normal and cancerous human colorectal tissues withgel-assisted digestion and iTRAQ labeling mass spectrometry Jinn-Shiun Chen1,2, Kuei-Tien Chen3, Chung-Wei Fan2,4, Chia-Li Han5, Yu-Ju C
Trang 1normal and cancerous human colorectal tissues with
gel-assisted digestion and iTRAQ labeling mass
spectrometry
Jinn-Shiun Chen1,2, Kuei-Tien Chen3, Chung-Wei Fan2,4, Chia-Li Han5, Yu-Ju Chen5, Jau-Song Yu6, Yu-Sun Chang7, Chih-Wei Chien5, Chien-Peng Wu5, Ray-Ping Hung3and Err-Cheng Chan3
1 Department of Surgery, Chang Gung Memorial Hospital, Tao Yuan, Taiwan
2 College of Medicine, Chang Gung University, Tao Yuan, Taiwan
3 Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Tao Yuan, Taiwan
4 Department of Colorectal Surgery, Chang Gung Memorial Hospital, Keelung, Taiwan
5 Institute of Chemistry, Academia Sinica, Taipei, Taiwan
6 Department of Cell and Molecular Biology, Chang Gung University, Tao Yuan, Taiwan
7 Molecular Medicine Research Center, Chang Gung University, Tao Yuan, Taiwan
Introduction
Colorectal cancer (CRC) remains one of the most
prevalent cancers in the western world and the third
highest cause of cancer mortality in Taiwan [1] CRC
is thought to evolve into invasive cancer from
adeno-Keywords
biomarker; colorectal cancer; mass
spectrometry; membrane protein; proteomic
profile
Correspondence
E.-C Chan, Department of Medical
Biotechnology and Laboratory Science,
Chang Gung University, 259 Wen-Hua 1st
Road, Kweishan, Taoyuan, Taiwan, China
Fax: +886 3 2118741
Tel: +886 3 2118800 (ext 5220)
E-mail: chanec@mail.cgu.edu.tw
Note
Jinn-Shiun Chen, Kuei-Tien Chen and
Chung-Wei Fan contributed equally to this
article
(Received 12 January 2010, revised 9 April
2010, accepted 17 May 2010)
doi:10.1111/j.1742-4658.2010.07712.x
The aim of this study was to uncover the membrane protein profile differ-ences between colorectal carcinoma and neighboring normal mucosa from colorectal cancer patients Information from cellular membrane proteomes can be used not only to study the roles of membrane proteins in fundamen-tal biological processes, but also to discover novel targets for improving the management of colorectal cancer patients We used solvent extraction and a gel-assisted digestion method, together with isobaric tags with related and absolute quantitation (iTRAQ) reagents to label tumoral and adjacent normal tissues in a pairwise manner (n = 8) For high-throughput quantifi-cation, these digested labeled peptides were combined and simultaneously analyzed using LC-MS⁄ MS Using the shotgun approach, we identified a total of 438 distinct proteins from membrane fractions of all eight patients After comparing protein expression between cancerous and corresponding normal tissue, we identified 34 upregulated and eight downregulated pro-teins with expression changes greater than twofold (Student’s t-test,
P< 0.05) Among these, the overexpression of well-established biomarkers such as carcinoembryonic antigens (CEACAM5, CEACAM6), as well as claudin-3, HLA class I histocompatibility antigen A-1, tapasin and mito-chondrial solute carrier family 25A4 were confirmed by western blotting
We conclude that gel-assisted digestion and iTRAQ labeling MS is a poten-tial approach for uncovering and comparing membrane protein profiles of tissue samples that has the potential to identify novel biomarkers
Abbreviations
CEA, carcinoembryonic antigen-related cell adhesion molecule 5; CLDN, claudin; CLDN3, claudin-3; CLDN4, cluadin-4; CRC, colorectal carcinoma; HLA, human leukocyte antigen; HLA-A1, HLA class I histocompatibility antigen A-1; iTRAQ, isobaric tags with related and absolute quantitation; SLC25A4, mitochondrial solute carrier family 25A4; TAPBP, tapasin.
Trang 2matous polyps by acquired mutations in various genes
[2] Development from adenoma into carcinoma takes
5–15 years, and there is therefore plenty of
opportu-nity for early intervention Approximately half of
patients diagnosed with colorectal cancer die within
5 years of diagnosis, although an early diagnosis
sig-nificantly improves patients’ outcomes Unfortunately,
few biomarkers are available for CRC analyses and
none is sufficiently sensitive for screening purposes [3]
Therefore, it is of great interest to identify proteins
whose levels are consistently altered in CRC, both to
improve the diagnosis and monitoring of CRC patients
and because their function may reveal insight into
critical events in tumorigenesis
Various proteomic technologies have been used to
search for new biomarkers in colorectal cancer [4–10]
There is increasing interest in sample prefractionation
to reduce proteome complexity and gain deeper insight
into the proteome This strategy is particularly useful
for low-abundance proteins such as membrane
pro-teins Membrane proteins account for 30% of the
proteome and play critical roles in many biological
functions such as cell signaling, cell–cell interactions,
communication, transport mechanisms and energy [11]
Information from membrane proteomes will help us
understand the role of these proteins in fundamental
biological processes, and it may also help us discover
novel targets for biomedical therapeutics to improve
patient management during pathogenesis [12] Thus,
global analysis of membrane proteins in CRC may
provide an important source of diagnostic or
prognos-tic markers such as carcinoembryonic antigen-related
cell adhesion molecule 5 (CEA)
Although high-throughput proteomic technologies
can provide comprehensive analyses of soluble proteins,
the analysis of membrane proteins has lagged behind
because of their low concentration and high
hydropho-bicity New tools and strategies are needed so that
membrane fractions from cancer cells can be screened
for candidate biomarkers In this study, we utilized a
technology combining gel-assisted digestion, isobaric
tags with related and absolute quantitation (iTRAQ)
labeling and LC-MS⁄ MS for quantitative analysis of
the membrane proteome of colorectal tissue In brief,
membrane proteins were solubilized with various types
of detergents at high concentrations and subsequently
incorporated into polyacrylamide gels without
electro-phoresis Excess detergent was removed prior to protein
digestion so that it would not interfere with the
LC-MS⁄ MS analysis In addition, we also utilized a recently
developed and widely used multiplexed quantitation
strategy based on iTRAQ isobaric reagents [13–15] The
iTRAQ labeling strategy offers enhanced identification
confidence and quantitation accuracy for proteomic research, especially for low-abundance proteins [16,17]
We used iTRAQ labeling together with gel-assisted digestion and mass spectrometry to detect differences
in the protein expression profiles of membrane frac-tions from tumoral and adjacent normal mucosa from colorectal cancer patients Differentially expressed pro-teins were identified by mass spectrometry and verified
by western blotting Initial validation studies confirmed the expression of claudin-3 (CLDN3) as a tumor-asso-ciated antigen in colorectal cancer We also uncovered some candidates, such as HLA class I histocompatibil-ity antigen A-1 (HLA-A1), tapasin (TAPBP) and mito-chondrial solute carrier family 25A4 (SLC25A4), as potential biomarkers for monitoring CRC
Results
Quantitative analysis of membrane proteins from paired tumoral and adjacent normal tissue of CRC patients
A total of eight tumor tissues and eight matched normal tissues were collected from eight CRC patients (Table S1) and protein expression was compared between each tumor and adjacent normal tissue using LC-MS⁄ MS analysis (Fig 1) In our previous study using the same proteomic platform, quantitation of four independently purified membrane fractions from HeLa cells gave high accuracy (< 8% error) and precision (< 12% relative SD), demonstrating a high degree of consistency and reproducibility of this quantitation platform [18] We used the same quantitative strategy to enhance identifi-cation confidence and quantitation accuracy for proteo-mic research A total of 438 proteins from both the tumor and normal tissue of eight patients was identified (false discovery rate = 2.25%) Figure 1 illustrates the flowchart for quantitative analysis of membrane pro-teins of the CRC samples and reveals 215, 299, 191 and
208 proteins from four 4-plex iTRAQ LC-MS⁄ MS experiments, respectively Statistical analysis of the expression level from eight CRC patients revealed changes in the expression of 42 proteins by more than twofold within 95% confidence levels (Student’s t-test;
P< 0.05) of individual variation Among the 42 identi-fied proteins, 34 were upregulated and eight were down-regulated (Table S2)
Differential protein expression analysis in CRC with hierarchical clustering
Cluster analysis was performed on our identified proteins to evaluate the relation between deregulated
Trang 3proteins and colorectal tissue samples and to identify
interesting protein expression clusters We initially
uncovered 438 proteins from eight CRC patients and
estimated their expression by comparing tumor tissues
with adjacent normal tissues By using a hierarchical
clustering analysis, a clear distinction of expression
patterns enabled the clustering of these proteins into
several characteristic profiles, which split the 438
proteins into two main clusters: either upregulated (in
red) or downregulated (in green) (Fig 2) In cluster
group 1, six proteins were notably downregulated in tumor tissues, including collagen I alpha-1 chain ()3.3-fold, P < 0.001), collagen I alpha-2 chain ()2.5-fold, P< 0.001), biglycan ()1.7-fold,
P = 0.12), mimecan ()2.1-fold, P < 0.05), actin of aortic smooth muscle ()2.0-fold, P < 0.05) and myo-sin-11 ()1.7-fold, P = 0.11) In cluster group 2, 46 proteins were notably upregulated in tumor tissues, including isoform 1 of surfeit locus protein 4 (2.8-fold, P < 0.05), ITGB2, VDAC1, ADP⁄ ATP translo-case 1 (SLC25A4; 2-fold, P< 0.05), HLA-A1, VDAC2 and VDAC3, among others Using cluster analysis with hierarchical partitioning of the expres-sion profiles of identified proteins, the results from cluster groups 1 and 2 confirmed 73.8% (31 ⁄ 42) of the previously selected differentially expressed proteins (more than twofold within 95% confidence levels nof individual variation; Table S2) and added other inter-esting candidates, such as cytochrome c oxidase sub-unit 7C, NADH-ubiquinone oxidoreductase chain 4
or microsomal glutathione S-transferase 3 as possible CRC markers For many of these proteins, there was
a remarkable homogeneity of upregulated or down-regulated expression across the eight pairs of CRC samples Moreover, there were different cluster groups
of proteins with less uniform patterns across the eight patients
Functional classification of proteins identified in CRC
Proteins identified by mass spectrometry were classified
by subcellular location and molecular function (Fig 3) To better understand the probable roles of the membrane proteomes in terms of their biological functions, the subcellular localization and molecular functions of the 438 identified proteins were classified using the gene ontology (GO) consortium The subcel-lular locations of these proteins are shown in Fig 3A
We analyzed a total of 438 proteins, and 51% were found to be membrane bound or membrane associ-ated Among these, 27% were shown to be in the plasma membrane, including CEACAM5, CEACAM6, VDAC1, VDAC3, isoform 1 of tapasin (TAPBP), SLC25A4, HLA-A1, CLDN3, ITGB2, Galectin-3 and keratin type II cytoskeletal 8, and 24% were shown to
be in organelle membranes (mitochondria or mem-brane-bound vesicles), including SEC11C, VDAC2 and cytochrome c oxidase subunit I It is unclear whether the differentially identified mitochondrial proteins are related to the disease or whether they are sampling artifacts Another 17% were shown to be in the extra-cellular space, including biglycan, collagen III alpha-1
Purification of membrane proteins from
adjacent non-tumor (N) and Tumor (T)
tissues of CRC patients
Gel-assisted digestion
iTRAQ labeling
LC-MS/MS analysis
Dataset A
Dataset B
19 30 9 52 23
22
18 22 10
Dataset C
Dataset D
iTRAQ Quantitation by Multi-Q
A-1
A-1
B-1
114 115 116 117
114 115 116 117 114 115 116 117
114 115 116 117
N T
N T N T N T N T N T N T N T
Fig 1 Methods for LC-MS ⁄ MS analysis and evaluation of
database search results Schematic describing the mixing of four
samples separately labeled with an iTRAQ tag onto the same run,
followed by simultaneous identification and quantification for data
analysis.
Trang 4chain, S100A8 and S100A9 Figure 3B shows the
molecular function categorization of the proteins
iden-tified in CRC patients Regarding major molecular
functions, the proteins were mostly associated with
binding functions (29.9%; S100A8, Galectin-3, keratin
type II cytoskeletal 8), transporter activity (17.1%;
VDAC1, VDAC2, VDAC3, TAPBP, SLC25A4) and
catalytic activity (12.8%; cathepsin G, mitochondrial
cytochrome c1 heme protein, component of pyruvate
dehydrogenase complex mitochondrial precursor) A
small number of proteins were also found associated
with structural molecule activity (collagen I alpha-1
chain, collagen I alpha-2 chain, tubulin beta chain),
molecular transducer activity (ITGB2,
interferon-induced transmembrane protein 1, integrin alpha-6 and
integrin alpha-M), signal transducer activity (S100A9,
HLA-A1, CLDN3) and motor activity For a few
proteins (19.4%), no molecular function has yet been
annotated
Validation of differentially expressed proteins in CRC patients by western blotting
To further validate the results obtained from the rela-tive compararela-tive expression studies with LC-MS⁄ MS,
we examined the expression status of several of the identified proteins using western blotting These repre-sentative proteins were selected based on changes of more than twofold in in their expression within the 95% confidence level (Student’s t-test; P < 0.05) of individual variation In cases where the antibodies were suitable for western blotting, we tested their reac-tivity with CRC samples as a means of verification Protein extracts from normal and tumoral tissues from another 16 patients were resolved by SDS⁄ PAGE and blotted onto poly(vinylidene difluoride) membranes (Table S2) Figure 4 shows a representative compilation of immunoblotting for these proteins These representative proteins included CLDN3,
COL1A1 COL1A2 BGN OGN ACTA2
SURF4 PRG2 SEC11C GPSN2 STT3A S100A8 ITGB2 TSPAN8 VDAC1 HLA-DRA TRAM1 LBR TMEM109 ANXA4 SLC25A4 HLA-A1 SLC25A6 SLC25A5 ATP5H COX7C ATP5F1 MT-ND4 MT-CO2 MT-ND2 COX5B MT-ATP6 COX4l1 CLDN3 MTCH2 ATP1B1 SLC25A24 MT-CO1 ATP2A2 SSR1 ZCD1 NNT SLC25A1 SQRDL MGST3 TAPBP ATP5J2 ATP5L VDAC3 VDAC2 PHB2 PHB
MYH11
Fig 2 Clustering analysis of colorectal
can-cer samples The 438 proteins expressed in
the eight CRC patients were classified into
two main groups via hierarchical clustering
analysis.
Trang 5HLA-A1, SLC25A4 and TAPBP The results of the
western blot analysis in the tumoral and normal tissues
confirmed the LC-MS⁄ MS results The expression
levels of CLDN3, HLA-A1 and SLC25A4 were signifi-cantly higher in tumor compartment from CRC patients (P < 0.05) The protein expression of TAPBP
Unknown 13%
A
B
Cytoplasm 11%
Nucleus 3%
Cytoskeleton 5%
Extracellular space 17%
Organelle membrane 24%
(12.8%) (5.9%)
(1.4%) (3.7%)
(9.8%)
(17.1%) (19.4%)
Catalytic activity
Molecular transducer activity
Signal transducer activity
Structual molecule activity
Transporter activity
Unknown
Number of identified proteins
Motor activity
Plasma membrane
27%
Fig 3 Classification of the identified pro-teins (A) Subcellular localization (B) Molec-ular function classification of identified proteins from CRC patients Classification and annotation were performed using the Ingenuity Pathway Analysis Knowledge Base and Gene Ontology (GO) consortium.
A
250
B 248 B 246 B 247 C 245 C 232 D 260 D 326 B 336 B 338 B 339 C 345 C 360 C 363 D 374 D 403
N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T
B
251
B 248 B 132 B 247 C 252 C 245 D 260 D 319 B 344 B 357 B 367 B 373 C 385 C 395 D 404 D 422
N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T
B
251
B 246 B 248 B 132 C 232 C 234 D 260 D 325 A 352 B 355 B 370 B 378 C 380 C 387 D 389 D 411
N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T
B
246
140
120
100
80
60
40
Density (CLDN3/actin) 20
0
120 100 80 60 40
20 0
120 140
100 80 60 40
Density (HLA-A1/actin) 20
Normal Tumor Normal Tumor Normal Tumor
Normal Tumor
0
120 140
P < 0.05
P < 0.05
P = 0.2
P < 0.05
ACTIN
SLC25A4
ACTIN
HLA-A1
ACTIN
TAPBP
ACTIN
CLDN3
100 80 60 40
Density (SLC25A4/actin) 20
0
B 247 B 248 B 132 C 252 C 245 D 306 D 325 A 361 B 368 B 384 B 390 C 393 C 410 D 421 D 424
N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T
Fig 4 Expression levels of CLDN3, HLA-A1, TAPBP and SLC25A4 in CRC samples as measured by western blotting In total, 16 pairs of tissue samples including tumor tissue (T) and matched normal tissues (N) were examined Actin was used as a loading control.
Trang 6was still differential, although less pronounced
TAP-BP was upregulated in 12 of 16 CRC samples, but
downregulated or not obviously changed between
tumoral and matched normal samples in another four
tissue pairs Upregulation of CEA was not analyzed
by immunoblot analysis However, it has been
unequivocally demonstrated in several earlier studies,
using immunohistochemistry and immunoassays, that
CEA expression is significantly elevated in neoplastic
epithelium when compared with matched normal
mucosa, and this was confirmed by our iTRAQ
label-ing MS analysis These results demonstrate that some
of the proteins identified by LC-MS⁄ MS could serve
as potential markers in future studies of CRC
Discussion
This study was aimed at identifying membrane
pro-teins differentially expressed between colorectal cancer
and normal tissue We utilized iTRAQ labeling
RPLC-MS⁄ MS to explore the membrane protein profiles in
paired CRC tissue samples A commonly used strategy
is multidimensional chromatography, where a first
dimension, usually the strong cation-exchange
chroma-tography, is combined with the second dimension
RP-HPLC However, the limited amount of membrane
proteins extracted (5 lgÆsample)1, a total of 20 lg for
an iTRAQ analysis) from precious colorectal tissues
restricted the use of fractionation prior to MS analysis
In our method, we decided to analyze the sample
directly by RPLC-MS⁄ MS three times to obtain a
con-fident protein identification result Using the iTRAQ
labeling mass spectrometry, a total of 438 proteins
were identified by our proteomic platform
To better understand the roles of these identified
proteins, they were grouped and analyzed according to
their possible pathogenic roles The clustering and
molecular functions of the identified proteins can
provide clues about their roles in the pathogenesis of
CRC In general, factors that contribute to the
patho-genesis of CRC include the accumulation of mutations
and the deregulation of gene expression Of particular
interest is the fact that a significant number of the
pro-teins identified as differentially upregulated in tumor
tissues may be functionally involved in the CRC
tumorigenesis Several clinically well-known
biomar-kers, such as CEACAM 5 and 6, were overexpressed
in tumor tissues, compared with the matched normal
colorectal tissues in our study Although CEA is not
an adequate screening tool for colorectal cancer
patients, the assessment of CEA levels for prognosis
has been shown to be an important variable in
predict-ing postoperative outcomes Data from studies on
postoperative colorectal cancer patients have demon-strated that measurement of CEA every 3 months for
at least 3 years is a valuable and cost-effective compo-nent of follow-up [3]
Our findings are in line with the results of several proteomics analyses Alfonso et al used a 2D-DIGE based approach to detect differentially expressed mem-brane proteins of colorectal cancer tissues An impor-tant implication of the study is the conclusion that annexin A2, annexin A4 and VDAC appear as poten-tial markers of interest for colorectal cancer diagnosis [19] A recent report detecting the changes of protein profiles associated with the process of colorectal tumorigenesis to identify specific protein markers for early colorectal cancer detection and diagnosis or as potential therapeutic targets VDAC1, annexin A2 and Keratin 8 variant have been identified [20] Madoz-Gurpide et al tested seven potential markers (ANXA3, BMP4, LCN2, SPARC, SPP1, MMP7 and MMP11) for antibody production and⁄ or validation ANXA3 was confirmed to be overexpressed in colorectal tumoral tissues [7] Kim et al [21] analyzed CRC tissues using 2D difference in-gel electrophoresis on a narrow-range IPG strip and suggested S100A8 and S100A9 as candidates for serological biomarkers in combination with other serum markers that aid CRC diagnosis Using our strategy combining gel-assisted digestion, iTRAQ labeling and LC-MS⁄ MS analysis, identical or similar proteins were identified, including VDAC1, VDAC2, VDAC3, ANXA4 (2.5-fold, P < 0.05), ANXA5 (6.5-fold, P < 0.05), S100A8 (9.5-fold,
P< 0.05) and S100A9 (8.5-fold, P < 0.05)
In addition to the well-known biomarkers and colo-rectal cancer-associated proteins such as CEA, CEA-CAM 6, VDAC and ANXA4, we identified several other proteins that may be potential novel markers for monitoring CRC but have not been unequivocally associated with colorectal carcinoma Overexpression
of CLDN3, HLA-A1, TAPBP and SLC25A4 in colorectal cancer has not been prominently reported, and there is interest in developing these proteins as diagnostic and prognostic markers for this disease In western blotting analysis, CLDN3, HLA-A1 and SLC25A4 showed the best discriminatory power between tumoral and normal tissue Our data provide important clues for the identification of differentially expressed membrane-associated proteins in CRC, and uncover several avenues for study of their roles in CRC carcinogenesis Some of their functional roles and implications in CRC are discussed below
CLDN3 was highly expressed in cancer tissues when tested by LC-MS⁄ MS and western blotting CLDN3 belongs to the claudin (CLDN) family, which consists
Trang 7of 23 proteins that are essential for the formation of
tight junctions in epithelial and endothelial cells [22]
Specifically, CLDN1, -3, -4, -5, -7, -10 and -16 have
been found to be altered in various cancers [23]
Over-expression of these proteins in cancer is unexpected,
but recent work suggests that claudins may be involved
in the survival of and invasion by cancer cells [24,25]
In addition, because claudins are surface proteins, they
may represent useful targets for various therapeutic
strategies Interestingly, Clostridium perfringens
entero-toxin is a ligand for CLDN3 and CLDN4 proteins,
and binding of the toxin to these claudins leads to
rapid cytolysis of cells [26] Preclinical studies have
suggested that Clostridium perfringens enterotoxin may
be effective against CLDN3- and CLDN4-expressing
malignancies [27,28] In our study, we found that
over-expression of CLDN3 is significantly associated with
CRC In a previous study, CLDN3 expression was
analyzed in 12 adenocarcinoma tissues and their paired
normal mucosa, and was shown to be upregulated
1.5-fold in CRC [29] It would be worthwhile to further
elucidate the value of this protein as a diagnostic
and⁄ or prognostic marker for CRC and to further
understand its role in the survival and⁄ or invasion in
CRC cancer cells
SLC25A4 was also significantly increased in CRC
tissues compared with matched normal tissues The
solute carrier family 25 (SLC25) consists of proteins
that are functionally and structurally related and that
construct the transporters of a large variety of
mole-cules [30] Following LC-MS⁄ MS and western blotting
analyses, SLC25A4 showed differential expression
between tumor and normal tissues This protein could
be a valuable diagnostic marker or a target for
moni-toring patients’ conditions
HLA-A1 was highly expressed in cancer tissues
when tested by LC-MS⁄ MS and western blot methods
Expression of human leukocyte antigen (HLA) class I
presenting tumor-associated antigens on the tumor cell
surface is considered to be a prerequisite for effective
T-lymphocyte activation [31] As a consequence, HLA
class I antigens can be downregulated or lost on
malig-nant cells, and these variations may be associated with
a poor prognosis [32,33]
In our study, expression of HLA-A1, determined by
LC-MS⁄ MS and western blotting, was upregulated in
colorectal cancer in comparison with normal tissues
Although these results may appear controversial, only
a few studies have reported the clinical impact of HLA
class I expression in colorectal cancer, with contrasting
results Some studies have shown no significant
corre-lation between staining intensity of HLA class I
expression and survival [34,35], whereas others found
that HLA class I expression correlated with the prog-nosis of CRC patients [36,37]
TAPBP may upregulate the expression of HLA class I molecules, and it was found to be upregulated
in cancer tissues in this study using LC-MS⁄ MS and western blotting TAPBP plays multiple roles in the peptide-loading complex; it stabilizes the complex, aids
in the appropriate selection of peptides, maintains appropriate HLA class I redox status and enhances TAP and HLA class I levels [38,39]
In summary, the strategy combining gel-assisted digestion and iTRAQ labeling LC-MS⁄ MS has proven
to be a potential means of identifying proteins in the membrane fraction from CRC tumoral samples Some
of the representative candidates, such as CLDN3, HLA-A1 and SLC25A4, appear to be promising mark-ers for the detection of colorectal cancer
Materials and methods
Materials
Monomeric acrylamide⁄ bisacrylamide solution (40%,
29 : 1) was purchased from Bio-Rad (Hercules, CA, USA) Trypsin (modified, sequencing grade) was obtained from Promega (Madison, WI, USA) The BCA and Bradford protein assay reagent kits were obtained from Pierce (Rock-ford, IL, USA) SDS was purchased from GE Healthcare (Central Plaza, Singapore) Ammonium persulfate and N,N,N¢,N¢-tetramethylenediamine were purchased from Amersham Pharmacia (Piscataway, NJ, USA) EDTA was purchased from Merck (Darmstadt, Germany) Tris(2-carb-oxyethyl)-phosphine hydrochloride, triethylammonium bicarbonate, Na2CO3, NaCl, sucrose, magnesium chloride hexahydrate (MgCl2), Hepes, methyl methanethiosulfonate, trifluoroacetic acid and HPLC-grade acetonitrile were pur-chased from Sigma-Aldrich (St Louis, MO, USA) Formic acid was purchased from Riedel de Haen (Seelze, Ger-many) Water was obtained from Milli-QUltrapure Water Purification Systems (Millipore, Billerica, MA, USA)
Patients and tumors
Clinical tissue samples from 56 patients with colorectal can-cer were taken from freshly isolated surgical resections in the operating room at the Chang Gung Memorial Hospital, Tao Yuan, Taiwan Malignant tissue (determined by pathological assessment) and adjacent normal tissue were prepared from the same resection All formalin-fixed paraffin-embedded tumor blocks from equivalent specimens from the same tumor tissue were inspected for quality and tumor content, and a single representative tumor block from each case, containing at least 70% neoplastic cells, was selected for the study Normal tissue was obtained
Trang 8from the distal edge of the resection at least 10 cm from
the tumor Written informed consent from all respective
patients was obtained before surgery in accordance with
medical ethics and approval by Human Clinical Trial
Com-mittee at Chang Gung Memorial Hospital A total of eight
tissue pairs containing tumoral and adjacent normal tissue
were collected and analyzed by gel-assisted digestion and
iTRAQ labeling MS Other tissue pairs were utilized to
ver-ify potential targets from the above-mentioned LC-MS⁄ MS
analysis Patients who had received any chemo- and⁄ or
radiotherapeutic treatment before surgery were excluded
from this study
Isolation of membrane proteins from tumoral
and adjacent normal tissues
After surgery, paired tumoral and adjacent normal tissues
were obtained from the same CRC patient and stored at
)80 C Frozen tissues were unfrozen rapidly in a 37 C
water bath, washed with 0.9% (w⁄ v) NaCl solution to
remove blood, resuspended in STM solution (5 gÆmL;
0.25 m sucrose, 10 mm Tris⁄ HCl, 1 mm MgCl2) with
pro-tease inhibitors (protein : protein inhibitor = 100 : 1, v⁄ v)
and homogenized with a homogenizer (Polytron System PT
1200 E, Luzernerstrasse, Switzerland) The nuclei were
removed by centrifugation at 260 g for 5 min at 4C, and
the postnucleus supernatant was centrifuged at 1500 g for
10 min at 4C The pellet was mixed with two-thirds the
original homogenate volume of a 0.25 m STM solution
con-taining protease inhibitors and resuspended in a
homoge-nizer with three strokes of the loose-fitting pestle followed
by one stroke of the tight-fitting pestle (Kimble⁄ Kontes,
Vineland) The resulting solution was centrifuged at
12 000 g for 1 h at 4C to pellet the membrane proteins
The pellet was washed twice with 1 mL of ice-cold 0.1 m
Na2CO3(pH 11.5), dissolved in 50 lL of 90% (v⁄ v) formic
acid to determine the membrane protein concentration by
Bradford assay, and then vacuum dried to obtain a
mem-brane pellet for subsequent proteolysis reactions
Digestion of membrane proteins
Purified membrane proteins were subjected to gel-assisted
digestion [18] In detail, the membrane protein pellet was
resuspended in 50 lL of 6 m urea, 5 mm EDTA and 2%
(w⁄ v) SDS in 0.1 m triethylammonium bicarbonate and
incubated at 37C for 30 min until completely dissolved
Proteins were chemically reduced by adding 1.28 lL of
200 mM Tris(2-carboxyethyl)-phosphine and alkylated by
adding 0.52 lL of 200 mm methyl methanethiosulfonate at
room temperature for 30 min To incorporate proteins into
a gel directly in an Eppendorf vial, 18.5 lL of
acrylam-ide⁄ bisacrylamide solution (40%, v ⁄ v, 29 : 1), 2.5 lL of
10% (w⁄ v) ammonium persulfate, and 1 lL of 100%
N,N,N¢,N¢-tetramethylenediamine was applied to the
membrane protein solution The gel was cut into small pieces and washed several times with 1 mL of triethylam-monium bicarbonate containing 50% (v⁄ v) acetonitrile The gel samples were further dehydrated with 100% acetonitrile and then completely dried by SpeedVac Proteolytic diges-tion was then performed with trypsin (protein⁄ trypsin =
10 : 1, g⁄ g) in 25 mm triethylammonium bicarbonate with incubation overnight at 37C Peptides were extracted from the gel using sequential extraction with 200 lL of 25 mm triethylammonium bicarbonate, 200 lL of 0.1% (v⁄ v) trifluoroacetic acid in water, 200 lL of 0.1% (v⁄ v) trifluo-roacetic acid in acetonitrile and 200 lL of 100% acetoni-trile The solutions were combined and concentrated in a SpeedVac
iTRAQ labeling and LC-ESI MS/MS analysis
To label peptides with the iTRAQ reagent (Applied Biosys-tems, Foster City, CA, USA), one unit of label (defined as the amount of reagent required to label 100 lg of protein) was thawed and reconstituted in ethanol (70 lL) by vor-texing for 1 min The resulting peptides from the normal tissue of one patient were labeled with iTRAQ114and pep-tides from tumor tissue of the same patient were labeled with iTRAQ115 The resulting peptides from normal tissue
of another patient were labeled with iTRAQ116 and pep-tides from tumor tissue were labeled with iTRAQ117 and incubated at room temperature for 1 h The same proce-dures were performed in the peptides from nontumor and tumor tissues of the remaining patients Labeled peptides (5 lg each) were then pooled, vacuum dried and resus-pended in 0.1% (v⁄ v) trifluoroacetic acid (40 lL) for further desalting and concentration using Oasis HLB uElution (Waters Corporation, Milford, MA, USA) All MS⁄ MS experiments for peptide identification were performed using a Waters nanoACQUITY UPLC pump system and a Waters Q-Tof premier mass spectrometer (Waters Corp.) equipped with a nano-ESI source The
(buffer A) containing 0.1% formic acid in water and an organic mobile phase (buffer B) containing 0.1% (v⁄ v) formic acid in acetonitrile Desalting of the samples was performed for 1.5 min with 99% buffer A using a C18
trapping column (5 lm, 20 mm· 180 lm id; Waters Corp.) Samples were separated using a Waters ACQUI-TY BEH C18Column (1.7 lm, 250 mm· 75 lm; Waters Corp.) at 300 nLÆmin)1using a 120 min gradient
During each LC injection, the mass spectrometer was operated in ESI positive V mode with a resolving power of
10 000 The voltage applied to produce an electrospray was 2.85 kV and the cone voltage was 35 eV Argon was intro-duced as a collision gas and the collision flow rate was 0.35 mLÆmin)1 Data acquisition was operated in the data directed analysis mode This mode included a full MS scan (m⁄ z 400–1600, 0.6 s) and an MS ⁄ MS scan (m ⁄ z 100–1990,
Trang 91.2 s each scan) sequentially on the three most intense ions
present in the full scan mass spectrum Mass accuracy was
calibrated with a synthetic human [Glu1]-Fibrinopeptide B
solution (500 fmolÆlL)1) due to the use of a
NanoLock-Spray source and sampled every 30 s The collision energies
were used to fragment each peptide ion on the basis of its
mass-to-charge (m⁄ z) values
Data processing and analysis
For protein identification, data files from LC-MS⁄ MS were
searched against the non-redundant International Protein
Index human sequence database v3.29 [40] (68 161
sequences) from the European Bioinformatics Institute
using the mascot algorithm (v2.2.1, Matrix Science,
Lon-don, UK) Peak lists were generated and processed using
mascot distillerv2.1.1.0 (Matrix Science) Search
param-eters for peptide and MS⁄ MS mass tolerance were
± 0.1 Da and ± 0.1 Da, respectively, with allowance for
two missed cleavages made from the trypsin digest and
var-iable modifications of deamidation (Asn, Gln), oxidation
(Met), iTRAQ (N-terminal), iTRAQ (Lys) and methyl
methanethiosulfonate (Cys) Only proteins with a protein
identification confidence interval of > 95% were
confi-dently assigned When unique peptides were identified to
multiple members of a protein family, proteins with the
highest sequence coverage were selected from the mascot
search output To evaluate the false discovery rate, we
repeated the searches against a random database using
identical search parameters and validation criteria
For protein quantitation, we used multi-q [41] software
to analyze the iTRAQ data Raw data files from the Waters
Q-Tof premier mass spectrometer were converted into files
of mzXML format using masswolf (Institute for Systems
Biology, Seattle, WA, USA), and the search results in
mascot were exported in the xml data format After the
data conversions, multi-q selected unique iTRAQ-labeled
peptides with confident MS⁄ MS identification (mascot
score‡ 40), detected signature ions (m ⁄ z = 114, 115, 116,
117), and performed automated quantitation of peptide
abundance For the detector dynamic range filter, signature
peaks with ion counts < 30 were filtered out by multi-q
To calculate protein ratios, the ratios of quantified unique
iTRAQ peptides were weighted according to their peak
intensities to minimize the standard deviation The final
protein quantitation results were exported to an output file
in csv data format
Clustering analysis
A total of 438 identified proteins were clustered based on
normal Euclidean distance between them and average
link-age The treeview program was used to observe the
hierar-chical partitioning of expression profiles of identified
proteins
Annotations
For subcellular localization and molecular function annota-tions, all the proteins identified in this study were analyzed using the Ingenuity Pathway Analysis Knowledge Base (http://www.ingenuity.com/) and gene ontology (GO) con-sortium [42]
Western blot and statistical analysis
Immunoblots of selected proteins were performed using tissue lysates from both tumoral and adjacent normal samples to confirm the LC-MS⁄ MS findings In total, tissue lysates from another patients with CRC were examined by immunoblotting Briefly, each tissue sample was mixed with electrophoresis sample buffer containing 2% SDS and 5% 2-mercaptoethanol and boiled for 5 min Proteins were separated by electrophoresis on 12% denaturing polyacryl-amide gels and transferred to poly(vinylidene difluoride) membranes These blots were blocked with 5% skim milk and then probed with the appropriate primary antibody
SLC25A4 mAb, Abnova, Taipei, Taiwan; HLA Class 1 A1 antibody, Abcam; Tapasin antibody, Abcam) at a dilution
of 1 : 1000 for 2 h, followed by incubation for 1 h with peroxidase-conjugated secondary antibody at room temper-ature The blots were visualized by ECL and then exposed
to Kodak biomax light films The immunoblot images were acquired by Imagemaster (Amersham Pharmacia Biotech,
NJ, USA) The protein level of each band was quantified by densitometry and analyzed with multi gauge version 2.0
software (Fuji PhotoFilm, Tokyo, Japan) Data were analyzed by an unpaired t-test using the statistical software spss⁄ windows 11.0 statistical package (SPSS Inc, Chicago, IL, USA) P values of < 0.05 were considered statistically significant
Acknowledgements
This work was supported by grants (CMRPD160097 and CMRPG371431) from Chang Gung University and Memorial Hospital, Taiwan
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