in Cancer Cells - American Chemical Society

Article pubs.acs.org/jnp

Pharmacogenomic Characterization of Cytotoxic Compounds from Salvia of f icinalis in Cancer Cells Onat Kadioglu and Thomas Efferth* Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany S Supporting Information *

ABSTRACT: Salvia of f icinalis is used as a dietary supplement with diverse medicinal activity (e.g. antidiabetic and antiatherosclerotic effects). The plant also exerts profound cytotoxicity toward cancer cells. Here, we investigated possible modes of action to explain its activity toward drug-resistant tumor cells. Log10IC50 values of two constituents of S. off icinalis (ursolic acid, pomolic acid) were correlated to the expression of ATP-binding cassette (ABC) transporters (P-glycoprotein/ABCB1/ MDR1, MRP1/ABCC1, BCRP/ABCG2) and epidermal growth factor receptor (EGFR) or mutations in RAS oncogenes and the tumor suppressor gene TP53 of the NCI panel of cell lines. Gene expression profiles predicting sensitivity and resistance of tumor cells to these compounds were determined by microarray-based mRNA expressions, COMPARE, and hierarchical cluster analyses. Furthermore, the binding of both plant acids to key molecules of the NF-κB pathway (NF-κB, I-κB, NEMO) was analyzed by molecular docking. Neither expression nor mutation of ABC transporters, oncogenes, or tumor suppressor genes correlated with log10IC50 values for ursolic acid or pomolic acid. In microarray analyses, many genes involved in signal transduction processes correlated with cellular responsiveness to these compounds. Molecular docking indicated that the two plant acids strongly bound to target proteins of the NF-κB pathway with even lower free binding energies than the known NF-κB inhibitor MG-132. They interacted more strongly with DNA-bound NF-κB than free NF-κB, pointing to inhibition of DNA binding by these compounds. In conclusion, the lack of cross-resistance to classical drug resistance mechanisms (ABCtransporters, oncogenes, tumor suppressors) may indicate a promising role of the both plant acids for cancer chemotherapy. Genes involved in signal transduction may contribute to the sensitivity or resistance of tumor cells to ursolic and pomolic acids. Ursolic and pomolic acid may target different steps of the NF-κB pathway to inhibit NF-κB-mediated functions.

W

Furthermore, S. off icinalis has been used in traditional European as well as Chinese medicine for memory improvement and against dementia.13,14 The traditional use of S. of f icinalis could be pharmacologically verified. The activity of this herb on memory, cognitive impairment, anxiety disorders, and Alzheimer’s disease has been clinically tested.15−17 S. of f icinalis is also efficient in the treatment of hot flashes and erythema and revealed in vitro activity toward Herpes simplex viruses 1 and 2.18−20 In addition, sage’s cytotoxicity toward cancer cells has been recently described, e.g., toward C32 melanoma, renal ACHN kidney cancer, LNCaP prostate carcinoma, and MCF-7 breast cancer cell lines.21 S. off icinalis extracts inhibited proliferation and induced apoptosis in HCT115 and CO115 colon carcinoma cell lines.22 The formation of tumor blood vessels represents an important determinant of tumor growth in vivo. S.

ith more than 900 species, the genus Salvia belongs to the largest families of Angiospermae. The name is derived from the Latin word salvare, which means to heal, referring to the fact that many Salvia species are traditionally used as medicinal and dietary herbs. The aromatic leaves of Salvia of f icinalis L. and S. f ruticosa MILL. have been used as a spice for fat-rich and heavy meals to foster digestion. The beneficial effects have been confirmed in animal experiments. S. off icinalis infusion improved the liver antioxidant status in mice and rats.1 Furthermore, sage (S. off icinalis) is used as an additive for sausage as an oxidative stabilizer.2 Dietary chia seed (S. hispanica L.) and the Chinese S. milthiorhizza Bunge attenuated blood glucose levels and improved adiposity,3 normalized hypertriacylglycerolemia, and acted in an antidiabetic manner in animals and human beings.4−6 S. milthiorhizza also prevented atherosclerotic plaque formation.7−11 Sage has also been used in agricultural livestock, e.g., S. triloba L. as a dietary supplement to improve egg production in quail.12 © XXXX American Chemical Society and American Society of Pharmacognosy

Received: December 13, 2014

A

DOI: 10.1021/np501007n J. Nat. Prod. XXXX, XXX, XXX−XXX

Journal of Natural Products

Article

off icinalis inhibited angiogenesis in vitro and in vivo.23 Three diterpenoid quinones (royleanone, horminone, and acetylhorminone) isolated from the roots of S. off icinalis induced alkalilabile DNA damage and apoptosis in Caco-2 colon carcinoma HepG2 hepatoma cells.24 A major problem of current cancer chemotherapy is the development of resistance, leading to failure of treatment with fatal outcomes for patients. Therefore, novel drugs are needed to combat drug resistance. Whether or not the cytotoxic activity of sage species can be exploited to treat otherwise drug-resistant tumor cells is not well understood as of yet. Starting with the chemoprofiles of three Salvia species (S. dorisiana, S. off icinalis, and S. sclarea), we focused on two phytochemicals derived from S. off icinalis, i.e., ursolic acid and pomolic acid. These two compounds have been investigated in detail in a panel of tumor cell lines of the National Cancer Institute (NCI, USA). We addressed the question of whether the expression and mutational status of well-known drug resistance genes (ABCB1, ABCB5, ABCC1, ABCG2) and oncogenes and tumor suppressor genes (EGFR, RAS, TP53) can influence the cellular response toward ursolic acid and pomolic acid in these cell lines. Furthermore, microarray-based mRNA expression data were subjected to COMPARE and hierarchical cluster analyses to identify gene expression profiles that correlated with sensitivity or resistance of the cell line panel to these two phytochemicals.

■

RESULTS AND DISCUSSION Chemoprofiling of Different Salvia Species. As a first step, we attempted to establish chemoprofiles for three Salvia species (S. dorisiana, S. off icinalis, and S. sclarea). We subjected the chemical compositions of these Salvia species (Dr. Duke’s Phytochemical and Ethnobotanical Databases; http://www.arsgrin.gov/cgi-bin/duke/farmacy2.pl) to hierarchical cluster analysis (Figure 1). A total of 209 phytochemicals have been included in the analysis, which are listed in detail in Supplementary Figure 1. Fifteen compounds were commonly found in all three Salvia species, and these compounds clustered together (cineole, pinenes, terpineols, caryophyllenes, camphene, geraniol, limonene, linalool, myrcene, cymene). Another 30 compounds were found in two of the three species, whereas all other compounds were found in only one Salvia species. This dendrogram demonstrates the considerable divergence of chemical composition in the Salvia species. In the present investigation, we first applied hierarchical cluster analysis for chemical profiling of three Salvia species. Phytochemical profiling has been used in the past to test the hypothesis that the phytochemical constitution of plants can be used for taxonomy of plants. While this approach seems attractive at first sight, its taxonomic value is controversial, because the phytochemical constitution can vary within the same species due to varying external stimuli and growth conditions.25 Although our analysis was limited to only three Salvia species, we have no information on Salvia-specific chemoprofiles, because the 15 compounds commonly present in all three species have a wide distribution over many plant families. For our further experimentation, we selected two compounds from S. off icinalis, which are present in this but not the other two species, i.e., ursolic acid and pomolic acid. Nevertheless, these two compounds are also not specific for S. off icinalis, and they can be found in other plants as well. Cross-Resistance Profiles of Ursolic Acid and Pomolic Acids. To explore possible modes of action, we correlated the

Figure 1. Dendrogram obtained by hierarchical cluster analysis of phytochemical constituents of Salvia of f icinalis, S. dorisania, and S. sclarea. Chemoprofiling of plants by cluster analysis may be termed “herbalomics”. A detailed view of this figure listing the compound names is provided in Supplementary Figure 1.

log10IC50 values of the NCI cell line panel for ursolic and pomolic acids with those of 76 clinically established anticancer drugs (Table 1). Statistically significant correlations were found between these two compounds and several alkylating agents, platinum compounds, antimetabolites, and DNA topoisomerase II inhibitors. All of these anticancer drugs act on DNA, indicating that ursolic acid and pomolic acid might also damage DNA. A few more correlations were found to drugs from other drug classes (Table 1). Our investigation of the cross-resistance profiles of ursolic and pomolic acids toward standard anticancer drugs demonstrated significant correlations between these compounds and several DNA-damaging agents (alkylating agents, platinum compounds, antimetabolites, and DNA topoisomerase II). This indicates that these plant acids may act on DNA. Indeed, cytotoxic concentrations of ursolic acid caused DNA damage and enhanced ionizing radiation-induced apoptosis in cancer cells.26,27 However, noncytotoxic doses of ursolic acid prevent DNA damage induced by other genotoxic compounds.28,29 Comparable results for pomolic acid have not been documented in the literature yet. Tumor-Type-Dependent Activity of Ursolic and Pomolic Acids. A total of 17 compounds of S. off icinalis B



Article

Table 1. Cross-Resistance Profile of Ursolic and Pomolic Acid toward Established Anticancer Drugs in the NCI Cell Line Panel test compound

pomolic acid

alkylating drugs

thiotepa

R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value R-value P-value

chlorambucil melphalan azathioprin platinum compounds

cisplatin carboplatin

antimetabolites

pemetrexed hydroxyurea mercatopurin gemcitabine

epipodophyllotoxins, DNA topoisomerase 2 inhibitors antihormones

etoposide tamoxifen anastrozol

a

tyrosine kinase inhibitors

vemurafenib

varia

zoledronate

ursolic acid

pomolic acid

0.21 0.026a 0.224 0.048a 0.227 0.056 0.229 0.048 0.286 0.016a 0.255 0.029a 0.269 0.024 0.429a 5.44 × 10−4a 0.331a 0.006a 0.363a 0.003a 0.195 0.079 0.193 0.080 0.106 0.238 −0.035 0.401 0.201 0.071

0.487a 1.91 × 10−4a 0.348a 0.005a 0.324a 0.009a 0.334a 0.008a 0.356a 0.005a 0.328a 0.008a 0.410a 0.001a 0.311a 0.013a 0.421a 9.53 × 10−4a 0.251 0.034 0.277 0.022 0.335a 0.008a 0.332a 0.010a 0.350a 0.011a 0.319a 0.011a 0.315a 0.011a

R > 0.30 and P < 0.05.

be valuable to kill otherwise drug-resistant tumors. To analyze this hypothesis in more detail, we investigated well-known mechanisms of anticancer drug resistance, e.g., ABC-transporter genes (ABCB1, ABCB5, ABCC1, ABCG2), oncogenes, and tumor suppressor genes (EGFR, RAS, TP53). Classical Mechanisms of Drug Resistance. It can be speculated that the varying sensitivities of the tumor cell lines to ursolic and pomolic acids may be due to differences in the expression of the drug resistance mechanisms in the tumor cells. Therefore, we correlated the log10IC50 values of these two compounds for the cell line panel with different parameters of the ATP-binding cassette (ABC) efflux transporter Pglycoprotein/MDR1/ABCB1. We used the mRNA expression as determined by microarray hybridization or RT-PCR of the Pglycoprotein-encoding MDR1/ABCB1 gene as well as gain of DNA at the chromosomal locus 7p21 (where the MDR1/ ABCB1 gene is located) and the intracellular rhodamine 123 (R123) accumulation rates. R123 is a P-glycoprotein substrate, and the flow cytometric determination of R123 uptake can serve as a functional assay for P-glycoprotein activity.30,31 As shown in Table 2,32 none of these parameters revealed significant correlations with the log10IC50 values for ursolic acid and pomolic acid, indicating that their cellular sensitivity was not related to the expression of MDR1/ABCB1 or the activity of P-glycoprotein. For comparison, the established anticancer drug daunorubicin was used as a positive control.

have been mined in the NCI database. Seven of them were either not active or minimally active. The 10 cytotoxic compounds are depicted in Figure 2A. Of them, caffeic acid and hispulidin were the least active substances, while ursolic acid and pomolic acid were the most active ones. Therefore, we have chosen ursolic and pomolic acids for further investigations. Despite the structural similarity of both phytochemicals, they showed overlapping but partly differing activity profiles (Figure 2B and C). If the average log10IC50 values over the entire NCI panel of cell lines were diversified regarding their tumor types, renal cancer and leukemia cell lines were the most sensitive to ursolic acid, whereas lung cancer and colon cancer cells were most resistant to this compound (Figure 2B). For comparison, leukemia and melanoma cells were the most sensitive, while ovarian and colon cancer cell lines were the most resistant to pomolic acid (Figure 2C). Ursolic acid and pomolic acid revealed overlapping cytotoxicity profiles in the NCI cell line panel. Clinically established anticancer drugs frequently show high sensitivity toward leukemia, while solid cancers tend to be more resistant to cytostatic drugs. While leukemia cell lines also belonged to the most sensitive tumor cell lines upon treatment with ursolic and pomolic acids, melanoma, renal, lung, and colon cancer cell lines also responded well to these two compounds. These tumor types are known for their unresponsiveness to standard anticancer drugs, indicating that ursolic acid and pomolic might C



Article

Daunorubicin is a well-known substrate of P-glycoprotein. As expected, the log10IC50 values for daunorubicin significantly correlated with all of these P-glycoprotein/MDR1/ABCB1 parameters (Table 2).32 The microarray data for MDR1/ABCB1 mRNA expression have been validated by correlation of these mRNA expressions with those of mRNA expression values obtained by RT-PCR. As expected, a statistically significant relationship has been found (Table 2). Similarly, significant relationships have been observed between microarray-based mRNA expression to the gain of DNA at chromosomal locus 7q21 and cellular R123 accumulation (Table 2). In addition to ABCB1, the correlation of log10IC50 of ursolic acid and pomolic acid to other ABC-transporters has been analyzed. Neither compound significantly correlated to the mRNA expression of ABCB5 (Table 3),33 MRP1/ABCC1 (Table 4),34,35 or BCRP/ABCG2 (Table 5).36 By contrast, control compounds known from the literature to be substrates of these transporters revealed significant correlations, as can be expected (Tables 3−5). Again, microarray-based mRNA expressions of these ABC-transporters in the cell line panel correlated with RT-PCR-based mRNA expression values, indicating the correctness of microarray hybridizations (Tables 3−5). Taken together, these results indicate that neither ursolic nor pomolic acid are substrates of these four ABC-transporters and that multidrug resistance phenotypes caused by these drug efflux transporters do not confer cross-resistance to ursolic and pomolic acid. ABC-transporters are not the only cause of pleiotropic drug resistance. Oncogenes and tumor suppressor genes also confer drug resistance in addition to their role in carcinogenesis.37−39 Therefore, we investigated whether or not EGFR and RAS oncogenes and the tumor suppressor gene TP53 influence cellular response to ursolic and pomolic acids. As can be seen in Tables 6−8, neither overexpression nor mutation of these genes affected responsiveness of the cell lines to ursolic acid and pomolic acid.40−42 For comparison, significant correlations have been observed for standard anticancer agents, i.e., erlotinib (for EGFR), 5-fluorouracil (for TP53), and melphalan (for RAS). Another determinant of tumor cell line susceptibility toward anticancer drugs is their proliferative activity. We have chosen cell doubling time and cell cycle distribution as proliferation markers and correlated them with the log10IC50 values of ursolic acid and pomolic acid. Whereas a classical drug such as 5fluorouracil revealed significant correlations to cell doubling times and S-phase cell cycle fractions, ursolic and pomolic acid did not (Table 9).43 ABC-transporters have been well characterized during the past years to expel a large number of anticancer drugs and xenobiotic compounds leading to survival of cancer cells. The clinical relevance of the major ABC-transporters ABCB1, ABCC1, and ABCG2 for failure of chemotherapy and worse survival prognoses of cancer patients has been shown.44 ABCB5 represents a novel resistance-mediating ABC-transporter, which is expressed in cancer stem-like cells. Therefore, it is important to develop novel anticancer drugs that kill ABC-transporterexpressing multidrug-resistant tumors. Our data show that ursolic acid and pomolic acid have the potential to kill such cells. Drug resistance is frequently multifactorial in nature, and other mechanisms contribute to the failure of chemotherapy

Figure 2. Mean log10IC50 values of cytotoxic phytochemicals from Salvia of f icinalis for tumor cell lines of the NCI drug screening panel as assayed by the sulforhodamine B test. Mean values and standard deviations are shown for the entire cell line panel (A) or grouped according to the tumor origin of the cell lines (B, C). D



Article

Table 2. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to the Gain of the Chromosomal Locus of the ABCB1 Gene (7q21), Expression of ABCB1 mRNA (by Microarray and RT-PCR), and P-Glycoprotein Function (Cellular Rhodamine 123 Accumulation) in the NCI Cell Line Panela ABCB1 expression (microarray) 7q21 (chromosomal locus of ABCB1 gene) ABCB1 expression (microarray) ABCB1 expression (RT-PCR) rhodamine 123 accumulation

Rvalue Pvalue Rvalue Pvalue Rvalue Pvalue Rvalue Pvalue

ABCB1 expression (RT-PCR)

rhodamine 123 accumulation

ursolic acid (log10IC50, M)

pomolic acid (log10IC50, M)

daunorubicin (log10IC50, M)

0.966b

0.012

0.561b

0.045

0.098

0.597b

4.24 × 10−33b

0.467

5.08 × 10−6b

0.379

0.254

4.82 × 10−6b

0.799b

0.717b

−0.030

0.074

0.684b

1.77 × 10−12b

8.65 × 10−11b

0.414

0.302

1.57 × 10−8b

−0.262

0.021

0.579b

0.037

0.445

4.19 × 10−6b

−0.077

0.039

0.544b

0.290

0.392

1.51 × 10−5b

0.564b 8.30 × 10−6b

a

Daunorubicin was used as positive control, as it is a well-known substrate of P-glycoprotein.32 The analysis was performed by means of Pearson’s rank correlation test. bP < 0.05 and R > 0.3.

Table 3. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to the Expression of ABCB5 mRNA (by Microarray Analyses and RT-PCR) in the NCI Cell Line Panela ABCB5 expression (microarray) ABCB5 expression (RT-PCR)

R-value P-value R-value P-value

ABCB1 expression (RT-PCR)



maytansine (log10IC50, M)

0.790b 3.24 × 10−14b

0.118 0.193 0.265 0.0242b

−0.132 0.174 −0.014 0.460

0.454b 6.67 × 10−4b 0.402b 0.0034b

a

Maytansine was used as a positive control, as it is a well-known substrate of ABCB5.33 The analysis was performed by means of Pearson’s rank correlation test. bP < 0.05 and R > 0.3.

Table 4. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to DNA Gene Copy Number and ABCC1 mRNA Expression (by Microarray and RT-PCR) in the NCI Cell Line Panela ABCC1 expression (microarray) DNA gene copy number ABCC1 expression (microarray) ABCC1 expression (RT-PCR)

R-value P-value R-value P-value R-value

0.388b 0.0001b

ABCC1 expression (RTPCR)


0.115 0.220 0.449b 8.83 × 10−4b

P-value

a

Vinblastine was used as a positive control, as it is a well-known substrate of ABCC1. correlation test. bP < 0.05 and R > 0.3.


vinblastine (log10IC50, M)

−0.036 0.398 0.032

0.165 0.121 0.145

0.429b 0.001b 0.399b

0.411 0.141

0.152 −0.090

0.002b 0.299

0.181

0.286

0.036b

34,35

The analysis was performed by means of Pearson’s rank

cells.47,48 These results corroborate data of the present investigation demonstrating that both plant-derived acids are cytotoxic toward cell lines independent of their expression level or mutational status of ABC-transporters, oncogenes, or tumor suppressor genes. COMPARE and Hierarchical Cluster Analyses of mRNA Microarray Data. Since classical factors of drug resistance did not determine responsiveness of cancer cells to ursolic and pomolic acid, the question arises about other molecular determinants. We studied the transcriptome-wide RNA expression of the NCI cell lines by COMPARE analyses and correlated the microarray-based mRNA expression data set with the log10IC50 values for ursolic and pomolic acids. This is a hypothesis-generating bioinformatical approach to identify novel putative factors associated with cellular response to

too. It is well known that slowly growing tumors are more resistant to chemotherapy than fast growing ones.45 Mutated tumor suppressor genes and activated oncogenes cause not only carcinogenesis but also drug and radiation resistance.37−39 Therefore, it is a pleasing result that we did not find significant correlations between cellular responsiveness toward ursolic and pomolic acids and expression or mutational status of EGFR, RAS, or TP53, indicating that tumor cells resistant due to alterations in these genes can be efficiently killed. Although the bioactivity of ursolic acid has been investigated in the past (e.g., sedative, anti-inflammatory, antibacterial, antidiabetic, and antiulcer activity), the cytotoxic activity toward cancer cells is not well understood.46 The same is true for pomolic acid. However, it is interesting that pomolic acid exerted cytotoxic activity toward otherwise drug-resistant tumor E



Article

Table 5. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to the Expression of ABCG2 mRNA (by Microarray and RT-PCR) and ABCG2 Protein (by Western Blot) in the NCI Cell Line Panela ABCG2 expression (RTPCR) ABCG2 expression (microarray) ABCG2 expression (RT-PCR) ABCG2 expression (Western blot)

ABCG2 expression (Western blot)



pancratistatin (log10IC50, M)

R-value

0.391b

0.905b

−0.014

−0.020

0.323b

P-value R-value

0.001b

8.59 × 10−23b 0.370b

0.459 −0.041

0.445 0.132

0.006b 0.071

0.381 0.018

0.173 0.044

0.295 0.346b

0.447

0.380

0.004b

0.002b

P-value R-value P-value

a

36

Pancratistatin was used as a positive control, as it is a well-known substrate of ABCG2. The analysis was performed by means of Pearson’s rank correlation test. bP < 0.05 and R > 0.3.

Table 6. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to EGFR Gene Copy Number, Expression of EGFR mRNA (by Microarray and RNAse Protection Assay), and EGFR Protein (by Protein Array) in the NCI Cell Line Panela EGFR expression (microarray) EGFR gene copy number EGFR expression (microarray) EGFR expression (RNase protection) EGFR expression (protein array)

Rvalue Pvalue Rvalue Pvalue Rvalue Pvalue Rvalue Pvalue

EGFR expression (RNase protection)

EGFR expression (protein array)

EGFR expression (Western blot)



erlotinib (log10IC50, M) −0.245

0.638b

0.428b

0.455b

0.221

0.062

0.049

2.11 × 10−8b

4.03 × 10−4b

1.48 × 10−4b

0.045b

0.324

0.362

0.029b

0.639b

0.710b

0.628b

−0.049

0.159

−0.458b

3.46 × 10−8

1.51 × 10−10b

4.01 × 10−8b

0.361

0.128

0.448b

0.438b

0.075

−0.042

2.35 × 10−4b

2.97 × 10−4b

0.294

0.384

−0.050

0.030

−0.376b

0.359

0.416

0.001b

0.542b 4.73 × 10−6b

1.15 × 10−4b 0.409b 7.08 × 10−4b

a

Erlotinib was used as a positive control, as it is a well-known tyrosine kinase inhibitor of EGFR.40 The analysis was performed by means of Pearson’s rank correlation test. bP < 0.05 and R > 0.3 (or R < −0.3).

these compounds. The scale rankings of genes obtained by COMPARE computation were subjected to Pearson’s rank correlation tests. The top 10 genes with direct and the top 10 genes with inverse correlation coefficients are shown in Table 10 for ursolic acid and in Table 11 for pomolic acid. As can be expected, these genes can be assigned to different functional groups. However, it is interesting that many of the identified genes code for proteins involved in signal transduction. This is true for 12 of the 20 genes identified by COMPARE analysis for ursolic acid (MNDA, WAS, OPRL1, CNTLN, PTGER2, RSU1, RASSF4, ZNF324B, GULP1, ZSCAN5A, STK36, ATP2B3) (Table 10) and for six genes identified for pomolic

Table 7. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to the Mutational Status of pan-RAS (HRAS, K-RAS, N-RAS) in the NCI Cell Line Panela

H-RAS, K-RAS, NRAS mutations

Rvalue Pvalue



melphalan (log10IC50, M)

−0.004

0.139

0.367b

0.489

0.166

0.002b

a

Melphalan was used as a positive control, as its cellular response is known to be determined by RAS.41 The analysis was performed by means of Pearson’s rank correlation test. bP < 0.05 and R > 0.3 (or R < −0.3).

Table 8. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to the Mutational Status of TP53 (cDNA Sequencing) and p53 Function (Yeast Functional Assay) in the NCI Cell Line Panela

TP53 mutation (cDNA sequencing) P53 function (yeast functional assay)

R-value P-value R-value P-value

P53 function (yeast functional assay)



5-fluorouracil (log10IC50, M)

0.869b 1.69 × 10−17b

−0.017 0.450 −0.066 0.324

−0.091 0.263 −0.061 0.342

−0.502b 3.50 × 10−5b −0.436b 5.49 × 10−4b

a

5-Fluorouracil was used as a positive control, as its cellular response is known to be determined by TP53.42 The analysis was performed by means of Pearson’s rank correlation test. bP < 0.05 and R > 0.3 (or R < −0.3). F



Article

Table 9. Correlation of Log10IC50 Values for Ursolic Acid and Pomolic Acid to Parameters of Tumor Growth (Cell Doubling Time and Cell Cycle Phases) in the NCI Cell Line Panela G0/G1 phase cell doubling time G0/G1 phase S phase G2/M phase

R-value P-value R-value P-value R-value P-value R-value P-value

0.232 0.062

S phase −0.3.77b 0.005b −0.670b 2.46 × 10−7b

G2/M phase



0.181 0.117 −0.398b 0.003b −0.415b 0.002b

0.142 0.158 0.169 0.149 −0.243 0.065 0.119 0.232

0.122 0.021 −0.128 0.219 −0.218 0.091 0.470b 0.001b

5-fluorouracil (log10IC50, M) 0.627b −6b

(7−14) × 10

0.067 0.330 −0.301b 0.022b 0.288 0.027b

a

5-Fluorouracil was used as a positive control, as it is a well-known proliferation-dependent anticancer drug.43 The analysis was performed by means of Pearson’s rank correlation test. bP < 0.05 and R > 0.3 (or R < −0.30).

Table 10. Correlation of Constitutive mRNA Expression of Genes Identified by COMPARE Analyses with Log10IC50 Values of Ursolic Acid for the NCI Tumor Cell Linesa COMPARE coefficient

pattern ID

GenBank accession

gene abbreviation

0.516 0.511 0.508

GC14029 GC149193 GC96243

N29376 AA609661 U12707

MNDA SLC35E4 WAS

0.498 0.484 0.48 0.479 0.478

GC65642 GC154643 GC150408 GC168792 GC96412

AI458306 AF348323 AA884069 AW170610 U19487

NTM OPRL1 PATE2 CNTLN PTGER2

0.477 0.47

GC85023 GC77964

AW025092 AI890191

RSU1 RASSF4

−0.51 −0.497

GC159193 GC102009

AI744673 AI376685

ZNF324B MTHFD2L

−0.473

GC182218

NM_003053

SLC18A1

−0.45

GC171490

BC001103

GULP1

−0.448 −0.446

GC159092 GC67165

AI742584 AI567488

ANKDD1A SUV420H2

−0.44 −0.437 −0.435 −0.429

GC98564 GC52761 GC46214 GC35439

W27720 AA934047 AA551070 U57971

PCDH9 ZSCAN5A STK36 ATP2B3

gene name

gene function

myeloid cell nuclear differentiation antigen solute carrier family 35, member E4 Wiskott-Aldrich syndrome (eczemathrombocytopenia) neurotrimin opiate receptor-like 1 prostate and testis expressed 2 centlein, centrosomal protein prostaglandin E receptor 2 (subtype EP2), 53 kDa Ras suppressor protein 1 Ras association (RalGDS/AF-6) domain family member 4 zinc finger protein 324B methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2-like solute carrier family 18 (vesicular monoamine), member 1 GULP, engulfment adaptor PTB domain containing 1 ankyrin repeat and death domain containing 1A suppressor of variegation 4−20 homologue 2 (Drosophila) protocadherin 9 zinc finger and SCAN domain containing 5A serine/threonine kinase 36 ATPase, Ca2+ transporting, plasma membrane 3

transcriptional regulation putative transporter effector protein for Rho-type GTPases and actin filament reorganization adhesion molecule G-protein coupled opioid receptor unknown phosphorelay sensor kinase G-protein coupled receptor Ras signal transduction pathway potential tumor suppressor. KRAS effector protein transcriptional regulation methylenetetrahydrofolate dehydrogenase drug transmembrane transporter signal transducer unknown histone methyltransferase cell-adhesion protein transcriptional regulation serine/threonine protein kinase calcium-transporting ATPase

a

Positive correlation coefficients indicate direct correlations to log10IC50 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 GeneCard database of the Weizman Institute of Science, Rehovot, Israel (http://bioinfo.weizmann.ac.il/cards/index.html).

acid (MEF2C, ITGA5, STYX, RAB3IP, DUSP8, FGFR4) (Table 11). The genes identified by COMPARE analyses were subsequently subjected to hierarchical cluster analyses. Only the mRNA expression data of the cell lines, but not the log10IC50 values of ursolic acid or pomolic acid, were included. Each three main cluster branches appeared in the dendrogram for ursolic acid as well as for pomolic acid (Figure 3). Using the chi-square test, we analyzed the distribution of cells being sensitive or resistant to these drugs. In the dendrogram for ursolic acid (Figure 3A), cluster 1 contained only sensitive and cluster 2 mainly resistant cell lines, while cluster 3 was of a mixed type. This distribution of sensitive and resistant cell lines

was statistically significant (P = 0.002; Table 12), indicating that the expression of these genes caused dendrogram branching in a way that gene expression predicted cellular responsiveness to ursolic acid. Comparable results were obtained for pomolic acid. Again, the dendrogram contained three main cluster branches (Figure 3B), whose cell lines significantly differed in their sensitivity to pomolic acid (P = 0.013; Table 12). The evaluation of the microarray data that correlated with cellular responsiveness to ursolic acid and pomolic acid identified many genes involved in signal transduction processes. Although the role of genes encoding signal transduction proteins for cellular responsiveness toward ursolic acid and G



Article

Table 11. Correlation of Constitutive mRNA Expression of Genes Identified by COMPARE Analyses with Log10IC50 Values of Pomolic Acid for the NCI Tumor Cell Linesa COMPARE coefficient

a

pattern ID

GenBank accession

gene abbreviation

0.627 0.592 0.577

GC15414 GC83370 GC181573

N93502 AL118637 NM_002205

TPST2 MEF2C ITGA5

0.553 0.543 0.542 0.541

GC170966 GC34584 GC153399 GC32396

AW968935 L33799 AF153882 U33822

STYX PCOLCE PDLIM4 MAD1L1

0.540 0.535 0.522 −0.544

GC29735 GC149439 GC179584 GC84493

D13628 AA683602 N48613 AW014298

ANGPT1 FKBP7 BEND6 LSM4

−0.528 −0.498

GC16626 GC14481

AA016250 N47234

TPD52 MOBKL1A

−0.486 −0.485 −0.48 −0.478 −0.471 −0.47 −0.466

GC79947 GC183178 GC13816 GC33280 GC37892 GC15229 GC36821

AI961177 NM_004420 N58020 L03840 AJ002308 N81028 X74929

RAB3IP DUSP8 GP9 FGFR4 SYNGR2 FAM183A KRT8

gene name tyrosylprotein sulfotransferase 2 myocyte enhancer factor 2C integrin, alpha 5 (fibronectin receptor, alpha polypeptide) serine/threonine/tyrosine interacting protein procollagen C-endopeptidase enhancer PDZ and LIM domain 4 MAD1 mitotic arrest deficient-like 1 (yeast) angiopoietin 1 FK506 binding protein 7 BEN domain containing 6 LSM4 homologue, U6 small nuclear RNA associated (S. cerevisiae) tumor protein D52 MOB1, Mps One Binder kinase activator-like 1A (yeast) RAB3A interacting protein (rabin3) dual specificity phosphatase 8 glycoprotein IX (platelet) fibroblast growth factor receptor 4 synaptogyrin 2 family with sequence similarity 183, member A keratin 8

gene function sulfotransferase transcriptional activator fibronectin receptor pseudophosphatase metalloproteinase inhibitor unknown component of the spindle-assembly checkpoint inhibiting anaphase angiogenesis regulator protein folding unknown unknown unknown unknown guanine nucleotide exchange factor (GEF) protein phosphatase platelet adhesion to blood vessels tyrosine-protein kinase unknown, putative vesicle protein unknown cytoskeletal element

For details see Table 10.

pomolic acid is still unknown, signal transduction pathways in general play important roles in cancer drug resistance.49−52 Therefore, it can be assumed that the genes involved in signal transducing processes identified in the present investigation may contribute to sensitivity or resistance of tumor cells to ursolic and pomolic acids. In addition to ursolic acid and pomolic acid, S. off icinalis contains numerous other phytochemicals. Previously, we investigated the essential oil isolated from this plant, which was cytotoxic toward cancer cells.53 Microarray gene expression analyses revealed that genes involved in cancer, cellular growth and proliferation, cell death, cell morphology, cell cycle, gene expression, and DNA repair were the most prominent pathways associated with the cytotoxicity of essential oil from S. off icinalis. The three most significantly regulated pathways by sage were aryl hydrocarbon receptor signaling, cell cycle (G1/S checkpoint) regulation, and p53 signaling.53 It can be assumed that different phytochemicals from the same plant act on different cellular mechanisms, leading to complementary activities against cancer cells. Molecular Docking. In order to evaluate the binding of ursolic acid and pomolic acid to NF-κB pathway proteins, in silico molecular docking calculations were conducted. The results are summarized in Table 13. Residues labeled bold in Table 13 reside at the relevant pharmacophore region on the target protein. The docking poses of the compounds and MG-132 (a wellknown NF-kB inhibitor used as control) are depicted in Figure 4. The two plant acids showed stronger interaction compared to MG-132 against all target proteins. Ursolic acid and pomolic acid showed stronger interaction with the NF-κB−DNA complex as with NF-κB alone. The binding energies were −9.87 ± 0.03 and −10.23 ± 0.01 kcal/mol, respectively. They

form hydrogen bonds with the bound DNA. Interestingly, the compounds docked to the ATP binding site of I-κB kinase β with binding energies of −8.98 ± 0.02 and −9.25 ± 0.01 kcal/ mol. They docked to the DNA binding site of NF-κB with binding energies of 9.09 ± 0.18 and 8.52 ± 0.00 kcal/mol. Ursolic acid docked to the I-κB kinase β-NEMO interaction site with a binding energy of −7.66 ± 0.01 kcal/mol. It has previously been reported that ursolic acid and pomolic acid act in an anti-inflammatory manner54 and that S. off icinalis activates the proinflammatory transcription factor NF-κB.55 Remarkably, NF-κB is also a key player in cancer biology. NFκB mediates resistance toward diverse cancer therapeutics by inhibition of apoptosis, and inhibition of NF-κB sensitizes cancer cells toward anticancer drugs (e.g., doxorubicin, imatinib), cytotoxic phytochemicals (e.g., curcumin), biological agents (e.g., β-IFN, TRAIL), and radiation.56−62 This broad spectrum of resistances represents another multiple drug resistance profile, which is different from the one mediated by ABC-transporters. Therefore, it is reasonable to suggest that ursolic and pomolic acid affect the function of NF-κB in drug-resistant tumor cells. For this reason, we performed molecular docking analyses to study their binding to NF-κB, I-κB kinase β, I-κB kinase β−NEMO complex and NF-κB−DNA complex in silico. Our results indicate that ursolic acid and pomolic acid strongly bind to the pharmacophores of target proteins with even lower free binding energies than the known inhibitor MG-132. Moreover, they more strongly interacted with DNA-bound NFκB than free NF-κB, pointing out the DNA-binding inhibition by these compounds. This observation was supported by the result that the compounds dock to the DNA binding site of free NFκB. Hence, our molecular docking results indicate that H



Article

Figure 3. Hierarchical cluster analysis of microarray-based mRNA expression of genes obtained by COMPARE analyses obtained for (A) ursolic acid and (B) pomolic acid. The dendrograms show the clustering of the NCI cell line panel according to the degrees of relatedness between cell lines.

■

ursolic and pomolic acid may target different steps of the NF-

CONCLUSION

S. of f icinalis is a well-known medicinal plant, although its κB pathway to inhibit NF-κB-mediated functions.

anticancer activity has sparsely been investigated as of yet. The I



Article

reported and deposited at the NCI Web site (http://dtp.nci.nih. gov).65,66 COMPARE analyses were performed to produce rankordered lists of genes expressed in the NCI cell lines. The methodology has been described previously in detail as a tool to identify candidate genes for drug resistance and sensitivity.67−70 To derive COMPARE rankings, a scale index of correlation coefficients (R-values) was created from log10IC50 values of test compounds and microarray-based mRNA expression values. Greater mRNA expression correlated with enhanced drug resistance in the standard COMPARE approach, whereas greater mRNA expression in cell lines indicated drug sensitivity in reverse COMPARE analyses. Pearson’s correlation test was used to calculate significance values and rank correlation coefficients as a relative measure for the linear dependency of two variables. This test was implemented into the WinSTAT Program (Kalmia). For hierarchical cluster analysis, objects were classified by calculation of distances according to the closeness of betweenindividual distances by means of hierarchical cluster analysis. All objects were assembled into cluster trees (dendrograms). Merging of objects with similar features leads to cluster formation, where the length of the branch indicates the degree of relation. Distances of subordinate cluster branches to superior cluster branches serve as criteria for the closeness of clusters. Thus, objects with tightly related features were clustered closely, while separation of objects in the dendrogram increased with progressive dissimilarity. Previously, cluster models have been validated for gene expression profiling and for approaching molecular pharmacology of cancer.71−74 In the present investigation, we applied hierarchical clustering according to the WARD method using the WinSTAT program (Kalmia, Cambridge, MA, USA). The program automatically omitted missing values, and the closeness of two joined objects was calculated by the number of data points they contained. Variables have been automatically standardized by the program by transforming the data with a mean = 0 and a variance = 1. Molecular Docking. Molecular docking was performed to predict the interaction energy and geometry of ursolic acid and pomolic ligands with target proteins: I-κB kinase β, I-κB kinase β−NEMO (NFκB essential modulator) complex, NF-κB, and NF-κB−DNA complex. The protocol for molecular docking was previously reported by us.75 X-ray crystallography-based protein structures were obtained from Protein Data Bank (http://www.rcsb.org/pdb)I-κB kinase β (PDB ID:3RZF), I-κB kinase β−NEMO complex (PDB ID: 3BRT), NF-κB (p52/RelB heterodimer, PDB ID: 3DO7), NF-κB−DNA complex (p50/p65 heterodimer bound to DNA, PDB ID: 1VKX)and set as the rigid receptor molecules. A known NF-κB inhibitor, MG-132, has

Table 12. Separation of Clusters of NCI Cell Lines Obtained by Hierarchical Cluster Analysis Shown in Figure 3 in Comparison to Drug Sensitivitya ursolic acid sensitive resistant pomolic acid sensitive resistant

partition

cluster 1

cluster 2

cluster 3

χ2-test

−4.86 M

4 11

10 11

12 3

P = 0.013

a

The median log10IC50 value (−4.86 M) for each compound was used as a cutoff to separate tumor cell lines as being “sensitive” or “resistant”.

results of the present investigation indicate that sage may be a promising candidate plant for cancer drug development, either as a whole plant or as a resource for the isolation of cytotoxic compounds. Ursolic acid and pomolic acid may serve as lead compounds for the generation of derivatives with improved pharmacological features. A key question will be whether or not concentrations that show cytotoxic activity in vitro in cell lines can be reached in vivo to exert anticancer activity in living organisms.

■

EXPERIMENTAL SECTION

Cell Lines. The cancer cell lines of the Developmental Therapeutics Program of NCI (U.S.A.) consisted of a series of nonsmall-cell lung cancer, colon cancer, renal cancer, ovarian cancer, tumor cells of the central nervous system, leukemia, melanoma, prostate carcinoma, and breast cancer. Their origin and processing have been previously reported.63 These tumor lines have been used to determine the cytotoxicity of ursolic acid and pomolic acid from S. of f icinalis and of several standard anticancer agents as positive controls. Cytotoxicity Assays. The cytotoxicity of ursolic and pomolic acids as well as of standard anticancer drugs in the NCI cell line panel was measured by the sulforhodamine B assay.64 The 50% inhibition concentrations calculated from dose−response curves and converted to logarithmic values (log10IC50) have been deposited in the NCI database (http://dtp.nci.nih.gov). COMPARE and Cluster Analyses of Microarray Data. The mRNA microarray hybridization of the NCI cell lines has been

Table 13. Molecular Docking of Ursolic Acid and Pomolic Acid on NF-κB Pathway Proteins (for Details See Figure 4)

I-κB kinase β MG132 pomolic acid ursolic acid I-κB kinase β−NEMO complex MG132 pomolic acid ursolic acid NF-κB MG132 pomolic acid ursolic acid NF-κB−DNA complex MG132 pomolic acid ursolic acid

binding energy (kcal/mol)

predicted inhibition constant (μM)

number of residues involved in hydrophobic interactions

−6.26 ± 0.19 −8.98 ± 0.02 −9.25 ± 0.01

26.75 ± 8.90 0.26 ± 0.01 0.17 ± 0.00

12 8 9

−3.99 ± 0.37 −6.55 ± 0.02 −7.66 ± 0.01

1370.17 ± 927.11 15.70 ± 0.52 2.44 ± 0.03

4 6 5

−6.05 ± 0.38 −9.09 ± 0.18 −8.52 ± 0.00

42.41 ± 29.46 0.22 ± 0.07 0.57 ± 0.00

10 10 9

Arg117, Lys273 (on RelB) Lys308 (on RelB)

−7.23 ± 0.21 −9.88 ± 0.03 −10.23 ± 0.01

2.04 ± 1.36 0.06 ± 0.00 0.03 ± 0.00

6 7 11

thymine8, Lys123 (on p65) guanine4, Glu222 (on p65) adenine6, Lys218 (on p65)

J

residues involved in hydrogen bond Asp103 Thr23, Asp103 Asp103, Lys106

Arg73 (on NEMO) Arg101 (on NEMO)



Article

Figure 4. continued

K



Article

Figure 4. Molecular docking studies of pomolic acid and ursolic acid to NFKB pathway proteins. MG132 is in blue, pomolic acid in red, and ursolic acid in green bond representation. (A) Docking poses into the ATP binding site of IKK (PDB code: 3RZF in yellow cartoon representation). (B) Docking poses into the interaction site of the IKK−NEMO complex (PDB code: 3BRT in pink cartoon representation). (C) Docking poses into the DNA binding site of NFKB (PDB code: 3DO7 in violet cartoon representation). (D) Docking poses into the DNA binding site of the NFKB−DNA complex (PDB code: 1VKX in gray cartoon representation). been used as a standard.76 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 AutodockTools-1.5.7rc1 using the Lamarckian genetic algorithm.77 The corresponding lowest binding energies and predicted inhibition constants were obtained from the docking log files (dlg). Mean ± SD of binding energies were L



Article

calculated from three independent dockings. Visual Molecular Dynamics (VMD) was used to depict the docking poses of ursolic acid and pomolic acid for each target protein.

■

(25) Wink, M.; Botschen, F.; Gosmann, C.; Schäfer, H.; Waterman, P. G. In Biochemistry of Plant Secondary Metabolism, 2nd ed.; Wink, M., Ed.; Blackwell, Annual Plant Reviews; 2010; Vol. 40. (26) Messner, B.; Zeller, I.; Ploner, C.; Frotschnig, S.; Ringer, T.; Steinacher-Nigisch, A.; Ritsch, A.; Laufer, G.; Huck, C.; Bernhard, D. Atherosclerosis 2011, 219, 402−408. (27) Koh, S. J.; Tak, J. K.; Kim, S. T.; Nam, W. S.; Kim, S. Y.; Park, K. M.; Park, J. W. Free Radical Res. 2012, 46, 339−345. (28) Ramos, A. A.; Pereira-Wilson, C.; Collins, A. R. Mutat. Res. 2010, 692, 6−11. (29) Ma, J. Q.; Ding, J.; Xiao, Z. H.; Liu, C. M. Int. Immunopharmacol. 2014, 21, 389−395. (30) Tapiero, H.; Munck, J. N.; Fourcade, A.; Lampidis, T. J. Cancer Res. 1984, 44, 5544−5549. (31) Efferth, T.; Löhrke, H.; Volm, M. Anticancer Res. 1989, 9, 1633− 1637. (32) Kosztyu, P.; Bukvova, R.; Dolezel, P.; Mlejnek, P. Chem. Biol. Interact. 2014, 219C, 203−210. (33) Sztiller-Sikorska, M.; Koprowska, K.; Majchrzak, K.; Hartman, M.; Czyz, M. PLoS One 2014, 9, e90783. (34) Cole, S. P.; Sparks, K. E.; Fraser, K.; Loe, D. W.; Grant, C. E.; Wilson, G. M.; Deeley, R. G. Cancer Res. 1994, 54, 5902−5910. (35) Breuninger, L. M.; Paul, S.; Gaughan, K.; Miki, T.; Chan, A.; Aaronson, S. A.; Kruh, G. D. Cancer Res. 1995, 55, 5342−5347. (36) Deeken, J. F.; Robey, R. W.; Shukla, S.; Steadman, K.; Chakraborty, A. R.; Poonkuzhali, B.; Schuetz, E. G.; Holbeck, S.; Ambudkar, S. V.; Bates, S. E. Anticancer Drug Screen. Mol. Pharmacol. 2009, 76, 946−956. (37) El-Deiry, W. S. Curr. Opin. Oncol. 1997, 9, 79−87. (38) El-Deiry, W. S. Oncogene 2003, 22, 7486−7495. (39) Shetzer, Y.; Solomon, H.; Koifman, G.; Molchadsky, A.; Horesh, S.; Rotter, V. Carcinogenesis 2014, 35, 1196−1208. (40) Sandler, A. Oncology (Williston Park) 2003, 17, 17−22. (41) Niimi, S.; Nakagawa, K.; Yokota, J.; Tsunokawa, Y.; Nishio, K.; Terashima, Y.; Shibuya, M.; Terada, M.; Saijo, N. Br. J. Cancer 1991, 63, 237−241. (42) Mueller, H.; Eppenberger, U. Anticancer Res. 1996, 16, 3845− 3848. (43) Fujii, R.; Seshimo, A.; Kameoka, S. Int. J. Clin. Oncol. 2003, 8, 72−78. (44) Efferth, T. Resistance to Targeted ABC Transporters in Cancer; Springer: New York, 2014. (45) Efferth, T.; Konkimalla, V. B.; Wang, Y. F.; Sauerbrey, A.; Meinhardt, S.; Zintl, F.; Mattern, J.; Volm, M. Clin. Cancer Res. 2008, 14, 2405−2412. (46) Zang, L. L.; Wu, B. N.; Lin, Y.; Wang, J.; Fu, L.; Tang, Z. Y. Chin. J. Integr. Med. 2014, 20, 72−79. (47) Fernandes, J.; Weinlich, R.; Castilho, R. O.; Amarante-Mendes, G. P.; Gattass, C. R. Cancer Lett. 2005, 245, 315−320. (48) Vasconcelos, F. C.; Gattass, C. R.; Rumjanek, V. M.; Maia, R. C. Invest. New Drugs 2007, 25, 525−533. (49) Klempner, S. J.; Myers, A. P.; Cantley, L. C. Cancer Discovery 2013, 3, 1345−1354. (50) Niederst, M. J.; Engelman, J. A. Sci. Signal 2013, 6, re6. (51) Namani, A.; Li, Y.; Wang, X. J.; Tang, X. Biochim. Biophys. Acta 2014, 1843, 1875−1885. (52) Stewart, D. J. J. Natl. Cancer Inst. 2014, 106, djt356. (53) Sertel, S.; Eichhorn, T.; Plinkert, P. K.; Efferth, T. HNO 2011, 59, 1203−1208. (54) Banno, N.; Akihisa, T.; Tokuda, H.; Yasukawa, K.; Higashihara, H.; Ukiya, M.; Watanabe, K.; Kimura, Y.; Hasegawa, J.; Nishino, H. Biosci. Biotechnol. Biochem. 2004, 68, 85−90. (55) Deeb, S. J.; El-Baba, C. O.; Hassan, S. B.; Larsson, R. L.; GaliMuhtasib, H. U. Front. Biosci. (Elite Ed.) 2011, 3, 410−420. (56) Schwartz, S. A.; Hernandez, A.; Mark Evers, B. Surg. Oncol. 1999, 8, 143−153. (57) Deeb, D.; Jiang, H.; Gao, X.; Hafner, M. S.; Wong, H.; Divine, G.; Chapman, R. A.; Dulchavsky, S. A.; Gautam, S. C. Mol. Cancer Ther. 2004, 3, 803−812.

ASSOCIATED CONTENT

S Supporting Information *

This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*Tel: +49 06131-39-25751. Fax: +49 06131-39-23752. E-mail: eff[email protected]. Notes

The authors declare no competing financial interest.

■

REFERENCES

(1) Lima, C. F.; Andrade, P. B.; Seabra, R. M.; Fernandes-Ferreira, M.; Pereira-Wilson, C. J. Ethnopharmacol. 2005, 97, 383−389. (2) Zhang, L.; Lin, Y. H.; Leng, X. J.; Huang, M.; Zhou, G. H. Meat Sci. 2013, 95, 145−150. (3) Ho, H.; Lee, A. S.; Jovanovski, E.; Jenkins, A. L.; Desouza, R.; Vuksan, V. Eur. J. Clin. Nutr. 2013, 67, 786−788. (4) Chicco, A. G.; D’Alessandro, M. E.; Hein, G. J.; Oliva, M. E.; Lombardo, Y. B. Br. J. Nutr. 2009, 101, 41−50. (5) Poudyal, H.; Panchal, S. K.; Waanders, J.; Ward, L.; Brown, L. J. Nutr. Biochem. 2012, 23, 153−162. (6) Oliva, M. E.; Ferreira, M. R.; Chicco, A.; Lombardo, Y. B. Prostaglandins Leukot. Essent. Fatty Acids 2013, 89, 279−289. (7) Wu, Y. J.; Hong, C. Y.; Lin, S. J.; Wu, P.; Shiao, M. S. Arterioscler Thromb. Vasc. Biol. 1998, 18, 481−486. (8) Fang, Z. Y.; Lin, R.; Yuan, B. X.; Liu, Y.; Zhang, H. Life Sci. 2007, 81, 1339−1345. (9) Fang, Z. Y.; Lin, R.; Yuan, B. X.; Yang, G. D.; Liu, Y.; Zhang, H. J. Ethnopharmacol. 2008, 115, 217−222. (10) Xu, S.; Little, P. J.; Lan, T.; Huang, Y.; Le, K.; Wu, X.; Shen, X.; Huang, H.; Cai, Y.; Tang, F.; Wang, H.; Liu, P. Arch. Biochem. Biophys. 2011, 515, 72−79. (11) Huang, M.; Xie, Y.; Chen, L.; Chu, K.; Wu, S.; Lu, J.; Chen, X.; Wang, Y.; Lai, X. Phytother. Res. 2012, 26, 944−948. (12) Ç abuk, M.; Eratak, S.; Alçicek, A.; Bozkurt, M. Sci. World J. 2014, 23, 573470. (13) Perry, E. K.; Pickering, A. T.; Wang, W. W.; Houghton, P.; Perry, N. S. J. Altern. Complementary Med. 1998, 4, 419−428. (14) Wu, T. Y.; Chen, C. P.; Jinn, T. R. Taiwan J. Obstet. Gynecol. 2011, 50, 131−135. (15) Perry, E.; Howes, M. J. CNS Neurosci. Ther. 2011, 17, 683−698. (16) Sarris, J.; McIntyre, E.; Camfield, D. A. CNS Drugs 2013, 27, 301−319. (17) Miroddi, M.; Navarra, M.; Quattropani, M. C.; Calapai, F.; Gangemi, S.; Calapai, G. CNS Neurosci. Ther. 2014, 20, 485−495. (18) Nolkemper, S.; Reichling, J.; Stintzing, F. C.; Carle, R.; Schnitzler, P. Planta Med. 2006, 72, 1378−1382. (19) Reuter, J.; Jocher, A.; Hornstein, S.; Mönting, J. S.; Schempp, C. M. Planta Med. 2007, 73, 1190−1191. (20) Rahte, S.; Evans, R.; Eugster, P. J.; Marcourt, L.; Wolfender, J. L.; Kortenkamp, A.; Tasdemir, D. Planta Med. 2013, 79, 753−760. (21) Loizzo, M. R.; Tundis, R.; Menichini, F.; Saab, A. M.; Statti, G. A.; Menichini, F. Anticancer Res. 2007, 27, 3293−3299. (22) Xavier, C. P.; Lima, C. F.; Fernandes-Ferreira, M.; PereiraWilson, C. Nutr. Cancer 2009, 61, 564−571. (23) Keshavarz, M.; Mostafaie, A.; Mansouri, K.; Bidmeshkipour, A.; Motlagh, H. R.; Parvaneh, S. Phytother. Res. 2010, 24, 1526−1531. (24) Slamenová, D.; Masterová, I.; Lábaj, J.; Horváthová, E.; Kubala, P.; Jakubíková, J.; Wsólová, L. Basic Clin. Pharmacol. Toxicol. 2004, 94, 282−290. M



Article

(58) Bednarski, B. K.; Ding, X.; Coombe, K.; Baldwin, A. S.; Kim, H. J. Mol. Cancer Ther. 2008, 7, 1827−1835. (59) Chen, W.; Wang, X.; Bai, L.; Liang, X.; Zhuang, J.; Lin, Y. J. Cell. Biochem. 2008, 105, 554−561. (60) Choi, B. H.; Kim, C. G.; Lim, Y.; Shin, S. Y.; Lee, Y. H. Cancer Lett. 2008, 259, 111−118. (61) Lounnas, N.; Frelin, C.; Gonthier, N.; Colosetti, P.; Sirvent, A.; Cassuto, J. P.; Berthier, F.; Sirvent, N.; Rousselot, P.; Dreano, M.; Peyron, J. F.; Imbert, V. Int. J. Cancer 2009, 125, 308−317. (62) Tracey, L.; Streck, C. J.; Du, Z.; Williams, R. F.; Pfeffer, L. M.; Nathwani, A. C.; Davidoff, A. M. Leukemia 2010, 24, 806−812. (63) Alley, M. C.; Scudiero, D. A.; Monks, A.; Hursey, M. L.; Czerwinski, M. J.; Fine, D. L.; Abbott, B. J.; Mayo, J. G.; Shoemaker, R. H.; Boyd, M. R. Cancer Res. 1988, 48, 589−601. (64) Monks, A.; Scudiero, D.; Skehan, P.; Shoemaker, R.; Paull, K.; Vistica, D.; Hose, C.; Langley, J.; Cronise, P.; Vaigro-Wolff, A. J. Natl. Cancer Inst. 1991, 83, 757−766. (65) Scherf, U.; Ross, D. T.; Waltham, M.; Smith, L. H.; Lee, J. K.; Tanabe, L.; Kohn, K. W.; Reinhold, W. C.; Myers, T. G.; Andrews, D. T.; Scudiero, D. A.; Eisen, M. B.; Sausville, E. A.; Pommier, Y.; Botstein, D.; Brown, P. O.; Weinstein, J. N. Nat. Genet. 2000, 24, 236− 244. (66) Amundson, S. A.; Do, K. T.; Vinikoor, L. C.; Lee, R. A.; KochPaiz, C. A.; Ahn, J.; Reimers, M.; Chen, Y.; Scudiero, D. A.; Weinstein, J. N.; Trent, J. M.; Bittner, M. L.; Meltzer, P. S.; Fornace, A. J., Jr. Cancer Res. 2008, 68, 415−424. (67) Wosikowski, K.; Schuurhuis, D.; Johnson, K.; Paull, K. D.; Myers, T. G.; Weinstein, J. N.; Bates, S. E. J. Natl. Cancer Inst. 1997, 89, 1505−1515. (68) Evans, A.; Bates, V.; Troy, H.; Hewitt, S.; Holbeck, S.; Chung, Y. L.; Phillips, R.; Stubbs, M.; Griffiths, J.; Airley, R. Cancer Chemother. Pharmacol. 2008, 61, 377−393. (69) Fagan, V.; Bonham, S.; Carty, M. P.; Saenz-Méndez, P.; Eriksson, L. A.; Aldabbagh, F. Bioorg. Med. Chem. 2012, 20, 3223− 3232. (70) Luzina, E. L.; Popov, A. V. Eur. J. Med. Chem. 2012, 53, 364− 373. (71) Efferth, T.; Fabry, U.; Osieka, R. Leukemia 1997, 11, 1180− 1186. (72) Villeneuve, D. J.; Parissenti, A. M. Curr. Top. Med. Chem. 2004, 4, 1329−1345. (73) Zeeberg, B. R.; Liu, H.; Kahn, A. B.; Ehler, M.; Rajapakse, V. N.; Bonner, R. F.; Brown, J. D.; Brooks, B. P.; Larionov, V. L.; Reinhold, W.; Weinstein, J. N.; Pommier, Y. G. BMC Bioinf. 2011, 12, 52. (74) Reinhold, W. C.; Sunshine, M.; Liu, H.; Varma, S.; Kohn, K. W.; Morris, J.; Doroshow, J.; Pommier, Y. Cancer Res. 2012, 72, 3499− 3511. (75) Zeino, M.; Zhao, Q.; Eichhorn, T.; Hermann, J.; Müller, R.; Efferth, T. J. Biosci. Med. 2013, 3, 31−43. (76) Zanotto-Filho, A.; Braganhol, E.; Schröder, R.; de Souza, L. H.; Dalmolin, R. J.; Pasquali, M. A.; Gelain, D. P.; Battastini, A. M.; Moreira, J. C. Biochem. Pharmacol. 2011, 81, 412−424. (77) Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J. J. Comput. Chem. 2009, 30, 2785− 2791.

N


in Cancer Cells - American Chemical Society

Recommend Documents