrice. 3. Results 3.1. Sch A sensitizes NSCLC cells to Gefi The intriguing research findings of dibenzocyclooctadiene lignans on reversing multidrug resistance aroused our great interest to explore whether it also could impact on targeted therapies resistance. Thus, the anti-proliferative effects of five dibenzocyclooctadiene ligands (Fig. 1A) were tested by means of colorimetric MTS assays and using the Gefisensitive cell line HCC827 and the Gefi-resistant HCC827/GR cell line. The cytotoxic effect of Gefi on HCC827/GR cells was significantly lower than on HCC827 cells (the IC50 value increased from 0.558 μM to 13
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
activity was dose-dependent. To elucidate the mechanisms of Sch A-induced apoptosis-sensitization, we examined the expression levels of the apoptosis-related proteins Cleaved-caspase-3, Cleaved-PARP and Bcl-2 by Western blot. As shown in Fig. 4B, the expression of pro-apoptotic molecules (CleavedPARP and Cleaved-caspase-3) was significantly upregulated, while the anti-apoptotic protein Bcl-2 was downregulated.
3.4. Sch A binds directly to IKKβ and suppresses the IKKβ/NF-κB signaling pathway in HCC827/GR cells The transcription factor NF-κB and its associated regulatory factors IκB kinases (IKKs) are involved in the pathogenesis of a variety of hematologic and solid tumor malignancies (e.g., NSCLC, gastric cancer, breast tumors, and so on). Furthermore, recent studies have indicated that crosstalk between the EGFR- and NF-κB-activating cascades is closely linked to the development of resistance to EGFR inhibitors. Previously, Song et al. showed that Sch A suppressed the activation of the LPS-induced IKKβ-IκB-NF-κB signaling pathway in a dose-dependent manner. Therefore, we speculated that the sensitizing effects of Sch A could involve inhibition of the IKKβ/NF-κB signaling pathway. TNF-α, a well-known NF-κB activator, was used to activate the IKKβ/ NF-κB pathway. The phosphorylation and total protein expression levels of IKKβ and IκB were analyzed by Western blot. IKKβ and IκB are two important upstream modulators of the NF-κB signal transduction cascade. Because IκB phosphorylation is catalyzed by IKK in the canonical NF-κB signaling pathway and occurs downstream of IKK phosphorylation, we measured the phosphorylated levels of IKKβ and IκB at two different time points (0.5 h in the case of p-IKKβ, and 1 h in the case of p-IκB). This approach was described in the previously quoted study (Song et al., 2016). The results showed that Sch A dose-dependently decreased IKKβ and IκB phosphorylation levels following stimulation with TNF-α (10 ng/ml) for 0.5 h and 1 h, respectively (Fig. 5A). As a transcription factor, NF-κB must translocate to the nucleus to regulate target gene transcription. Thus, we also measured the subcellular localization of NF-κB p65 after treatment with Sch A. The levels of NF-κB p65 subunit in the nuclear and cytosolic fractions were quantified by Western blot. As shown in Fig. 5B, Sch A effectively blocked the nuclear translocation of NF-κB. Furthermore, considered the supposed role of NF-κB pathway in determining resistance to gefitinib, the levels of pIKKβ, p-IκBα and NF-κB in gefitinib-resistant cells treated with Sch A, gefitinib, or Sch A plus gefitinib was determined. The result (Fig. 5C and D) demonstrated that treatment of gefitinib enhanced the level of pIKKβ, p-IκBα and NF-κB nuclear translocation, while combined treatment of Sch A and gefitinib effectively downregulated IKKβ/NF-κB signaling activation induced by gefitinib. Based on these results, it indicated that the mechanism of the synergistic effect of Sch A might be through inhibition of IKKβ/NF-κB signal activation. To investigate whether Sch A bound directly to IKKβ, SPR experiments were performed. As shown in Fig. 5E, the SPR response value increased gradually with elevated Sch A concentrations and a low equilibrium dissociation constant (KD) of 37.3 μM was determined. The interaction between Sch B and IKKβ, as well as the binding capacity of Sch A to IKKα, was examined to rule out artificial effects in SPR. As shown in Fig. S2A, Sch B also binds to IKKβ with a slightly higher KD value than Sch A, which was consistent with the results of Zeng et al. that Sch B could inhibit LPS-induced IKKα/β activation and was supposed to bind IKKβ protein (Zeng et al., 2012). To identify whether Sch A selectively binds to IKKβ, the interaction between Sch A and IKKα was also determined. The results (Fig. S2B) demonstrated that the binding of Sch A to IKK alpha was greatly lower than IKKβ, suggesting Sch A might be a potential selective IKKβ inhibitor. Collectively, these results indicate that Sch A bound directly to IKKβ, preventing the phosphorylation of IKKβ and IκB, and the subsequent nuclear translocation of NF-κB.
Fig. 2. Sch A sensitized the anti-growth activity of Gefi against the Gefi-resistance cell line. (A) Effect of combination therapy with Gefi and SchA on viability of HCC827GR cells. Cell viability was determined by MTS assay. (B) Effect of combination therapy with Gefi and SchA on HCC827GR cells clone formation. Cells were exposed to each group for 24 h, and cultured in fresh medium for 14 days. Visualized colonies were photographed. The data were obtained from three independent experiments performed in triplicate, and the representative photos were shown.
3.2. Gefi and Sch A combination induces NSCLC cell cycle arrest To examine whether the sensitizing effect of Sch A involved cell cycle arrest, the proportion of cells in G0/G1, S, and G2/M phases were counted by flow cytometry. The results showed that combined treatment with Gefi and Sch A effectively arrested HCC827/GR cells at the G0/G1 phase, indicating that Sch A significantly elevated the cell cycle arresting capacity of Gefi (Fig. 3A). On the other hand, Western blot results demonstrated that Sch A enhanced the inhibitory effect of Gefi on the expression of cell cycle-related proteins (Cyclin D1, Cdk-4) in a synergistic manner (Fig. 3B). These results suggested that Sch A synergized with Gefi to arrest the cells in the G0/G1 phase by reducing the expression of cell cycle-related proteins. 3.3. Combined Gefi and Sch A treatment induces HCC827/GR cell apoptosis Double staining with annexin V-FITC and PI was performed to determine whether Sch A could enhance the pro-apoptotic effect of Gefi (Crowley et al., 2016). The flow cytometry results showed that when compared with single agent treatment, the early- and late-stage apoptosis rates of HCC827/GR cells were both greatly increased after combined treatment with Gefi and Sch A (Fig. 4A). Moreover, increasing the concentration of Sch A in the combined treatment group promoted cancer cell apoptosis to a greater extent, suggesting that its sensitizing 14
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 3. Combination treatment with Gefi and Sch A induced the cell cycle arrest of HCC827GR cells. (A) HCC827GR cells were treated with Sch A alone (20 or 40 μM), Gefi alone (15 μM) or treated with combination of Sch A (20 or 40 μM) and Gefi (15 μM) for 24 h, and the cell cycle distribution was analyzed by the cytometry (Becton Dickinson FACSCalibor, BD Biosciences, Franklin Lakes, NJ). And the representative histogram of the cell cycle distribution was shown. Data are presented as the mean ± S.D. of three independent experiments conducted in triplicate. “ns” means no significance; *P < 0.05; **P < 0.01. (B) Western blot analysis of cell cycle related protein Cyclin D1 and Cdk-4. Actin was shown as the control of equal loading.
atoms of IKKβ and the heavy atoms of Sch A were analyzed, as shown in Fig. 6A. This analysis indicated that the RMSD values of the backbone atoms of IKKβ showed only small fluctuations after 120 ns and those of Sch A were relative stable during the whole conventional MD simulation. In addition, conformational alignment of the initial structure with the last snapshots of the conventional MD simulation showed that these two structures were very similar, with only minor adjustments (Fig. 6B). These findings indicated that we had determined a relatively
3.5. Analysis of the Sch A binding site by molecular modeling Firstly, molecular docking experiments were conducted to predict the possible interaction between IKKβ and Sch A. 200 ns conventional MD simulations were performed to obtain the equilibrated and stable MD trajectories. To validate whether the studied systems reached equilibrium and to monitor the dynamic stability of the IKKβ/Sch A complex, the root mean square deviations (RMSDs) of all the backbone
Fig. 4. Combination treatment with Gefi and Sch A promoted HCC827GR cells apoptosis. (A) HCC827GR cells were treated with Sch A alone (20 or 40 μM), Gefi alone (15 μM) or treated with combination of Sch A (20 or 40 μM) and Gefi (15 μM) for 24 h, Apoptosis was assessed by Annexin V/propidium iodide (PI) staining. Data are presented as the mean ± S.D. of three independent experiments conducted in triplicate. “ns” means no significance; *P < 0.05; **P < 0.01. (B) The total protein was extracted and the expression of Bcl-2, Bax, Cleaved-caspase-3 and Cleaved-PARP was examined by Western blot. Actin was shown as the control of equal loading. 15
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 5. The effect of Sch A on IKK〈beta〉 pathway, and interaction between Sch A and IKKβ. (A) After HCC827GR cells were treated with TNF-α (10 ng/ml) with or without Sch A (5, 10, 20 and 40 μM) for 0.5 h (p-IKKβ assay) or 1 h (p-IκB assay), the phosphorylated and total IKKβ and IκB proteins were determined by Western blot. All data are shown as the mean ± S.D. from independent experiments performed in triplicate. (B) After HCC827GR cells were treated with TNF-α (10 ng/ml) with or without Sch A (20 and 40 μM) for 2 h, NF-κB p65 levels in the nucleus and cytoplasm were determined by Western blot. (C, D) HCC827GR cells were treated with Gefi alone (15 μM), Sch A alone (40 μM), or treated with combination of Gefi (15 μM) and Sch A (40 μM) for 24 h, the phosphorylated and total IKKβ and IκB proteins (C), and the NF-κB p65 levels in the nucleus and cytoplasm (D) were determined by Western blot. (E) Direct-binding affinity between Sch A and IKKβ was demonstrated by SPR. Kd: disocciation constant, Ka: association constant, KD: equilibrium dissociation constant.
conformational distributions of IKKβ were analyzed by principal component analysis (PCA) and plotted against time (Fig. 7C). As shown in Fig. 7C, the PCA results indicated that the conformational distributions of IKKβ were dynamic during the 0–240 ns simulation, and finally, stabilized during the 240–300 ns simulation. Finally, a free energy map was constructed to further illustrate the relationship between the conformational and energetic changes (Fig. 7D). As shown in Fig. 7D, four energetic deep wells were observed in the free energy map during the simulation time, indicating that the GaMD simulation could sample a much larger conformational space. Additionally, the energetic deep well observed during the simulation period of 240–300 ns suggested that the IKKβ/Sch A complex had achieved stability. Overall, combining the SPR results with the two simulation methods, we concluded that the detailed action mode of Sch A might be directly bind to IKKβ kinase domain through several key hydrophobic interactions. At last, to further verify our speculation, the binding affinity of Sch A and IKKβ mutant was detected by SPR. In the molecular dynamic simulations results, IKKβ Ile165 was identified as the most contributed residue of the interaction between Sch A and IKKβ. Thus, mutated IKKβ (I165A) plasmid was constructed and expressed under the induction of IPTG. After purified by nickel column, gradient imidazole elution and
reasonable binding conformation for our IKKβ/Sch A complex. Thereafter, the last 40 ns of the conventional MD simulation trajectory were used for the energetic analysis. To determine the role of individual residues in the protein-ligand recognition patterns, the binding free energies were decomposed by means of the MM/GBSA method. As illustrated in Fig. 6C and D, the most 10 contributed residues are Ile-165, Leu-21, Val-29, Val-152, Met96, Cys-99, Tyr-98, Gly-102, Gly-24, Val-74. The predominant residues were hydrophobic amino acids, indicating that nonpolar interactions may contribute to the binding of Sch A to IKKβ. To further validate the stability of the predicted conformation determined by conventional MD simulation, we conducted GaMD simulation, which is an enhanced MD simulation method that provides unconstrained enhanced sampling to speed up the conformational sampling process. As shown in Fig. 7A, the RMSD values of the backbone atoms of IKKβ quickly reached a steady state after 70 ns and those of Sch A were also relatively stable during the whole GaMD simulation, agreeing with the results obtained with conventional MD simulation. An overlay of the initial structure (yellow, Fig. 7B) with the last snapshot (light pink, Fig. 7B) showed that they were quite similar, with conformational adjustments within a certain range. Furthermore, the
16
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 6. Structural and energetic analysis of Sch A to the active binding site of IKKβ by conventional MD simulation. (A) RMSD curves for the 200 ns conventional MD simulation; (B) Alignment of the initial structure (yellow) and the last snapshot (green) of IKKβ/Sch A; (C) Most 10 contributed residues between IKKβ and Sch A; (D) Structural analysis of the most 10 contributed key residues of IKKβ to Sch A. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
et al., 2011). Moreover, it has been reported that NF-κB activation resulted in upregulated integrin αvβ3 expression, driving tumor stemness and resistance to EGFR inhibitors by interacting with KRAS (Seguin et al., 2014). All these studies indicated that combined therapy with an EGFR TKI and an inhibitor of NF-κB signaling could enhance the therapeutic effect in some EGFR-TKI-resistant NSCLC tumors. However, the high-toxicity of NF-κB inhibitors prohibits their potential clinical application. In recent years, the discovery of highly effective and relatively nontoxic anticancer compounds derived from natural products has transformed this field into a hotspot of cancer therapy research. Similarly, the search for agents derived from natural products which can effectively reverse tumor resistance is also receiving extensive attention. A good example is resveratrol, a natural polyphenol compound found in a large variety of plant species (in particular, mulberries, peanuts and grapes) which can both reverse the resistance of cancer cells to chemotherapeutic drugs (such as doxorubicin, gemcitabine and pemetrexed), and Gefi-resistance in NSCLC cells (Hu et al., 2014). Therefore, in this study we focused our attention on Sch A, another promising compound derived from a natural product, and investigated whether it could potentially counteract the intractable problem of Gefi resistance. Although there are already some studies on Sch A-mediated reversal of chemotherapeutic resistance (5-FU, doxorubicin), research on the effects of Sch A on Gefi-resistance or to other targeted therapies has not been conducted. In this study, we found that Sch A selectively enhanced the sensitivity to Gefi in resistant cells. Thus, adding Sch A strongly increased the anti-proliferative, cell cycle arrest and proapoptotic effects of Gefi in resistant cells. It is worth noting that the results we obtained with Sch A surpassed even our own initial predictions. The MTS and clone formation assays both demonstrated that Sch A significantly reversed Gefi-resistance. By means of Western blot and SPR assays, we found that Sch A was a potential IKKβ inhibitor. Through determination the influence of IKKβ/NF-κB signaling after Gefi
cleavage of His-tag by thrombin, targeted mutated IKKβ protein was obtained and used for SPR assay. As shown in Fig. 7E, Sch A indeed lost the binding with IKKβ protein. Therefore, this data further verified the direct interaction and the binding site between Sch A and IKKβ. 4. Discussion Gefi, the first anti-EGFR agent to enter clinical development and the first in its class to be approved for clinical use, is a well-tolerated treatment for advanced NSCLC. It is approved as first-line treatment for patients with metastatic NSCLC harboring activating EGFR mutations by US FDA in 2015. Unfortunately, the vast majority of patients with metastatic lung cancer who initially benefit from Gefi eventually develop resistance. During the past few years, different pathways involved in the development of resistance have been described. The EGFR second-site T790M mutation is considered the most common resistance mechanism to EGFR TKIs, and accounts for acquired EGFR TKI resistance in over 50% of NSCLC patients. The second-generation EGFR TKI afatinib and the third-generation anti-EGFR TKIs, such as osimertinib (AZD9291), and rocelitinib (CO-1686), were subsequently developed to covalently bind to EGFR Cys-797, but acquired resistance to these irreversible inhibitors inevitably appeared (Thress et al., 2015). In addition, a bypass signaling pathway (such as MET amplification, HER2 upregulation or constitutive NF-κB activities) was identified in patients with resistance to EGFR TKIs. As a crucial regulator of survival, apoptosis and migration of cancer cells, the IKKβ/NF-κB signaling pathway has not only been linked to the oncogenic potential of the EGFR, but is also involved in the acquisition of EGFR TKIs resistance. By applying an RNA interference-based screening approach, Bivona et al. found that knocking down several components of the NF-κB pathway specifically enhanced cell death induced by EGFR TKIs, and that genetic or pharmacologic inhibition of NF-κB enhanced erlotinib-induced apoptosis in erlotinib-resistant EGFR-mutant lung cancer cells (Bivona 17
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
Fig. 7. Structural and energetic analysis of Sch A to the active binding site of IKKβ by GaMD simulation. (A) RMSD curves for the 300 ns GaMD simulation; (B) Alignment of the initial structure (yellow) and the last snapshot (green) of IKKβ/Sch A; (C) PCA scatter plot of 150,000 snapshots from GaMD simulations along the first two principal components and plotted against time; (D) Free energy map calculated from the first two principal components. (E) The binding affinity of Sch A with IKKβ I165A mutant was determined by SPR assay. The indicated concentration of Sch A was also set as 20, 40, 60, 80, 100 μM. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
BMS345541 and ML-120B. A distinct structural feature of Sch A is that it has no nitrogen atoms. Thus, it would be difficult for it to form hydrogen bonds with its target. Our molecular docking and simulation analyses showed that IKKβ inhibition by Sch A depended mainly on hydrophobic interactions with the hydrophobic cavity of IKKβ, a result that was highly consistent with the molecular characteristics of Sch A and the IKKβ mutants SPR assay results. Overcoming the low water solubility of Sch A by modifying its structure to improve its druggability has always been one of the most important problems in Sch A research. According to our molecular modeling results, introducing a long-chain hydrophilic moiety in the aromatic ring oriented away from the ATP pocket may effectively increase its water solubility, as well as its inhibitory activity. Moreover, it can be observed that the adjacent Cys-99 residue is located near the octane ring of Sch A, suggesting that adding a Michael receptor at this position may greatly enhance its binding
treatment alone or combination treatment, we further confirmed the synergistic effect of Sch A was mediated by inhibition of IKKβ/NF-κB pathway. Based on the crosstalk that exists between the EGFR and IKKβ/NF-κB signaling cascades and which has been linked to the development of EGFR TKIs resistance, and to their important roles sustaining proliferation and survival, and preventing apoptosis, we believe that combined treatment with Gefi and Sch A may exert a potent lethal effect on Gefi-resistant NSCLC cells. Another important contribution of this study was the utilization of molecular docking and dynamic simulations to analyze the underlying mode of interaction and the key binding site between Sch A and its target IKKβ. Previous studies on the identification of IKKβ inhibitors were mostly based on measurement of their biological activity (IKKβ inhibition) without understanding the mechanisms. This is quite different from the newer and well-known IKKβ inhibitors, such as TPCA-1, 18
European Journal of Pharmacology 855 (2019) 10–19
H. Xian, et al.
affinity towards IKKβ.
Ares, L., 2017. Lung cancer: current therapies and new targeted treatments. Lancet 389, 299–311. Hrustanovic, G., Lee, B.J., Bivona, T.G., 2013. Mechanisms of resistance to EGFR targeted therapies. Cancer Biol. Ther. 14, 304–314. Hu, Y., Li, C., Li, H., Li, M., Shu, X., 2014. Resveratrol-mediated reversal of tumor multidrug resistance. Curr. Drug Metabol. 15, 703–710. Kazandjian, D., Blumenthal, G.M., Yuan, W., He, K., Keegan, P., Pazdur, R., 2016. FDA approval of gefitinib for the treatment of patients with metastatic EGFR mutationpositive non-small cell lung cancer. Clin. Cancer Res. 22, 1307–1312. Kong, D., Zhang, D., Chu, X., Wang, J., 2018. Schizandrin A enhances chemosensitivity of colon carcinoma cells to 5-fluorouracil through up-regulation of miR-195. Biomed. Pharmacother. 99, 176–183. Lee, H.J., Zhuang, G., Cao, Y., Du, P., Kim, H.J., Settleman, J., 2014. Drug resistance via feedback activation of Stat3 in oncogene-addicted cancer cells. Cancer Cell 26, 207–221. Lee, K., Ahn, J.H., Lee, K.T., Jang, D.S., Choi, J.H., 2018. Deoxyschizandrin, isolated from Schisandra berries, induces cell cycle arrest in ovarian cancer cells and inhibits the protumoural activation of tumour-associated macrophages. Nutrients 10. Liu, S., Misquitta, Y.R., Olland, A., Johnson, M.A., Kelleher, K.S., Kriz, R., Lin, L.L., Stahl, M., Mosyak, L., 2013. Crystal structure of a human IkappaB kinase beta asymmetric dimer. J. Biol. Chem. 288, 22758–22767. Loncharich, R.J., Brooks, B.R., Pastor, R.W., 1992. Langevin dynamics of peptides: the frictional dependence of isomerization rates of N-acetylalanyl-N'-methylamide. Biopolymers 32, 523–535. Miao, Y.L., Sinko, W., Pierce, L., Bucher, D., Walker, R.C., McCammon, J.A., 2014. Improved reweighting of accelerated molecular dynamics simulations for free energy calculation. J. Chem. Theory Comput. 10, 2677–2689. Miller, B.R., McGee, T.D., Swails, J.M., Homeyer, N., Gohlke, H., Roitberg, A.E., 2012. MMPBSA.py: an efficient program for end-state free energy calculations. J. Chem. Theory Comput. 8, 3314–3321. Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J., 2009. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791. Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C., Ferrin, T.E., 2004. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612. Seguin, L., Kato, S., Franovic, A., Camargo, M.F., Lesperance, J., Elliott, K.C., Yebra, M., Mielgo, A., Lowy, A.M., Husain, H., Cascone, T., Diao, L., Wang, J., Wistuba II, , Heymach, J.V., Lippman, S.M., Desgrosellier, J.S., Anand, S., Weis, S.M., Cheresh, D.A., 2014. An integrin beta(3)-KRAS-RalB complex drives tumour stemness and resistance to EGFR inhibition. Nat. Cell Biol. 16, 457–468. Shostak, K., Chariot, A., 2015. EGFR and NF-kappaB: partners in cancer. Trends Mol. Med. 21, 385–393. Solomon, B.J., Kim, D.W., Wu, Y.L., Nakagawa, K., Mekhail, T., Felip, E., Cappuzzo, F., Paolini, J., Usari, T., Tang, Y., Wilner, K.D., Blackhall, F., Mok, T.S., 2018. Final overall survival analysis from a study comparing first-line Crizotinib versus chemotherapy in ALK-mutation-positive non-small-cell lung cancer. J. Clin. Oncol. 36, 2251–2258. Song, F., Zeng, K., Liao, L., Yu, Q., Tu, P., Wang, X., 2016. Schizandrin a inhibits microglia-mediated neuroninflammation through inhibiting TRAF6-NF-kappab and jak2-stat3 signaling pathways. PLoS One 11, e0149991. Szopa, A., Ekiert, R., Ekiert, H., 2017. Current knowledge of Schisandra chinensis (Turcz.) Baill. (Chinese magnolia vine) as a medicinal plant species: a review on the bioactive components, pharmacological properties, analytical and biotechnological studies. Phytochemistry Rev. 16, 195–218. Takezawa, K., Pirazzoli, V., Arcila, M.E., Nebhan, C.A., Song, X., de Stanchina, E., Ohashi, K., Janjigian, Y.Y., Spitzler, P.J., Melnick, M.A., Riely, G.J., Kris, M.G., Miller, V.A., Ladanyi, M., Politi, K., Pao, W., 2012. HER2 amplification: a potential mechanism of acquired resistance to EGFR inhibition in EGFR-mutant lung cancers that lack the second-site EGFRT790M mutation. Cancer Discov. 2, 922–933. Tan, W.L., Jain, A., Takano, A., Newell, E.W., Iyer, N.G., Lim, W.T., Tan, E.H., Zhai, W., Hillmer, A.M., Tam, W.L., Tan, D.S.W., 2016. Novel therapeutic targets on the horizon for lung cancer. Lancet Oncol. 17, e347–e362. Thress, K.S., Paweletz, C.P., Felip, E., Cho, B.C., Stetson, D., Dougherty, B., Lai, Z., Markovets, A., Vivancos, A., Kuang, Y., Ercan, D., Matthews, S.E., Cantarini, M., Barrett, J.C., Janne, P.A., Oxnard, G.R., 2015. Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M. Nat. Med. 21, 560–562. Yun, C.H., Mengwasser, K.E., Toms, A.V., Woo, M.S., Greulich, H., Wong, K.K., Meyerson, M., Eck, M.J., 2008. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc. Natl. Acad. Sci. U. S. A. 105, 2070–2075. Zeng, K.W., Zhang, T., Fu, H., Liu, G.X., Wang, X.M., 2012. Schisandrin B exerts antineuroinflammatory activity by inhibiting the Toll-like receptor 4-dependent MyD88/ IKK/NF-kappaB signaling pathway in lipopolysaccharide-induced microglia. Eur. J. Pharmacol. 692, 29–37. Zhang, Z.L., Jiang, Q.C., Wang, S.R., 2018. Schisandrin A reverses doxorubicin-resistant human breast cancer cell line by the inhibition of P65 and Stat3 phosphorylation. Breast Canc. 25, 233–242.
5. Conclusion In summary, Gefi and Sch A in combination acted synergistically to inhibit proliferation, arrest the cell cycle and promote apoptosis of Gefiresistant NSCLC cells. Mechanistically, our results demonstrated that Sch A could bind directly to IKKβ, preventing the gefitinib induced phosphorylation of IKKβ and of the downstream molecule IκB, and reducing the nuclear translocation of NF-κB p65. Finally, molecular docking and molecular simulations of the Sch A/IKKβ complex revealed that stable binding between these two molecules depended on hydrophobic interactions. Author contributions H.X. and W.F. designed and performed the research. J.Z., wrote and revised the manuscript; All authors read and revised the final manuscript. Acknowledgments This study was supported by grants from the Special fund project for technology innovation of Foshan City (No. 2014AG10003). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ejphar.2019.04.016. Conflicts of interest The authors declare no conflict of interest. References Berendsen, H.J.C., Postma, J.P.M., Vangunsteren, W.F., Dinola, A., Haak, J.R., 1984. Molecular-dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690. Bivona, T.G., Hieronymus, H., Parker, J., Chang, K., Taron, M., Rosell, R., Moonsamy, P., Dahlman, K., Miller, V.A., Costa, C., Hannon, G., Sawyers, C.L., 2011. FAS and NFkappaB signalling modulate dependence of lung cancers on mutant EGFR. Nature 471, 523–526. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A., 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca - Cancer J. Clin. 68, 394–424. Chen, Z., Fillmore, C.M., Hammerman, P.S., Kim, C.F., Wong, K.K., 2014. Non-small-cell lung cancers: a heterogeneous set of diseases. Nat. Rev. Canc. 14, 535–546. Chong, C.R., Janne, P.A., 2013. The quest to overcome resistance to EGFR-targeted therapies in cancer. Nat. Med. 19, 1389–1400. Crowley, L.C., Marfell, B.J., Scott, A.P., Waterhouse, N.J., 2016. Quantitation of apoptosis and Necrosis by annexin V binding, propidium iodide uptake, and flow cytometry. Cold Spring Harb. Protoc. 11. Engelman, J.A., Zejnullahu, K., Mitsudomi, T., Song, Y., Hyland, C., Park, J.O., Lindeman, N., Gale, C.M., Zhao, X., Christensen, J., Kosaka, T., Holmes, A.J., Rogers, A.M., Cappuzzo, F., Mok, T., Lee, C., Johnson, B.E., Cantley, L.C., Janne, P.A., 2007. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science 316, 1039–1043. Ercan, D., Xu, C., Yanagita, M., Monast, C.S., Pratilas, C.A., Montero, J., Butaney, M., Shimamura, T., Sholl, L., Ivanova, E.V., Tadi, M., Rogers, A., Repellin, C., Capelletti, M., Maertens, O., Goetz, E.M., Letai, A., Garraway, L.A., Lazzara, M.J., Rosen, N., Gray, N.S., Wong, K.K., Janne, P.A., 2012. Reactivation of ERK signaling causes resistance to EGFR kinase inhibitors. Cancer Discov. 2, 934–947. Gottlich, C., Muller, L.C., Kunz, M., Schmitt, F., Walles, H., Walles, T., Dandekar, T., Dandekar, G., Nietzer, S.L., 2016. A combined 3D tissue engineered in vitro/in silico lung tumor model for predicting drug effectiveness in specific mutational backgrounds. JoVE, e53885. Hirsch, F.R., Scagliotti, G.V., Mulshine, J.L., Kwon, R., Curran Jr., W.J., Wu, Y.L., Paz-
19