Cytotoxicity of the bisphenolic honokiol from Magnolia officinalis against multiple drug-resistant tumor cells as determined by pharmacogenomics and molecular docking.
A main problem in oncology is the development of drug-resistance. Some plant-derived lignans are established in cancer therapy, eg. the semisynthetic epipodophyllotoxins etoposide and teniposide. Their activity is, unfortunately, hampered by the ATP-binding cassette (ABC) efflux transporter, P-glycoprotein. Here, we investigated the bisphenolic honokiol derived from Magnolia officinalis. P-glycoprotein- overexpressing CEM/ADR5000 cells were not cross-resistant to honokiol, but MDA-MB-231 BRCP cells transfected with another ABC-transporter, BCRP, revealed 3-fold resistance. Further drug resistance mechanisms analyzed study was the tumor suppressor TP53 and the epidermal growth factor receptor (EGFR). HCT116 [p53.sup.-/-] did not reveal resistance to honokiol, and EGFR-transfected U87.MG EGFR cells were collateral sensitive compared to wild-type cells (degree of resistance: 0.34). To gain insight into possible modes of collateral sensitivity, we performed in silico molecular docking studies of honokiol to EGFR and EGFR-related downstream signal proteins. Honokiol bound with comparable binding energies to EGFR (-7.30 [+ or -]0.01 kcal/mol) as the control drugs erlotinib (-7.50[+ or -] 0.30 kcal/mol) and gefitinib (-8.30[+ or -]0.10 kcal/mol). Similar binding affinities of AKT, MEK1, MEK2, STAT3 and mTOR were calculated for honokiol (range from -9.0 [+ or -]0.01 to 7.40 [+ or -]0.01 kcal/mol) compared to corresponding control inhibitor compounds for these signal transducers. This indicates that collateral sensitivity of EGFR-transfectant cells towards honokiol may be due to binding to EGFR and downstream signal transducers. COMPARE and hierarchical cluster analyses of microarray-based transcriptomic mRNA expression data of 59 tumor cell lines revealed a specific gene expression profile predicting sensitivity or resistance towards honokiol.
Magnolia officinalis Honokiol
Despite the break-taking progresses in cancer biology during the past decades, the cure from the disease is still not reality for the majority of the patients. A main reason for this nonsatisfactory situation is the development of drug-resistance, which ultimately leads to the failure of chemotherapy and fatal outcome for patients. Unfortunately, drug resistance occurs not only with long-established cytotoxic drugs, but also with the more recent small molecules and therapeutic antibodies, which are directed against specific targets in tumor cells. Hence, the competition to identify and develop novel drugs goes on. Drug resistance is frequently multifactorial and several mechanisms account for unresponsiveness of tumors towards drugs. The multiplicity of mechanisms can be categorized as acting up- or down-stream of the actual drug target or at the target site itself (Efferth and Grassmann 2000; Efferth and Volm, 2005).
ATP-binding cassette (ABC) transporter P-glycoprotein (MDR1/ABCB1) or the breast cancer resistance protein (BCRP/ABCG2), which are well-known proteins playing role in upstream mechanisms, extrude multiple drugs out of cancer cells leading to multidrug-resistance phenomena (Efferth, 2001; Gillet et al., 2007). Proteins playing role in downstream drug resistance mechanism are for instance tumor suppressors and oncoproteins. After damage of target molecules (DNA, microtubules, DNA topoisomerases etc.), they regulate DNA repair, cell cycle arrest, proliferation and apoptosis and thereby ultimately influence the fate of tumor cells on death or survival, even if the actual treatment targets are damaged.
The dimension of multifactorial drug resistance can be illustrated with the epipodophyllotoxins etoposide and teniposide. They are semisynthetic derivatives of podophyllotoxin, a lignan derived from American mandrake (Podophyllum peltatum L). This herb has been traditionally used by the First Nations in North America to treat warts. Epipodophyllotoxins are well-established in clinical oncology since decades. Their activity is however hampered by P-glycoprotein, which pumps etoposide and teniposide out of tumor cells leading to drug resistance (Lum et al., 1993; Efferth et al., 2008). Thereby, insufficient concentration of drug molecules can be achieved at the actual target of epipodophyllotoxins, DNA topoisomerase II and subsequently cells survive. On the other hand, mutations in the tumor suppressor TP53 gene, which is a regulator of apoptosis, lead to loss of function and cell death will not be triggered by p53, even if etoposide successfully targets DNA topoisomerase II with lethal damage in tumor cells (Blandino et al., 1999). Thus, inhibition of apoptosis to drug resistance (el-Deiry, 2003). In addition to tumor suppressor genes, activation of oncogenes by mutation or over-expression also affects apoptosis and causes drug resistance (el-Deiry, 1997)
Fortunately, lignans are not inherently prone to resistance by these mechanisms. Recently, we described another lignan, (-)-sesamin, whose cytotoxicity is not curtailed by both P-glycoprotein and its close relative, ABCB5 (Saeed et al., 2014). As a part of our ongoing studies, we now investigated the bisphenolic honokiol. This compound can be found in Magnolia officinalis (Rehder & Wilson) and other species of this genus and is traditionally used in Chinese and Japanese medicine. Recent studies in vitro and in vivo reported various pharmacological activities of honokiol, e.g. anti-arrhythmic, anti-inflammatory, anti-thrombotic, anxiolytic, antimicrobial and other activities (Arora et al., 2012). Interestingly, honokiol inhibited growth of xenograft transplants derived from various tumor types in nude mice (Chen et al., 2004, 2010; Singh et al., 2013).
In the present study, we investigated whether P-glycoprotein, BCRP, TP53 and the oncogenic epidermal growth factor receptor (EGFR) as classical mechanisms of drug resistance play a role in the cellular response to honokiol. To gain deeper insight into cellular and molecular modes of action of honokiol, we performed in silico molecular docking studies of honokiol to EGFR and EGFR-related downstream signal proteins as well as bioinformatical COMPARE and hierarchical cluster analyses of microarray-based transcriptomic mRNA expression data of 60 tumor cell lines of the National Cancer Institute, USA (http://dtp.nci.nih.gov).
Material and methods
Leukemic CCRF-CEM cells were cultured as previously described (Efferth et al., 2003a). Drug resistance of Pglycoprotein/MDR1/ABCB1-overexpressing CEM/ADR5000 cells was maintained in 5000 ng/ml doxorubicin (Kimmig et al. 1990). Breast cancer cells transduced with a control vector (MDA-MB-231 -pcDNA3) or with cDNA for the breast cancer resistance protein BCRP/ABCC2 (MDA-MB-231-BCRP clone 23) were generated and maintained as reported (Doyle et al. 1998). The mRNA expression of MDRl and BCRP in the resistant cell lines has been reported (Efferth et al. 2003b; Gillet et al. 2004).
Human wild-type HCT116 colon cancer cells ([p53.sup.+/+]) as well as knockout clones ([p53.sup.-/-]) derived by homologous recombination (Bunz et al. 1998) were a generous gift from Dr. B. Vogelstein and H. Hermeking (Howard Hughes Medical Institute, Baltimore, MD) and cultured as described (Bunz et al. 1998).
Human glioblastoma multiform U87.MG cells transduced with an expression vector harboring an epidermal growth factor receptor (EGFR) gene with a deletion of exons 2 through 7 (U87.MG. AEGFR) has been reported previously (Huang et al. 1997). Transduced and non-transduced cell lines were kindly provided by Dr. W.K. Cavenee (Ludwig Institute for Cancer Research, San Diego, CA). Human HepG2 hepatocellular carcinoma cells and AML12 normal heptocytes were obtained from the American Type Cell Culture Collection (ATCC, USA).
The panel of 59 human tumor cell lines of the Developmental Therapeutics Program of the National Cancer Institute (NCI, USA) consisted of leukemia, melanoma, non-small cell lung cancer, colon cancer, renal cancer, ovarian cancer, breast cancer, and prostate carcinoma cells as well as tumor cells of the central nervous system (Alley et al. 1998). Cells were assayed by means of a sulforhodamine B assay (Rubinstein et al. 1990).
Resazurin cell growth inhibition assay
The resazurin (Promega, Mannheim, Germany) reduction assay (O'Brien et al. 2000) was used to assess the cytotoxicity as previously described (Kuete and Efferth, 2013). Each assay was conducted at least three times, with two replicates each. Cell viability was evaluated based on a comparison with untreated cells. IC50 values were determined as concentrations required to inhibit 50% of cell proliferation and were calculated from a calibration curve by linear regression using Microsoft Excel.
mRNA microarray data of the NCI tumor cell line panel are available (Scherf et al. 2000; Staunton et al. 2001) through the NCI website (http://dtp.nci.nih.gov). For hierarchical cluster analysis, objects were classified into dendrograms by calculating distance according to the closeness of between-individual distances by means of the Ward method (WinSTAT program, Kalmia, Cambridge, MA, USA). Cluster models have previously been validated for gene expression profiling and for approaching molecular pharmacology of cancer (Efferth et al. 1997; Scherf et al. 2000). The application of this method for pharmacogenomics of phytochemicals has been described in detail (Efferth et al., 2011).
COMPARE analyses were performed to produce rank-ordered lists of genes expressed in the NCI cell lines as previously described (Pauli et al. 1989; Wosikowski et al., 1997). Briefly, every gene of the NCI microarray database was ranked for similarity of its mRNA expression to the log10IC5o values for honokiol.To derive COMPARE rankings, a scale index of correlation coefficients (R-values) was created.
Pearson's correlation test was used to calculate significance values and rank correlation coefficients as a relative measure of the linear dependency of two variables (WinSTAT, Kalmia). The chi-squared test was applied to bivariate frequency distributions of pairs of nominal scaled variables (WinSTAT, Kalmia). It was used to calculate significance values (P-values) and rank correlation coefficients (R-values) as a relative measure of the linear dependency of two variables.
Molecular docking is a robust predictive method to evaluate the interaction energy and geometry of ligands with target proteins. The protocol for molecular docking was previously reported by us (Zeino et al., 2013). X-ray crystallography-based structures of proteins that involved in EGFR downstream signaling cascades were obtained from Protein Data Bank (http://www.rcsb.org/pdb); EGFR (PDB ID:), ART (PDB ID: 3E87), mTOR (PDP ID: 4JSP), MEK1 and MEK2 (PDB IDs: and respectively) and homology-modelled structure of human STAT3 created by us from murine crystal structure (PDB ID:) were set as the rigid receptor molecule. Human and murine STAT3 protein sequences were aligned with EMBOSS Needle (http://www.ebi.ac.uk/Tools/psa/embossneedle/), then the aligned region of human STAT3 was used to create homology models with MODELLER 9.11 (Fiser and Sali, 2003; Venkatachalam et al., 2003) by using mouse STAT3 (PDB ID:) as template structure. Two water molecules in close proximity to Lys340 were kept within the murine STAT3 structure since they were described to play critical role in interaction with DNA (Becker et al., 1998). The best homology model to be used for molecular docking studies was determined with Swiss-MODEL structure assessment tool (http://swissmodel.expasy.org/). For each protein, known inhibitor/s has/have been selected as standard, Erlotinib and Gefitinib for EGFR (Park et al., 2012), GSK690293 for AKT (Rouse et al., 2009), Sirolimus for mTOR (Yang et al., 2013), PD184352 for both MAPKKs (English and Cobb, 2002) and NSC74859 for STAT3 (Siddiquee et al., 2007) to compare their binding affinity and geometry with honokiol. A grid box was then constructed to define docking spaces in each protein according to its pharmacophores. Docking parameters were set to 250 runs and 2,500,000 energy evaluations for each cycle. Docking was performed three times independently by Autodock4 and with AutodockTools1.5.7rcl (Morris et al., 2009) using the Lamarckian Algorithm. The corresponding lowest binding energies and predicted inhibition constants were obtained from the docking log files (dig). Mean [+ or -] SD of binding energies were calculated from three independent docking. Visual Molecular Dynamics (VMD) was used to depict the docking poses of honokiol and the inhibitors for each target protein.
Response of drug-resistant tumor cell lines towards honokiol
We first analyzed honokiol in multidrug-resistant Pglycoprotein (MDR1/ABCBI)-overexpressing CEM/ADR5000 cells and drug-sensitive parental CCRF-CEM cells using a resazurin assay. The degree of resistance of CEM/ADR5000 cells was calculated by dividing the [IC.sub.50] value of this cell line by the [IC.sub.50] value of the parental CCRF-CEM cells. None or only a weak cross-resistance of the CEM/ADR5000 cells was obtained (1.59-fold, Table 1).
As a second cell model for multidrug resistance, we tested MDA-MB-231 cells transfected with BCRP/ABCC2 and compared them with cells transfected with pcDNA control vector. The BCRP-transfectants were 3.0-fold more resistant to honokiol than the mock vector-transfected cells (Table 1).
To mimic loss of functional in the TP53 gene, we used knockout HCT116 (p53/) cells and compared their response to wild-type HCT116 ([p53.sup.+/+]) cells. As shown in Table 1, the TP53-knockout cells were slightly more sensitive to honokiol than the TP53 wild-type cells (degree of resistance: 0.84).
Interestingly, U87.MG cells transfected with a deletion-activated EGFR cDNA were considerably more sensitive to honokiol than their wild-type counterpart. We found a degree of resistance of 0.38 (Tablet).
Normal AML10 hepatocytes were more resistant to honokiol than HepG2 hepatocellular carcinoma cells, indicating that the cytotoxic effects of honokiol may display tumor specificity at least to some extent (Table 1).
Tumor-type dependent response towards honokiol
If the average [log.sub.10] [IC.sub.50] values over the entire range of 59 cell lines were diversified regarding their tumor types, colon cancer cell lines were most resistant towards honokiol, whereas leukemia cell lines were most sensitive (Fig. 1).
COMPARE and hierarchical cluster analyses of mRNA microarray data
We studied the transcriptome-wide RNA expression by COMPARE analyses and mined the database of the NCI by correlating the mRNA expression data with the [log.sub.10] [IC.sub.50] values for honokiol. This is a hypothesis-generating bioinformatical approach allowing to find novel putative molecular determinants of cellular response to honokiol. The scale rankings of genes obtained by COMPARE computation were subjected to Pearson's rank correlation tests. The thresholds for correlation coefficients were R> 0.50 for direct correlations and R [left arrow] 0.50 for inverse correlations. The genes fulfilling these criteria are shown in Table 2. As can be expected, these genes can be assigned to different functional groups such as cell death regulators (CASP2, ING2, MDM4, NAIP), transcriptional and translational regulation (DEPDC1, GABPB1, LHFPL2, NFIB, P0LR3C, RPL34, RPS3A, RPS21, RPS25, TFDP2, TRIM24, ZBTB1, ZBTB38, ZFP112), oxidative stress response (SPATS2L, GSTT2B, NQO1), DNA maintenance and processing (BAHCC1, FANCA, H1ST1H3G, IK, KDM4C, MCM7, PRB3, RNASEH2B, SNRPE, TFDP2), blood coagulation (FGA, MATR3, PROCR, P1K3CG), signal transduction (ANXA2, ARHGAP19, C7orf47, CCDC50, DTX3, FHL2, P1K3CG, RALB, T1CAM2), cytoskeletal components (BCL7A, DYNC1LI2, SEPT10, SEPT11), transport functions (ABCC1, FXYD2, S100A6, SCNN1G, XP05), or others (ADAM22, ALDH3A2, FAM161A, HDDC2, HLA-F).
As a next step, the genes identified by COMPARE analyses and Pearson's rank correlation tests were subjected to hierarchical cluster analyses. Only the mRNA expression data, but not the [log.sub.10][IC.sub.50] values of honokiol for the 59 cell lines were included into the cluster analysis. Three main cluster branches appeared (Fig. 2). The first cluster contained 30 cell lines, the second cluster 23 and the third cluster 6 tumor lines. The distribution of cell lines being either sensitive or resistance to honokiol was significantly different between the three clusters As the [log.sub.10] [IC.sub.50] values of honokiol were not included into the cluster analysis, we now analyzed whether or not the obtained gene expression profile predicted sensitivity or resistance of cell lines to honokiol. Indeed, cluster 1 contained significantly more resistant cell lines than cluster 3, while cluster 2 was of an intermediate type (Table 3), indicating that the expression of these genes caused dendrogram branching in a way that honokiol-sensitive and -resistant cell lines were grouped clustered in different clusters.
To have a closer insight into the phenomenon of hypersensitivity of U87.MG cells transfected with a deletion-activated EGFR cDNA to honokiol, we investigated in silico the interaction of honokiol with proteins that are involved in EGFR downstream signaling cascades. For docking, we have chosen the active site for each protein in which phosphorylation and thereafter signaling cascade activation takes place. Honokiol bound preferentially at the same binding sites of known inhibitors in the kinase domains for EGFR and AKT, in the DNA binding domain for STAT3, and in the non-competitive binding pocket for homologous mitogen-activated protein kinases (MEK1 and MEK2) with slight differences in binding affinities, whereas in mTOR, honokiol docked at the substrate binding site and mTOR's inhibitor sirolimus docked on its active site FKBP12-rapamycin binding (FRB) domain, indicating that honokiol may indeed inhibit both mTOR complexes (mTORC1 and mTORC2) by competing with ATP at substrate binding site. The results are illustrated in Table 4. Docking positions of honokiol and known inhibitors into binding pockets of different macromolecules were depicted in Fig. 3.
The central topic of the present investigation was to analyze the possibilities to treat otherwise drug-resistant tumor cells with honokiol. Because of tumor resistance to a broad spectrum of anticancer drugs (Efferth et al., 2008), there is an urgent requirement for the development of novel drugs with improved features.
Out of the 48 ABC-transporter gens in the human genome, MDR1/ABCB1 and BCRP/ABCG2 have been extensively investigated in human tumors and frequently found to be correlated to response to chemotherapy and survival times of patients (Tamaki et al., 2011). For this reason, we have investigated the cross-resistance of MDR1- or BCRP-overexpressing tumor cells to honokiol. It turned out that there was a low, but clearly visible cross-resistance to honokiol (3-fold) in MDA-MB-231 BCRP cells as compared to MDA-MB-231 pcDNA control cells. Although the degree of cross-resistance of BCRP-transfectants was only 3, it has to be taken into mind that patients may frequently die rather from tumors with low degrees of drug resistant than from tumors with higher degrees of drug resistance (Belvedere and Dolfini, 1993). Thus, a 3-fold cross-resistance to honokiol may indicate that BCRP-expressing tumors may not properly respond to honokiol, if this compound will be applied in the clinic.
On the other hand, there was no cross-resistance of MDR1/ABCB1-expressing CEM/ADR5000 cells to honokiol. This is a remarkable result, since CEM/ADR5000 cells are highly resistant to anthracyclines, Vinca alkaloids, taxanes and other anticancer drugs (Efferth et al., 2008). Our results can be taken as a clue that honokiol may be used to treat MDR1-expressing tumors with good chances to kill refractory tumors. Our result that P-glycoprotein did not affect the cytotoxicity of honokiol in CEM/ADR5000 cells is corroborated by data of Lin et al. (2012) showing that honokiol traversed the blood-brain barrier in vivo, because P-glycoprotein is a major mechanism of the blood-brain barrier.
There is a long-lasting debate in the literature about the clinical relevance for chemo- and radioresistance of functional inactivation of the tumor suppressor TP53 (el-Deiry, 2003). Wild-type TP53 has been regarded as the guardian of the genome (Lane, 1992), because this protein maintains cellular integrity upon detrimental damage by xenobiotic, carcinogenic substances, including anticancer drugs. Lesions in DNA and other cellular structures are recognized by TP53 leading to cell cycle arrest and DNA repair. If damage exceeds the cellular repair capability, TP53 may sense the induction of apoptosis. Both DNA repair and apoptosis ultimately maintain organismic health, because damaged cells will be removed from the body. TP53 mutations lead to loss of these functions causing carcinogenesis, because unrepaired lesions may be transduced to subsequent cell generations during cell division, a process where drug-resistant TP53-mutated tumors may still be sensitive to honokiol. This may have important implications for a clinical use of honokiol as cancer drug. This result warrants more detailed investigation in the future.
The epidermal growth factor receptor (EGFR) represents an important oncogene that transduces not only growth signals in cancer cells, but also triggers tumor progression in term of invasion and metastasis (Baselga, 2002; Lui and Grandis, 2002). ECFR-overexpression is a well-known worse prognostic factor, which is associated with unfavorable response to chemotherapy and short survival times of cancer patients. The development of small molecule inhibitors (e.g. erlotinib and gefitinib) or therapeutic antibodies (e.g. cetuximab) specifically targeting EGFR in tumor cells and leaving normal tissues without EGFR-overexpression untouched was a major improvement in modern cancer pharmacology. Unfortunately, specific point mutations in the EGFR gene confer resistance to EGFR-targeting drugs and novel treatment solutions are required as well. It was an unexpected, but pleasing result that U87-MG.[DELTA]EGFR cells were more sensitive to honokiol than non-transfectant U87.MG wild-type cells. The phenomenon of hypersensitivity (=collateral sensitivity) is known from MDR1/ABCB1-expressing multidrug-resistant cells, and it has been suggested that multidrug-resistant tumor cells could be treated with compounds provoking collateral sensitivity to induce tumor shrinkage and beneficial outcomes for patients (Pluchino et al., 2012; Saeed et al., 2013). Collateral sensitivity in EGFR-overexpressing cancer cells is a new phenomenon and is described in the present investigation for the first time to the best of our knowledge.
The question arises about the molecular mechanisms to explain the increased sensitivity of EGFR-overexpressing tumor cells towards honokiol. We speculated that honokiol might bind to EGFR and/or EGFR-related downstream signaling proteins thereby silencing signal transduction. For this reason, we performed molecular docking studies triggering the main EGFR signal transducers in cancer cells, e.g. AKT, STAT3, MAPKKs, and the transcription factor mTOR. Indeed, our calculations indicated that honokiol might silence EGFR-related downstream signaling by inhibiting the activity of several signal transducers.
Deletion of extracellular domain of EGFR that occurred in some tumors leads to ligand independent receptor activation, consequently dimerization and autophosphorylation of key tyrosine residues within C-terminal on EGFR kinase domain recruit cytoplasmic proteins that contain Src homology 2 (SH2) and phsophotyrosine binding domains (Yarden and Sliwkowski, 2001), thereby, initiate intracellular downstream signaling pathways, P13K/Akt/mTOR, Ras/Raf/MAPK and JAK/STAT leading to activation of genes transcription and cell cycle progression. Interestingly, the results of these in silico docking studies indeed indicated that honokiol may bind to these key molecules of EGFR-related signaling pathways with comparable binding affinities as well-known small molecule inhibitors. These results may be taken as a clue that honokiol kills EGFR-expressing, drug-resistant tumor cells by binding to EGFR and downstream signal transducers at their active sites leading to disruptions in signaling cascades, thereby, inhibition of tumor growth and induction of cell death. This point of view is supported by recent experimental evidence that honokiol may shut off phosphorylation of signal transduction proteins involved in EGFR-signaling such as Akt, MAPKKs, STAT3 and mTOR (Park et al., 2009; Leeman-Neill et al., 2010).
The remarkable collateral sensitivity of honokiol in EGFR-expressing, otherwise drug-resistant U87.MG[DELTA]EGFR cells may open the possibility that combination treatments of established anticancer drugs together with honokiol may result in synergistic tumor cell killing, Indeed, this has been repeatedly reported for several anticancer drugs as well as for radiotherapy (Hu et al., 2008; Jiang et al., 2008; Leeman-Neill et al., 2010; Wang et al., 2010; Arora et al., 2011; Cheng et al., 2011; He et al., 2011; Tian et al., 2013). If one imagines that honokiol would find its way into the clinics, it can be expected that this compound will be rather used as part of a combination therapy than as monotherapy. Silencing EGFR and its downstream signaling routes by honokiol may, therefore, be a valuable strategy to re-sensitize drug-resistant tumors. This is a fascinating perspective for further investigations to corroborate this hypothesis.
As for most natural products (Efferth and Koch, 2011), it can be expected that the bioactivity of honohiol is determined by multiple rather than by single factors. Therefore, we performed COMPARE and hierarchical cluster analyses of transcriptomewide microarray-based mRNA expressions. We identified a panel of genes, whose expression was significantly correlated with the logl0IC50 values for honokiol of the NCI cell line panel. Genes of diverse functional groups and signaling routes appeared: Cytoskleleton (BCL7A, DYNC1L12, SEFT10, SEPT11), transcriptional and translational regulation (DEPDC1, GABPB1, LHFPL2, NFIB, POLR3C, RPL34, RPS3A, RPS21, RPS25, TFDP2, TRIM24, ZBTB1, ZBTB38, ZFP112), cell death (CASP2, INC2, MDM4, NAIP), DNA maintenance and processing (BAHCC1, FANCA, HIST1H3G, IK, KDM4C, MCM7, PRB3, RNASEH2B, SNRPE, TFDP2), signal transduction (ANXA2, ARHGAP19, C7orf47, CCDC50, DTX3, FHL2, P1K3CG, RALB, TICAM2), transport functions (ABCC1, FXYD2, S100A6, SCNN1C, XP05), oxidative stress response (SPATS2L, GSTT2B, NQ01), blood coagulation (FGA, MATR3, PROCR, PIK3CG), and others (ADAM22, ALDH3A2, FAM161A, HDDC2, HLA-F).
With few exceptions, most of these genes were not reported before to confer resistance to standard anticancer drugs nor to honokiol. ABCC1 is another ABC-transporter (multidrug resistance-related protein 1, MRP1), known to confer another MDR-phenotype than the one caused by P-glycoprotein (ABCB1/MDR1) and BCRP/ABCG2 (Efferth, 2001; Gillet et al., 2007).
However, some of the pathways and functional groups are well-known to be associated with drug resistance. For instance, the cell death machinery (Hickman, 1992; Kondo et al., 2005; Efferth and Greten, 2012; Kamal et al., 2014; Vanden Berghe et al., 2014), the cytoskeleton (Kavallaris, 2010; Kanakkanthara et al., 2013), DNA maintenance and processing (Bouwman and Jonkers, 2012; Curtin, 2013), and oxidative stress response (Landriscina et al., 2009; Myatt et al., 2011) have been described to mediate drug resistance. It is remarkable that a number of genes involved in blood coagulation appeared in our approach. This result fits well with the anti-thrombotic activity of honokiol that has been previously described (Hu et al., 2005; Zhang et al., 2007). This result indicates that this cancer cell-focused approach may not only unravel cancer-related molecular mechanisms, but also modes of actions of natural products unrelated to their cytotoxic activity against cancer cells. This might have important implications for the detection of biological mechanisms of natural products in general.
Conflict of interest
There is no conflict of interest of the authors.
We are grateful to a stipend of the National Research Center (Khartoum, Sudan) to M.S. and a stipend of the Alexander von Humboldt Foundation (Bonn, Germany) to V.K.
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Abbreviations: ABC, ATP-binding cassette; Akt, v-Akt murine thymoma viral oncogene homolog 1; BCRP, breast cancer resistance protein; EGFR, epidermal growth factor; MAPK, mitogen-activated protein kinase; MDR, multidrug resistance; mTOR, mammalian target of rapamycin; PI3 K, phosphoinositid-3-kinase; STAT3, signal transducer and activator of transcription.
Received 31 May 2014
Received in revised form 15 June 2014
Accepted 21 July 2014
Mohamed Saeed (a), Victor Kuete (a,b), Onat Kadioglu (a), Jonas Bortzler (a), Hassan Khalid (c), Henry Johannes Gretende, Thomas Efferth (a,*)
(a) Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Germany
(b) Department of Biochemistry, Faculty of Science, University of Dschang, Cameroon
(c) Medicinal and Aromatic Plants Research Institute (MAPRI), National Centre for Research, Khartoum, Sudan
(d) Abel Salazar Biomedical Sciences Institute, University of Porto, Portugal
(e) Heidelberg School of Chinese Medicine, Heidelberg, Germany
* Corresponding author. Tel.: +49 6131 3925751; fax: +49 6131 3923752.
E-mail address: firstname.lastname@example.org (T. Efferth).
Table 1 Cytotoxicity of honokiol towards sensitive and drug-resistant cancer cell lines and normal cells as determined by the resazurin reduction assay. Resistance Cell line [IC.sub.50] Degree of mechanism ([micro]-M) resistance * P-glycoprotein CCRF-CEM 20.99 [+ or -] 0.04 1.29 (MDRl/ABCBt) CEM/ADR5000 27.15 [+ or -] 0.34 BCRP/ABCG2 MDA-MB-231 15.36 [+ or -] 1.20 3.00 pcDNA MDA-MB-231 46.15 [+ or -] 1.20 BCRP TP53 Han 6 51.96 [+ or -] 2.74 0.84 ([p53.sup.+/+]) Hail 6 43.59 [+ or -] 1.43 ([p53.sup.-/-]) EGFR U87.MG 88.91 [+ or -] 2.22 0.38 U87.MG[DELTA] 33.83 [+ or -] 7.32 EGFR Tumor versus HepG2 75.92 [+ or -] 6.61 >2 normal AM LI 2 >150.00 * The degree of resistance was determined as the ratio of [IC.sub.50] value of the resistant/lCso sensitive cell line. Table 2 Correlation of constitutive mRNA expression of genes identified by COMPARE analyses with [log.sub.10] [IC.sub.50] values of honokiol for 59 tumor cell lines. Coefficient Symbol Name Function -0.596 BCL7A B-Cell CLL/Lymphoma F-actin cross- 7A linking protein -0.575 TRIM24 Tripartite Transcriptional motif-containing 24 coactivator interacting with nuclear receptors and coactivators; modulation of transcription of target genes -0.564 CASP2 Caspase 2, Apoptosis execution apoptosis-related cysteine peptidase RNA -0.563 ZNF397 Zinc finger protein DNA-dependent 397 transcriptional repressor -0.563 ING2 Inhibitor Of Growth Induction of Family. Member 2 apoptosis by p53 activation -0.559 MATR3 Matrin 3 Forming the internal fibrogranular network -0.553 RPL34 Ribosomal protein RNA binding L34 -0.553 MRPL55 Mitochondrial Structural ribosomal protein constituent of L55 ribosome -0.552 SNRPE Small nuclear Involved in histone ribonucleoprotein 3'-end processing polypeptide E -0.551 NA1P NLR family, Anti-apoptotic apoptosis inhibitory protein and sensor protein component of the NLRC4 inflammasome -0.549 ZBTB17 Zinc finger and BTB Transcription factor domain containing 17 playing a role in lymphocyte and embryonic development -0.547 FXYD2 FXYD domain Forming the receptor containing ion site for cardiac transport regulator glycoside binding 2 +modulate transport function of sodium ATPase -0.543 RNASEH2B Ribonuclease H2, Endonuclease subunit B -0.541 PRR3 Proline rich 3 Nucleic acid binding; zinc ion binding -0.538 MCM7 Minichromosome Component of maintenance complex putative replicative component 7 helicase -0.533 HDDC2 HD domain containing Protein and metal 2 ion binding + phosphoric diester hydrolase activity -0.533 RPS3A Ribosomal protein Plays a role in S3A erythropoiesis -0.531 H1ST1H3G Histone cluster 1, Core component of H3g nucleosome -0.529 ADAM22 ADAM Ligand for integrin, metallopeptidase a non-catalytic domain 22 metalloprotease- like protein -0.527 SEPT11 Septin 11 Filament-forming cytoskeletal GTPase -0.526 FANCA Fanconi anemia, DNA repair protein complementation group A -0.526 XP05 Exportin 5 Nuclear export of proteins -0.522 RPS21 Ribosomal protein Structural S21 constituent of ribosome;protein N- terminus binding -0.52 BAHCC1 BAH domain and DNA binding coiled-coii containing 1 -0.519 RPS25 Ribosomal protein RNA binding and S25 structural constituent of ribosome -0.516 GABPB1 GA binding protein Transcription factor transcription of genes with factor, beta subunit mitochondrial 1 function -0.516 TFDP2 Transcription factor Stimulates E2F- Dp-2 (E2F dependent dimerization partner transcription which 2) is involved in cell cycle regulation or DNA replication -0.513 FGA Fibrinogen alpha Polymerize into chain fibrin; cofactor in platelet aggregation -0.513 LOC283788 FSHD region gene 1 Unknown pseudogene -0.512 POLR3C Polymerase (RNA) III DNA-dependent RNA (DNA directed) polymerase polypeptide C (62kD) -0.51 FAM161A Family with sequence Ciliogenesis similarity 161, member A -0.509 C7orf47 Chromosome 7 open Inhibition of PPP1CA reading frame 47 phosphatase activity -0.509 SCNN1G Sodium channel, Electrodiffusion of non-voltage-gated 1, luminal sodium gamma subunit -0.507 MDM4 Mdm4 p53 binding Inhibition of cell protein homolog cycle arrest and (mouse) apoptosis -0.506 IK IK cytokine, Chromatin binding down-regulator of HLAII -0.506 PIK3CG Phosphatidylinositol- Generates PIP3; 4,5-bisphosphate involved in immune 3-kinase, catalytic reaction and subunit gamma thrombosis -0.505 ARHGAP19 Rho GTPase GTPase activator for activating protein the Rho-type GTPases 19 0.525 PCYT1A Phosphate Controls cytidylyltransferase phosphatidylcholine 1, choline, alpha synthesis 0.496 ASPH Aspartate Asp or Asn beta-hydroxylase hydroxylation in EGF domains of proteins; membrane-bound Ca(2+)-sensing protein 0.49 DTX3 Deltex homolog 3 Notch regulation (Drosophila) 0.488 PROCR Protein C receptor, Controls blood endothelial coagulation through binding activated protein C 0.48 DEPDC1 DEP domain Transcriptional containing 1 corepressor 0.48 DYNC1L12 Dynein, cytoplasmic Component of 1, light cytoplasmic dynein 1 intermediate chain 2 0.479 TMEM62 Transmembrane Unknown protein 62 0.476 TICAM2 Toll-like receptor Regulation of MYD88- adaptor molecule 2 independent pathway 0.475 LRRC49 Leucine rich repeat Unknown containing 49 0.467 ZBTB38 Zinc finger and BTB Transcriptional domain containing 38 activator 0.463 RALB v-ral simian Multifunctional leukemia viral GTPase oncogene homolog B (ras related; GTP binding protein) 0.462 NQOl NAD(P)H Serves as quinone dehydrogenase, reductase involved quinone 1 in detoxification pathways and biosynthetic processes 0.454 KDM4C Lysine (K)-specific Histone demethylase, demethylase 4C demethylates 'Lys- 9' and 'Lys-36' 0.446 CCDC50 Coiled-coii domain EGFR signaling containing 50 0.444 NFIB Nuclear factor I/B Recognizes and binds the palindromic sequence 5'- TTGGCNNNNNGCCAA-3' 0.444 ABCC1 ATP-binding Export of organic cassette, sub-family anions and drugs + C (CFTR/MRP), member ATP-dependent 1 transport 0.441 LHFPL2 Lipoma HMGIC fusion Protein encoding partner-like 2 0.438 HLA-F Major Antigen presentation histocompatibility complex, class I, F 0.436 FHL2 Four and a half LIM Molecular domains 2 transmitter 0.436 SPATS2L Spermatogenesis Protection of associated, oxidative stress serine-rich 2-like 0.434 S100A6 S100 calcium binding Calcium sensor protein A6 0.43 GSTT2B Glutathione Conjugation of S-transferase theta reduced glutathione 2B (gene/pseudogene) to electrophiles 0.43 RRBP1 Ribosome binding Ribosome receptor protein 1 0.429 ALDH3A2 Aldehyde Catalyzes oxidation dehydrogenase 3 of long-chain family, member A2 aliphatic aldehydes to fatty acids 0.427 C2orf55 Chromosome 2 Open Unknown Reading Frame 55 0.427 ZFP112 Zinc finger protein DNA binding; zinc 112 homolog (mouse) ion binding 0.424 SEPT10 Septin 10 Filament-forming cytoskeletal CTPase 0.423 ANXA2 Annexin A2 Calcium-regulated membrane-binding protein Positive correlation coefficients indicate direct correlations to [log.sub.10][IC.sub.50] values, negative ones indicate inverse correlations. Information on gene functions was taken from the OMIM database, NCI, USA (http://www/ncbi/nlm/nih/gov/Omim/) and from the CeneCard database of the Weizman Institute of Science, Rehovot, Israel (http://bioinfo.weizmann.ac.il/cards/ index.html). Table 3 Separation of clusters of 59 NCI cell lines obtained by hierarchical cluster analysis shown in Fig. 2 in comparison to drug sensitivity. Partition Cluster 1 Cluster 2 Sensitive [less than 7 16 or equal to] -4.81 M Resistant >-4.81 M 23 7 Cluster 3 [chi.sup.2]-test Sensitive 6 P=1.21 x [10.sup.-4] Resistant 0 The median [log.sub.10][IC.sub.50] value (-4.810 M) for each compound was used as cut-off to separate tumor cell lines as being "sensitive" or "resistant". Table 4 Molecular docking results of proteins involved in EGFR downstream signal transduction, lowest binding energy, predicted inhibition constant (Pki) and amino acids (AA) involved in hydrogen bonding for each inhibitor and honokiol have been shown. Each docking experiment has been repeated three times. Receptor protein Inhibitor Lowest binding energy (kcal/mol) EGFR Erlotinib -7.50 [+ or -] 0.30 Gefitinib -8.30 [+ or -] 0.10 Honokiol -7.30 [+ or -] 0.01 Akt CSK690693 -10.97 [+ or -] 0.03 Honokiol -7.40 [+ or -] 0.01 mTOR Sirolimus -7.10 [+ or -] 0.02 Honokiol -8.00 [+ or -] 0.00 MEK1 PD184352 -10.10 [+ or -] 0.09 Honokiol -8.50 [+ or -] 0.00 MEK2 PDl84352 -10.90 [+ or -] 0.01 Honokiol -9.00 [+ or -] 0.01 STAT3 NSC74859 -8.03 [+ or -] 0.02 Honokiol -7.84 [+ or -] 0.32 Receptor protein Pki([micro]M) AA involved in H-bonds EGFR 3.20 [+ or -] 1.20 MET769, CYS773 0.82 [+ or -] 0.16 LYS721 4.80 [+ or -] 0.01 LYS721.THR766 Akt 0.01 [+ or -] 0.00 LYS160, GLY164 3.50 [+ or -] 0.10 LYS181, ASP293 mTOR 6.50 [+ or -] 0.2 MET2089, GLU2083 1.40 [+ or -] 0.00 PR02229 MEK1 0.04 [+ or -] 0.01 LYS97 0.62 [+ or -] 0.00 SER212 MEK2 0.01 [+ or -] 0.00 -- 0.30 [+ or -] 0.02 SER216 STAT3 1.79 [+ or -] 0.05 GLY254, SER255 1.30 [+ or -] 0.70 LEU252, LEU310