Evidence-Based Complementary and Alternative Medicine

Volume 2013 (2013), Article ID 341724, 14 pages

http://dx.doi.org/10.1155/2013/341724

Research Article

Cytotoxicity and Pharmacogenomics of Medicinal Plants from Traditional Korean Medicine

Victor Kuete,1,2 Ean-Jeong Seo,² Benjamin Krusche,² Mira Oswald,² Benjamin Wiench,² Sven Schröder,³ Henry Johannes Greten,4,5 Ik-Soo Lee,⁶ and Thomas Efferth²

¹Department of Biochemistry, Faculty of Science, University of Dschang, Cameroon

²Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany

³HanseMerkur Center for Traditional Chinese Medicine, University Hospital Eppendorf, Hamburg, Germany

⁴Biomedical Sciences Institute Abel Salazar, University of Porto, Portugal

⁵Heidelberg School of Chinese Medicine, Heidelberg, Germany

⁶College of Pharmacy, Chonnam National University, Gwangju, Republic of Korea

Received 25 March 2013; Accepted 24 April 2013

Academic Editor: Sookyung Lee

Copyright © 2013 Victor Kuete et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Aim. The present study was designed to investigate the cytotoxicity of a panel of 280 Korean medicinal plants belonging to 73 families and 198 species against human CCRF-CEM leukemia cells. Selected phytochemicals were investigated in more detail for their mode of action. Methods. The resazurin assay was used to determine cytotoxicity of the plant extracts. Microarray-based mRNA expression profiling, COMPARE, and hierarchical cluster analyses were applied to identify which genes correlate with sensitivity or resistance to selected phytochemicals of the Korean plants. Results. The results of the resazurin assay showed that cytotoxicity extracts tested at 10 μg/mL from 13 samples inhibited proliferation more than 50% ( μg/mL) and the most active plants are Sedum middendorffianum (15.33%) and Lycoris radiata (17.61%). Out of 13 selected phytochemicals from these plants, hopeaphenol and deoxynarciclasine were the most cytotoxic ones. Genes from various functional groups (transcriptional or translational regulation, signal transduction, cellular proliferation, intracellular trafficking, RNA metabolism, endoplasmic/sarcoplasmic reticulum function, etc.) were significantly correlated with response of tumor cell lines to these two compounds. Conclusion. The results provide evidence on the possible use of selected Korean medicinal plants and chemical constituents derived from them for the treatment of tumors.

1. Introduction

Since ancient times, humans have derived many benefits from medicinal plants. A variety of different medicinal plants have traditionally been used in Asian cultures as medicinal plants to treat cancers [1]. The pharmacological screening of plants is an important mean for the discovery of new, safe, and effective drugs. Over 50,000 plants, possess therapeutic virtues in the world, and about 80% of human use herbal medicines at least once in their life [2, 3]. Medicinal plants through the multiplicities of their chemical constituents are important for the discovery of new substances active against tumors and other cancers. Screenings of medicinal plants used as anticancer drugs have provided modern medicine with effective cytotoxic pharmaceuticals. More than 60% of the approved anticancer drugs in the United State of America (from 1983 to 1994) were from natural origin [4, 5].

Traditional Korean medicine is widely used in Korea and is the primary health care system for more than 20% of the population [6, 7]. As previously emphasized in a regional demographic survey, about 30–40% of the Korean population had used complementary and alternative medicine (including traditional Korean medicine) within a 5-year period [6, 8].

In the pharmacopoeia of many countries worldwide including Korea, there is still a serious lack of information on the use of medicinal plants in the treatment of cancer. However, it has been recommended that ethnopharmacological usages such as for immune and skin disorders, inflammatory, infectious, parasitic, and viral diseases should be taken into account when selecting plants used to treat cancer, since these reflect disease states bearing relevance to cancer or cancer-like symptoms [9, 10]. In general, leukemia cells are often more sensitive to cytotoxic drugs than adherent cancer cells, making them a suitable tool for primary bioactivity screenings. Hence, the present work was designed to investigate the cytotoxicity of a panel of 280 Korean medicinal plants against human CCRF-CEM leukemia cells. Based on the bioactivity of the extracts, selected phytochemicals of active plants were analyzed in more detail. Genes determining sensitivity or resistance to selected compounds were identified by microarray-based mRNA expression profiles and hierarchical cluster analyses in a panel of tumor cell lines of the National Cancer Institute, USA.

2. Material and Methods

2.1. Plant Material and Extraction

Medicinal plants used in the present work were collected at different localities of the Republic of Korea and provided by Prof. Ik-Soo Lee (College of Pharmacy, Chonnam National University, Kwangju, South Korea). The plants were identified at the National herbarium, where voucher specimens were deposited under the references numbers (see Supplementary material available online at http://dx.doi.org/10.1155/2013/341724). The extraction of the air-dried and powdered plant material was conducted using methanol (HPLC grade) with either ASE 300 (Dionex) or a sonicator (Branson Ultrasonics) at 50°C. The extracts were then conserved at 4°C until further use.

2.2. Cell Culture

Human CCRF-CEM leukemia cells were obtained from Professor Axel Sauerbrey (University of Jena, Jena, Germany). Cells were maintained in RPMI 1640 containing 100 units/mL penicillin and 100 mg/mL streptomycin and supplemented with heat-inactivated 10% fetal bovine serum (FBS), in a humidified environment at 37°C with 5% CO₂. Doxorubicin ≥ 98.0% (Sigma-Aldrich, Schnelldorf, Germany) was used as a positive (cytotoxic) control.

2.3. Resazurin Cell Growth Inhibition Assay

Alamar Blue or Resazurin (Promega, Mannheim, Germany) reduction assay [11] was used to assess the cytotoxicity of the studied samples. The assay tests cellular viability and mitochondrial function. Briefly, aliquots of  cells/mL were seeded in 96-well plates, and extracts were added immediately. After 24 h incubation, 20 μL resazurin 0.01% w/v solution was added to each well and the plates were incubated at 37°C for 1-2 h. Fluorescence was measured on an automated 96-well Infinite M2000 Pro plate reader (Tecan, Crailsheim, Germany) using an excitation wavelength of 544 nm and an emission wavelength of 590 nm. Doxorubicin was used as positive control. The concentration of DMSO was kept at or below 0.1% in all experiments. Each assay was done at least three times, with three replicates each. All samples were tested at a single concentration of 10 μg/mL.

2.4. COMPARE and Hierarchical Cluster Analysis of mRNA Microarray Data

The mRNA microarray hybridization of the NCI cell line panel has been described [12, 13] and the date has been deposited 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 [12–14]. 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 distances 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 IC₅₀ values for selected phytochemicals 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 [12]. COMPARE analyses were performed by a web-based algorithm (http://dtp.nci.nih.gov/) to produce rank-ordered lists of genes expressed in the NCI cell lines. The methodology has been described previously in detail [15]. Briefly, every gene of the NCI microarray database was ranked for similarity of its mRNA expression to the IC₅₀ values for the corresponding compound. Scale indices of correlations coefficients (-values) were created. Pearson’s correlation coefficients with positive algebraic signs indicate that greater mRNA expression in cell lines correlate with enhanced drug resistance, whereas coefficients with negative algebraic signs indicate that greater mRNA expression in cell lines was associated with drug sensitivity. Pearson’s correlation test and test were implemented into the WinSTAT Program (Kalmia). The one-way ANOVA at 95% confidence level was used for statistical analysis.

3. Results

3.1. Cytotoxic Activity

In the present work, we screened 280 plant extracts derived from traditional Korean medicine belonging to 73 plant families and 198 species (see Supplementary Table) for their cytotoxicity against human CCRF-CEM leukemia cells.

The results of the cytotoxicity assay (Figure 1; Supplementary Table) indicated that is among the 280 studied plant extracts, 58 promoted the growth (% growth > 100%) of CCRF-CEM cells, while the remaining samples showed various extents of growth inhibition. Out of these extracts, 207 extracts showed no or a weak inhibition of cancer cell proliferation (<50%) at the tested concentration of 10 μg/mL. However, 13 samples induced the proliferation of less 50% cells (IC₅₀ < 10 μg/mL). These samples included Sedum middendorffianum whole plant (15.33%), Lycoris radiata underground parts (17.61%), Camellia japonica fruits (22.28%), Myrica rubra stem-stem bark (27.61%), Lycoris radiata leaves (27.73%), Vitis flexuosa aerial parts (29.32%), Sedum takesimense whole plant (32.39%), Sorbaria sorbifolia var. stellipila stems (34.98%), Isodon japonicus whole plant (44.15%), Sageretia theezans leaves and stems (46.35%), Rubus corchorifolius aerial part (46.81%), Alnus japonica stem-stem bark (48.43%), and Meliosma oldhamii stem-stem bark (49.94%) (Figure 1). The traditional used and reported bioactivities are compiled in Table 1.

Table 1: Korean plants with cytotoxic activity.

Plants (and family)
Traditional uses
Part used
Previously reported activity of the plant
Reported chemical constituents

Alnus  japonica  Steudel (Betulaceae)
In oriental traditional medicine as remedies for fever, hemorrhage, diarrhea, and alcoholism [43]
Stems-stem bark
Hepatoprotective and antioxidant activities [44], antiviral activity against the influenza virus [45]
1,7-Bis-(3,4-dihydroxyphenyl)-3-hydroxyheptane-5-O-β-D-xylopyranoside; 1,7-bis-(3,4-dihydroxyphenyl)-heptane-3-O-β-D-glucopyranosyl(1→3)-β-D-xylopyranoside; 1,7-bis-(3,4-dihydroxyphenyl)-heptane-3-O-β-D-apiofuranosyl(1→6)-β-D-glucopyranoside; 1,7-bis-(3,4-dihydroxyphenyl)-heptane-5-O-β-D-glucopyranoside; 1,7-bis-(3,4-dihydroxyphenyl)-5-hydroxylheptane; 1,7-bis-(3,4-dihydroxyphenyl)-heptane-3-one-5-O-β-D-glucopyranoside; oregonin; hirsutanonol; hirsutenone; platyphylloside [44]; tannins (alnusjaponins A and B); 5-O-galloyl-(−)-shikimic acid, 2,3-(S)-hexahydroxydiphenoyl-D-glucose, 4,6-di-O-galloyl-D-glucose, 1,4-di-O-galloyl-β-D-glucose, 4,6-(S)-valoneoyl-D-glucose; strictinin; gemin D; pedunculagin; praecoxin A; flosin A; stachyurin; casuarinin [46], lupeol; betulin; betulinic aldehyde; 3-acetoxybetulinic aldehyde, β-sitosterol [45]

Camellia japonica L.  (Theaceae)
Cosmetic protectant to keep the skin and hair healthy and as a soothing agent [47]
Fruits
Antibacterial activity [48], inhibitor of human immunodeficiency virus type 1 protease [49], Epstein-Barr virus inhibitor [50], antimetastasis activity [51], antioxidant activity [52, 53], inhibitor of human type I procollagen production [54], and antiallergic responses [55], anti-inflammatory [47]
3β,18β-dihydroxy-28-norolean-12-en-16-one; 18β-hydroxy-28-norolean-12-ene-3,16-dione; camelliagenin A, B, and C [56], camellenodiol 3-O-β-D-galactopyranosyl(1→2)[β-D-xylopyranosyl(1→2)-β-D-galactopyranosyl(1→3)]-β-D-glucuronopyranoside; camellenodiol 3-O-4′′-O-acetyl-β-D-galactopyranosyl(1→2)[β-D-xylopyranosyl(1→2)-β-D-galactopyranosyl(1→3)]-β-D-glucuronopyranoside; camellenodiol 3-O-(β-D-galactopyranosyl(1→2)[β-D-xylopyranosyl(1→2)-β-D-galactopyranosyl(1→3)]-6′-methoxy-β-D-glucuronopyranoside; maragenin II 3-O-(β-D-galactopyranosyl(1→2)[β-D-xylopyranosyl(1→2)-β-D-galactopyranosyl(1→3)]-6′-methoxy-β-D-glucuronopyranoside, camellioside A; camellioside B [57]

Isodon japonicus  (Burman f.) H. Hara (Labiatae)
Antibacterial, anti-inflammation, and anthelmintic [58]
Whole plant
Cytotoxicity on K562 human leukemia cells and immunomodulatory activity [16], antibacterial activity for plant constituents [59]
Isadonol; epinodosin; sodoponin; epinodosinol [60, 61]; epinodosin; oridonin; taihangjaponicain A; lushanrubescensin J; bisjaponins A and B [58]; isodonal, trichodonin; nodosin; enmein; oridonin; enmein-3-acetate [59]

Lycoris  radiata  (L'Her.) Herbert  (Amaryllidaceae)
Laryngeal trouble, furuncle, carbuncle, suppurative wounds [62]
Leaves, underground parts
Cytotoxicity against B16F10 melanoma cells [17]
Different types of alkaloids (crinine-type; galanthamine-type; lycorine-type homolycorine-type; tazettine-type; narciclasine-type; and lycorine-type alkaloids); trisphaeridine; galanthine; bicolorine; 11-hydroxyvittatine; 8-O-demethymaritidine;O-demethylgalanthamine; O-demethyllycoramine [63]

Meliosma  oldhamii  Miq. ex. Maxim. (Sabiaceae)
Liver ailments [64]
Stems-stem bark
Low Cholinesterase inhibition (12–19% at 5 mg/mL) [65], moderate alpha glucosidase activity [64]
—

Myrica  rubra  Sieb. and Zucc. (Myricaceae)
Diarrhea; gastroenteritis in China [66]
Stems-stem bark
Antioxidant activity [67]; anti-influenza virus activity [68]
Taraxerone; taraxerol; myricadiol; sitosterol; 28-hydroxy-D-friedoolean-14-en-3-one (); myricanol 5-O-β-D-glucopyranosyl-(1→3)-β-D-glucopyranoside; myricanol 5-O-α-L-arabinofuranosyl-(1→6)-β-D-glucopyranoside; isomyricanone [69]; cyanidin-3-O-glucoside; myricetin; quercetin-3-O-rutinoside [67]

Rubus  corchorifolius  L. f. (Rosaceae)
Stomachache, diarrhea, and dysentery [18]
Whole plant
Antioxidant activity of essential oil [70]
Ent-kauran-3β, 16β, 17, 19-tetrol; ent-2-carbonyl-16β-hydroxy-kauran-17β-D-glucoside [18]; rubusin A; quercetin; kaempferol [25]

Sageretia  theezans  (L.) Brongn  (Rhamnaceae)
Tea materials [71]
Leaves, Stems
Antioxidant activity [72]
7-O-methylmearnsitrin; myricetrin, kaempferol 3-O-α-L-rhamnopyranoside, europetin 3-O-α-L-rhamnoside, and 7-O-methyl quercetin 3-O-alpha-L-rhamnopyranoside; 7-O-methylmearnsetin 3-O-rhamnoside [71, 72]

Sedum  middendorffianum Maxim. (Crassulaceae)
—
Whole plant
—
kaempferol; quercetin; myricetin; arbutin [24]

Sedum  takesimense  Nakai (Crassulaceae)
—
Whole plant
Antioxidant activities [26]
Ferulic acid; caffeic acid; gallic acid; methyl gallate; myricetin; quercetin; luteolin; rhodalin; rhodalidin; luteolin-7-O-β-D-glucoside; arbutin; 1-(4-hydroxyphenyl)-2-(3,5-dihydroxyphenyl)-2-hydroxyethanone; gossypetin-8-O-β-D-xylopyranoside; 2,6-di-O-galloylarbutin [26]

Sorbaria  sorbifolia  (L.) A. Br. var.  stellipila  MAX.  (Rosaceae)
—
Stems
Antioxidant activities, cytotoxicity [73, 74]
Sutherlandin-5-trans-p-coumarate; cardiosdiospermin-5-(4-hydroxy) benzoate [75]; noreugenin; wogonin; 5,7,3′,4′-tetrahydroxy-3-methoxyflavone; protocatechuic acid; benzoic acid; emodin; daucosterol [76]; 5,2′,4′-trihydroxy-6,7,5′-trimethoxyflavone; succinic acid; p-hydroxybenzoic acid [77]

Vitis flexuosa  Thunb. (Vitaceae)
—
Aerial parts
—
Flexuosol A; gnetin A; (+)-epsilon-viniferin; vitisin A; hopeaphenol [78]

(—): not reported; the complete list of the tested plants is available in supplementary material.

Figure 1: Cytotoxicity of the 13 most active samples from selected Korean medicinal plants extract (at 10 μg/mL) and doxorubicin (1 μM) on CCRF-CEM leukemia cells. Data with different superscript letters are significantly different (). (See Supplementary Data for the overview of the cytotoxicity of all 280 tested extracts. Are shown mean values ± SD of each five measurements.) The established anticancer drug, doxorubicin was used as positive control (black bar).

3.2. Cytotoxicity of Phytochemicals Derived from Koran Medicinal Plants

We searched the literature on the chemical constituents of the cytotoxic Korean plants (Table 1). Subsequently, we mined the NCI database for these compounds. Thirteen compounds with average log₁₀IC₅₀ values over the entire NCI cell line panel below −4.0 M are depicted in Figure 2. Hopeaphenol and deoxynarciclasine were the most cytotoxic compounds with log₁₀IC₅₀ values of  M (=0.346 μM) and  M (=2.33 μM), respectively.

Figure 2: Mean log₁₀IC₅₀ values for selected phytochemicals derived from Korean medicinal plants for tumor cell lines from the NCI cell line panel.

If the average IC₅₀ values over the entire range of cell lines were diversified regarding their tumor types, colon cancer and melanoma cell lines were most sensitive towards hopeaphenol, whereas leukemia and kidney cancer cell lines were most resistant (Figure 3(a)). The cell lines reacted in a different manner towards deoxynarciclasine. Leukemia and colon cancer were most sensitive towards this compound, whereas breast cancer and prostate cancer cell lines were most resistant (Figure 3(b)).

(a)

(b)

Figure 3: Cytotoxic activity of hopeaphenol (a) and deoxynarciclasine (b) towards cell lines of different tumor types (mean ± SD).

3.3. Cross-Resistance of the NCI Cell Line Panel towards Hopeaphenol and Deoxynarciclasine

The log₁₀IC₅₀ values of hopeaphenol and deoxynarciclasine were correlated with clinically well-established anticancer drugs. The cell line panel showed statistically significant correlations between deoxynarciclasine and doxorubicin, daunorubicin, vincristine, paclitaxel, cisplatin, melphalan and carmustin. By contrast, cross-resistance was not found between hopeaphenol and these standard drugs, indicating that hopeaphenol might be useful for the treatment of otherwise drug-resistant tumors (Table 2). Interestingly, an inverse correlation was found between the log₁₀IC₅₀ values for hopeaphenol and the epidermal growth factor receptor tyrosine kinase inhibitor, erlotinib. Such a relationship was not found between deoxynarciclasine and erlotinib (Table 2).

Table 2: Cross-resistance profile of a panel of cell lines towards hopeaphenol and deoxynarciclasine determined by correlating the IC₅₀ values by Pearson’s correlation test.

Hopeaphenol
Deoxynarciclasine

Doxorubicin

    -value
<0.30
0.340

    -value
>0.05
0.010

Daunorubicin

    -value
<0.30
0.430

    -value
>0.05
0.001

Vincristine

    -value
<0.30
0.331

    -value
>0.05
0.012

Paclitaxel

    -value
<0.30
0.330

    -value
>0.05
0.012

Cisplatin

    -value
<0.30
0.300

    -value
>0.05
0.020

Melphalan

    -value
<0.30
0.388

    -value
>0.05
0.004

Carmustin

    -value
<0.30
0.394

    -value
>0.05
0.003

Erlotinib

    -value
−0.353
>−0.30

    -value
0.004
>0.05

Pearson's rank correlation test.

3.4. COMPARE and Hierarchical Cluster Analyses of mRNA Microarray Data

We further investigated the microarray-based transcriptomic mRNA expression by COMPARE analyses to test whether the responses of the tumor cells lines to hopeaphenol and deoxynarciclasine were associated with specific gene expression profiles. For this reason, we mined the transcriptome-wide mRNA expression database of the NCI and correlated the expression data with the IC₅₀ values for both compounds. This represents a hypothesis-generating bioinformatical approach, which allows the identification of novel putative molecular determinants of cellular response for hopeaphenol and deoxynarciclasine. The scale rankings of genes obtained by COMPARE computation were subjected to Pearson’s rank correlation tests. The thresholds for correlation coefficients were for direct correlations and for inverse correlations. The genes fulfilling these criteria are shown in Table 3 (for hopeaphenol) and Table 4 (for deoxynarciclasine). These genes were from diverse functional groups for hopeaphenol (Table 3). For deoxynarciclasine, genes were found involved in transcription (ILF2, BCLAF1, MATR3, PSPC1, ZBTB11, CNOT8, IKZF5, SF1, EBP, and MYBL1), RNA metabolism (HNRNPA1, LARS, SFRS1, FARSA, NUDT21, DDX39, and SERBP1), translation (GOT1, NGDN), cellular proliferation (CWF19L1, MKI67, HSPA9, MLF1IP, and DCBLD2), intracellular trafficking (OPTN, SNX6, and RAB11FIP5), endoplasmic/sarcoplasmic reticulum function (SLN, DNAJC10, LMAN2L, SEC24D, and KDELR2), signal transduction (FRAT2, CHUK, GNA12, and LPP), and others.

Table 3: Genes identified by standard or reverse COMPARE analyses, whose mRNA expression in a panel of 60 cell lines correlated with IC₅₀ values for hopeaphenol.

Pearson’s correlation coefficient
Experimental ID
Gene symbol
Name
Function

0.552
GC63503
HMGN4
High mobility group nucleosomal binding domain 4
Binds nucleosomal DNA

0.527
GC190712
UAP1
UDP-N-acetylglucosamine pyrophosphorylase 1
Nucleotidyltransferase

0.521
GC45602
TBC1D2
TBC1 domain family, member 2
GTPase activator

0.521
GC188142
ERBB2IP
Erbb2 interacting protein
Receptor adapter, structural constituent of cytoskeleton

0.515
GC26884
PRPS1
Phosphoribosyl pyrophosphate synthetase 1
Involved in nucleotide synthesis

0.513
GC38343
CNNTAL1

Unknown

−0.591
GC97260
MANBA
Mannosidase, beta A, lysosomal
Exoglycosidase

−0.574
GC73531
FGF9
Fibroblast growth factor 9 (glia-activating factor)
Cell growth and differentiation during development

−0.534
GC55495
CYP7B1
Cytochrome P450, family 7, subfamily B, polypeptide 1
Monooxygenase

−0.516
GC94617
GABRA3
Gamma-aminobutyric acid (GABA) A receptor, alpha 3
Neurotransmission

−0.515
GC184017
HES1
Hairy and enhancer of split 1, (Drosophila)
Transcriptional repressor

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 Weizmann Institute of Science, Rehovot, Israel (http://bioinfo.weizmann.ac.il/cards/index.html).

Table 4: Genes identified by standard or reverse COMPARE analyses, whose mRNA expression in a panel of 60 cell lines correlated with IC₅₀ values for deoxynarciclasine.

Pearson’s correlation coefficient
Experimental ID
Gene symbol
Name
Function

0.572
GC74997
SNAP25
Synaptosomal-associated protein, 25 kDa
Regulation of neurotransmitter release

0.56
GC32186
OPTN
Optineurin
Maintenance of membrane trafficking

0.557
GC52658
FAM116B
Family with sequence similarity 116, member B
Unknown

0.531
GC187393
ZDHHC7
Zinc finger, DHHCtype containing 7
Palmitoyltransferase

0.529
GC12575
CLEC9A
C-type lectin domain family 9, member A
Endocytic receptor for necrotic cells

0.528
GC188718
SNX6
Sorting nexin 6
Involved in intracellular trafficking

0.528
GC154565
RAB11FIP5
RAB11 family interacting protein 5 (class I)
Involved in protein trafficking

0.527
GC18484
NTAN 1
N-terminal asparagine amidase
Ubiquitin-dependent turnover of intracellular proteins

0.526
GC16433
DNAJC10
DnaJ (Hsp40) homolog, subfamily C, member 10
Endoplasmic reticulum cochaperone

0.524
GC10009
PCOLCE2
Procollagen C-endopeptidase enhancer 2
Binds to procollagens

0.524
GC45803
LMAN2L
Lectin, mannose-binding 2-like
Regulation of export from the endoplasmic reticulum

0.524
GC82947
NGDN
Neuroguidin, EIF4E binding protein
Involved in the translational repression

0.519
GC75800
SEC24D
SEC24 family, member D (S.  cerevisiae)
Transport of ER-derived vesicles

0.517
GC90440
KDELR2
KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein retention receptor 2
Retention of luminal endoplasmic reticulum proteins

0.514
GC173264
GNA12
Guanine nucleotide binding protein (G protein) α12
Transducer in transmembrane signaling

0.512
GC170473
MYBL1
V-Myb myeloblastosis viral oncogene homolog (avian)-like 1
Transcriptional activator

0.512
GC175225
LPP
LIM domain containing preferred translocation partner in lipoma
Signal transduction from cell adhesion sites to the nucleus

0.511
GC14006
ITGB5
Integrin, β5
Receptor for fibronectin

0.509
GC150035
DCBLD2
Discoidin, CUB, and LCCL domain containing 2
Involved in tumor progression

0.508
GC73833
NCEH1
Neutral cholesterol ester hydrolase 1
Promotes tumor cell migration

0.507
GC177466
AHNAK
AHNAK nucleoprotein
Involved in neuronal cell differentiation

0.502
GC40315
ADAL
Adenosine deaminaselike
Putative nucleoside deaminase

0.502
GC28174
MPG
N-methylpurine-DNA glycosylase
Hydrolysis of alkylated DNA

0.501
GC18079
CR1
Complement component (3b/4b) receptor 1 (Knops blood group)
Mediates cellular binding of particles and immune complexes

−0.658
GC18354
ILF2
Interleukin enhancer binding factor 2, 45 kDa
Transcription

−0.608
GC44240
NDUFA2
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2, 8 kDa
Part of mitochondrial membrane respiratory chain

−0.605
GC65788
HNRNPA1
Heterogeneous nuclear ribonucleoprotein A1
Involved in RNA replication

−0.589
GC43237
CWF19L1
CWF19-like 1, cell cycle control (S. pombe)
Cell cycle control

−0.572
GC174320
BCLAF1
BCL2-associated transcription factor 1
Death-promoting transcriptional repressor

−0.572
GC40779
LARS
Leucyl-tRNA synthetase
Editing of tRNA

−0.567
GC151509
FRAT2
Frequently rearranged in advanced T-cell lymphomas 2
Wnt signaling regulator

−0.561
GC175610
IK
IK cytokine, downregulator of HLA II
May bind to chromatin

−0.555
GC30213
SLN
Sarcolipin
Sarcoplasmic reticulum proteolipid

−0.553
GC162737
ANKHD2

Unknown

−0.552
GC17532
SFRS28

Unknown

−0.55
GC185046
SFRS1
Serine/arginine-rich splicing factor 1
Splicing regulator

−0.55
GC37292
FARSA
Phenylalanyl-tRNA synthetase, α subunit
Phenylalanine-tRNA ligase

−0.548
GC32318
MKI67
Antigen identified by monoclonal antibody Ki-67
Cell proliferation

−0.548
GC149101
MATR3
Matrin 3
Regulator of transcription

−0.547
GC150688
PSPC1
Paraspeckle component 1
Regulator of androgen receptor-mediated gene transcription

−0.546
GC80581
ZBTB11
Zinc finger and BTB domain containing 11
Regulator of transcription

−0.546
GC52470
MLF1IP
MLF1 interacting protein
Involved in mitotic progression

−0.545
GC81490
NUDT21
Nudix (nucleoside diphosphate linked moiety X)-type motif 21
Involved in pre-mRNA 3′-processing

−0.543
GC33504
DDX39
DEAD (Asp-Glu-Ala-Asp) box polypeptide 39A
Involved in pre-mRNA splicing

−0.543
GC35760
NDUGB8

Unknown

−0.539
GC36766
CHUK
Conserved helix-loop-helix ubiquitous kinase
Involved in NF-kappa-B signaling

−0.538
GC33463
POLD1
Polymerase (DNA directed), 1, catalytic subunit
DNA synthesis

−0.537
GC83208
SERBP1
SERPINE1 mRNA binding protein 1
Regulation of mRNA stability

−0.536
GC153558
CNOT8
CCR4-NOT transcription complex, subunit 8
Transcription factor

−0.535
GC17771
GOT1
Glutamic-oxaloacetic transaminase 1, soluble (aspartate aminotransferase 1)
Involved in amino acid metabolism

−0.535
GC43091
NUDCD2
NudC domain containing 2
Unknown

−0.534
GC31949
HSPA9
Heat shock 70 kDa protein 9 (mortalin)
Control of cell proliferation and cellular aging

−0.533
GC91403
IKZF5
IKAROS family zinc finger 5 (Pegasus)
Transcriptional repressor

−0.533
GC31641
SF1
Splicing factor 1
Transcriptional repressor

−0.532
GC161453
KDM2B
Lysine (K)-specific demethylase 2B
Histone demethylase

−0.53
GC35520
EBP
CCAAT/enhancer binding protein (C/EBP), gamma
Transcription factor

−0.53
GC10226
ABCB7
ATP-binding cassette, sub-family B (MDR/TAP), member 7
Heme transport

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 Weizmann Institute of Science, Rehovot, Israel (http://bioinfo.weizmann.ac.il/cards/index.html).

As a next step, the genes identified by COMPARE analyses and Pearson’s rank correlation tests were subjected to hierarchical cluster analyses. Only the mRNA expression levels but not the IC₅₀ values of the compounds for the cell line panel were used for cluster analyses. Four cluster branches were found in the hopeaphenol-related dendrogram (Figure 4) and three clusters were obtained in the deoxynarcilasine-related cluster analysis (Figure 5). Remarkably, the distribution of cell lines sensitive or resistance to both drugs considerably varied between the different clusters of the dendrograms. Since the IC₅₀ values of the compounds were not included into the cluster analysis, we could address the question whether the gene expression profiles alone are sufficient to predict sensitivity or resistance of cell lines to the compounds.

Figure 4: Dendrograms obtained by hierarchical cluster analysis of microarray-based gene expressions for hopeaphenol of the panel of NCI cell lines. The dendrograms were obtained by clustering using the WARD method.

Figure 5: Dendrograms obtained by hierarchical cluster analysis of microarray-based gene expressions for deoxynarciclasine of the panel of NCI cell lines. The dendrograms were obtained by clustering using the WARD method.

As shown in Table 5, the distributions of cell lines sensitive or resistant to hopeaphenol and deoxynarciclasine, respectively, were significantly different between the clusters of the dendrograms, indicating that the expression of these genes was not only responsible for the branching of the dendrograms, but also predicted sensitivity or resistance of these cell lines towards these compounds.

Table 5: Separation of clusters of 60 cancer cell lines obtained by hierarchical cluster analysis for hopeaphenol and deoxynarciclasine. The log₁₀ IC₅₀ median values (M) of each compound were used as cut-off values to define cell lines as being sensitive or resistant. was considered as not significant ( test).

Sensitive
Resistant
test

Hopeaphenol

Partition*
<−5.80
>−5.80

Cluster 1
0
7

Cluster 2
0
4

Cluster 3
15
0

Cluster 4
12
16

Deoxynarciclasine

Partition*
<−6.40
>−6.40

Cluster 1
3
22

Cluster 2
13
2

Cluster 3
7
0

log₁₀ IC₅₀ (M).

4. Discussion

4.1. Cytotoxic Activity

The cytotoxicity observed in Isodon. japonicus in this work is in concordance with previous reports. In effect, the cytotoxic effect of I. japonicus extract was reported against five human cancer cell lines (IC₅₀ values below 10 μg/mL on stomach MKN-45, breast MCF-7, leukemia K562, colon HT29, and lung A549 cell lines) with leukemia K562 (IC₅₀ : 2.70 μg/mL) being the most sensitive [16]. Also, the ethanol extract of Lycors radiata exhibited significant antiproliferative effect against B16F10 melanoma cells and induced apoptosis through the activation of p38 and AP-1 pathway [17]. The present report, therefore, provides evidence on the activity of L. radiata not only against cell lines derived from solid tumors but also derived from the hematopoietic system, that is, CCRF-CEM leukemia cells. Sorbaria sorbifolia was cytotoxic towards HepG-2 cells via induction of apoptosis and cell cycle arrest [18], and evidence of this activity towards CCRF-CEM leukemia cells is provided herein.

In the US NCI plant screening program, a crude extract is generally considered to have in vitro cytotoxic activity, if the IC₅₀ value following incubation between 48 and 72 h is less than 20 μg/mL [19]. In this study, we reduced the cutoff point to 10 μg/mL. All extracts were tested at this concentration and only samples with an inhibitory effect >50% were considered to have highly promising activities against leukemia cells. Under this condition, 13 samples from 12 medicinal plants (Figure 1) were identified as promising anticancer products and should be screened for more cancer cell lines. It is noteworthy that only Lycoris radiata exhibited a significant (<50% growth proliferation) activity with both aerial and subterraneal parts, suggesting that different parts of the plant should be considered when screening the cytotoxicity of medicinal plants.

To the best of our knowledge, the cytotoxic effect of several active plants (Table 1) against leukemia cells was reported here for the first time. Nevertheless, some of the analyzed plants contain compounds with known cytotoxicity against cancer cells. In fact, the diarylheptanoids [1,7-bis-(3,4-dihydroxyphenyl)-heptane-3-O-β- D-glucopyranosyl(1→3)-β-D-xylopyranoside; 1,7-bis-(3,4-dihydroxyphenyl)-heptane-3-O-β- D-apiofuranosyl(1→6)-β-D-glucopyranoside; 1,7-bis-(3,4-dihydroxyphenyl)-heptane-5-O-β- D-glucopyranoside, 1,7-bis-(3,4-dihydroxyphenyl)-5-hydroxyheptane; 1,7-bis-(3,4-dihydroxyphenyl)-heptane-3-one-5-O-β-D-glucopyranoside; oregonin; hirsutanonol; hirsutenone; 1,7-bis-(3,4-dihydroxyphenyl)-5-hydroxyheptane-3-O-β- D-xylopyranoside and platyphylloside], isolated from the bark of A. japonica, showed cytotoxic activities on human, B16 mouse melanoma cells and SNU-1 gastric cancer cell lines with IC₅₀ values varying from 17.02 to 55.47 μM [20]. Interestingly, the extract of the stem bark of this plant exhibited significant antiproliferative activity on leukemia CCRF-CEM cells (Figure 1), inducing less than 25% growth at 10 μg/mL. In addition, Kim et al. [21] reported the presence of two well-known cytotoxic compounds, betulin and lupeol [22] in the extract of this plant. Apoptosis induction via Fas-mediated pathway was also reported in human MCF-7 breast adenocarcinoma cells by prodelphinidin B-2 3,3′-di-O-gallate, a constituent of M. rubra [23]. The presence of such cytotoxic compounds could explain the good activity of the crude extract of A. japonica and M. rubra. Similarly, the presence of cytotoxic compounds such as quercetin or ferulic acid [22], in R. corchorifolius and S. middendorffianum extracts [24, 25] and Sedum takesimense [26] could also provide some explanation on their antiproliferative potentials. Nonetheless, it is interesting to know that the activity does not only depend on the presence of a cytotoxic substance in a plant, but also their quantities and possible interaction with other plant constituents. For example, cytotoxic compounds such as incanone {known to be active on HL60 leukemia cells (IC₅₀ value of 6 µM) [25])} or verbascoside [27] {active on human HEP-2 larynx epidermoid carcinoma, human RD rhabdomyosarcoma and human MCF-7 breast adenocarcinoma cell lines [28]} were reported in Caryopteris incana. But extract from Caryopteris incana did not show significant activities against CCRF-CEM leukemia cells in the present study.

We screened the NCI database of the Developmental Therapeutics Program of the NCI for the constituents of our panel of Korean medicinal plants. The two most cytotoxic compounds were hopeaphenol and deoxynarciclasine. Both compounds have been previously reported to be cytotoxic [29–32]. While hopeaphenol’s activity has been demonstrated in mouse tumors in vitro and in vivo [31, 32], the present investigation shows that this compounds also active against human tumor cells. To the best of our knowledge, the mechanisms of action of these two compounds have not been investigated in the past and are addressed in our analysis for the first time.

4.2. COMPARE and Hierarchical Cluster Analyses of mRNA Microarray Data

To gain insight into possible modes of action of both phytochemicals, we investigated gene expression profiles of the NCI cell line panel. By microarray-based gene expression and COMPARE analyses, we correlated the IC₅₀ values for both compounds of 60 tumor cell lines with transcriptomic mRNA expression levels of this cell line panel [12]. This approach has been successfully used to unravel the mode of action of novel compounds [33]. Cluster and COMPARE analyses are also useful for comparing gene expression profiles with IC₅₀ values for investigational drugs to identify candidate genes for drug resistance [34, 35] and to identify prognostic expression profiles in clinical oncology [36, 37].

Eleven genes passing the correlation thresholds of and were identified to significantly correlate with sensitivity or resistance to hopeaphenol. Except for fibroblast growth factor 9 [38], none of them have been associated with response of tumor cells to cytostatic drugs. This point of view is supported by the fact that the NCI cell lines did not exert cross-resistance between hopeaphenol and anticancer drugs, such as doxorubicin, vincristine, cisplatin, and others.

The genes correlating with the response of tumor cells to deoxynarciclasine were also not known to confer drug resistance as of yet, but many of them belong to functional groups, which are associated with response to chemotherapy. For example, transcriptional regulation, signal transduction, and cell proliferation are well-known processes influencing the success of cancer chemotherapy [39–42]. This might also explain why the NCI cell line panel exhibited cross-resistance between deoxynarciclasine and standard chemotherapy. On the other hand, genes involved in cellular trafficking or endoplasmic/sarcoplasmic reticulum functions have not been recognized as possible mechanisms of drug resistance of tumors. This finding merits more detailed investigations in the future.

4.3. Conclusion

The present work provides evidence that some plants derived from Korean medicine could be useful in the treatment of leukemia and supports the advanced investigation of the most active plants extracts. It also provides first pharmacological data on the cytotoxicity of some plants, such as Adenophora racemosa, Cinnamomum japonicum, Eurya japonica, Sedum middendorffianum, and Vitis flexuosa. The identification of cytotoxic phytochemicals from these plants, for example, hopeaphenol, and deoxynarciclasine, and the investigation of their possible molecular modes of action may foster the development of novel treatment strategies against otherwise drug-resistant and refractory tumors.

Conflict of Interests

No potential conflict of interests was disclosed.

Authors’ Contribution

Victor Kuete and Ean-Jeong Seo contributed equally to this paper.

Acknowledgments

The authors are thankful to Mrs. Christine Köppel for her technical assistance performing the cytotoxicity assays and Mrs. Ilona Zirbs for her secretarial support.

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