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Artemisinin derivatives induce iron-dependent cell death (ferroptosis) in tumor cells.

ABSTRACT

Background: Apoptosis and other forms of cell death have been intensively investigated in the past years to explain the mode of action of synthetic anticancer drugs and natural products. Recently, a new form of cell death emerged, which was termed ferroptosis, because it depends on intracellular iron. Here, the role of genes involved in iron metabolism and homeostasis for the cytotoxicity of ten artemisinin derivatives have been systematically investigated.

Material and methods: [Log.sub.10][IC.sub.50] values of 10 artemisinin derivatives (artesunate, artemether, arteether, artenimol, artemisitene, arteanuin B, another monomeric artemisinin derivative and three artemisinin dimer molecules) were correlated to the microarray-based mRNA expression of 30 iron-related genes in 60 cell lines of the National Cancer Institute (NCI, USA) as determined in 218 different microarray hybridization experiments. The effect of desferoxamine and ferrostatin-1 on the cytotoxicity of artenimol of CCRF-CEM cells was determined by resazurin assays. The mRNA expression of TFRC was exemplarily validated by immunohistochemical detection of transferrin receptor protein expression.

Results: The mRNA expression of 20 genes represented by 59 different cDNA clones significantly correlated to the [log.sub.10] [IC.sub.50] values for the artemisinins, including genes encoding transferrin (TF), transferrin receptors 1 and 2 (TFRC, TFR2), cerulopasmin (CP), lactoferrin (LTF) and others. The ferroptosis inhibitor ferrostatin-1 and the iron chelator deferoxamine led to a significantly reduced cytotoxicity of artenimol, indicating ferroptosis as cell death mode.

Conclusion: The numerous iron-related genes, whose expression correlated with the response to artemisinin derivatives speak in factor for the relevance of iron for the cytotoxic activity of these compounds. Treatment with ferroptosis-inducing agents such as artemisinin derivatives represents an attractive strategy for cancer therapy. Pre-therapeutic determination of iron-related genes may indicate tumor sensitivity to artemisinins. Ferroptosis induced by artemisinin-type drugs deserve further investigation for individualized tumor therapy.

Keywords:

Cancer

Cell death

Iron

Microarray

Pharmacogenomics

Introduction

Artemisia annua L. (Asteraceae) has been used in traditional Chinese medicine for two millennia. In the second half of the 20th century, the antimalarial activity of artemisinin (1) from A. annua has been unraveled. This compound and its semisynthetic derivatives artemether (2) and artheether (3) are established drugs to combat infections with Plasmodium falciparum and Plasmodium vivax (Tu, 2011). As found by us and others in the 1990s, artemisinin derivatives also exert anticancer activity in vitro and in vivo (Efferth et al. 1996; Lai and Singh 1995). Interestingly, iron is a crucial determinant of activity of artemisinin-type drugs both in malaria and cancer. In both diseases, the activity of the drugs is associated with the presence of iron. This metal is present in large excess bound to hemoglobin in erythrocytes and fosters the cleavage of artemisinin's endoperoxide bridge in a Fenton-type chemical reaction. This leads to the generation of reactive oxygen species (ROS), which induce cell death of the Plasmodia parasites (Haynes et al., 2013). A comparable situation occurs in tumors. It is well known that the iron content is higher in tumors than in normal tissues explaining at least in part the preferential cytotoxicity of artemisinins towards tumor cells compared to normal cells (Shterman et al. 1991). The exquisite susceptibility of tumor cells to artemisinins upon co-administration of holotransferrin or ferrous iron was reported by us and others (Lai and Singh 1995; Efferth et al. 2004; Kelter et al. 2007).

The best known mode of cell death is apoptosis, a programmed form of cell death occurring both in healthy and diseased cells (Cotter 2009). Two different pathways--an extrinsic receptor-driven and intrinsic mitochrondria-driven signaling cascade lead to the activation of caspases, which finally execute dying cells. In addition, other forms of cell death have been described. Autophagy, necroptosis, and mitoptosis may be induced instead or in parallel to apoptosis upon challenge of cells with toxic insults Gain et al. 2013). The different forms of cell death have been intensively investigated for cytotoxic natural products (Efferth 2012; Lin and Tongyi 2014; Safarzadeh et al. 2014).

Recently, a novel type of caspase-independent non-apoptotic cell death has been described (Dixon and Stockwell 2014). It was termed ferroptosis, because it is dependent on the intracellular presence of iron. Ferric iron favors ROS generation and thereby induce of ferroptosis. It has been shown that RAS-mutated tumor cells commit programmed cell death with concomitant increases of ROS levels and decreases of mitochondrial sizes. The exact mechanism of ferroptosis has not been clarified yet. Intracellular cysteine import mediated by a glutamate-cysteine-antiporter systems in the cell membrane suppresses ferroptosis. Cysteine is needed for the synthesis if glutathione and glutathione prevents the accumulation of lipid peroxides. Ferroptosis may also occur by inhibition of glutathione peroxidase 4 (GPX4).

Erasin, an oncogenic RAS-selective lethal compound, as well as the kinase inhibitor sorafenib have been identified to inhibit the cysteine-glutamate antiporter complex [x.sub.c.sup.-] and to induce irondependent, oxidative cell death (Dixon et al. 2012, 2014). Ferrostatin-1 and deferoxamine are iron-depleting agents that inhibit ferroptosis (Louandre et al. 2013; Skouta et al. 2014).

Artemisinin exerts exquisite anticancer activity in vitro (Efferth et al. 1996, 2001, 2002, 2003b) and in vivo (Dell'Eva et al. 2004; Du et al. 2010; Li et al. 2007; Ma et al. 2011). Compassionate uses on single cancer patients as well as first clinical trials to treat veterinary and human tumors speak for the potential of artemisinin-type compounds as novel anticancer drugs (Berger et al. 2005; Breuer and Efferth 2014; Jansen et al. 2011; Krishna S 2014; Rutteman et al. 2013; Singh 2002; Zhang et al. 2008).

Artesunate (5) induced cell death in cancer cells by both the intrinsic and extrinsic pathways of apoptosis as well as autophagy and necroptosis (Button et al. 2014; Wang et al. 2012). In light of the multiplicity of cell death modes activated by artemisinin-type compounds, it is reasonable to hypothesize that they may also induce ferroptosis. There are already important hints that speak for this hypothesis. The role of holotransferrin and ferric iron in the form of iron (II)-glycine sulfate (Ferrosanol[R]) for the enhancement of tumor-inhibiting effects of artemisinins is well documented (Efferth et al. 2004; Kelter et al. 2007; Lai and Singh 1995).

However, the role of other proteins involved in iron metabolism and homeostasis for the activity of artemisinin-type drugs has not been addressed in detail as of yet. Therefore, genes encoding proteins involved in iron metabolism were systematically studied by a pharmacogenomic approach. To identify possible modes of action, the microarray-based mRNA expression of iron-related genes were correlated in the cell line panel of the National Cancer Institute (NCI), USA with the [log.sub.10][IC.sub.50] values for 10 different artemisinin derivatives and found a large number of iron-related genes correlating with response to artemisinins. The names and chemical structures of the 10 artemisinin derivatives are shown in Fig. 1. Using the iron-depleting agents, deferoxamine and ferrostatin-1, the cytotoxic activity was diminished, which is another proof for ferroptosis as relevant cell death mode of artemisinin compounds.

Material and methods

Cell lines

The cancer cell lines of the Developmental Therapeutics Program of NCI (MD, USA) consisted of a series of non-small cell lung cancer, colon cancer, renal cancer, ovarian cancer cells, tumor cells of the central nervous system, leukemia, melanoma, prostate carcinoma, and breast cancer. Their origin and processing have been previously reported (Alley et al. 1988).

Cytotoxicity assays

The cytotoxicity of 10 artemisinin derivatives (see Fig. 1) in NCI cell line panel was measured by the sulforhodamine B assay (Monks et al. 1991). The 50% inhibition concentrations calculated from dose response curves and converted to logarithmic values ([log.sub.10][IC.sub.50]) have been deposited in the NCI database (http://dtp.nci.nih.gov).

The resazurin assay has been used to measure the effect of ferroptosis inhibitors on the cytotoxicity of CCRF-CEM-cells. The performance of the resazurin assay has been previously reported (Kuete et al. 2011). Ferrostatin-1 and deferoxamine have been used as ferroptosis inhibitors (Sigma-Aldrich, Taufkirchen, Germany). The compounds have been pre-incubated at concentrations of 50 [micro]M (ferrostatin-1) or 0.1 mM (deferoxamine) for 1 h prior to application of artenimol to allow precipitation of cellular iron. Then, artenimol (4) has been applied in concentration in a range between 0.001 and 100 [micro]M.

COMPARE and cluster analyses of microarray data

The mRNA microarray hybridization of the NCI cell lines has been reported and deposited at the NCI website (http://dtp.nci.nih.gov) (Amundson et al. 2008; 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 as a tool to identify candidate genes for drug resistance and sensitivity (Evans et al. 2008; Fagan et al. 2012; Luzina and Popov 2012; Wosikowski et al. 1997). To derive COMPARE rankings, a scale index of correlations coefficients (R-values) was created from [log.sub.50][IC.sub.50] values of test compounds and microarray-based mRNA expression values. Greater mRNA expression correlate 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 relative measure for the linear dependency of two variables. This test was implemented into the WinSTAT Program (Kalmia Co, MA, USA).

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 cluster trees (dendrograms) by the algorithm included into the WINSTAT program (Kalmia Co, MA, USA). The cluster analyses were run using the WARD method. Previously, cluster models have been validated for gene expression profiling and for approaching molecular pharmacology of cancer (Efferth et al. 1997; Reinhold et al. 2012; Villeneuve and Parissenti 2004; Zeeberg et al. 2011).

Immunocytochemistry

Cytopsin preparations were prepared from CCRF-CEM leukemia cells by centrifugation at 800 rpm for 10 min to glass slides. The cells were air-dried and ethanol fixed. For determination of transferrin receptor protein expression, the UltraVision polymer detection kit (Thermo Fisher Scientific GmbH, Dreieich, Germany) was used according to manufacturer's instructions.. Endogenous peroxidase activity was blocked by immersing the slides in 3% [H.sub.2][0.sub.2] for 10 min at room temperature. After rinsing for 5 min in PBS, non-specific binding was blocked by Ultra Vision Block (Thermo Fisher Scientific GmbH) for another 5 min. Slides were incubated in a humidified chamber over night at 4[degrees] C with primary antibody (monoclonal antibody 10F11, dilution 1:40; Thermo Fisher Scientific). After rinsing for 5 min in PBS, Primary Antibody Amplifier Quanto (Thermo Fisher Scientific GmbH) was applied for 10 min at room temperature. Slides were washed in PBS for 5 min and then HRP Polymer Quanto (Thermo Fisher Scientific GmbH) was applied for 10 min, a wash step (5 min) followed. Afterwards, 30 [micro]l diaminobenzidine (DAB) Quanto chromogen (Thermo Fisher Scientific GmbH) was mixed with 1 ml DAB Quanto substrate and applied to the slides for 5 min. After washing in PBS for 5 min, the tissues were counterstained in hemalaun solution (Merck KGaA, Darmstadt, Germany) and rinsed in PBS for 5 min, followed by running tap water (10 min). Cytospin preparations were dehydrated (2x1 min 70% ethanol, 2x1 min 96% ethanol, 2x 1 min 100% ethanol, 2x 5 min xylol, 1x2 min xylol) and embedded using Entellan (Merck KGaA).

Results

Cytotoxicity of artemisinin derivatives towards NCI cell line panels

The cytotoxicity of 10 artemisinin derivatives has been tested over a dose range from [10.sup.-8] to [10.sup.-4] M in 60 tumor cell lines. The chemical structures of artemisinin and these 10 derivatives are depicted in Fig. 1 and coded as (1) to (11).

COMPARE and cluster analyses of microarray data

The mean [log.sub.10] [IC.sub.50] values of the ten compounds were in a range from -4.14 to -6.81 M. Artemisinin dimer hemisuccinate (11) was most cytotoxic. Then, the [log.sub.10] [IC.sub.50] values were correlated for these compounds with the baseline mRNA expression levels of 30 genes in 60 cell lines represented by determined in 218 different microarray hybridization experiments. These genes have been selected, because they are involved in cellular iron metabolism and homeostasis. The mRNA expression of 20 genes represented by 59 different DNA clones significantly correlated to the [log.sub.10] [IC.sub.50] values for the ten derivatives with a significance level of P < 0.05 and a correlation coefficient of R < -0.2 and R > 0.2, respectively. The function of these genes and their relationship to iron metabolism and homeostasis are listed in Table 1.

The genes identified by Pearson correlation test were subjected to hierarchical cluster analysis. A cluster image map was constructed using the p-values derived from the correlations of the mRNA expressions of these genes and the [log.sub.50][IC.sub.50] values for the 10 artemisinin-type substances (Fig. 2). Grey boxes indicate statistical significance (p < 0.05), white boxes non-significance (p > 0.05). The dendrogram on the right side of Fig. 2 shows that four major branches can be separated from each other (termed clusters 1-4). Then, a second cluster analysis was performed using the 10 artemisinin derivatives, which provided a dendrogram with two branches termed clusters A and B (Fig. 2, bottom). The cluster image map that has been constructed from these two dendrograms shows that the number of p-values below 0.05 (gray boxes) in clusters 2 and 4 were more or less equally distributed among the compounds. However, more significant correlations with < 0.05 were observed in cluster 1 for compounds belonging to cluster A drugs and in cluster 3 for compounds of cluster B (Fig. 2).

The question arose, why the cluster image map separated the 10 derivatives into two clusters (clusters A and B). The mean [log.sub.10][IC.sub.50] values were subjected for all 60 cell lines for each compound and subjected to chi-square test. The purpose was to see, whether or not the genes included into the cluster image map of Fig. 2 separate the derivatives according to their cytotoxicity. Indeed, cluster A contained significantly more highly cytotoxic substances than cluster B at a cutoff value of-5.20 M (see insert of Fig. 2; P = 0.038).

All iron-related genes were correlated with the response of tumor cells to the derivatives. This clearly indicates that genes involved in cellular iron metabolism and homeostasis may indeed regulate the activity of these compounds in cancer cells. Nevertheless, differences in the gene expression patterns were also apparent. Compounds with higher cytotoxic activity (mean [log.sub.10][IC.sub.50] across all 60 cell lines of <-5.20 M) were assembled in cluster A, while the weaker active compounds tend to appear in cluster B. It can be concluded that it is possible to recognize derivatives with toxicity only the basis of this specific gene expression profile, because the [log.sub.10][IC.sub.50] values of these ten compounds have not been included into the cluster analysis.

It can be assumed that higher cytotoxicity may be associated with the expression of certain genes, e.g. genes that clustered in cluster 1 (CP, TFRC, TFR2, SDHB, FTL, FTHL5, LTF, UQCRFS1). This speculation needs, however further investigation, since other clones of the same genes partly assembled in clusters 2-4, indicating that the relationship between expression of certain genes and high cytotoxicity might not be very tight.

The genes subjected to the cluster analysis in Fig. 2 were derived from different microarray hybridization platforms. In Fig. 3, the mRNA microarray data obtained by Affymetrix U95 microarrays and hierarchical cluster analysis were selected. The resulting dendrogram is can be separated into four main branches. Subsequently, the median [log.sub.10][IC.sub.50] values of the 10 derivatives were used as cutoff values to define the 60 cell lines as being sensitive or resistant to the corresponding compounds. The distribution of sensitive and resistant cell lines among the four cluster branches is shown in Table 2. Interestingly, the gene expression profile included into this cluster analysis revealed significant distribution pattern for all derivatives except artemisitene (7) and arteanuine B (8). Thus, the expression pattern of these iron-related genes is sufficient to explain the activity of the majority of derivatives analyzed in this investigation.

As a next step, the correlations of all 10 derivatives were addressed to selected genes. Two genes were selected, whose mRNA expression directly correlated with the [log.sub.10][IC.sub.50] values of the compounds (positive R-vaiues) and four genes with inverse correlations to the artemisinins (negative R-values). The direct and indirect correlations are illustrated for artesunate (5) and artemisinin dimer 1 (9) in the left and middle columns of Fig. 4. To depict the correlations to all 10 derivatives, net-like representations (oncobiograms) were used (Fig. 4, right column). The axes in the oncobiograms show correction coefficients (R-values). The direct correlations between mRNA expressions of the CP and NFU1 genes and [log.sub.10][IC.sub.50] values to the ten compounds revealed positive R-values (Fig. 4A and B), while the inverse correlations for the OGFOD1, TFRC, CISD1, and ISCU genes resulted in negative R-values (Fig. 4C-F). The CP expression correlated with high R-values to [log.sub.50][IC.sub.50] values of all compounds except arteaunuine B (8) (Fig. 4A), while NFU1 expression strongly correlated to all compounds (Fig. 4B). The inverse correlations showed a mixed picture. Some compounds correlated well with some variations in the negative R-values to OGFOD1, TFRC, CISD1, and ISCU gene expression (Fig. 4C-F).

To cross-validate the microarray-results, the microarray data were correlated for selected genes obtained from different platforms with each other. As shown in Table 3, the mRNA expression values in 60 tumor cell lines for the CP gene all correlated with each other independent on the microarray platform (Stanford, Affymetrix U95 U95v2, U133, U133A/U133B). Comparable results were obtained for the OGFOD1 and TFRC genes for the Affymetrix microarrays U133 and U133A/U133B.

TFRC expression

The results of the present investigation and also previously published data indicate that the transferrin receptor encoded by the TFRC gene plays an important role for iron-mediated cytotoxicity of the derivatives. Therefore, the TFRC expression was investigated in each of the 60 tumor cells lines. As shown in Fig. 5, there was a large variation of TFRC expression not only between cell lines of different tumor types, but also between individual cell lines of the same tumor type. Colon cancer cell lines had a much higher TFRC expression than renal cancer cell lines. The TFRC mRNA expression has been exemplarily confirmed by immunohistochemistry. Membrane-bound and cytosolic expression of transferrin receptor protein can be seen in the insert of Fig. 5. After binding of transferrin to its receptor, this complex is internalized into cancer cells by endocytosis. This explains, why both membrane-bound and granula-like cytosolic staining patterns were observed in CCRF-CEM cells.

Ferroptosis inhibition

As a validation that the associations between iron-related genes to the response to artemisinin derivatives may be due to induction of iron-dependent cell death, CCRF-CEM cells were treated with artenimol (4) with and without addition of the ferroptosis inhibitor ferrostatin-1 and with the iron chelator deferoxamine. The dose response curve of artenimol demonstrated an [IC.sub.50] value of 4.3 [micro]M (Fig. 6). The addition of 50 [micro]M ferrostatin 1 decreased artenimol's cytotoxic effect by 4 fold with an [IC.sub.50] of 18 [micro]M, whereas the addition of 0.1 mM deferoxamine led to a nearly complete loss of cytotoxicity of artenimol (4). This is a clear indication that artenimol (4) indeed induced ferroptosis. Ferrostatin-1 and deferoximine are known as ferroptosis-inhibitors. Therefore, these two compounds were used as control compounds to prove, whether iron is indeed of crucial importance for the cytotoxic action of artemisinin-type drugs towards cancer cells.

Discussion

It is long known that artemisnin and its derivate do not only kill Plasmodia parasites, but also cancer cells. During the past decade, we and others have shown that the activity of artemsinin derivatives depend on the presence of ferric iron ions (Lai and Singh 1995; Efferth et al. 2004; Kelter et al. 2007). Recently, a novel mode of cell death called ferroptosis (iron dependent non-apoptotic cell death) has been described (Dixon et al. 2012, 2014; Dixon and Stockwell 2014). Systematical re-evaluating microarray-based mRNA expression indicates that artemisinin induce iron-dependent cell death (ferroptosis). In the present investigation, we clearly found evidence that the mode of action of 10 artemisinin derivatives are due to ferroptosis. Ferrous irons cause oxidative stress. There is mounting evidence that artemisinins generate ROS and lead to oxidative stress in cancer cells, which in turns leads to the activation of antioxidant stress responses (Efferth et al. 2003a, 2007; Efferth and Oesch 2004; Hamacher-Brady et al. 2011; Horwedel et al. 2010). Antioxidant detoxification systems such as the glutathione redox cycle, thioredoxin redox cycle, detoxifying enzymes such as catalase, superoxide dismutase etc., and the Nrf2 pathway all contribute to counteract oxidative stress posed by artemisinins. These data fit well to the concept of ferroptosis, where ferric ions induce oxidative stress and cell death.

Among the diverse genes whose mRNA expression in the 60 tumor lines correlated significantly with response to the artemisinins, we were especially interested in the transferrin receptor, which encoded by the TFRC gene. TFRC may serve as biomarker to select tumors with good responsiveness to artemisinins. This hypothesis should be investigated in more detail, as it represents an attractive strategy for individualized tumor therapy with artemisinin-type drugs. The role of the transferrin receptor for cancer biology in general has been previously reported. This receptor exerts growth regulatory functions and is most frequently over-expressed in rapidly growing tumors (Daniels et al. 2006), although few exceptions are known, e.g. in hepatocellular carcinoma occuring in a background of hemochromatosis, where tumors are depleted in iron compared to non-tumor tissue. The expression of transferrin receptor is of prognostic significance for several tumor types (Ucar and Gurer 2003; Whitney et al. 1995; Yang et al. 2001). Ferrous iron can either be bound to transferrin or to other proteins before uptake. The role of the other iron-related genes, e.g. CP, ODFOD 1 and 2, ISCU, NFU1 etc. is less well understood for cancer therapy and prognosis, but deserves more attention in the future.

In conclusion, the present investigation demonstrated that artemisinin derivatives do not only kill tumor cells by induction of apoptosis, autophagy or necroptosis as shown in the past, but also by ferroptosis. The numerous iron-related genes, whose expression correlated with the response to the derivatives speak in factor for the relevance of iron for the cytotoxic activity of these compounds. The mixture of different modes of cell death makes this class of compounds to attractive candidates for cancer therapy. The redundancy of biological mechanisms is a frequent phenomenon in biology, which assures that a desired endpoint effect can be reached with high reliability. In the case of artemisinins, it can be speculated that apoptosis-resistant cells may still die due to induction of non-apoptotic forms of cell death, such as autophagy, necroptosis, or as shown in the present paper by ferroptosis. The full therapeutic potential of this perspective needs to be explored in more detail in the future.

ARTICLE INFO

Article history:

Received 26 June 2015

Revised 3 August 2015

Accepted 5 August 2015

Conflict of interest

The authors declare not to have any conflict of interest.

Acknowledgment

The secretarial assistance of Mrs. Ilona Zirbs is gratefully acknowledged.

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Edna Ooko (a), Mohamed E.M. Saeed (a), Onat Kadioglu (a), Shabnam Sarvi (a), Merve Colak (a), Kaoutar Elmasaoudi (a), Rabab Janah (a), Henry J. Greten (b), Thomas Efferth (b), *

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

(b) Biomedical Sciences Institute Abel Salazar, University of Porto, Portugal, and Heidelberg School of Chinese Medicine, Heidelberg, Germany

Abbreviations: DAB, diaminobenzidine; [IC.sub.50], 50% inhibition concentration; NCI, National Cancer Institute USA; ROS, reactive oxygen species; TFRC, transferrin receptor.

* Corresponding author. Tel.: +49 6131 39 25751; fax: +49 6131 39 23752.

E-mail address: efferth@uni-mainz.de (T. Efferth).

http://dx.doi.org/10.1016/j.phymed.2015.08.002

Table 1
Meta-data of genes shown in the cluster analysis of Fig. 1, whose
mRNA expression correlated with [log.sub.10]IC50 values of 10
artemisinins in 60 tumor cell lines.

                Genebank accession
Gene symbol     numbers                   Gene name

OGFOD1          AA536054, AI817242,       2-Oxoglutarate and
                  NM_018233                 iron-dependent oxygenase
                                            domain containing 1
OGF0D2          AI431902, AI431902,       2-Oxoglutarate and
                  NM_024623, AI309636,      iron-dependent oxygenase
                  AI309636, AI431902,       domain containing 2
                  NM_024623
1REB2           N52559                    Iron-responsive element
                                            binding protein 2
SLC40A1         AA044844, AI082754,       Solute carrier family 40
                  AU156956                  (iron-regulated
                                            transporter), member 1
CP              N50654, M13699,           Ceruloplasmin (ferroxidase)
                  AI963654, AI922198,
                  NM_00009S, AI684991,
                  AI922198
LTF             W86890                    Lactotransferrin
TF              NMJ501063                 Transferrin
TFRC            H93194, X01060, M11507,   Transferrin receptor
                  NM 003234, BC001188,      (p90, CD71)
                  N76327
TFR2            AF067864, NM_003227       Transferrin receptor 2
FTMT            AA470110                  Ferritin mitochondrial
FTH1            L20941, AA083483,         Ferritin, heavy
                  NM_002032                 polypeptide 1
FTHL5, FTH1P5   J04755                    Ferritin, heavy
                                            polypeptide-like 5,
                                            (pseudogene 5)
FTL             AW026066, BG538564,       Ferritin, light polypeptide
                  BG537190
CISD1           AI761052, AI827828,       CDGSF1 iron sulfur domain 1
                  NM_018464
ISCU            U47101, AY009128          Iron-sulfur duster scaffold
                                            homologue (E. coli)
NFU1            AA858071, NMJJ15700       NFUl iron-sulfur cluster
                                            scaffold homologue
                                            (S. cerevisiae)
PIR             N47572                    Pirin (iron-binding nuclear
                                            protein)
UQCRFS1         AA035338                  Ubiquinol-cytochrome c
                                            reductase, Rieske
                                            iron-sulfur polypeptide 1
SDHB            AW294107                  Succinate dehydrogenase
                                            complex, subunit B,
                                            iron-sulfur (Ip)
FDX1            N89718                    Ferredoxin 1
FDXR            J03826                    Ferredoxin reductase

Gene symbol     Gene function

OGFOD1          Iron ion binding protein; oxidoreductase
OGF0D2          Iron ion binding protein; oxidoreductase
1REB2           RNA-binding protein that binds to iron-responsive
                  elements (IRES) of mRNA species. Binding to the IRE
                  element in the 5'-UTR ferritin mRNA represses its
                  mRNA translation. Binding to the 3'-UTR of the
                  transferrin receptor mRNA inhibits its degradation
SLC40A1         Involved in iron export. Mediates iron efflux in the
                  presence of ferroxidases (e.g. ceruloplasmin)
CP              Copper-binding (6-7 atoms per molecule) glycoprotein
                  with ferroxidase activity oxidizing Fe(2+) to
                  Fe(3+) without releasing radical oxygen species. It
                  is involved in iron transport across the cell
                  membrane
LTF             Transferrins are iron binding transport proteins
                  which can bind two Fe(3+) ions. Lactotransferrin is
                  a major iron-binding and multifunctional protein
                  found in exocrine fluids such as breast milk and
                  mucosal secretions. Has antimicrobial activity
                  (antibacterial, antifungal, antiviral) as well as
                  anabolic, differentiating and anti-apoptotic
                  effects on osteoblasts and can also inhibit
                  osteoclastogenesis. Stimulates the TLR4 signaling
                  pathway and NF-[kappa]B activation. Inhibits
                  neutrophil granulocyte migration to sites of
                  apoptosis, if secreted by apoptotic cells.
                  Stimulates VEGFA-mediated endothelial cell
                  migration and proliferation
TF              Transferrins are iron binding transport proteins,
                  which can bind two [Fe.sup.3+] ions. Transferrin
                  transports iron from sites of absorption and heme
                  degradation to those of storage and utilization.
                  Serum transferrin also stimulates cell
                  proliferation
TFRC            Cellular uptake of iron occurs by endocytosis of
                  ligand-occupied transferrin receptor into
                  endosomes. Endosomal acidification leads to iron
                  release. The apotransferrin-receptor complex is
                  then recycled to the cell surface with a return
                  to neutral pH and the concomitant loss of affinity
                  of apotransferrin for its receptor. Regulated by
                  cellular iron levels by binding of iron regulatory
                  (IRP1,1RP2) to iron-responsive elements in the
                  3'-UTR. Up-regulated upon proteins mitogenic
                  stimulation
TFR2            Mediates cellular uptake of transferrin-bound iron in
                  a non-iron dependent manner. Transferrin receptor 2
                  is involved in the cellular transport of iron
                  (alpha form) similar to TFRC
FTMT            Ferrooxidase, ferric ion binding protein. Stores iron
                  in a soluble, non-toxic, readily available form.
                  Important for iron homeostasis. Iron is taken up in
                  ferrous iron and deposited as ferric hydroxides
                  after oxidation
FTH1            Similar function as mitochondrial ferritin. Also
                  plays a role in delivery of iron to cells
FTHL5, FTH1P5   Unknown function; pseudogene
FTL             Similar function as mitochondrial ferritin. Also
                  plays a role in delivery of iron to cells
CISD1           Metal ion binding protein. Involved in regulating
                  maximal capacity for electron transport and
                  oxidative phosphorylation. Involved in Fe-S cluster
                  shuttling and/or in redox reactions
ISCU            Iron ion binding protein. Involved in the assembly or
                  repair of the [Fe-S] clusters present in
                  iron-sulfur proteins
NFU1            Iron-sulfur scaffold protein, which assembles
                  [4Fe-2S] clustrers and delivers them to target
                  proteins
PIR             Transcriptional coregulator of NF-xB which
                  facilitates binding of NF-xB proteins to target
                  kappa-B genes in a redox-state-dependent manner
UQCRFS1         Metal ion binding protein; oxidoreductase. Component
                  of the uniquinol-cytochrome c reductase complex
                  (complex III) in the respiratory chain generating
                  an electrochemical potential coupled to ATP
                  synthesis
SDHB            Metal ion binding protein; oxidoreductase.
                  Iron-sulfur protein (IP) subunit of succinate
                  dehydrogenase (SDH) that is involved in complex II
                  of the mitochondrial electron transport chain and
                  is responsible for transferring electrons from
                  succinate to ubiquinone (coenzyme QJ
FDX1            Ion sulfur electron transport protein for
                  mitochondrial P450s. Essential for the synthesis of
                  steroid hormones. Participates in the reduction of
                  mitochondrial cytochrome P450 for steroidogenesis.
                  Transfers electrons from adrenodoxin reductase to
                  CYP11A1, which catalyzes cholesterol side-chain
                  cleavage
FDXR            Electron transport flavoprotein for mitochondrial
                  P450s. Serves as the first electron transfer
                  protein in all the mitochondrial P450 systems.
                  Including cholesterol side chain cleavage in all
                  steroidogenic tissues

Gene information was taken from the OMIM database, National Cancer
Institute, 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).

Table 2
Separation of clusters of 60 tumor cell lines obtained by the
hierarchical cluster analysis and color image mapping shown in Fig. 3
in comparison to the response of the cell lines to 10 artemisinin
derivatives.

                 Partition
                 [(log.sub.10][IC.sub.50], m)       Cluster 1

2    Sensitive   <-4.0                              2
     Resistant   [greater than or equal to] -4.0    0
3    Sensitive   <-4.355                            2
     Resistant   >-4.355                            0
4    Sensitive   <-6.165                            1
     Resistant   >-6.165                            1
5    Sensitive   <-5.30                             2
     Resistant   >-5.30                             0
6    Sensitive   <-6.28                             2
     Resistant   >-6.28                             0
7    sensitive   <-5.46                             0
     Resistant   >-5.46                             1
8    Sensitive   <-5.185                            0
     Resistant   >-5.185                            2
9    Sensitive   <-6.98                             2
     Resistant   >-6.98                             0
10   Sensitive   <-5.21                             2
     Resistant   >-5.21                             0
11   Sensitive   <-7.21                             2
     Resistant   >-7.21                             0

                 Cluster 2   Cluster 3   Cluster 4   Chi square test

2    Sensitive    6           2           5
     Resistant    9          13          18          p = 0.039
3    Sensitive   12           6           8
     Resistant    4           8          18          p = 0.018
4    Sensitive   12           4           9
     Resistant    3          11          17          p = 0.014
5    Sensitive   12           7           9
     Resistant    3           8          15          p = 0.033
6    Sensitive   12           4          10
     Resistant    3          11          16          p = 0.007
7    sensitive    9           4          11
     Resistant    4           9           9          Not significant
8    Sensitive    9           4          13
     Resistant    7          11          12          Not significant
9    Sensitive   11           8           8
     Resistant    5           8          18          p = 0.045
10   Sensitive   11           5          10
     Resistant    4          10          16          p = 0.041
11   Sensitive   13           3          10
     Resistant    1          11          16          p = 3.921 x
                                                       [10.sup.-4]

Not significant, p > 0.05.

The median [log.sub.10][IC.sub.50] values of the ten compounds were
used as cutoffs to separate tumor cell lines as being "sensitive" or
"resistant".

Table 3
Correlation of microarray-based mRNA expression values for selected
genes obtained from hybridization with different microarray
platforms. The analysis was performed by means of Pearson's rank
correlation test.

Gene     Microarray platforms     Pearson       Affymetrix ul33
                                  correlation
                                  test

CP       Stanford                 R-value       0.408
                                  p-value       6.1 x [10.sup.-4]
         Affymetrix U95v2         R-value       0.395
                                  p-value       9.81 x [10.sup.-4]
         Affymetrix U133A/U133B   R-value       0.498
                                  p-value       3.52 x [10.sup.-5]
OGFOD1   Affymetrix U133A/U133B   R-value       0.627
                                  p-value       6.77 x [10.sup.-8]
TFRC     Affymetrix U133A/U133B   R-value       0.614
                                  p-value       1.51 x [10.sup.-7]

Gene     Microarray platforms     Pearson       Microarray platforms
                                  correlation   Affymetrix
                                  test          U133a/U133b

CP       Stanford                 R-value       0.374
                                  p-value       0.002
         Affymetrix U95v2         R-value       0.869
                                  p-value       5.17 x [10.sup.-19]
         Affymetrix U133A/U133B   R-value
                                  p-value
OGFOD1   Affymetrix U133A/U133B   R-value
                                  p-value
TFRC     Affymetrix U133A/U133B   R-value
                                  p-value
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Author:Ooko, Edna; Saeed, Mohamed E.M.; Kadioglu, Onat; Sarvi, Shabnam; Colak, Merve; Elmasaoudi, Kaoutar;
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Date:Oct 15, 2015
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