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Quantitative iTRAQ LC-MS/MS Proteomics Reveals the

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Quantitative iTRAQ LC-MS/MS Proteomics Reveals the Proteome Profiles of
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DF-1 Cells after Infection with Subgroup J Avian Leukosis Virus
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Xiaofei Li, Qi Wang, Yanni Gao, Xiaole Qi, Yongqiang Wang, Honglei Gao, Yulong
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Gao*, Xiaomei Wang*
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Division of Avian Infectious Diseases, State Key Laboratory of Veterinary
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Biotechnology,
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Agricultural Sciences, Harbin 150001, China
Harbin Veterinary Research Institute, Chinese Academy of
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Xiaofei Li E- mail address: lixiaofei2338@sina.com
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Qi Wang E-mail address: wangqi60815@163.com
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Yanni Gao E- mail address: yngao03@sina.com
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Xiaole Qi E- mail address: qxl@hvri.ac.cn
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Yongqiang Wang E- mail address: yqw_19@yahoo.com
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Honglei Gao E- mail address: ghl@hvri.ac.cn
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Yulong Gao E-mail address: ylg@hvri.ac.cn
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Xiaomei Wang E-mail address: xmw@hvri.ac.cn
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Abstract
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Avian leukosis virus subgroup J (ALV-J) is an avian oncogenic retrovirus that can
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induce various clinical tumors and has caused severe economic losses in China. To
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improve our understanding of the host cellular responses to virus infection and the
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pathogenesis of ALV-J infection, we applied isobaric tags for relative and absolute
28
quantification
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chromatography-tandem mass spectrometry to detect the protein changes in DF-1
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cells infected and mock-infected with ALV-J. A total of 75 cellular proteins were
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significantly changed, including 33 up-regulated proteins and 42 down-regulated
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proteins. The reliability of iTRAQ-LC MS/MS was confirmed via real- time RT-PCR.
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Most of these proteins were related to the physiological functions of metabolic
34
processes, biosynthetic processes, responses to stimuli, protein binding, signal
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transduction, cell cytoskeleton, etc. We also found some proteins that play important
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roles in apoptosis and oncogenicity. The differentially expressed proteins identified
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may provide valuable information to elucidate the pathogenesis of virus infection and
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virus-host interactions.
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Keywords: avian leukosis virus, cellular proteins, change, iTRAQ
(iTRAQ)
labeling
coupled
with
multidimensional
liquid
40
41
42
43
44
2
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1. Introduction
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The J subgroup of avian leukosis virus (ALV-J), which belongs to the Retroviridae
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family, was first isolated from white meat-type chickens in the United Kingdom in
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1988
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immunosuppression effects in both naturally and experimentally infected chickens
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[2-3]. In China, ALV-J-associated myeloid leukosis in chickens was first reported in
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1999 [4]. ALV-J can induce various tumors, growth retardation and production
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problems. In addition, in recent years, it has become widespread in many parts of our
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country and leads to severe economic losses in the poultry industry.
[1].
It
can
predominantly
lead
to
myeloid
leukosis
(ML)
and
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The pathogenesis of virus infection and the mechanism through which the virus
55
interacts with host cells remain unclear. During virus infection, the proteins of host
56
cells may be significantly changed. It is now possible to use proteomic techniques to
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identify the changes in protein abundance that indicate host cellular responses to virus
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infection and provide useful information to obtain a better understanding of the
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pathogenesis of virus infection [5–8]. Kvaratskhelia et al. [9] applied enzymatic
60
digestion coupled with mass spectrometry (MS) to detect the sites of glycosylation on
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the surface of avian leukosis virus subgroup A (ALV-A) and found that carbohydrates
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may play an important role in receptor binding.
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To explore the possible mechanisms of virus infection, we used isobaric tags for
64
relative and absolute quantification (iTRAQ) combined with multidimensional liquid
65
chromatography (LC) and tandem MS analysis to perform a quantitative proteomic
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analysis of DF-1 cells infected with ALV-J [10]. To the best of our knowledge, no
3
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previous study had used the iTRAQ LC-MS/MS proteomics strategy to investigate the
68
differently expressed proteins in ALV-J- infected DF-1 cells. The iTRAQ labeling
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technology could greatly increase the identification sensitivity and quantitation
70
accuracy of proteomic analyses through a multiplexed quantitation strategy [11]. The
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results showed that 75 proteins were significantly changed after ALV-J infection.
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These changed proteins may provide valuable information to study the molecular
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mechanisms underlying ALV-J pathogenesis.
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2. Materials and methods
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2.1. Reagents
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The iTRAQ Reagent Multi-Plex Kit was acquired from Applied Biosystems (Foster
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City, CA, USA). A multidimensional liquid chromatographer (RIGOL 3220) was
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purchased
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chromatographic column, 250×4.6 mm i.d., filler particles diameter: 5 µm) was
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acquired from Agela Co., Ltd. (Tianjin, China). The LC-MS/MS instrument
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(Q-Exactive) was obtained from Thermo Fisher Scientific.
from
RIGOL,
and
the
chromatographic
column
(Agela,
C18
82
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2.2. Cell culture and virus infection
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DF-1 cells (ATCC accession number: CRL-12203) were cultured in Dulbecco’s
85
modified Eagle medium (DMEM; HyClone, Beijing, China) supplemented with 10%
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fetal bovine serum (FBS) and 100 μg/mL streptomycin and penicillin at 37°C in a 5%
87
CO2 atmosphere. ALV-J strain HPRS-103 (GenBank: Z46390) was kindly provided
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by Professor Venugopal Nair. DF-1 cells cultured in flasks to approximately 80%
4
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confluence were infected with 0.5 mL of 103.5 /mL 50% tissue culture infectious doses
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(TCID50) of ALV-J for 144 h. Uninfected DF-1 cells served as mock-infected cells.
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2.3. Indirect immune fluorescence assay (IFA)
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At 144 h post-infection, the infected DF-1 cells were washed twice with PBS and
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fixed with anhydrous ethanol for 20 min. The fixed cells were then incubated with
95
mouse anti-P27 monoclonal antibody (prepared in our lab) at 37°C for 60 min. After
96
washing three times with PBST (0.01 M PBS, pH 7.2, 0.05% Tween 20), the cells
97
were incubated with goat anti- mouse IgG conjugated to FITC (Sigma, USA) at 37°C
98
for another 60 min. Finally, the cells were observed under a Carl Zeiss Vision
99
microscope (ZEISS Axio Observer D1) after three washes with PBST.
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2.4. Protein extraction, digestion and labeling with iTRAQ reagents
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Infected and mock- infected DF-1 cells were washed twice with PBS. The cells
103
were lysed in a lysis buffer (9 M urea, 4% CHAPS, 1% DTT, and 1% IPG buffer).
104
The mixtures were centrifuged at 15,000 g and 4°C for 15 min. The supernatant was
105
collected, and the protein concentration was determined using the Bradford protein
106
assay [12] (Bio-Rad Laboratories). Then, 100 µg of protein was mixed overnight with
107
four volumes of cold (-20°C) acetone and then dissolved using the dissolution buffer.
108
After being reduced, alkylated and digested with trypsin, the samples were labeled
109
following the manufacturer’s instructions described in the iTRAQ protocol. The
110
labeled samples were pooled for further analysis.
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2.5. LC-MS/MS and database searches
112
The iTRAQ- labeled sample mixtures were then fractionated by strong cation
113
exchange (SCX) chromatography on a high-performance liquid chromatography
114
(HPLC) system (RIGOL 3220; Beijing, China) using a chromatographic column
115
(Agela, C18 chromatographic column, 250×4.6 mm i.d., filler particles diameter : 5
116
µm Tianjin, China). Mobile phase A consisted of 2% ACN-98% H2 O (pH 10.0), and
117
mobile phase B consisted of 98% ACN-2% H2 O (pH 10.0). The solvent gradient was
118
the following: 5%-8%B for 1 min, 8%-32% B for 24 min, 32%-95% B for 2 min, 95%
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for 4 min, and 95%-5% B for 1 min. The column temperature was 45°C, the flow rate
120
was 0.7 mL/min and the detection wavelength was 214 nm. Peptides were collected
121
every minute within the effective gradient from 8% to 32%. A total of 27 fractions
122
were collected and then dried.
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The dried fractions were dissolved in 1.9% ACN/98% H2 O/0.1% FA aqueous
124
solution and combined into nine samples. The samples were centrifuged at 12,000 × r
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for 3 min, and the supernatant was collected. The supernatant was then analyzed using
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the EASY- nLC-1000 liquid phase interfaced with a Q Exactive mass spectrometer
127
(Thermo Fisher). The chromatographic conditions are the following: liquid phase,
128
EASY-nLC-1000; enriching column, C18, 5 µm, ID100 µm, 20- mm length;
129
separation column, C18, 3 µm, ID75 µm 120- mm length; mobile phase A, 1.9% ACN
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+ 98% H2 O + 0.1%FA; mobile phase B, 98% ACN + 1.9% H2 O + 0.1% FA; and flow
131
rate, 450 nl/min.
132
6
133
Elution conditions:
Time
0
24
30
31
38
B%
3
16
30
90
90
134
The data were acquired at 38 min. The spray voltage was 2.0 KV, the capillary
135
temperature was 320°C, the collision energy was 30, and the acquisition quality range
136
was 300-1400 da.
137
The relative quantification and protein identification were performed with the
138
Protein Discoverer software (version 1.2) using the built- in mascot as the search
139
engine.
140
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2.6. Real-time RT-PCR
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The primers (Table 1) were synthesized by BoShi Biotechnology Company (Harbin,
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China). The gene was amplified from the genomic DNA of DF-1 cells by polymerase
144
chain reaction (PCR). The PCR-amplified products were separated in a 2% agarose
145
gel and then purified using a DNA Gel Extraction kit (Axygen Biotechnology Limited,
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Hangzhou City, China). The products were then ligated into the pZeroBack/blunt
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vector (Tiangen Biotech Co., Ltd., Beijing, China), and the sequence was verified.
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The plasmid DNA was used as the standard to construct the standard curve via SYBR
149
Green real- time RT-PCR. The total cellular RNA of the infected or mock- infected
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DF-1 cells was extracted using the RNeasy Mini kit (QIAGEN, China) according to
151
the manufacturer’s protocol. Reverse transcription was performed using a PrimeScript
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II First-Strand cDNA Synthesis Kit (TaKaRa, China) as described in the protocol. The
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real-time RT-PCR was performed using the Roche LightCycler® 480 Real- Time PCR
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System.
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Table 1 Primer sequences for real-time RT-PCR.
Gene
Sequence
Size
BLOC1S5
F-TATATGAGCGGGGCAGGCCCT
150bp
R-TTCCCCGACATCCTTGAT
keratin
F-ATGTCCCGCTCCGTCAGCTTC
150bp
R-AGAGCCCAGGTTGTAGAGGCT
HMG14
F-ATGCCGAAGAGAAAGGTG
140bp
R-TCAGATTTATCCTTAGCCGCC
AACS
F-ATGTCCCGCGAGCCCGAGATT
150bp
R-CACTGACCACTGGTATAAGTC
157
158
2.7. Bioinformatics analysis
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The functional annotation of the 75 proteins in DF-1 cells that were significantly
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changed after infection with ALV-J was performed using the GOSlimViewer tool of
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the Agbase database (http://www.agbase.msstate.edu/) [13]. In addition, we aimed to
162
determine how ALV-J interacts with the host cellular proteins and how they affect the
163
function of host cells. The identified proteins were inputted into the STRING database
164
to obtain the protein-protein interaction network [14, 15] (http://string.embl.de/).
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3. Results
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3.1. Confirmation of ALV-J infection in DF-1 cells by IFA
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To confirm the DF-1 cells were infected by ALV-J, IFA was used to detect the viral
168
P27 antigen. The results showed clear green fluorescence in ALV-J-infected DF-1
8
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cells 144 h post- infection, whereas the uninfected DF-1 cells exhibited no green
170
fluorescence (Fig. 1).
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172
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Fig. 1. Identification of DF-1 cells infected with ALV-J by IFA. (A) DF1 cells infected
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with ALV-B. (B) Normal uninfected DF1 cells.
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3.2. Protein profile obtained by iTRAQ LC–MS/MS analysis
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To explore the differences in the protein expression levels after virus infection, the
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total proteins of ALV-J-infected and mock- infected DF-1 cells were extracted for
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iTRAQ- LC–MS/MS analysis. A total of 1091 proteins were detected, including 75
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proteins in DF-1 cells that were significantly changed infection with ALV-J for 144 h
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(Table 2). These differently expressed proteins were divided into two clusters:
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up-regulated and down-regulated. The number of up-regulated proteins was 33,
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whereas the number of down-regulated proteins was 42.
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9
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Table 2 List of significant differentially expressed proteins identified by iTRAQ
186
analysis of DF-1 cells infected with ALV-J.
Accession
Protein name
Protein score
no.
Fold change
Protein
in expression
MW
Protein
PI
Cluster 1:Tendency to up-regulation (33)
O73612
Ephrin-B1 GN=EFNB1
34.98
1.667
36.8
8.87
F1P187
Gephyrin (Fragment) GN=GPHN
0.00
1.560
77.4
5.38
P12274
Non-histone chromosomal protein HMG-14B GN=HMG14
0.00
1.473
11.2
9.63
E1BTX9
Serine/threonine-protein phosphatase
39.24
1.429
73.4
8.34
P08286
Histone H1.10
476.50
1.315
22.0
11.18
E1C281
PHD finger protein 6 GN=PHF6
0.00
1.281
41.0
8.62
Q5ZJ02
DBIRD complex subunit ZNF326 GN=ZNF326
45.04
1.280
63.5
5.78
Q5ZIK4
Protein yippee-like GN=YPEL5
0.00
1.276
13.8
7.31
Q5F3J5
Proteasome activator complex subunit 3 GN=PSME3
143.05
1.274
29.5
6.19
Q5F3Z5
DnaJ homolog subfamily B me mber 6 GN=DNAJB6
0.00
1.263
36.7
8.84
F1NB51
Zinc finger E-box-binding homeobox 1 GN=ZEB1
41.75
1.258
123.1
5.02
F1NLA7
Zinc finger CCCH domain-containing protein 11A GN=ZC3H11A
43.83
1.254
79.0
8.16
F1P5W3
Ephrin-B1 (Fragment) GN=EFNB1
34.98
1.249
32.7
8.46
F1NXG2
WW domain-binding protein 4 (Fragment) GN=WBP4
0.00
1.235
45.2
5.73
P08267
Ferritin heavy chain GN=FTH
65.41
1.226
21.1
6.21
Q6K1L7
Probable RNA-binding protein EIF1AD GN=eif1ad
0.00
1.210
21.2
4.79
F1NEY0
Syndecan (Fragment) GN=CPQ
45.65
1.208
19.9
4.70
F1NMD7
Pre-mRNA-splicing factor RBM22 GN=RBM22
21.07
1.205
46.7
8.54
O93481
Chromobox protein (CHCB2) GN=CBX3
0.00
1.190
19.8
5.12
F1NFJ0
DNA replication licensing factor MCM3 GN=MCM3
46.00
1.188
91.3
5.74
E1C9E9
DCN1-like protein GN=DCUN1D5
0.00
1.183
27.2
5.77
F1NAQ1
Vascular endothelial growth factor A GN=VEGFA
0.00
1.179
25.1
9.10
F1NLU6
Enhancer of mRNA-decapping protein 3 GN=EDC3
49.54
1.175
56.0
7.17
P16527
Myristoylated alanine-rich C-kinase substrate GN=MARCKS
30.67
1.174
27.7
4.44
10
Q5ZMC9
Nuclear distribution protein nudE homolog 1 GN=NDE1
0.00
1.173
39.5
5.11
Q5ZII6
Protein kish-A GN=TMEM167A
27.01
1.173
8.0
8.92
Q5ZIL9
KIF1-binding protein homolog GN=kbp
0.00
1.167
69.0
5.21
F1NFP5
Arginine--tRNA ligase, cytoplasmic GN=RARS
184.95
1.158
75.4
6.98
E1C4V1
ATP synthase-coupling factor 6, mitochondrial GN=ATP5J
111.05
1.157
12.5
9.33
Q90595
Transcription factor MafF GN=MAFF
37.09
1.155
16.6
9.74
R4GJF8
TAR DNA-binding protein 43 GN=TARDBP
131.97
1.155
42.2
6.19
Q6B7Z6
Polymyositis/scleroderma autoantigen 1 GN=EXOSC9
0.00
1.151
49.3
5.54
E1C7X8
S-adenosylmethionine synthase GN=LOC427292
14.44
1.150
43.2
6.62
Cluster 2: Tendency to down-regulation (42)
R4GKA6
Collagen alpha-2(VI) chain GN=COL6A2
181.18
0.850
102.4
5.48
E1BXS2
Guanine nucleotide-binding protein G(i) subunit alpha-1 GN=GNAI1
147.69
0.850
40.4
5.97
Q90927
Nuclear factor 1 GN=cNFI-A4
0.00
0.850
54.6
8.31
E1BUI0
tRNA pseudouridine synthase (Fragment) GN=PUSL1
55.04
0.849
33.7
9.64
Q90617-3
Isoform LAMP-2C of Lysosome-associated membrane glycoprotein 2
149.29
0.849
46.4
6.43
GN=LAMP2
Q90733
COUP transcription factor 2 GN=NR2F2
0.00
0.848
45.4
8.28
P12957-2
Isoform Brain l-cad of Caldesmon GN=CALD1
0.00
0.847
58.8
8.44
F1N9D8
Cathepsin B GN=CTSB
133.09
0.847
37.6
5.86
A4GTP0
Galectin
66.44
0.846
25.7
8.27
Q08392
Glutathione S-transferase
108.74
0.846
25.3
8.88
F1N965
Frizzled-7 GN=FZD7
0.00
0.842
62.7
7.99
E1C3U7
Lysyl oxidase homolog 2 GN=LOXL2
0.00
0.842
86.9
6.49
F1NGX1
Integrin alpha-V GN=ITGAV
172.35
0.841
114.3
5.58
E1BRJ4
DNA-directed RNA polymerase GN=POLR3B
0.00
0.840
127.4
8.54
Q8AXV1
Endophilin-A1 GN=SH3GL2
65.55
0.839
39.9
5.47
F1NMF6
Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1 GN=PLOD1
198.96
0.837
84.3
6.74
F1P2F0
Collagen alpha-3(VI) chain GN=COL6A3
642.87
0.836
339.4
6.68
R4GFM0
FERM, RhoGEF and pleckstrin domain-containing protein 1 GN=FARP1
0.00
0.835
119.5
8.15
11
F1N8G4
Diphthamide biosynthesis protein 2 GN=DPH2
0.00
0.834
52.1
5.54
H9L0H3
Alpha-actinin-4 (Fragment) GN=ACTN4
515.20
0.834
71.6
6.09
Q5F4B1
Phosphoglycolate phosphatase GN=PGP
39.98
0.833
33.0
5.73
P56673
Pituitary homeobox 1 GN=PITX1
20.32
0.833
34.5
9.11
Q90611
72 kDa type IV collagenase GN=MMP2
267.53
0.829
74.9
5.49
F1NME2
Integrin beta GN=ITGB5
51.04
0.828
88.4
6.71
F1NBZ7
Serine/threonine-protein phosphatase GN=PPP3CA
0.00
0.827
60.6
5.83
P51890
Lumican GN=LUM
44.33
0.826
38.6
6.52
B3TZC1
PNPLA7 GN=PNPLA7
0.00
0.821
147.6
7.17
Q5ZLG0
Acetoacetyl-CoA synthetase GN=AACS
189.47
0.818
74.3
6.49
F1NLD4
Inhibitor of nuclear factor kappa-B kinase subunit alpha GN=CHUK
0.00
0.817
86.1
6.32
F1NFE0
Collagen alpha-1(VI) chain GN=COL6A1
110.52
0.812
107.9
5.90
F1N9N4
Stathmin-3 GN=NPC2
34.96
0.808
16.2
6.51
F1NPX5
SH3 domain-binding glutamic acid-rich-like protein (Fragment)
106.54
0.802
12.9
4.88
GN=SH3BGRL
P01038
Cystatin
39.09
0.799
15.3
7.69
Q8QG94
Suppressor of fused GN=SUFU
46.78
0.786
53.7
5.33
Q90Y35-2
Isoform 2 of Zinc finger protein 622 GN=ZNF622
0.00
0.783
42.6
6.65
F1NMZ3
Hemoglobin subunit epsilon GN=HBE
37.75
0.761
16.6
8.91
Q5ZK77
Biogenesis of lysosome-related organelles complex 1 subunit 5
34.23
0.759
22.6
7.06
GN=BLOC1S5
F1P0D2
Glutamine synthetase (Fragment) GN=LOC417253
0.00
0.758
44.9
7.02
F1NJT4
Fibronectin GN=FN1
1373.06
0.752
259.0
6.11
Q155F6
Tumor necrosis factor-inducible protein 6 GN=TNFIP6
44.59
0.747
30.7
6.02
F1NJT3
Fibronectin GN=FN1
1373.06
0.727
273.1
5.64
O93532
Keratin, type II cytoskeletal cochleal
95.26
0.693
53.8
6.10
187
188
Fold change=infected/control. Fold change >1 indicates up regulation, and fold change <1
189
indicates down regulation.
12
190
3.3. Functional classifications of the identified proteins
191
To annotate the functions of the 75 significantly changed proteins identified in our
192
study, the proteins were submitted to GORetriever (http://www.agbase.msstate.edu/)
193
for analysis. Three types of annotations were obtained using the website: molecular
194
functions, biological processes and cellular components.
195
The biological process annotation revealed that the significantly changed proteins
196
were involved in metabolic process (19%), macromolecule metabolic process (12%),
197
regulation
198
nucleobase-containing compound metabolic process (10%), response to stimulus (7%),
199
and various other activities (31%) (Fig. 2, biological process).
of
biological
process
(11%),
biosynthetic
processes
(10%),
200
The molecular function annotation revealed that these differently expressed
201
proteins were involved in protein binding (30%), nucleic acid binding (21%),
202
hydrolase activity (11%), transferase activity (5%), receptor activity (5%),
203
oxidoreductase activity (4%), and various other activities (24%) (Fig. 2, molecular
204
function).
205
The cellular component annotation revealed that the altered proteins were
206
associated with the following cellular components: intracellular (28%), cytoplasm
207
(24%), nucleus (17%), membrane (15%), extracellular region (5%), chromosome (3%)
208
and various others (8%) (Fig. 2, cellular component).
13
209
210
211
Fig. 2. Functional annotation of the differently expressed proteins according to their
212
biological process, molecular function and cellular component.
213
214
3.4. Validation of the iTRAQ data by real-time RT-PCR
215
To confirm the results of the differentially expressed proteins identified by iTRAQ
216
LC-MS/MS analysis, real-time RT-PCR was performed to detect the transcript
217
expression levels of the genes after ALV-J infection. We generated four standard
218
curves to determine the gene expression of BLOC1S5, keratin, HMG14 and AACS in
219
ALV-J-infected and mock- infected DF-1 cells. The results showed that HMG14 was
220
up-regulated (Fig. 3), whereas BLOC1S5, AACS and keratin were down-regulated
221
(Fig. 3). The RT-PCR results were consistent with the results of the iTRAQ
222
LC-MS/MS analysis (Table 2), confirming that the iTRAQ data were reliable.
14
223
224
Fig. 3 Transcriptional profiles of the significantly changed proteins in ALV-J-infected
225
DF-1 cells. The error bars represent the standard deviations.
226
227
3.5. Protein-protein interaction analysis
228
The mechanism through which the virus interacts with host cells remains unclear,
229
and oncogenicity is an important index of the pathogenicity of ALV-J. During virus
230
infection, some proteins of host cells may be significantly changed. As a result, the
231
functions of the changed proteins will also be altered. In our study, we aimed to
232
determine whether the significantly changed proteins that were identified have some
233
relationship with apoptosis or ALV-J-induced oncogenicity. We searched the STRING
234
database to analyze the protein-protein interactions between the differently expressed
235
proteins and PARK7, PTENP1, AKT1, PIK3CA (PI3K), and VDAC (Fig. 4). These
236
proteins are known to have some relationship with tumor-associated process and
15
237
apoptosis. The protein-protein interaction networks may provide valuable information
238
to further investigate the possible mechanism of ALV-J-induced oncogenicity.
239
240
Fig. 4 The protein-protein interaction between the identified proteins and the tumor-
241
or apoptosis-associated proteins analyzed by the STRING software. An edge was
242
drawn with up to seven differently colored lines, representing the existence of the
243
seven types of evidence used for predicting the associations: a red line indicates the
244
presence of fusion evidence; a yellow line indicates text mining evidence; a purple
245
line indicates experimental evidence; a blue line indicates co-occurrence evidence; a
16
246
light blue line indicates database evidence; a green line indicates neighborhood
247
evidence; a black line indicates coexpression evidence.
248
4. Discussion
249
Proteomics is a relatively novel technology that has been used for the detection of
250
the host cellular proteins response to virus infection [16, 17]. Isobaric tags for relative
251
and absolute quantification (iTRAQ) combined with multidimensional liquid
252
chromatography (LC) and tandem MS analysis are a powerful tool for quantitative
253
proteomic analysis that has been widely applied in many studies [18-20]. In this study,
254
we first applied the iTRAQ approach to identify the differential protein expression
255
profiles of DF-1 cells infected with ALV-J. Using the iTRAQ LC-MS/MS technology,
256
the significantly changed proteins were mostly associated with metabolic process,
257
signal transducer activity, cell cytoskeleton, oxidoreductase activity, response to
258
stimulus and immune responses. In addition, some apoptosis and tumor-associated
259
proteins (VEGF-A, ACTN4 and METAP2) were also identified by the iTRAQ
260
LC-MS/MS technology.
261
4.1. Alterations of tumor-associated proteins
262
Vascular endothelial growth factor A (VEGF-A) is an important inducer of
263
angiogenesis [21]. As has been shown in many reports, up-regulated VEGF-A can
264
induce tumor formation via some unique signaling pathways [22-23]. In addition,
265
VEGF-A, which is known as a positive regulator, contributes to tumor growth and
266
promotes tumor formation [24-25]. Previous studies described a threshold level of
267
proteins to promote tumorigenesis, which indicated the expression level of one protein
268
needs to reach the threshold level before promoting tumorigenesis [26-27]. Studies in
17
269
our lab showed that the increased replication of ALV-J increased the expression of
270
VEGF-A, indicating an increased opportunity for ALV-J to push the expression level
271
of VEGF-A to reach the threshold level to promote tumorigenesis [27]. In this study,
272
we found that VEGF-A is over-expressed in DF-1 cells after infection with ALV-J.
273
The results further suggested that VEGF-A is closely associated with ALV-J- induced
274
tumorigenesis and may also suggest a novel molecular mechanism for better
275
understanding of the higher oncogenicity of ALV-J.
276
Alpha-actinins (ACTNs) were classified into cytoskeleton proteins, while ACTN4
277
has some other unique functions, such as signal transduction, protein expression
278
regulation and nuclear transport. Histological analyses of cancer tissues showed a
279
strong correlation between ACTN4 expression and tumorigenesis in several types of
280
cancers [28-30]. Furthermore, up-regulated ACTN4 in cancer cells has been suggested
281
as a biomarker for drug resistance and malignant cell invasion [31-34]. Previous
282
studies showed that ALV-J infection in DF-1 cells led to rapid increase in Akt
283
phosphorylation and the phosphorylation of Akt was PI3K-dependent [35]. PI3K/Akt
284
pathway also regulates viral replication of ALV-J [35]. Furthermore, AKT interacts
285
with ACTN4 and ACTN4 is a functional partner of AKT [36]. Therefore, the
286
up-regulated ACTN4 observed in this study may be associated with tumorigenesis
287
induced by ALV-J through PI3K/Akt pathway. This may provide useful information to
288
elucidate the mechanism of ALV-J induced tumorigenesis and may also become
289
potential therapeutic targets to control ALV-J infections.
290
METAP2 was considered to have some relationship with angiogenesis inhibition
18
291
[37]. In addition, METAP2 can block B cell differentiation into plasma cells [38].
292
Some viruses, whose primary target cells are B cells, can clinically induce tumor
293
formation. Therefore, down-regulation of METAP2 in this study may influence the
294
function of B cells, which may provide evidences to explain why ALV-J infection can
295
result in immune suppression and tumorigenesis.
296
4.2. Redox regulation
297
Peroxiredoxins (PRDXs), a family of peroxidases as antioxidant enzymes, can
298
support tumor maintenance and survival through protecting cells from apoptosis
299
induced by oxidative stress [39-41]. Previous study indicated that liver cells
300
transfected with PRDX6 siRNA resulted in an increase in peroxide- induced
301
cytotoxicity by apoptosis, which implies that decrease of PRDX6 promotes apoptosis
302
[42]. Therefore, down-regulated PRDX6 in this study suggests that ALV-J infection
303
may weaken the anti-apoptotic function of PRDX6.
304
In addition, PRDX1 was found to be up-regulated in this study. Previous study
305
indicated that the mice which lacking PRDX1 have several malignant cancers,
306
including sarcomas, carcinomas and lymphomas [43]. These malignancies are
307
associated with low expression of PRDX1, which suggests that PRDX1 may function
308
as a tumor suppressor [43]. Studies also indicated that PRDX1 interacts with the
309
c-Myc oncogene and can inhibit its transcriptional activity [44] and high expression of
310
PRDX1 appears to be associated with less aggressive breast cancers [45]. Therefore,
311
up-regulation of PRDX1 in this study may result from the defense of host cells
312
responses to the ALV-J infection.
19
313
4.3. Cytoskeleton proteins and ALV-J infection
314
Cytoskeleton proteins are involved in the maintenance of cell morphology,
315
regulation of protein synthesis, endocytosis, cell movement, and cell- to-cell
316
attachment [46-47]. As determined through iTRAQ LC-MS/MS analysis, some
317
cytoskeleton proteins were identified to be significantly changed in DF-1 cells after
318
infection with ALV-J. Isoform 2 of the F-actin-capping protein subunit beta isoforms
319
1 and 2 (CAPZB) can regulate the growth of actin filaments, and actin filaments play
320
a vital role in the maintenance of cell morphology [48]. Furthermore, actin-related
321
protein 3 (ACTR3) and actin-related protein 5 (ACTR5) were also found to be
322
changed. The low expression of these proteins revealed that the cytoskeletal proteins
323
were disrupted during infection with ALV-J. In addition, the differential expression of
324
these proteins may be due to the interaction between the virus and host cellular
325
proteins after infection with ALV-J.
326
It has been reported that keratins have become the standard detection marker for
327
tumor cells and were also the most common marker to identify tumor cells [49].
328
Previous studies showed that before tumor cells got the ability to migrate and invade
329
the host, they need undergo epithelial–mesenchymal transition, during which process
330
the cytoskeletons are rearranged and epithelial markers, such as keratins, claudins and
331
E-cadherin are observed to be down-regulated [49-52]. Immunohistochemical
332
analysis showed low expression of keratin was associated with a higher tumor grade
333
in breast cancer [52]. Previous study indicated that acetoacetyl-CoA synthetase
334
(AACS) was found in tumor tissues and plays important roles in metabolic processes
20
335
of tumors [53]. Whether or not the down-regulated keratin and AACS in this study
336
were associated with tumorigenesis induced by ALV-J infection needs to be further
337
investigated.
338
Conclusions
339
In summary, our study was the first to use iTRAQ LC-MS/MS to detect cellular
340
responses to ALV-J infection in DF-1 cells. A total of 75 significantly changed
341
proteins were identified. These differently expressed proteins may provide useful
342
information for elucidating the molecular mechanism underlying the interaction
343
between ALV-J and DF-1 cells and will also facilitate our understanding of the
344
pathogenesis of ALV-J infection.
345
Acknowledgments
346
This study was funded by the National Natural Science Foundation of China
347
(31372437 and 31201923) and the earmarked fund for the Modern Agro- industry
348
Technology Research System (no. nycytx-42-G3-01).
349
Conflict of interest
350
The authors declare no conflict of interest.
351
Supplementary data
352
Table S1
353
by the iTRAQ-LC–MS /MS analysis.(DOCX)
354
References
355
[1] Payne, L.N., Howes, K., Gillespie, AM., Smith, L.M. Host range of Rous sarcoma
356
virus pseudotype RSV (HPRS-103) in 12 avian species: support for a new avian
Differentially expressed proteins in ALV-J-infected DF-1 cells identified
21
357
retrovirus envelope subgroup, designated J. J Gen Virol. 1992; 73(Pt 11): 2995–2997.
358
[2] Payne L.N., Gillespie A.M., Howes K. Recovery of acutely transforming viruses
359
from myeloid leukosis induced by the HPRS-103 strain of avian leukosis virus. Avian
360
Dis. 1993; 37(2):438–450.
361
[3] Witter R. Avian tumor viruses: persistent and evolving pathogens. Acta Vet Hung.
362
1997; 45(3): 261–266.
363
[4] Du Y., Cui Z., Qin A. Subgroup J of avian leukosis viruses in China. China Poult.
364
Sci. 1999; 3: 1-4.
365
[5] Narayan R., Gangadharan B., Hantz O., Antrobus R., Garcia A., Dwek R.A.,
366
Zitzmann N. Proteomic analysis of HepaRG cells: a novel cell line that supports
367
hepatitis B virus infection. J Proteome Res. 2009; 8(1): 118–122.
368
[6] Alfonso P., Rivera J., Hernaez B., Alonso C., Escribano J.M. Identification of
369
cellular proteins modified in response to African swine fever virus infection by
370
proteomics. Proteomics 2004; 4 (7): 2037– 2046.
371
[7] Toda T., Sugimoto M., Omori A., Matsuzaki T., Furuichi Y., Kimura N. Proteomic
372
analysis of Epstein–Barr virus-transformed human B- lymphoblastoid cell lines before
373
and after immortalization. Electrophoresis. 2000; 21(9): 1814–1822.
374
[8] Sun J., Jiang Y., Shi Z., Yan Y., Guo H., He F., Tu C. Proteomic alteration of
375
PK-15 cells after infection by classical swine fever virus. J Proteome Res. 2008; 7(12):
376
5263 – 5269.
377
[9] Kvaratskhelia, M., Clark, P.K., Hess, S., Melder, D.C., Federspiel, M.J., Hughes,
378
S.H. Identification of glycosylation sites in the SU component of the Avian
22
379
Sarcoma/Leukosis virus Envelope Glycoprotein (Subgroup A) by mass spectrometry.
380
Virology. 2004; 326 (1):171–181.
381
[10] Ross P.L., Huang Y.N., Marchese J.N., Williamson B., Parker K., Hattan S., et al.
382
Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive
383
isobaric tagging reagents. Mol Cell Proteomics. 2004; 3(12):1154–1169.
384
[11] Seggerson K., Tang L., Moss E.G. Two genetic circuits repress the
385
Caenorhabditis elegans heterochronic gene lin-28 after translation initiation. Dev Biol.
386
2002;15: 243(2):215–225.
387
[12] Bradford M.M. A rapid and sensitive method for the quantitation of microgram
388
quantities of protein utilizing the principle of protein–dye binding. Analytical
389
Biochemistry. 1976; 72: 248–254.
390
[13] Liu J., Bai j., Lu Q., Zhang L., Jiang Z., Michal J.J., He Q., Jiang P.
391
Two-Dimensional Liquid Chromatography–Tandem Mass Spectrometry Coupled with
392
Isobaric Tags for Relative and Absolute Quantification(iTRAQ) Labeling Approach
393
Revealed First Proteome Profiles of Pulmonary Alveolar Macrophages Infected with
394
pulmonary alveolar macrophages infected with porcine circovirus type 2. J
395
Proteomics. 2013; 79: 72-86.
396
[14] Thanthrige-Don N., Abdul-Careem M.F., Shack L.A., Burgess S.C., Sharif S.
397
Analyses of the spleen proteome of chickens infected with Marek’s disease virus.
398
Virology. 2009; 390(2): 356–367.
399
[15] Liu J., Bai j., Lu Q., Zhang L., Jiang Z., Michal J.J., He Q., Jiang P.
400
Two-Dimensional Liquid Chromatography–Tandem Mass Spectrometry Coupled with
23
401
Isobaric Tags for Relative and Absolute Quantification(iTRAQ) Labeling Approach
402
Revealed First Proteome Profiles of Pulmonary Alveolar Macrophages Infected with
403
Porcine Reproductive and Respiratory Syndrome Virus. J Proteome Res. 2013; 11(5):
404
2690 – 903.
405
[16] Maxwell K.L., Frappier L. Viral proteomics. Microbiol Mol Biol Rev. 2007;71:
406
398– 411.
407
[17] Zheng X., Hong L., Shi L., Guo J., Sun Z., Zhou J. Proteomics analysis of host
408
cells infected with infectious bursal disease virus. Mol Cell Proteomics. 2008; 7(3):6
409
12–25.
410
[18] Seshi B. An integrated approach to mapping the proteome of the human bone
411
marrow stromal cell. Proteomics. 2006; 6(19): 5169-5182.
412
[19] Zhang H., Lv L., Liu H., Cui L., Chen G., Bi P., Li Z. Profiling the potential
413
biomarkers for cell differentiation of pancreatic cancer using iTRAQ and 2-D
414
LC-MS/MS. Proteomics Clin Appl. 2009; 3(7): 862-871.
415
[20] Muraoka S., Kume H., Watanabe S., Adachi J., Kuwano M., Sato M., Kawasaki
416
N., Kodera Y., Ishitobi M., Inaji H., Miyamoto Y., Kato K., Tomonaga T. Strategy for
417
SRM-based verification of biomarker candidates discovered by iTRAQ method in
418
limited breast cancer tissue samples. J Proteome Res. 2012; 11(8): 4201-4210.
419
[21] Takahashi S. Vascular endothelial growth factor (VEGF), VEGF receptors and
420
their inhibitors for antiangiogenic tumor therapy. Biol Pharm Bull. 2011;
421
34(12):1785–1788.
422
[22] Catalano A., Romano M., Martinotti S., Procopio A. Enhanced expression of
24
423
vascular endothelial growth factor (VEGF) plays a critical role in the tumor
424
progression potential induced by simian virus 40 large T antigen. Oncogene.
425
2002;21(18):2896 –2900.
426
[23] Ohtani K., Usuda J., Ichinose S., Ishizumi T., Hirata T., et al. High expression of
427
GADD-45alpha and VEGF induced tumor recurrence via upregulation of IL-2 after
428
photodynamic therapy using NPe6. Int J Oncol. 2008;32(2):397– 403.
429
[24] Lorenzon E., Colladel R., Andreuzzi E., Marastoni S., Todaro F., Schiappacassi
430
M., Ligresti G., Colombatti A., Mongiat M. MULTIMERIN2 impairs tumor
431
angiogenesis and growth by interfering with VEGF-A/VEGFR2 pathway. Oncogene.
432
2012; 31(26):3136–3147.
433
[25] Oka N., Soeda A., Inagaki A., Onodera M., Maruyama H., Hara A., Kunisada T.,
434
Mori H., Iwama T. VEGF promotes tumorigenesis and angiogenesis of human
435
glioblastoma stem cells. Biochem Biophys Res Commun .2007;360(3):553–559.
436
[26] Li Q., Ishikawa T.O., Oshima M., Taketo M.M. The threshold level of
437
adenomatous polyposis coli protein for mouse intestinal tumorigenesis. Cancer Res.
438
2005; 65(19):8622–8627.
439
[27] Wang Q., Gao Y.L., Wang Y.Q., Qin L.T., Qi X.L., Qu Y., Gao H.L., Wang X.M..
440
A 205-nucleotide deletion in the 3' untranslated region of avian leukosis virus
441
subgroup J, currently emergent in China, contributes to its pathogenicity. J Virol.
442
2012;86(23):12849-60.
443
[28] Honda K., Yamada T., Hayashida Y., Idogawa M., Sato S., Hasegawa F., Ino Y.,
444
Ono M., Hirohashi S. Actinin-4 increases cell motility and promotes lymph node
25
445
metastasis of colorectal cancer. Gastroenterology. 2005;128(1):51–62.
446
[29] Welsch T., Keleg S., Bergmann F., Bauer S., Hinz U., Schmidt J. Actinin-4
447
expression in primary and metastasized pancreatic ductal adenocarcinoma. Pancreas.
448
2009; 38(8):968–976.
449
[30] Yamamoto S., Tsuda H., Honda K., Kita T., Takano M., Tamai S., Inazawa J.,
450
Yamada T., Matsubara O. Actinin-4 expression in ovarian cancer: A novel prognostic
451
indicator independent of clinical stage and histological type. Modern Pathology. 2007;
452
20(12):1278–1285.
453
[31] Fellenberg J., Dechant M.J., Ewerbeck V., Mau H. Identification of
454
drug-regulated genes in osteosarcoma cells. International Journal of Cancer. 2003;
455
105(5):636–643.
456
[32] He J., Whelan S.A., Lu M., Shen D., Chung D.U., Saxton R.E., Faull K.F.,
457
Whitelegge J.P., Chang H.R. Proteomic-based biosignatures in breast cancer
458
classification and prediction of therapeutic response. International Journal of
459
Proteomics. 2011; 2011:1–16.
460
[33] Kikuchi S., Honda K., Tsuda H., Hiraoka N., Imoto I., Kosuge T., et al.
461
Expression and gene amplification of actinin-4 in invasive ductal carcinoma of the
462
pancreas. Clinical Cancer Research. 2008;14(17):5348–5356.
463
[34] Zhou C., Zhong Q., Rhodes L.V., Townley I., Bratton M.R., Zhang Q., et al.
464
Proteomic analysis of acquired tamoxifen resistance in MCF-7 cells reveals
465
expression signatures associated with enhanced migration. Breast Cancer Research.
466
2012; 14(2):R45.
26
467
[35] Feng S.Z., Cao W.S., Liao M. The PI3K/Akt pathway is involved in early
468
infection of some exogenous avian leukosis viruses. Journal of General Virology.
469
2011;92:1688-1697.
470
[36] Ding Z., Liang J., Lu Y., Yu Q., Songyang Z., Lin S.Y., Mills G.B. A
471
retrovirus-based
472
AKT1-binding partners. Proc Natl Acad Sci USA. 2006;103(41):15014-9.
473
[37] Selvakumar P., Lakshmikuttyamma A., Dimmock J.R., Sharma R.K.
474
Methionine aminopeptidase 2 and cancer. Biochim Biophys Acta. 2006;1765(2):
475
148–154.
476
[38] Priest R.C., Spaull J., Buckton J., Grimley R.L., Sims M., Binks M., Malhotra R.
477
Immunomodulatory activity of a methionine aminopeptidase-2 inhibitor on B cell
478
differentiation. Clinical and Experimental Immunology.2009;155(3):514–522.
479
[39] Kim S.Y., Jo H.Y., Kim M.H., Cha Y.Y., Choi S.W., et al. H2O2-dependent
480
hyperoxidation of peroxiredoxin 6 (Prdx6) plays a role in cellular toxicity via
481
up-regulation of iPLA2 activity. J Biol Chem. 2008; 283: 33563–33568.
482
[40] Fatma N., Kubo E., Sen M., Agarwal N., Thoreson W.B., et al. Peroxiredoxin 6
483
delivery attenuates TNF-alpha-and glutamate-induced retinal ganglion cell death by
484
limiting ROS levels and maintaining Ca2+ homeostasis. Brain Res. 2008;1233:
485
63–78.
486
[41] Neumann C.A., Fang Q. Are peroxiredoxins tumor suppressors? Curr Opin
487
Pharmacol. 2007;7: 375–380.
488
[42] Walsh B., Pearl A., Suchy S., Tartaglio J., Visco K., et al. Overexpression of
protein
complementation
assay
screen
reveals
functional
27
489
Prdx6 and resistance to peroxide- induced death in Hepa1-6 cells: Prdx suppression
490
increases apoptosis. Redox Rep. 2009;14:275–284.
491
[43] Neumann C.A., Krause D.S., Carman C.V., Das S., et al. Essential role for the
492
peroxiredoxin Prdx1 in erythrocyte antioxidant defence and tumour suppression.
493
Nature. 2003;424(6948):561-5.
494
[44] Egler R.A., Fernandes E., Rothermund K., Sereika S., de Souza-Pinto N., Jaruga
495
P., Dizdaroglu M., Prochownik E.V. Regulation of reactive oxygen species, DNA
496
damage, and c-Myc function by peroxiredoxin 1. Oncogene. 2005;24:8038–8050.
497
[45] Pei-Jou Chua E-HL, Chunhua G., George W-C Y, Puay-Hoon T., Boon-Huat B.
498
Clinicopathological correlation of peroxiredoxin I in breast cancer. In Proceedings of
499
the World Medical Conference. Prague, Czech Republic: 2011.
500
[46] Radulovic M., Godovac-Zimmermann J. Proteomic approaches to understanding
501
the role of the cytoskeleton in host-defense mechanisms. Expert Rev Proteomics.
502
2011;8(1):117–126.
503
[47] Zou W., Ke J., Zhang A., Zhou M., Liao Y., Zhu J., Zhou H., Tu J., Chen H., Jin
504
M. Proteomics analysis of differential expression of chicken brain tissue proteins in
505
response to the neurovirulent H5N1 avian influenza virus infection. Journal of
506
Proteome Research. 2010;9(8):3789–3798.
507
[48] Casella J.F., Maack D.J., Lin S. Purification and initial characterization of a
508
protein from skeletal muscle that caps the barbed ends of actin filaments. J Biol
509
Chem.1986;261(23): 10915–10921.
510
[49] Joosse S.A., Hannemann J., Spötter J., Bauche A., Andreas A., Müller V., Pantel
28
511
K. Changes in keratin expression during metastatic progression of breast cancer:
512
impact on the detection of circulating
513
2012;18(4):993-1003.
514
[50] Thiery J.P., Sleem an J.P. Complex networks orchestrate epithelial- mesenchymal
515
transitions. Nat Rev Mol Cell Biol. 2006; 7(2):131– 42.
516
[51] Radisky D.C. Epithelial- mesenchymal transition. J Cell Sci. 2005;118:4325– 6.
517
[52] Willipinski-Stapelfeldt B., Riethdorf S., Assmann V., Woelfle U., Rau T., Sauter
518
G., Heukeshoven J., Pantel K. Changes in cytoskeletal protein composition indicative
519
of an epithelial- mesenchymal transition in human micrometastatic and primary breast
520
carcinoma cells.Clin Cancer Res. 2005;11(22):8006-14.
521
[53] Tisdale M.J. Role of acetoacetyl-CoA synthetase in acetoacetate utilization by
522
tumor cells. Cancer Biochem Biophys. 1984 ;7(2):101-107.
tumor
cells.
Clin
Cancer
Res.
523
29
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