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Master’s degree in Proteomics & Bioinformatics

Master’s report

August 2008 – March 2009

"Quantitative proteomic analysis of bile

from patients suffering from malignant biliary strictures"

Paola MALASPINA ANTINORI

Supervisor: Annarita FARINA

Laboratory: Clinical Proteomics Group, Department of Structural Biology and Bioinformatics,

Faculty of Medicine, Geneva, Switzerland

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TABLE OF CONTENT

ABSTRACT ............................................................................................................................................ 3

ABBREVIATIONS ................................................................................................................................ 3

INTRODUCTION .................................................................................................................................. 4

Biliary strictures ................................................................................................................................... 4

Main causes of malignant strictures ..................................................................................................... 4

The need for new diagnostic markers .................................................................................................. 5

Bile as a source of biomarkers ............................................................................................................. 5

Bile composition: a trouble for protein analysis .................................................................................. 5

Bile proteomics .................................................................................................................................... 6

A quantitative approach ....................................................................................................................... 7

RESEARCH PLAN ............................................................................................................................. 10

MATERIALS AND METHODS ........................................................................................................ 12

Materials............................................................................................................................................. 12

Methods .............................................................................................................................................. 12

Sample collection ........................................................................................................................... 12

Sample purification ........................................................................................................................ 12

Removal of cellular debris ........................................................................................................... 12

Delipidation ................................................................................................................................. 12

Desalting ...................................................................................................................................... 13

Determination of protein concentration ......................................................................................... 13

iTRAQ procedure ........................................................................................................................... 14

Standard spiking .......................................................................................................................... 14

Protein denaturation, reduction and alkylation ............................................................................ 14

PNGase F deglycosilation ............................................................................................................ 14

Trypsin digestion ......................................................................................................................... 14

iTRAQ labeling ........................................................................................................................... 14

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OFFGEL™ fractionation ............................................................................................................... 15

Purification on reversed-phase extraction cartridge .................................................................... 15

OFFGEL™ isoelectrofocalization ............................................................................................... 16

Fraction cleaning on reversed-phase extraction cartridge ........................................................... 17

HPLC-MALDI/TOF-TOF analysis ................................................................................................ 17

High performance Liquid Chromatography ................................................................................ 17

MALDI-TOF/TOF mass spectrometry ........................................................................................ 17

Protein identification ...................................................................................................................... 18

Protein quantitation ........................................................................................................................ 18

RESULTS ............................................................................................................................................. 19

Peptide fractionation .......................................................................................................................... 19

Isotopic correction and manipulation bias for iTRAQ quantitation ................................................... 19

Identification of bile proteins by MALDI-TOF/TOF ........................................................................ 19

Protein quantitation ............................................................................................................................ 23

DISCUSSION ....................................................................................................................................... 27

Sample preparation and fractionation by OFFGEL™ ....................................................................... 27

Bovis Taurus β-lactoglobulin (LACB) normalization ....................................................................... 27

Protein identification .......................................................................................................................... 27

Protein quantitation ............................................................................................................................ 28

Proteins overexpressed in all cancers ............................................................................................. 28

Proteins specifically overexpressed in one cancer ......................................................................... 29

Proteins overexpressed in cancers and chronic pancreatitis ........................................................... 30

On the data consistency .................................................................................................................. 31

CONCLUSIONS .................................................................................................................................. 32

PERSONAL CONCLUSIONS ............................................................................................................ 33

AKNOWLEDGEMENTS ................................................................................................................... 34

REFERENCES ..................................................................................................................................... 35

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ABSTRACT

Biliary strictures share very similar symptoms but may have benign or malignant aetiologies. An early and

differential diagnosis can help clinicians to find the right therapy, at the benefit of the patients. The identification of

protein markers can help discriminating between the different diseases and therefore could assist in this choice.

Quantitative proteomics could provide the mean to search for differentially expressed proteins in benign and

malignant biliary strictures. We decided to use this approach on bile samples from four patients with biliary strictures

caused by pancreatic adenocarcinoma and cholangiocarcinoma (malignant), gallstones and chronic pancreatitis

(benign).

We used OFFGEL™ fractionation to separate peptides after protein tryptic digestion in combination with iTRAQ

labelling and MALDI-TOF/TOF tandem mass spectrometry to perform the quantitative protein profiling of the four

bile samples.

Overall 130 proteins were identified and quantified, comparing all four conditions. Fourteen proteins were found

overexpressed in both cancers, two in pancreatic adenocarcinoma, two in cholangiocarcinoma and nineteen in cancers

and chronic pancreatitis.

Information provided by this preliminary screening may be a useful guide for further studies, helping to select a

number of potential biomarkers to be validated by immunoblotting assay.

ABBREVIATIONS

AC, pancreatic adenocarcinoma; CC, cholangiocarcinoma; G, gallstones; P, chronic pancreatitis; iTRAQ, isobaric tag

for relative and absolute quantitation; LACB, bovin β-lactoglobulin; IEF, IsoElectric Focusing; IPG, immobilized pH

gradient Gel; CID, collision induced dissociation; RP-HPLC, reverse phase-high performance liquid chromatography;

LC, liquid chromatography; MS/MS, tandem mass spectrometry; MALDI, matrix-assisted laser desorption/ionisation;

TOF, time of flight; CSF, cerebro-spinal fluid; BSA, bovine serum albumin; TEAB, triethylammonium hydrogen

bicarbonate buffer; SDS, sodium dodecyl sulfate; TFA, trifuoroacetic acid; IAA, iodoacetamide; TCEP, tris-(2-

carboxyethyl)-phosphine hydrochloride; LACB, bovin β-lactoglobulin; ERCP, endoscopic retrograde

cholangiopancreatography; HLB, hydrophilic-lipophylic balance; SPE, solid-phase extraction; CH3CN, acetonitrile;

pI, isoelectric point; ID, internal diameter; NH4PO4, ammonium dihydrogen phosphate; HCl, using hydrochloric acid .

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INTRODUCTION

Biliary strictures

Biliary strictures consist in an abnormal narrowing of the common bile duct impeding the outflow of bile from the

liver to the small intestine (Fig. 1).

Figure 1. Examples of malignant strictures.

They may have various aetiologies some benign and some malignant. Benign strictures are usually the result of a

surgical trauma but may be also the consequence of pancreatitis, primary sclerosing cholangitis (PSC), bile duct stones

and other less frequent conditions [1]. Malignant biliary strictures may be caused by a primary bile duct tumor

(cholangiocarcinoma) or by extensions of adjacent tumors (pancreatic adenocarcinoma, hepatocarcinoma, ampullary

carcinoma, gallbladder carcinoma) [2]. Patients with a bile duct stricture may stay asymptomatic for a long time, until

the reduction of the duct lumen blocks the bile flow. However, even when symptoms appear, their aspecific nature

(e.g. abdominal pain, cholangitis, anorexia, nausea, vomiting, cachexia) makes the definitive diagnosis and treatment

of the stricture very difficult.

Main causes of malignant strictures

Pancreatic adenocarcinoma is the major cause of malignant biliary strictures. According to the estimates of the

American Cancer Society, it was the fourth cause of the death by cancer in 2008 with about 37900 deaths. It is a very

aggressive cancer that can stay silent for years and became symptomatic when it has already spread to proximal

organs and lymphatic system. Only 7% of the cases are detected in an early stage. The late diagnosis and the

aggressive nature of this cancer are the main reasons why only 10 to 15 % of the patients can be treated by surgery [3].

Considering all the stages of the disease the 1- and 5-year relative survival rates are 24% and 5%, respectively. Even

for those people diagnosed with local disease, the 5-year survival is only 20%.

Cholangiocarcinoma arises from the bile ducts in the distal (intra-pancreatic), proximal, or intra-hepatic portion. Its

incidence markedly increased during the last decades [4, 5], and it represents approximately 3% of all gastrointestinal

malignant diseases [6]. As for pancreas adenocarcinoma, cholangiocarcinoma has a very poor prognosis [7] and

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surgical resection is the only potentially curative therapy, because it is most often clinically silent until it reaches an

advanced stage.

The need for new diagnostic markers

Tools that are currently available to differentiate between benign and malignant biliary stenosis, including

laboratory tests, imaging techniques, cytopathological examination of endoscopic biliary samples and intraductal

ultrasonography, are plagued by a poor sensitivity and/or specificity and allow an accurate diagnosis in only 40-60%

of the patients [2]. Moreover, proposed serum markers such as carbohydrate antigens CA19-9 [8], CA242 [9], tumor

isoenzymes such as tumor-M2-pyruvate kinase [10, 11] and onocofetal antigens [12] have limited diagnostic value

[13]. Particularly CA19-9, which is the only one used in everyday practise for pancreatic cancer and

cholangiocarcinoma diagnosis, has a sensitivity of 70-90% and a specificity of 90% but false positive results are found

with obstructive jaundice and chronic pancreatitis even in absence of bile duct strictures [5]. Furthermore, the

expression of CA19-9 is dependent of Lewis phenotype and is uninformative for 7% of the population who is Lewis

negative. The antibody used in the assays is directed against high-molecular-weight mucin glycoproteins coated with

sialylated blood-group epitopes such as sialyl Lewis [13].

The discovery of new markers for early detection and differentiation of malignant strictures from benign ones is

therefore awaited in clinical practice.

Bile as a source of biomarkers

Bile is an essential body fluid constantly produced by the liver through the active release of primary solutes by the

hepatocytes followed by the passive movement of water in response to the osmotic gradient [14]. The fluid is drained

by biliary ducts from the liver into the gallbladder, where it is stored and concentrated approximately ten folds. After

meals, in response to stimulation by cholecystokinin, the gallbladder releases concentrated bile into the duodenum

through the common bile duct. Bile is mainly a way of excretion of metabolic breakdown products but it is also crucial

in lipids absorption through the emulsifying action of the bile salts on fats in the small intestine. In biomarker

discovery, one postulates that diseased tissues release proteins in body fluids via various mechanisms, such as leakage

from damaged and dying cells or secretion [15]. Accordingly, bile could be extremely valuable to detect biomarkers of

pathologies that involve the biliary tract or surrounding tissues, such as liver and pancreas (the common bile duct

passes through the head of the pancreas). Particularly for pancreas adenocarcinoma and cholangiocarcinoma the

probability of leakage of proteins from cancer cells into bile can be considered as high since bile is in direct contact

with the tumor due to the fact that both cancers may occlude the bile duct.

Bile composition: a trouble for protein analysis

Despite an obvious clinical interest, protein analysis in bile poses many more problems than in any other body fluid

as a result of the massive concentration of interfering compounds. A list of the main components of bile fluid is given

in Table 1.

Bile salts, phospholipids, and cholesterol, are the main organic solutes and constitute more than 90% of biliary

components (by dry weight) [16, 17]. As water insoluble, these components are present in bile in mixed micellar and

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vesicular forms. In addition to polar lipids, bile contains also a number of other organic anions of both endogenous

and exogenous origin (e.g. bilirubin diglucuronide, estrogen glucuronides, estrogen sulphates, xenobiotics). Proteins,

instead, account for approximately only 5% of bile components by dry weight (protein concentration varies with the

technique employed [18] because pigments from bile interfere with colorimetric reactions altering the measurement

[16].

The presence in bile of supramolecular assemblies of lipids, organic compounds and salts in such a

disproportionate rate, makes impossible the direct analysis of protein from crude bile. A pre-treatment of the sample is

required to allow good quality investigation; it usually involves removal of lipids and salts by using several

purification methods including solvent extraction, dialysis, microfiltration, protein precipitation as well as a wide

range of fractionation procedures.

Table 1. The main components of hepatic and gallbladder bile

Bile proteomics

Proteomics has proven to be a valid approach to discover new biochemical markers and to identify unique panels

of proteins associated with malignancies [19, 20]. It could therefore represent a promising approach to investigate bile

fluid collected from strictures, allowing an early detection of tumor-associated proteins. Nevertheless, due its

complexity, only a limited number of proteomic studies have been carried out on bile with the specific intent to search

for cancer markers [21, 24]. Kristiansen and colleagues have been precursors by performing the first large-scale

proteomic analysis of bile collected from a patient with a cholangiocarcinoma [21]. Among the identified proteins,

several corresponded to known cancer-associated proteins previously shown to be overexpressed in

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cholangiocarcinomas (MUC2, MUC16, [25, 26]) or not previously reported in this types of cancer (MAC-2BP,

lipocalin 2, DMBT1).

A second large-scale proteomic study was recently published by Farina and collegues [24]. The analysis of bile

samples collected from patients having a biliary stricture caused by a pancreas adenocarcinoma allowed the

identification of several proteins known to be associated to cancer. Among them, two proteins, previously reported in

studies on pancreatic cancer, exhibited an altered expression in bile in case of malignant strictures (MUC1 and

CEACAM6).

Despite of interesting results, current bile proteomic studies stay only descriptive. The selection of candidate

proteins for immunovalidation essentially relies on bibliographic data and, hence, is restricted to known biomarkers.

In addition, potential biomarkers are selected without knowing whether the biliary concentration of the candidate

protein changes between different pathologies of interest. In order to circumvent these limitations, a quantitative

proteomic analysis of samples from patients affected with different diseases could represent a further step for

malignant strictures investigations.

A quantitative approach

A number of protein or peptide labeling methods are available to quantify proteins by mass-spectrometry in

biological samples and iTRAQ [27] is one of the most commonly used. It consists of isobaric reagents that react with

the primary amines of peptides (N-terminus and side chain of lysine). Four to eight tags are commercially available,

allowing labeling as many different samples and thus the comparison of multiple physiological states. Figure 2

illustrates the chemistry and mass spectrometric behavior of the reagents. The overall mass of the tag is maintained

constant by balancing the mass with different isotopes of carbon, oxygen and nitrogen thus enabling the four identical

derivatized peptides from different samples to elute simultaneously during the chromatographic separation. The

mixture of tagged peptides with identical m/z (mass over charge ratio) appears as a single peak in the MS spectrum

but, after the collision induced dissociation (CID), the charged reporter ions is released from each peptide appearing as

a distinct mass in the MS/MS spectrum. The peak areas of the reporter ions yield the information of the relative

abundance of peptides and hence of proteins from the four different sources.

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Figure 2. (A) The complete reagent molecule. (B) The different isotopes incorporated in the reporter and balance

groups. (C) A mixture of four identical peptides marked with four different labels appears as an undistinguishable

precursor in MS but, after CID fragmentation, the reporter group ions are measured as distinct masses. (D) An

example of signature reporter ions from peptides mixed in ratios 1:5:2:10 (modified from [27]).

To avoid overlapping peaks during LC elution step, which could affect relative quantitation by summing up peaks

areas from different peptides, a method for peptides pre-fractionation is required prior to LC-MS/MS analysis. A

recently developed fractionation technique, OFFGEL™ has proved to be compatible with quantitative iTRAQ

labeling [28]. Based on isoelectric focusing (IEF), it uses immobilised pH gradient gel (IPG) strips to separate proteins

or peptides according to their isoelectric point (pI) [29]. The sample is placed in plastic sequential wells positioned on

top of an IPG strip. The gel of the IPG strip buffers a thin layer of the solution from the respective well and the

peptides (or proteins) in solution are thus charged according to their pI and the pH imposed by the gel. Two electrodes

are placed at the extremities of the multiwell device, with the anode at the low pH side and the cathode at the high pH

side. An electric field is then applied to the gel. The wells are isolated from each other and the charged peptides travel

through the gel underneath the wall separating the compartments, until they reach the position where they are neutral

(pI). The peptides are finally recovered in solution at a pH equal to their pI (Figure 3). The resolution of the separation

can be varied using the 12 or 24 multiwell device, the pH range and the length of the IPG strips.

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Figure 3. (A) The IPG gel strip is rehydrated and tightly sealed against the wells; the diluted sample is equally

distributed in fractionation buffer in all wells in the frame. (B) High voltage is applied to the ends of the gel strip

and peptides and ampholytes migrate through the gel until they reach a position where the pH equals their pI (C).

After fractionation, the separated peptides are recovered in solution.

According to a recent paper [30], the combination of OFFGEL™, iTRAQ and MALDI represent a valuable approach

to improve proteome coverage. OFFGEL™, in fact, provides a good fractionation technique for complex samples,

especially when protein digestion prior to iTRAQ chemical labeling enhances the complexity of the mixture or when a

good loading capacity is needed. iTRAQ, on the other hand, appears to increase MALDI ionization allowing the

identification of less abundant proteins.

A combination of OFFGEL™ fractionation, iTRAQ labeling, nano RP-HPLC separation and MALDI-TOF/TOF

mass spectrometry has been successfully applied to the analysis of plasma samples [28] and tested in our own group

on CSF. Therefore, we decided to start from that point in order to perform a quantitative analysis of human bile.

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RESEARCH PLAN

The aim of this study was to compare protein expression in bile samples from patients with biliary strictures having

different aetiologies (cholangiocarcinoma, pancreatic adenocarcinoma, gallstones and chronic pancreatitis), using a

quantitative proteomic approach. Differences in protein content could be indicative of potential biomarkers for a

specific disease.

The choice of bile as our research material was dictated by the fact that this body fluid is in direct contact with

tumors causing biliary strictures. We therefore hypothesized that bile could collect protein biomarkers released by

diseased tissues.

We used a combination of peptide labelling with iTRAQ reagents, OFFGEL™ peptide fractionation, HPLC

peptide separation and MALDI-TOF/TOF mass spectrometry for bile proteins identification and relative quantitation.

Data acquired from the mass spectrometer were analyzed by using the Phenyx protein identification software.

The intensities of the reporter ions (iTRAQ labels) retrieved from the mass spectrometer for all the validated

peptides were treated to correct for experimental bias and the tag isotopic impurities. Peptide ratios, representing

protein relative abundances between the four different conditions, were calculated. Differentially expressed proteins

were evaluated in order to assess the validity of our approach and to find potential candidate proteins for validation as

biomarkers for pancreatic adenocarcinoma and cholangiocarcinoma.

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Figur e X : Experime ntal w orkflow : fo u r bi le samp les

PN Gase F d igestion Trypsin digest ion

iTRAQ

lab elling

MALDI- TOF/TOF

Pro teins identification Pro teins q uantification

HP LC separation

Samp les

preparation

Mix

Cholangio carcinoma

Pancreatic adenocarcinoma

carcinoma

Gallstones Chronic pancraetitis

Figure 4. Experimental workflow. Bile from four patients with cholangiocarcinoma, pancreatic adenocarcinoma, gallstones and

chronic pancreatitis are centrifuged, delipided and desalted. Asparagine-linked oligosaccharides are digested with PNGase F and

then proteins are digested with trypsin. Peptides mixtures are labeled with different iTRAQ reagents (114, 115, 116, 117) and then

mixed. Pooled peptides are fractionated by OFFGEL™, separated by nano RP- HPLC and analyzed by MALDI-TOF/TOF.

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MATERIALS AND METHODS

Materials

Water was purified using a MilliQ system from Millipore (Bedford, MA). The solid phase for delipidation

CleanasciteTM

was from Biotech (North Brunswick, NJ) and Centricon Ultracel YM-3 filter were from Millipore

(Bedford, MA). Bovin serum albumin (BSA) and Bradford Protein Assay were from Bio-Rad (Hercule, CA).

Triethylammonium hydrogen bicarbonate buffer (TEAB), sodium dodecyl sulfate (SDS) and trifuoroacetic acid (TFA)

were from Fluka (Buch, Switzerland). Iodoacetamide (IAA), tris-(2-carboxyethyl)-phosphine hydrochloride (TCEP),

α-cyano-4-hydroxycinnamic acid, peptide-N-glycosilase F (PNGase F), trypsine type IX-S from porcine pancreas,

bovin β-lactoglobulin (LACB) from bovine milk and acetonitrile (CH3CN) Chromasolv for HPLC were from Sigma-

Aldrich (St.Louis, MO). Water for chromatography LiChrosolv was from Merck (Darmstadt, Germany). iTRAQ

reagents of a multi-plex kit were from Applied Biosystems (Foster City, CA). Hydroxylamine solution (50 wt % in

water) was from Aldrich (Milwaukee, WI). OASIS® HLB reversed-phase extraction cartridges (1cc, 10 and 30mg)

were from Waters (Millford, MA). Linear IPG strips (24 cm, pH 3-10) were from GE-Healthcare (Uppsala, Sweden).

Magic C18 AQ 5-µm resin was from Michrom BioResources (Auburn, CA).

Methods

Sample collection

Human bile samples were collected during endoscopic retrograde cholangiopancreatography (ERCP) from four

patients with biliary strictures of different aetiologies: pancreatic adenocarcinoma, cholangiocarcinoma, chronic

pancreatitis, gallstones.

A volume of 10 to 30 mL of bile was collected upstream the stricture before contrast medium injection. The bile

samples were immediately transported on ice, aliquoted and stored at -80 ºC until analysis.

Patient informed consent was obtained and the protocol was approved by the Ethical Committee of the Geneva

University Hospital.

Sample purification

Removal of cellular debris

Bile samples were thawed on ice and 400 µL of each sample was centrifuged at 16.000 g at 4 ºC for 10 minutes.

The pellet was discarded.

Delipidation

The delipidation procedure was carried out using CleanasciteTM

, a non-ionic adsorbent solid-phase which

selectively removes lipids and mucinous impurities. The supernatant of each sample was mixed with 100 µL of

CleanasciteTM

(volume ratio CleanasciteTM

: sample = 1:4) and kept under mild agitation at 4 ºC for 1 hour to increase

the agglomeration of fine lipids.

After centrifugation at 16.000 g at 4 ºC for 2 minutes, the supernatants were recovered in new tubes.

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Desalting

Removal of salts and small molecules was carried out using a Centricon Ultracel YM-3 filter with a 3000 Da

molecular weight cut-off. The filters were prerinsed with MilliQ water to eliminate the traces of protective glycerin

from the membrane and checked for potential filter damage.

Delipided samples were then transferred onto the Centricon reservoir and centrifuged at 6400 g for 30 minutes,

until half of the initial volume had passed through the filter.

A volume of 250 µL of MilliQ water was added twice to each sample in the Centricon reservoir and the

centrifugation step was repeated until the added volume of water has passed through the filter. Finally the sample

reservoir was placed upside down in a new vial to recover the salt-cleared sample and centrifuged at 700 g for 2

minutes (Fig. 5).

Figure 5. Sample desalting. The sample was loaded on the upper reservoir, washed with MilliQ water, centrifuged

and recovered in a new tube after inversion of the upper reservoir. (Modified from MICROCON® Centrifugal Filter

Devices User Guide)

Determination of protein concentration

The protein concentration was determined using the Bio-Rad Protein Assay based on the Bradford method [31].

This is a dye-binding assay in which a differential color change of a dye occurs in response to various concentrations

of protein. The absorbance maximum for an acidic solution of Coomassie® Brilliant Blue G-250 dye shifts from 465

nm to 595 nm when binding to protein occurs. The Coomassie blue dye binds to primarily basic and aromatic amino

acid residues, especially arginine and changes its color from reddish-brown to blue.

BSA was used as the calibration standard to generate a standard curve. Three different dilutions of BSA from a 1

µg/µL stock solution were prepared in triplicates (5, 8, 12 µg/mL) in Acryl-Cuvettes Sarstetd, Nümbrecht, Germany).

The absorbance at 595 nm for each dilution was measured on a 2100 pro spectrophotometer (Amersham Biosciences

now GE Healthcare, Piscataway, NJ) and plotted on an Excel graph as a function of the concentration. Two dilutions

of each sample, in triplicates, were prepared in the Acryl-Cuvettes using 2 and 4 µL of the delipided/desalted bile

sample, the absorbance was measured (y axis) and the concentration (x axis) calculated using the linear regression

function on the Excel graph.

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iTRAQ procedure

Standard spiking

From this point on, only water for chromatography LiChrosolv was used.

In order to end up with 200 µg of proteins for the OFFGEL™ fractionation, we calculated the volume of each

sample containing 50 µg and dissolved it in TEAB 0.5 M at pH 8.0 to obtain a final volume of 100 µL. An equal

amount of LACB from bovine milk was added to each solution (1 µg for 50 µg of proteins) to act as a standard

reference in order to normalize the data and compensate for the manipulation bias (Tab.3).

Table 3. Samples preparation prior to digestion and OFFGEL™

Sample Volume for 50 µg of proteins

(µL)

TEAB

(µL)

Final volume

(µL)

LACB (0.1 µg /µL)

(µL)

CC 39.2 60.8 100 10

AC 33.3 66.7 100 10

G 36.6 63.4 100 10

P 12.5 87.5 100 10

The final pH of the sample solution should be comprised between 7 and 8.5 to optimize trypsin digestion, thus the

pH of the solution was adjusted to pH = 8.2, using hydrochloric acid (HCl) 2.5%.

Protein denaturation, reduction and alkylation

Protein denaturation was carried out adding 1 µL of sodium SDS 1% to each sample. In order to reduce the

disulfide bonds, 2 µL of TCEP 50mM was used and the reaction was performed at 60°C for 60 minutes. Finally, the

thiol groups of the cysteine residues were blocked with 1 µL IAA 400 mM, leaving the reaction mixture at room

temperature, in the dark, under gentle agitation.

PNGase F deglycosilation

Digestion of asparagine-linked (N-linked) oligosaccharides from glycoproteins was achieved adding 10 µL of

PNGase F from a 0.5 U/ µL stock solution. The reaction mixture was incubated at 37°C for 3 hours.

Trypsin digestion

20 µL of freshly prepared trypsin (0.2 µg/ µL in TEAB 0.5 M) were added to have a protein/trypsin ratio of 50:1

w/w. The digestion was performed overnight at 37°C.

iTRAQ labeling

iTRAQ reagents from a multi-plex kit with m/z =114, 115, 116 and 117 were thawed and centrifuged. 70 µL of

ethanol were added in each reporter tube according to the supplier protocol. After brief vortexing and centrifugation to

collect the solution at the bottom of the tubes, each reporter tag was added in a specific sample. The pairings sample-

tag are given in Table 4.

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Table 4. Sample-iTRAQ label pairing

Sample Reporter

CC (Cholangiocarcinoma) 114

AC (Adenocarcinoma) 115

G (Gallstones) 116

P (Chronic Pancreatitis) 117

The reaction mixture was left at room temperature for 60 minutes. In order to block the reaction, 8 µL of

hydroxylamine 5% was added in each tube and left for 15 minute at room temperature.

The tagged samples were then combined and the original tubes were washed twice with 30 µL of water and

centrifuged each time for an optimum recovery of the labeled peptides.

The pooled sample was evaporated under vacuum and frozen at – 20°C.

OFFGEL™ fractionation

Purification on reversed-phase extraction cartridge

To remove interfering substances prior to OFFGELTM

fractionation, we used OASIS® HLB extraction cartridges

packed with a reversed-phase copolymer (30 mg) and mounted on a Visiprep™ SPE Vacuum Manifold (Sigma-

Supelco, Park City, Bellefonte State, PA) (Fig. 6).

Figure 6. Visiprep™ SPE Vacuum Manifold

(Supelco Bullettin 910)

The dried sample was dissolved in 1.5 mL of a 95% H2O/5% CH3CN/0.1% TFA solution. The column was

conditioned with 5% H2O/95% CH3CN/0.1% TFA in four steps of 1 mL each. The equilibration was performed in

four steps of 1mL each with 95% H2O/5% CH3CN/0.1% TFA and then the sample solution (1.5 mL) was loaded on

the column. The sample trapped in the solid phase was washed in three steps with 95% H2O/5% CH3CN/0.1% TFA.

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The column was placed on a metallic stand and finally eluted by hand with 2 mL of 50% H2O/50% CH3CN/0.1% TFA

in a 2 mL Eppendorf tube.

The eluted sample was evaporated under vacuum and frozen at -200C.

OFFGEL™ isoelectrofocalization

The OFFGEL™ fractionation was performed on an Agilent 3100 OFFGEL™ instrument (Santa Clara, CA) using

24 cm IPG strips with a linear pH gradient ranging at 3-10, to obtain 24 peptide fractions.

Figure 7. Agilent 3100 OFFGEL™ fractionator with an IPG strip and multiwell frame mounted

A stock solution containing glycerol 7.5% and IPG Buffer pH 3-10 0.09% was prepared. The dried sample, from

the initial 200 µg of bile proteins, was re-suspended in 720 µL of water and 2.88 mL of stock solution were added to

take the sample solution to a final volume of 3.6 mL (150 µL per well x 24 wells). The IPG strip was installed in one

lane of the tray, covered with the wells frame and rehydrated with stock solution 1x according to the manufacturers’

protocol (Fig. 7). The tray was positioned on the instrument platform and 150 µL of sample solution were pipetted in

each of the 24 wells. The focusing method used to run the sample was set on the instrument; the limiting parameters

are showed in Table 5.

Table 5. OFFGEL™ settings

Peptide fractions were recovered from the wells when the total voltage reached 50 kVh and transferred in 24 tubes.

The pH of the 24 fractions was measured to check the focalization efficiency.

The 24 OFFGEL™ fractions were evaporated under vacuum and frozen at -20°C.

Volt/hour Voltage Current Power Time

50 Kv/h 8000 volts 50 μA 200 mW 100 h

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Fraction cleaning on reversed-phase extraction cartridge

In order to clean the sample from ampholytes and other interfering substances, the 24 OFFGEL™ fractions were

individually dissolved in 1 mL 95% H2O/5% CH3CN/0.1% TFA and purified by using OASIS® HLB reversed-phase

extraction cartridges (10 mg). The 24 columns were previously conditioned with 5% H2O/95% CH3CN/0.1% TFA in

two steps of 1mL each. The equilibration was performed in two steps of 1mL each with 95% H2O/5% CH3CN/0.1%

TFA and then the sample solutions (1 mL each fraction) were loaded onto the columns. The sample trapped in the

solid phase was washed in three steps with 95% H2O/5% CH3CN/0.1% TFA. The columns were placed one by one on

a metallic stand and finally eluted by hand with 1 mL of 50% H2O/50% CH3CN/0.1% TFA in 1.5 mL Eppendorf

tubes.

The eluted fractions were evaporated under vacuum and frozen at -20°C until HPLC-MALDI-TOF/TOF analysis.

HPLC-MALDI/TOF-TOF analysis

High performance Liquid Chromatography (HPLC)

Samples were separated by HPLC using a Waters 2790 separation module (Milford, MA) equipped with a flow

splitter.

Each dried OFFGEL™ fraction was dissolved in 200 µL of SOLUTION A (98% H2O / 3% CH3CN / 0.1% TFA)

and vortexed for two minutes. 21 µL of each sample solution were used for the analysis, after a centrifugation step at

7000 g for 3 minutes. Two runs of liquid chromatography were planned, each with 12 different OFFGEL™ fractions.

A homemade 0.1 mm ID x 100 mm column packed with 5 µm Å Magic C18 AQ resin was mounted on a

homemade LC- robot. The composition of the solutions used for the gradient was:

• SOLUTION A: 98% H2O / 3% CH3CN / 0.1%

• SOLUTION B: 5% H2O / 95% CH3CN / 0.1%

The separation gradient was run as follow: 0-2 minutes 90% A, then to 50% A at 45 minutes, and 98% B at 50

minutes at an estimated flow rate of 400 nL/minute.

Every 60 seconds a chromatographic fraction was deposited by the robot on a 384 well-MALDI plate for a total of

64 fractions per sample, 6 samples per plate and 4 plates. A time delay of 15 minutes was allowed between each

sample.

MALDI-TOF/TOF mass spectrometry

The four MALDI plates were spotted by hand with the matrix (5 mg of α-cyano-4-hydroxycinnamic acid, 1 mL

50%H2O / 50%CH3CN/1% TFA, 10µl of NH4PO4 1M) and analyzed with a MALDI-TOF/TOF 4800 mass

spectrometer from Applied Biosystems (Foster City, CA). The spectra were acquired in positive reflector ion mode

with an m/z scan range of 800-4000 Th (1000 shots with a laser intensity of 3900 in arbitrary units). After selection of

20 most intense precursors, MS/MS experiments (1500 shoots with a laser intensity of 4500 in arbitrary units) were

realized at medium collision energy.

18

Protein identification

Peak lists were generated using the software of the instrument. The resulting .mgf files were combined for all the

OFFGEL™ fractions and searched against the uniprot_sprot (56.1 of 02-Sep-2008) database using Phenyx interface

(Gene Bio, Geneva, Switzerland) operating on a local server. Homo sapiens taxonomy was specified. Variable amino

acid modifications were oxidized methionine and deamidation of asparagines. Fixed modifications were iTRAQ-

labeled peptides on the amino terminus and on the side chains of lysine. Trypsin was selected as the enzyme, with a

maximum of 1 missed cleavage. The peptide and fragment ion tolerance was, respectively, 1.1 Da and 800 ppm for

Phenyx search. “Turbo” search mode was selected. The acceptance criteria were set to: AC score 6.85, peptide z-score

6.85, peptide p-value 1 x 10-4

. Conflict resolution was accepted.

Protein quantitation

Data analysis was performed to obtain information about the relative abundance of identified proteins in each

pathological condition.

Areas of the reporter ions released during CID fragmentation of iTRAQ labeled precursor ions, were corrected to

reduce manipulation bias thanks to LACB spiking. A purity correction was also applied taking into account the

isotopic impurities of the commercial labels. The reporter intensities were normalized by the sum of all reporter

intensities. Normalized intensities for each reporter were averaged for all peptides from the same protein and used to

calculate the relative abundances. The quantification values for the proteins were calculated averaging the

corresponding peptide intensities. Ratios between protein relative abundances in the four different conditions

(cholangiocarcinoma, adenocarcinoma, gallstones and chronic pancreatitis) were calculated to search for differentially

expressed proteins which could potentially be considered for validation as biomarkers for cancer diseases.

19

RESULTS

Peptide fractionation

Deglicosilated tryptic peptides, obtained from in-solution digestion of purified bile samples collected from patients

diagnosed with cholangiocarcinoma (n=1), pancreatic adenocarcinoma (n=1), gallstones (n=1) and chronic

pancreatitis (n=1), were labeled with iTRAQ reagents, mixed and fractionated by OFFGEL™ electrophoresis

according to their pI.

To validate the efficiency of the fractionation procedure, in the analytical phase of the experiment, we measured

the pH of each OFFGEL™ fraction and obtained the results summarized in Table 6 and Figure 8.

Table 6. OFFGEL™ fractions and measured pH in each well

Fraction n. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

pH 2.68 2.89 3.78 4.24 4.64 4.94 5.20 5.54 5.74 6.00 6.25 6.40 6.58 6.80 7.03 7.21 7.44 7.63 7.70 7.93 8.17 8.42 8.90 9.02

Figure 8. Graphic of the pH values measured in the 24 OFFGEL™ fractions

As an additional analysis, we verified the distribution of each identified peptide in the OFFGEL™ fractions. The

results are summarized in Figure 9.

20

Figure 9. Fractionwise distribution of identified peptides in the OFFGEL™ fractions. On the x-axis is reported

the number of fractions; on the y-axis is reported the percentage of peptides identified in one or more fractions

Isotopic correction and manipulation bias for iTRAQ quantitation

Before tryptic digestion, an equal amount of LACB was added to each sample to serve as a normalization standard

to correct any manipulation bias that could affect the relative quantification of the iTRAQ procedure. The peak list

search with Phenyx, using Bos Taurus taxonomy, identified LACB with 10 unique tryptic peptides, 39 valid peptides,

56% sequence coverage and a protein score of 111.9 (fpr 4.43%).

A correction was applied to account for the isotopic impurities intrinsic in the iTRAQ reporters. LACB ratios

obtained for reporter ions 114:115:116:117 were 1.06:1.11:0.64:1.19 instead of the theoretical 1:1:1:1 showing a

manipulation bias affecting in particular the sample labeled with the 116 reporter. Thanks to the spiking of LACB a

correction of the reporter peak areas for all samples was applied and the corrected intensities gave ratios 1:1:0.99:1.

Identification of bile proteins by MALDI-TOF/TOF

Each OFFGEL™ fraction was separated by nano RP-HPLC and the obtained chromatographic fractions were

analyzed by MALDI-TOF/TOF tandem mass spectrometry. The resulting peak lists from all 24 fractions were merged

and searched against uniprot_sprot (56.1 of 02-Sep-2008) database using Phenyx interface. The results of the protein

identifications are detailed in Table 7.

Overall, 130 proteins were identified in the pooled samples (≥ 95% confidence). Proteins identified with at least

two distinct tryptic peptides amounted to 67, the remaining 63 being identified with one unique peptide (not shared

with other proteins).

21

Table 7. List of identified proteins

Proteins identified with two or more unique peptides

# AC ID Description Score #Pept % Cov

1 P02768_CHAIN_0 ALBU_HUMAN Serum albumin 609.42 236 80

2 P02787_CHAIN_0 TRFE_HUMAN Serotransferrin (Transferrin) 285.33 64/64 39 3 P15144_CHAIN_0 AMPN_HUMAN Aminopeptidase N (hAPN) 177.37 26/26 20

4 P01009_CHAIN_0 A1AT_HUMAN Alpha-1-antitrypsin 167.05 28/28 42

5 P02790_CHAIN_0 HEMO_HUMAN Hemopexin 158.12 30/30 36 6 P01833_CHAIN_1 PIGR_HUMAN Secretory component 156.33 33/33 23

7 P00738_CHAIN_0 HPT_HUMAN Haptoglobin beta chain 153.03 32/32 34

8 P68871_CHAIN_0 HBB_HUMAN LVV-hemorphin-7 147.27 40/40 85 9 Q9HC84_CHAIN_0 MUC5B_HUMAN Mucin-5B (MUC-5B) 142.74 22/22 2

10 Q9BYE9_CHAIN_0 PCD24_HUMAN Protocadherin-24 (PC-LKC) 130.02 24/24 11

11 P02774_CHAIN_0 VTDB_HUMAN Vitamin D-binding protein 114.35 14/14 33 12 P02647_CHAIN_1 APOA1_HUMAN Apolipoprotein A-I(1-242) 107.87 17/17 42

13 P25311_CHAIN_0 ZA2G_HUMAN Zinc-alpha-2-glycoprotein 106.2 12/12 35

14 P01011_CHAIN_0 AACT_HUMAN Alpha-1-antichymotrypsin His-Pro-less 96.36 17/17 25 15 P01011_CHAIN_1 AACT_HUMAN Alpha-1-antichymotrypsin His-Pro-less 96.16 18/18 24

16 P19440 GGT1_HUMAN Gamma-glutamyltranspeptidase 1 92.31 15/15 15

17 P01024_CHAIN_0 CO3_HUMAN Complement C3c alpha' chain frag. 2 85.42 10/10 7 18 O95497_CHAIN_0 VNN1_HUMAN Pantetheinase (Vanin-1) 83.8 13/13 16

19 P01857 IGHG1_HUMAN Ig gamma-1 chain C region 79.1 18/18 30

20 P69905_CHAIN_0 HBA_HUMAN Hemoglobin subunit alpha 69.74 26/26 41 21 P05155_CHAIN_0 IC1_HUMAN Plasma protease C1 inhibitor 69.54 6/6 13

22 P01871 IGHM_HUMAN Ig mu chain C region 67.96 12/12 17

23 P01861 IGHG4_HUMAN Ig gamma-4 chain C region 65.99 23/23 25 24 P00450_CHAIN_0 CERU_HUMAN Ceruloplasmin 64.34 14/14 8

25 P04264_CHAIN_0 K2C1_HUMAN Keratin, type II cytoskeletal 1 60.07 7/7 13

26 P01859 IGHG2_HUMAN Ig gamma-2 chain C region 55.45 21/21 20 27 P60174_CHAIN_0 TPIS_HUMAN Triosephosphate isomerase 53.13 6/6 28

28 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 50.22 6/6 19

29 P04217_CHAIN_0 A1BG_HUMAN Alpha-1B-glycoprotein 49.86 5/5 12 30 P01834 IGKC_HUMAN Ig kappa chain C region 48.3 12/12 50

31 P01042_ISOFORM_LMW KNG1_HUMAN Low molecular weight growth-promoting fact. 46.37 9/9 7 32 P0C0L4 CO4A_HUMAN Complement C4 gamma chain 45.28 5/5 3

33 P01008_CHAIN_0 ANT3_HUMAN Antithrombin-III (ATIII) 44.45 6/6 16

34 P01876 IGHA1_HUMAN Ig alpha-1 chain C region 42.93 13/13 11 35 P01860 IGHG3_HUMAN Ig gamma-3 chain C region 41.56 15/15 12

36 P10909_CHAIN_0 CLUS_HUMAN Clusterin alpha chain. 38.14 6/6 11

37 P01842 LAC_HUMAN Ig lambda chain C regions 37.74 5/5 42 38 P80188_CHAIN_0 NGAL_HUMAN Neutrophil gelatinase-associated lypocalin 37.5 5/5 23

39 P05109 S10A8_HUMAN Protein S100-A8 (MRP-8) (CFAG) 37.08 5/5 25

40 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 35.87 3/3 8 41 P01023_CHAIN_0 A2MG_HUMAN Alpha-2-macroglobulin 35.72 4/4 4

42 P02652_CHAIN_1 APOA2_HUMAN Apolipoprotein A-II(1-76). 35.67 10/10 28

43 P04406_CHAIN_0 G3P_HUMAN Glyceraldehyde-3-phosphate dehydrogenase 35.21 3/3 11 44 P01019_CHAIN_0 ANGT_HUMAN Angiotensin-3 (Ang III) 32.26 9/9 7

45 P06702 S10A9_HUMAN Protein S100-A9 (MRP-14) (P14) 32.02 4/4 37

46 Q08380_CHAIN_0 LG3BP_HUMAN Galectin-3-binding protein (Mac-2-BP) 31.23 3/3 7 47 P02656_CHAIN_0 APOC3_HUMAN Apolipoprotein C-III 30.78 5/5 43

48 P01877 IGHA2_HUMAN Ig alpha-2 chain C region 27.87 11/11 8

49 P01591 IGJ_HUMAN Immunoglobulin J chain 27.54 5/5 21 50 P61769_CHAIN_1 B2MG_HUMAN Beta-2-microglobulin form pI 5 25.07 2/2 20

51 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 25.02 3/3 7

52 P43652_CHAIN_0 AFAM_HUMAN Afamin (Alpha-Alb) 23.77 2/2 4 53 P60709_CHAIN_0 ACTB_HUMAN Actin, cytoplasmic 1 23.63 3/4 8

54 P08473_CHAIN_0 NEP_HUMAN Neprilysin 21.59 2/2 3

55 P00915_CHAIN_0 CAH1_HUMAN Carbonic anhydrase 1 (CA-I) 21.49 2/2 9 56 P04431_CHAIN_0 KV123_HUMAN Ig kappa chain V-I region Walk 21.45 3/3 22

57 P02760 AMBP_HUMAN Trypstatin 21.45 3/3 7

58 P02753_CHAIN_3 RET4_HUMAN Plasma retinol-binding protein... 21.16 4/4 12 59 P02749_CHAIN_0 APOH_HUMAN Beta-2-glycoprotein 1 20.79 3/3 7

60 P13473_CHAIN_0 LAMP2_HUMAN Lysosome-associated membrane glycoprot.2 20.21 3/3 5

61 P08263_CHAIN_0 GSTA1_HUMAN Glutathione S-transferase A1 19.52 3/3 9 62 Q13228 SBP1_HUMAN Selenium-binding protein 1 18.28 2/2 4

63 Q07654_CHAIN_0 TFF3_HUMAN Trefoil factor 3 (hP1.B) 17.76 2/2 22

64 P04004_CHAIN_1 VTNC_HUMAN Somatomedin-B 16.83 2/2 8 65 P02649_CHAIN_0 APOE_HUMAN Apolipoprotein E (Apo-E) 16.69 5/5 6

22

66 P06733_CHAIN_0 ENOA_HUMAN Alpha-enolase (NNE) 16.18 2/2 6

67 P15586_CHAIN_0 GNS_HUMAN N-acetylglucosamine-6-sulfatase 15.93 2/2 4

Proteins identified with one unique peptide

# AC ID Description Score #Pept % Cov

1 P02766_CHAIN_0 TTHY_HUMAN Transthyretin 18.97 4/4 10

2 P02763_CHAIN_0 A1AG1_HUMAN Alpha-1-acid glycoprotein 1 16.02 2/2 8

3 P01777 HV316_HUMAN Ig heavy chain V-III region TEI 15.63 2/2 16

4 P01764_CHAIN_0 HV303_HUMAN Ig heavy chain V-III region VH26 15.18 1/1 19

5 P23141_CHAIN_0 EST1_HUMAN Liver carboxylesterase 1 14.51 1/1 2

6 P04155_CHAIN_0 TFF1_HUMAN Trefoil factor 1 13.95 1/1 20

7 P01034_CHAIN_0 CYTC_HUMAN Cystatin-C 12.47 1/1 9

8 P40199_CHAIN_0 CEAM6_HUMAN Carcinoembryonic antigen-related adh. mol.6 12.35 1/1 5

9 P02750_CHAIN_0 A2GL_HUMAN Leucine-rich alpha-2-glycoprotein 12.23 1/1 4

10 P00747_CHAIN_3 PLMN_HUMAN Plasmin light chain B 12.23 1/1 2

11 P04433 KV309_HUMAN Ig kappa chain V-III region VG 11.87 5/5 8

12 Q9Y624_CHAIN_0 JAM1_HUMAN Junctional adhesion molecule A 11.76 1/1 4

13 P07148 FABPL_HUMAN Fatty acid-binding protein, liver 11.54 1/1 9

14 P16070_ISOFORM_15 CD44_HUMAN CD44 antigen (ECMR-III) 11.53 1/1 4

15 Q86SQ4_CHAIN_0 GP126_HUMAN Probable G-protein coupled receptor 11.15 3/3 1

16 P13688_ISOFORM_G CEAM1_HUMAN Carcinoembryonic antigen-related adh. mol.1 11.02 1/1 4

17 P07858_CHAIN_2 CATB_HUMAN Cathepsin B heavy chain 10.96 1/1 8

18 Q7Z7D3_CHAIN_0 VTCN1_HUMAN V-set domain-containing T-cell act. Inhibitor1 10.85 1/1 4

19 Q5TFQ8_CHAIN_0 SIRBL_HUMAN Signal regulatory protein beta 10.49 1/1 4

20 P20142 PEPC_HUMAN Gastricsin 10.14 3/3 2

21 P15941_ISOFORM_7 MUC1_HUMAN Mucin-1 subunit beta 10.11 1/1 4

22 P62158_CHAIN_0 CALM_HUMAN Calmodulin (CaM) 10.09 2/2 11

23 P05543_CHAIN_0 THBG_HUMAN Thyroxine-binding globulin 9.58 1/1 2

24 Q6UWV6_CHAIN_0 ENPP7_HUMAN Ectonucleotide pyrophosphatase 9.43 1/1 2

25 P09237_CHAIN_0 MMP7_HUMAN Matrilysin (MMP-7) (Matrin) 9.08 1/1 4

26 P02654_CHAIN_0 APOC1_HUMAN Apolipoprotein C-I (Apo-CI) 9.08 1/1 16

27 P29622_CHAIN_0 KAIN_HUMAN Kallistatin 9.08 2/2 2

28 P08571_CHAIN_1 CD14_HUMAN Monocyte differentiation antigene 9.04 1/1 4

29 P02771_CHAIN_0 FETA_HUMAN Alpha-fetoprotein 8.9 1/1 1

30 P04040_CHAIN_0 CATA_HUMAN Catalase 8.7 1/1 2

31 P04080 CYTB_HUMAN Cystatin-B 8.61 1/1 12

32 P07477_CHAIN_2 TRY1_HUMAN Alpha-trypsin chain 2 8.57 1/1 6

33 Q6IA69 NADE1_HUMAN Glutamine-dependent NAD(+) synthetase 8.51 1/1 1

34 Q7L0X0_CHAIN_0 K0644_HUMAN Leucine-rich repeat-containing prot. KIAA0664 8.49 1/1 2

35 P14384_CHAIN_0 CBPM_HUMAN Carboxypeptidase M (CPM) 8.47 1/1 2

36 P06727_CHAIN_0 APOA4_HUMAN Apolipoprotein A-IV (Apo-AIV) 8.25 1/1 2

37 Q96SI1_ISOFORM_2 KCD15_HUMAN BTB/POZ domain-containing protein 8.22 1/1 4

38 P01781 HV320_HUMAN Ig heavy chain V-III region GA 8.2 2/2 8

39 P01617 KV204_HUMAN Ig kappa chain V-II region TEW 8.09 1/1 12

40 Q86UN3_CHAIN_0 R4RL2_HUMAN Reticulon-4 receptor-like 2 8.06 1/1 2

41 P00441_CHAIN_0 SODC_HUMAN Superoxide dismutase [Cu-Zn] 7.96 1/1 5

42 P19652_CHAIN_0 A1AG2_HUMAN Alpha-1-acid glycoprotein 2 7.89 1/1 9

43 P27487_CHAIN_1 DPP4_HUMAN Dipeptidyl peptidase 4 soluble 7.84 2/2 1

44 Q9H0M0_ISOFORM_2 WWP1_HUMAN NEDD4-like E3 ubiquitin-protein 7.82 1/1 7

45 P05981_CHAIN_1 HEPS_HUMAN Serine protease hepsin catalytic chain 7.69 1/1 3

46 P01620 KV302_HUMAN Ig kappa chain V-III region SI 7.68 3/3 15

47 Q5JV73 FRPD3_HUMAN FERM and PDZ domain-containing protein 7.6 1/1 1

48 Q7KZF4 SND1_HUMAN Staphylococcal nuclease domain conteing prot. 7.55 1/1 1

49 Q96PD5_CHAIN_0 PGRP2_HUMAN N-acetylmuramoyl-L-alanine amidase 7.39 1/1 4

50 P08559_CHAIN_0 ODPA_HUMAN Pyruvate dehydrogenase E1 component 7.35 2/2 3

51 Q6ZNR8 PRR17_HUMAN Proline-rich protein 17 7.24 1/1 4

52 Q15311_CHAIN_0 RBP1_HUMAN RalA-binding protein 1 7.19 1/1 1

53 Q8NEV4 MYO3A_HUMAN Myosin IIIA 7.18 1/1 0

54 Q7Z4G4_ISOFORM_3 TRM11_HUMAN tRNA guanosine-2'-O-methyltransferase 7.18 1/1 3

55 P49327 FAS_HUMAN Fatty acid synthase 7.13 1/1 0

56 Q96HE9 PRR11_HUMAN Proline-rich protein 11 7.1 1/1 2

23

57 Q7Z591_ISOFORM_6 AKNA_HUMAN AT-hook-containing transcription factor 7.06 1/1 2

58 Q14999 CUL7_HUMAN Cullin-7 (CUL-7) 7.01 1/1 1

59 P04179_CHAIN_0 SODM_HUMAN Superoxide dismutase [Mn], mitochondrial 7.01 1/1 7

60 P30086_CHAIN_0 PEBP1_HUMAN Hippocampal cholinergic neuros... 7 1/1 4

61 Q9NZR1 TMOD2_HUMAN Tropomodulin-2 (N-Tmod) 6.92 1/1 2

62 P09493_ISOFORM_2 TPM1_HUMAN Tropomyosin alpha-1 chain 6.87 1/1 3

63 Q96KE9 BTBD6_HUMAN BTB/POZ domain-containing protein 6.86 1/1 2

Protein quantitation

The quantitation of each protein was estimated as a ratio between reporter relative abundances as follow:

malignant/malignant (AC/CC);

malignant/benign (AC/G; CC/G; AC/P; CC/P);

benign/bening (P/G).

A protein was considered to be differentially expressed in one sample when a 1.90 fold change was measured (ratio

=< 0.5 or >=1.90).

Combining data from the comparison of the obtained ratios, proteins were grouped into several categories

according to their specific overexpression in malignant samples. In order to check the reliability of the analysis, also

proteins exclusively overexpressed in pancreatitis and gallstones were listed. The results are summarized in Table 8

and detailed ratios values are listed in Table 9.

Table 8. Summary table of the categories of proteins obtained by comparing proteins ratios from the four samples

Protein Category Criteria Number of hits

Overexpressed in all cancers AC ,CC > P , G 14

Overexpressed in pancreatic cancer AC > CC, P, G 2

Overexpressed in cholangiocarcinoma CC > AC, P, G 2

Overexpressed in cancers and pancreatitis AC , CC, P > G 19

Overexpressed in pancreatitis P > AC, CC, G 14

Overexpressed in gallstones G > AC, CC, P 12

24

Table 9. List of proteins with at least 1.9 fold abundance change in: a) both cancers, b) pancreatic

adenocarcinoma, c) cholangiocarcinoma, d) cancers and chronic pancreatitis, e) gallstones.

ID Description AC/CC CC/G AC/G CC/P AC/P P/G Overexpression

a) Overexpressed by at least 1.9 fold in both cancers

A1AT_HUMAN Alpha-1-antitrypsin 1.8 2.6 4.7 3.5 6.5 0.7

CC AC CC AC

MUC5B_HUMAN Mucin-5B (MUC-5B) 0.9 3.1 2.8 3.2 2.9 1.0

CC AC CC AC

VTDB_HUMAN Vitamin D-binding protein 0.7 6.4 4.6 4.8 3.5 1.3

CC AC CC AC

IC1_HUMAN Plasma protease C1 inhibitor 0.9 3.7 3.2 2.6 2.2 1.4

CC AC CC AC

KNG1_HUMAN Low molecular weight growth-promoting factor 1.8 2.5 4.4 1.9 3.4 1.3

CC AC CC AC

CLUS_HUMAN Clusterin alpha chain 1.1 2.9 3.4 3.1 3.6 0.9

CC AC CC AC

AMBP_HUMAN Trypstatin(alpha-1-microglobulin) 0.9 2.9 2.6 2.7 2.4 1.1

CC AC CC AC

APOE_HUMAN Apolipoprotein E (Apo-E) 1.5 3.1 4.7 2.9 4.5 1.0

CC AC CC AC

A1AG1_HUMAN Alpha-1-acid glycoprotein 1 0.9 2.5 2.3 2.4 2.2 1.1

CC AC CC AC

CEAM6_HUMAN Carcinoembryonic antigen-related adhesion molecule 6 0.8 4.8 3.6 3.3 2.5 1.4

CC AC CC AC

MUC1_HUMAN Mucin-1 subunit beta (MUC1-beta) 0.6 3.8 2.4 3.6 2.2 1.1

CC AC CC AC

MMP7_HUMAN Matrilysin (MMP-7) (Matrin) 0.9 2.8 2.7 2.0 1.9 1.4

CC AC CC AC

K0644_HUMAN Leucine-rich repeat-containing pro KIAA0664 0.9 7.0 6.6 5.5 5.1 1.3

CC AC CC AC

PRR17_HUMAN Proline-rich protein 17 0.8 5.6 4.2 6.6 5.0 0.9

CC AC CC AC

b) Overexpressed by at least1.9 fold in pancreatic adenocarcinoma

K1C9_HUMAN Keratin, type I cytoskeletal 9 8.3 0.3 2.1 0.7 5.7 0.4 AC

AC

AC

NADE1_HUMAN Glutamine-dependent NAD(+) synthetase 2.4 1.8 4.3 1.9 4.7 0.9 AC

AC CC AC

c) Overexpressed by at least two fold in cholangiocarcinoma

EST1_HUMAN Liver carboxylesterase 1 0.4 3.7 1.3 3.5 1.3 1.1

CC

CC

PEPC_HUMAN Gastricsin 0.2 4.9 0.9 8.6 1.5 0.6

CC

CC

d) Overexpressed by at least 1.9 fold in cancers and pancreatitis (Inflammation?)

ALBU_HUMAN Serum albumin [CHAIN 0] 1.0 4.4 4.4 1.1 1.1 4.1

CC AC

P

LG3BP_HUMAN Galectin-3-binding protein (mac-2-BP) 0.8 5.5 4.4 2.8 2.3 1.9

CC AC CC AC P

TRFE_HUMAN Serotransferrin (Transferrin) 1.5 2.5 3.8 0.7 1.0 3.7

CC AC

P

APOA1_HUMAN Apolipoprotein A-I(1-242) 1.7 3.1 5.2 1.2 2.0 2.6

CC AC

AC P

CO3_HUMAN Complement C3c alpha' chain fragment 1.2 3.0 3.7 0.5 0.6 6.5

CC AC

P

IGHG1_HUMAN Ig gamma-1 chain C region 1.2 2.0 2.4 0.2 0.3 9.4

CC AC

P

IGHG4_HUMAN Ig gamma-4 chain C region 1.5 2.4 3.6 0.2 0.2 15.4

CC AC

P

IGHG2_HUMAN Ig gamma-2 chain C region 1.5 2.2 3.3 0.2 0.3 13.0

CC AC

P

NGAL_HUMAN Neutrophil gelatinase-associated lipocalin 0.7 6.3 4.1 2.9 1.9 2.2

CC AC CC AC P

S10A8_HUMAN Protein S100-A8 (MRP-8) (CFAG) 1.2 2.8 3.2 0.2 0.2 16.5

CC AC

P

S10A9_HUMAN Protein S100-A9 (MRP-14) (P14) 1.6 9.2 15.1 0.2 0.2 60.7

CC AC

P

HV303_HUMAN Ig heavy chain V-III region VH26 1.3 2.3 3.0 0.8 1.0 2.9

CC AC

P

TFF1_HUMAN Trefoil factor 1 0.9 5.1 4.6 2.3 2.1 2.2

CC AC CC AC P

CYTC_HUMAN Cystatin-C 2.0 25.7 51.5 0.4 0.8 68.0 AC CC AC

P

CATB_HUMAN Cathepsin B heavy chain 1.3 2.6 3.4 1.1 1.4 2.4

CC AC

P

FETA_HUMAN Alpha-fetoprotein 0.8 3.1 2.5 1.2 1.0 2.5

CC AC

P

FRPD3_HUMAN FERM and PDZ domain-containing protein 0.8 3.9 3.0 1.6 1.3 2.4

CC AC

P

MYO3A_HUMAN Myosin IIIA 1.0 4.3 4.2 1.5 1.5 2.9

CC AC

P

25

CUL7_HUMAN Cullin-7 (CUL-7) 1.0 2.1 2.2 1.1 1.1 2.0

CC AC

P

e) Overexpressed by at least 1.9 fold in chronic pancreatitis

HPT_HUMAN Haptoglobin beta chain.. 1.2 1.1 1.3 0.3 0.4 3.2

P

TPIS_HUMAN Triosephosphate isomerase 0.8 1.6 1.2 0.7 0.6 2.2

P

IGKC_HUMAN Ig kappa chain C region 1.4 1.0 1.4 0.2 0.3 4.1

P

CO4A_HUMAN Complement C4 gamma chain 1.5 1.1 1.6 0.5 0.8 2.1

P

LAC_HUMAN Ig lambda chain C regions 1.4 0.9 1.3 0.3 0.4 3.0

P

G3P_HUMAN Glyceraldehyde-3-phosphate dehydrogenate 0.7 1.9 1.3 0.5 0.4 3.7

P

KV123_HUMAN Ig kappa chain V-I region Walk 1.2 1.6 1.9 0.6 0.7 2.8

P

KV309_HUMAN Ig kappa chain V-III region VG 1.0 1.0 1.0 0.4 0.4 2.4

P

APOC1_HUMAN Apolipoprotein C-I (Apo-CI) 1.2 1.0 1.3 0.2 0.3 4.3

P

CYTB_HUMAN Cystatin-B 1.3 1.1 1.4 0.4 0.6 2.5

P

SND1_HUMAN Staphylococcal nuclease domain 1.2 1.5 1.9 0.3 0.4 4.4

P

AKNA_HUMAN AT-hook-containing transcription factor 1.6 0.8 1.3 0.4 0.7 2.0

P

BTBD6_HUMAN BTB/POZ domain-containing protein 0.9 1.1 1.0 0.6 0.5 2.0

P

PRR11_HUMAN Proline-rich protein 11 1.0 1.2 1.2 0.4 0.4 3.2

P

f) Overexpressed in gallstones (ratios others/G < 0.5)

AMPN_HUMAN Aminopeptidase N (hAPN) 1.0 0.2 0.2 1.1 1.1 0.2

G G

G

PCD24_HUMAN Protocadherin-24 (PC-LKC) 0.8 0.4 0.3 1.3 1.0 0.3

G G

G

GGT1_HUMAN Gamma-glutamyltranspeptidase 1 0.9 0.1 0.1 1.2 1.0 0.1

G G

G

VNN1_HUMAN Pantetheinase (Vanin-1) 0.8 0.3 0.2 1.1 0.9 0.3

G G

G

NEP_HUMAN Neprilysin (Neutral endopeptidase 24.11) 1.1 0.1 0.1 0.7 0.8 0.1

G G

G

GSTA1_HUMAN Glutathione S-transferase A1 0.7 0.5 0.4 1.9 1.4 0.3

G G

G

GP126_HUMAN Probable G-protein coupled receptor 0.9 0.2 0.2 1.3 1.1 0.1

G G

G

CEAM1_HUMAN Carcinoembryonic antigen-related adhesion molecule 1 0.6 0.2 0.1 1.1 0.6 0.2

G G

G

SIRBL_HUMAN Signal regulatory protein beta 0.9 0.3 0.2 1.2 1.1 0.2

G G

G

ENPP7_HUMAN Ectonucleotide pyrophosphatase 0.8 0.5 0.4 1.4 1.1 0.3

G G

G

DPP4_HUMAN Dipeptidyl peptidase 4 soluble 1.0 0.4 0.4 1.3 1.3 0.3

G G

G

ODPA_HUMAN Pyruvate dehydrogenase E1 component 1.2 0.2 0.3 1.3 1.6 0.2

G G

G

g) Ambiguous

HBB_HUMAN LVV-hemorphin-7 3.4 1.1 3.6 0.1 0.3 11.3 AC

AC

P

HBA_HUMAN Hemoglobin subunit alpha 2.8 0.9 2.5 0.1 0.3 7.5 AC

AC

P

FETUA_HUMAN Alpha-2-HS-glycoprotein 6.3 1.3 8.3 0.2 1.4 6.1 AC

AC

P

APOA2_HUMAN Apolipoprotein A-II(1-76) 2.4 1.2 2.8 0.2 0.4 7.5 AC

AC

P

LAMP2_HUMAN Lysosome-associated membrane glycoprotein 2 1.2 1.8 2.1 2.1 2.6 0.8

AC CC AC

ZA2G_HUMAN Zinc-alpha-2-glycoprotein 1.1 1.9 2.1 1.9 2.0 1.0

CC AC

AC

PIGR_HUMAN Secretory component 0.7 1.9 1.3 3.6 2.5 0.5

CC

CC AC

AACT_HUMAN Alpha-1-antichymotrypsin His-Pro-less 0.9 1.1 1.1 2.3 2.1 0.5

CC AC

AACT_HUMAN Alpha-1-antichymotrypsin His-Pro-less 0.9 1.2 1.1 2.6 2.3 0.5

CC AC

K2C1_HUMAN Keratin, type II cytoskeletal 1 3.7 0.4 1.7 1.1 4.0 0.4 AC

AC

A1BG_HUMAN Alpha-1B-glycoprotein 1.4 1.6 2.2 1.0 1.4 1.6

AC

ANT3_HUMAN Antithrombin-III (ATIII) 1.5 1.9 2.9 1.7 2.6 1.1

AC

AC

IGHG3_HUMAN Ig gamma-3 chain C region 1.5 1.7 2.5 0.1 0.2 11.8

AC

P

A2MG_HUMAN Alpha-2-macroglobulin 1.3 1.7 2.1 0.3 0.4 4.8

AC

P

B2MG_HUMAN Beta-2-microglobulin form pI 5 0.8 2.9 2.2 1.7 1.3 1.7

CC AC

26

K1C10_HUMAN Keratin, type I cytoskeletal 10 2.5 0.5 1.2 0.6 1.5 0.8 AC

ACTB_HUMAN Actin, cytoplasmic 1 0.7 2.0 1.4 1.0 0.7 2.0

CC

P

CAH1_HUMAN Carbonic anhydrase 1 (CA-I) 1.5 1.6 2.3 0.4 0.6 3.6

AC

P

TTHY_HUMAN Transthyretin 2.0 0.9 1.8 0.7 1.4 1.3 AC

TFF3_HUMAN Trefoil factor 3 (hP1.B) 0.8 3.7 2.8 2.0 1.5 1.8

CC AC CC

VTNC_HUMAN Somatomedin-B 1.3 1.6 2.2 1.4 1.9 1.2

AC

GNS_HUMAN N-acetylglucosamine-6-sulfatase 1.5 0.9 1.2 1.6 2.4 0.5

AC

PLMN_HUMAN Plasmin light chain B 1.7 2.2 3.8 1.4 2.4 1.6

CC AC

AC

JAM1_HUMAN Junctional adhesion molecule A 1.2 0.9 1.0 4.0 4.6 0.2

CC AC

FABPL_HUMAN Fatty acid-binding protein, liver 2.2 0.5 1.1 1.5 3.3 0.3 AC

AC

CD44_HUMAN CD44 antigen (ECMR-III) 0.8 2.3 1.9 1.4 1.1 1.6

CC

VTCN1_HUMAN V-set domain-containing T-cell activation inhibitor 1 0.7 2.6 1.7 3.4 2.3 0.8

CC

CC AC

THBG_HUMAN Thyroxine-binding globulin 0.9 1.0 0.9 3.0 2.6 0.3

CC AC

KAIN_HUMAN Kallistatin 1.1 2.0 2.3 1.5 1.7 1.4

CC AC

CD14_HUMAN Monocyte differentiation antigene 0.7 1.7 1.3 3.0 2.2 0.6

CC AC

APOA4_HUMAN Apolipoprotein A-IV (Apo-AIV) 2.7 0.4 1.1 1.3 3.5 0.3 AC

AC

SODC_HUMAN Superoxide dismutase [Cu-Zn] 1.3 0.7 0.9 1.8 2.4 0.4

AC

A1AG2_HUMAN Alpha-1-acid glycoprotein 2 0.8 2.9 2.2 1.5 1.2 1.9

CC AC

P

PGRP2_HUMAN N-acetylmuramoyl-L-alanine amidase 1.6 1.5 2.3 0.8 1.2 1.8

AC

TRM11_HUMAN tRNA guanosine-2'-O-methyltransferase 1.3 1.6 2.1 0.5 0.7 2.9

AC

P

PEBP1_HUMAN Phosphatidylethanolamine-binding protein 1 0.7 1.0 0.7 3.2 2.3 0.3

CC AC

27

DISCUSSION

Sample preparation and fractionation by OFFGEL™

Many different techniques have been employed in the past to clean bile from lipids and organic salts which

strongly interfere with proteins analysis [16]. In this study, we combined centrifugation for removal of cell debris, and

mucinous impurities, treatment with a non-ionic adsorbent for lipids removal, and desalting using 3-kDa size

exclusion ultrafiltration device. Despite all these purification steps, the color of the samples ranging from pale yellow

to green-brown didn’t varied considerably suggesting that bile pigments were still present in the fluid. During the

purification step with the OASIS® reverse-phase cartridge, a greenish color tainted the resin and was not cleared away

by the column washing steps, sticking to the phase and finally eluting with peptides.

After speed-vacuum evaporation, a green pellet was present at the bottom of the tube. Several steps of heating at 60

0C and sonication were performed to solubilize the solid deposit in water. Even so, a brief centrifugation before

loading the sample on the OFFGEL™ instrument revealed the presence of green particles precipitated at the bottom of

the tube. After the recovery of the focalized peptides from the wells, a visual inspection of the IPG strip revealed that,

in the acidic part of the strip (pH 2-6), the gel was colored in green, probably because of the presence of pigments

(e.g. bilirubin, biliverdin). The permanence of contaminants in the sample could be assumed to impede the peptide

migration then, to evaluate the quality of the peptide focalization during the OFFGELTM

fractionation the pH of each

fraction was measured. The results of the measurements highlighted that pH intervals between adjacent fractions

varied from about 0.2 to 0.4 pH units, which is a good result considering that, theoretically:

pH range (7) / number of fractions (24) = 0.3 pH units (Fig.8).

Ideally, a good focalization procedure should also allow separating individual peptides into one or two fractions,

with minimum overlapping. The analysis of the individual peptides distribution showed that 89.9 % of the peptides

focalized in one (53.7%) or two fractions (22.2%) confirming the quality of the fractionation procedure (Fig. 9).

Bovis Taurus β-lactoglobulin (LACB) normalization

The correction of the bias introduced during sample manipulation allowed us to avoid an error on the measured

intensities of the iTRAQ reporter 116 that would have deeply affected the quantification analysis under-estimating the

contribution of the reporter 116 assigned to the gallstones sample.

Protein identification

Proteins identified with the highest number of peptides were derived from plasma (e.g. albumin, transferring,

haptoglobin, hemoglobin, alpha-1-antitrypsin, alpha-1-antichimotrypsin, vitamin D binding protein, hemopexin,

complement factors, ceruloplasmin, angiotensinogen, immunoglobulin-like proteins). These proteins are known to be

released in bile either via a transcellular or a paracellular pathway at the hepatic level [17]. Because of this, bile

proteomic analysis suffers, as plasma, from the presence of high abundance proteins, whose peptides hide the less

abundant ones. Purification steps can indeed assist in identifying low abundance proteins but are also responsible for

major losses [34].

28

We found also other proteins originating from hepatocytes, or from the epithelial cells lining the gallbladder or the

common bile duct: mucin glycoproteins [32], lysosomal proteins (e.g. lysosome-associated membrane glycoprotein 2,

lysosomal glycosidases, N-acetylglucosamine-6-sulfatase, cathepsin) [17], secretory components (e.g. polymeric

immunoglobulin receptor), mitochondrial proteins (e.g. superoxide dismutase), plasma-membrane proteins

(aminopeptidase N) and endoplasmatic reticulum proteins (e.g. liver carboxylesterase 1).

What is relevant for the present study is that some of the identified proteins have already been found implicated in

pancreatic cancer, e.g. carcinoembryonic antigen-related cell adhesion molecule 6 precursor, mucin-1, clusterin,

matrilysin precursor, neutrophil gelatinase-associated lipocalin, [24, 22, 65, 68].

Protein quantitation

Proteins overexpressed in all cancers

Some of the proteins that we found increased in pancreatic adenocarcinoma and cholangiocarcinoma are derived

from plasma (alpha-1-antitrypsin, plasma protease C1 inhibitor, alpha-1-acid glycoprotein), probably as a consequence

of the inflammatory syndrome that accompanies most cancers [35-36]. Interestingly, we found increased levels of

some cancer associated proteins, whose overexpression in cancer samples have been demonstrated in previous works

by various detection methods.

Three of these proteins have been already validated in bile samples:

Mucin-1, a highly glycosylated membrane protein carring the carbohydrate antigen CA 19-9, is a well

exstablished serum marker of biliary and pancreatic cancers and was found up-regulated in both cancer samples in our

work.

Another glycoprotein, GPI-anchored, CEACAM 6 (or carcinoembryonic antigen-related cell adhesion

molecule 6) is overexpressed in both cancers, confirming data from Farina et al. [24]. They compared by

immunoblotting two samples from chronic pancreatitis, six from pancreatic adenocarcinoma, two from gallstones and

three from cholangiocarcinoma. CEACAM 6 was increased in 3/3 cases of cholangiocarcinomas, 5/6 cases of

pancreatic adenocarcinoma, one faint band was visible in one case of pancreatitis and no bands in gallstones samples.

CEACAM 6 involvement in tumor cell migration, invasion and formation of distant metastases has been demonstrated

in previous studies [37-40].

Matrylisin (MMP-7) has a wide substrate specificity and can promote cancer invasion by proteolytic cleavage

of extracellular matrix and basement membrane proteins, such as fibronectin, collagen type IV, laminin, elastin,

entactin, osteopontin, and cartilage proteoglycan aggregates. Furthermore, this enzyme activates other

metalloproteinases facilitating tumor invasion. In line with this finding, it was reported that MMP-7 expression in

humans is correlated with invasiveness of cancer tissues of the esophagus, stomach, colon, liver, and pancreas [41-42].

In this study, it is overexpressed in cancers samples with just a value (CC/P) at the limit (>= 1.9) but Farina et al. [24]

tested its expression levels in pancreatic cancer, cholangiocarcinoma, chronic pancreatitis and gallstones samples and

found that it was not detectable in all cancer samples.

Among the other proteins that we found increased in cancer samples there is mucin 5B (MUC5B), that is a secreted

gel-forming mucin which proposed function is to protect the mucosal surface from infections and physical traumas

[43]. According to the same source, it is likely that secreted mucins form small gels or aggregates around foreign

29

bodies or dead cells and eliminate them from the mucosal surface. MUC5B is expressed mainly in bronchus glands

and also in submaxillary glands, endocervix, gallbladder and pancreas [44] and its increased expression in bile could

be a local response to cell damage induced by the tumor. Since specific commercial antibodies already exist, it could

be interesting to evaluate its overexpression in more bile samples.

Remarkably, a protein overexpressed in cancers samples is clusterin, a secreted heterodimeric glycoprotein that

serves as a heat-shock protein. Clusterin has various activities that influence many basic cell functions, including cell

remodeling, differentiation, apoptosis, and cell proliferation [45-47] but its specific role in these processes is still

under debate. Clusterin has been reported to be overexpressed in anaplastic lymphoma [48] and various carcinomas

[49-50]. The results of a study from Min-Jue Xie et al. [65], conducted on tissue samples from patients affected by

pancreatic cancer, chronic pancreatitis, mucinous cystadenoma and normal tissues, suggest that the expression of

clusterin is upregulated in all diseases, but not in normal pancreas. Data from these studies are stimulating and further

investigations in bile sample could help elucidating its role in cancers.

Proteins specifically overexpressed in one cancer (cholangiocarcinoma or pancreatic adenocarcinoma)

In the present study two proteins were found overexpressed in the sample from the patient affected by

cholangiocarcinoma: gastricsin (pepsinogen C) and liver carboxylesterase 1.

Gastricsin or pepsinogen C expression is increased in the cholangiocarcinoma sample with calculated ratios:

AC/CC=0.18, CC/G=4.95, CC/P=8.62. Identified with one unique peptide from three distinct MS/MS spectra and

protein coverage of 2%, it is known to be involved in some human cancers [51-52] and was also identified in a

proteomic analysis of pancreatic juice from patients with pancreatic cancer [53]. Merino AM et al. [66] examined by

immunohistochemistry the expression of gastrcsin in human carcinomas of various origins. They analyzed 268 tumors

and 80 (29.8%) showed positive staining for gastricsin. These positive tumors included 12 gastric (38.7% of the 31

examined cases), nine pancreatic (42.8%), two renal (20%), 12 prostatic (40%), three bladder (27.3%), 14 endometrial

(29.7%), 18 ovarian (40%) carcinomas and 10 melanomas (50%). Additionally a study conducted using cDNA

microarrays found that a cluster of extrapancreatic foregut markers, including pepsinogen C and TFF1, was

overexpressed in pancreatic intraepithelial neoplasia (PanIN) compared with microdissected normal duct epithelium

[54]. The authors speculated that the development of early PanIN was associated with characteristic changes in

epithelial differentiation, including the ectopic appearance of a gastric epithelial phenotype. Interestingly, invasive

pancreatic cancer is thought to develop through a series of noninvasive duct lesions characteristic of PanIN. Using

gene expression profiling, Sato N et al.[67] found pepsinogen C among the most highly expressed genes in intraductal

papillary mucinous neoplasms suggesting a differentiation into a gastric-type epithelial phenotype during the

neoplastic evolution. Furthermore, pepsinogen C is known to be a strong predictor for favorable outcome in patients

with various cancers, including breast [55], gastric [55] and pancreatic cancer [57]. Summarizing, gastricsin, normally

expressed in gastric mucosa, can be overexpressed in some cancers and could have a prognostic value toward a

favorable outcome. Commercial antibodies directed against gastricsin are available and could allow investigating its

expression on more diseased and normal bile samples.

Liver carboxylesterase 1 is the other protein found overexpressed in the sample from the cholangiocarcinoma.

Localized mainly in liver and lung, this enzyme is involved in biotransformation of both endogenous and exogenous

30

compounds to polar products to facilitate their elimination [58]. A further validation will be necessary to confirm its

overexpression in more bile samples.

Moreover, two proteins were found overexpressed in the sample from the patient with pancreas adenocarcinoma:

keratin 9 and glutamine-dependent NAD(+)synthetase .

Keratin 9 was identified with 3 uniques peptides and shows an increased expression in pancreatic adenocarcinoma

(AC/CC=8.28, AC/G=2.14, AC/P=5.56). It is a high-molecular-weight type I (acidic pI) keratin, normally found in

complex epithelia. It could be considered as a contaminant from skin particles but it could be interesting to evaluate its

presence on a larger number of bile samples, since a number of keratins have already been described as cancer-

associated proteins [69].

Glutamine-dependent NAD(+)synthetase 1 was also increased in the pancreatic adenocarcinoma sample. It belongs

to the NAD synthetase family that catalyzes the final step in the biosynthesis of nicotinamide adenine dinucleotide

(NAD), which is a coenzyme in metabolic redox reactions, a precursor for several cell signaling molecules, and a

substrate for protein posttranslational modifications. The implication of NAD in many and foundamental cellular

activities induces an interest toward glutamine-dependent NAD(+)synthetase 1 and its overexpression should be

further tested in bile samples.

Proteins overexpressed in cancers and chronic pancreatitis

Cancers and chronic pancreatitis share many overexpressed proteins, which is expected since cancers are

frequently characterized by an inflammatory syndrome [53] and chronic pancreatitis is a long-standing inflammation

of the pancreas. Proteins involved in chronic inflammation such as S100-A8, S100-A9, immunoglobulins,

serotransferrin, complement C3 are, indeed, highly expressed in both conditions.

Moreover patients with chronic pancreatitis are at risk for developing cancer [59] and could therefore have similar

profiles at same stages of the desease. Chen et al.[59] in their work on tissue from patients with chronic pancreatitis,

pancreatic adenocarcinoma and normal controls, using ICAT labeling, found that diseased samples had 40% of the

differentially expressed proteins in common.

Interestingly, galectin-3-binding protein (Mac-2-BP), was found significantly increased by enzyme-linked

immunosorbent assay (ELISA) in bile from patients with biliary tract carcinoma in comparison with benign deseases

and primary sclerosing cholangitis [60]. The authors proposed it as a marker of cancer of the bilary tract. In the

present study, Mac-2-BP is overexpressed in both cancers but also in pancreatitis, even if at a lower extent.

Another interesting protein overexpressed in both cancers and chronic pancreatitis is neutrophil gelatinase-

associated lipocalin (NGAL) [68]. It is considered as an immunomodulator since it is up-regulated in epithelial cells

under different inflammatory conditions and it has been found increased in multiple human cancers [61].

31

On the data consistency

Other interesting data are nested into the analysis that we performed on human bile, many of them supporting the

validity of our approach and our choice of considering for quantitation also the proteins identified with one unique

tryptic peptide.

An example is represented by aminopeptidase-N. Isolated from the vesicular carrier of biliary lipids, it has been

proposed as a cholesterol crystallization-promoting by Núñez L et al. [62] and was find significantly overexpressed in

the sample from the patient with gallstones compared to the other conditions (CC/G=0.24, AC/G=0.24, P/G=0.22).

Another one is protocadherin-24 which is down-regulated in a number of liver and colon cancers and whose ratios

in our studies are very low when compared to gallstones.

Finally, many immunoglobulins appear to be overexpressed in pancreatitis compared to the other conditions as

expected in chronic inflammation.

32

CONCLUSIONS

The aim of this study was to perform, for the first time, a quantitative proteomic analysis on bile samples from

patients with biliary stenosis of different aetiologies: pancreatic adenocarcinoma, cholangiocarcinoma, gallstones and

chronic pancreatitis. Overall 130 proteins (63 with more than 2 unique peptides) were identified and relative

quantitation was performed on all of them, calculating ratios between all four conditions. Fourteen proteins were

found overexpressed in both cancers, two in pancreatic adenocarcinoma, two in cholangiocarcinoma, nineteen in

cancers and chronic pancreatitis, fourteen in pancreatitis, twelve in gallstones.

The workflow included sample preparation, protein digestion, iTRAQ labeling, OFFGEL fractionation, HPLC

separation and MALDI-TOF/TOF mass spectrometry.

Many purification steps were performed to clean the sample during sample preparation or fractionation, thus

introducing a potential loss of proteins and, as a consequence, of cancer biomarkers. Nevertheless, a number of

interesting proteins were found differentially expressed in the four samples. Some of these proteins (CEACAM6,

mucin-1, mac-2-binding protein) had already been found increased in bile from cancer patients by previous studies

confirming the validity of our approach.

Among the overexpressed proteins, gastricsin and glutamine-dependent NAD(+)synthetase , from the patient with

cholangiocarcinoma, keratin 9 and liver carboxylesterase 1 from the patient with pancreatic adenocarcinoma, mucin

5B and clusterin from both patients, deserve further investigations.

33

PERSONAL CONCLUSIONS

The day when I first arrived in the laboratory and I had a hard time finding the right tip for the right pipette seems

now a distant past. The competence and the pedagogic skills of Annarita Farina guided me through the months spent

at the bench. I learned not just how to use the instruments but also to understand the principles underneath the

different techniques, in order to get the best out of them. Many steps are required to go from crude bile samples to the

analysis on the mass spectrometer; accuracy and timing were everyday practice. Moreover I learned to read and

understand a protocol, to keep truck of every detail of the manipulations, to respect the common working space and

many other things that are part of the routine of every researcher. When we finally gathered the results of the iTRAQ

quantitation on the four bile samples, I thought that the most difficult part of the work was done but it was time to

analyze the results. It was a though work and I relied entirely on Annarita Farina and on the experience of other

persons from the laboratory. It requires a very critical attitude and an accurate research work in the literature to make

sense of the results. Proteins stop being just names on paper and burst to life. The variations in expression levels

between different samples have to be interpreted in the biological context and evaluated to draw conclusions on their

significance.

I’m aware that I had the chance to participate in a very rich experiment, from sample preparation, proteins

digestion, iTRAQ labeling, OFFGEL peptides fractionation, HPLC peptides separation, MALDI-TOF/TOF mass

spectrometry to protein identification, relative quantification and analysis of the results, in a friendly and stimulating

environment. I have been confronted with my limits and relied on other more experienced researchers to overcome

them, which contributed to enrich me both from a professional and a personal point of view.

34

AKNOWLEDGEMENTS

First of all I would like to thank Pierre Lescuyer and Annarita Farina for giving the opportunity to an

inexperienced person as was to participate in this enriching experiment. They introduced me with contagious

enthusiasm into the world of research and assisted me until the end. I particularly wish to thank Annarita for sharing

her professional experience with me and for making me feel at home in her laboratory. Her positive attitude was the

main source of my motivation in the difficult moments.

I really appreciated the friendly atmosphere and the generous assistance of many persons from other groups. No

question remained unanswered and I was always given assistance when I needed it.

I would also like to thank the coordinator of the Masters program, Dr Patricia Palagi, for encouraging me in the

first place when I still doubted if engaging in the Master and for her support throughout the duration of the course.

But if I’m here today is because my husband one day came home saying that he had found something really

interesting at the University of Geneva and encouraged me to take my chance.

35

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