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Cytotoxic activity of secondary metabolites derived from Artemisia annua L. towards cancer cells in comparison to its designated active constituent artemisinin.

doi: 10.1016/j.phymed.2011 06.008

ARTICLEINFO

Keywords:

Artemisinin

Caner

Drug

resistance

Microarry

Natural

products

Pharmacogenomics

Trypanosoma

Artemisia annua L (sweet wormwood, qinhao) has traditionally been used in Chinese medicine. The isolation of artemisinin from Artemisia annua and its worldwide accepted application in malaria therapy is one of the showcase success stories of phytomedicine during the past decades. Artemisinin-type compounds are also active towards other protozoal or viral diseases as well as cancer cells in vitro and in vivo. Nowadays, Artemisia annua tea is used as a self-reliant treatment in developing countries. The unsupervised use of Artemisia annua tea has been criticized to foster the development of artemisinin resistance in malaria and cancer due to insufficient artemisinin amounts in the plant as compared to standardized tablets with isolated artemisinin or semisynthetic artemisinin derivatives. However, artemisinin is not the only bioactive compound in Artemisia annua. In the present investigation, we analyzed different Artemisia annua extracts. Dichloromethane extracts were more cytotoxic (range of [IC.sub.50]: 1.8-14.4 [micro]g/ml) than methanol extracts towards Trypanosoma b. brucei (TC221 cells). The range of [IC.sub.50] values for HeLa cancer cells was 54.1-275.5 [micro]g/ml for dichloromethane extracts and 276.3-1540.8 [micro]g/ml for methanol extracts. Cancer and trypanosomal cells did not reveal cross-resistance among other compounds of Artemisia annua, namely the artemisinin-related artemisitene and arteanuine B as well as the unrelated compounds, scopoletin and 1,8-cineole. This indicates that cells resistant to one compound retained sensitivity to another one These results were also supported by microarray-based mRNA expression profiling showing that molecular determinants of sensitivity and resistance were different between artemisinin and the other phytochemicals investigated. [c] 2011 Elsevier GmbH. All rights reserved.

Introduction

Artemisia annua L (sweet wormwood, qinghao) has traditionally been used in China for the treatment of fever and chills. Artemisinin has been identified as the anti-malarial principle of the plant, and artemisinin derivatives are nowadays established as anti-malarial drugs with activity towards otherwise drug-resistant Plasmodium infections. Though originally growing in Asia and Europe, the plant is cultivated in Africa and used as tea for the treatment of malaria. Artemisinin-type compounds are not only active towards malaria, but also towards a variety of other diseases such as infections with Schistosoma, Leishmania, Trypanosoma, a wide variety of viruses and human cancer cell lines in vitro and in vivo, and even plant crown gall tumors (Efferth et al. 2003, 2008; Efferth 2005, 2007, 2009; Dell'Eva et al. 2004; Ullrich et al. 2009; Nibret and Wink 2010).

Despite artemisinin's global application in malaria therapy is one of the showcase success stories of pharmacognosy during the past decades, there are still some contradictions which have not satisfactorily been addressed as yet. Artemisinin is not or hardly water soluble, but the traditional use in Chinese medicine is based on water preparations such as tea or decoction. Nowadays, Artemisia annua tea is used as a self-reliant treatment for malaria in developing countries (Mueller et al. 2000; de Ridder et al. 2008; RITAM Artemisia annua Task Force 2006). Especially the unsupervised use of Artemisia annua tea has been criticized. It has been argued that the use of suboptimal concentrations of artemisinin would facilitate the development of resistance (Jansen 2006). It is, however, a general biological phenomenon that medicinal plants contain rather many than single pharmacologically active phytochemical compounds. This is well-known and thoroughly discussed for many years as one of the advantages of phytotherapy (including traditional Chinese medicine) compared to classical Western medicine (Wink 2008). In Artemisia annua, more than 50 different phytochemicals have been recorded (Dr. Duke's Phytochemical and Ethnobotanical Databases: http://www.arsgrin.gov/cgi-bin/duke/farmacy2.pl). It can, hence, be hypothesized that the development of resistance, which is not recorded for the use of this plant in traditional Chinese medicine, might not take place. Rather the plant provides a sort of combination therapy which engraves or even prevents the development of resistance to single bioactive plant constituents. The bioactivity of other constituents of Artemisia annua apart from artemisinin has, however, not adequately been addressed as yet.

In an effort to evaluate, whether other constituents than artemisinin in Artemisia annua may also reveal cytotoxicity towards cancer cells we focused on two artemisinin-related compounds, arteanuine B and artemisitene, and two other compounds without structural similarity to artemisinin, scopoletin and 1,8-cineole, which are also present in this plant. First, we analyzed the inhibitory activity of these compounds towards human HeLa cervical cancer cells. To prove a broader bioactivity in addition to cytotoxicity towards cancer cells, the inhibitory action towards Trypanosoma has been investigated. As a second step, we analyzed cross-resistance profiles of these compounds in the NCI panel of cell lines derived from different tumor types. Microarray-based mRNA expression profiling and COMPARE analyses revealed that different sets of genes correlated with the ICso values for these compounds, indicating that the missing cross-resistance of arteanuine B, artemisitene, 1,8-cineole or scopoletin towards artemisinin may be based on different transcriptomic expression profiles determining sensitivity or resistance to these compounds.

Materials and methods

Plant material

Artemisia annua was obtained from different sources to test the variability of different specimens. Artemisia annua specimen A was obtained from a pharmacy in Germany. The origin of this specimen was China, the harvest date is unknown. Artemisia annua specimen B was grown in Tansania/Bunda and was obtained from the non-governmental organisation, Anamed (pulverized leaves; harvest date: 2008). Further specimens were also provided by Anamed: specimen C (grown in Winnenden, Germany, screen leftovers and thin caulis, harvest date: 2007) and specimen D (Winnenden, chaff and thick caulis, harvest date: 2007). A further specimen was purchased on a medicinal plant market in Shanghai, China (specimen E; harvest date: 2005).

These plant samples were macerated in dichloromethane or methanol, and left on a shaker for two days. The extracts were filtered and evaporated to dryness under reduced pressure using Rotavapor as described (Nibret and Wink 2010).

Phytochemicals

Artemisinin was obtained from Sigma-Aldrich (Taufkirchen, Germany) and artesunate was purchased from Saokim Ltd. (Hanoi, Vietnam). Arteanuine B and artemisitene were obtained from the drug repository of the Developmental Therapeutics Program of the National Cancer Institute (NCI, Bethesda, MA, USA). Scopoletin and 1,8-cineole were purchased from Sigmal-Aldrich (Taufkirchen, Germany). The chemical structures of the compounds are depicted in Fig. 1.

Determination of plant constituents by GLC-MS

The analysis was carried out on a Hewlett-Packard gas chromatograph (GC 5890II, Hewlett PACKARD; Bad Homburg, Germany) equipped with OV-1 column (30m x 0.25mm x 0.25 [micro]m; Ohio Valleys, Ohio, USA). The capillary column was directly coupled to a quadrupole mass spectrometer (SSQ7000, Thermo-Finnigan, Bremen, Germany). The operation conditions were previously described (Nibret and Wink 2010).

Cell lines

TC221 Trypanosoma brucei brucei cells, the causative agent of Nagana epidemic, were grown in Baltz medium supplemented with 20% inactivated fetal bovine serum (FBS) and 0.001% ([beta]-mercaptoethanol as previously described (Baltz et al. 1985).

Human HeLa cervical carcinoma cells were cultured in DMEM complete medium supplemented with 10% inactivated FBS, 1% antibiotics (penicillin, streptomycin) and 1 % NEA. Cells were maintained in a humified atmosphere containing 5% [CO.sub.2] at 37[degrees].

The panel of human tumor cell lines of the Developmental Therapeutics Program of NCI consisted of leukemia, melanoma, non-small cell lung cancer, colon cancer, renal cancer, ovarian cancer cells, tumor cells of the central nervous system, prostate carcinoma, and breast cancer. Their origin and processing have previously been described (Alley et al. 1988). These cell lines were employed to determine the cytotoxicity of the isolated compounds and of topotecan, irinotecan and SN-38 as positive controls.

Cytotoxicity assays

Sensitivity of TC221 and HeLa cells towards extracts and pure compounds was determined using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cell viability assay (Mosmannl983). 1 x [10.sup.5] cells/ml were seeded in 96 well plates and cultured for 24 h. They were treated with 0-5 mg/ml of Artemisia annua extract 0-5 mg/ml pure compound for 24 h. Medium was exchanged with fresh medium containing 1 mg/ml MTT and incubated for 4 h. The formazan crystals were dissolved in 100 [um]l DMSO. The absorbance was measured at 570 nm with a Tecan Safire II Reader (Tecan, Mannedorf, Switzerland). All experiments were performed in triplicates and repeated three times. The viability results of TC221 cells were additionally confirmed by counting under the microscope. The results were expressed as percentage of the control set at 100%.

The cytotoxicity of phytochemical compounds towards the NCI cell line panel was evaluated by determining the [IC.sub.50] (concentration resulting in 50% inhibition) using a modification of the sulforhodamine B assay (SRB, Sigma, MI, USA; Monks et al. 1991).

Statistical analyses

The mRNA microarray hybridization of the NCI cell line panel has been described (Scherf et al. 2000; Amundson et al. 2008) and the date das been depositied at the NCI website (http://dtp.nci.nih.gov). For hierarchical cluster analysis, objects were classified by calculation of distances according to the closeness of between-individual distances by means of hierarchical cluster analysis. All objects were assembled into a cluster tree (dendrogram). The merging of objects with similar features leads to the formation of a cluster, where the length of the branch indicates the degree of relation. The distance of a subordinate cluster to a superior cluster represents a criterion for the closeness of clusters as well as for the affiliation of single objects to clusters. Thus, objects with tightly related features appear together, while the separation in the cluster tree increases with progressive dissimilarity. Previously, cluster models have been validated for gene expression profiling and for approaching molecular pharmacology of cancer(Efferthetal. 1997; Scherf et al. 2000). Hierarchical cluster analyses applying the WARD method were done with the WinSTAT program (Kalmia, Cambridge, MA, USA). Missing values were automatically omitted by the program, and the closeness of two joined objects was calculated by the number of data points they contained. In order to calculate dis tances between all variables included in the analysis, the program automatically standardizes the variables by transforming the data with a mean = 0 and a variance =1.

For COMPARE analysis, the mRNA expression values of genes of interest and [IC.sub.50] values for artemisinin, arteanuine B, artemisitene, scopoletin, and 1,8-cineole of the NCI cell lines were selected from the NCI database (http://dtp.nci.nih.gov). The mRNA expression has been determined by microarray analyses as reported (Scherf et al. 2000). COMPARE analyses were performed to produce rank-ordered lists of genes expressed in the NCI cell lines. The methodology has been described previously in detail (Wosikowski et al. 1997). Briefly, every gene of the NCI microarray database was ranked for similarity of its mRNA expression to the [IC.sub.50] values for the corresponding compound. To derive COMPARE rankings, a scale index of correlations coefficients (R-values) was created. In the standard COMPARE approach, greater mRNA expression in cell lines correlate with enhanced drug resistance, whereas in reverse COMPARE analyses greater mRNA expression in cell lines indicated drug sensitivity.

Pearson's correlation test was used to calculate significance values and rank correlation coefficients as relative measure for the linear dependency of two variables. This test was implemented into the WinSTAT Program (Kalmia).

[FIGURE 1 OMITTED]

Results

Cytotoxicity of extracts and single constituents of Artemisia annua towards trypanosomes

Dichloromethane or methanol extracts of six different Artemisia annua samples of different origin have been tested for their activity to inhibit the growth of trypanosomes. All extracts inhibited trypanosomal growth in a dose-dependent manner, albeit at different efficacy (Fig. 2). The [IC.sub.50] values were calculated from the dose response curves (Table 1, Supplementary file). The dichloromethane extracts were more cytotoxic (range of [IC.sub.50]: 1.8-14.4 [um]g/ml) than the methanol extracts (range of IC50: 10.8-77.5 [um]g/ml).

Three phytochemicals have been included into the analysis, which have been described as constituents of Artemisia annua (Dr. Duke's Phytochemical and Ethnobotanical Databases; http://www.ars-grin.gov/cgi-bin/duke/farmacy2.pl), i.e. artemisinin, 1,8-cineole, and scopoletin. As shown in Fig. 3 and Table 1, Supplementary file, all compounds inhibited trypanosomal growth. Of them, 1,8-cineole was the most potent one with an IC50 value of 64.6 [um]g/ml. As control drug, the semisynthetic artemisinin-derivative artesunate was used. The [IC.sub.50] value of artesunate for trypanosomes was 2.3 [um]g/100 [um]l).
Table 1

Relative abundance of artemisinin, arteanuine B and scopoletin
in dichloromethane and methanol extracts of different Artemisia
annua specimens as measured by GLC-MS.

Specimen        Solvent         Artemisinin  Arteanuine B  Scopole

A                         MeOH        9.96%         9.96%   80.07%
A         [CH.sub.2][Cl.sub.2]       18.75%        39.20%   42.05%
B                         MeOH       47.88%            0%   52.12%
B         [CH.sub.2][Cl.sub.2]       92.60%            0%    7.40%
C                         MeOH        5.87%            0%   94.13%
C         [CH.sub.2][Cl.sub.2]       35.41%          6.6%   57.98%
D                         MeOH        6.93%            0%   93.07%
D         [CH.sub.2][Cl.sub.2]        6.40%            0%   93.60%
E                         MeOH        5032%         7.62%   42.05%
E         [CH.sub.2][Cl.sub.2]       59.75%        13.70%   26.55%

[CH.sub.2][Cl.sub.2], dichloromethane; MeOH, methanol.


Cytotoxicity of single constituents and extracts of Artemisia annua towards HeLa cervical cancer cells

Furthermore, we tested the activity of the Artemisia extracts towards HeLa cells (Fig. 2). All extracts tended to be less active towards HeLa cells as compared to trypanosomes. Comparable to the results obtained for trypanosomes, dichloromethane extracts were more active towards HeLa cells than methanol extracts. The range of [IC.sub.50] values was 54.1-275.5 ([um]g/ml for dichloromethane extracts and 276.3-1540.8 [um]g/ml for methanol extracts.

The dose-response curves of individual terpenoids are shown in Fig. 3. The [IC.sub.50] values in Table 1, Supplementary file show that artemisinin was more cytotoxic than 1,8-cineoIe or scopoletin. Artesunate as control drug revealed the highest cytotoxicity.

In addition to extracts of Artemisia annua and its chemical constituents, we also tested established trypanosomal drugs as positive control. The [IC.sub.50] values for diminazene, DL-a- difluoromethylornithine, metronidazole, ornidazole, and suramin in TC221 cells were in a range of 0.9-18.8 [um]g/ml (Table 1, Supplementary file). Remarkably, the activity of dichloromethane extracts of Artemisia annua ([IC.sub.50] range: 1.8-14.4 [um]g/ml) was comparable to that of these established drugs. The activity of these try-panosomal drugs towards HeLa cancer cells was weak ([IC.sub.50] range: 170.6-1502.6 [um]g/ml).

A phytochemical investigation of the extracts by GLC-MS revealed artemisinin, arteanuine B, and scopoletin in all extracts (Table 1). Artemisitene and 1,8-cineole, both of which have been reported as components of Artemisia annua (http://www.ars-grin.gov/cgi-bin/duke/farmacy2.pl) were not detected in our samples.

Cytotoxicity of phytochemicals from Artemisia annua towards the NCI cell line panel

In addition to HeLa cervical carcinoma cells, we investigated the activity of artemisinin, arteanuine B, artemisitene 1,8-cineole, and scopoletin towards cell lines of different other tumor origin. The [IC.sub.50] values for artemisinin, 1,8-cineole and scopoletin as well as two additional compounds, which are also constituents of Artemisia annua (arteanuine B and artemisitene) have been determined over a dose range of [10.sup.-8]-[10.sup.-4] M in the NCI panel of tumor cell lines and deposited at the database of the NCI's Developmental Therapeutics Program (www.dtp.nci.nih.gov). The [log.sub.10] [IC.sub.50] mean values for these cell lines grouped according to their tumor type are shown in Table 2. Across all cell lines, artemisitene was the most cytotoxic compound, whereas artemisinin and scopoletin were less active. Arteanuine B and 1,8-cineole showed intermediate inhibitory activity. Interestingly, artemisitene and 1,8-cineole exhibited different activity profiles. Leukemia ceil lines were most sensitive towards arteanuine B and artemisitene, whereas breast cancer cell lines were most affected by 1,8-cineole. Renal cancer cells were most resistant towards arteanuine B and artemisitene, and ovarian cancer cells showed the lowest inhibition by 1,8-cineole (Table 2).
Table 2

50%.inhibition.concentration ([log.sub.10] [IC.sub.50]) values
(M) for five phytochemicals of the NCI cell line panel grouped
according to tumor types. The [log.sub.10] [IC.sub.50] values
(mean [ + or - ] SEM were determined by the sulforhodamine assay.

          Artemisinin  Scopoletin  1,8-CineoIe  ArtemisiteneB

All        -4.059 ([+  -4.172 ([+   -4.937 ([+  -5.134 ([+ or
tumors    or -]0,013)          or  or -]0.061)       -]0.036)
                         -]0.037)
Leukemia   -4.243 ([+  -4.334 ([+   -4.646 ([+  -5.425 ([+ or
          or -]0.067)          or  or -]0.094)       -]0.074)
                         -]0.133)
Breast     -4.053 ([+  -3.928 ([+   -6.191 ([+  -5.164 ([+ or
Ca        or -]0.039)      or -]0      or -]0)       -]0.103)
Colon Ca   -4.019 ([+  -4.102 ([+   -4.656 ([+   -5,187([+ or
          or -]0.012)  or -]0.098  or -]0.090)       -]0.104)
Melanoma   -4.002 ([+  -4.077 ([+   -4.735 ([+  -5.275 ([+ or
          or -]0.002)  or -]0.046  or -]0.146)       -]0.084)
Brain      -4.026 ([+  -4.247 ([+   -4.656 ([+  -5.087 ([+ or
tumors    or -]0.026)          or  or -]0.159)       -]0.146)
                         -]0.129)
Lung Ca    -4.078 ([+  -4.209 ([+   -4.950 ([+  -4.904 ([+ or
          or -]0.030)          or   or -]0.122       -]0.069)
                         -]0.089)
Ovarian    -4.060 ([+  -4.096 ([+   -4.370 ([+  -4.958 ([+ or
ca        or -]0.031)          or   or -]0.068       -]0.095)
                         -]0.062)
Renal Ca   -4.028 ([+  -4.223 ([+   -5.025 ([+  -4.519 ([+ or
          or -]0.016)          or  or -]0.216)       -]0.081)
                         -]0.128)

          Arteanuine

All       -5.372 ([+
tumors            or
            -]0.045)
Leukemia  -5.881 ([+
                  or
            -]0.090)
Breast    -5.564 ([+
Ca           or -]0)
Colon Ca  -5.451 ([+
                  or
            -]0.109)
Melanoma  -5.425 ([+
                  or
            -]0.080)
Brain     -5.266 ([+
tumors            or
            -]0.076)
Lung Ca   -5.227 ([+
                  or
            -]0.106)
Ovarian   -5.137 ([+
ca                or
            -]0.136)
Renal Ca  -4.077 ([+
                  or
            -]0.055)


The [IC.sub.50] values of these cell lines were subjected to Pearson's correlation test to investigate, whether cell lines resistant to one phytochemical were also resistant to another compound. The rationale behind this approach was to test the cross-resistance profile of the cell lines. Although the relationships between artemisinin, arteanuine B and artemisitene revealed P-levels below 0.05. the correlation coefficients where rather weak (R-----0.6; Table 3). The [IC.sub.50] values for 1,8-cineole and scopoletin did not correlate with those for artemisinin. Although scopoletin cytotoxicity was associated with those of 1,8-cineole and artemisitene (P = 0.04), the correlation coefficients were weak (R > 0.03).
Table 3

Cross-resistance profile of the NCI cell line panel towards
five phytochemicals from Artemisia annua as determined by
correlating the IC5o values by Pearson's correlation test.

                         Artemisinin  Arteanuine    Artemisitene
                                               B

1,8-Cineole   P-Value    -0.121       -0.152        0.232
              R-Value     0.190       0.152         0.043
 Scopoletin   P-Value     0.070       -0.117        0.231
              R-Value     0.305       0.215         0.042
Artemisitene  P-Value      0330         0.347
              R-Value     0.007       0.008
Arteanuine B  P-Value     0.263
              R-Value     0.025

              Scopoletin

1,8-Cineole     0.229
                0.045
 Scopoletin
Artemisitene
Arteanuine B


As a control experiment, when the cross-resistance profiles of the well-known phytochemical and established anti-cancer drug, camptothecin and its derivatives (topotecan, irinotecan, and SN-38) were analyzed, significant relationships at sufficient high correlation coefficients were found (P < 0.05 and R > 0.6; Table 4), indicating pronounced cross-resistance among these drugs.
Table 4
Cross-resistance profile of the NCI cell line panel towards
camptothecin, SN-38, irinotecan. and topotecan as control drugs.

                      Camplothecin     Topotecan   Irinotecan

SN-38       P-Value          0.718         0.746        0.594
            R-Value         7.49 x        5.69 x       3.49 x
                      [10.sup.-11]  [10.sup.-12]  [10.sup.-7]
Irinotecan  P-               0.698         0.682
            Value
            R-Value         1.24 x        5.38 x
                      [10.sup.-11]  [10.sup.-11]
Topotecan   P-Value          0.855
            R-Value       4.1713 x
                     [10.sup.-21]'


Cluster analysis of [IC.sub.50] values of the NCI cell line panel for constituents of Artemisia annua

To mimic the activity of several compounds in the plant by a computational approach, we subjected the [IC.sub.50] values of artean-uine B, artemisitene, 1,8-cineole and scopoletin to hierarchical cluster analysis. The intention was to investigate whether or not clusters with high [IC.sub.50] values for these four phytochemicals also reveal a significant probability for high [IC.sub.50] values for artemisinin. If cell lines resistant to artemisinin were also resistant to the other compounds, these cell lines would cluster together in one of the dendrogram branches obtained. Vice versa, sensitive cell lines would cluster together in another branch of the dendrogram.

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

The dendrogram in Fig. 4A shows three distinct branches. Most importantly, none of them significantly correlated with the [IC.sub.50] values for artemisinin. To test whether these cluster branches contain cell lines with different sensitivity to arteanuine B, artemisitene, 1,8-cineole or scopoletin. The distribution of cell lines among the three dendrogram branches in Fig. 4A shows that clusters 1 and 2 contained cell lines resistant to arteanuine B, artemisitene, or scopoletin (Table 5). Cluster 3 contained cell lines sensitive to the three compounds. These relationships were statistically significant (Table 5, [x.sup.2] test). A significant relationship to 1,8-cineole was not observed in this dendrogram. Even if we subjected only the [IC.sub.50] values for the two artemisinin-related compounds, arteanuine B and artemisitene, to hierarchical cluster analysis, no significant retionship to [IC.sub.50] values for artemisinin was observed in the cluster tree (Fig. 4B), whereas the distribution of cell lines sensitive or resistant to arteanuine or artemisitene was statistically different (Table 5). Similarly, clustering of [IC.sub.50] values for 1,8-cineole and scopoletin did not result in a dendrogram, which separates cell lines according to their resistance or sensitivity towards artemisinin (Fig. 4C). However, significant relationships were obtained for 1,8-cineole and scopoletin (Table 5). As a control, we subjected the [IC.sub.50] values for topotecan, irinotecan and SN-38 to hierarchical cluster analysis. As expected, the dendrogram branches containing cell lines resistant to topotecan, irinotecan or SN-38 were also resistant to camptothecin with statistical significance (Fig. 4D). Vice versa, cell lines sensitive to topotecan, irinotecan or SN-38 were also sensitive to camptothecin.
Table 5
Separation of clusters of the NCI cell line panel obtained by
hierarchical cluster analysis shown in Fig. 4 in comparison to
phytochemical constituents of Artemisia annua. The [IC.sub.50]
median values of each compound were used as cut-off values to
define cell lines as being sensitive or resistant. n.s., not
significant(p >0.05).

                         Sensitive  Resistant  X2 test

Clustering of all four phytochemicals

Artemisinin   Cluster 1          3         11
(n = 63)      Cluster 2         10         18
              Cluster 3         12          9  n.s.
Arteanuine B  Cluster 1          5          6
(n = 56)      Cluster 2          5         20
              Cluster 3         18          2  P=1.76xl0 5
Artemisitene  Cluster 1          3         11
(n = 56)      Cluster 2         17         10
              Cluster 3          8          7  P= 0.040
1,8-Cineole   Cluster 1          3         11
(n = 56)      Cluster 2         16         10
              Cluster 3          9          7  n.s.
Scopoletin    Cluster 1          0         14
(n-56)        Cluster 2         16         11
              Cluster 3         12          3  P= 0.001
Clustering of arteanuine B and artemisitence
Artemisinin   Cluster 1          5          9
(n-63)        Cluster 2         13         23
              Cluster 3          7          6  n.s.
Arteanuine B  Cluster 1          2         10
(n = 55)      Cluster 2         13         17
              Cluster 3         13          0  P=8.05x10-5
Artemisitene  Cluster 1          0         14
(n = 54)      Cluster 2         22          8
              Cluster 3          5          5  P = 3.48xl0"5
Clustering of 1,8-cineole and scopoletin
Artemisinin   Cluster 1          9         15
(n-55)        Cluster 2          0          5
              Cluster 3         14         12  n.s.
1,8-Cineole   Cluster 1          3         22
(n"56)        Cluster 2          3          2
              Cluster 3         22          4  P=1.3x 10"9
Scopoletin    Cluster 1          5         19
(n-56)        Cluster 2          5          0
              Cluster 3         18          9  P=3.09xl0-'
Control clustering of topotecan, irinotean and SN-38
Camptothecin  Cluster 1          1         17
(n = 69)      Cluster 2         11         17
              Cluster 3         23          0  P = 4.36x10-9
Topotecan     Cluster 1          1         17
(n = 69)      Cluster 2         12         16
              Cluster 3         23          0  P=6.40x10"y
Irinotecan    Cluster 1          2         16
(n = 69)      Cluster 2         11         17
              Cluster 3         23          0  P=2.38xl0"8
SN-38         Cluster 1          0         17
(n = 59)      Cluster 2         10         12
              Cluster 3         20          0  P=8.46x 10-9


mRNA microarray and COMPARE analyses

We further investigated the microarray-based transcriptome-wide mRNA expression by COMPARE analyses to test, whether sensitivity and resistance to the compounds were correlated with expression of similar or different sets of genes. First, standard COMPARE analyses were performed. Lowest [IC.sub.50] values) of cell lines were correlated with the lowest mRNA expression levels of genes. Then, a reverse COMPARE analysis was done which correlated lowest [IC.sub.50] values with the highest gene expression level. Genes with correlation coefficients of R > 0.6 (standard COMPARE) and R < -0.6 (reverse COMPARE) are listed in Table 6. Importantly, no genes appeared in association with more than one of the phytochemicals, indicating that different genes may determine cellular response to these phytochemicals and that the weak or missing cross-resistance was reflected at genetic level.

Chemoprofiling of different Artemisia species

As exemplarily shown for five phytochemicals from Artemisia annua, the cellular response towards these compounds was considerably different. Therefore, we attempted to establish a chemoprofile for different Artemisia species. We subjected the chemical compositions of 11 Artemisia species (Dr. Duke's Phytochemical and Ethnobotanical Databases: http://www.arsgrin.gov/cgi-bin/duke/farmacy2.pl) to hierarchical cluster analysis. A total number of 546 phytochernicals has been included into the analysis (Supplementary Table 2) They have been subjected to hierarchical cluster analysis. As shown in the dendrogram of Fig. 5, Artemisia annua clustered closely together with A abrotanum, A. cina, A maritima, A palens, and A vulgaris in one branch of the dendrogram, while A herba-alba, A absinthium, A capillaris, A. salsoloides, and A dracunculus were less closely clustered. This dendrogram demonstrates that the considerable divergence of chemical composition in the Artemisia species enables specific clustering and separation of the species.

[FIGURE 5 OMITTED]

Discussion

Whether or not cancer cells and protozoans develop resistance towards artemisinin-type compounds is a long standing discussion in malaria therapy. In vitro, it was possible to generate Plasmodium strains with acquired resistance towards artemisinin (Walker et al., 2000). The relevance for the in vivo situation was unclear, since drug-resistant Plasmodium strains have worldwide not been detected for many years. It was only recently that artemisinin-resistant P. falciparum isolates emerged at the Thailand-Cambodian border (Witkowski et al. 2010). Resistance to high dose artesunate was associated with a quiescence mechanism involving overexpression of heat shock proteins and erythrocyte surface proteins and downregulation of cell cycle regulators and DNA biosynthesis proteins. A role of genetic polymorphisms in the pfmdr1 gene has been discussed (Pickard et al. 2003). Since drug resistance to novel and effective drugs belong to the major threats of chemotherapy, WHO recommended not to use artemisinins as monotherapy, but only in combination with other antimalarials. WHO also critically acknowledged the use of artemisinin in any other form than tablets or capsules, i.e. Artemisia annua tea (www.who.int/entity/medicines/publications/traditional/ArtemisiaStatement.pdf).

In this context, it is also important to address the question, whether or not arternisinin-type drugs induce resistance in cancer cells. While artemisinin cross-resistance can be tested in multidrug-resistant cell lines (Efferth et al. 2003), stable artemisinin-resistant cell line has not been described thus far. In the present investigation, we focused on phytochemicals in Artemisia annua in addition to artemisinin. While >50 compounds have been described in Artemisia annua (http://www.ars-grin.gov/cgi-bin/duke/farmacy2.pl), we exemplarily selected four compounds of interest, two artemisinin-type compounds (artemisitene and arteanuine B) and two unrelated ones (1,8-cineole, scopoletin). All of these compounds revealed cytotoxicity towards cancer and try-panosomal cells, but no cross-resistance of the NCI cell line panel was observed between artemisinin and these four phytochemicals. A general concept of drug resistance has been described by Goldie and Coldman (1985). Starting point of this seminal work were observations with bacterial strains which acquired resistance towards viruses by spontaneous mutations (Luria and Delbruck 1943). Goldie and Coldman and later on other groups developed mathematical models which explained drug resistance of tumors on the basis of spontaneous mutations of single cells. Upon drug treatment, such resistant cells have a survival advantage compared to the majority of non-mutated sensitive cells and overgrow the entire tumor cell population (Dy and Adjei 2008). Sublethal drug concentrations act as an evolutionary selection pressure for the development of resistant tumors. This can be prevented by the simultaneous treatment with a second drug. The assumption is that small subpopulations resistant to one drug are not resistant at the same time to a second drug. Therefore, they are killed by the second drug and development of resistance to the first drug is avoided. This is the basic principle of combination chemotherapy for tumors developed in the 1970s and 1980s and still well established in clinical oncology up to now. Transferring this concept to medicinal plants e.g. A annua provides a similar scenario: small subpopula-tions resistant to artemisinin do not survive when they are treated with 1,8-cineole. Hence, artemisinin resistance of the entire tumor cell population may be avoided. This point of view has not extensively been discussed so far in the field of phytotherapy. We have shown that artemisinin-resistant cell lines are not cross-resistant to other compounds of Artemisia annua such as 1,8-cineole. Therefore, when it is apparent that artemisinin-resistant subclones of a tumor can efficiently be killed by 1,8-cineole preventing the emergence of artemisinin-resistant tumors.
COMPARE        ID        GenBank   Symbol
coefficient
Artemisinin -
standard
COMPARE
0.819          GC54762   AB023220  USP20

0.771          GC183783  NM005187  CBFA2T3

0.766          GC96878   U43185    STAT5 A

0.765          GC101502  Z22576    CD69

0.756          GC100401  X59834    GLUL

0.753          GC182328  NM003189  TALI

0.752          CC166288  AL590118  SERHL2
0.742          GC90143   M6358990  None
0.734          GC31019   AF054186  EEF1E1

0.734          GC33623   X07109    PRKCB1

0.733          GC165496  AL161952  None
0.727          GC182816  NM003888  ALDH1A2

0.725          GC183873  NM005320  HIST1H1D

0.723          GC187047  NM016520  C9orf78

0.72           GC36324   Z93241    None
0.711          GC81738   AJ245416  LSM2

0.71           GC191046  U08626    None
0.706          GC186656  NM015905  TRIM24

0.7            GC32454   M6358990  None
0.7            GC29282   Z82206    None
0.693          GC156430  AI335888  ATP9B
0.686          GC188575  NM020993  BCL7A

0.676          GC182953  NM004117  FKBP5
0.668          GC174612  BF056790  LOC91431

0.668          GC99087   W60897    ZNRD1

0.66           GC32262   AF001862  FYB

Artemisinin -
reverse
COMPARE
-0.63          GC11521   H29810    None
-0.601         GC13148   H55766    None
Artemisitene
- standard
COMPARE
0.712          GC31589   T89651    None

0.669          GC37806   D14530    RPS23


0.644          GC30164   AF054187  NACA

0.634          GC37651   Z25749    None
0.628          GC15116   N68924    None
0.628          GC148889  AA524093  FBX041

0.626          GC192507  Z98950    None
0.624          GC16228   W78173    None
0.62           GC34926   X79563    RPS21

0.62           GC38852   ABO19409  None
0.618          GC34785   AC004537  None
0.616          GC37574   S79522    RPS27A

0.612          GC30713   U68140    NVL
0.61           GC17766   W87741    MYC

0.608          GC37836   U59151    DKC1

0.605          GC36655   U14966    RPL5

0.604          GC36609   X80822    RPL18A

0.602          GC30254   AA044823  RPL27

Artemisitene
- reverse
COMPARE
-0.653         GC182056  NM002844  PTPRK

-0.627         GC18210   AA009800  GSTT2B

-0.615         GC97127   U53204    PLEC1

-0.609         GC91265   N26926    GNA11

-0.601         GC31852   AF037339  CLPTM1

Arteanuine B
- reverse
COMPARE
-0.619         GC60519             ZNF488

1,8-Cineole -
standard
COMPARE
0.646          GC147599  AF169689  PCDHA6
0.634          GC176788  BF792773  FIBCD1

0.627          GC167434  AV710838  BC02
0.615          GC97912   U91512    NINJ1
0.612          GC165127  AL136870  KIAA1787
0.612          GC167406  AV706915  MTHFD2L

0.602          GC98312   W23068    HSPB8

0.601          GC160320  AI831738  DDX59

1,8-Cineofe -
reverse
COMPARE
-0.683         GC64100   AI375128  FSD2

Scopoletin -
standard
COMPARE
0.618          GC183972  NM005463  None
0.606          GC17951   AA001636  None

COMPARE        Name                               Function
coefficient
Artemisinin -
standard
COMPARE
0.819          Ubiquitin specific peptidase 20    Ubiquitin
                                                  thiolesterase,
                                                  peptidase
0.771          Core-binding factor, runt domain,  Transcription
               a subunit 2; translocated to, 3    factor

0.766          Signal transducer and activator    Signal transducer
               of transcription 5                 and transcription
                                                  factor

0.765          CD69 molecule                      Transmembrane
                                                  receptor
0.756          Glutamate-ammonia ligase           Glutamate ammonia
               (glutamine synthetase)             ligase

0.753          T-cell acute lymphocytic leukemia  Transcription
               1                                  regulator
0.752          Serine hydrolase-like 2            Serine hydrolase
0.742          Not specified                      Unknown
0.734          Kukaryotic translation elongation  Translation
               factor 1 epsilon 1                 factor

0.734          Protein kinase C, [beta] 1         Calcium-activated
                                                  protein kinase
0.733          Not specified                      Unknown
0.727          Aldehyde dehydrogenase 1 family,   Oxidoreductase
               member A2

0.725          Histone cluster 1, H1d             Involved in
                                                  histone
                                                  condensation
0.723          Chromosome 9 open reading frame    Unknown
               78

0.72           Not specified                      Unknown
0.711          LSM2 homologue, U6 small nuclear   Protein kinase;
               RNA associated (S. cerevisiae)     involved in
                                                  pre-mRMA splicing
0.71           Not specified                      Unknown
0.706          Tripartite motif-containing 24     Transcription
                                                  coactivator
0.7            Not specified                      Unknown
0.7            Not specified                      Unknown
0.693          ATPase, class II, type 9B          ATPase
0.686          B-cell CLL/Iymphoma 7A             Putative F-actin
                                                  cross-linking
                                                  protein

0.676          FK506-binding protein 5            FK506 binding
0.668          Prematurely terminated mRNA decay  Zinc ion binding
               factor-like

0.668          Zinc ribbon domain containing 1    Transcription
                                                  regulator
0.66           FYN-binding protein                Adapter protein of
               (FYB-120/130)                      FYN and LCP2
                                                  signaling
Artemisinin -
reverse
COMPARE
-0.63          Not specified                      Unknown
-0.601         Not specified                      Unknown
Artemisitene
- standard
COMPARE
0.712          Transcribed locus, strongly        Unknown
               similar to NP.775369.1 ribosomal
               protein L36A

0.669          Ribosomal protein S23              Structural
                                                  constituent of
                                                  ribosome
0.644          Nascent polypeptide-associated     Binds nascent
               complex a subunit                  proteins emerging
                                                  from ribosome

0.634          Not specified                      Unknown
0.628          Not specified                      Unknown
0.628          F-box protein 41                   Component of
                                                  ubiquitin ligase
                                                  complex

0.626          Not specified                      Unknown
0.624          Not specified                      Unknown
0.62           Ribosomal protein S21              Structural
                                                  constituent of
                                                  ribosome
0.62           Not specified                      Unknown
0.618          Not specified                      Unknown
0.616          Ribosomal protein S27a             Structural
                                                  constituent of
                                                  ribosome
0.612          Nuclear VCP-Iike                   Unknown
0.61           Avian myelocytomastosis viral      Transcription
               (v-myc) oncogene homologue         factor

0.608          Dyskeratosis congenita 1,          Required for
               dyskerin                           ribosome
                                                  biogenesis and
                                                  telomere
                                                  maintenance

0.605          Ribosomal protein L5               Structural
                                                  constittient of
                                                  ribosome
0.604          Ribosomal protein L18a             Structural
                                                  constituent of
                                                  ribosome
0.602          Ribosomal protein L27              Structural
                                                  constituent of
                                                  ribosome
Artemisitene
- reverse
COMPARE
-0.653         Protein tyrosine phosphatase,      Regulation of cell
               receptor type, K                   contact and
                                                  adhesion

-0.627         Glutathione S-transferase theta    Detoxification of
               2B                                 electrophiles.
                                                  Phase II enzyme

-0.615         Plectin 1, intermediate filament   Structural
               binding protein 500 kDa            constituent of
                                                  muscle

-0.609         Guanine nucleotide binding         Signal transducer
               protein (G protein), a 11 (Gq
               class)
-0.601         Cleft lip and palate associated    Cell
               transmembrane protein 1            differentiation

Arteanuine B
- reverse
COMPARE
-0.619         Zinc finger protein 488            Transcriptional
                                                  repressor
1,8-Cineole -
standard
COMPARE
0.646          Protocadherin [alpha] 9            Cell adhesion
0.634          Fibrinogen C domain containing 1   Receptor binding
                                                  activity
0.627          [beta] -Carotene oxygenase 2       Oxidoreductase
0.615          Ninjurin 1                         Cell adhesion
0.612          KIAA1787 protein                   Unknown
0.612          MethyienetetrabydrofoJate          Oxidoreductase
               dehyhdro-genase(NADP+-dependent)
               2-like

0.602          Heat shock protein 22 kDa protein  Chaperone
               8

0.601          DEAD (Asp-Glu-Ala-Asp) box         ATP-dependent
               polypeptide 59                     helicase
1,8-Cineofe -
reverse
COMPARE
-0.683         Fibronectin type HI and SPRY       Unknown
               domain containing 2
Scopoletin -
standard
COMPARE
0.618          Not specified                      Unknown
0.606          Not specified                      Unknown

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).


Our in vitro model with single compounds tested towards a panel of cell lines does not reflect all complex interactions in herbal mixtures of compounds. The activity of a mixture of compounds is constituted by additive and synergistic compound interactions. Synergistic interactions need a common mechanism, e.g. a common target where they bind to or a common pathway they inhibit. From an evolutionary point of view, synergistic compound interactions are not likely formed by chance. They need a co-evolution under appropriate selection pressure. The biosynthesis of different phytochemicals in plants with different models of action (as illustrated by different gene expression profiled) does not necessarily require co-evolutionary conditions. It can, therefore, be speculated that additive compound interactions occur with higher probability than synergistic ones.

The missing cross-resistance of the NCI cell line panel to several phytochemicals of Artemisia annua speaks for a sustained activity of Artemisia annua extracts, even if cancer cells would resist the detrimental effects of one of these compounds. Whether this result can be transferred from the in vitro to the in vivo situation remains to be seen. In addition to cellular determinants of sensitivity and resistance, the response of living organisms towards cytotoxic compounds is influenced by other factors such as immune system, angiogenesis, pharmacokinetics, etc. A clinical study with a limited number of patients showed that Artemisia annua tea was capable to reduce the parasitic load of malaria patients, but that the recrudescence rates were high (Mueller et al. 2004). Artemisia annua tea is widely used outside the official health care systems by local people and non-governmental organizations in Africa. Therefore, clinical validation is still warranted to see whether the lack of pronounced cross-resistance profiles in vitro among the four phytochemicals and artemisinin may indicate that herbal Artemisia annua extracts reveal activity towards heterogenous cancer cell populations with different genetic background and response rates towards these natural products.

This point of view is also supported by the microarray-based mRNA expression profiles identified by COMPARE and hierarchical cluster analyses. These expression profiles did not only considerably differ between artemisinin and the structurally unrelated compounds 1,8-cineole and scopoletin, but also to the related arteanuine B and artemisitene. This indicates that the molecular determinants of sensitivity and resistance of these phytochemicals are not identical and that different signaling routes and genetic networks may be active in tumor cells upon treatment with artemisinin compared to the other natural products analyzed. This is also consistent with recent pharmacogenomic data obtained for artesunate, a semisynthetic derivative of artemisinin (Sertel et at 2010).

Among the various Artemisia annua extracts which have been analyzed in the present study, a considerable heterogeneity of inhibitory activity was observed. This heterogeneity was obvious among samples of different origin and points to an important aspect in phytotherapy in general. Differing bioactivities mirror biological variability between different plant individuals as well as differences in growth and cultivation conditions (soil composition, climate, harvest, processing and storage conditions). The differences observed in the present investigation clearly show the necessity for standardized cultivation conditions (e.g. by the rules of good agricultural practice, GAP, etc.).

In addition to heterogeneous bioactivities between different samples, we also found varying cytotoxicities of extracts from different parts of the plant obtained from the same plant sample. Most active were methanol extracts from leaves, while dichloromethane extracts from chaff and thick caulis revealed the weakest growth inhibitory activity. This is a well-known phenomenon in pharmacognosy and a specific terminology has been established characterizing the morphological structure of a plant, e.g. flos, fructus, semen kerb[R], folia, summhates, ramulus, stipes, caulis, lignum, cortex, radix, bulbus, rhizoma, and tuber drugs. Commonly, Artemisia annua is used as herbal drug (Herba Artemisiae annuae). The data found in our investigation showed that the leaves were more active than other parts of the plant. Hence, the leave drug (Folia Artemisiae annuae) is more recommendable. This is true for the cytotoxic activity towards both cancer cells and trypanosomal cells. Our data are in accord with previous results on the antitrypanosomal activity of artemisinins and Artemisia annua extracts (Mishina et al. 2007; Nibret and Wink 2010). Furthermore, we showed here that phytochemicals in addition to artemisinin such as 1,8-cineole and scopoletin also revealed activity against trypanosomal cells. Hence, the bioactivity of Artemisia annua is not solely due to artemisinin.

Another point that has to be critically discussed is that dichloromethane extracts were more cytotoxic than the corresponding methanol extracts. This indicates that phytochemicals solved in non-polar solvents such as dichloromethane are more cytotoxic and/or present in higher amounts than compounds that can be found in polar methanol extracts. Artemisinin as best known phytochemical of Artemisia annua cannot be solved in polar solvents such as methanol or water. This may raise doubts about the activity of tea preparations for unsupervised self-reliant treatments. Either the tea might be inactive or other compounds than artemisinin confer bioactivity of Artemisia annua tea. Another possibility is that tea preparation might contain solving mediators facilitating the solubility of artemisinin.

Finally, we applied hierarchical cluster analysis for chemical profiling of diverse Artemisia 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, although not all species have been analyzed to the same extent. While this approach seems attractive at first sight, its taxonomic value has been controversially discussed, because the phytochemical constitution can vary within the same species due to varying external stimuli and growth conditions (Wink 2003; Wink et al. 2010). In the present investigation, we found that artemisinin was only present in Artemisia annua, but not in other Artemisia species. Although our analysis was limited to only 11 Artemisia species, we can conclude that artemisinin is not a lead compound for the genus Artemisia. These results are consistent with other investigations showing that artemisinin is present in some, but not ail Artemisia species (Liersch et al. 1986; Luo et al. 1991; Tan et al. 1998; Erdemoglu et al. 2007; Nibret and Wink 2010). Furthermore, there was no other compound present in all Artemisia species analyzed, indicating that there may also be no other Artemisia-specific lead compound. Some natural products appeared in more than one Artemisia species, e.g. 1,8-cineole or scopoletin. These compounds have a wide distribution over many plant families.

In conclusion, the present study showed that there is no pronounced cross-resistance among different phytochemicals of Artemisia annua. This result obtained by cytotoxicity assays was confirmed by microarray-based mRNA expression profiles, which revealed individual pharmacogenomic signatures with no overlap of genes for each of the phytochemicals tested.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at doi:10.l0l6/j.phymed.20l 1.06.008.

0944-7113/$ - see front matter [c] 2011 Elsevier GmbH. All rights reserved, doi: 10.1016/j.phymed.2011.06.008

* Corresponding author at: Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, University of Mainz, Staudinger Weg 5, 55128 Mainz, Germany, Tel.: +49 6131 392575; fax: +49 6131 3923752.

E-mail address: efferth[congruent to]uni-mainz.de (T. Efferth).

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Thomas Efferth (a), (*), Florian Herrmann (b), Ahmed Tahrani (b), Michael Wink (b)

a Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry,University of Mainz, Mainz, Germany

b Department of Biology, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Heidelberg, Germany
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Author:Efferth, Thomas; Herrmann, Florian; Tahrani, Ahmed; Wink, Michael
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Article Type:Report
Geographic Code:4EUGE
Date:Aug 15, 2011
Words:8328
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