COURRENT PINION A systems biology approach for diagnostic ...€¦ · After launching this project 10 years ago, we have made more than 40000 plasmids, printed the encoded proteins

REVIEW

CURRENTOPINION A systems biology approach for diagnostic and

vaccine antigen discovery in tropical infectiousdiseases

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www.co-infectiousdiseases.com

Li Liang and Philip L. Felgner

Purpose of review

There is a need for improved diagnosis and for more rapidly assessing the presence, prevalence, andspread of newly emerging or reemerging infectious diseases. An approach to the pathogen-detectionstrategy is based on analyzing host immune response to the infection. This review focuses on a proteinmicroarray approach for this purpose.

Recent findings

Here we take a protein microarray approach to profile the humoral immune response to numerousinfectious agents, and to identify the complete antibody repertoire associated with each disease. The resultsof these studies lead to the identification of diagnostic markers and potential subunit vaccine candidates.These results from over 30 different organisms can also provide information about common trends in thehumoral immune response.

Summary

This review describes the implications of the findings for clinical practice or research. A systems biologyapproach to identify the antibody repertoire associated with infectious diseases challenge using proteinmicroarray has become a powerful method in identifying diagnostic markers and potential subunit vaccinecandidates, and moreover, in providing information on proteomic feature (functional and physicallyproperties) of seroreactive and serodiagnostic antigens. Combining the detection of the pathogen with acomprehensive assessment of the host immune response will provide a new understanding of thecorrelations between specific causative agents, the host response, and the clinical manifestations of thedisease.

Keywords

antigen, immune response, protein microarray, serodiagnostic

Division of Infectious Diseases, Department of Medicine, University ofCalifornia, Irvine, California, USA

Correspondence to Philip L. Felgner, Division of Infectious Diseases,Department of Medicine, University of California, Irvine, CA 92697, USA.Tel: +1 949 824 1407; fax: +1 949 824 9675; e-mail: [email protected]

Curr Opin Infect Dis 2015, 28:438–445

DOI:10.1097/QCO.0000000000000193

INTRODUCTION

A major component of the adaptive immuneresponse to infection is the generation of protectiveand long-lasting humoral immunity. Analyses ofantibody responses against different infectiousagents are critical for diagnosing infectious diseases,understanding pathogenic mechanisms, and thedevelopment and evaluation of vaccines. Proteinmicroarrays are well suited to identify, quantify,and compare individual antigenic responses follow-ing exposure to infectious agents. It can now evalu-ate antibody responses to hundreds, or eventhousands, of recombinant antigens at one time.These large-scale studies have uncovered new anti-genic targets, provided new insights into vaccineresearch and yielded an overview of immunoreac-tivity against almost the entire proteome of certainpathogens. This technology can be applied to the

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development of improved serodiagnostic tests,discovery of subunit vaccine antigen candidates,epidemiologic research, and vaccine development,as well as providing novel insights into infectiousdisease and the immune system. In this review, wewill discuss the use of protein microarrays as apowerful tool to define the humoral immuneresponse to bacteria and viruses.

Factors governing selection of the particularantigens recognized are unclear [1,2]. It is not

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KEY POINTS

� Protein microarray is a powerful tool in identifyingdiagnostic markers and potential subunit vaccinecandidates.

� Full proteome microarrays have provided valuableinformation on antigen predictions.

� Certain proteomic features (functional and physicallyproperties) have been identified to be associated withseroreactive and serodiagnostic antigens.

� Naive Bayes classification further improves thesensitivity and specificity of this in-silico predictivealgorithm.

Antibody prediction for infectious diseases Liang and Felgner

uncommon for viruses encoding a small numberof proteins to generate antibodies against eachencoded protein. But for infectious agents contain-ing hundreds or thousands of proteins only a subsetof the proteome is recognized and little is knownabout the extent or the characteristics of this subsetof antigens. Methods for making a complete empiri-cal accounting of the immunoproteome havelimitations, particularly when the genome of theorganism is large. The Protein Microarray Labora-tory at University of California Irvine has developeda highly efficient method to determine the humoralimmune response to microbial antigens. We haveapplied this approach to more than 30 medicallyimportant infectious microorganisms [3–33]including Mycobacterium tuberculosis, Plasmodiumfalciparum, Plasmodium vivax, Brucella melitensis,Chlamydia trachomatis, Francisella tularensis, Burkhol-deria pseudomallei, Coxiella burnetii, Borrelia burgdor-feri, Salmonella enterica Typhi, Rickettsia prowazekii,Rickettsia rickettsii, Orientia tsutsugamushi, Bartonellahenselae, Leptospira interrogans, Toxoplasma gondii,Candida albicans, Schistosoma mansoni, and virusesincluding vaccinia herpes simplex viruses 1&2,varicella zoster virus, Epstein–Barr virus, humanpapillomaviruses, HIV, dengue, influenza, WestNile virus, yellow fever, Saint Louis encephalitis,Japanese encephalitis, and chikungunya viruses.After launching this project 10 years ago, we havemade more than 40 000 plasmids, printed theencoded proteins on 25 000 microarrays and probedthe arrays with 15 000 serum specimens in order todetermine disease-associated antibody profiles inpeople infected with each agent. These chips canbe probed with sera from infected patients to deter-mine the immunodominant antigens for each agentand the methodology is amenable to the screeningof sera from very large cohorts numbering in thethousands. When seroreactive and serodiagnostic

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antigen subsets from different infectious agentsare printed onto the same array, the chip can dis-criminate between patients infected with differentagents and also identify individuals with coinfec-tions or multiple infections. We have shown thatthe individual proteins printed on these arrayscapture antibodies present in serum from infectedindividuals and the amount of captured antibodycan be quantified using fluorescent secondary anti-body. In this way, a comprehensive profile of anti-bodies that result after infection or exposure can bedetermined that is characteristic of the type ofinfection and the stage of disease [9,10,31].

Here we summarize the approximate seroreac-tive and serodiagnostic antigens that were identifiedand published in 30 different organisms, and discussthe antibody response predictions from classifi-cation of reactive antigens based on functionaland physical properties.

PROTEIN MICROARRAY PRODUCTION,PROBING, AND ANALYSIS

Genes were amplified and cloned using a high-throughput PCR and recombination method [29].Open reading frames from genomic DNA or cDNAwere identified and amplified using gene-specificprimers containing about 20 bp nucleotide exten-sion complementary to ends of linearized pXT7vector, which allows homologous recombinationbetween the PCR product and pXT7 vector in com-petent Escherichia coli cells. The resulting fusionproteins also harbored a hemagglutinin epitope atthe 30 end and polyhistidine at the 50 end. Plasmidswere expressed at 24 8C in a 16 h in-vitro transcrip-tion/translation E. coli system (expressway kits fromInvitrogen). For no DNA controls, no plasmid DNAwas added to the same amount of reagent fromin-vitro transcription/translation E. coli system totest E. coli background reactivity. For microarrays,10 ml of reaction was mixed with 3.3 ml 0.2% Tween20 to give a final concentration of 0.05% Tween 20,and printed onto nitrocellulose coated glass FASTslides (Whatman) using an Omni Grid 100 micro-array printer (Genomic Solutions). Sera sampleswere diluted in E. coli lysate (Mclab). Slides wereincubated in biotin-conjugated secondary antibody(Jackson ImmunoResearch) and detected by incu-bation with streptavidin-conjugated SureLight P-3(Columbia Biosciences). Microarray slides werescanned and analyzed using a Perkin Elmer Scan-Array Express HT or Genepix microarray scanner.Intensities were quantified. All signal intensitieswere corrected for spot-specific background. Allforeground values were transformed and normal-ized using a robust linear model or nonlinear

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HT PCRCloning

OutputProteinInput

Plasmid

4 hrs

Cell free in vitroExpression

GenomicDNA

2, 000+genes perweek

KN(R)Linear vector

T7

ORF

Laser confocal microarray scanner

HAHISATG

Printing

Probing

Analysis

FIGURE 1. Microarray production, printing, and analysis. Thousands of genes of interest were cloned using a high-throughputmethod, in-vitro expressed in an Escherichia coli cell free system. Protein microarrays were then produced, probed, and datawere analyzed as described in the text. ORF, open reading frame.

Tropical and travel-associated diseases

variance stabilizing normalization to remove sys-tematic effects [24,34,35] (Fig. 1).

PERCENTAGE OF SEROREACTIVEANTIGENS

Discovery of novel antigens associated with infec-tious diseases is fundamental to the development ofserodiagnostic tests and protein subunit vaccinesagainst existing and emerging pathogens. Throughover 10 years of effort, we have identified over 1000antigens associated with infections or vaccinations in30 different organisms (Table 1) [3–6,9–17,23,25

&&

,31–33,36

&

,37–39,40&&

,41,42,43&

,44,45&

,46&

,47&&

],accounting for around 2–5% of bacterial genome;20–57% of viral genome; and 10–45% of parasitegenomes. Antigens differentially reactive amonginfected and healthy controls comprise even smallerpercentage of the genome size: from 0.3 to 3% forbacteria; 16 to 40% for viruses, and 2 to 18% forparasites. Borrelia burgdorferi, however, generatehigher antibody responses against approximately15% of polypeptides during natural infection, ofwhich half are differentially reactive between natu-rally infected and uninfected individuals [10].

Antigens were classified as ‘seroreactive’ withmean reactivity greater than 2–3 standard devi-ations above the mean of the negative controls inmost organisms; and differentially reactive antigensare classified by Benjamini-Hochberg adjusted P

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value smaller than 0.05 by comparing the negativegroup with infected or vaccinated individuals.

Full proteome microarrays were constructed foronly a limited number of bacterial species; however,other data were published using partial arrays con-taining only partial proteome, and may over-represent the percentages of seroreactive and sero-diagnostic antigens in the full proteome because thesubset of proteins on the array was selected based onantigenic features seen previously.

ENRICHMENT ANALYSIS REVEALSPHYSICAL PROPERTIES AND CELLULARFUNCTIONS ASSOCIATED WITHIMMUNOGENICITY

Another application for these empirical data is totrain an algorithm to predict reactive antigens insilico, and several studies from our group applyenrichment analyses to identify proteomic featuresthat tend to be seen more frequently in the seror-eactive and serodiagnostic antigen sets [12,17,23].

Efforts to predict antigenicity have relied on afew computational algorithms predicting signalpeptide sequences (signalP), transmembranedomains (TMHMM), or subcellular localization(Psort). The current database from this proteinmicroarray approach contains quantitative antibodyreactivity data against 40 000 proteins derivedfrom 30 infectious microorganisms and more than



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Tab

le1

.A

nov

ervi

ewof

sero

reac

tive

antig

ens

and

diffe

rent

ially

reac

tive

antig

ens

iden

tifie

din

diffe

rent

orga

nism

s

Ca

teg

ory

Tota

l#

pro

tein

s#

com

ple

ted

a%

com

ple

ted

%re

act

ive

ant

igen

s%

dif

fere

ntia

llyre

act

ive

ant

igen

sR

efer

ence

s

Virus

esRe

trovi

ruse

s:H

IV1&

2(fi

vesu

btyp

es,

four

clad

es)

83

74

89%

28%

[36

&

]

HPV

viru

ses

(13

type

s)104

104

100%

57%

16%

[32]

Orth

opox

viru

ses:

thre

ety

pes

260

260

100%

20

–22%

–[9

,31]

Her

pes

viru

ses:

HSV

1&

2,

VZV

,EB

V300

270

90%

28.5

–42%

25.9

–41.7

%[3

7]

Flav

ivirus

es:

WN

V,

deng

ue,

YF,

SLE,

JEA

50

50

100%

NP

NP

Alp

havi

ruse

s:C

hiku

ngun

yaC

10

10

100%

NP

NP

Bact

eria

Bruc

ella

mel

itens

isB

3198

3046

95%

4%

1.1

0%

[12]

Chl

amyd

iatra

chom

atis

911

894

98%

2.2

3%

0.5

6%

[3,2

5&&

]

Chl

amyd

iam

urid

arum

911

900

99%

NP

NP

Myc

obac

terium

tube

rcul

osis

C3

990

3899

98%

9.1

3%

0.2

6%

[33]

Fran

cise

llatu

lare

nsis

A1

933

1741

90%

2.7

6%

[11,2

3]

Cox

iella

burn

etiib

B2,0

65

2000

97%

1–2.5

%(Ig

G);

13.1

%(Ig

M)

0.6

–3.3

%(G

);5.1

%(M

)[1

5,3

8]

Borr

elia

burg

dorfer

i1

640

1293

79%

15.5

0%

7.9

7%

[10]

Burk

hold

eria

pseu

dom

alle

iB

5728

1205

21%

8.9

6%

4.0

6%

[6]

Lept

ospi

rain

terr

ogan

sb3

667

3359

92%

5.6

9%

1.0

7%

[39,4

0&&

]

Salm

onel

laen

terica

Typh

ibB

4318

4000

93%

7.3

1%

(GM

)3.4

%(G

M)

[41]

Orien

tiats

utsu

gam

ushi

C1

400

1400

100%

NP

NP

Rick

etts

iarick

etsi

iB

900

900

100%

NP

NP

Barto

nella

hens

elae

B1

493

1433

96%

7.3

3%

3.6

3%

[17]

Ente

ric

toxo

gen

icEs

cher

ichi

aco

liN

PN

P

Para

site

sC

lost

ridi

umdi

ffici

le4000

3297

82%

NP

NP

Plas

mod

ium

falc

ipar

um5

268

4320

82%

13

–21%

2.1

–3.9

%[5

,42,4

3&

]

Plas

mod

ium

viva

x5

300

2200

42%

8.0

4%

–[4

4,4

5&

]

Schi

stos

oma

man

soni

/jap

onic

umc

9000

300

3%

9.8

–44.7

%–

[4,4

6&

]

Toxo

plas

ma

gon

dii

8155

1015

12%

18%

[13]

Nec

ator

amer

ican

us12

000

564

5%

3.9

0%

[47

&&

]

Tryp

anos

oma

cruz

i15

099

240

2%

NP

NP

Hum

anTr

ypan

osom

abr

ucei

8529

214

3%

NP

NP

Hum

anA

utoi

mm

une

arra

yd21

000

1800

9%

2%

[16]

Tota

l1

21

312

>40

000

33%

EBV

,Ep

stei

n–Ba

rrvi

rus;

HIV

,hu

man

imm

unod

efic

ienc

yvi

rus;

HPV

,hu

man

papi

llom

avirus

;H

SV,

herp

essi

mpl

exvi

rus;

JE,

Japa

nese

ence

phal

itis;

SLE,

Sain

tLou

isen

ceph

aliti

s;V

ZV

,va

rice

llazo

ster

viru

s;W

NV

,W

estN

ilevi

rus;

YF,

yello

wfe

ver.

aN

onpu

blis

hed

data

incl

uded

.bBo

thIg

Gan

dIg

Mw

ere

incl

uded

inco

mpu

tatio

n.c IgG

and

IgE

subt

ype

resp

onse

sw

ere

mea

sure

d.dPe

mph

igus

vulg

aris

antib

odie

sw

ere

mea

sure

d.


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Table 2. Physical properties and cellular functions associated with immunogenity in bacteria, virus, and parasites

Enriching features Bacteria Viruses Parasites

Predicted functions COG U Intracellular traffickingand secretion

Virion membrane GO function protein binding, catalytic,transporter, transferase activities

COG M-Cell envelope biogenesis,outer memberane

Membrane proteins,core proteins

GO function enzymatic, regulator

COG N-Cell motility andsecretion

GO function structural molecule and ionchannel activity

COG O Posttranslational modifi-cation, protein turnover, chaper-ones

GO ATP biosynthetic process, proteol-ysis; metabolic

COG S GO transport process

Predicted computationally TMHMM¼1 TMHMM TMHMM¼1 to 10

Signal P> ¼0.7 Signal peptide

pSortb Outer Membrane pSort Outer Membrane

pSortb Periplasmic

pI 0–5 pI 7–9

Expression Expression evidence by massspectrometry

Abundance of expression Abundance of expression

COG, Clusters of Orthologous Groups; GO, gene ontology; pI, isoelectric point; TMHMM, transmembrane domains.


30 million data points derived from 15 000 patientsera. Interrogation of these data sets has revealedmore than 10 proteomic features that are associatedwith antigenicity allowing an in-silico proteinsequence and functional annotation-based approachto triage the least likely antigenic proteins from thosethat are more likely to be antigenic.

These proteomic enrichment features (Table 2)are: functionally annotated Clusters of OrthologousGroups of proteins (U, M, N, and O) or gene ontol-ogy function and process; computationally pre-dicted features (TMHMM, Signal peptide, pSortOutermembrane, pSort Periplasmic, and isoelectricpoint (pI) less than 5 for bacteria, and pI 7–9 forparasites); and abundance of expression. Thisapproach applied to B. melitensis predicts 37% ofthe bacterial proteome containing 91% of the anti-gens empirically identified by probing proteomemicroarrays [12].

Parasite toxoplasma gondii proteins were assignedby gene ontology functions. Proteins involved inprotein binding, catalytic activity, transporteractivity, and transferase activity were significantlyenriched [13]. Proteins with enzymatic activityother than kinase activity were enriched at 2.0 fold,and proteins with enzyme regulator activity, struc-tural molecule activity, and ion channel activitywere also highly enriched. Proteins with gene ontol-ogy null functions, or involved in nucleotide andnucleic acid binding were underrepresented [13].

Proteins were also assigned by gene ontologyprocess classification. Proteins involved in ATP



biosynthetic process were enriched. Several proteinsinvolved in transport were also significantlyenriched, including ion transport, protein trans-port, vesicle mediated transport, and other transportfunctions. Proteins involved in metabolic process,proteolysis, and signal peptide processing were alsoenriched. Conversely, proteins not assigned withgene ontology process categories were significantlyunderrepresented (0.5 fold; P value 3.301�10�21)[13].

An examination with the Pf proteins on themicroarray based on gene ontological analysisrevealed that approximately 40% of the immuno-genic proteins are expressed in the membrane of theparasite or host erythrocyte and that they are over-represented in the biological process categories of‘pathogenesis,’ ‘cytoadherence to microvascula-ture,’ ‘antigenic variation,’ and ‘rosetting’ [5].

The data set of Vaccinia viral proteins alsoallowed us to identify properties of viral proteinsthat were associated with immunogenicity. Wefound that membrane and core proteins, proteinswith late or early/late temporal expression, andproteins with transmembrane domains were over-represented in the immunoreactive antigen setrelative to the whole proteome. These predictorsare strongest in MVA profiles, as the antibody profileto MVA is more heavily skewed toward structuralproteins. In contrast, early proteins were under-represented relative to the whole proteome, andthere was negligible influence of molecularweight, pI, or the presence of a signal sequence



Table 3. Naive Bayses classification of Brucella melitensis and Salmonella enterica

Top ranked antigens

Brucella melitensis Salmonella enterica

% of totalseroreactive

% of totalserodiagnostic

% of totalseroreactive

% of totalserodiagnostic

1% 6% 15% 4% 5%

2% 11% 21% 4% 9%

5% 27% 45% 9% 25%

10% 44% 82% 19% 39%

20% 63% 91% 38% 64%

25% 72% 94% 45% 72%

50% 88% 100% 72% 86%

75% 94% 100% 100% 96%

100% 100% 100% 100% 100%


on immunogenicity. Vaccinia antigens are eitherabundant components of MV particles, such asA10 and L4 [48], or are expressed at high levels ininfected cells, such as I1 and WR148 [49,50]. Theirabundance may contribute to immunogenicityonce released from infected cells, particularly if, likeD13 [51], such proteins have a propensity for self-assembly into macromolecular structures.

Analysis was also done for the herpes simplexvirus-1 antibody profile based on gene ontologycomponent classifiers according to the database atwww.uniprot.org. The percentage of the total num-ber of genes assigned to each gene ontology com-ponent present in the proteome and in theseroreactive antigens was determined, and the ratiowas used to determine the fold enrichment. Theanalysis revealed 12 proteins on the array that wereassigned the gene ontology component virion mem-brane, of which nine were seroreactive. Tegumentproteins were not enriched in the seroreactive anti-gen set [37].

Overall, our data show that the antibody profileis not a random assortment of specificities, butstrongly biased toward the recognition of certainproteomic features. Why we do not observe anti-bodies to all intracellular proteins expressed frominfected cells remains unclear. It is also interesting,to note, that the rules that determine immunoge-nicity might be different from those that defineprotection.

NAIVE BAYES CLASSIFICATION

Individual proteomic features provide some infor-mation about the likelihood of a protein beingseroreactive; however, using all of these featurestogether leads to a better segregation of the hitsfrom the rest of the proteome. To analyze the



relationship between all of these features and theseroreactivity of the proteins in a rigorous manner,we used a naive Bayes formulation [52].

We applied a naive Bayes classification approachto assign a relative numerical score to each antigenin the B. melitensis (Bm) proteome. This score reflectsthe relative likelihood that a protein will be reactivebased on its functionally annotated or computation-ally predicted features. Our analyses indicates that91% of serodiagnostic antigens are predictable fromthe top 20% of the genome ranked by this naiveBayes classification approach, and the antigens withenriched features in the top 20% of the genomeaccount for 100% of serodiagnostic antigens withthese features. Without this naive Bayes classifi-cation approach, we would have to clone 37% ofthe genome with enriching features to obtain 91%of serodiagnostic antigens. This analysis greatlyenhances the predictive efficiency compared withprevious studies, will provide a basis for targetedscreens of entire proteomes based on likelihoodof seroreactivity, and help determine trends in thehumoral immune response to gram-negativebacteria. The same approach has been applied toS. enterica and revealed that we would need toscreen only 25% of the genome to be able to identify72% of serodiagnostic antigens (Table 3).

CONCLUSION

The development of protein arrays for profilingthe antibody response generated upon exposureto an infectious agent has allowed for new insightinto the humoral immune response and the identi-fication of potential subunit vaccine candidatesand new diagnostics. No other existing approachcan provide such a thorough perspective of thehumoral immune response to infection. Moreover,



http://www.uniprot.org/


it provides a systematic foundation formation onproteomic features (functional and physically pro-perties) of seroreactive and serodiagnostic antigens.The information presented here will allow futureprotein microarray screening to focus efforts onportions of the proteome that most likely containseroreactive proteins, and may also be useful forunderstanding the antibody responses to bacteria,viruses, and parasites.

Acknowledgements

None.

Financial support and sponsorship

This work was supported by NIH grants U01AI078213,AI089686 and AI095916.

Conflicts of interest

There are no conflicts of interest.

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& of special interest&& of outstanding interest
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2. Rappuoli R. Reverse vaccinology, a genome-based approach to vaccinedevelopment. Vaccine 2001; 19:2688–2691.

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COURRENT PINION A systems biology approach for diagnostic ...€¦ · After launching this project 10 years ago, we have made more than 40000 plasmids, printed the encoded proteins