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Page 1: Biomarker discovery for ovine paratuberculosis (Johne's disease) by proteomic serum profiling

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Comparative Immunology, Microbiology and Infectious Diseases 34 (2011) 315– 326

Contents lists available at ScienceDirect

Comparative Immunology, Microbiologyand Infectious Diseases

j o ur nal homep age : w ww.elsev ier .com/ locate /c imid

iomarker discovery for ovine paratuberculosis (Johne’s disease) byroteomic serum profiling

. Zhong1, D. Taylor, D.J. Begg, R.J. Whittington ∗

aculty of Veterinary Science, The University of Sydney, NSW, Australia

r t i c l e i n f o

rticle history:eceived 4 November 2010eceived in revised form 17 February 2011ccepted 10 March 2011

eywords:ELDIheeparatuberculosisiomarkerass spectrometryiagnosisroteomic fingerprint

a b s t r a c t

Paratuberculosis (Johne’s disease) is a chronic granulomatous enteritis affecting ruminantsand other species. It is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Inthis study, surface enhanced laser desorption ionization time-of-flight mass spectrome-try (SELDI TOF-MS) was used as a platform to identify candidate biomarkers from sheepserum. Multivariate biomarker models which aimed to differentiate sheep with paratuber-culosis and vaccinated-exposed sheep from unexposed animals were proposed based onclassification and regression tree (CART) and linear discriminant analysis (LDA) algorithmsfrom two array types. The accuracy of classification of sheep into unexposed or exposedgroups ranged from 75 to 100% among models. SELDI was used to monitor protein profilechanges over time during an experimental infection trial by examining sera collected at 4-,8- and 13-months post infection. Although three different SELDI instruments were used,nine consistent proteomic features were observed associated with exposure to MAP. Two of

the putative serum biomarkers were purified from serum using chromatographic methodsand were identified as transthyretin and alpha haemoglobin by tandem mass spectrometry.They belong to highly abundant, acute phase reactants in the serum proteome and havealso been discovered as serum biomarkers in human inflammatory conditions and cancer.

to the p

Their relationship

. Introduction

Paratuberculosis (Johne’s disease) of ruminants isaused by Mycobacterium avium subsp. paratuberculosisMAP) and is characterized by chronic granulomatousnteritis, lymphadenitis and progressive emaciation. Itas been reported worldwide. Paratuberculosis typically

rogresses through subclinical and clinical stages. Cur-ent diagnostic tests include indirect detection of theost immune response to infection or direct tests for

∗ Corresponding author at: Faculty of Veterinary Science, The Univer-ity of Sydney, 425 Werombi Road, Camden, NSW 2570, Australia.el.: +61 2 9351 1619; fax: +61 2 9351 1618.

E-mail address: [email protected]. Whittington).

1 Present address: Bioanalytical Mass Spectrometry Facility, The Uni-ersity of New South Wales, Sydney, New South Wales, Australia.

147-9571/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.cimid.2011.03.001

athogenesis of Johne’s disease remains to be elucidated.© 2011 Elsevier Ltd. All rights reserved.

the microorganism. Serologic tests such as enzyme-linkedimmunosorbent assays (ELISA), which rely on the detec-tion of antibodies produced during the humoral responseare more useful at a later stage of infection. In the earlystage, no single diagnostic test can definitively diagnoseinfection, although gamma interferon detection has shownsome promise [1,2]. Faecal culture or PCR tests are lim-ited to detecting animals that are already shedding theorganism into the environment [3–6] and for the latterthere is need to avoid potential cross-reactivity with envi-ronmental mycobacteria that contain IS900-like insertionsequences [7,8]. There is need for a new diagnostic test to bedeveloped that has greater sensitivity and specificity thancurrent tests, and especially one that has the capacity to

detect animals in the early stage of infection.

The emerging field of proteomics provides an approachto analyse the distribution and abundance of proteins, andidentify and characterize individual proteins of interest.

Page 2: Biomarker discovery for ovine paratuberculosis (Johne's disease) by proteomic serum profiling

crobiolog

316 L. Zhong et al. / Comparative Immunology, Mi

Mass spectrometry (MS) is now commonly used to detectand identify proteins. Surface-enhanced laser desorptionionisation time-of-flight (SELDI TOF) MS is a proteomictool distinct from two dimensional gel electrophoresisand matrix assisted laser desorption ionisation MS, whichare non-selective and have been used in some veterinarystudies. SELDI technology has been applied in biomedi-cal research in particular to identify potential diagnosticmarkers for ovarian, prostate, breast, renal as well as headand neck cancer. Recently, this technique has been used ininvestigating viral [9–11] and bacterial infections [12,13].There have been few such studies in veterinary medicine– fascioliasis in sheep [14], Dichelobacter nodosus vacci-nal responses [15] and a mouse model of transmissiblespongiform encephalopathy [16] – but in each putativebiomarkers were discovered.

In this study, we assessed the potential of proteomicprofiling using SELDI analysis of whole serum to identifybiomarkers in sheep with MAP exposure. Whole serumwas used based on the findings of a previous validationstudy in sheep [15]. Protein profiles at 4-, 8- and 13-monthspost infection were monitored, and two of the putativebiomarkers were purified from serum and identified.

2. Materials and methods

2.1. Sample characteristics

2.1.1. Cross-sectional study in naturally andexperimentally infected sheep.

In this first experiment, a total of 89 sheep aged from 2 to4 years were selected and divided into groups: unexposed,vaccinated-exposed and infected (Table 1). Unexposedsheep (n = 29) were adult female Merino sheep selectedfrom a property at Armidale in the New England regionof New South Wales in which paratuberculosis had notpreviously been reported and where sheep were shownto be free of Johne’s disease by repeated testing usingantibody ELISA, faecal culture of the whole flock and tis-sue culture of cull sheep [17]. Vaccinated-exposed sheep(n = 30) were 2–3 year old adult female Merinos selectedfrom the University of Sydney Farm “Arthursleigh” at Maru-lan, New South Wales; they were administered 1 ml ofoil adjuvant GudairTM vaccine subcutaneously at threemonths of age. These sheep potentially were exposed toMAP on the farm from birth by grazing pasture that wascontaminated naturally by a MAP-infected flock. Infectedsheep (n = 30) were comprised of 26 naturally infectedfemale adult Merinos, and four experimentally infectedfemale cross-bred sheep. The naturally infected sheepwere obtained from a property at Bathurst, New SouthWales and were housed in pens at Camden for two weeksprior to sample collections. They were fed a mixture oflucerne chaff and lupins. The four experimentally infectedsheep were obtained from the property at Armidale andwere challenged orally with 1 × 107 MAP strain Telford 9.2three times at weekly intervals from four months of age

(from trial 3 in [17]); the serum samples were collected20 months after last challenge. Infection of each sheep inthe infected group was confirmed by culture of intestinaltissues and associated lymph nodes [18] and histopathol-

y and Infectious Diseases 34 (2011) 315– 326

ogy [19]. Histological classification was according to thePerez grades: multibacillary (3b) (n = 10 sheep); early pau-cibacillary (3a) (n = 12 sheep) and; late paucibacillary (3c)(n = 8 sheep) (Table 1).

2.1.2. Longitudinal study of experimentally infectedsheep

In this second experiment, paratuberculosis–freeMerino lambs (n = 57) were acquired from the Armidaleproperty and at three months of age, were randomlyallocated into three groups: (i) unexposed control group(n = 20); (ii) exposed by inoculation to gut homogenatefrom an infected sheep (n = 19); and (iii) exposed to a pureculture of MAP (n = 18) as previously described (trial 6 in[17]). Between the 8- and 13-month sample collections,11 animals were euthanased – 7 animals from group(ii) and 2 from group (iii) which had clinical signs ofparatuberculosis and 2 clinically normal sheep from thecontrol group. After the 13-month post inoculation samplecollection, all remaining animals were euthanased withintravenous sodium pentobarbital. Mesenteric lymphnodes and intestinal tissues were collected at necropsyto enable the status of all animals to be confirmed byhistological and cultural examinations. At the end of trial,25 sheep were classified as infected, 12 from group (ii)and 13 from group (iii) while 32 sheep were classified asuninfected (Table 2). Serum samples were collected at 4-,8- and 13-months post inoculation and were subjected toSELDI analysis as described in Table 2.

All sheep in both experiments were the outbred progenyof natural mating using multiple sires and were managedunder conventional Australian sheep farming conditions bygrazing in open paddocks on unimproved pasture, unlessotherwise stated above, and had ad libitum access to water.

2.2. Sample preparation and SELDI array processing

Sample collection from the jugular vein and serumpreparation for SELDI analysis and ProteinChip array pro-cessing were described previously [15]. Based on pilotexperiments (data not shown), two array types, immo-bilized metal affinity capture (IMAC 30) arrays activatedwith NiSO4 and strong anion exchange (Q10) arrays wereselected. One serum sample from an unexposed sheep wasused as a quality control (QC) sample and was added toone spot of each array. Duplicates of all samples were pro-cessed. Each array (chip) had 8 test spots.

2.3. SELDI data acquisition and cluster generation

Arrays were analysed on Protein Biology System II(PBSIIc) SELDI TOF mass spectrometers (Ciphergen). Threeinstruments, designated as M1, M2 and M3 were usedin this study. The spectra in the first experiment wereacquired from M1 and M2. In the second experiment thespectra of sera 4-months post infection were acquired fromM2 while those for sera 8- and 13-months post infection

were acquired from M3. The three instruments had thesame model number, but were calibrated separately andindependently. M1 belonged to institution I and M2 and M3belonged to institution II. Peak detection was performed
Page 3: Biomarker discovery for ovine paratuberculosis (Johne's disease) by proteomic serum profiling

L. Zhong et al. / Comparative Immunology, Microbiology and Infectious Diseases 34 (2011) 315– 326 317

Table 1Animal population characteristics in experiment 1.

Group No. of animals Age Gender Breed Histological examinationa Tissue cultureb Faecal cultureb

Unexposed 29 3 Female Merino 29 negative 29 negative 29 negativeVaccinated-exposed 30 2 Female Merino N/A N/A 30 negativeInfected 26 3–4 Female Merino 26 positive 26 positive 26 positive

4 2 Female Cross-bred 4 positive 4 positive 4 positive

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a Histological examination of terminal ileum (3 sites), jejunum (3 siteleum and jejunum; lesion grades were multibacillary (n = 10 sheep); earl

b Determined by growth in radiometric culture system (BACTEC 12B) a

sing ProteinChip software version 3.0 (Ciphergen). UsingMAC arrays, only low molecular weight spectra were col-ected while both low and high molecular weight spectra

ere collected when using the Q10 arrays. Spectra wereormalized to total ion current intensity starting and end-

ng at the m/z of the collection ranges. Cluster generationas performed with ProteinChip Biomarker Wizard soft-are version 3.1 (Ciphergen).

.4. Data analysis

.4.1. Univariate analysisThe Significance Analysis of Microarray (SAM) algo-

ithm (Stanford University) was used for univariatenalysis to identify significant proteomic features betweenontrasting animal populations as described previously20,21].

.4.2. Multivariate analysisConstruction of classification and regression trees

CART) was performed using Biomarker Pattern SoftwareBPS) version 5.0.2 (Ciphergen), as described by Grajskit al. [22] with modifications. The V-fold cross-validation

unction was used to evaluate the accuracy of each deci-ion tree. Multivariate linear discriminant analysis (LDA)as performed in MiniTab (MiniTab for Windows 12.21,iniTab Inc., State College, PA, USA).

able 2nimal population characteristics in experiment 2.

Group No. ofanimals

Histologicalexaminationa

Tissuecultureb

No. of animalsinfectedc

4-Minfe

No.samcolle

Unexposed 20 All negative All neg-ative

– 20

Pure culture 20 13 positive 13 pos-itive

13 18

Gut homogenate 19 14 positive 13 pos-itive

12 19

a Histological examination of terminal ileum (3 sites), jejunum (3 sites) and oleum and jejunum.

b Determined by growth in radiometric culture system (BACTEC 12B) at 37 ◦C wulosis (MAP) was confirmed by IS900 PCR amplification.

c An animal was classified as infected if it had positive results in both histologyd In the infection groups only samples from confirmed infected sheep were proce

nd discarded.e Eleven animals, which included 7 animals from the gut homogenate group, 2 f

he control group, were euthanized at 14 months of age due to poor body conditi

ne section of mesenteric lymph node corresponding to each section ofacillary (n = 12) and late paucibacillary (n = 8).ithin 12 weeks; the presence of MAP was confirmed by IS900 PCR.

2.5. Reproducibility of multivariate models created fromspectra from two different SELDI instruments

Spectra obtained from the same IMAC 30 arrays read onmachines M1 and M2 were compared to enable a prelim-inary assessment of whether between-machine variationwould affect the discovery of putative biomarkers usingCART analysis.

2.6. Purification and identification of biomarkers fromserum

Multiple chromatographic steps, including depletion ofdominant components such as immunoglobulin and albu-min, metal affinity, ion exchange and gel filtration wereapplied to purify candidate biomarkers. As the nature ofspectral peaks is unknown, a trial and error approach isnecessary to determine the optimal procedures for purifi-cation of each peak. Three purification schemes weredeveloped (Fig. 1). At each step of purification, all frac-tions were collected, concentrated, buffer exchanged, andexamined by gel electrophoresis. SELDI was used to deter-mine whether fractions contained the peaks of interest.In purification scheme I, three affinity chromatography

resins (Protein G, Protein A and CibacronTM Blue F3G-A) were evaluated to remove highly abundant proteins(immunoglobulin and/or albumin) from sheep serum. Fol-lowing this, the depleted serum containing the proteins

onths postction

8-Months postinfection

13-Months postinfection

plescted

No.samplesprocessedon SELDI

No.samplescollected

No.samplesprocessedon SELDId

No.samplescollectede

No.samplesprocessedon SELDI

20 20 19 18 18

18 18 12 16 11

19 19 12 12 5

ne section of mesenteric lymph node corresponding to each section of

ithin 12 weeks; the presence of Mycobacterium avium subsp. paratuber-

and tissue culture.ssed on SELDI; one infected and one unexposed sample were haemolyzed

rom the pure culture challenge group and 2 clinically normal sheep fromon.

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318 L. Zhong et al. / Comparative Immunology, Microbiology and Infectious Diseases 34 (2011) 315– 326

otein pu

Fig. 1. Schematic diagram showing pr

of interest was separated based on molecular size usinggel filtration chromatography. In purification scheme II,ion exchange chromatography was employed to sepa-rate sheep serum according to differences in the netsurface charge of its constituent proteins, followed byfurther separation using gel filtration. The depletion ofhigh abundance proteins from serum, though enrichinglow abundant species, may also lead to the loss of lowmolecular weight proteins carried by large molecules.As candidate serum biomarkers were identified usingnon-depleted reduced serum probed on IMAC 30 Ni2+

arrays which bind proteins through histidine, cystine andphosphorylated amino acids, in purification scheme IIIsimilar conditions were used: non-depleted denaturedserum was applied to Ni2+ ion chelated affinity chro-matographic resin to capture such proteins. The successfulapproach determined for two proteins of interest was asfollows.

Starting with 1 ml sheep serum, two candidate pro-teins were eluted from an anion exchange HiTrap Q HP,5 ml column (GE) at pH 7 on an ÄKTApurifier (GE Health-care) protein purification system. Purified fractions wereexamined by SELDI and by SDS–PAGE on 10–20% lineargradient Tris-Tricine gels (Ready Gel®, Bio-Rad Laborato-ries). Bands of interest that were observed in silver orCoomassie Brilliant blue stained gels were excised andplaced into a 1.5 ml microcentrifuge tube. Two hundredmicrolitres of a solution comprising 50% (v/v) methanol and10% (v/v) acetic acid was added and the tube was shakenfor 30 min. After removal of the solvent, 100 �l acetonitrile(ACN, Sigma) was added and shaken for 15 min. The proteinwas then extracted with 15 �l of a solution that contained50% (v/v) formic acid, 25% (v/v) ACN, 15% (v/v) isopropanoland 10% (v/v) deionized water for 2 h at room tempera-ture with vigorous shaking, and then 2 �l of the extract was

transferred to an NP20 array for further analysis by SELDIMS.

Immunodetection by Western blotting was performedto confirm the identity of transthyretin. The primary anti-

rification schemes used in this study.

body was polyclonal anti-human transthyretin producedin rabbit (1:1000 dilution) (DakoCytomation) and sec-ondary antibody was peroxidise-conjugated anti-rabbitIgG (whole molecule) produced in goat (1:1000 dilutionin PBS) (Sigma).

The excised bands of interest were placed into a1.5 ml screwcap tube and subjected to trypsin diges-tion. Coomassie blue stain was removed by incubatinggel bands in 200 �l NH4HCO3 (25 mM) in 25% (v/v)acetonitrile until clear. Gel bands were digested by incu-bation with 40 �l DTT (10 mM) in NH4HCO3 (50 mM)for 30 min at 37 ◦C. After removal of the solvent, thebands were incubated with 40 �l iodoacetamide (25 mM)in NH4HCO3 (50 mM) for 30 min at 37 ◦C. The bandswere then washed with 50 �l acetonitrile twice, 10 mineach and 40 �l trypsin (∼100 ng) in NH4HCO3 (25 mM)was added then the solution incubated at 37 ◦C for14 h. After the trypsin digest, the bands were washedwith 50 �l deionized water with 0.1% (v/v) formic acidand 100 �l of acetonitrile for 15 min. The extractedpeptides were dried under vacuum and dissolved in10 �l deionized water with 0.05% (v/v) heptafluorobu-tyric acid and 1% (v/v) formic acid, and then subjectedto liquid chromatography–tandem mass spectrometry(LC–MS/MS) analysis. Digested peptides firstly were sep-arated by nano-liquid chromatography (LC) using a Cap-LCautosample system (Waters, Milford, MA). Samples wereconcentrated and desalted using a micro C18 precol-umn (500 �m × 2 mm, Michrom Bioresources, Auburn, CA),which was connected to an Ultima API hybrid quadrupletime-of-flight (Q-TOF) tandem mass spectrometer (Micro-mass, Manchester, UK). Peak lists were generated byMassLynx (Micromass) using the mass measure programand searched against the NCBInr non-redundant proteindatabase using the Mascot (version 2.1, Matrix Science,

London, England). The ions score significance thresh-old was set to 0.5 and each protein was provided witha probability based Mowse (Molecular Weight Search)score [23].
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. Results

.1. Proteomic features in the serum of adult sheepxposed to MAP

SELDI TOF MS was used to identify proteomic fea-ures between contrasting animal populations: (1) infectedheep versus unexposed sheep and (2) sheep vaccinatedith GudairTM vaccine and exposed to MAP versus unex-osed sheep. For logistical reasons, in the unexposed group,2 of 29 sheep were used initially to generate spectra onhe IMAC 30 arrays while all 29 sheep were used on the Q10rrays (Table 1). An animal exposed to MAP early in life mayecome a ‘carrier’ and not develop disease until much later

n life. At this early stage, ante-mortem tests such as faecalulture or blood tests do not show positive results. There-ore it is difficult to be sure that an exposed animal is notnfected unless necropsy is performed to obtain tissues forulture of MAP. One aim of this study was to identify serumroteomics features in sheep which were exposed but notbviously infected.

.1.1. Univariate analysisUsing SAM analysis at a median false discovery rate

f zero, a panel of significant differentially expressedroteomic features was identified between contrastingopulations (Table 3). When infected animals were com-ared to unexposed controls, 10 proteins were foundo be up-regulated (range 1.6–3.2-fold) and another 10ere down-regulated (1.7–4.6-fold). Similarly, a total of

0 proteins had significant expression level changes inhe vaccinated-exposed animals; 16 were up-regulated1.2–6.5-fold) and 4 were down-regulated (1.7–5-fold)Table 3). There were a number of differentially expressedroteins common to both infected and vaccinated-exposednimals relative to the control group but with a slightlyifferent fold change in each group. Proteins with massesf 7936, 8496, 8595, 8651, 8702, 10,230, 38,902 and77,495 Da were up-regulated to similar levels in both

nfected and vaccinated-exposed animals. Three pro-eins with masses of 11,570, 13,752 and 13,812 Da wereown-regulated in both vaccinated-exposed and infectednimals.

.1.2. Multivariate analysis

.1.2.1. Comparison of infected versus unexposed animals.iomarker Pattern Software (Ciphergen) was used to gen-rate classification and regression tree (CART) models toimultaneously find peaks and identify protein patternshat separated infected sheep or vaccinated-exposed sheeprom unexposed animals. CART model I was generatedsing 104 spectra derived on IMAC 30 arrays from 52 serumamples (30 infected and 22 unexposed sheep, tested inuplicate). This model had three decision nodes, basedn the intensity of three proteins with masses of 13,752,651 and 8305 Da. The 13,752 Da protein was used as theoot decision node (Fig. 2). In cross-validation, this model

chieved a sensitivity of 90.0% in identifying infected ani-als (27 of 30) and a specificity of 90.9% in identifying

nexposed animals (20 of 22) (Table 4). Similarly, CARTodel II was developed using 118 spectra obtained on

y and Infectious Diseases 34 (2011) 315– 326 319

Q10 arrays from 59 serum samples (30 infected and 29unexposed sheep, tested in duplicate). Interestingly, thismodel had only one decision node based upon a proteinof 8263 Da and this protein was 4.6-fold down-regulatedin the univariate analysis (Fig. 3). Using this decision algo-rithm, 93.1% (27 of 29) unexposed animals and 83.3% (25of 30) infected animals were correctly classified following10-fold cross validation (Table 4).

Linear discriminant analysis (LDA) was used as an alter-native to CART analysis to identify a subset of proteins thatcould discriminate infected sheep from unexposed sheep.LDA model I was generated using spectra obtained fromIMAC 30 arrays and five proteins with masses of 13,752,10,230, 8305, 16,074 and 8651 Da were identified (Table 4).Compared to CART model I, the 13,752, 8305 and 8651 Daproteins were common and the 13,752 Da protein was cho-sen as the best discriminator as well as the root decisionnode in both models. LDA model I correctly identified 20of 22 unexposed animals and 27 of 30 infected animals inthe cross-validation, which was the same accuracy as CARTmodel I (Table 4). Similarly, LDA model II was generatedusing spectra derived from the Q10 arrays and consisted oftwo proteins with masses of 8263 and 8496 Da. The 8263 Daprotein had the greatest discriminatory power and was alsoselected as the root decision node in CART model II. Theaccuracy of this model was close to that of CART model II(Table 4).

3.1.2.2. Comparison of vaccinated-exposed versus unexposedanimals. CART model III was developed from 104 spec-tra generated from 52 serum samples applied in duplicateon IMAC 30 arrays (Fig. 4). This classification tree hadtwo decision nodes with masses of 8596 and 13,752 Da,and the former protein was chosen as the root deci-sion node. This classification model correctly identified 21of 22 unexposed animals and 29 of 30 vaccinated ani-mals in the 10-fold cross-validation (Table 5). Notably,the 13,752 Da protein was involved also in CART model Ito differentiate infected from unexposed sheep. Similarly,CART model IV was generated using 118 spectra obtainedon Q10 arrays from 59 serum samples (30 vaccinated-exposed and 29 unexposed sheep tested in duplicate). Themodel contained five decision nodes based on intensityof four proteins with masses of 8496, 74,786, 8263 and13,634 Da, with the 8496 Da protein chosen as the rootbiomarker (Fig. 5). Although the 13,634 Da protein wasnot significantly altered in the univariate analysis, it waspart of classification algorithm in the multivariate analysis.This model correctly classified 24 of 30 vaccinated-exposed sheep and 24 of 29 unexposed animals followingcross-validation (Table 5).

LDA model III was developed using a linear combina-tion of four proteins of masses, 8595, 13,752, 9691 and9320 Da. The protein of mass 8595 Da was identified as hav-ing the maximum contribution to the model, and notablywas also selected as the root node in CART model III. Thisdiscriminant model correctly identified 20 of 22 unexposed

animals and all vaccinated-exposed animals during thecross-validation process (Table 5). LDA model IV was cre-ated using four proteins, with masses of 8496, 93,973, 8353and 77,494 Da acquired from Q10 arrays. In comparison to
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320 L. Zhong et al. / Comparative Immunology, Microbiology and Infectious Diseases 34 (2011) 315– 326

Fig. 2. CART model I: infected versus unexposed samples. This classification decision tree was generated from IMAC 30 array spectra. 52 serum sampleswere used as a learning set to generate a decision tree to distinguish sera obtained from sheep infected with paratuberculosis from those from unexposedsheep. There are three decision nodes, based on the intensity of three different proteins with masses of 13,752, 8651 and 8305 Da. Intensity cut-points areshown after the mass of each protein. Terminal nodes determine whether a sample is classified as unexposed or infected.

Fig. 3. CART model II: infected versus unexposed samples. This classification decision tree was generated from Q10 array spectra. Spectra from 59 serumsamples were used as a learning set to generate a decision tree to distinguish sera obtained from sheep infected with paratuberculosis from unexposedsheep. There is one decision node, based on the intensity of one protein with mass of 8263 Da. Terminal nodes determine whether a sample is classified asunexposed or infected.

Fig. 4. CART model III: vaccinated-exposed versus unexposed samples. This classification decision tree was generated from IMAC 30 array spectra. Spectrafrom 52 serum samples were used as a learning set to generate a decision tree to distinguish sera obtained from sheep vaccinated with GudairTM vaccinefrom unexposed, un-vaccinated sheep. There are two decision nodes, node 1 and node 2, based on intensity of two different proteins with masses of 8595and 13,752 Da, respectively. Terminal nodes determine whether a sample is classified as unexposed or vaccinated.

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L. Zhong et al. / Comparative Immunology, Microbiology and Infectious Diseases 34 (2011) 315– 326 321

Table 3Differentially expressed proteomic features between unexposed versus infected animals and unexposed versus vaccinated-exposed animals from twoarray types by univariate analysis in order of m/z.

No. m/z Infection Vaccination-exposure q-Valuea ProteinChip array Peak used

Effect Fold change Effect Fold change CART LDA

1 2184.2 – – Up 2.56 0 Q102 5167.0 – – Down −1.97 0 IMAC 303 5188.2 – – Up 1.79 0 IMAC 304 5212.2 – – Up 2.57 0 IMAC 305 7936.8 Up 1.64 Up 2.08 0 IMAC 306 8263.0 Down −4.63 – – 0 Q10 Xb Xb

7 8305.4 Down −2.65 – – 0 IMAC 30 Xb Xb

8 8353.2 Down −1.91 – – 0 Q109 8496.5 Up 1.70 Up 2.94 0 Q10 Xc Xb,c

10 8595.9 Up 1.60 Up 6.50 0 IMAC 30 Xc Xc

11 8651.0 Up 2.91 Up 2.58 0 IMAC 30 Xb Xb

12 8702.1 Up 1.82 Up 2.79 0 Q1013 8725.5 Down −1.85 – – 0 IMAC 3014 9259.9 Up 1.95 Up 1.76 0 Q1015 9320.6 – – Up 1.79 0 IMAC 30 Xc

16 9461.3 – – Up 1.69 017 9691.6 – – Up 1.95 0 IMAC 30 Xc

18 10,230.6 Up 2.16 Up 1.77 0 IMAC 30 Xb

19 11,569.9 Down −2.18 Down −1.73 0 IMAC 3020 13,752.7 Down −3.49 Down −4.96 0 IMAC 30 Xb,c Xb,c

21 13,812.2 Down −3.06 Down −4.09 0 IMAC 3022 38,901.8 Up 3.20 Up 3.74 0 Q1023 44,534.4 Down −2.44 – – 0 Q1024 47,114.7 Down −1.70 – – 0 Q1025 74,786.4 Down −2.23 Up 1.21 0 Q10 Xc

26 77,494.6 Up 2.52 Up 3.81 0 Q10 Xc Xc

a q-Value: this is the lowest false discovery rate at which the protein is called significant in the SAM analysis. It is like the P value, but adapted to thea

iate mod in mult

Cwtpwc(s

TCd

nalysis of a large number of proteins.b Peaks selected to differentiate infected versus unexposed in multivarc Peaks selected to differentiate vaccinated-exposed versus unexposed

ART model IV, the best discriminator (8496 Da protein)as also the root decision node while the other three pro-

eins were different to those used in the CART model. Tworoteins selected in LDA model IV (93,973 and 8353 Da)

ere not significantly altered in univariate analysis. In the

ross-validation, this model correctly identified 26 of 3086.7%) vaccinated sheep and 27 of 29 (93.1%) unexposedheep (Table 5).

able 4lassification accuracy of models developed in experiment 1: classification and

erived from infected versus unexposed sheep.

Unexposed

Correct/total

CART model I – using spectra generated from IMAC 30 arrays using M1Training set resubstitution results 21/22

Training set cross-validation results 20/22

CART model II – using spectra generated from Q10 arrays using M1Training set learning results 27/29

Training set cross-validation results 27/29

CART model V – model generated from IMAC 30 arrays using M2Training set learning results 24/29

Training set cross-validation results 22/29

LDA model I – using spectra generated from IMAC 30 arrays using M1Training set resubstitution results 20/22

Training set cross-validation results 20/22

Discriminant function: scores = 10.67 + 0.80 M8305 − 0.11 M8651 − 2.40 M10LDA model II – using spectra generated from Q10 arrays using M1

Training set resubstitution results 28/29

Training set cross-validation results 28/29

Discriminant function: scores = 9.08 + 0.22 M8263 − 0.12 M8496

els.ivariate models.

3.1.3. Reproducibility of multivariate models betweeninstruments M1 and M2

To evaluate reproducibility of spectra between SELDImachines, 52 serum samples probed on IMAC 30 arrays

were examined on two instruments, M1 and M2. Whileunivariate analysis of spectra acquired from both SELDIinstruments resulted in similar significant proteomic fea-tures being identified and with similar magnitudes of fold

regression tree (CART) and linear discriminate analysis (LDA) of spectra

Infected

Percent Correct/total Percent

95.4 28/30 93.390.9 27/30 90.0

93.1 25/30 83.393.1 25/30 83.3

82.7 27/30 90.075.9 22/30 75.3

90.9 29/30 96.990.9 27/30 90.0

230 + 3.19 M13752 − 0.28 M16074

96.5 26/30 86.696.5 26/30 86.6

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Table 5Classification accuracy of models developed in experiment 1: classification and regression tree (CART) and linear discriminate analysis (LDA) of spectraderived from vaccinated-exposed versus unexposed sheep.

Unexposed Vaccinated-exposed

Correct/total Percent Correct/total Percent

CART model III – using spectra generated from IMAC 30 arrayTraining set resubstitution results 22/22 100.0 29/30 96.7Training set cross-validation results 21/22 95.5 29/30 96.7

CART model IV – using spectra generated from Q10 arrayTraining set learning results 29/29 100.0 27/30 90.0Training set cross-validation results 24/29 82.7 24/30 80.0

LDA model III – using spectra generated from IMAC 30 arrayTraining set resubstitution results 20/22 90.9 30/30 100.0Training set cross-validation results 20/22 90.9 30/30 100.0Discriminant function: scores = 11.79 + 0.39 M8595 − 1.31 M9320 + 5.26 M9691 − 4.62 M13752

LDA model IV – using spectra generated from Q10 array

0.91 M8

Training set resubstitution results 27/29

Training set cross-validation results 27/29Discriminant function: scores = 10.14 + 0.23 M8496 − 8.78 M93793 −

change, the intensities of the majority of the peaks in allsamples generated in M1 were higher than in M2. Impor-tantly, two proteins with masses of 13,752 and 13,812 Da,had significant alteration between exposed and unexposedsheep using the spectra acquired from M1, but were notresolved properly using M2. The 13,752 Da protein hadbeen selected as the root decision node in CART model Ias shown in Fig. 1.

These data indicated that spectral patterns developedusing multivariate analysis may be instrument-specific.Reproducibility was determined to be sufficient for

biomarker discovery using more than one instrument, butwith loss of power due to machine variation. To test this,CART model V was generated using 118 spectra acquired

Fig. 5. CART model IV: vaccinated-exposed versus unexposed samples. This classi59 serum samples were used as a learning set to generate a decision tree to distiunexposed sheep. There are five decision nodes, based on the intensity of four prdetermine whether a sample is classified as vaccinated or unexposed.

93.1 26/30 86.793.1 26/30 86.7

353 + 1.31 M77494

from M2 and derived from IMAC 30 arrays from 59 serumsamples which were used in experiment 1 but includ-ing 7 more unexposed sheep. The model contained threedecision nodes, based on intensity of three proteins withmasses of 8305, 8651 and 11,569 Da, with the 8305 Daprotein chosen as the root decision node. In the V-foldcross validation, this decision model correctly identified22 of 29 unexposed animals and 22 of 30 infected sheep(Table 4). Comparison of CART model V from machine M2and CART model I from machine M1 confirmed a similarmodel structure. Two common proteins with masses of

8305 and 8651 Da were selected in both models. However,the root decision node in the two CART models was dif-ferent: in CART model I, a 13,752 Da protein was chosen

fication decision tree was generated from Q10 array spectra. Spectra fromnguish sera obtained from sheep vaccinated with GudairTM vaccine fromoteins with masses of 8496, 74,786, 8263 and 13,634 Da. Terminal nodes

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ut it was not present in the CART model V mainly dueo the low resolution and lower intensity of this protein inpectra from M2. Instead, the 8305 Da protein was selectedy the algorithm to be the root decision node in CARTodel V. Even though the two CART models were simi-

ar, the discrepancy between the key root decision nodesn the two models made the validation of biomarker pat-erns generated from M1 using the spectra obtained from

2 impossible. These results indicate that model valida-ion must be performed using spectra acquired from theame SELDI instrument.

.2. Proteomic features in early stages of MAP infection

Sera from experimentally infected sheep collected overime were examined using IMAC 30 arrays to determinehether there were differences in the serum proteome

ssociated with time elapsed since infection. Due to instru-ent service difficulties, two instruments M2 and M3 were

mployed: sera collected 4-months post infection werexamined on M2 while sera collected 8- and 13-monthsost infection were analysed on M3.

.2.1. Univariate analysisUnivariate analysis of contrasting samples at each time

oint revealed that significant proteomic features wereifferent between the time points. At 4-months post inoc-lation, 8 differentially expressed protein features were

dentified; at 8-months, only one peptide, with a mass of651 Da remained 2.17-fold up-regulated in the infectednimals and at 13-months post inoculation, no peptide wasound to be significantly increased in the infected sheepnd only two peptides, with masses of 5691 and 8305 Da,ere found to be down-regulated relative to the unexposed

ontrols (Table 6).A comparison of the differentially expressed protein

eatures identified at these three time points with thosedentified in infected 3 year old sheep revealed severalifferences. The peptide with a mass of 8305 Da had a 2.6-old decrease in infected sheep, but showed no significanthange until 13-months post inoculation. The peptide with

mass of 8651 Da had a 2.2-fold increase in 3 year oldnfected sheep and also showed a more than 2-fold up-egulation at both 4-months and 8-months post infection,ut no significant alteration was observed at 13-months.wo peptides with masses of 7936 and 13,812 Da showed

1.6-fold increase and a 3-fold decrease, respectively, in 3ear old infected sheep, but were not altered at 4-, 8- and3-months post infection (Table 6). These results indicatedhat proteomic features are likely to be distinct at differentimes after infection.

.3. Purification of candidate proteins

Even without knowing the identity of the proteins in theiomarker patterns, the CART models differentiated con-rasting animal samples as shown in the results. However,

he characterization and identification of these proteins

ay provide an insight into disease progression. Nine pro-eins were observed to be common to different stages ofnfection in this study and two were selected for purifi-

y and Infectious Diseases 34 (2011) 315– 326 323

cation and identification: the 8496 Da protein which wasthe root decision node in CART model IV to discriminatevaccinated-exposed sheep and unexposed controls; the13,752 Da protein which was the root decision node inCART model I to differentiate infected sheep from con-trols. The following purification strategy was successful:1 ml sheep serum was separated on an anion exchangecolumn (HiTrap Q) at pH 7 and fractions that containedthe peaks of interest were further separated by gel fil-tration. The two candidate biomarkers were eluted andidentified. At each step of purification, all fractions werecollected, concentrated and examined by gel electrophore-sis. The band of interest was excised from the gel, and twosteps were performed to confirm the identity of the protein.Firstly, SELDI TOF-MS using a normal phase NP20 array, anon-specific surface, to show that the observed molecularweight of the protein extracted from the gel was consistentwith the candidate biomarker identified during the previ-ous biomarker discovery experiments. Secondly, the bandwas subjected to in gel trypsin digestion and examinedby LC–MS/MS to obtain the identity of any proteins thatmay be present. The 8496 Da marker was derived from thealpha haemoglobin chain while the 13,752 Da marker wasderived from transthyretin (TTR). The identity of TTR wasconfirmed by Western blotting using a specific antibody; afaint but specific band was observed in the fractions thatcontained this protein (Fig. 6).

4. Discussion

Paratuberculosis has a prolonged incubation period,measured in years, and during the initial pre-clinical stages,there are no effective ante-mortem tests to distinguishexposed, infected and non-exposed sheep. The general aimof this study was to identify candidate biomarkers relatedto the pathogenesis of paratuberculosis. It was divided intotwo parts: firstly a study of adults to identify differencesbetween exposed/infected/vaccinated sheep and a group ofcontrols which had not been exposed to MAP, and secondlyan attempt to discover changes during the progression ofthe infection following exposure of young sheep.

In the first part, samples from 89 sheep (30 infected, 30vaccinated-exposed, 29 unexposed controls) were used togenerate proteomic profiles by SELDI TOF-MS. There werea number of differentially expressed proteins common toboth infection and vaccination-exposure, but fold changediffered between these contrasts. Proteins with masses of8496, 8595, 8651, 8702, 9259, 10,230, 38,902 and 74,786 Dawere all up-regulated in both infected and vaccinated-exposed groups while four proteins with masses of 11,569,13,752, 13,812 and 74,786 Da had decreased expressionlevel. The overlap of proteins identified in response toinfection and vaccination-exposure relative to unexposedcontrols was not unexpected as the vaccinated animalswere exposed to MAP and some of them may have beeninfected. Conceptually, the sensitization from vaccine mayhave initiated immune responses overlapping those due to

infection and produced similar serum protein profiles.

To enhance the selection of proteins of significance,two independent multivariate analysis tools, CART andLDA, were used. A comparison of the proteins selected

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324 L. Zhong et al. / Comparative Immunology, Microbiology and Infectious Diseases 34 (2011) 315– 326

Table 6Comparison of differentially expressed proteomic features between experimentally infected and unexposed animals at 4-, 8- and 13-months post inocu-lation for those proteins which were significantly altered in 3 year old infected sheep, in order of m/z.

m/z SELD instrument M2 SELDI instrument M3 SELDI instrument M2

4-Months post infection 8-Months post infection 13-Months post infection Experiment 1 infected 3 years old

Fold change Infection effect Fold change Infection effect Fold change Infection effect Fold change Infection effect

4365 1.33 Up4632 1.24 Up5691 1.44 Up −1.45 Down7936 1.60 Up8028 −2.13 Down8305 −1.71 Down −2.60 Down8651 2.24 Up 2.17 Up 2.10 Up8725 2.22 Up −1.56 Down

11,569 1.54 Up

13,812

16,041 −1.54 Down

by the two multivariate methods for both infected andvaccinated-exposed versus unexposed animals showedthat the primary discriminator/root decision nodes werethe same in both methods, and the accuracy of both meth-ods in cross-validation was very similar. An examinationof the proteins utilized in both models revealed some thatwere increased and some were decreased in intensity. Thissuggests that the disease or vaccination status of the ani-mal can elevate or diminish the levels of circulating serumproteins or peptides, and these changes could be used to

detect the presence of disease or vaccination.

Throughout the study, the instability of SELDI instru-ments and poor availability of service created enormous

Fig. 6. Confirmation of the identity of the 13,752 Da protein as transthyretin. (A)

blot with commercial anti-transthyretin polyclonal antibody. Lane 1, PageRulerexchange column; lane 3, fraction p4-G1, separation of P4 by gel filtration; lane 4,transthyretin.

−2.36 Down−3.03 Down

difficulties and necessitated use of three SELDI instru-ments, designated as M1, M2 and M3. The first part of thestudy was conducted using M1 and M2, which were main-tained and calibrated separately and independently in twoinstitutions. A comparison of the spectra generated fromthe two instruments revealed higher intensity of spectra inM1 than M2. The most important difference was that the13,752 Da protein, which was the main discriminator in themultivariate CART model I for infected versus unexposedanimals (Fig. 1), could not be properly resolved on the sec-

ond instrument M2; the root decision node was replaced byan 8305 Da protein to produce CART model V. Therefore thevalidation of candidate biomarker patterns generated from

SDS–PAGE 10–20% Tricine gel stained with Coomassie blue; (B) WesternTM prestained protein marker; lane 2, fraction P4 collected from anion

fraction p4-G2, separation of P4 by gel filtration. Arrowed bands indicate

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1 using the spectra acquired from independent serumamples tested on an instrument like M2, and potentiallyther machines, would be very difficult. Machine-specificultivariate models would therefore be needed; to gener-

te these one option would be to produce spectra from eachnstrument using a common set of arrays. Despite theseifficulties, the models produced by the two instrumentsad common features, sufficient to inform experi-ents to purify and identify candidate biomarkers (see

elow).In the second part of the study, samples from a longitu-

inal experimental infection trial were analysed to discoverrotein profile changes as the infection developed overime. Univariate analysis of spectra obtained at 4-, 8- and3-months post infection of lambs revealed that at thearly stage of infection (4-months), the number of differen-ially expressed proteomic features was greater than thosedentified at 8- and 13-months post infection (Table 6).owever, a larger study would be required to ascertainhether the changes observed consistently correlate withisease progression. Differences in results generated fromlder infected sheep and those in the experimental infec-ion trial were noted and may be partly due to differencesn the age of sheep in the two trials, the stage of infectionnd the instrument used (Table 6). The sheep used in thexperimental trial were only 17 month of age when theyere euthanized, compared to ∼3 year old in the natural

nfection, and time since infection was not known for indi-idual naturally infected sheep. The implications are thatxperimental design parameters must be described fullyo enable inferences to be made from biomarker discoverytudies, and comparisons made between studies.

Chromatographic methodologies were used to purifyandidate proteins. Transthyretin (TTR) (13,752 Da)howed significantly decreased expression in both infectednd vaccinated-exposed sheep compared to unexposedontrols and was the root decision node in CART model Io differentiate infected sheep from controls (Fig. 1). TTR is

55 kDa homotetramer in serum and a major transporterf thyroxine, tri-iodothyronine and vitamin A throughssociation with retinol-binding protein [24]. The samerotein has been shown to be regulated in human tuber-ulosis patients [12] and was also identified as a negativeiagnostic biomarker in two ovarian cancer studies usingELDI technology [25,26]. Interestingly, transthyretin washown to be significantly increased in paratuberculosisnfected cattle compared to controls using the iTRAQ

ethod [27]; the direction of change was in contrast tohe results observed in this study.

Another candidate serum biomarker identified in thistudy was alpha-haemoglobin (8496 Da). This proteinhowed significant up-regulation of expression in vacci-ated sheep and a moderate increase in infected sheepTable 3). In CART model IV, the 8496 Da protein was cho-en as the root decision node to discriminate vaccinatedheep and unexposed controls. Haemoglobin is knowno be involved in dioxygen binding and transport. In a

ecent study, alpha-haemoglobin was identified as an up-egulated serum biomarker in patients with chronic liverisease [11] and for the diagnosis and prognosis of ovarianancer [28].

y and Infectious Diseases 34 (2011) 315– 326 325

Both alpha-haemoglobin and TTR are highly abun-dant in serum. The likelihood of disease marker detectiondepends on a combination of the concentration of themarker and the sensitivity of the detection method in acomplex biological sample. Evidence is building to sug-gest that the lowest detection limit of SELDI technologyis around the �g/ml level [29], while the concentration ofcommon tumour markers is in the ng/ml range. Given thehigh degree of complexity of the serum proteome, whichspans 12 orders of magnitude, together with the low abun-dance of putative disease markers and the limited detectioncapacity of the SELDI instrument, it is not surprising thatmost of the biomarkers identified in serum by this tech-nology are high abundance proteins.

Even though some studies suggested that a single acute-phase protein was not a satisfactory biomarker for thediagnosis of cancer, when combined with other biomark-ers, it may enable a sensitive and specific diagnosis [26,30].Recently, this phenomenon has been termed ‘host responseprotein amplification cascade’, where post-translationalmodification and metabolism of host response proteinscan amplify the signal of low-abundance, biologicallyactive disease markers [31]. As the identified SELDIpeaks found in the present study were acute-phasereactants or their fragments or isoforms, their relation-ships with the pathogenesis of Johne’s disease remainunclear.

There is no doubt that SELDI TOF-MS is a useful toolin biomedical research, even though its reliability and sta-bility have been questioned worldwide [32]. However,more systematic investigations should be made to exam-ine the effects of various experimental variables, such asdilution, lipid interference and degree of haemolysis. Addi-tional studies are needed to achieve an optimized operatingprocedure and appropriate experimental design to ensurereliable results. The inability of SELDI to detect lower abun-dance serum proteins may be improved with the use ofnew-generation of SELDI technology and more advancedfractionation techniques. Prior fractionation of serum, forexample to remove high abundance proteins such as albu-min, transferrin and the globulins, may lead to removalof significant features as these are carriers of low abun-dance proteins [33]. Though blood is readily accessible,other sample types, such as tissue or organ-specific sam-ples, should also be considered. Moreover, it is tedious andcostly to purify and obtain the protein identity of a SELDIpeak of interest. The use of a quantitative proteomic pro-filing approach, such as iTRAQ labelling, may thus be moreuseful in biomarker discovery [27].

A good biomarker is one that is specific, robust andapplicable to clinical usage [34]. The proteomic studiesdescribed in this study must be extended to a validationphase to demonstrate the usefulness of these proteins.The candidate biomarkers need to be characterized ina large independent cohort, preferably in a multicenterstudy. Quantitative measurements should be carried out inthe validation step to confirm that the candidate proteins

are significantly different between diseased and controlanimals. Currently the most commonly used quantitativeapproaches are immunoassays, mainly ELISA. Recently,several authors have outlined the successful use of mass
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spectrometry-based methods for quantifying specific pro-teins within complex mixtures [35,36].

Acknowledgements

This study was funded by Meat and Livestock Australiaand The University of Sydney. MS–MS analysis was per-formed at the Bioanalytical Mass Spectrometry Facility(BMSF) at the University of New South Wales. Our thanksare extended to Navneet Dhand and Peter Thomson for sta-tistical advice; Robert Baxter and Liping Chung at KollingInstitute of Medical Research for access to one of the SELDIinstruments; Satoko Kawaji for advice with protein analy-sis.

References

[1] Stabel JR. Production of gamma-interferon by peripheral bloodmononuclear cells: an important diagnostic tool for detection of sub-clinical paratuberculosis. J Vet Diagn Invest 1996;8(3):345–50.

[2] Stabel JR, Whitlock RH. An evaluation of a modified interferon-gamma assay for the detection of paratuberculosis in dairy herds.Vet Immunol Immunopathol 2001:7969–81.

[3] Whittington RJ, Marsh I, Turner MJ, McAllister S, Choy E, Eamens GJ,et al. Rapid detection of Mycobacterium paratuberculosis in clinicalsamples from ruminants and in spiked environmental samples bymodified BACTEC 12B radiometric culture and direct confirmationby IS900 PCR. J Clin Microbiol 1998:36701–7.

[4] Kawaji S, Taylor DL, Mori Y, Whittington RJ. Detection of Mycobac-terium avium subsp. paratuberculosis in ovine faeces by directquantitative PCR has similar or greater sensitivity compared to radio-metric culture. Vet Microbiol 2007;125(1–2):36–48.

[5] Christopher-Hennings J, Dammen MA, Weeks SR, Epperson WB,Singh SN, Steinlicht GL, et al. Comparison of two DNA extractions andnested PCR, real-time PCR, a new commercial PCR assay, and bacterialculture for detection of Mycobacterium avium subsp. paratuberculosisin bovine feces. J Vet Diagn Investig 2003;15(2):87–93.

[6] Khare S, Ficht TA, Santos RL, Romano J, Ficht AR, Zhang SP, et al. Rapidand sensitive detection of Mycobacterium avium subsp. paratubercu-losis in bovine milk and feces by a combination of immunomagneticbead separation-conventional PCR and real-time PCR. J Clin Microbiol2004;42(3):1075–81.

[7] Cousins DV, Whittington R, Marsh I, Masters A, Evans RJ, KluverP. Mycobacteria distinct from Mycobacterium avium subsp. paratu-berculosis isolated from the faeces of ruminants possess IS900-likesequences detectable by IS900 polymerase chain reaction: implica-tions for diagnosis. Mol Cell Probes 1999;13(6):431–42.

[8] Englund S, Bolske G, Johansson KE. An IS900-like sequence found ina Mycobacterium sp other than Mycobacterium avium subsp. paratu-berculosis. FEMS Microbiol Lett 2002;209(2):267–71.

[9] Gobel T, Vorderwulbecke S, Hauck K, Fey H, Haussinger D, Erhardt A.New multi protein patterns differentiate liver fibrosis stages and hep-atocellular carcinoma in chronic hepatitis C serum samples. World JGastroenterol 2006;12(47):7604–12.

10] Pang RT, Poon TC, Chan KC, Lee NL, Chiu RW, Tong YK, et al. Serumproteomic fingerprints of adult patients with severe acute respira-tory syndrome. Clin Chem 2006;52(3):421–9.

11] Trak-Smayra V, Dargere D, Noun R, Albuquerque M, Yaghi C,Gannage-Yared MH, et al. Serum proteomic profiling of obesepatients: correlation with pathology and evolution after bariatricsurgery. Gut 2008.

12] Agranoff D, Fernandez-Reyes D, Papadopoulos MC, Rojas SA, Herb-ster M, Loosemore A, et al. Identification of diagnostic markersfor tuberculosis by proteomic fingerprinting of serum. Lancet

2006;368(9540):1012–21.

13] Buhimschi CS, Bhandari V, Hamar BD, Bahtiyar MO, Zhao G,Sfakianaki AK, et al. Proteomic profiling of the amniotic fluidto detect inflammation, infection, and neonatal sepsis. PLoS Med2007;4(1):e18.

[

y and Infectious Diseases 34 (2011) 315– 326

14] Rioux MC, Carmona C, Acosta D, Ward B, Ndao M, Gibbs BF, et al. Dis-covery and validation of serum biomarkers expressed over the firsttwelve weeks of Fasciola hepatica infection in sheep. Int J Parasitol2007.

15] Zhong L, Taylor DL, Whittington RJ. Proteomic profiling of ovineserum by SELDI-TOF MS: optimisation, reproducibility and feasibil-ity of biomarker discovery using routinely collected samples. CompImmunol Microbiol Infect Dis 2010;33(1):47–63.

16] Barr JB, Watson M, Head MW, Ironside JW, Harris N, Hogarth C, et al.Differential protein profiling as a potential multi-marker approachfor TSE diagnosis. BMC Infect Dis 2009:9.

17] Begg DJ, de Silva K, Di Fiore L, Taylor DL, Bower K, Zhong L, et al. Exper-imental infection model for Johne’s disease using a lyophilised, pureculture, seedstock of Mycobacterium avium subspecies paratubercu-losis. Vet Microbiol 2010:141301–11.

18] Whittington RJ, Marsh I, McAllister S, Turner MJ, Marshall DJ, FraserCA. Evaluation of modified BACTEC 12B radiometric medium andsolid media for culture of Mycobacterium avium subsp. paratuber-culosis from sheep. J Clin Microbiol 1999;37(4):1077–83.

19] Perez V, Garcia Marin JF, Badiola JJ. Description and classificationof different types of lesion associated with natural paratuberculosisinfection in sheep. J Comp Pathol 1996;114(2):107–22.

20] Poon TC, Chan KC, Ng PC, Chiu RW, Ang IL, Tong YK, et al. Serialanalysis of plasma proteomic signatures in pediatric patients withsevere acute respiratory syndrome and correlation with viral load.Clin Chem 2004;50(8):1452–5.

21] Poon TC, Yip TT, Chan AT, Yip C, Yip V, Mok TS, et al. Comprehen-sive proteomic profiling identifies serum proteomic signatures fordetection of hepatocellular carcinoma and its subtypes. Clin Chem2003;49(5):752–60.

22] Grajski KA, Breiman L, Viana Di Prisco G, Freeman WJ. Classificationof EEG spatial patterns with a tree-structured methodology: CART.IEEE Trans Biomed Eng 1986;33(12):1076–86.

23] Pappin DJ, Hojrup P, Bleasby AJ. Rapid identification of proteins bypeptide-mass fingerprinting. Curr Biol 1993;3(6):327–32.

24] Peterson PA. Characteristics of a vitamin A-transporting protein com-plex occurring in human serum. J Biol Chem 1971;246(1):34–43.

25] Kozak KR, Su F, Whitelegge JP, Faull K, Reddy S, Farias-Eisner R. Char-acterization of serum biomarkers for detection of early stage ovariancancer. Proteomics 2005;5(17):4589–96.

26] Zhang Z, Bast Jr RC, Yu Y, Li J, Sokoll LJ, Rai AJ, et al. Three biomarkersidentified from serum proteomic analysis for the detection of earlystage ovarian cancer. Cancer Res 2004;64(16):5882–90.

27] Seth M, Lamont EA, Janagama HK, Widdel A, Vulchanova L, Stabel JR,et al. Biomarker discovery in subclinical mycobacterial infections ofcattle. PLoS ONE 2009;4(5):e5478.

28] Woong-Shick A, Sung-Pil P, Su-Mi B, Joon-Mo L, Sung-Eun N, Gye-Hyun N, et al. Identification of hemoglobin-alpha and -beta subunitsas potential serum biomarkers for the diagnosis and prognosis ofovarian cancer. Cancer Sci 2005;96(3):197–201.

29] Diamandis EP. Point: proteomic patterns in biological fluids:do they represent the future of cancer diagnostics? Clin Chem2003;49(8):1272–5.

30] Malik G, Ward MD, Gupta SK, Trosset MW, Grizzle WE, Adam BL,et al. Serum levels of an isoform of apolipoprotein A-II as a potentialmarker for prostate cancer. Clin Cancer Res 2005;11(3):1073–85.

31] Fung ET, Yip TT, Lomas L, Wang Z, Yip C, Meng XY, et al. Classificationof cancer types by measuring variants of host response proteins usingSELDI serum assays. Int J Cancer 2005;115(5):783–9.

32] Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOFprotein patterns in serum: comparing datasets from different exper-iments. Bioinformatics 2004;20(5):777–85.

33] Villar-Garea A, Griese M, Imhof A. Biomarker discovery from body flu-ids using mass spectrometry. J Chromatogr B: Analyt Technol BiomedLife Sci 2007;849(1–2):105–14.

34] Lescuyer P, Hochstrasser D, Rabilloud T. How shall we use theproteomics toolbox for biomarker discovery? J Proteome Res2007;6(9):3371–6.

35] Kiernan UA. Quantitation of target proteins and post-translational

modifications in affinity-based proteomics approaches. Expert RevProteomics 2007;4(3):421–8.

36] Lu Y, Bottari P, Aebersold R, Turecek F, Gelb MH. Absolute quan-tification of specific proteins in complex mixtures using visibleisotope-coded affinity tags. Methods Mol Biol 2007:359159–76.