Printer Friendly

Cytotoxicity of the indole alkaloid reserpine from Rauwolfia serpentina against drug-resistant tumor cells.


Background: The antihypertensive reserpine is an indole alkaloid from Rauwolfia serpentina and exerts also profound activity against cancer cells in vitro and in vivo. The present investigation was undertaken to investigate possible modes of action to explain its activity toward drug- resistant tumor cells.

Material and methods: Sensitive and drug-resistant tumor cell lines overexpressing P-giycoprotein (ABCB1/MDR1), breast cancer resistance protein (ABCG2/BCRP), mutation-activated epidermal growth factor receptor (EGFR), wild-type and p53-knockout cells as well as the NCI panel of cell lines from different tumor origin were analyzed. Reserpine's cytotoxicity was investigated by resazurin and sulforhodamine assays, flow cytometry, and COMPARE and hierarchical cluster analyses of transcriptome- wide microarray-based RNA expressions.

Results: P-glycoprotein- or BCRP overexpressing tumor cells did not reveal cross-resistance to reserpine. EGFR-overexpressing cells were collateral sensitive and p53- Knockout cells cross-resistant to this drug compared to their wild-type parental cell lines. Reserpine increased the uptake of doxorubicin in P-glycoproteinoverexpressing cells, indicating that reserpine inhibited the efflux function of P- glycoprotein. Using molecular docking, we found that reserpine bound with even higher binding energy to P- glycoprotein and EGFR than the control drugs verapamil (P-glycoprotein inhibitor) and erlotinib (EGFR inhibitor). COMPARE and cluster analyses of microarray data showed that the mRNA expression of a panel of genes predicted the sensitivity or resistance of the NCI tumor cell line panel with statistical significance. The genes belonged to diverse pathways and biological functions, e.g. cell survival and apoptosis, EGFR activation, regulation of angiogenesis, cell mobility, cell adhesion, immunological functions, mTOR signaling, and Wnt signaling.

Conclusion: The lack of cross-resistance to most resistance mechanisms and the collateral sensitivity in EGFRtransfectants compared to wild-type cells speak for a promising role of reserpine in cancer chemotherapy. Reserpine deserves further consideration for cancer therapy in the clinical setting.




Cluster analysis

Collateral sensitivity

Molecular docking



Reserpine is an indole alkaloid derived from the roots of Rauwolfia serpentina and serves as potent antihypertensive drug (Panda et al. 2012) It represents an established second line treatment against hypertension (Milne and Pinkney-Atkinson 2004; Pillay 2009).

Reserpine mediates the depletion of neurotransmitters from postganglionic nerve endings, which consequently lower arterial pressure and total peripheral resistance ultimately leading to decreased heart rates and cardiac output. Its administration together with diuretics effectively lowers arterial pressure and significantly reduces morbidity and mortality related to hypertension (Bakris and Frohlich 1989).

Early studies started at the mid-1950s reported anti-tumor effect of reserpine in vivo independent from its cardiovascular action. Experimental studies in mice bearing advanced leukemia, reserpine increased animals' life span by three-fold (Burton et al. 1956). Other studies reported the anti-tumor activity of reserpine in different mouse sarcomas in vivo (Belkin and Hardy 1957; Nelson et al. 1981).

Since the 1980s, attention was paid toward phenomena related to chemotherapy failure (Efferth 2001; Gillet et al. 2007; Eichhorn and Efferth 2012) and the ATP binding cassette (ABC) transporter, P-glycoprotein (ABCB1), which confers multidrug resistance (MDR) by energy-dependent drug efflux process (Hall et al. 2009). Drugs such as verapamil, quinidine, tamoxifen, progesterone, rapamycin, cyclosporins and others were found to inhibit P-glycoprotein and to overcome MDR (Arced 1993). Interestingly, reserpine was also reported as P-glycoprotein inhibitor. It suppressed photolabeling of P-glycoprotein with a vinblastine analogue in MDR cell lines (Akiyama et al. 1988). Reserpine and other indole alkaloids enhanced the sensitivity of MDR cancer cells toward cytotoxic agents (Beck et al. 1988; Zamora et al. 1988).

In this study, we investigated the role of reserpine in different cancer cell lines in an approach to understand possible molecular modes of action. Since MDR is multifactorial in nature and other mechanisms in addition to P-glycoprotein also contribute to unresponsiveness of tumors, we also investigated several other mechanisms. The ABCtransporter BCRP/ABCG2, the mutated tumor suppressor gene p53 and the activated epidermal growth factor receptor (EGFR) all mediate drug resistance (El-Deiry 1997,2003; Gillet et al. 2007). Therefore, we investigated, whether or not cell lines expressing these genes reveal cross-resistance to reserpine. The intention was to prove, whether reserpine could be used to bypass drug-resistance and to eradicate otherwise unresponsive tumors by reserpine.

Material and methods

Cell culture

Drug-sensitive CCRF-CEM and multidrug-resistant CEM/ADR5000 leukemia cell lines were cultured in RPMI 1640 medium, supplemented with 10% fetal bovine serum (FBS) (Invitrogen) and 1% penicillin (100 U/ml)-streptomycin (100 [micro]g/ml) (PIS) antibiotic (Invitrogen) and incubated in humidified 5% C[O.sub.2] atmosphere at 37[degrees]C.

Breast cancer cells transduced with control vector (MDA-MB231-pcDNA3) or with a cDNA for the breast cancer resistance protein BCRP (MDA-MB-231BCRP clone 23), human wild-type HCT116 [(p53.sup.+/+]) colon cancer cells as well as knockout clones HCT116 ([p53.sup.-/-]) derived by homologous recombination, non-transduced human U87MG glioblastoma multiforme cells and U87MG cells transduced with an expression vector harboring an epidermal growth factor receptor (EGFR) gene with a genomic deletion of exons 2 through 7 (U87MG.AEGFR) were all maintained in DMEM medium, supplemented with 10% FBS and 1% penicillin-streptomycin and incubated under standard conditions as described for leukemia cell lines.

The resistance of the different resistant cell lines has been maintained by using 5000 ng/ml doxorubicin for CEM/ADR5000, 400 [micro]g/ ml geneticin for U87MG.[DELTA]EGFR and HCT116 ([p53.sup.-/-]) and 800 ng/ml of the same compound for MDA-MB-231 BCRP clone 23.

The glioblastoma cell lines were kindly provided by Dr. W. K. Cavenee (Ludwig Institute for Cancer Research, San Diego, CA). Transfected breast cancer cell lines were a generous gift from Dr. B. Vogelstein and H. Hermeking (Howard Hughes Medical Institute, Baltimore, MD). The leukemia cells were kindly provided by Dr. J. Beck (Department of Pediatrics, University of Greifswald, Greifswald, Germany).

Cytotoxicity assay

The cytotoxicity of reserpine has been investigated using the resazurin reduction assay (Borra et al. 2009). Resazurin is an indicator dye, which is reduced in viable cells to highly fluorescent resorufin, in contrast to non-viable cells, which lost their metabolic capability and are not able reduce resazurin. In total 96-well cell culture plate (Thermo Scientific, Germany) were seeded with 20,000 cells/well in a total volume of 100 [micro]L, and then treated with different concentrations of reserpine diluted in 100 [micro]L medium. Adherently growing cells were allowed to attach overnight and treated after 24 h. The cells were incubated with reserpine for 72 h. Then, 0.01% of resazurin (Sigma-Aldrich, Germany) diluted in double distilled water (dd[H.sub.2]0) was added (20 [micro]L/well) and incubated for another 4 h. Infinite M2000 Pro[TM] plate reader (Tecan, Germany) was used to measure the fluorescence using an excitation wavelength of 544 nm and an emission wavelength of 590 nm. Experiments were performed three times with at least with six replicates per experiment. The 50% inhibition concentrations ([IC.sub.50]) were calculated from dose response curves of each cell using Microsoft Excel 2013 software.

Doxorubicin uptake assay

Flow cytometry has been used to measure the retention of doxorubicin. Doxorubicin is substrate of P-glycoprotein and its inherent fluorescence was used to assess the efflux activity of this drug transporter. CCRF-CEM and CEM/ADR5000 cells were seeded in phenol red-free RPMI 1640 medium in a concentration of 5 x [10.sup.5] cells/well in 12 well plates at a total volume 500 [micro]L. Then, cells were treated with medium containing 10 [micro]M doxorubicin with and without reserpine (15 [micro]M). Cells were incubated at 37[degrees]C in an atmosphere containing 5% C[0.sub.2] for 3 h, which is the time required for maximum doxorubicin uptake (Krishan and Hamelik, 2005). Finally, cells were washed to remove free doxorubicin. Cells were measured on LSR-Fortessa FACS analyzer (Becton-Dickinson, Germany) equipped with argon blue laser, the excitation and emission wavelength of doxorubicin were 488 nm and 610/20 nm, respectively. Only living cells, identified by DAP1 to stain dead cells were considered for the analyses. Data were processed by Flowjo software. Three controls were taken, unstained cells to determine auto-fluorescence, cells treated with doxorubicin alone to measure the efflux efficacy, and in combination with verapamil as positive control for a P-glycoprotein inhibitor.

Molecular docking

AutoDock4 (Hetenyi and Van Der Spoel 2002) was used for molecular docking calculations of reserpine. A homology model human ABCB1 based on the crystal structure of murine P-glycoprotein was previously constructed by us (Zeino 2014; Tajima et al. 2014) and used in the present investigation for binding site determination of reserpine. Verapamil was included in our analyses as control drug for a well-known P-glycoprotein inhibitor.

Molecular docking of reserpine to EGFR was done on the ATP-binding site of EGFR kinase domain. The crystal structure of EGFR was retrieved from The Protein Data BANK (Database code PDB1 Ml 7; Erlotinib which is an irreversible tyrosine kinase inhibitor of EGRF was taken as control drug for docking. The 3D structure of reserpine, verapamil and erlotinib were downloaded from PubChem (

Drug binding residues of ABCB1 were identified as His61, Gly64, Leu65, Met69, Ser222, Leu304, Ile306, Tyr307, Phe336, Leu339, Ile340, Ala342, Phe343, Gln725, Phe728, Phe732, Leu762, Thr837, Ile868, Gly872, Phe942, Thr945, Tyr953, Leu975, Phe978, Ser979, Val982, Gly984, Ala985, Met986, Gly989, Gln990, and Ser993 (Aller et al. 2009). At the other hand, residues identified as binding sites for EGFR include Leu620, Leu694, Phe699, Val702, Ala719, Lys721, Met742, Leu764, Thr766, Gln767, Met769, Pro770, Cys773, Thr830 and Asp831 (Yadav et al. 2014).

Crude BDP structures of the receptors proteins were refined, energy minimized and polar hydrogens were added and saved as pdbqt files. Then, the grid map parameters were set to cover the defined residues. Numbers of runs and energy evaluations were reset to 250 and 25,000,000, respectively. Docking calculations were performed using Lamarckian Genetic Algorithm. For image visualization of docking results, Visual Molecular Dynamics (VMD) software was used.

COMPARE and hierarchical cluster analyses of microarray data

Messenger RNA expression profiles of 60 human cancer cell lines were deposited at the database of the Developmental Therapeutics Program (DTP) of the National Cancer Institute (NCI) (http://dtp.nci.

Compare analysis (Pauli et al. 1989) was performed to correlate IC50 values for reserpine and microarray-based transcriptomewide mRNA expression levels in the NCI cell line panel. According to gene expression levels, resistance and sensitive candidate genes were determined using standard and reverse COMPARE as described (Villeneuve and Parissenti 2004; Zeeberg et al. 2011; Reinhold et al. 2012).

Pearson's rank correlation test was used (W1NSTAT program, Kalmia) for calculation of significance values (p-values) and ranking correlation coefficients (R-values) as a relative measure of the linear dependency of two variables. The median log10 IC50 value was taken as a cut-off threshold for determination of cell lines being sensitive or resistant to reserpine.

Hierarchical cluster analysis (WARD method) groups objects into clusters according to similarities and closeness between them. For calculation of distances of all variables involved in the analysis, the program automatically standardizes the variables by transforming the data with a mean = 0 and a variance = 1. Then, cluster trees were performed.


Resazurin reduction assay

The cytotoxic effects of reserpine have been investigated in different cancer cell lines (Fig. 1) to gain insight into the mechanisms of action underlying the activity of the compound. Reserpine exerted 50% cell viability inhibition in parental CCRF-CEM cells at a concentration of 14.52 [+ or -] 1.62 [micro]M and of 13.2 [+ or -]1.02 [micro]M in P- glycoprotein-overexpressing, multidrug-resistant CEM/ADR5000 cells. These results clearly show that multidrug-resistant cells did not exhibit cross-resistance to reserpine, although these cells are highly resistant to established anticancer drugs such as anthracyclines, Vinca alkaloids, taxanes, epipodophyllotoxins and others (Efferth et al. 2008).

Treatment of drug-sensitive MDA-MB-231-pcDNA3 and BRCP-transfected, multidrug-resistant MDA-MB-231 -BCRP (clone 23) resulted in [IC.sub.50] values of 34.33 [+ or -] 10.38 [micro]M and 40.6 [+ or -] 6.84 [micro]M, respectively.

U87MG cells transfected with mutation-activated EGFR (U87MG.AEGFR) were much more sensitive toward reserpine ([IC.sub.50]: 9.15 [+ or -] 2.67 [micro]M) than wild-type cells (87.98 [+ or -] 11.84 [micro]M), indicating that EGFR-overexpressing cells display collateral sensitivity (hypersensitivity) to reserpine.

Finally, we compared the responsiveness of p53 wild-type and knockout cells toward reserpine. HCT116 ([p53.sup.+/+]) colon cancer cells were sensitive to reserpine ([IC.sub.50] : 30.07 [+ or -] 7.57 [micro]M), while HCT116 ([p53.sub.-/-]) cells were resistant to reserpine.

Doxorubicin uptake assay

Doxorubicin uptake was measured in terms of fluorescence intensity, which can be taken as a measure for intracellular accumulation of the drug. CCRF-CEM and P-glycoprotein-overexpressing CEM/ADR5000 cells were both incubated with doxorubicin with or without reserpine. CCRF-CEM cells that do not express P-glycoprotein were sensitive to doxorubicin and neither reserpine nor the control drug verapamil shows any effect on doxorubicin accumulation.

By contrast, CEM/ADR5000 cells showed only intracellular low fluorescence intensity of doxorubicin (Fig. 2). The fluorescence intensity of doxorubicin considerably increased after addition of 3.75 [micro]M reserpine (0.25 x [IC.sub.50] value) and even more increased after incubation with 15 [micro]M reserpine ([IC.sub.50] value). The retention effect of reserpine ([IC.sub.50] value) was 2.04-fold higher than of verapamil. It is important to note that there were no auto-fluorescent effects from either tested cancer cell lines.

Molecular docking of reserpine to ABCB1 and ECFR

Reserpine showed a high binding energy for human P-glycoprotein/ABCBl (-9.65 [+ or -] 0.79 kcal/mol), even higher than that of the control drug verapamil (-8.57 [+ or -] 0.13 kcal/mol) (Table 1). Two hydrogen bonds with drug binding residues (Gly226 and Lys234) interacted with reserpine. Interestingly, reserpine bound to the same drug binding site as verapamil (Fig. 3A). The residues involved in this interaction were Ser222, Pro223, Gly226, Ala229, Ala230, Lys234, Phe 303, Ile306, Tyr310, Leu339, Ala342, Phe 343 and Gly 346.

Molecular docking of reserpine to the tyrosine kinase domain of EGFR (PDB 1M17) revealed a low binding energy (-7.32 [+ or -] 1.12 kcal/ mol). Two hydrogen bonds were involved in this interaction (Lys721 and Met769). It is remarkable that reserpine revealed a higher binding affinity than the control EGFR inhibitor, erlotinib (-5.93 [+ or -] 0.3 kcal/mol). Both reserpine and erlotinib shared the same residues involved in hydrophobic interaction (Fig. 3B), i.e. Leu694, Lys721, Glu738, Leu768, Met769, Gly772, Cys773, Asp813, Arg817, Leu820 and Asp831.

Cross-resistance of reserpine to established anticancer drugs

To get a clue on possible modes of actions of reserpine, we correlated the [log.sub.10][IC.sub.50] values of the NCI cell lines to reserpine with those of 87 standard drugs. The cellular responses of 4 out of 7 anti-hormonal drugs significantly correlated with those of reserpine (= 57%). Alkylating drugs were also frequently correlated to reserpine. Seven of 13 alkylating agents (= 54%) revealed significant correlations to reserpine (p < 0.05; R > 0.30). Comparable results were obtained for mTOR inhibitors (2/4 drugs = 50%). Intermediate correlation rates were found for DNA topoisomerase inhibitors (3/8 drugs = 37.5%) antimetabolites (2/15 drugs = 13%), as well as tyrosine kinase inhibitors (1/13 drugs = 8%). No correlations were found for platin compounds, tubulin inhibitors, and epigenetic inhibitors (Fig. 4). These results may indicate that reserpine exerts multiple modes of action, a feature which is frequently observed with phytochemicals (Efferth and Koch, 2011).

3.5. COMPARE and cluster analyses of microarray data

COMPARE and cluster analyses of microarray data have shown that cell lines of different tumor types show different degrees of sensitivity to reserpine (Fig. 5). In an approach to identify molecular pharmacology and mechanism underlying responsiveness to reserpine, candidate genes have been identified using transcriptome-wide COMPARE analysis. Using Pearson test, genes were ranked whose mRNA expression directly or inversely correlate with the log10IC5o values for reserpine of all NCI cell lines (Table 2). Only genes with correlation coefficients of R > 0.5 and R < -0.5 were considered for direct inverse correlations respectively.

These genes belonged to pathways and biological functions that presumably determined responsiveness of tumor cells to reserpine, e.g. cell survival and apoptosis (TRAFh SULFi), EGFR activation (EFEMP1, EPS15LF), regulation of angiogenesis (ANCPTL4), cell mobility (ACTA2, FISPB3, CNNi), cell adhesion (POSTN, MOG), immunological function (CFI, LBP, IU7B), mTOR signaling (PPAPDC3) and Wnt signaling pathway (DIXDC1, SFRP2, CPZ, ZRANB1).

Hierarchical cluster analysis was performed using the mRNA expression values listed in Table 2. As shown in Fig. 6, the resulting dendrogram could be separated into four main branches. The median value of the [log.sub.10][IC.sub.50] values of reserpine was then used as cutoff value to define cell lines as being sensitive or resistant to reserpine. The distribution of sensitive and resistant cell lines is shown in Table 3. Interestingly, a statistically significant distribution pattern across the four dendrogram branches was observed. The majority of cell lines in clusters 1 and 2 were reserpine-resistant, whereas those in clusters 3 and 4 were mainly sensitive (P = 0.00437; [chi square] test). Since only mRNA values but not [log.sub.10][IC.sub.50] values were included into the cluster analysis, the mRNA expression profile alone was sufficient to predict sensitivity or resistance to reserpine. Clusters 1 and 2 contain both sensitive and resistant cell lines to reserpine whereas clusters 3 and 4 contain only resistant cell lines to reserpine.


Cross-resistance and collateral sensitivity of drug-resistant cell lines

Our results showed that reserpine has novel cytotoxic features that may be beneficial for the treatment of drug-resistant tumors. P-glycoprotein-overexpressing cells were not cross-resistant to reserpine, indicating that this compound may be valuable to eradicate MDR tumor cell populations of refractory tumors. The early reports from the 1950s on the anticancer activity of reserpine in vivo (Burton et al. 1956; Belkin and Hardy 1957) did not went into further mode of action analyses, since major molecular mechanisms were still unknown at that time. For instance, apoptosis as important mechanisms of cell death (Kerr et al. 1972) and P-glycoprotein as mediator of multidrug resistance (Juliano and Ling 1976) came only up in the 1970s. Later on, it has been indeed demonstrated that reserpine triggers apoptotic cell death, which explains the anticancer activity of this drug independent of its calcium channel blocking, antihypertensive effects. Reserpine induces cell death by activation of the TRAIL-mediated apoptotic pathway leading to up-regulation of BAX, down-regulation of BCL-2 as well as activation of caspase-3 and caspase-8 (Cantarella et al. 2009). In the present investigation, we found that reserpine reversed doxorubicin resistance of P-glycoprotein overexpressing CEM/ADR5000 cells. Reserpine is an effective modulator for P-glycoprotein, which increases intracellular doxorubicin accumulation by binding with high affinity to this ABC-transporters leading to efficient drug efflux inhibition. These results are in accord to previous data reporting on the photoaffinity labeling of P-glycoprotein and its inhibition by reserpine (Qian and Beck 1990). The inhibition of P-glycoprotein's efflux function by reserpine was subsequently confirmed by other authors (Schlemmer and Sirotnak 1994; Bhat et al. 1995; Jette et al. 1995; Sarver et al. 2002). In addition to these studies, we did not only confirm reserpine's inhibitory activity on P-glycoprotein's function, but we also suggested the amino acids responsible for reserpine's binding in the pharmacophore of P-glycopotein by molecular docking. Taken all these results together, reserpine can be understood as a "two-in-one" drug. It kills tumor cells due to its profound cytotoxicity, and at the same time it inhibits P-glycoprotein's efflux enhancing the activity of standard drugs such as doxorubicin.

It was pleasing to observe BCRP-transfectant tumor cells did also not exert cross-resistance to reserpine. BCRP (ABCG2) represents another ABC transporter that confers resistance to a broad spectrum of anticancer drugs in various tumor types, including human breast carcinoma, colon carcinoma, gastric carcinoma, fibrosarcoma, and myeloma (Doyle and Ross 2003). The fact that many of the established anticancer drugs clinically fail, because of the activity of ABC-transporters such as P-glycoprotein and BCRP illustrates the necessity to identify and develop novel anticancer drugs. Our data give reason to hope that reserpine is a promising candidate drug to improve the effectiveness of cancer chemotherapy.

In this context, it was interesting to observe that reserpine was hypersensitive (collateral sensitive) to tumor cells transfected with a mutation-activated form of the epidermal growth factor receptor (EGFR). This is an important oncogene, which leads not only to carcinogenesis, but also to tumor progression, metastasis and worse prognosis for survival time of cancer patients (Chanprapaph et al. 2014). EGFR modulates cell survival, proliferation, angiogenesis and migration through activation of PI3K, MAPK and STAT3 signaling pathways (Taylor 2012). EGFR overexpression has been found in several tumor types, including glioblastoma, colorectal cancer, head and neck squamous cell carcinoma, non-small cell lung cancer, breast, renal, ovarian, bladder, prostate and pancreatic cancers (Gomez et al. 2013). EGFR overexpression also confers resistance to anticancer drugs (Wykosky et al. 2011). In the present investigation, we were able to identify a potential novel role for reserpine. The results that EGFR-transfected cells were collateral sensitive to this drug compared to non-transfected control cells and that reserpine binds with even higher affinity than erlotinib to the tyrosine kinase domain of EGFR indicates that reserpine may act as EGFR kinase inhibitor. This hypothesis warrants further more detailed investigations in the future.

Another important determinant of anticancer drug resistance is the tumor suppressor gene p53. Mutations in p53 lead to functional inactivation causing deregulation of cell cycle arrest, DNA repair, and apoptosis induction (Liao et al. 2014). Our results with p53-knockout tumor cells showed that a loss of p53 function was associated with decreased cytotoxicity toward reserpine compared to wild-type p53proficient cells.

Cross-resistance pattern of the NCI cell line panel between reserpine and standard drugs

We performed a large correlation analysis between the [log.sub.10][IC.sub.50] values of reserpine and those of 87 standard anticancer drugs with the aim to identify possible modes of action of reserpine. Interestingly, the highest correlation rates were found to anti-hormonal drugs (57%) and DNA alkylating agents (53%) followed by mTOR inhibitors (50%). These results are supported by hints from the literature.

Reserpine has previously been investigated in vivo endocrinic disruptor screening assay and was found to interact with the estrogen (Ohta et al. 2012; Koch et al. 1980) reported chemopreventive effects of reserpine. Prolonged estrogen exposure induce pituitary tumors. The authors found that elevated serum prolactin levels in rats induced by ethinyl estradiol were slightly reduced by reserpine.

Alkylating agents kill cancer cells by alkylation of DNA and subsequent induction of apoptosis. The interaction of reserpine to binds to the DNA repair enzyme MSH2, thereby triggering the MSH2dependent cell-death pathway (Vasilyeva et al. 2009). In this context it is important to mention that despite the inhibition of DNA repair, reserpine did not induce genetic damage and was not genotoxic (Tsutsui et al. 1994; Kevekordes et al. 1999) indicating that reserpine may not be carcinogenic.

The correlation of reserpine to two out of four mTOR inhibitors (sirolimus, temsirolimus) is a novel find and has not been recognized before. The hypothetical inhibitory effects of reserpine on estrogen receptor and related downstream signaling pathways deserve more detailed investigation in the future.

COMPARE and cluster analyses of microarray data

Apart from the low-level cross-resistance of HCT-116 [p53.sup.-/-] cells to reserpine compared to wild-type HCT-116 [p53.sup.+/+] cell, the pother drug-resistant cell lines overexpressing ABCB1, ABCG2, or EGFR did not reveal cross-resistance to reserpine. This indicates that the responsiveness of tumor cells toward reserpine may be determined by other factors. For this reason, we applied COMPARE and hierarchical cluster analyses of microarray-based transcriptome-wide mRNA expressions to find out, whether the expression of other than the classical drug resistance genes mentioned above correlate with reserpine resistance among the NCI cell line panel. COMPARE analysis has been reported as valuable method to identify the mode of action of anticancer drugs using the NCI tumor panel (Wosikowski et al. 2000; Evans et al. 2008; Fagan et al. 2012; Luzina and Popov 2012). Furthermore, microarray hybridization and clustering techniques have been widely applied for mechanistic studies of established and novel drugs in cancer research (for instance see Reinhold et al. 2012; Villeneuve and Parissenti 2004; Zeeberg et al., 2011)

COMPARE analysis revealed genes belonging to different functional classes, whose mRNA expression correlated to [log.sub.10][IC.sub.50] values of reserpine. Remarkably, genes involved in EGFR activation were identified (EFEMP1, EPS15LI). This result and the fact that U87MG EGFR transfected cells were collateral sensitive toward reserpine compared to non-transfectant wild-type U87MG cells indicate that EGFR signaling may indeed influence reserpine's cytotoxicity toward tumor cells. Further experiments shall analyze the signaling processes of EGFR upon reserpine treatment.

Furthermore, mRNA expression of genes involved in the regulation of cell survival and apoptosis (TRAFI, SULF1) and angiogenesis (ANCPTL4) were observed to correlate to cellular responsiveness to reserpine. Both apoptosis induction and the formation of new blood vessels in tumors are important factors determining tumor survival and growth. Therefore, apoptosis-inducing and anti-angiogenic drugs became effective weapons in the fight against cancer (Efferth 2010; Wahl et al. 2011; Cheng et al. 2012; Zihlif et al. 2012; Krusche et al. 2013; Seo et al. 2013). Our data indicate that reserpine kills tumors by induction of apoptosis and inhibition of tumor blood vessel formation.

One gene involved in mTOR signaling appeared in our analysis (PPAPD3). Since reserpine was also correlated with the activity of mTOR inhibitors (sirolimus, temsirolimus), it can be hypothesized that reserpine might disturb mTOR-related signaling routes.

Furthermore, the expression of a number of genes involved in cell mobility (ACTA2, HSP3, CNN1), cell adhesion (POSTN, MOC) and immunological functions (CF/, LBP, IL17B) appeared in the COMPARE analysis. These biological functions are all involved in the regulation of tumor microenvironment and metastasis, indicating that reserpine might inhibit metastatic spread of tumors.

In this context, the WNT signaling pathway is important not only for metastasis, but also for cancer stem-like cells (Efferth 2012). Whether reserpine reveals cytotoxicity toward stem cells warrants further investigations.

The fact that reserpine that revealed less profound cytotoxicity toward P-glycoprotein-, BCRP- or EGFR-expressing but p53-mutated tumor cells raises the question, whether or not these favorable results might be translatable to the clinical situation. Reserpine has been repeatedly shown to inhibit tumor growth in mice (Nelson et al. 1981), indicating that reserpine might not only exert anticancer activity not only in vitro and in animals, but possibly also in human cancer patients. Furthermore, reserpine has been clinically applied for decades to treat cardiovascular diseases. Therefore, using reserpine for cancer therapy as novel indication should not be out of reach.

Conclusion and perspectives

The anticancer activity of reserpine in vivo is known since the 1950s (Burton et al. 1956; Belkin and Hardy 1957). However, reserpine had not been further developed as cancer drug, probably due to high toxicity. The effects of reserpine on the cardiovascular system may appear as severe side effects in normotonic cancer patients, but may even be beneficial in comorbid cancer patients that suffer from hypertonia. Furthermore, reserpine may serve as lead compound to develop novel derivatives with cytotoxicity against drug-resistant tumors, but without activity on the cardiovascular system.

Speculating that reserpine would make its way into clinical oncology, it will be used as part of combination therapy regimens rather than as monotherapy. In this context, the inhibition of P-glycoprotein and increase of intracellular drug accumulation is a pleasing feature leading to improved cancer killing efficiencies. Previously, it has been reported that reserpine does not only improve the activity of anticancer drugs involved in MDR, but also of alkylating drugs probably by interfering with DNA repair mechanisms (Wakusawa et al. 1984).

Although the antihypertensive reserpine has already been described in the 1950s to reveal anticancer activity in vivo, its true potential has not been recognized at that time to our point of view. In the present investigation, we reinvestigated this drug because it reveals surprisingly profound activity toward otherwise drug-resistant tumors.

Cancer patients, preferentially older ones, suffer not only from their tumor but also from high blood pressure. Together with the activity of reserpine against drug-resistant tumor cells, reserpine might be a valuable supplement to combination therapy protocols to treat comorbid patients with cancer and hypertension.

In conclusion, the results of the present investigation speak for a promising role of reserpine in cancer chemotherapy due to its activity toward drug-resistant tumor cells. Reserpine deserves reconsideration and re-evaluation for cancer therapy in the clinical setting.

Conflict of interest

The authors declare they have no conflict of interest.


Article history:

Received 2 November 2014

Revised 7 December 2014

Accepted 12 January 2015

Abbreviations: ABC, ATP-binding cassette; BCRP, Breast cancer resistance protein; EGFR, Epidermal growth factor receptor; MAPK, Mitogen-activated protein kinase; MDR, Multidrug resistance; MSH2, MutS homologue 2, mismatch repair enzyme; mTOR, Mammalian target of rapamycin; NCI, National Cancer Institute; PI3K, Phosphoinositid-3-kinase; STAT3, Signal transducer and activator of transcription 3.


Akiyama, S., et at, 1988. Most drugs that reverse multidrug resistance also inhibit photoaffinity labeling of P-glycoprotein by a vinblastine analog. Mol. Pharmacol. 33 (2), 144-147.

Aller, S.G., et al., 2009. Structure of P-glycoprotein reveals a molecular basis for polyspecific drug binding. Science 323 (5922), 1718-1722.

Arced, R.J., 1993. Clinical significance of P-glycoprotein in multidrug resistance malignancies. J. Amer. Soc. Hematol. 81,2215-2222.

Bakris, G.L., Frohlich, E.D., 1989. The evolution of antihypertensive therapy: An overview of four decades of experience. J. Amer. College Cardiol. 14 (7), 15951608.

Beck, W.T., et al., 1988. Effect of indole alkaloids on multidrug resistance and labeling of P-glycoprotein by a photoaffinity analog of vinblastine. Biochem. Biophys. Res. Commun. 53 (3), 959-966.

Belkin, M., Hardy, W., 1957. Effect of reserpine. Science 125 (3241), 233-234.

Bhat, U.G., Winter, M.A., Pearce, H.L., Beck, W.T., 1995. A structure-function relationship among reserpine and yohimbine analogues in their ability to increase expression of mdrl and P-glycoprotein in a human colon carcinoma cell line. Mol. Pharmacol. 48 (4), 682-689.

Borra, R.C., et al., 2009. A simple method to measure cell viability in proliferation and cytotoxicity assays. Braz. Oral Res. 23 (3), 255-262.

Burton, R., et al., 1956. Reticular activating system of brain stem and "animal hypnosis" antileukemic action of reserpine. Science 125 (3239), 156-157,

Cantarella, G, Di Benedetto, G, Martinez, G, Loreto, C, dementi, G, Cantarella, A, Prato, A, Bernardini, R, 2009. Amylin prevents TRAIL-mediated apoptotic effects of reserpine in the rat gastric mucosa. Peptides 30 (8), 1466-1472.

Chanprapaph, K., Vachiramon, V., Rattanakaemakorn, P., 2014. Epidermal growth factor receptor inhibitors: A review of cutaneous adverse events and management. Dermatol. Res. Pract. 2014, 734249.

Cheng, J.-J., Tsai, T.-H., Lin, L-C, 2012. New alkaloids and cytotoxic principles from Sinomenium acutum. Planta Med. 78 (17), 1873-1877.

Doyle, L.A., Ross, D.D., 2003. Multidrug resistance mediated by the breast cancer resistance protein BCRP (ABCG2). Oncogene 22 (47), 7340-7358.

Efferth, T., 2010. Personalized cancer medicine: From molecular diagnostics to targeted therapy with natural products. Planta Med. 76(11), 1143-1154.

Efferth, T., et al., 2008. Prediction of broad spectrum resistance of tumors towards anticancer drugs. Clin. Cancer Res. 14 (8), 2405-2412.

Efferth, T., 2012. Stem cells, cancer stem-like cells, and natural products. Planta Med. 78 (10), 935-942.

Efferth, T., 2001. The human ATP-binding cassette transporter genes: From the bench to the bedside. Curr. Mol. Med. 1 (1), 45-65.

Efferth, T., Koch, E., 2011. Complex interactions between phytochemicals. The multitarget therapeutic concept of phytotherapy. Current Drug Targets 12 (1), 122-132. Eichhorn, T., Efferth, T., 2012. P-glycoprotein and its inhibition in tumors by phytochemicals derived from Chinese herbs. J. Ethnopharmacol. 141 (2), 557-570. El-Deiry, W.S., 1997. Role of oncogenes in resistance and killing by cancer therapeutic agents. Curr. Opin. Oncol. 9(1), 79-87.

El-Deiry, W.S., 2003. The role of p53 in chemosensitivity and radiosensitivity. Oncogene 22 (47), 7486-7495.

Evans, A., et al., 2008. Glut-1 as a therapeutic target: Increased chemoresistance and HIF-l-independent link with cell turnover is revealed through COMPARE analysis and metabolomic studies. Cancer Chemother. Pharmacol. 61 (3), 377-393.

Fagan, V., et al., 2012. COMPARE analysis of the toxicity of an iminoquinone derivative of the imidazo[5,4-f]benzlmidazoles with NAD(P)H:quinone oxidoreductase 1 (NQO1) activity and computational docking of quinones as NQO1 substrates. Bioorg. Med. Chem. 20 (10), 3223-3232.

Gillet, J.-P., Efferth, T., Remade, J., 2007. Chemotherapy-induced resistance by ATPbinding cassette transporter genes. Biochim. Biophys. Acta 1775 (2), 237-262.

Gomez, G.G., et al., 2013. Therapeutic resistance in cancer: microRNA regulation of EGFR signaling networks. Cancer Biol. Med. 10 (4), 192-205.

Hall, M.D., Handley, M.D., Gottesman, M.M., 2009. Is resistance useless? Multidrug resistance and collateral sensitivity. Trend. Pharmacol. Sci. 30 (10), 546-556.

Hetenyi, C., Van Der Spoel, D., 2002. Efficient docking of peptides to proteins without prior knowledge of the binding site. Prot. Sci. 11 (7), 1729-1737.

Jette, L, Murphy, G.F., Leclerc, J.M., Beliveau, R., 1995. Interaction of drugs with Pglycoprotein in brain capillaries. Biochem. Pharmacol. 50 (10), 1701-1709.

Juliano, R.L., Ling, V., 1976. A surface glycoprotein modulating drug permeability in Chinese hamster ovary cell mutants. Biochim. Biophys. Acta 455 (1), 152-162.

Kerr, J.F., Wyllie, A.H., Currie, A.R., 1972. Apoptosis: A basic biological phenomenon with wide-ranging implications in tissue kinetics. Brit. J. Cancer 26 (4), 239- 257.

Kevekordes, S., et al., 1999. SOS induction of selected naturally occurring substances in Escherichia coli (SOS chromotest). Mut. Res. 445 (1), 81-91.

Koch, M., et al., 1980. Induction of pituitary tumours and hyperprolactinemia in female rats by estrogens. The effect of apomorphine, reserpine and L-dopa. Arch. Toxicol, 89-91.

Krishan, A., Hamelik, R.M., 2005. Flow cytometric monitoring of fluorescent drug retention and efflux. Methods Mol. Med. Ill, 149-166.

Krusche, B., Arend, J., Efferth, T., 2013. Synergistic inhibition of angiogenesis by artesunate and captopril in vitro; and in vivo. Evid. Based Complement. Alternat. Med. 2013,454783.

Liao, J., et al., 2014. New insights into p 53 functions through its target microRNAs. J. Mol. Cell Biol. 6 (3), 206-213.

Luzina, E.L, Popov, A. V, 2012. Synthesis, evaluation of anticancer activity and COMPARE analysis of N-bis(trifluoromethyl)alkyl-N'-substituted ureas with pharmacophoric moieties. Euro. J. Med. Chem. 53,364-373.

Milne, F.J., Pinkney-Atkinson, V.J., 2004. Hypertension guideline 2003 update. S. Afr. Med. J. 94 (3 Pt 2), 220-225 pp. 209-16, 218.

Nelson, M., Nelson, D.S., Hopper, K.E., 1981.1. Tumor growth in mice with depressed capacity to mount inflammatory responses: Possible role of macrophages. Amer. J. Pathol. 104(2), 114-124.

Ohta, R., et al., 2012. Ovariectomized mouse uterotrophic assay of 36 chemicals. J. Toxicol. Sci. 37 (5), 879-889.

Panda, S.K., et al., 2012. Phyto-pharmacognostical studies and quantitative determination of reserpine in different parts of Rauwolfia (spp.) of Eastern Odisha by UV spectroscopy Method. Asian J. Plant Sci. Res. 2 (2), 151-162.

Pauli, K.D., et al., 1989. Display and analysis of patterns of differential activity of drugs against human tumor cell lines: Development of mean graph and COMPARE algorithm.). Natl. Cancer Inst. 81,1088-1092.

Pillay, T., 2009. A comparison of two methods for measuring anti-hypertensive drug use: Concordance of use with South African standard treatment guidelines. Bullet. World Health Organ. 87 (6), 466-471.

Qian, X., Beck, W.T., 1990. Binding of an optically pure photoaffinity analogue of verapamil, LU-49888, to P-glycoprotein from multidrug-resistant human leukemic cell lines. Amer. Assoc. Cancer Res. 50,1132-1137.

Reinhold, W.C., et al., 2012. CellMiner: A web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCi-60 cell line seL Cancer Res. 72 (14), 3499-3511.

Sarver, J.G., Klis, W.A., Byers, J.P., Erhardt, P.W., 2002. Microplate screening of the differential effects of test agents on Hoechst 33342, rhodamine 123, and rhodamine 6 G accumulation in breast cancer cells that overexpress P-glycoprotein. J. Biomol. Screen. 7(1), 29-34.

Schlemmer, S.R., Sirotnak, F.M., 1994. Functional studies of P-glycoprotein in insideout plasma membrane vesicles derived from murine erythroleukemia cells overexpressing MDR 3. Properties and kinetics of the interaction of vinblastine with P-glycoprotein and evidence for its active mediated transport. J. Biol. Chem. 269 (49), 31059-31066.

Seo, E., et al., 2013. Antiangiogenic activity and pharmacogenomics of medicinal plants from traditional Korean medicine. Evid. Based Complement. Alternat. Med. 2013, 131306.

Tajima, Y., et al., 2014. Nitensidine A, a guanidine alkaloid from Pterogyne nitens, is a novel substrate for human ABC transporter ABCB1. Phytomedicine 21 (3), 323-332.

Taylor, T.E., Furnari, F.B., Cavenee, W.K., 2012. Targeting EGFR for treatment of gliboblastoma: Molecular basis to overcome resistance. Curr. Cancer Drug Target. 12 (3), 197-209.

Tsutsui, T., et al., 1994. Reserpine-induced cell transformation without detectable genetic effects in Syrian hamster embryo cells in culture. Carcinogenesis 15 (1), 11 - 14.

Vasilyeva, A., et al., 2009. Small molecule induction of MSH2-dependent cell death suggests a vital role of mismatch repair proteins in cell death. DNA Rep. 8 (1), 103-113.

Villeneuve, D.J., Parissenti, A.M., 2004. The use of DNA microarrays to investigate the pharmacogenomics of drug response in living systems. Curr. Topic. Med. Chem. 4 (13), 1329-1345.

Wahl, 0., et al., 2011. Inhibition of tumor angiogenesis by antibodies, synthetic small molecules and natural products. Curr. Med. Chem. 18 (21), 3136-3155.

Wakusawa, S., Miyamoto, IC, Koshiura, R., 1984. Increase of sensitivity and uptake of vinblastine by reserpine in rat ascites hepatoma. Japan. J. Pharmacol. 36 (2), 187195.

Wosikowski, K., et al., 2000. Reduced growth rate accompanied by aberrant epidermal growth factor signaling in drug resistant human breast cancer cells. Biochim. Biophys. Acta 1497 (2), 215-226.

Wykosky, J., et al., 2011. Therapeutic targeting of epidermal growth factor receptor in human cancer: Successes and limitations. Chin. J. Cancer 30 (1), 5-12.

Yadav, I.S., et al., 2014. Ensemble docking and molecular dynamics identify knoevenagel curcumin derivatives with potent anti-EGFR activity. Gene 539 (1), 82-90.

Zamora, J.M., Pearce, H.L., Beck, W.T., 1988. Physical-chemical properties shared by compounds that modulate multidrug resistance in human leukemic cells. Mol. Pharmacol. 33 (4), 454-462.

Zeeberg, B.R., et al., 2011. RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis. BMC Bioinform. 12,52.

Zeino, M., et al., 2014. The ability of molecular docking to unravel the controversy and challenges related to P-glycoprotein--A well-known, yet poorly understood drug transporter. Invest. New Drug 32 (4), 618-625.

Zihlif, M., et al., 2012. Screening the antiangiogenic activity of medicinal plants grown and sold in Jordan. Planta Med. 78 (3), 297-301.

Sara A.A. Abdelfatah, Thomas Efferth *

Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University of Mainz, Staudinger Weg 5, 55128 Mainz, Germany

* Corresponding author. Tel.: +49 6131 3925751; fax: +49 6131 3923752.

E-mail address: (T. Efferth).

Table 1
Molecular docking of reserpine to P-glycoprotein/ABCBl and tyrosine
kinase domain of EFGR.

Target    Compound    Lowest binding          Mean binding
protein               energy (kcal/mol)       energy (kcal/mol)

ABCB1     Reserpine   -9.65 ([+ or -] 0.34)   -9.12 ([+ or -] 0.79)
          Verapamil   -8.57 ([+ or -] 0.13)   -8.57 ([+ or -] 0.12)
EGFR      Reserpine   -7.86 ([+ or -] 1.57)   -7.32 ([+ or -] 1.12)
          Erlotinib   -6.81 ([+ or -] 0.18)   -5.93 ([+ or -] 0.3)

Target    Compound    Residues involved   Number of residues
protein               in hydrogen bond    involved in
                                          hydrophobic interaction

ABCB1     Reserpine   Gly226 Lys234       14
          Verapamil   Lys234              15
EGFR      Reserpine   Lys721 Met769       10
          Erlotinib   Met769 Cys773       13

Target    Compound    pKi * (nM)

ABCB1     Reserpine   92.75 ([+ or -] 42.2)
          Verapamil   526.79 ([+ or -] 117.04)
EGFR      Reserpine   531.2 ([+ or -] 361.08)
          Erlotinib   10230 ([+ or -] 0.34)

* pKi: Predicted inhibitory constant.

Table 2
COMPARE analysis of genes, whose microarray-based mRNA expression
correlated with [log.sub.10] [IC.sub.50] values for reserpine in a
panel of 43 cell lines.

R-value   Gene symbol    Genbank     Pattern ID

Standard compare (resistance genes)

 0.666    MYBPH          NM_004997   CC183640
 0.651    SLC9A9         AI536067    GC66696
 0.641    OLFML2B        AL050137    GC83013
 0.636    BBS9           AF095771    GC152697
 0.631    EFEMP1         AI740711    GC159025
 0.621    CPI            U83411      GC97760
 0.618    TRAF1          U19261      GC32739
 0.617    FMOl           NM_002021   GC181437
 0.617    TMEM132B       AI435595    GC157072
 0.615    CTNNA2         M94151      GC34818
 0.614    ANGPTL4        AF169312    GC153504
 0.613    SULF1          A1479175    GC157295
 0.611    CFI            Y00318      GC101200
 0.607    CH25H          AF059214    GC56092
 0.604    HSPB3          NM_006308   GC184572
 0.603    ACTA2          X13839      GC35741
 0.602    DIXDC1         AF070621    GC26930
 0.602    SFRP2          A1246042    GC61488
 0.601    POSTN          D13666      GC85573
 0.599    ZNF385D        NM_024697   GC189574
 0.596    ATP8B2         AB032963    GC151263
 0.595    C10orf72       AL080114    GC83204
 0.594    CHI3L1         M80927      GC179212
 0.594    CNN1           D17408      GC37204
 0.591    COL1A2         AA628535    GC149250
 0.591    LBP            NM_004139   GC182970
 0.591    ODF2           AL138382    GC165390
 0.591    CLCA2          NM_006536   GC184752
 0.59     PPAPDC3        BC006362    GC172554
 0.59     DCTN3          W26651      GC30840
 0.589    RBP4           NM_006744   GC184918
 0.589    BGN            NM_001711   GC181189
 0.588    MOG            U64565      GC28275
 0.586    SMAP2          AI702142    GC71360
 0.583    PSMB9          AI758695    GC73064
 0.58     LDB2           NM_001290   GC180889
 0.578    IL17B          NM 014443   GC186079
 0.573    EPS15L1        AV710549    GC167431
 0.573    LOC284542      BF060736    GC174719

Reverse COMPARE (Sensitive genes)

-0.635    C1orf86        H41433      GC13018
-0.632    ZRANB1         AW269836    GC169305
-0.592    MGC70870       AA278816    GC42843
-0.59     CTBP2          AF016507    GC55340
-0.586    CCND1          T89175      GC11864
-0.585    LOCI00287615   AI700233    GC71205
-0.574    CMTM4          W46185      GC15744
-0.552    LARP1B         AK027164    GC163652
-0.539    KCNQ4          H26683      GC12683
-0.534    DOCK6          AI198543    GC60349
-0.53     MYL12B         U26162      GC191176
-0.526    SMC5           N70436      GC15093
-0.524    LOC285147      H23213      GC87116
-0.524    ZNF205         AF060865    GC37936
-0.523    S1K2           AA142956    GC41241
-0.519    TMED5          N49615      GC14323
-0.519    AGAP1          AB029022    GC37681
-0.518    RBMS2          R99202      GC13107
-0.513    TM9SF3         N93209      GC15318
-0.512    SJ8SIA3        N62107      GC14703
-0.512    HSPC159        AK025603    GC163255
-0.507    SPATA20        H15544      GC11293
-0.506    ZNF652         AA057773    GC17251
 0.506    MRPL17         AI141700    GC59526
-0.504    C13orf1        N30354      GC14024
-0.501    TAC1           N47310      GC14485
-0.5      GCOM1          N51713      CC14400

R-value   Gene symbol    Gene name

Standard compare (resistance genes)

 0.666    MYBPH          Myosin binding protein H
 0.651    SLC9A9         Solute carrier family 9
 0.641    OLFML2B        Olfactomedin-like 2B RNA
 0.636    BBS9           Bardet-Biedl syndrome 9
 0.631    EFEMP1         EGF-containing fibulin-like extracellular
                           matrix protein 1
 0.621    CPI            Carboxypeptidase Z
 0.618    TRAF1          TNF receptor-associated factor 1
 0.617    FMOl           Flavin containing monooxygenase 1
 0.617    TMEM132B       Transmembrane protein 132B
 0.615    CTNNA2         Catenin (cadherin-associated protein),
                           [alpha] 2
 0.614    ANGPTL4        Angiopoietin-like 4
 0.613    SULF1          Sulfatase 1
 0.611    CFI            Complement factor 1
 0.607    CH25H          Cholesterol 25-hydroxylase
 0.604    HSPB3          Heat shock 27 kDa protein 3
 0.603    ACTA2          Actin, [alpha] 2, smooth muscle, aorta
 0.602    DIXDC1         DIX domain containing 1

 0.602    SFRP2          Secreted frizzled-related protein 2
 0.601    POSTN          Periostin, osteoblast specific factor
 0.599    ZNF385D        Zinc finger protein 385D
 0.596    ATP8B2         ATPase, class 1, type 8B, member 2
 0.595    C10orf72       Chromosome 10 open reading frame 72
 0.594    CHI3L1         Chitinase 3-like 1 (cartilage
 0.594    CNN1           Calponin 1, basic, smooth muscle
 0.591    COL1A2         Collagen, type 1, [alpha]
 0.591    LBP            Lipopolysaccharide binding protein
 0.591    ODF2           Outer dense fiber of sperm tails 2
 0.591    CLCA2          Chloride channel accessory 2
 0.59     PPAPDC3        Phosphatidic acid phosphatase type 2 domain
                           containing 3
 0.59     DCTN3          Dynactin 3 (p22)
 0.589    RBP4           Retinol binding protein 4, plasma
 0.589    BGN            Biglycan
 0.588    MOG            Myelin oligodendrocyte glycoprotein
 0.586    SMAP2          Small ArfGAP2
 0.583    PSMB9          Proteasome (prosome, macropain) subunit, beta
                           type, 9 (large multifunctional peptidase 2)
 0.58     LDB2           LIM domain binding 2
 0.578    IL17B          Interleukin 17B
 0.573    EPS15L1        Epidermal growth factor receptor pathway
                           substrate 15-like
 0.573    LOC284542      Hypothetical protein LOC284542

Reverse COMPARE (Sensitive genes)

-0.635    Clorf86        Chromosome 1 open reading frame 86
-0.632    ZRANB1         Zinc finger, RAN-binding domain containing 1
-0.592    MGC70870       C-terminal binding protein 2 pseudogene
-0.59     CTBP2          C-terminal binding protein 2
-0.586    CCND1          Cyclin D1
-0.585    LOCI00287615   Hypothetical protein LOCI 00287615
-0.574    CMTM4          CKLF-like MARVEL transmembrane domain
                           containing 4
-0.552    LARP1B         La ribonucleoprotein domain family, member 1B
-0.539    KCNQ4          Potassium voltage-gated channel, KQT-like
                           subfamily, member 4
-0.534    DOCK6          Dedicator of cytokinesis 6
-0.53     MYL12B         Myosin, light chain 12B, regulatory
-0.526    SMC5           Structural maintenance of chromosomes 5
-0.524    LOC285147      Hypothetical protein LOC285147
-0.524    ZNF205         Zinc finger protein 205
-0.523    S1K2           Salt-inducible kinase 2
-0.519    TMED5          Transmembrane emp24 protein transport domain
                           containing 5
-0.519    AGAP1          ArfGAP with GTPase domain, ankyrin repeat
                           and PH domain 1
-0.518    RBMS2          RNA binding motif, single stranded
                           interacting protein 2
-0.513    TM9SF3         Transmembrane 9 superfamily member 3
-0.512    SJ8SIA3        ST8 [alpha]-N-acetyl-neuraminide
                           [alpha]-2,8-sialyltransferase 3
-0.512    HSPC159        Galectin-related protein
-0.507    SPATA20        Spermatogenesis associated 20
-0.506    ZNF652         Zinc finger protein 652
 0.506    MRPL17         Mitochondrial ribosomal protein L17
-0.504    C13orfl        Chromosome 13 open reading frame 1
-0.501    TAC1           Tachykinin, precursor 1
-0.5      GCOM1          GRINL1A complex locus RNA

R-value   Gene symbol    Gene function

Standard compare (resistance genes)

 0.666    MYBPH          Structural constituent of muscle
 0.651    SLC9A9         Solute hydrogen antiporter activity
 0.641    OLFML2B        Extracellular matrix binding
 0.636    BBS9           Required for ciliogenesis
 0.631    EFEMP1         Epidermal growth factor-activated receptor
 0.621    CPI            Metallocarboxypeptidase activity
 0.618    TRAF1          Regulation of cell survival and apoptosis
 0.617    FMOl           N,N-dimethylaniline monooxygenase activity
 0.617    TMEM132B       Molecular function
 0.615    CTNNA2         Structural constituent of cytoskeleton
 0.614    ANGPTL4        Protein with hypoxia-induced expression in
                           endothelial cells acts as a regulator of
 0.613    SULF1          Diminishes proliferation, and facilitates
 0.611    CFI            Inactivates complement subcomponents
 0.607    CH25H          Cholesterol 25-hydroxylase activity
 0.604    HSPB3          Inhibitor of actin polymerization
 0.603    ACTA2          Cell motility
 0.602    DIXDC1         Positive effector of the Wnt signaling
 0.602    SFRP2          Modulator of Wnt signaling
 0.601    POSTN          Cell attachment and adhesion
 0.599    ZNF385D        Nucleic acid binding
 0.596    ATP8B2         Nucleotide binding
 0.595    C10orf72       Protein binding
 0.594    CHI3L1         Important role in the capacity of cells to
                           respond to and cope with environmental
 0.594    CNN1           Actin binding
 0.591    COL1A2         Protein binding
 0.591    LBP            Affinity enhancer for CD14, facilitating its
                           association with lipopolysaccharides
 0.591    ODF2           Structural molecule activity
 0.591    CLCA2          May act as a tumor suppressor in breast and
                           colorectal cancer
 0.59     PPAPDC3        Negative regulator of myoblast
 0.59     DCTN3          Involved in spindle assembly and cytokinesis
 0.589    RBP4           Retinol transporter activity
 0.589    BGN            May be involved in collagen fiber assembly
 0.588    MOG            Mediates homophilic cell-cell adhesion
 0.586    SMAP2          ARF GTPase activator activity
 0.583    PSMB9          Threonine-type endopeptidase activity
 0.58     LDB2           Transcription factor binding transcription
                           factor activity
 0.578    IL17B          Stimulates the release of tumor necrosis
                           factor [alpha] and IL-1-[beta]
 0.573    EPS15L1        Role in receptor-mediated endocytosis
 0.573    LOC284542      Unknown

Reverse COMPARE (Sensitive genes)

-0.635    Clorf86        Polyubiquitin binding
-0.632    ZRANB1         Ubiquitin thiolesterase activity/positive
                           regulator of Wnt signaling pathway
-0.592    MGC70870       Unknown
-0.59     CTBP2          Transcription corepressor activity
-0.586    CCND1          Cyclin-dependent protein serine/threonine
                           kinase regulator activity
-0.585    LOCI00287615   Unknown
-0.574    CMTM4          Cytokine activity
-0.552    LARP1B         Nucleic acid binding
-0.539    KCNQ4          Delayed rectifier potassium channel activity
-0.534    DOCK6          Guanyl-nucleotide exchange factor activity
-0.53     MYL12B         Myosin regulatory subunit
-0.526    SMC5           DNA double-strand repair
-0.524    LOC285147      Unknown
-0.524    ZNF205         Transcriptional regulation
-0.523    S1K2           Activates insulin signaling pathway
-0.519    TMED5          Vesicular protein trafficking, maintenance
                           of Golgi apparatus
-0.519    AGAP1          GTPase-activating protein
-0.518    RBMS2          Nucleotide binding
-0.513    TM9SF3         Unknown
-0.512    SJ8SIA3        [alpha]-N-acetylneuraminatea
                           [alpha]-2,8-sialyltransferase activity
-0.512    HSPC159        Carbohydrate binding
-0.507    SPATA20        Fertility regulation
-0.506    ZNF652         Transcriptional repressor
 0.506    MRPL17         Structural constituent of ribosome
-0.504    C13orfl        Protein binding
-0.501    TAC1           Active peptides which excite neurons
-0.5      GCOM1          Unknown

Only correlations with cut-offs of R > 0.5 and R < -0.5 were
considered. Gene function information was retrieved from Gene Card
database (

Table 3
Separation of clusters of cancer cell lines obtained by hierarchical
cluster analysis for reserpine. The [log.sub.10] [IC.sub.50] median
value (M) of reserpine was used as cut-off.

Partition              Cluster 1   Cluster 2   Cluster 3   Cluster 4

Sensitive   <-4.80 M   3            5          8           5
Resistant   >-4.80 M   8           11          2           0

P = 0.00437 ([chi square]-test)
COPYRIGHT 2015 Urban & Fischer Verlag
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2015 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Abdelfatah, Sara A.A.; Efferth, Thomas
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Date:Feb 15, 2015
Previous Article:Natural lignans from Arctium lappa modulate P-glycoprotein efflux function in multidrug resistant cancer cells.
Next Article:Clinical tolerability and pharmacokinetics of Erigerontis hydroxybenzene injection: results of a randomized phase I study in healthy Chinese...

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters