Download Proteomics analysis reveals involvement of Krt17 in areca nut

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Proteomics Analysis Reveals Involvement of Krt17 in Areca NutInduced Oral Carcinogenesis
Chang-Hsu Chiang,†,‡,# Chih-Ching Wu,†,‡,§,∥,# Li-Yu Lee,⊥ Yi-Chen Li,† Hao-Ping Liu,∇ Chia-Wei Hsu,§
Ya-Ching Lu,† Joseph T. Chang,*,⊗ and Ann-Joy Cheng*,†,‡
†
Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Kwei-Shan, Tao-Yuan
333, Taiwan
‡
Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Kwei-Shan, Tao-Yuan 333, Taiwan
§
Molecular Medicine Research Center, Chang Gung University, Kwei-Shan, Tao-Yuan 333, Taiwan
∥
Department of Otolaryngology-Head & Neck Surgery, Chang Gung Memorial Hospital, Linkou 333, Taiwan
⊥
Department of Pathology, Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan
∇
Department of Veterinary Medicine, National Chung Hsing University, Tai-Chung 402, Taiwan
⊗
Department of Radiation Oncology, Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan
S Supporting Information
*
ABSTRACT: The areca nut is a known carcinogen that causes oral cancer in individuals in
Southeast Asia, but the molecular mechanism that leads to this malignancy is still unclear. To
mimic the habit of areca nut chewing, our laboratory has established four oral cancer cell
sublines (SAS, OECM1, K2, C9), which have been chronically exposed to areca nut extract
(ANE). To elucidate the molecular basis of areca nut-induced oral carcinogenesis, the
differential proteomes between oral cancer cells and the ANE-treated sublines were
determined using isobaric mass tag (iTRAQ) labeling and multidimensional liquid
chromatography−mass spectrometry (LC−MS/MS). Over 1000 proteins were identified in
four sublines, and 196 proteins were found to be differentially expressed in at least two ANEtreated sublines. A bioinformatic analysis revealed that these proteins participate in several
pathways, and one of the most prominent pathways was the regulation of epithelial to
mesenchymal transition (EMT). In all, 24 proteins including Krt17 were confirmed to be
differentially expressed in the ANE-treated sublines. To reveal additional information on the
mechanism of ANE-induced carcinogenesis, Krt17 was further investigated. Krt17 knockdown
significantly suppressed ANE-induced cell migration and invasion and modulated the EMT process. Furthermore, in a murine
model of carcinogen-induced (arecoline cocktail, an active compound of ANE) oral cancer, Krt17 was significantly up-regulated
in all hyperplastic tissues and in carcinoma tissues (p < 0.001). In conclusion, we have identified a proteome of oral cancer cells
that is associated with chronic areca nut exposure. Krt17 was demonstrated to contribute to areca nut-induced oral malignancy.
The results of this study contribute to risk assessment, disease prevention and other clinical applications associated with areca
nut-induced oral cancer.
KEYWORDS: areca nut, oral carcinogenesis, iTRAQ, epithelial to mesenchymal transition, Krt17
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INTRODUCTION
Oral cancer is one of the most common cancers worldwide; it is
prevalent in Southeast Asia1,2 and is more common in middleaged males.1−3 Epidemiologic studies have shown a strong
association between oral cancer and environmental carcinogens,
especially as a result of the use of tobacco and the areca nut.1,3,4 In
Southeast Asia, the areca nut is a primary cause of oral
precancerous and cancerous lesions, and an estimated 28-fold
higher incidence of oral cancer has been observed among areca
nut chewers compared with nonchewers.3 In 2004, the
International agency for Research on Cancer recognized the
areca nut as a Group 1 carcinogen.5
Previously, many studies have reported on the possible
mechanisms of how the areca nut leads to the genesis of oral
© 2016 American Chemical Society
cancer. For example, the areca nut induces DNA damage,
epigenetic alterations and elicits cytotoxicity in human epithelial
cells.6,7 The areca nut also triggers inflammatory responses
through the up-regulation of several meditators, including
prostaglandin E2, cyclooxygenase-2, and IL-6.8−10 Areca nut
exposure also induces the production of reactive oxygen species
(ROS),11−13 which further results in cell growth arrest, apoptosis
or autophagy.13−16 However, most reports on the molecular and
cellular effects of the areca nut often used short-term and highdose cellular treatments as research models, which do not
Received: February 16, 2016
Published: July 19, 2016
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between being used by areca nut chewers and cellular
administration in this study, we estimated that the highest
treatment concentration (500 μg/mL) was comparable to the
exposure by a light chewer per day (10 pieces of extract in 1 mL),
and 20% of the exposure by a heavy chewer per day (50 pieces of
extract in 1 mL).
recapitulate the conditions of patients with long-term, habitual
exposure to areca nuts.
Recently, proteomic approaches have been successfully used
to analyze the proteomes of individuals with many malignant
diseases, including oral cancer.17,18 Methods of quantitative
proteomics, such as isobaric tag for relative and absolute
quantification (iTRAQ) with tandem mass spectrometry (MS),
provides more advanced technology for a global survey of
differential proteomes between samples.19 The iTRAQ
technique employs a 4-plex set of amine reactive isobaric tags
for the derivatization of peptides at the N-terminus and at lysine
side chains. During MS analysis, the same peptides that are
labeled with any of the isotopic tags are indistinguishable
(isobaric). Upon fragmentation during MS/MS, signature
reporter ions are produced, which provide quantitative
information on the peptides that originate from the different
protein samples. The application of iTRAQ technology has been
shown to be relevant in the discovery of promising cancer
biomarkers.20,21
Previous studies have attempted to conduct a global survey to
identify the molecular profiles that are associated with areca nutinduced oral diseases. Transcriptomic microarray analyses have
been used to compare cancer cell lines treated with areca nut
integrants,22,23 or to directly compare normal mucosa with oral
cancer tissues from areca nut chewing patients.24−27 However, a
major disadvantage of clinical sample investigation lies in the
heterogeneity among individuals. In addition, the transcriptomic
alterations are not directly linked to cellular function. Thus, far,
to our knowledge, no proteomic assay that addresses areca nutinduced oral pathogenesis has been developed. To reduce
heterogeneity and to obtain proteomic data associated with areca
nut-induced oral cancer, we performed the iTRAQ-based
analysis with four areca nut-exposed oral cancer cell sublines in
this study. These sublines were chronically exposed to areca nut
extract (ANE) over the long-term (3 months) and at a low-dose
(IC30, dose for 30% growth inhibition) to recapitulate the
experience of oral cancer patients with areca nut chewing habit. A
software suite was used to identify statistically significant
pathways, and RT-PCR was applied to confirm potential
molecules. The most promising molecule was Krt17, which
was further validated via cell and animal-based studies with
respect to its critical role in the regulation of malignant
phenotypes. Our study and the subsequent discovery of novel
biomarkers provide additional knowledge and a foundation for
further application of the identified molecules in the management of areca nut-induced oral diseases.
■
Preparation of Cell Extracts and Digestion of Protein
Mixtures for Proteome Analysis
Cells were lysed in buffer containing 100 mM triethylammonium
bicarbonate (TEABC, Sigma-Aldrich, St. Louis, MO, USA) and
0.1% RapiGest SF (Waters Corporation, Milford, MA, USA) on
ice for 15 min. The cell lysate was collected, and the protein
mixtures were subjected to in-solution tryptic digestion in a
manner similar to that described in the Supporting Information.30
iTRAQ Reagent Labeling
The peptides were labeled with iTRAQ reagent (Applied
Biosystems, Foster City, CA, USA) as described in the
Supporting Information. In the first experimental set, iTRAQ
114 and 115 reagents were combined with peptide mixtures from
parental and ANE-trained SAS cells, respectively. The peptides of
parental and ANE-trained OECM1 cells were labeled with
iTRAQ 116 and 117, respectively. For set 2, iTRAQ 114 and 115
reagents were added to the peptide mixtures of parental and
ANE-trained CGHNK2 cells, respectively. The peptides from
parental and ANE-trained CGHNC9 cells were labeled with
iTRAQ 116 and 117, respectively.
Peptide Fractionation and LC−MS/MS Analysis by
LTQ-Orbitrap PQD
For strong cation exchange (SCX) chromatography, the iTRAQlabeled peptides were loaded onto a BioBasic SCX column
(Thermo Electron, CA, USA), as described in the Supporting
Information. After SCX chromatography, each peptide fraction
was loaded across a trap column (Zorbax 300SB-C18, 0.3 × 5 mm,
Agilent Technologies, Wilmington, DE, USA) and separated on
a resolving 10 cm analytical C18 column (inner diameter, 75 μm)
with a 15-μm tip (New Objective, Woburn, MA, USA). The
peptides were eluted as described in the Supporting Information.
The reversed-phase LC apparatus was online coupled to a mass
spectrometer. Peptides were analyzed with LTQ-Orbitrap
Discovery (Thermo Fisher Scientific, CA, USA) and selected
for MS/MS using the PQD operating mode as described in the
Supporting Information.30
Sequence Database Search and Quantitative Data Analysis
The MS/MS spectra were searched against the Swiss-Prot
human sequence database (released Jun 15, 2010, selected for
Homo sapiens, 20 367 entries) using the Mascot search engine
(Matrix Science, London, UK; version 2.2.04). The detailed
parameters for protein identification are described in the
Supporting Information. Protein identification and quantification were validated using the default setting of open source transproteomic pipeline (TPP) software (Version 4.0). We used
ProteinProphet probability scores ≥0.95 to ensure an overall
false-positive rate of less than 1.0%. Proteins were quantified
using the Libra program, which is a module within the TPP
software package. Each quantified protein contained at least
three LIBRA peptides. The other detailed settings for protein
quantification are described in the Supporting Information.
iTRAQ ratios were log2 transformed and normalized as
described in the Supporting Information. Proteins with log2
ratios below the mean of all log2 ratios minus one standard
MATERIALS AND METHODS
Cell Lines and the Treatment of ANE
Three oral cancer cell lines (OECM1, SAS, and CGHNC9) and
one immortalized normal keratinocyte cell line (CGHNK2)
were used in this study.28 The immortalized normal keratinocyte
cells were maintained in KSFM medium (Life Technologies, Inc.,
Gibco BRL, Rockville, MD, USA), while cancer cell lines were
grown in 100% DMEM or RPMI 1640 medium supplemented
with 10% fetal bovine serum (FBS) (Life Technologies, Inc.). All
cells were cultured at 37 °C in a humidified atmosphere of 5%
CO2. These cell lines were chronically treated with an IC30 dose
of ANE for 3 months to establish ANE-trained sublines, as
previously described.29 The IC50 doses were 680, 430, 500, and
1200 μg/mL for CGHNK2, SAS, CGHNC9, and OECM1 cells,
respectively. For the relevance of effective concentration of ANE
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medium supplemented with 1% FBS, cell migration toward the
gap area was imaged. All experiments were performed
independently at least twice, and the average results are shown.
In each sample, the invasion ability was quantified via a
comparison of the distance of the cell-free gap after normalization to the control group.
deviation (SD) of all log2 ratios were considered to be underexpressed. Proteins above the mean plus one SD were considered
to be overexpressed.
Bioinformatic Analysis of Functional Pathways
The analysis of the functional pathways of differentially expressed
proteins affected by the areca nut was performed using MetaCore
analytical suite (GeneGo Inc., St Joseph, MI, USA), as previously
described.31 MetaCore was applied to calculate the statistical
significance (p value) based on the probability of assembly from a
random set of nodes (genes) of the same size as the input gene
list. To obtain a functional network of the differentially expressed
genes, we applied the network paths algorithm to establish global
pathways associated with chronic areca nut exposure.
Cell Invasion Assay
The invasive abilities of the cells were determined after the cells
were cultured on a polycarbonate membrane coated with
Matrigel (Becton Dickinson Biosciences, Franklin Lakes, NJ,
USA) in a Millicell invasion chamber (Millipore, Billerica, MA,
USA), as previously described.36 Briefly, after transfection of the
specific sh-Krt plasmids, cells in medium supplemented with 1%
FBS were seeded in the upper chamber, which was precoated
with Matrigel. The lower chamber contained complete culture
medium (containing 10% FBS) to trap invading cells. After
incubation at 37 °C, the cells that invaded the reverse side of the
chamber were stained with crystal violet and imaged. All the
experiments were performed at least twice independently, and
the typical results were shown. In each sample, the invasion
ability was quantified via a comparison of the density of the
crystal violet dye after normalization to the control group.
Reverse Transcription Quantitative Polymerase Chain
Reaction (RT-qPCR)
Total RNA extraction and the RT-qPCR assay were performed in
a manner similar to a previously described method.32 Briefly, the
PCR and cDNA synthesis were performed in a Bio-Rad
MiniOpticon real-time PCR detection system using SyBr
Green Supermix reagents (Life Technologies, Inc.). The PCR
primers used in this study are listed in Table S1. The PCR results,
which were recorded as quantitation cycle (Cq) values, are
presented as relative fold expression.
Immunohistochemistry (IHC) and Hematoxylin and Eosin
(H&E) Staining
Protein Extraction and Western Blot Analysis
The IHC assay was performed as previously described.37 Briefly,
murine tumors were fixed in 10% neutral buffered formalin and
embedded in paraffin. The slides were then stained with an antiKrt17 antibody (purchased from Santa Cruz Biotech., CA, as
described above). The DakoCytomation REAL EnVision
detection system (Dako, Carpinteria, CA, USA) was used for
further staining and color development according to the
manufacturer’s instructions. In addition, each sample was also
counterstained with H&E according to the manufacturer’s
suggested protocol (Zymed Laboratories Inc.). The stained
sections were examined by microscopy. The immunoreactivity
was evaluated using the H-scoring system, by assessment of the
staining intensity and the fraction of cells.38
Protein extraction and Western blot analysis were performed as
previously described.33 Briefly, cellular proteins were extracted,
separated by SDS-PAGE, and transferred to a nitrocellulose
membrane. The membrane was exposed to primary antibodies
and then incubated with horseradish peroxidase-conjugated
secondary antibodies. The membranes were developed using an
ECL developing solution (Millipore, Darmstadt, Germany)
followed by autoradiography. The primary antibodies used in this
study are listed in Table S2.
shRNA Construction and Cellular Transfection
Krt17-targeted shRNA was designed as a 22-nt sense and
antisense hairpin that was complementary to the Krt17 mRNA
sequence 5′- GAT CCG ATC CTC ACA GCC ACC GTG GAA
GCT TGC ACG GTG GCT GTG AGG ATC TTT TTT GGA
AGC −3′. The shRNA was cloned into the pTOPO-U6 plasmid
vector to produce the sh-Krt17 plasmid in a manner similar to
that of a previously described method.34 This sh-Krt17 was
subcloned into a pCI-neo plasmid and was used for the stable
transfection of sh-Krt17 cells.34
For the plasmid transfection, cells were seeded at a density of 5
× 105 in a 100 mm dish and were cultured for 16 h. When the
cells reached 60% confluency, they were transfected with 6 μg of
shRNA plasmid or the empty vector plasmid using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) in Opti-MEM
reduced serum media (Invitrogen, Carlsbad, CA, USA). After 16
h, the Opti-MEM media was replaced with fresh complete media.
The stably transfected cellular clones were selected using G418
antibiotic solution, which is a neomycin reagent (Sigma, St Louis,
MO, USA).
Spontaneous Induction of Tumor in Mouse
In all, 28 female C57BL/6 mice at 6 weeks of age were used in
this study, which was approved by the Institutional Animal Care
and Use Committee. The mice were handled in accordance with
the Animal Care and Use Guidelines of the Chang Gung
University. For the tumorigenic induction, arecoline, which is the
active carcinogenic compound of the areca nut (Sigma-Aldrich,
St. Louis, MO, USA), and 4-nitroquinoline 1-oxide (4-NQO)
(Sigma-Aldrich) were given to the mice after the compounds
were dissolved in the drinking water in a manner similar to what
has been previously described.39,40 These 28 mice were randomly
assigned to the experimental group (n = 12), which received both
4-NQO (200 μg/mL) and arecoline (500 μg/mL), the control
group (n = 8), which received DMSO (1/100X), or the mock
group (n = 8), which received only normal drinking water.
In the first 8 weeks, the carcinogen solution was locally
smeared over the inner mouth area once per week in addition to
its administration through the drinking water. Thereafter, the
carcinogen solution was administered only in the drinking water
until week 28. The body weight of the mice and the induced oral
lesions, such as hyperplasia, dysplasia, or squamous cell
carcinomas, were carefully observed every week. The developed
tumors were dissected, fixed in 10% formaldehyde, and subjected
to a pathological examination and IHC analysis. In regards to the
Cell Migration Assay
Cell migration was determined using an in vitro wound healing
assay, as previously described.35 After transfection of the specific
sh-Krt plasmids, 3.5 × 104 cells were seeded in ibidi culture
inserts (ibidi LLC, Verona, WI, USA) on top of a 6-well plate.
After 8 h of incubation, the culture inserts were detached to form
a cell-free gap in the cell monolayer. After changing to the culture
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Figure 1. Identification of differentially expressed proteins in areca nut extract (ANE)-trained cell sublines. (A) A schematic diagram illustrates the
workflow of proteomic profiling after induction by ANE with iTRAQ-based analysis. These four ANE sublines were labeled in parallel with the
corresponding iTRAQ reporters. iTRAQ labeling was performed using equal amounts of proteins from pooled parental and ANE-trained sublines
followed by SCX chromatography. The fractions were subjected to LC−MS/MS analysis in an LTQ-Orbitrap Velos mass spectrometer. The data were
searched and collected with the Proteome Discoverer program using Mascot software as the search engine. The numbers of identified proteins are
shown as Venn diagrams. (B) The numbers of proteins identified or quantified in two iTRAQ-based experiments. (C) The numbers of proteins that
were determined to be up-regulated or down-regulated in four ANE sublines, including SAS, OECM1, CGHNK2 and CGHNC9 cells. Venn diagrams
show the overlap between proteins identified or quantified in the two experiments. The total number of proteins identified or quantified in each
experiment is listed in brackets. (D) The relative expression levels of 196 significant proteins in ANE-trained sublines compared with the parental cells.
survival analysis, a mouse was defined as “dead” when the mouse
was actually dead, when the tumor size was over 7 mm in length,
or when the body weight was less than half of the average body
weight in the control group when food and water were available.
■
parental control cell lines (Figure 1A). As shown in Figure 1A,
for experimental set 1, iTRAQ 114 and 116 reagents were
combined with peptides from parental SAS and OECM1 cells,
respectively. Peptides from ANE-trained SAS and OECM1 cells
were labeled with iTRAQ 115 and 117 reagents, respectively
(Table S3). In set 2, iTRAQ 114 and 115 reagents were added to
peptide mixtures of parental and ANE-trained CGHNK2 cells,
respectively. Peptides from parental and ANE-trained CGHNK2
cells were labeled with iTRAQ 116 and 117 reagents, respectively
(Table S4). The iTRAQ-labeled samples were then analyzed by
two-dimensional LC−MS/MS for the quantitative proteomic
analysis. The two-dimensional fractionation of the labeled
peptides involved the use of an offline SCX separation in the
first dimension, followed by an online reverse-phase fractionation. Each fraction was analyzed using an LTQ-Orbitrap
Discovery system. The resulting MS/MS spectra were analyzed
using the Swiss-Prot human sequence database with the Mascot
search engine. The search results were further evaluated using the
open-source TPP software with stringent criteria regarding
RESULTS
Proteomic Profiling of Areca Nut Extract (ANE)-Trained
Sublines
To establish ANE-trained sublines, three oral cancer cell lines
(OECM1, SAS, and CGHNC9) and one immortalized noncancerous keratinocyte cell line (CGHNK2) were treated with
ANE at an IC30 dose for 3 months. These cells have been
functional characterized possessing tumorigenic phenotypes,29
and the results of immortalized keratinocytes were shown as
example (Figure S1). To identify proteins that were differentially
expressed in ANE-treated cells compared with the controls, we
included two experimental sets of iTRAQ-based quantitative
proteomics analyses (set 1 and set 2), each of which contained
four measurements of two ANE-trained sublines and two
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Table 1. List of Proteins Differentially Expressed in Areca Nut Extract (ANE)-Trained Cell Lines
log2 ratio of ANE-trained/parental cells
protein name (protein accession number, gene name)
SAS
OECM1
CGHNK2
CGHNC9
1.016
0.637
1.515
0.815
−0.146
0.057
1.180
0.716
0.655
0.646
0.807
0.699
0.628
1.186
0.791
0.862
1.387
0.601
3.158
−0.327
−0.193
0.281
−0.070
0.123
0.070
0.475
0.210
−0.041
0.371
0.246
−0.456
−0.026
−0.115
0.959
0.628
0.742
−0.085
−0.177
−0.085
0.030
0.381
−0.225
−0.026
−0.177
−0.130
−0.258
−0.327
0.110
0.186
−0.310
−0.130
NA
0.070
0.043
−0.292
NA
0.173
−0.209
0.781
1.492
0.814
0.937
0.748
1.022
0.877
0.966
0.907
1.180
1.461
1.253
0.877
0.765
0.276
0.472
0.276
−0.321
0.472
0.626
1.192
0.626
0.781
0.679
0.662
1.366
0.861
0.966
0.714
0.679
0.731
1.116
1.180
−0.771
0.205
0.299
0.644
0.980
0.798
0.644
1.180
0.662
0.814
0.697
0.922
0.892
1.063
0.714
0.781
1.344
−0.034
NA
−0.678
−0.186
0.299
NA
−1.430
−0.321
1.241
1.404
0.659
0.342
0.484
0.342
−0.278
0.051
NAb
NA
NA
NA
NA
NA
0.398
0.525
0.650
0.687
0.342
0.525
0.376
0.431
0.515
0.584
0.484
0.442
0.504
0.342
0.515
0.420
0.342
0.574
1.146
−0.313
−0.065
−0.192
−0.065
−0.591
−0.065
0.146
−0.035
−0.260
0.198
0.079
0.120
−0.192
0.307
0.120
0.185
0.159
0.354
0.515
0.409
0.603
0.494
0.545
0.452
0.504
−0.343
0.245
−1.442
−1.217
3.009
2.448
1.767
−0.044
NA
NA
NA
NA
NA
NA
0.161
0.666
−0.928
−0.876
1.711
0.775
−1.217
−0.002
0.970
−0.461
0.180
1.876
−1.643
0.670
−0.360
−0.044
0.880
−0.687
0.117
3.671
3.576
2.760
3.857
2.668
3.452
3.335
2.780
2.983
2.887
3.139
2.427
2.863
3.013
2.474
2.673
3.382
2.961
2.576
2.574
2.634
2.744
3.489
2.546
3.383
Overexpressed proteinsa
High mobility group protein HMGI-C (P52926, HMGA2)
Keratin, type I cytoskeletal 17 (Q04695, KRT17)
SH3 domain-containing kinase-binding protein 1 (Q96B97, SH3KBP1)
PDZ and LIM domain protein 5 (Q96HC4, PDLIM5)
Bystin (Q13895, BYSL)
Histone deacetylase 2 (Q92769, HDAC2)
Catalase (P04040, CAT)
3-hydroxyacyl-CoA dehydrogenase type-2 (Q99714, HSD17B10)
Uridine-cytidine kinase 2 (Q9BZX2, UCK2)
Charged multivesicular body protein 2b (Q9UQN3, CHMP2B)
Receptor expression-enhancing protein 5 (Q00765, REEP5)
Ubiquitin carboxyl-terminal hydrolase 4 (Q13107, USP4)
Transmembrane and coiled-coil domain-containing protein 7 (Q9C0B7, TMCO7)
Cellular retinoic acid-binding protein 2 (P29373, CRABP2)
Thrombospondin-1 (P07996, THBS1)
Epiplakin (P58107, EPPK1)
ATPase WRNIP1 (Q96S55, WRNIP1)
AT-rich interactive domain-containing protein 1A (O14497, ARID1A)
Plasminogen activator inhibitor 2 (P05120, SERPINB2)
Serpin B5 (P36952, SERPINB5)
E3 SUMO-protein ligase RanBP2 (P49792, RANBP2)
Double-strand break repair protein MRE11A (P49959, MRE11A)
Adenine phosphoribosyltransferase (P07741, APRT)
Histone H1.4 (P10412, HIST1H1E)
Histone H1.3 (P16402, HIST1H1D)
Integrin alpha-6 (P23229, ITGA6)
Myristoylated alanine-rich C-kinase substrate (P29966, MARCKS)
Transcription activator BRG1 (P51532, SMARCA4)
Endoplasmic reticulum resident protein 44 (Q9BS26, ERP44)
NACHT, LRR and PYD domains-containing protein 2 (Q9NX02, NLRP2)
Pre-mRNA branch site protein p14 (Q9Y3B4, SF3B14)
Telomere length regulation protein TEL2 homologue (Q9Y4R8, TELO2)
Aldo-keto reductase family 1 member C2 (P52895, AKR1C2)
Isocitrate dehydrogenase [NADP] cytoplasmic (O75874, IDH1)
Solute carrier family 2, facilitated glucose transporter member 1 (P11166, SLC2A1)
Peptidyl-prolyl cis−trans isomerase B (P23284, PPIB)
26S proteasome non-ATPase regulatory subunit 8 (P48556, PSMD8)
Phosphatidylserine synthase 1 (P48651, PTDSS1)
Tubulin-specific chaperone A (O75347, TBCA)
Pyruvate dehydrogenase E1 component subunit beta, mitochondrial (P11177, PDHB)
Integrin beta-4 (P16144, ITGB4)
Probable ATP-dependent RNA helicase DDX5 (P17844, DDX5)
Endoplasmic reticulum resident protein 29 (P30040, ERP29)
60S ribosomal protein L15 (P61313, RPL15)
Aminoacyl tRNA synthase complex-interacting multifunctional protein 2 (Q13155, AIMP2)
PCI domain-containing protein 2 (Q5JVF3, PCID2)
Probable ATP-dependent RNA helicase DDX46 (Q7L014, DDX46)
Hsc70-interacting protein (P50502, ST13)
WD40 repeat-containing protein SMU1 (Q2TAY7, SMU1)
Eukaryotic translation initiation factor 4 gamma 3 (O43432, EIF4G3)
Heterogeneous nuclear ribonucleoprotein H (P31943, HNRNPH1)
Mitochondrial import inner membrane translocase subunit TIM44 (O43615, TIMM44)
Glucose-6-phosphate 1-dehydrogenase (P11413, G6PD)
High mobility group protein B2 (P26583, HMGB2)
Splicing factor, arginine/serine-rich 3 (P84103, SFRS3)
Terminal uridylyltransferase 7 (Q5VYS8, ZCCHC6)
Thioredoxin domain-containing protein 5 (Q8NBS9, TXNDC5)
Far upstream element-binding protein 2 (Q92945, KHSRP)
2985
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Table 1. continued
log2 ratio of ANE-trained/parental cells
protein name (protein accession number, gene name)
SAS
OECM1
CGHNK2
CGHNC9
RNA-binding protein Raly (Q9UKM9, RALY)
Protein transport protein Sec31A (O94979, SEC31A)
Brain acid soluble protein 1 (P80723, BASP1)
PDZ domain-containing protein GIPC1 (O14908, GIPC1)
Cathepsin D (P07339, CTSD)
Laminin subunit beta-1 (P07942, LAMB1)
ERO1-like protein alpha (Q96HE7, ERO1L)
Mediator of RNA polymerase II transcription subunit 14 (O60244, MED14)
Drebrin (Q16643, DBN1)
Protein S100-A14 (Q9HCY8, S100A14)
Testin (Q9UGI8, TES)
Keratin, type II cytoskeletal 7 (P08729, KRT7)
Glutathione S-transferase omega-1 (P78417, GSTO1)
Spermine synthase (P52788, SMS)
Copine-1 (Q99829, CPNE1)
Calpain-1 catalytic subunit (P07384, CAPN1)
Integrin alpha-3 (P26006, ITGA3)
Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial (P31040, SDHA)
Squalene synthase (P37268, FDFT1)
Transgelin-2 (P37802, TAGLN2)
Coronin-1B (Q9BR76, CORO1B)
[Pyruvate dehydrogenase [acetyl-transferring]]-phosphatase 1, mitochondrial (Q9P0J1, PDP1)
UPF0727 protein C6orf115 (Q9P1F3, C6orf115)
7-dehydrocholesterol reductase (Q9UBM7, DHCR7)
Glutamate dehydrogenase 1, mitochondrial (P00367, GLUD1)
Glucosidase 2 subunit beta (P14314, PRKCSH)
Translation initiation factor eIF-2B subunit beta (P49770, EIF2B2)
3′(2′),5′-bisphosphate nucleotidase 1 (O95861, BPNT1)
Epithelial cell adhesion molecule (P16422, EPCAM)
Probable ATP-dependent RNA helicase DDX6 (P26196, DDX6)
Signal recognition particle 14 kDa protein (P37108, SRP14)
Transaldolase (P37837, TALDO1)
Synaptic vesicle membrane protein VAT-1 homologue (Q99536, VAT1)
60 kDa SS-A/Ro ribonucleoprotein (P10155, TROVE2)
D-dopachrome decarboxylase (P30046, DDT)
Prostaglandin E synthase 3 (Q15185, PTGES3)
Cytochrome c oxidase subunit 2 (P00403, MT-CO2)
Heat shock 70 kDa protein 1A/1B (P08107, HSPA1A)
Estradiol 17-beta-dehydrogenase 12 (Q53GQ0, HSD17B12)
Protein NDRG1 (Q92597, NDRG1)
Charged multivesicular body protein 4b (Q9H444, CHMP4B)
Double-strand-break repair protein rad21 homologue (O60216, RAD21)
Peroxisomal multifunctional enzyme type 2 (P51659, HSD17B4)
Underexpressed proteinsa
Rab3 GTPase-activating protein catalytic subunit (Q15042, RAB3GAP1)
Ribonucleases P/MRP protein subunit POP1 (Q99575, POP1)
Small subunit processome component 20 homologue (O75691, UTP20)
Probable dimethyladenosine transferase (Q9UNQ2, DIMT1L)
Methylosome protein 50 (Q9BQA1, WDR77)
dCTP pyrophosphatase 1 (Q9H773, DCTPP1)
Fatty acid-binding protein, epidermal (Q01469, FABP5)
Lysophospholipid acyltransferase 7 (Q96N66, MBOAT7)
Nuclear receptor corepressor 2 (Q9Y618, NCOR2)
C-Jun-amino-terminal kinase-interacting protein 4 (O60271, SPAG9)
Lamin-A/C (P02545, LMNA)
Glycogen phosphorylase, liver form (P06737, PYGL)
Electron transfer flavoprotein subunit alpha, mitochondrial (P13804, ETFA)
Protein S100-A2 (P29034, S100A2)
Squalene synthase (P37268, FDFT1)
0.148
0.515
0.515
0.505
0.505
0.505
1.343
0.534
0.465
0.371
0.424
0.371
0.455
0.413
0.403
0.495
0.646
0.655
0.966
0.534
0.673
0.655
0.424
0.403
0.327
−0.161
−0.146
0.293
0.070
0.030
−0.012
0.084
0.360
0.270
−0.026
0.186
−0.130
0.234
0.258
NA
NA
−0.070
1.207
0.180
1.008
1.142
−0.186
0.103
−2.678
0.845
0.814
1.560
0.765
0.626
0.697
0.781
−0.508
−0.591
−0.321
0.607
0.077
−1.678
−0.871
−0.871
−0.321
−0.591
0.299
0.697
0.626
0.830
0.765
0.922
0.845
0.922
1.142
0.877
1.430
1.008
−0.123
−0.321
−0.286
−1.286
NA
NA
0.388
0.701
0.678
0.079
−0.387
0.431
0.865
0.659
−0.313
NA
0.319
−0.828
−0.081
−0.465
0.079
0.632
0.555
−0.209
−0.226
−1.050
−0.485
−0.127
0.009
−0.127
−0.209
0.247
0.319
0.331
0.319
0.079
0.093
−0.176
0.146
−0.159
−0.802
0.079
−0.527
0.331
0.641
0.319
0.331
0.594
0.442
0.564
0.861
2.693
−0.225
−1.023
0.420
1.527
1.019
2.979
NA
1.173
1.553
0.092
−0.132
1.423
0.192
0.394
2.509
2.225
2.047
2.386
2.005
3.033
4.444
2.271
2.360
0.807
1.826
−0.778
2.401
2.278
2.204
2.127
2.241
2.380
1.995
2.032
2.092
2.214
2.170
2.302
2.175
2.014
2.229
1.893
−0.310
−0.055
−0.852
−0.475
−0.475
−0.903
−0.778
−0.535
−0.475
0.258
0.392
0.360
0.002
−0.258
0.966
−1.508
−0.771
−1.155
−1.093
−1.155
−0.871
0.531
1.344
0.022
−1.034
−0.771
−0.724
−0.771
−1.034
−1.678
−0.681
−0.387
0.146
NA
NA
0.515
−0.704
−0.569
−0.728
−0.387
−0.527
−0.728
−0.387
−0.728
−0.485
−0.889
−1.954
1.042
NA
NA
0.742
1.327
0.245
−0.480
−0.002
0.758
−0.242
1.143
0.046
2.386
2986
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Table 1. continued
log2 ratio of ANE-trained/parental cells
protein name (protein accession number, gene name)
Prolyl endopeptidase (P48147, PREP)
Puromycin-sensitive aminopeptidase (P55786, NPEPPS)
Myosin light polypeptide 6 (P60660, MYL6)
Coactosin-like protein (Q14019, COTL1)
Non-POU domain-containing octamer-binding protein (Q15233, NONO)
Neutral amino acid transporter B(0) (Q15758, SLC1A5)
Proteasome activator complex subunit 2 (Q9UL46, PSME2)
Nuclear pore complex protein Nup50 (Q9UKX7, NUP50)
Mitochondrial import receptor subunit TOM40 homologue (O96008, TOMM40)
Cytochrome c oxidase subunit 4 isoform 1, mitochondrial (P13073, COX4I1)
Interleukin-1 alpha (P01583, IL1A)
Cleavage stimulation factor subunit 2 (P33240, CSTF2)
Protein S100-A10 (P60903, S100A10)
Rho GDP-dissociation inhibitor 1 (P52565, ARHGDIA)
Proteasomal ubiquitin receptor ADRM1 (Q16186, ADRM1)
Inosine triphosphate pyrophosphatase (Q9BY32, ITPA)
Serine/arginine repetitive matrix protein 2 (Q9UQ35, SRRM2)
N-alpha-acetyltransferase 10, NatA catalytic subunit (P41227, NAA10)
Tubulin alpha-1A chain (Q71U36, TUBA1A)
V-type proton ATPase 16 kDa proteolipid subunit (P27449, ATP6 V0C)
Remodeling and spacing factor 1 (Q96T23, RSF1)
Histone deacetylase 1 (Q13547, HDAC1)
Acyl-protein thioesterase 1 (O75608, LYPLA1)
4F2 cell-surface antigen heavy chain (P08195, SLC3A2)
ATP synthase subunit O, mitochondrial (P48047, ATP5O)
Ras-related C3 botulinum toxin substrate 1 (P63000, RAC1)
60S ribosomal protein L38 (P63173, RPL38)
Prohibitin-2 (Q99623, PHB2)
Ribosome production factor 2 homologue (Q9H7B2, RPF2)
Sideroflexin-1 (Q9H9B4, SFXN1)
Ubiquitin-conjugating enzyme E2 variant 2 (Q15819, UBE2V2)
Mitochondrial inner membrane protein (Q16891, IMMT)
LIM domain and actin-binding protein 1 (Q9UHB6, LIMA1)
Dynamin-like 120 kDa protein, mitochondrial (O60313, OPA1)
V-type proton ATPase subunit C 1 (P21283, ATP6 V1C1)
60S ribosomal protein L40 (P62987, UBA52)
AP-2 complex subunit beta (P63010, AP2B1)
Protein transport protein Sec24A (O95486, SEC24A)
Elongation factor 1-beta (P24534, EEF1B2)
Protein mago nashi homologue (P61326, MAGOH)
Nuclease-sensitive element-binding protein 1 (P67809, YBX1)
Sorbitol dehydrogenase (Q00796, SORD)
Cullin-1 (Q13616, CUL1)
Splicing factor 3B subunit 4 (Q15427, SF3B4)
Sec1 family domain-containing protein 1 (Q8WVM8, SCFD1)
Cytosolic nonspecific dipeptidase (Q96KP4, CNDP2)
N-alpha-acetyltransferase 50, NatE catalytic subunit (Q9GZZ1, NAA50)
Malate dehydrogenase, cytoplasmic (P40925, MDH1)
1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase beta-3 (Q01970, PLCB3)
AP-2 complex subunit mu (Q96CW1, AP2M1)
Heat shock factor-binding protein 1 (O75506, HSBP1)
Dedicator of cytokinesis protein 7 (Q96N67, DOCK7)
RNA-binding protein 14 (Q96PK6, RBM14)
Ras-related protein Rab-1B (Q9H0U4, RAB1B)
26S proteasome non-ATPase regulatory subunit 4 (P55036, PSMD4)
26S proteasome non-ATPase regulatory subunit 14 (O00487, PSMD14)
Cytoplasmic dynein 1 light intermediate chain 1 (Q9Y6G9, DYNC1LI1)
Acyl-protein thioesterase 2 (O95372, LYPLA2)
2′-5′-oligoadenylate synthase 1 (P00973, OAS1)
2987
SAS
OECM1
CGHNK2
CGHNC9
0.434
0.198
0.186
0.016
−0.085
−0.292
0.043
−0.012
−0.619
−0.515
NA
NA
NA
−0.418
NA
NA
−0.041
NA
NA
NA
NA
NA
−0.292
−0.418
−0.292
−0.275
−0.363
−0.310
−0.418
−0.399
−0.327
−0.399
−0.327
−0.327
−0.363
−0.345
−0.275
−0.399
−0.292
−0.437
−0.292
−0.275
−0.456
−0.418
−0.292
−0.437
−0.310
−0.026
0.057
0.043
−0.209
0.465
−0.070
0.198
0.057
NA
0.002
NA
NA
−1.286
−0.678
−0.871
−0.724
−1.093
−1.356
−0.820
−1.356
0.626
0.205
NA
NA
NA
−0.871
NA
NA
0.205
NA
NA
NA
NA
NA
−0.923
−0.771
−0.724
−0.678
−1.286
−1.034
−1.678
−0.724
0.570
−0.430
−0.469
−0.591
0.129
0.589
−0.063
0.205
0.229
−0.321
0.731
0.253
−0.186
−0.123
0.626
0.626
0.451
−0.634
−0.634
−0.678
−1.034
−1.678
−0.634
−1.219
−0.923
NA
−0.123
NA
NA
−0.387
−0.681
−0.406
−0.465
−0.387
−0.591
−0.445
−0.802
0.223
−0.143
−0.880
−0.880
−0.387
−0.368
−0.635
−0.777
−0.387
−0.445
−0.828
−1.020
−0.704
−0.387
−0.313
0.172
−0.050
−0.331
0.172
0.023
0.331
0.079
−0.368
−0.465
−0.368
−0.387
−0.635
−0.991
−0.591
0.065
−0.081
0.235
0.223
0.051
−0.278
0.133
−0.096
−0.020
−0.192
−0.368
−0.426
−0.368
−0.295
0.272
−0.112
0.023
0.235
−0.506
−0.527
−0.569
−0.387
1.466
1.493
2.067
1.498
0.500
0.394
0.491
0.346
−0.743
−0.839
−1.250
−0.839
−0.732
−0.461
−0.876
−0.732
−0.876
−1.080
−0.790
−1.020
−2.065
−0.709
1.072
1.984
0.486
1.579
1.962
2.009
0.405
1.389
0.640
0.436
0.136
−0.155
0.319
0.524
−0.058
−0.902
−0.529
−0.352
−0.590
−0.519
−0.334
−0.369
−0.743
−0.698
−0.415
1.155
1.002
0.567
−0.405
−0.283
−0.387
−0.415
−0.424
−0.378
−0.325
−0.490
−0.396
DOI: 10.1021/acs.jproteome.6b00138
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Table 1. continued
log2 ratio of ANE-trained/parental cells
protein name (protein accession number, gene name)
SAS
Keratin, type II cytoskeletal 6B (P04259, KRT6B)
High mobility group protein HMG-I/HMG-Y (P17096, HMGA1)
Calmodulin-like protein 3 (P27482, CALML3)
Galectin-7 (P47929, LGALS7)
Tumor protein D52 (P55327, TPD52)
Succinyl-CoA:3-ketoacid-coenzyme A transferase 1, mitochondrial (P55809, OXCT1)
Brain acid soluble protein 1 (P80723, BASP1)
Dual specificity mitogen-activated protein kinase kinase 1 (Q02750, MAP2K1)
Tight junction protein ZO-1 (Q07157, TJP1)
Ras suppressor protein 1 (Q15404, RSU1)
2,4-dienoyl-CoA reductase, mitochondrial (Q16698, DECR1)
Probable ATP-dependent RNA helicase DDX60 (Q8IY21, DDX60)
Protein TFG (Q92734, TFG)
Endophilin-A2 (Q99961, SH3GL1)
Tubulin-specific chaperone D (Q9BTW9, TBCD)
Signal recognition particle 68 kDa protein (Q9UHB9, SRP68)
Exportin-7 (Q9UIA9, XPO7)
NSFL1 cofactor p47 (Q9UNZ2, NSFL1C)
Retinoblastoma-binding protein 6 (Q7Z6E9, RBBP6)
60S ribosomal protein L12 (P30050, RPL12)
Coatomer subunit gamma-2 (Q9UBF2, COPG2)
NA
−0.041
NA
NA
NA
NA
0.515
NA
0.030
0.173
NA
NA
NA
NA
NA
NA
1.097
NA
NA
0.780
NA
OECM1
NA
0.662
NA
NA
NA
NA
1.142
NA
0.229
0.430
NA
NA
NA
NA
NA
NA
0.388
NA
NA
0.575
NA
CGHNK2
CGHNC9
−0.527
−0.569
−0.485
−1.050
−0.465
−0.613
−0.387
−0.387
−0.465
−0.387
−0.485
−0.426
−0.506
−0.681
−0.368
−0.485
−0.569
−0.445
−0.350
0.960
0.623
−0.291
−0.611
−0.325
−0.405
−0.387
−0.433
−1.023
−0.519
−0.500
−0.452
−0.480
−0.396
−0.291
−0.369
−0.519
−0.549
−0.317
−0.490
−0.611
0.414
0.331
a
Proteins with log2 ratios below the mean of all log2 ratios minus the SD of all log2 ratios were considered to be underexpressed in ANE-trained
cells (−0.246, −0.614, −0.343, and −0.268 for SAS, OECM1, CGHNK2 and CGHNC9, respectively), while proteins with ratios above the mean
plus the SD were deemed overexpressed in ANE-trained cells (0.366, 0.610, 0.316, and 1.932 for SAS, OECM1, CGHNK2 and CGHNC9,
respectively). bProteins that have not been identified or quantified are shown as not available (NA).
Functional Pathway Analysis Revealed a Broad Effect of
Areca Nut Induction
protein probability (≥0.95) and at least two peptide hits for one
protein identification (Figure 1A). The false discovery rate
(FDR) of protein detection was empirically determined through
a comparison of the data set against a random database using the
same search parameters and TPP cutoffs. The estimated FDR of
1.0% was calculated as the number of reverse proteins divided by
the number of forward proteins.
Using this approach, 1405 and 1324 proteins were identified
and quantified, respectively, in set 1. In addition, 1614 and 1542
proteins were detected and quantified, respectively, in set 2
(Figure 1B). The details of these proteins are summarized in
Tables S-3 and S-4. Among the identified proteins, 1166 were
detected in both set 1 and set 2, while 1114 were quantified in
both sets (Figure 1B). Proteins with log2 ratios below the mean
of all log2 ratios minus the SD of all log2 ratios were considered
to be under-expressed in ANE-trained cells (−0.246, −0.614,
−0.343, and −0.268 for SAS, OECM1, CGHNK2 and
CGHNC9, respectively), while proteins with ratios above the
mean plus the SD were deemed to be overexpressed in ANEtrained cells (0.366, 0.610, 0.316, and 1.932 for SAS, OECM1,
CGHNK2 and CGHNC9, respectively) (Table S5). On the basis
of the cutoffs, 145, 187, 183, and 244 proteins were overexpressed in ANE-trained SAS, OECM1, CGHNK2 and
CGHNC9 cells, respectively, compared with parental cells
(Figure 1C). Additionally, 126, 184, 207, and 242 were underexpressed in ANE-trained SAS, OECM1, CGHNK2 and
CGHNC9 cells, respectively (Figure 1C). Among these
differentially expressed proteins, 196 proteins were found to be
dysregulated in at least two ANE-trained sublines, including 101
proteins that were overexpressed and 95 that were underexpressed (Figure 1C and Table 1). The relative expression of
the 196 significant proteins is summarized in Figure 1D.
To obtain a global picture of the functional pathways that are
induced by chronic areca nut exposure, the 196 differentially
expressed proteins were imported into MetaCore for integrated
network analysis, for which an intersection algorithm was used.
Several pathways were significantly associated with chronic areca
nut extract exposure (P < 10−8). These include pathways that
involve cytoskeleton remodeling, regulation of epithelialmesenchymal transition (EMT), cell adhesion-associated
cytokines, angiogenesis via IL-8 signaling, activation of
adrenergic receptors by the EGFR pathway, and regulation of
cell survival and apoptosis via BAD phosphorylation (Figure 2A).
Apparently, areca nut exposure induced a broad effect on cellular
functions.
To explicitly determine the significant pathways induced by
chronic areca nut exposure, the EMT regulatory pathway was
surveyed. In all, 19 out of 47 (approximately 1/3) identified
molecules were matched to the dominant pathway (Figure 2B).
The following molecules were shown to be potentially involved:
secretory cytokines and growth factors (Wnt, TGF-β, EGF/
FGF2, IL-1β, TNF), cell membrane receptors (Fizzled, EGFR/
TGFR, E-cadherin), cytoplasmic proteins (Occludin, PAI1, ZO1, Tropomyosin, MMP9), and nuclear transcription factors
(CREB1, SP1, SMAD2, E2A, c-Jun). This indicates that EMT
was markedly induced through multiple molecular pathways.
Confirmation of 29 Molecules with Altered Expression in
ANE-Treated Cell Sublines
To further assess the significance of these molecules in response
to chronic areca nut exposure, 29 molecules were independently
examined by RT-qPCR, and the results from these four oral
parental cell lines were compared with those of the ANE-trained
sublines. The results of each molecule in the four paired samples
2988
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Figure 2. Bioinformatic analysis of the functional pathways of the 196 significant proteins induced by chronic areca nut exposure. (A) Functional
classification of 196 significant proteins using Metacore analysis software. (B) Illustration of the pathway of TGF-beta-induced EMT via MAPK. (C)
Verification of 29 molecules that were differentially expressed among four paired oral parental (PT) and ANE-trained subline cells by RT-qPCR. (D)
Overall view of the differential expression of 29 molecules between parental and ANE-exposed subline cells, with the average fold change (x-axis) and
statistical P value (y-axis) for each.
are summarized in Figure 2C and Table 2. Twenty-four
molecules were confirmed to exhibit more than a 1.5-fold
change in expression. To obtain an overall view of the differential
expression of these molecules, Figure 2D was plotted to illustrate
both the fold change and the statistical P values for each
molecule. As shown, most molecules exhibited significant
differential expression (>1.5-fold), and the difference observed
in ANE-trained sublines was significant (P < 0.05). These results
suggest a common set of molecules that are crucially affected by
chronic areca nut exposure.
Of the 24 molecules, the 8 proteins with most highly upregulated and the 4 proteins with most highly down-regulated
were further assessed via a cell-based study. Two oral cancer cell
lines (SAS and OECM1) were treated with various doses (0 to
100 μg/mL) of ANE, and the molecular expression levels were
determined by RT-qPCR analysis. The relative expression levels
of the 8 up-regulated genes are shown in Figure 3A and are
summarized in Figure 3B, whereas the relative expression levels
of the 4 down-regulated genes are shown in Figure 3C and are
summarized Figure 3D. As shown, Krt17, ERP44, WRNIP1,
DBN1, PDLIM5, ITGB4, HDAC2, and ITGA6 were all
significantly up-regulated in two ANE-trained sublines in a
dose-dependent manner (Figure 3A,B). Out of these, Krt17
demonstrated the highest increase, with 5.1-fold and 27.9-fold
elevations in SAS and OECM1 cells, respectively. Similarly,
although various levels were detected among different cell lines,
ARHGDIA, S100A2, SLCIA5 and RAB3GAP1 showed a
consistent trend of down-regulation in two ANE-trained sublines
(Figure 3C,D). These results further confirmed the significance
of these proteins in that they may play important roles associated
with areca nut-induced carcinogenesis.
Krt17 Contributed to ANE-Induced Cell Mobility via an
EMT-Associated Pathway
Because Krt17 showed a prominent level of up-regulation in
ANE-trained sublines, this molecule was selected for further
investigation. First, its expression status and protein level were
confirmed in four ANE-trained sublines. As shown in Figure 4A,
Krt17 was significantly elevated in four sublines, from 1.6- to 4.7fold. To further validate the effects of this molecule, oral cancer
cell lines were treated with various doses of ANE, and the protein
levels were determined. Krt17 was consistently induced in a
dose-dependent manner, and 2.4- to 8.1-fold increases were
observed in three cell lines tested at a dose of 500 μg/mL (Figure
4B).
To examine whether the phenotypic alterations induced by
areca nut exposure were associated with the Krt17 molecule, a
gene knockdown strategy using specific shRNA was applied.
Because cytoskeletal remodeling was a prominent mechanism
according to the bioinformatic network analysis (Figure 2A), the
function of cell mobility was examined. After confirmation of
Krt17 silencing in two oral cancer cell lines with stable
knockdown of Krt17 (Figure 5A), the effects of ANE induction
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(E-cadherin) and increased the levels of mesenchymal
biomarkers (N-cadherin and Slug) in two cell lines. However,
Krt17 knockdown caused resistance to this induction by ANE. In
either cell line, no significant alteration was observed in the
expression levels of any of these molecules. These results
suggested that ANE-induced EMT process was through a Krt17dependent manner.
It has been known that TGF-β participates in EMT process;
we therefore determined whether the ANE-Krt17-EMT
regulatory axis may be mediated by TGF-β. The expression of
TGF-β in response to ANE treatment was examined in both
parental oral cancer cell lines and Krt17-silencing cancer cell
lines. As shown in Figure 5F (for CGHNC9 cells) and Figure 5G
(for OECM1 cells), TGF-β was increased after ANE treatment in
oral cancer cells. Nevertheless, unlike EMT marker molecules,
Krt17 knockdown has no effect on TGF-β expression. These
results indicated that ANE rendered a broad effect to induce
several molecules including Krt17 and TGF-β. However, this
TGF-β induction was Krt17-independent and may not cross talk
with Krt-17 induced EMT mechanism. Taking together, our
results suggest that Krt17 contributed to ANE-induced cell
mobility, which was via an EMT-associated mechanism.
Table 2. List of 29 Molecules Differentially Expressed in ANE
Sublines As Analyzed by Q-PCR
gene symbol
KRT17
ERP44
WRNIP1
DBN1
PDLIM5
ITGB4
HMGA2
ITGA6
HMGCS1
SH3KBP1
NDUFAF2
GPRC5A
BYSL
CRNKL1
NDRG1
THBS1
IQGAP1
IL1A
WDR7
HDAC2
VIM
NT5E
TPM2
STAT1
RAB3GAP1
S100A2
ARHGDIA
SLC1A5
POP1
protein name
Overexpressed proteins
Keratin, type I cytoskeletal 17
Endoplasmic reticulum resident protein 44
ATPase WRNIP1
Drebrin
PDZ and LIM domain protein 5
Integrin beta-4
High mobility group protein HMGI-C
Integrin alpha-6
Hydroxymethylglutaryl-CoA synthase,
cytoplasmic
SH3 domain-containing kinase-binding
protein 1
Mimitin, mitochondrial
Retinoic acid-induced protein 3
Bystin
Crooked neck-like protein 1
Protein NDRG1
Thrombospondin-1
Ras GTPase-activating-like protein
IQGAP1
Interleukin-1 alpha
Methylosome protein 50
Histone deacetylase 2
Vimentin
5′-nucleotidase
Tropomyosin beta chain
Signal transducer and activator of
transcription 1
Underexpressed proteins
Rab3 GTPase-activating protein catalytic
subunit
Protein S100-A2
Rho GDP-dissociation inhibitor 1
Neutral amino acid transporter B
Ribonucleases P/MRP protein subunit
POP1
average
P value
8.110
7.718
7.431
4.860
4.203
3.905
3.349
3.032
2.797
−0.009
−0.627
−0.749
−0.363
−0.223
−0.549
−0.364
−0.063
−0.040
2.783
−0.167
2.770
2.632
2.578
2.530
2.228
2.223
1.963
−0.522
−0.102
−0.087
−0.426
−0.075
−0.032
−0.039
1.853
1.817
1.310
1.078
1.033
0.990
0.673
−0.574
−0.669
−0.260
−0.039
−0.990
−0.856
−0.430
−2.896
−0.214
−2.473
−1.940
−1.636
3.159
−0.250
−0.376
−0.982
−0.074
Krt17 Was Elevated in Oral Lesions in a Mouse Model of
Spontaneous Tumor Induction
To authenticate the molecular effect of Krt17 on ANE-induced
oral tumors, a mouse model of spontaneous tumorigenesis was
established. After carcinogen exposure for 28 weeks, all mice
were sacrificed, and the tongue tissues were dissected for
pathologic examination. In the mock or control group, no
obvious lesions were found in the tongue or in the inner mouth
area. However, in the arecoline treatment group, malignant
lesions were observed in the right or upper lateral tongue, some
of which had invaded deep into the skeletal muscle. In this group,
the incidence rate in the mice was 83% (10/12), including 42%
(5/12) that developed hyperplasia or dysplasia and 42% (5/12)
with verrucous hyperplasia (VH) or squamous cell carcinoma
(SCC) (Table 3). No lesions were observed in any other tissues,
such as the esophagus, liver, colon, kidney, spleen or stomach.
The dissected tongue tissues were subjected to immunostaining and pathological review. To examine whether Krt17 was
associated with arecoline-induced oral tumorigenesis, we
analyzed Krt17 protein expression in the tongue tissues of
mice by H&E stain and IHC. Examples of Krt17 levels in various
pathological states are shown in Figure 6A. In the nontreatment
group (mock or control), all tissues showed normal (i.e.,
negative) Krt17 expression (tissues a, b). In the arecoline
treatment group, the level of Krt17 expression varied among the
mice but was dependent on the pathologic status. In general,
grossly normal tissue (tissues c, d) showed weak Krt17 staining,
hyperplastic or dysplastic tissues (tissues e, f) displayed moderate
staining, and areas with verrucous hyperplasia or squamous cell
carcinoma (tissues g, h) showed strong Krt17 staining. The
Krt17 expression level was quantified by H-scoring system, which
assessed the staining intensity and the percentage of positive cells
in each tissue section. The results are shown in Figure 6B.
Compared with the mock group, the H-score of the control
group was slightly elevated (1.34 fold), but no significant
difference was observed (P = 0.201). However, the scores were
substantially higher in all tissues from the arecoline treatment
group, and these exhibited an increasing trend following
pathologic progression. On average, Krt17 was up-regulated by
4.54, 5.28, and 5.84 fold in normal tissues, hyperplastic/
on cellular migration and invasion were determined by in vitro
wound healing and Matrigel invasion assays, respectively. As
shown, the migratory ability of the cells was enhanced with an
increase in ANE treatment dose in CGHNC9 (Figure 5B) and
OECM1 (Figure 5C) cancer cell lines. The knockdown of Krt17
expression substantially inhibited ANE-induced cell migration, as
approximately 50% inhibition was observed at the highest dose of
ANE treatment in these two cell lines. Similarly, Krt17
knockdown significantly suppressed ANE-induced cell invasion,
as 95% and 55% suppression was observed at the highest ANE
dose in CGHNC9 (Figure 5D) and OECM1 cancer cell lines,
respectively (Figure 5E). These results indicated that ANE
promoted the migration and invasion of these cancer cells
through a Krt17 regulatory pathway.
Recent reports have indicated that EMT plays a significant role
in the facilitation of cell invasion.41,42 Therefore, we investigated
whether the contribution of Krt17 to ANE-induced cell mobility
is associated with EMT. The epithelial marker E-cadherin, the
mesenchymal marker N-cadherin, and the EMT regulator Slug
were examined in two oral cancer cell lines. As shown in Figure
5F for CGHNC9 cells and in Figure 5G for OECM1 cells,
although various levels of different molecules were detected,
ANE treatment reduced the expression of epithelial biomarkers
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Figure 3. Effects of the expression of 12 proteins in response to ANE treatment. The oral cancer cells SAS and OECM1 were treated with 50 or 100 μg/
mL of ANE for 24 h. The mRNA was extracted and subjected to qRT-qPCR analysis for each molecule. Relative expression levels were determined after
normalization to GAPDH expression (internal control). (A) The differential expression of 8 up-regulated genes induced by ANE: KRT17, ERP44,
WRNIP1, DBN1, PDLIM5, ITGB4, HMGA2, and ITGA6. (B) Relative expression levels of 8 up-regulated genes in SAS and OECM1 cells. (C)
Differential expression levels of 4 down-regulated genes induced by ANE: ARHGIA, S100A2, SLCIA5, and RAB3GAP1. (D) Relative expression levels
of 4 down-regulated genes in SAS and OECM1 cells.
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Figure 4. Krt17 protein expression is up-regulated by ANE. (A) Krt17 expression is up-regulated in response to chronic areca nut exposure, as observed
in four pairs of parental (PT) and ANE sublines by Western blot analysis. (B) Krt17 expression is up-regulated in a dose-dependent manner in three oral
cancer cell lines (OECM1, CGHNC9 and SAS) and one immortalized normal keratinocyte cell line (CGHNK2) upon short-term (24 h) ANE
treatment (0−500 μg/mL). In each sample, the relative expression of Krt17 was determined after normalization to the GAPDH expression level, as
shown in the right panel.
Over 1000 proteins were identified in four areca nut-trained
oral cell sublines. Out of these, 196 proteins were found to be
dysregulated in at least two sublines (Table 1), suggesting the
importance of the participation of these proteins in areca nutinduced pathogenesis. The 196 differentially expressed proteins
were imported into MetaCore for an integrated network analysis
by using an intersection algorithm. Unlike previous reports,6,7,23
the mechanisms that involve DNA damage or epigenetic
alterations were less significant in our study. This may be
explained by the lack of cytotoxicity and genotoxicity elicited by
this chronic areca nut-trained cellular model.29 However, the
regulation of cell migration, including cytoskeletal remodeling
and EMT, was the most prominent (Figure 2A). This result was
consistent with that of previous reports in that biological
pathways that mediate cell adhesion or tumor invasion were
commonly found to be dysregulated in patients with oral
cancer.25−27 Thus, the functional changes in cell mobility may be
a crucial effect of areca nuts during oncogenic transformation.
Currently, the molecular mechanism of ANE leading to oral
carcinogenesis is still unclear. Thus, far, there is no ANE
associated receptor found in cancer cells. However, arecoline, the
major abundant component of ANE, has been reported exerting
its effects through M1, M2, and M3 muscarinic acetylcholine
receptors in neuron and muscular cells.43−45 It may be worthy to
determine that whether ANE contributing to oral carcinogenesis
through muscarinic acetylcholine receptors as well. For
molecular pathways, our integrated network analysis (Figure
2A) revealed that ANE may render a broad effect of molecular
activations, including IL-8, EGFR, TGF-β, IGF-1, HSP70, and
TLR signaling pathways. Thus, ANE facilitates oral carcinogenesis may be through multisignaling mechanisms. This
postulate is awaited to be investigated.
Twenty-nine molecules were subjected to an independent
examination by RT-qPCR. Although various levels of these
proteins were observed among the different cell lines, 24
molecules were confirmed to exhibit more than a 1.5-fold
differential expression on average in the four cell lines tested
dysplastic issues, and in the case of VH/SCC, respectively,
according to gross pathology (P < 0.001 in all groups). This result
suggests that Krt17 was induced by arecoline and that it
contributed to the carcinogen-induced tumorigenesis in these
mice.
This elevated expression of Krt17 during carcinogenesis in
mice was further supported with clinical investigation. The Krt17
levels in oral cancer tissues from various clinical stages have been
determined using IHC method. As shown in Figure S2, the Krt17
expression in the early stage (stage I−II) cancers was lower than
that in the late-stage (stage III−IV) cancers. Since it is well
accepted that cancers with advanced stage are correlated with
poor prognosis, these results suggest that Krt17 expression may
be associated with poor prognosis in oral cancer patients.
■
DISCUSSION
Oral cancer is one of the most common malignant diseases
worldwide, and the areca nut is a primary carcinogen that causes
this cancer in Southeast Asia. Previously, several screening
methods, such as microarrays, have been used to globally survey
the gene alteration profiles of individuals with oral cancer caused
by areca nut exposure.22,24−27 However, across various studies,
little overlap among genes has been observed. These diverse
results may be due largely to the heterogeneity that is present in
different patient samples, various gene screening modalities or
underlying multifaceted patho-etiological mechanisms among
different sample sets. To reduce sample heterogeneity of areca
nut-induced oral carcinogenesis, in this study, we used four
isogenic sublines of oral cells that were chronically exposed to
areca nut extract, which were then compared with the
corresponding parental cells. To reveal the molecular basis of
the carcinogenic mechanism involved, we performed quantitative
proteomic analyses to globally determine the proteomic profile
and thus elucidate the relevant biological processes. To our
knowledge, this is the first report on proteomics relevant to areca
nut-associated diseases.
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Figure 5. Krt17 knockdown suppresses the effects of ANE-induced cell migration and invasion. (A) Krt17 expression was knockdown by Krt17-specific
shRNA in CGHNC9 and OECM1 cells. In each sample, the relative expression of Krt17 was determined after normalization to the GAPDH expression
level, as shown in the right panel. (B,C) The effects on cell migration as determined by an in vitro wound healing assay in CGHNC9 cells (B) and in
OEM1 cells (C). Cells in which Krt17 was stably knocked down and control cells were treated with various doses of ANE (0−100 μg/mL) for 24 h, and
the cell migration ability was determined, as described in the Methods section. Cell migration toward the gap was observed, imaged, and quantified at the
indicated times. (*p < 0.05, **p < 0.01, t test). (D,E) The effects on cell invasion as determined by a Matrigel invasion assay in CGHNC9 cells (D) and
in OEM1 cells (E). Cells in which Krt17 was stably knocked down and control cells were treated with various doses of ANE (0−100 μg/mL) for 24 h,
and the cell invasion ability was determined, as described in the Methods section. The cells that invaded through the Matrigel-coated membranes to the
lower chamber were stained, imaged, and quantified after 16 h. (*p < 0.05, **p < 0.01, t test). (F,G) Krt17 knockdown abolished ANE-induced
epithelial-mesenchymal transition (EMT). Relative expression levels of EMT-associated molecules in Krt17 knockdown cells and the control (parental)
CGHNC9 cells (F) and OECM1 cells (G). In each sample, the expression levels of an epithelial biomarker (E-cadherin) and mesenchymal biomarkers
(N-cadherin and Slug) were determined by real-time RT-PCR after normalization to GAPDH expression (internal control).
Krt17, which exhibited the most prominent alteration in
response to chronic areca nut treatment, was further investigated.
Krt17 is part of the keratin family and is multifunctional. In
epithelial tissues, a network of proteins links the nucleus to the
cell membrane through keratin filaments, in which transmembrane proteins provide the basis for cell−cell and cellextracellular matrix adhesion.46,47 Krt17 belongs to the type-1
(Figure 2C,D). These results suggest a high probability that the
proteomic analysis is valid in the present study. Among the 24
proteins, 12 molecules were further confirmed via cell-based
assays, and all of these showed a consistent trend of altered
expression in two cell lines (Figure 3). These results suggest a
panel of proteins that may participate in the mechanisms of areca
nut-induced pathogenesis.
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acidic epithelial keratin family, the members of which
heterodimerize with Krt6b to form an intermediate filament
network that attaches to desmosomes at points of cell−cell
contacts.46,47 In addition to its scaffolding function, Krt17 has
also been reported to be highly expressed in carcinomas,
presumably taking part in tumorigenesis via the regulation of
wound healing and cell growth, two processes that require rapid
cytoskeletal remodeling. For examples, Krt17 has been reported
overexpressed in malignant lesions of the cervix,48 associated
with the aggressiveness of head-neck cancer,49,50 and promotes
Table 3. Summarization for Incidences of Tumorigenesis
Induced by Arecoline
treatment
a
tissue status
mock (n = 8)
DMSO (n = 8)
Arecoline (n = 12)
health
hyperplasia
VH/SCCa
100% (8/8)
0% (0/8)
0% (0/8)
100% (8/8)
0% (0/8)
0% (0/8)
16.6% (2/12)
41.7% (5/12)
41.7% (5/12)
VH, verrucous hyperplasia; SCC, squamous cell carcinoma.
Figure 6. Krt17 is elevated in the oral lesions of mice with spontaneous tumor induction. (A) Immunohistochemistry (IHC) for the Krt17 protein in the
tongue tissues from mice with spontaneous tumor induction. The groups of mice were classified according to treatment condition and pathological
status as indicated above. The representative normal tissues without treatment (a, b), normal tissues with arecoline cocktail treatment (c, d), hyperplasia
or dysplasia (e, f), and verrucous hyperplasia (VH) or squamous cell carcinoma (HCC) (g, h) are shown. For each sample, the H&E stains are also
presented below. The immunoreactivity was evaluated by subjective assessment of the median staining intensity, as negative (0: no staining), weak (+1),
moderate (+2), or strong (+3). (B) The scatter dot plot of the H-scoring for Krt17 protein expression in each tongue specimen from mice with
spontaneously induced tumors. The groups of mice were classified according to treatment condition and pathological status as indicated below. (***p <
0.001, t test).
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skin hyperplasia.51 Consistently, our data showed that Krt17
contributes to the malignant phenotype, as the knockdown of
this protein attenuated areca nut-induced cell migration (Figure
5B,C) and cell invasion (Figure 5D,E) in two oral cancer cell
lines. Moreover, this phenotypic alteration is accompanied by
EMT (Figure 5F,G), which may be associated with the regulation
of the Krt17 molecule by phosphorylation.52
The function of Krt17 is further demonstrated in our animal
study. It was shown to be induced by arecoline cocktail and was
associated with tumorigenesis (Figure 6). Moreover, the
elevation of Krt17 in grossly normal tissue (Figure 6B) indicates
that this molecular change occurred much earlier than cellular
alterations. Therefore, Krt17 may play a role in early tumor
initiation during oral carcinogenesis. This implies that Krt17 may
serve as a biomarker for early screening or for susceptibility
assessment of areca nut-induced oral cancer. Nevertheless, the
mechanism by which Krt17 contributes to areca nut-induced
malignant transformation is currently unclear. Further characterization of the Krt17 signaling pathway is needed to discover its
molecular network and to provide additional insights concerning
the regulatory mechanism of areca nut-induced cancer
progression.
In conclusion, we have identified a proteome that is associated
with chronic areca nut exposure in oral cancer cells. We found
196 proteins that were commonly expressed in more than two
cell lines. An algorithm-based analysis revealed a broad cellular
effect of areca nut induction, while cytoskeletal remodeling via
EMT was the most significant. Twelve molecules, including
Krt17, were confirmed to be highly associated with areca nut
induction; Krt17 was further demonstrated to contribute to oral
transformation. An understanding of the mechanisms that
underlie areca nut-induced malignancies provides insight into
the management of oral cancers. Our study should contribute to
risk assessment, disease prevention and other clinical applications associated with areca nut-induced oral diseases.
■
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and the CGMH, Taiwan (CLRPD190014-6, CMRPD2B0053,
and BMRPC77).
■
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ASSOCIATED CONTENT
S Supporting Information
*
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.jproteome.6b00138.
Supporting methods, tables, and figures (PDF)
■
REFERENCES
AUTHOR INFORMATION
Corresponding Authors
*Tel: 886-3-3281200, ex 7008. Fax: 886-3-2118247. E-mail:
[email protected].
*Tel: 886-3-2118800, ex 5085. Fax: 886-3-2118247. E-mail:
[email protected].
Author Contributions
#
C.-H.C. and C.-C.W. contributed equally.
Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS
This work was supported by grants to Ann-Joy Cheng from the
Ministry of Science and Technology (MOST), Taiwan (1012314-B-182A-121-MY3) and Chang Gung Memorial Hospital
(CGMH), Taiwan (CMRPD1A0643 and CMRPD1E0032-3)
and grants to Chih-Ching Wu from the MOST (102-2320-B2995
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