A Burkholderia pseudomallei protein microarrayreveals serodiagnostic and cross-reactive antigensPhilip L. Felgnera,b,c,1, Matthew A. Kayalab,d, Adam Vigila, Chad Burka, Rie Nakajima-Sasakia, Jozelyn Pabloa,Douglas M. Molinac, Siddiqua Hirsta, Janet S. W. Chewe, Dongling Wange, Gladys Tane, Melanie Duffieldf, Ron Yangg,Julien Neelb,d, Narisara Chantratitah, Greg Bancrofti, Ganjana Lertmemongkolchaij, D. Huw Daviesa, Pierre Baldib,d,k,Sharon Peacockh,l, and Richard W. Titballg,i

aDepartment of Medicine, Division of Infectious Diseases, University of California, Irvine, CA 92697; bInstitute for Genomics and Bioinformatics, dDepartmentof Computer Science, and kDepartment of Biological Chemistry, University of California, Irvine, CA 92067; cAntigen Discovery, Inc., Irvine, CA 92618;eDefence Medical & Environmental Research Institute, DSO National Laboratories, 27 Medical Drive, #13-01, Singapore 117510; fDefence Science andTechnology Laboratory, Porton Down, Salisbury SP4 0JQ, United Kingdom; gSchool of Biosciences, Geoffrey Pope Building, University of Exeter, Exeter EX44QD, United Kingdom; iDepartment of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT,United Kingdom; jDepartment of Clinical Immunology, Khon Kaen University, Khon Kaen 40002, Thailand; hMahidol-Oxford Tropical Medicine ResearchUnit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand; and lCenter for Clinical Vaccinology and Tropical Medicine, NuffieldDepartment of Clinical Medicine, University of Oxford, Churchill Hospital, Oxford CB2 0QQ, United Kingdom

Edited by Peter Palese, Mount Sinai School of Medicine, New York, NY, and approved June 19, 2009 (received for review December 8, 2008)

Understanding the way in which the immune system responds toinfection is central to the development of vaccines and manydiagnostics. To provide insight into this area, we fabricated aprotein microarray containing 1,205 Burkholderia pseudomalleiproteins, probed it with 88 melioidosis patient sera, and identified170 reactive antigens. This subset of antigens was printed on asmaller array and probed with a collection of 747 individual seraderived from 10 patient groups including melioidosis patients fromNortheast Thailand and Singapore, patients with different infec-tions, healthy individuals from the USA, and from endemic andnonendemic regions of Thailand. We identified 49 antigens thatare significantly more reactive in melioidosis patients than healthypeople and patients with other types of bacterial infections. Wealso identified 59 cross-reactive antigens that are equally reactiveamong all groups, including healthy controls from the USA. Usingthese results we were able to devise a test that can classifymelioidosis positive and negative individuals with sensitivity andspecificity of 95% and 83%, respectively, a significant improve-ment over currently available diagnostic assays. Half of the reac-tive antigens contained a predicted signal peptide sequence andwere classified as outer membrane, surface structures or secretedmolecules, and an additional 20% were associated with pathoge-nicity, adaptation or chaperones. These results show that microar-rays allow a more comprehensive analysis of the immune responseon an antigen-specific, patient-specific, and population-specificbasis, can identify serodiagnostic antigens, and contribute to amore detailed understanding of immunogenicity to this pathogen.

antigen discovery � melioidosis � diagnostic � antigen prediction

Understanding the interaction of the immune system withbacteria is central to an understanding of the pathogenesis

of infectious disease, and also to the development of diagnosticsand vaccines. Yet there is an unmet need for a comprehensiveand unambiguous approach for quantifying the immune re-sponse to infection in an antigen-specific manner and on agenome-wide scale. The development of antibodies to individualcomponents of bacterial pathogens is 1 important element of theimmune response, and is especially relevant to the developmentof diagnostics and for therapies where antibodies play direct orindirect roles in the killing of pathogens or pathogen-infectedcells. Although protein antigens are generally assumed to makeup the majority of the bacterial antigens that are recognized byantibodies, the reasons why only some proteins evoke antibodyresponses are poorly understood. There is a general and intuitiveassumption that proteins that are displayed on the surface of thebacterium are more likely to evoke an antibody response. In thecase of proteins able to induce protective immunity there is some

evidence that this assumption is correct. An analysis of 72bacterial proteins included in vaccines or shown to induceprotective immunity in animal models of disease showed that 52(72%) possessed signal sequences and were therefore likely to belocated outside of the cell membrane (1). The assumption thatproteins able to induce protective immunity possess signalsequences is exploited in reverse vaccinology, where candidateprotective antigens are predicted from the genome sequence (2).

However, these studies fail to provide insight into the broaderquestion of the differential abilities of bacterial proteins toinduce antibody responses in a population. To a large extent, thedifficulties in addressing this question are a reflection of thelimitations of existing technologies for mapping the completesubset of proteins involved in the immune response against theinfectious agent. These technologies rely on chromatographic orelectrophoretic separation of proteins from bacteria or thescreening of expression libraries. These approaches are limitedby the different levels of expression of proteins in the native orrecombinant hosts, specific bacteria growth conditions if culti-vable, and are not conducive to high-throughput screening of alarge collection of serum samples.

We have previously developed array technology that allowsprotein microarrays to be constructed from the predicted pro-teome of a microorganism (3–7). These arrays can be used toaddress basic questions about the interactions of a given patho-gen with the host immune system (8–10), and allows the iden-tification of proteins which can be used as diagnostic reagents orfor inclusion in vaccines (3). The empirical data gathered fromthis type of array can also be used to evaluate and improve theaccuracy of in silico antigen prediction of a proteome.

We report here the development of a Burkholderia pseudoma-llei protein array. B. pseudomallei is the causative agent ofmelioidosis, a serious and often fatal infectious disease ofhumans. It is an important medical problem across Southeast

Author contributions: P.L.F., A.V., G.T., G.B., G.L., D.H.D., P.B., S.P., and R.W.T. designedresearch; M.A.K., A.V., R.N.-S., J.P., D.M.M., S.H., J.S.W.C., D.W., and N.C. performedresearch; P.L.F., M.A.K., A.V., C.B., G.T., M.D., R.Y., J.N., G.L., D.H.D., P.B., S.P., and R.W.T.analyzed data; and P.L.F., A.V., P.B., S.P., and R.W.T. wrote the paper.

Conflict of interest statement: P.L.F. and D.H.D. have patent applications related to proteinmicroarray fabrication and have stock positions with Antigen Discovery, Inc. D.M.M. is anemployee with Antigen Discovery, Inc.

This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.

1To whom correspondence should be addressed at: Department of Medicine, Division ofInfectious Diseases, University of California, 3501 Hewitt Hall, Irvine, CA 92697. E-mail:pfelgner@uci.edu.

This article contains supporting information online at www.pnas.org/cgi/content/full/0812080106/DCSupplemental.

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Asia and northern Australia, and is increasingly recognized inother tropical areas of the world including areas of SouthAmerica (11, 12). The global incidence of disease is not known,but in northeast Thailand, the disease accounts for 40% of alldeaths from community-acquired septicemia. The potential forthe bacterium to cause disease after inhalation has also resultedin the inclusion of this pathogen on the CDC category B list ofpotential biological warfare and bioterrorism agents (12). Herewe have used the protein array to map the antibody response in747 serum samples from well-defined melioidosis positive andnegative patients.

ResultsProtein Microarray Design and Construction. We devised a proteinarray of 1,205 proteins, including proteins predicted to be surfacelocated using PSORTb (13), components of the 3 different typeIII systems, components of the flagella, proteins identified asimmunoreactive from 2D gels, and 672 proteins selected atrandom. This array was probed with a collection of 88 sera frommelioidosis patients in Singapore (SI Text). One hundred seventyantigens with average signal intensity greater than 2.5 times thestandard deviation of the average negative control spots wereconsidered seroreactive. Of these, 80 proteins were encoded onchromosome 1, and 90 on chromosome 2. Three of the serore-active proteins were encoded in genomic islands (BPSL1705 inGI8, BPSS0663 in GI4, and BPSS1068 in GI15), and 2 proteinswere encoded in possible genomic islands (BPSS0402 andBPSL0739). Some large CDSs (coding sequences) were ex-pressed as overlapping fragments and for many of these, wefound reactivity against several of the fragments, providingevidence that epitopes are distributed throughout these proteins.For example, 6 polypeptides from the BPSS2053 CDS, encodinga 3,103 amino acid protein similar to the Ralstonia solanacearumprobable hemagglutinin-related protein, all reacted with me-lioidosis sera. Four polypeptides that form the BPSS1434 CDS,encoding a 2,178 amino acid protein similar to a Streptococcuspneumoniae cell wall surface anchor family protein, all reactedwith patient sera. Nine polypeptides from the BPSL1661 CDS,encoding a 3,229 amino acid protein similar to a Ralstoniasolanacearum putative hemagglutinin/hemolysin-related pro-tein, all reacted with melioidosis sera. In a previous study, 12 B.pseudomallei proteins were identified as immunoreactive by 2Dgels (14), and all of these proteins were identified as immuno-reactive using the array.

Mapping the Antigenic Profile. The seroreactive antigens wereprinted onto smaller arrays (Fig. 1), which we used to probe theentire collection of 747 serum samples from patients withmelioidosis and control sera from individuals in Singapore,Thailand, and the USA (Table S1 and SI Text). The Singaporeand Thailand melioidosis-positive and -negative cases werepatients entering the hospital with symptoms of melioidosis thatwere later confirmed to be either positive or negative formelioidosis. The Thailand cases were from Ubon Ratchathaniin Northern Thailand and healthy controls from the same regionwhere the disease is endemic. There were also healthy controlsfrom Southern Thailand (outside the endemic region) andpatients diagnosed with leptospirosis, other bacteremia, orfungemia.

The reactivity of 747 sera from different individuals is shown asa heatmap (Fig. 2) and as a histogram (Fig. S1) with patient samplesgrouped according to their clinical description. Thirty-one of themost reactive serodiagnostic antigens and 31 cross reactive antigensare shown. ‘Serodiagnostic’ antigens are defined as significantlydifferentially reactive between the Singapore melioidosis-positiveand -negative groups with Benjamini and Hochberg adjustedCyber-T P values �0.05, and ‘cross-reactive’ antigens had a P value�0.05 (Fig. 3). Analysis of variance was performed to detect

differences in signal intensity between groups. For the serodiag-nostic antigens there is a significant difference when comparingmelioidosis-positive patients to all other groups (P � 3.6 � 10�7),but the Singapore and Thailand melioidosis patients are not dif-ferent from each other (P � 0.55), and all melioidosis negativepatients are not different from each other (P � 0.91) (Fig. S1). Allof the sera react similarly to the cross-reactive antigens whetherfrom melioidosis-positive or -negative individuals, healthy subjectsfrom endemic or nonendemic areas, or patients with other infec-tions (P value � 0.08).

Determining a Set of Serodiagnostic Antigens. There were 2 distinctcollections of melioidosis-positive and -negative specimens usedfor this work, from different clinical investigators and clinicalsites in Thailand and Singapore, and different definitions of thecases and controls were used in each location. The incidence ofmelioidosis is relatively low in Singapore, and exposure to B.pseudomallei is considered to be an infrequent event. TheSingaporean cases were clinically confirmed melioidosis andpositive by the indirect hemagglutination assay (IHA) and about40% of these samples were proven culture positive. All of thecontrols for this collection lacked clinical signs of melioidosisdisease and were also IHA negative. The Northeast Thailandcollection is from a region where the disease is consideredendemic. All of the cases are proven culture positive, and thecontrols are culture negative but IHA positive subjects are notexcluded, because it is known that healthy individuals in thegeneral population in Northeast Thailand can be IHA positive.For all of the work reported here we used the Singaporeancases and controls to develop a classifier that can distinguishmelioidosis-positive and -negative subjects, and tested sensitivityand specificity of the classifier independently on the Thailandcollection.

Our goal was to establish a collection of antigens that could beused as a multiplex set to accurately distinguish the melioidosiscases from controls. As such, we studied the discriminatorypower of different sets of ORFs using receiver operating char-acteristic (ROC) curves. First, ROC curves were generated forindividual serodiagnostic antigens to assess their ability toseparate the control and disease cases. The serodiagnostic

No DNA

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Fig. 1. Construction of a B. pseudomallei Microarray. Arrays were printedcontaining 214 B. pseudomallei proteins, positive and negative control spots.The arrays were read in a laser confocal scanner, analyzed, and the datanormalized as described in the Materials and Methods section. Protein ex-pression efficiency was determined to be 99.2% by probing against a carboxy-terminal HA tag for quality control. Each array contains positive control spotsprinted from 4 serial dilutions of human IgG (HMIg), and the intensity of thesespots was similar for both serum samples. Each array also contained 6 ‘‘NoDNA’’ negative control spots, and the reactivity of these spots was low for bothserum samples. There are also 4 serially diluted EBNA1 (EBNA) protein controlspots that are reactive to varying degrees in different subjects, as expected,and provide a methodological control. The remaining spots on the array arein vitro transcription/translation reactions expressing 183 different B.pseudomallei proteins that were selected from our primary array analysis. Thesignal intensity of each antigen is represented by rainbow palette of blue,green, red, and white by increasing signal intensity. Representative microar-ray immunofluorescence images of individual patient sera are displayed.

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antigens were ranked by decreasing single antigen area under theROC curve (AUC) (Table S2). The top 5 ORFs all have an AUCgreater than 0.81, with GroEL (BPSL2697; AUC 0.89; Ben-jamini and Hochberg adjusted Cyber-T P value �10e-14) givingthe best single antigen discrimination. Heat shock proteins likeGroEL are often dismissed a priori for serodiagnostic utilitybecause of perceived cross-reactivity with heat shock proteinsfrom other organisms that would theoretically result in lowspecificity of the test; here, we find the heat shock protein GroELis the most significantly differentially reactive antigen and havesimilarly found serodiagnostic power from heat shock proteinsfrom other organisms (7). The 31st antigen has an AUC of 0.622which still exceeds the upper 95% confidence interval forrandom expectations for the AUC. To extend the analysis tocombinations of antigens, we used kernel methods and supportvector machines (15, 16) to build linear and nonlinear classifiers.As input to the classifier, we used the highest-ranking 1, 2, 5, 10,20, 25, 30, and 31 ORFs on the basis of either P value or singleantigen AUC and the results were validated with 10 runs of3-fold cross-validation. The boxplots for these predictions wereplotted and the results show that increasing the antigen numberfrom 1 to 2, 2 to 5, and 5 to 10 produced an improvement in theclassifier and a reduction in accuracy as the antigens increaseabove 20 due to over-fitting (Fig. 4A and Table S3).

The classifier threshold that yielded the highest accuracy in theSingapore data set was then used to predict the samples in the

Thailand collection. Using 20 antigens the classifier predicts 86%of the true positives and 98% of the true negatives fromSingapore, and 76% and 72% of the Thailand positives andnegatives, respectively. The healthy controls from North andSouth Thailand are predicted with 78% and 86% accuracy,respectively. Patients with other infections are predicted asmelioidosis negative with 69% to 100% accuracy, depending onthe group.

Improving Serodiagnostic Accuracy. To test the feasibility of usingthe serodiagnostic antigens on an alternative analytical platform,10 serodiagnostic proteins were printed onto an Optitran nitro-cellulose membrane (typically used for western blots) using aBioDot jet dispenser (SI Text). These 10 serodiagnostic proteinswere chosen for being highly significant and highly seroreactivein the protein microarray assay. The paper was then cut into 3mm strips to produce ‘immunostrips’ (also called ‘line blots’)(Fig. 4B). The individual strips were probed with 60 differentmelioidosis positive sera and 67 melioidosis negative sera fromthe Singapore collection. Reactive bands were visualized afterincubation with alkaline phosphatase conjugated anti-humansecondary antibody followed by substrate, and the band inten-sities quantified with ImageJ (17). Melioidosis patients reactedstrongly against the serodiagnostic antigens although the inten-sity pattern varied depending on the patient. Naïve and healthycontrol subjects had low reactivity against these serodiagnosticantigens.

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Fig. 2. Probing a collection of B. pseudomallei infected, uninfected, and healthy control sera from Singapore, Thailand, and the USA. Arrays containing 214B. pseudomallei proteins were probed with 747 melioidosis and nonmelioidosis sera organized into 11 groups as described in the text. The normalized intensityis shown according to the colorized scale with red strongest, bright green weakest, and black in between. The antigens are in rows and are grouped accordingto serodiagnostic and cross-reactive. The patient samples are in columns and sorted left to right by increasing average intensity to serodiagnostic antigens.

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Fig. 3. Serodiagnostic antigen discovery of melioidosis-positive patients from Singapore. The mean sera reactivity of the 214 antigens was compared betweenthe Singapore melioidosis-positive and Singapore melioidosis-negative groups. Antigens with Benjamini Hochberg corrected P value less than 0.05 are organizedto the left and cross-reactive antigens to the right. The 31 most reactive serodiagnostic and 31 of the most reactive cross-reactive antigens are shown.

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A classifier was trained to accurately diagnose the melioidosis-positive and -negative samples from Singapore, and the classifierwas tested against strips probed with the independent collectionof positive and negative samples from Thailand. ROC curveswere generated and compared to the microarray results (Fig.4C). The area under the immunostrip ROC curve was greaterthan the microarray, indicating a more accurate test can beobtained by transferring the serodiagnostic antigens discoveredby microarray to the immunostrip format. The results aresummarized in the contingency table (Fig. 4D). Increasing thenumber of antigens from 1 to 5 on the microarray results in amore accurate discrimination between positive and negativesamples, with maximum sensitivity and specificity of 90% and55%, respectively, for the Thailand samples. However, immu-nostrips containing the top 10 serodiagnostic antigens discov-ered by microarray discriminate the Thailand positive andnegative patient samples with 95% sensitivity and 83% specific-ity. This accuracy is greater than previously published assaysusing crude B. pseudmallei antigen ELISA and affinity-purifiedantigen ELISA, and a substantial improvement over the clinicalstandard IHA assay. (Table S3). This proof-of-concept diagnos-tic assay validates the set of serodiagnostic antigens and dem-onstrates the feasibility of transferring the antigens discovered bymicroarray to the immunostrip format to correctly classify B.pseudomallei positive sera using 10 differentially reactive sero-diagnostic antigens.

Functional Classification of the Reactive Antigens. We next classifiedthe serodiagnostic and cross-reactive antigens according toannotated and computationally predicted features. The com-plete analysis is in Table S4 and a summary in Table 1.Annotated features were taken from the Artemis database onthe Sanger website which has 2 categories of functional defini-tions called ‘Colour’ and ‘Class.’ Each of the 13 colors has afunctional definition, and each of the 5,853 proteins in thedatabase is assigned exactly 1 color. The results in Table 1 showthat only 2 of the 13 color classifications are significantly

enriched in both the serodiagnostic and cross-reactive antigensets. ‘Pathogenicity/adaptation/chaperone’ and ‘surface’ mole-cules account for 75% of all of the reactive antigens whereasthese functional categories account for only 38% of the proteinsprinted on the array (and 32% of the whole proteome). There are180 class definitions in Artemis, and proteins can belong to morethan 1 class; proteins classified as ‘chaperones’ and ‘outermembrane localized’ are significantly enriched in the serodiag-nostic antigen set by 12.3- and 4.6-fold, respectively. Proteinclassified as membrane/exported/lipoproteins are enrichedamong the cross reactive antigens, but not the serodiagnosticantigens. The PSORTb computational predictor shows thatcytoplasmic proteins are significantly underrepresented in thecross-reactive antigen set and cytoplasmic membrane proteinsare significantly enriched. Proteins predicted to be extracellularand outer membrane localized are significantly enriched, and thepresence of a signal peptide is a highly significant enrichmentfeature. We have expressed and probed 20% of the B. pseudoma-llei proteome and identified 49 serodiagnostic antigens and 59cross-reactive antigens. By probing the remaining 4,648 proteinsin the complete proteome, we may expect to identify a total of295 cross-reactive and 240 serodiagnostic antigens. Pathogenic-ity associated molecules and membrane structures account for32% of the entire proteome, but these 2 categories account for74% of the reactive antigens. Thus by fabricating protein arrayscontaining 1901 molecules in only these 2 categories, we expectto identify more than 70% of the reactive antigens.

The majority of these serodiagnostic antigens (36/49) wereencoded on chromosome 2 of B. pseudomallei K96243. (TableS2) Chromosome 2 has previously been suggested to be associ-ated with genes required for adaptation and survival in differentniches (18), and our finding that serodiagnostic antigens werepreferentially encoded on chromosome 2 is consistent with thishypothesis. The serodiagnostic proteins included a range ofknown or putative virulence associated proteins including com-ponents of TTSS3 (BPSS1532, BipB and BPSS1525, BopE)(19–21) and a type IV pilus protein PilO (BPSS1599) (22).

Mel. Negative Mel. Positive

BPSL2697BPSS1532BPSS0477BPSS1512BPSS1492BPSL2522BPSS1525BPSL2698BPSS0476BPSL3222

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affinity-purified antigen ELISA 87% 88%crude B. pseudomallei antigen ELISA 86% 83%

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Fig. 4. Development of a melioidosis classifier for improved diagnosis. (A) The graph shows 9 Boxplots for nonlinear classifiers with increasing number ofantigens. As the number of antigens increases up to 5 antigens, the classifier becomes more accurate. (B) Ten serodiagnostic antigens were printed ontonitrocellulose paper in adjacent stripes using a BioDot jet dispenser (SI Text). Strips were probed with patient sera diluted 1/200 followed by alkaline phosphataseconjugated secondary antibody and enzyme substrate. Weak reactivity in the naïve healthy controls can be readily distinguished from the strong reactivity ininfected subjects. Ten representative strips for each group are shown. (C) The graph shows the immunostrip ROC curves compared to the microarray ROC curvegenerated for with the same 10 antigens. (D) Percent diagnostic accuracy for each assay is listed. Microarray accuracy was calculated for 1, 2, 5, and 10 antigens.Thailand sera are from a previously well-characterized cohort of patients and represent a selected population of samples with definite melioidosis comparedto patients with an alternative diagnosis. Thailand sera samples were used for direct comparison of the microarray, ELISA, IHA, and immunostrip assays.

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We have also interfaced our dataset with data on genes whichare up-regulated in the hamster model of melioidosis (23). Genesencoding 6 of the 31 diagnostic signature proteins (BPSS0476;GroES: BPSL1445; putative lipoprotein BPSL2017; Di-hemecytochrome c peroxidase; BPSL2697; GroEL: BPSL3319; f lagel-lin; and BPSL2520: putative exported protein) were up-regulated in vivo. Chaperones and stress response proteins froma range of pathogens have frequently been found to react withthe appropriate convalescent sera (14, 24–28). Both GroEL andDNA K have previously been identified as reactive with serafrom melioidosis patients (14, 25).

DiscussionThis study is the most extensive use of a diverse collection ofpatient sera on a protein microarray. Of 1,205 proteins probed,49 antigens were identified that are significantly more reactive inpatients with melioidosis and 59 antigens were cross-reactive inhealthy people and patients with other infections. Becausemelioidosis is confined to relatively small localized regions of theworld, it might have been anticipated that immunoreactivityagainst this species would be localized as well. Our finding thatthese cross-reactive antigens also reacted equally with sera fromindividuals in the USA suggests that cross-reactive antibodiesmay be responsible for these signals. This finding contrastsmarkedly to our previous studies with Francisella tularensis (7,29), Borrelia burgdorferi (5), Plasmodium falciparum (6), andvaccinia (8), where reactivity of arrayed proteins with naïvepatient sera was minimal or not seen.

The antibodies in melioidosis negative sera may be indicativeof past exposure to bacteria related to B. pseudomallei whichpossess similar proteins. For example, BPSS0542 (glycosyl hy-drolase), BPSL1207 (polyribonucleotide nucleotidyltrans-ferase), and BPSS2136 (family S43 nonpeptidase homologue)are identical in a range of Burkholderia species such as Burk-holderia thailandensis, B. vietnamiensis, and B. cenocepacia. B.thailandensis is found in many regions of the world including SEAsia and the USA (30) and the B. pseudomallei and B. thailan-densis genomes are highly syntenic (31). Bacteria of the B.cepacia complex are also widely distributed worldwide and arefrequently the cause of complications in cystic fibrosis. All ofthese bacteria rarely cause disease in healthy individuals but it ispossible that subclinical disease results in the induction of animmune response to these bacteria and the development of

antibodies that react with the protein array. Even though cross-reactivity is widespread, some individuals within any region wereessentially immunologically naïve. The results reported heresuggest widespread exposure and cross-reactivity to nonpatho-genic Burkholderia sp., but these conclusions warrant corrobo-rating data from additional endemic and nonendemic regionsand comparative data on arrays from nonpathogenic strains.

The information from the array was used to devise an accurateprototype diagnostic test, using differentially reactive antigenswhile avoiding the cross-reactive ones. The microarray platformmay not necessary be the most sensitive to produce the mostaccurate test results. Serodiagnostic antigens discovered bymicroarray were transferred to the immunostrip platform, theSingaporean specimen collection was used to train the classifier,and the classifier tested on the Thailand collection. The immu-nostrip test allowed for the correct identification of 95% ofmelioidosis-positive samples and 83% of melioidosis-negativesamples, a major improvement over the most commonly useddiagnostic test (the IHA test) which identifies 79% and 72% ofpositive and negative samples, respectively. We regard themicroarray as a serodiagnostic antigen discovery platform. An-tigens discovered by microarray can be transferred to other moresensitive platforms to give more accurate test results.

The data we report here also provide a broader insight into theantigens that are recognized by the immune system after naturalinfection. Reactivity was not evenly distributed across the pro-teome and the cross-reactive and serodiagnostic antigens areselectively enriched with proteins from specific functional cat-egories. Membrane and secreted molecules accounted for 52%of the reactive antigens, but this class of molecule constitutesonly 29% of the proteins on the array. Similarly, pathogenicity-island related proteins and chaperones (as annotated in theArtemis database) accounted for 22% of the reactive antigensbut only 9% of the proteins printed on the large array were fromthis category. Three of the 6 annotated chaperones printed onthe array were serologically reactive, and all 3 of these wereserodiagnostic for melioidosis. PSORTb predicts 82 proteins(7% of the large array) to be extracellular or outmembranelocalized, and 32 (39%) are antigenic. Proteins with a signalpeptide are enriched on the reactive antigen lists and proteinslacking a signal peptide sequence are underrepresented. Pro-teins involved in central and intermediary metabolism are alsosignificantly underrepresented on the reactive antigen list. These

Table 1. Enrichment table

Counts Cross-reactive Serodiagnostic

Whole proteome Large chip Hits Fold enrich P value Hits Fold enrich P value

Artemis ’colour’ definitionspathogenicity/adaption/chaperones 368 106 13 * 2.50 1.2E-03 11 * 2.55 2.4E-03Surface (outer membrane, inner membrane,

secreted, surface structures)1533 352 31 * 1.80 1.8E-04 25 * 1.75 1.1E-03

All other categories 3952 747 15 — — 13 — —Total 5853 1205 59 49

Artemis ’class’ definitionsChaperones 30 6 0 0.00 1.0E �00 3 * 12.30 1.2E-03Membrane/exported/lipoproteins 397 174 15 * 1.76 2.1E-02 12 1.70 5.8E-02Outer membrane 81 48 2 0.85 1.0E �00 9 * 4.61 7.2E-05

Computational predictionsPSORTb cytoplasmic 1975 293 2 * 0.14 1.6E-05 7 0.59 1.2E-01PSORTb cytoplasmicmembrane 1054 94 11 * 2.39 4.3E-03 5 1.31 5.8E-01PSORTb extracellular 37 11 5 * 9.28 8.8E-05 3 * 6.71 8.3E-03PSORTb outermembrane 150 71 10 * 2.88 1.6E-03 14 * 4.85 2.2E-07PSORTb periplasmic 130 30 5 * 3.40 1.3E-02 2 1.64 3.5E-01PSORTb unknown 2507 706 26 * 0.75 2.2E-02 18 * 0.63 1.8E-03SignalP �� 0.7 1041 340 40 * 2.40 1.1E-10 30 * 2.16 9.7E-07

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examples illustrate that molecular recognition by the immunesystem is not stochastic. There is a preference for recognition ofsurface molecules, chaperones, and pathogenicity-related fac-tors, and the PSORTb outer membrane and extracellular com-putational tool predicts antigenicity. These results classifyingantigenicity by protein function are not out of line with expec-tations but they are far more quantitative and informative thanpreviously reported.

Seventy-four percent of the antigens fall into functionalannotation categories that are enriching features, but the other26% of the reactive antigens are from categories with proteomicfeatures that are underrepresented on the immunoreactive an-tigen list. We have not yet identified predictive features of thesemolecules that selectively target them for immune recognition.Some of the molecules with proteomic features that are under-represented on the hit list may be expressed at high levels in vivomaking them targets for immune recognition independent oftheir functional category. Although certain protein categoriesare enriched in the immunoreactive antigen list, there is nocategory that is entirely reactive. For example, there are 458proteins on the array in the pathogenicity and outer membranecategories, but only 17% of these molecules are reactive. Part ofthe explanation for nonreactivity may be that the proteinsexpressed in the cell free in vitro expression system are not in theproper conformation to be recognized by the antibodies. Forexample, we have shown that proteins requiring disulfide bondsfor their native conformation may not be seen unless in vitroexpression is carried out under oxidizing conditions (4). Lack ofposttranslational modification, incomplete folding of proteinsexpressed in vitro, or unfolding of proteins bound to nitrocel-lulose are other factors that could result in an underestimate ofthe total number of reactive antigens determined by this method,but our experience with other agents indicates that this effect isactually marginal (4). At this point, any in silico algorithm for

predicting the antigenic profile based on sequence data alonewill be imperfect because the majority of proteins predicted arenonreactive. Furthermore, there are categories of reactive mol-ecules with proteomic features that are not enriched in theantigenic profile, so they would not be predicted. The datapresented here highlight the need to improve in silico predictionalgorithms for antigen discovery and vaccine development.

These results provide significant insight into the antigenicityof a bacterial pathogen, while both confirming and overturningassumptions that have previously been made about the natureof immunoreactive proteins. In addition to contributing to amore profound understanding of the humoral immune re-sponse to infection, this work could lead to the developmentmore accurate tests for clinical diagnosis and for worldwidedisease surveillance.

Materials and MethodsProtein Microarray Chip Fabrication and Probing Methods. Protein microarraychips consisting of 1,205 B. pseudomallei antigens were fabricated as de-scribed previously (SI Text) (3, 4, 7).

SI. The description of the serum samples, immunostrip fabrication and prob-ing, and data analysis methods are in the SI Text.

ACKNOWLEDGMENTS. We thank Eng-Eong Ooi and Jimmy Loh (DefenceMedical and Environmental Research Institute, Singapore) for providing se-rum samples; Allen C. Cheng (University of Melbourne) for sera collection; Mrs.Vanaporn Wuthiekanun and Dr. Narisara Chatratita (Mahidol-Oxford TropicalMedicine Research Unit, Bangkok) for providing samples from Thailand andfor ELISA and IHA assays, respectively; Drs. Gavin Koh and Rapeephan Rat-tanawongnara for collection of clinical data; and Xiaolin Tan for assistancewith ANOVA analysis. The primer design and statistical analyses of this workwere supported primarily by National Institutes of Health Biomedical Infor-matics Training Program Grant 5T15LM007743 (to M.K.) and National ScienceFoundation Grants MRI EIA-0321390 and NSF 0513376 (to P.B.) and in part byNational Institutes of Health/National Institute of Allergy and Infectious Dis-eases Grants U01AI061363 and U54065359 (to P.F.).

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