in search of novel dengue biomarkers using seldi...
TRANSCRIPT
IN SEARCH OF NOVEL DENGUE BIOMARKERS USING SELDI-TOF MS AND
OTHER PROTEOMIC TECHNOLOGIES
by
Alexa Gilbert
A thesis submitted
in partial fulfillment of the requirements for the degree of Master of Science in
Experimental Medicine in the University of McGill University
Montreal, Québec
September 2010
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ABSTRACT
Surface-Enhanced, Laser-Desorption & Ionization, Time-Of-Flight Mass
Spectrometry (SELDI-TOF MS) permits the study of the protein/peptide content of
complex biological fluids such as serum, plasma, urine, cell lysates and tissue extracts.
This high throughput proteomic platform has been used to identify biomarkers for a wide
range of inflammatory, infectious and neoplastic conditions. The promising results
obtained in these varied conditions raised the possibility that SELDI could be applied to
dengue virus (DV) infection to develop urgently needed diagnostic tests.
Plasma from pediatric Thai patients with either primary (1°) dengue fever (DF)
(n=12) or dengue hemorrhagic fever (DHF) (n=9) as well as patients with either
secondary (2°) DF (n=24) or DHF (n=27) were available for the Discovery Study. In
addition, 15 samples from Thai patients admitted to hospital with other febrile illnesses
(OFI) were analyzed as controls. Validation was accomplished using sera samples from
30 confirmed 2° DF, 54 2° DHF and 30 OFI cases from Puerto Rico (PR). SELDI peaks
that discriminated between patients with DV infection and those with OFI were
considered potential diagnostic biomarkers. SELDI peaks that differed between
uncomplicated DV infection and DHF early in the course of infection were considered to
be potential prognostic biomarkers. For both cases, an initial p-value ≤ 0.05 and a 0.30 ≥
ROC-value ≥ 0.70 were used in screening potential biomarkers. Of these, only the peaks
that showed an intensity ratio between the two groups being compared of at least 2 and
followed the same pattern (i.e. either up- or down-regulated) in PR and Thai samples
were considered as candidate biomarkers.
Following these criteria, 33 peaks were selected that differentiated 2°DV from
OFI and 4 peaks between 2°DF and 2°DHF. Using Biomarker Pattern Software (BPS),
an algorithm was developed and tested using two independent data sets (i.e. PR as the
learning set and Thai as the testing set). A specificity and sensitivity ≥ 89% for
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diagnostic purposes was achieved. Unfortunately, no algorithm for prognostic purposes
with a specificity and sensitivity ≥70% using a single or a combination of the 4 selected
peaks was achieved. Guided by these results, the most promising candidate biomarkers
were identified using SDS-PAGE gels and tandem MS. More than 30 proteins were
identified, five of which were detected in samples from both countries (i.e. α2-
macroglobulin, complement component C3, plasma protease (C1) inhibitor,
serotransferrin inhibitor, serum albumin, and vitronectin precursor). The last, vitronectin
(Vn) precursor protein, was selected for further, in depth study.
Vn is an acute phase glycoprotein thought to be involved in vascular
inflammation and complement activation. Since hemorrhages and plasma leakage are
hallmarks of severe DV infection, it may be relevant that Vn also plays a role in the
fibrinolytic pathway. By binding and stabilizing type 1 plasminogen activator inhibitor
(PAI-1), it inhibits fibrinolysis. In Thai subjects, we found that Vn precursor could
differentiate between 1°DF and more severe DV infection (e.g.: DHF and dengue shock
syndrome). The Vn precursor was readily found by Western blot in serum/plasma from
healthy children as well as patients with OFI or 1°DF but it was not detectable in patients
with 1°DHF, 2° DF, or 2° DHF. Surprisingly, these results could not be confirmed in the
Puerto Rican samples although Vn does seem to be a biomarker if we consider the
concentration of Vn found in both set of samples. The genetic background of the
individuals studied and/or of the viral strains may account for these differences. The total
Vn plasma concentration was lower in 2°DHF (about 20% Vn of normal plasma content)
than in 2°DF samples (70% Vn of normal plasma) or in OFI and healthy samples with a
p-value < 0.01. PR samples followed the same trend (2°DF > 2°DHF) with a
significance of p-value<0.001.
Although clinical manifestations of a 1° and 2°DV infection is clinically
indistinguishable, more than 100 candidate biomarkers were found in Thai specimen that
differentiated between the 1° and 2° DV infection proteomes. This suggests that the
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plasma proteome of patients with a 1° infection is markedly different than that of a
patient with a 2° infection and that perhaps the underlying disease mechanism might
differ in subtle ways.
This study demonstrates the potential for high-throughput proteomics to develop
useful diagnostic and prognostic tests for DF/DHF. Additionally, such biomarker studies
may give unique insight into the mechanisms underlying the different manifestations of
DV infection.
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RÉSUMÉ
La spectrométrie de masse à desorption-ionisation laser assistée par matrice
(SELDI-TOF MS) permet l’étude du contenu protéomique de fluides biologiques
complexes tels du sérum, du plasma, d’urine, des lysats cellulaires et d’extraits de tissus.
Cette plateforme proéomique à haut débit a été utilisé pour identifier des biomarqueurs
dans une vaste panoplie de conditions inflammatoires, infectieuses, et néoplastiques. Ces
résultats prometteurs obtenus dans ces conditions variées soulèvent la possibilité que la
SELDI pourrait être appliquée à la maladie de la dengue afin de développer des tests
diagnostiques urgemment nécessités.
Le plasma de patients pédiatriques thaïlandais souffrant soit de la dengue (DF)
primaire (1°) (n=12) ou de la dengue hémorragique (DHF) (n=9) ainsi que de patients
avec de la DF secondaire (2°) (n=24) et de la DHF (n=27) étaient disponibles pour la
partie découverte de l’étude. De plus, 15 échantillons de patients thaïlandais admis à
l’hôpital avec d’autres maladies fébriles (OFI) ont été analysés et utilisés comme
contrôles. La validation a été accomplie en utilisant des échantillons de 30 cas confirmés
2°DF, 54 2°DHF et 30 cas OFI du Puerto Rico (PR). Les pics moléculaires obtenus de
l’analyse de ces échantillons par la SELDI-TOF MS qui permettent de discriminer entre
les patients infectés avec le virus de la dengue (DV) et ceux avec OFI sont considérés des
biomarqueurs diagnostiques potentiels, tandis que ceux permettant de différencier entre
les patients de DF et ceux de DHF tôt dans le cours de l’infection seraient potentiellement
des biomarqueurs pronostiques. Dans les deux circonstances, des valeurs initiales p ≤
0.05 et 0.30 ≥ ROC ≥0.70 furent utilisées afin de déterminer quels pics moléculaires
représentent des biomarqueurs potentiels. De ces pics, seulement ceux avec un ratio
d’intensité entre les deux groupes étant comparés ≥ 2 et démontrant une même tendance
(i.e. soit régulé à la hausse ou à la baisse) dans les deux ensembles de données (Thaï et
Portoricain) furent considérés comme biomarqueurs candidats.
Respectant ces critères, 33 pics furent sélectionnés pouvant différentier entre
2°DV et OFI et 4 pics entre 2°DF et 2°DHF. Avec l’aide du Biomarker Pattern Software
(BPS), un algorithme fut développé et testé utilisant deux ensembles de données
indépendants (i.e. PR pour l’ensemble apprentissage et Thaï pour l’ensemble test). Une
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spécificité et sensitivité ≥ 89% servant à des fins diagnostiques furent obtenues.
Malheureusement, ni avec l’aide d’un seul ni avec une combinaison des 4 pics
pronostiques pré-selectionnés un algorithme put-il être développé qui démontra une
spécificité et une sensitivité ≥70%. Guidé par ces résultats, les biomarqueurs candidats les
plus prometteurs furent identifiés utilisant des gels SDS-PAGE et le MS/MS. Plus de 30
protéines furent identifiés, cinq desquelles furent retrouvées dans les échantillons
provenant des deux pays (i.e. α2-microglobuline, complément C3, inhibiteur de la C1
estérase, inhibiteur de la serotransferrin, albumine, et vitronectine). La protéine
vitronectine (Vn) fut sélectionnée pour des études plus approfondies.
La vitronectine est une glycoprotéine de phase aiguë impliquée dans
l’inflammation vasculaire et dans l’activation du système de complément. Puisque les
hémorragies et les troubles de perméabilité capillaire sont les principales caractéristiques
des infections sévères de la dengue, il est pertinent de mentionner que la Vn joue aussi un
rôle dans le système fibrinolytique. En s’associant et stabilisant l’inhibiteur de
l’activateur du plasminogen (PAI-1), la Vn inhibe la fibrinolyse. Dans les patients
thaïlandais, nous démontrâmes que le précurseur de la Vn permet de différencier entre
1°DF et les cas plus sévères de la DV (ex. DHF et la dengue avec syndrome choc). La
présence du précurseur de la Vn fut démontrée par un western blot du sérum/plasma
d’enfant en santé en plus de patients avec OFI et 1°DF, mais ne fut pas détecté dans les
échantillons provenant des patients avec 1°DHF, 2°DF, ou 2°DHF. Étonnamment, ces
résultats ne purent être confirmés par les échantillons portoricains même si la Vn semble
être un biomarqueur si on considère sa faible concentration – par rapport aux contrôles -
dans les deux régions du monde. Les origines génétiques des individus étudiés et/ou la
souche virale peuvent expliquer certaines de ces différences. La Vn est présente à de
basses concentrations dans le plasma des patients avec 2° DHF (environ 20% de la
concentration normale) en comparaison avec les patients 2°DF (environ 70% de la
concentration normale) ou OFI et en santé avec une valeur significative p < 0.01. Les
échantillons portoricains démontrèrent la même tendance (2°DF > 2°DHF), avec une
valeur significative de p < 0.001.
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Malgré le fait que les manifestations cliniques entre une infection primaire et
secondaire de la dengue soient indiscernables, plus de 100 biomarqueurs candidats furent
détectés dans les spécimens thaïs pouvant différencier entre les protéomes d’une infection
primaire et secondaires de la DV. Ceci suggère que le protéome plasmatique d’un patient
souffrant d’une infection primaire diffère nettement de celui d’un patient avec une
infection secondaire et que le mécanisme sous-jacent de la maladie pourrait différer de
manières subtiles.
Cette étude atteste le potentiel des plateformes protéomiques à haut débit de
développer des tests diagnostique et pronostiques pour le DF/DHF. En outre, ces
biomarqueurs pourraient fournir une meilleure compréhension des différentes
manifestations de la maladie de la dengue.
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TABLE OF CONTENTS
ABSTRACT ...................................................................................................................... III
RÉSUMÉ ........................................................................................................................... VI
TABLE OF CONTENTS .................................................................................................. IX
DECLARATION ............................................................................................................... XI
ACKNOWLEDGMENTS ................................................................................................ XII
LIST OF TABLES .......................................................................................................... XIII
LIST OF FIGURES ........................................................................................................ XIV
CHAPTER 1: INTRODUCTION TO DENGUE VIRUS .................................................. 1
1.1 DENGUE VIRUS INFECTION .......................................................................................... 2
1.2 RISK FACTORS ASSOCIATED WITH DHF/DSS AND PATHOGENESIS .............................. 3
CHAPTER 2: MOTIVATION AND JUSTIFICATION FOR MASTER’S PROJECT .... 5
CHAPTER 3: CURRENT DENGUE VIRUS INFECTION MARKERS .......................... 8
3.1 CLINICAL MARKERS OF DISEASE SEVERITY ................................................................. 8
3.2 CYTOKINE PATTERNS OF THE INNATE IMMUNE RESPONSE ........................................... 9
3.3 CYTOKINE PATTERN OF THE ADAPTIVE IMMUNE RESPONSE ....................................... 10
3.4 ALTERATIONS IN SOLUBLE RECEPTORS DURING INFECTION ...................................... 10
3.5 MARKERS ASSOCIATED WITH ENDOTHELIAL CELL ACTIVATION AND INJURY ............ 11
CHAPTER 4: SURFACE-ENHANCED LASER DESORPTION/IONIZATION TIME-
OF-FLIGHT MASS SPECTROMETRY (SELDI-TOF MS) ........................................... 12
4.1 PROTEINCHIP ARRAYS .............................................................................................. 12
4.2 DESORPTION/IONIZATION PROCESS ........................................................................... 12
4.3 ION SEPARATION AND DETECTION ............................................................................. 13
4.4 MASS CALIBRATION AND DATA PROCESSING ............................................................ 14
4.5 GENERATION OF PEAK CLUSTERS .............................................................................. 15
CHAPTER 5: PROTEINCHIP PATTERN ANALYSIS SOFTWARE ........................... 16
5.1 CLASSIFICATION AND REGRESSION TREES (CART) METHODOLOGY ......................... 16
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CHAPTER 6: CONTRIBUTION OF THE SELDI TECHNOLOGY TO DENGUE
VIRUS INFECTION CHARACTERIZATION ................................................................ 19
CHAPTER 7: GENERAL METHODS ............................................................................. 21
7.1 PURIFIED VITRONECTIN PROTEIN DEGLYCOSYLATION BY PNGASEF ........................ 21
7.2 VITRONECTIN ELISA ............................................................................................... 21
7.3 ACQUISITION OF MS OF HUMAN PURIFIED VITRONECTIN PROTEIN ............................ 21
7.4 IMMUNOPRECIPITATION OF VITRONECTIN PROTEIN ................................................... 22
7.5 HIGH-ABUNDANCE SERUM PROTEIN DEPLETION ........................................................ 22
CHAPTER 8: DETECTION AND IDENTIFICATION OF BIOMARKERS FOR
DENGUE FEVER AND DENGUE HEMORRHAGIC FEVER IN PLASMA SAMPLES
FROM THAILAND AND PUERTO RICO USING SELDI-TOF MS TECHNOLOGY 23
8.1 INTRODUCTION ......................................................................................................... 24
8.2 MATERIALS AND METHODS ...................................................................................... 26
8.3 RESULTS ................................................................................................................... 30
8.4 DISCUSSION .............................................................................................................. 44
REFERENCES FOR CHAPTER 8 .......................................................................................... 48
CHAPTER 9: VITRONECTIN PRECURSOR PROTEIN AS A NEW LEAD IN
DENGUE PHYSIOPATHOLOGY ................................................................................... 51
9.1 PROTEIN IDENTIFICATION FROM THAI SAMPLES ........................................................ 52
9.2 VITRONECTIN PROTEIN CHARACTERIZATION ............................................................. 55
9.3 CHARACTERIZATION OF VITRONECTIN PROTEIN IN DENGUE SAMPLES ....................... 58
9.4 VITRONECTIN AND MASS SPECTROMETRY ................................................................. 63
9.5 DISCUSSION .............................................................................................................. 70
CHAPTER 10: SUMMARY AND CONCLUSION ........................................................ 76
REFERENCES .................................................................................................................. 86
APPENDIX A. SUPPLEMENTARY DATA ................................................................. 100
APPENDIX B. ETHIC AND CONSENT FORMS ........................................................ 109
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DECLARATION
I hereby declare that I am the sole author of this thesis. Scientific contributions have
been made by the following individuals: Dr. Brian J. Ward (McGill University, Montreal,
Canada), primary supervisor; Dr. Momar Ndao (National Reference Center for
Parasitology, Montreal, Canada), co-supervisor; Christine Straccini (National Reference
Center for Parasitology, Montreal, Canada), SELDI-TOF MS training and assistance; Dr.
Sukathida Ubol (Mahidol University, Bangkok, Thailand), donation of Thai DV infected
plasma samples; Takol Chareonsirisuthigul (Mahidol University, Bangkok, Thailand),
start-up of dengue project in Ward lab; Dr. Elizabeth Hunsperger (CDC, Puerto Rico,
USA), donation of Puerto Rican DV infected plasma samples; Dr. Bernard F. Gibbs
(McGill Sheldon Biotech Center, Montreal, Canada), protein identification in Thai
samples; Dr. Kurt Dejgaard (McGill University, Montreal, Canada), protein identification
in Puerto Rican samples.
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ACKNOWLEDGMENTS
This research project would not have been possible without the support of many people. I
wish to express my gratitude to my supervisor Dr. Brian Ward for his invaluable guidance
and suggestions and for instilling in me, through his endless enthusiasm, a love for
research. Likewise, I would like to thank Dr. Momar Ndao for his expertise and his
never-ending dedication to his lab. To Angela and Fabio, for always lending a hand when
needed as well as their invaluable friendship. A special thank you to Chrisine Straccini
for her skills and her inexhaustible patience! And to all the other members of the lab,
especially my fellow graduate students, who made this experience so much more
enjoyable.
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LIST OF TABLES
Table 8.1 Diagnostic biomarkers that can distinguish between secondary DV infection and
OFI at time of hospitalization (t1).
Table 8.2 Biomarkers that can distinguish between DF and OFI at time of hospitalization
(t1).
Table 8.3 Biomarkers that can distinguish between 2°DHF and OFI at time of
hospitalization (t1).
Table 8.4 Prognostic Biomarkers that can distinguish between DF and DHF at time of
hospitalization (t1).
Table 8.5 Non-redundant list of the 16 proteins identified.
Table 9.1 Non-redundant list of the 25 proteins identified.
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LIST OF FIGURES
Figure 2.1 Clinical manifestations during dengue infection.
Figure 4.1 Pre-processing of mass spectrum data.
Figure 5.1 Example of classification and regression tree output.
Figure 8.1 Heat map of biomarkers found in F1CSL that can differentiate between 1°DF
and 2°DF.
Figure 8.2 Heat map of biomarkers found in F6CSH that can differentiate between 1°DHF
and 2°DHF.
Figure 8.3 Biomarker pattern software based on CART analysis was used to generate
candidate diagnostic algorithms.
Figure 8.4 4-12% Bis-Tris NuPAGE gel of pooled PR samples.
Figure 8.5 Assessment of vitronectin precursor protein in Thai samples by Western blot
analysis.
Figure 9.1 4-12% Bis-Tris NuPAGE gel of pooled Thai samples.
Figure 9.2 Deglycosylation of purified human vitronectin by PNGaseF.
Figure 9.3 Chromatogram of two vitronectin isoforms.
Figure 9.4 Assessment of vitronectin precursor protein in Thai samples by Western blot
analysis.
Figure 9.5 Assessment of vitronectin precursor protein in PR samples by Western blot
analysis.
Figure 9.6 Total Vn levels in Thai samples at t1.
Figure 9.7 Total Vn levels in Puerto Rican samples.
Figure 9.8 SELDI-TOF MS of purified human vitronectin.
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Figure 9.9 MALDI-TOF MS of purified human vitronectin.
Figure 9.10 Effect of treatment of plasma samples with Seppro IgY 14 Spin Columns on
mass spectra at high and low laser intensity.
Figure 9.11 Western blot of vitronectin immunoprecipitation from depleted Thai plasma
samples.
Figure 9.12 SELDI-TOF MS of vitronectin immunoprecipitation from OFI Thai depleted
plasma samples.
Figure 9.13 Western blot of vitronectin protein from fractionated Thai plasma samples.
1
Chapter 1: Introduction to Dengue Virus
Records as far back as 992 A.D. in China describe a dengue-like syndrome.
However, the first epidemics of well-documented cases of what are believed to be dengue
occurred in 1779-1780 (75). Although a successful eradication program against the main
vector of dengue virus (DV), the Aedes aegypti mosquito, brought both dengue and yellow
fever under control in the 1950s and 1960s in the Americas, this program was halted in the
1970s. Mosquito populations rapidly recovered and, accompanying this rebound, there
was a dramatic re-emergence of dengue infection (48). During the last decade, the spread
of dengue worldwide has been fuelled by unprecedented population growth, unplanned
and uncontrolled urbanization, and the lack of mosquito control among other things (102).
Clinically-apparent disease due to DV has been described as the tip of the iceberg, since
less than 10% of symptomatic dengue cases are reported and 50-90% of all DV infections
are asymptomatic. It is estimated that 3.5 billion people, or almost half the world’s
population, are at risk of DV infection in more than 100 countries in the Americas,
Southeast Asia, western Pacific, Africa and the eastern Mediterranean (75). There are an
estimated 50 million cases of dengue infection each year with 500,000 cases of dengue
hemorrhagic fever (the most severe form of the disease) and at least 22,000 deaths, mostly
in children (7).
DV belongs to the Flavivirus genus of the Flaviviridae family that also includes
yellow fever, West Nile, tick-borne encephalitis (TBEV), and Japanese encephalitis
viruses. Four main serotypes distinguishable by neutralizing antibodies exist, all of which
can cause disease ranging in severity from the mildest form of dengue fever (DF), to the
most severe forms, dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS).
DV possesses an icosahedral core 40-50 nm in diameter, containing one of the 3 structural
proteins, the C protein. This protein encapsulates the 10,700 nucleotide positive-sense
RNA genome. Surrounding the core is a smooth lipid bilayer containing the other 2
structural proteins, the membrane protein (M), and the envelope glycoprotein (E) (7, 71).
Important biological properties of the virus reside in the E protein including receptor
binding and agglutination of erythrocytes as well as neutralising antibody induction. Anti-
E antibodies are a critical component of the protective immune response (96). DV also
2
possesses 7 non-structural proteins (NS1, NS2a, NS2b, NS3, NS4a, NS4b, NS5), of which
two, NS1 and NS3, are believed to be involved in pathogenesis. Upon primary infection
with DV, antibodies against E, NS1, and NS3 proteins are generated (44). Non-
neutralizing antibodies against these proteins tend to be cross-reactive and may contribute
to immunopathology (so-called antibody enhancement of disease; ADE). Life-long
immunity against the infecting serotype follows disease, but protection against the
heterologous strains is usually transient.
1.1 Dengue virus infection
The likelihood of exposure to infected mosquitos (A. aegypti or A. albopictus) is
what determines an individual’s risk for acquiring DV. As a result, by avoiding
mosquitoes and eliminating their breeding sites around the house and work place, an
individual might reasonably hope to decrease the risk of infection. However, there are
many factor’s beyond an individual’s control. Although most reported DV cases in the
United states are from travelers returning from endemic countries, there have been
epidemics as far north as Philadelphia and both A. aegypti and A. albopictus are present in
central and southern parts of the country (5). Such ‘vector-competent’ mosquito
populations are widely distributed in sub-tropical and even temperate environments across
the world in areas where dengue virus is not yet endemic. Once bitten by an infected
mosquito, there is typically an incubation period of up to 2 weeks. Most infections are
asymptomatic, especially in children under 15 years of age. In those who become
symptomatic, the disease manifestations can range from a mild febrile illness to fulminant
infection leading to death. Population-based studies have shown that the severity of the
disease increases with the patient’s age (25, 30, 37, 70). The 1997 WHO classification
guidelines grouped DV infection into 3 categories: undifferentiated fever, classical DF and
DHF. DF is an acute febrile disease often characterised by frontal headache, retroocular
pain, muscle and joint pain, nausea, vomiting, and rash (63). The fever usually ends
between 5-7 days after the onset of symptoms, often correlating with the appearance of
anti-DV antibodies in the circulation. The main pathophysiological difference between
DF and DHF is plasma leakage. Thrombocytopenia (platelet count ≤ 100,000/mm3) is
another characteristic of DHF and many patients have hemorrhagic manifestations such as
3
petechiae, purpuric lesions and ecchymoses (48). Moreover, DHF tends to follow a
secondary DV infection with a heterologous strain, supporting the antibody-dependent
enhancement (ADE) model (44, 49, 68, 93, 110). DHF can be further classified into four
severity grades (I to IV), with grades III and IV being defined as DSS. DSS is the most
severe form of the disease characterized by circulatory failure and a narrowing pulse
range. Once shock begins, the fatality rate can be as high as 44% if adequate medical
support is not available (i.e. intensive care) (93). Although new guidelines in 2009 were
published, the 1997 classification system continues to be widely used (6).
1.2 Risk factors associated with DHF/DSS and pathogenesis
Currently, there is no adequate animal model for either DF or DHF. As a result,
investigation of the mechanism(s) of variable pathogenesis have been indirect and
dreadfully slow. Despite this limitation, the severity of DV infection seems to be
influenced by a number of factors including pre-existing immunity from previous DV
exposures, time between infections, age, ethnicity, host genetic background, sequence of
infecting serotypes and viral genotype (75). After recovery from a primary infection, life-
long immunity specific for the DV serotype generally ensues. Some level of cross-
protective immunity for other serotypes is often generated but protection is usually
transient. Although the pathogenic mechanisms of DHF/DSS are still poorly understood,
one of the best-supported theories is that of antibody-dependent enhancement (ADE).
This theory postulates that, after an initial period of cross-serotype protection based upon
high overall titers, cross-reactive antibodies fall to non-neutralizing levels. Although
insufficient to neutralize the virus, these cross-reactive antibodies still bind to virus and
facilitate uptake into monocytes/macrophage cells via the Fc receptor. This facilitated
entry leads to increased viral replication and immune activation accompanied by cytokine
release (75). Cytokines and chemical mediators such as tumor necrosis factor (TNF),
interleukin-1 (IL-1), IL-2, IL-6, platelet-activating factor (PAF), complement activation
products C3a and C5a, and histamine have been suggested as key factors responsible for
increased vascular permeability observed in DHF/DSS (48). A related hypothesis to
explain the cytokine storm that accompanies DHF/DSS has been called “original antigenic
sin”. In this scenario, a secondary DV infection reactivates cross-reactive memory T-cells
4
and/or memory B cells specific for the previous rather than the current DV infection. This
immune ‘confusion’ might plausibly cause a delay in viral clearance and/or an increase in
cytokine secretion along with increased apoptosis of both infected and bystander cells
(75). Other groups believe that the severity of the disease is mostly influenced by the
virulence of the infecting DV. In this hypothesis, DHF/DSS-associated strains would be
transmitted more efficiently by mosquitoes and their replication fitness would be superior
to strains associated with DF (48). Although attractive as a hypothesis, such increases in
replication fitness have not yet been detected for any of the four common DV genotypes
leading many researchers to conclude that individual and community-levels differences in
clinical presentation and severity are primarily a function of immunologic and genetic
differences between people and populations in different geographic areas (eg: Southeast
Asia vs. the Americas) (50).
At the current time, there is no anti-dengue vaccine, there are no effective antiviral
drugs for DF/DHF and no intervention has been shown to limit the plasma leakage
associated with DV infection. Dengue treatment in 2010 is solely supportive, including
analgesic and antipyretic medications (but not ASA) and careful attention to fluid
management. Only when the molecular pathology of DHF is better understood will we be
able to treat the devastating complications of infection (77, 102). This underscores the
critical need for improved diagnostic tools in dengue so that disease can be recognized
early to maximize the patient’s chance of survival.
5
Chapter 2: Motivation and Justification for Master’s Project
Clinical findings alone are often not very helpful in distinguishing DF from other
febrile illnesses (OFI) such as the chikungunya, measles, malaria, leptospirosis, yellow
fever, influenza, West Nile, Japanese, and St Louis encephalitis (among many others)
(102, 106, 115).
A wide range of methods are available for the diagnosis of DV infection.
Serological assays include enzyme immunoassays (EIA), haemagglutination-inhibition
(HI) tests and complement fixation (CF). For the diagnosis of acute infection, the World
Health Organization (WHO) currently recommends the use of the dengue monoclonal
antibody (IgM)-capture EIA (MAC-EIA) which is inexpensive, simple, fast, and only
requires one blood sample (7). However, one has to keep in mind that IgM antibodies can
often only be detected 5-7 days after symptom onset and may persist in the blood of some
subjects for several months post-infection producing false-positives (115). Due to cross-
reactivity among the flaviviruses, any serologic test must therefore include the four dengue
serotypes as well as other flavivirus antigens as controls (115). Isolated positive IgG
results can be very difficult to interpret because the vast majority of people who live in the
tropics have been repeatedly exposed to one or more DV strains (as well as other
flaviviruses). As a result, paired acute and convalescent serum samples are often critical
for the adequate interpretation of any test (102).
The haemagglutination-inhibition (HI) test is slightly more sensitive than the EIA
but more manipulation of the samples is needed (i.e. more labour-intensive). Moreover,
this test does not differentiate between closely related flavivirus infections or different DV
serotypes. Paired sera are needed; consequently, the results are typically delayed for
weeks. The complement fixation (CF) test is a relatively good marker of recent infection
compared to detection of IgM antibodies but it is the least sensitive of the serological tests.
Direct virus detection in clinical specimens is another diagnostic approach.
Mosquito cells, larvae or adult mosquitoes are incubated with the clinical specimen (e.g.
serum). After amplification of the virus in infected cells, both the presence of DV and the
DV serotype can be determined using immunofluorescence. This test is convenient since
6
the samples are relatively stable for two weeks (115). However, this approach has
biosafety implications and days to weeks are necessary for virus isolation. Furthermore,
the cost of equipment and laboratory maintenance is high, particularly when mosquito
larvae or adults are used (64).
The flavivirus non-structural protein NS1 is a highly conserved and secreted
glycoprotein and a candidate protein for rapid diagnosis of dengue in endemic countries
(130). Commercially available ELISA kits are available for rapid detection of this DV Ag.
However, only acute-phase sera are positive and as the patient progresses towards
convalescence, NS1 becomes undetectable. Moreover, serotyping is not possible with this
method and the test may be cross-reactive to other flaviviruses (29, 84).
Viral RNA can be detected by reverse-transcription-polymerase chain reaction
(RT-PCR). This technique is also expensive and has high contamination risks associated
with sample manipulation. However, the test only takes a few hours to perform, is much
more sensitive and can differentiate between serotypes (115).
Virus isolation and nucleic acid based tests (e.g.: PCR) most reliably detect DV in
the days prior to symptom onset and are often negative once a child is admitted to the
hospital. As a result, there is a diagnostic ‘gap’ at the time of hospitalization when no test
is particularly reliable (Figure 2.1). The first few days of hospitalization can be critically
important for children with DV infection since deterioration to DHF/DSS is often
preceded by clinical defervescence. Indeed, many children in whom a diagnosis cannot be
made (and who appear to be improving) are discharged from hospital in the day or two
before DHF/DSS manifests. As a result, new diagnostic strategies to differentiate between
DF and OFI or that can predict progression to DHF/DSS are urgently needed.
7
Figure 2.1 Clinical manifestations during dengue infection. Diagnosis falls into two
stages: stage I, fever and viraemia accompanied by NS1 antigens in blood; and stage II,
the early post-febrile period lasting a few weeks when IgM and IgG antibodies are in
excess. During a primary infection, viraemia more or less coincides with fever. During a
secondary infection, viraemia can be 2 or 3 days long, whereas presence of NS1 antigens
in blood lasts somewhat longer.
8
Chapter 3: Current Dengue Virus Infection Markers
Clinical disease in DV infection can range from asymptomatic, through
undifferentiated fever and ‘classical’ DF, to DHF. The majority of DV infections in
children are asymptomatic or present as undifferentiated fever. Infections in older
children and adults are more likely to cause symptomatic DF. Only a minority of patients
will develop DHF, which is characterized by bleeding and plasma leakage and may lead to
shock and even death. At the current time, there is no animal model that can properly
mimic DHF. Human studies are thus essential in identifying predictors of severe illness.
Interpreting these studies is not a trivial task however. Human studies of infectious
diseases are notoriously heterogeneous in many respects: geography, genetics, co-
morbidities, etc. In particular, heterogeneity in timing of sample collection may hide
differences that exist between patients with different disease severity. Since severe DV
manifestations typically occur shortly after defervescence (i.e. apparent clinical
improvement), sampling needs to be done at this time (or serially) rather than at disease
onset, depending on the kind of markers of greatest interest to the researcher. Also,
differences in sample processing may have profound impact on the nature of biomarkers
discovered. For example, the use of plasma or serum may determine whether or not
markers released or activated during coagulation can be detected. Such biomarkers may be
of particular relevance in DV infection that targets the vascular endothelium. Currently,
there are no reliable clinical or laboratory markers to predict the development of DHF in a
given patient infected with DV although some patterns have been observed.
3.1 Clinical markers of disease severity
Plasma leakage is the main clinical feature that differentiates DHF from
DF. This manifestation typically lasts approximately 48 hours followed by rapid
resolution. Spontaneous bleeding at any site is common to both DF and DHF although it
is more severe in the latter. Even gastrointestinal tract bleeding has been observed in
some DHF cases. Reports of hepatic failure and encephalopathy have also been reported
in some severe cases. Another criterion for DHF diagnosis is thrombocytopenia (platelet
count < 100,000 cells/ mm3). Interestingly, a significant proportion of DF patients also
have a low platelet count. In fact, platelet numbers during the early febrile phase of the
9
illness are not significantly different between DF and DHF patients. The platelet count
progressively decreases until it reaches a nadir at time of defervescence in both DF and
DHF patients. Although this coincides with plasma leakage in DHF, thrombocytopenia
can not be regarded as an early indicator for DHF. On the other hand, the platelet count is
inversely correlated with the size of pleural effusion in DHF (92). Thrombocytopenia
may therefore serve as a marker for the extent of plasma leakage or perhaps as a tool for
monitoring disease progression (108).
Hepatomegaly and liver tenderness are common in both DF and DHF patients.
High serum levels of liver transaminases (AST and ALT) are present early in both DF and
DHF, although elevations are more pronounced in the latter (62, 63, 94). High levels of
liver enzymes in the circulation may serve as early markers for disease severity. However,
cut-off values that could potentially differentiate between DF and DHF remain undefined.
Viral load in serum samples also seems to correlate with disease severity since it
peaks early in the course of disease and abruptly drops at defervescence. Peak viraemia
has been observed to be higher in patients with DHF compared to patients with DF (81,
121). Moreover, serum levels of the soluble viral antigen, NS1, are higher within 72
hours of fever onset in patients who go on to develop DHF (83). A high viral load is
therefore a risk factor for the development of severe disease.
3.2 Cytokine patterns of the innate immune response
In vitro infection of cells such as monocytes/macrophages, dendritic cells
and endothelial cells with DV induces the production of cytokines such as TNF-α, IL-1,
IL-6, IL-10 and many chemokines (11, 18, 55, 58, 74, 82). Although cytokine levels have
been measured in humans infected with DV infection, the reported levels are not
consistent across studies. These differences may be due to sample timing, the detection
methods used, and/or lack of standardization in assigning disease severity.
TNF-α and IL-8 are cytokines with pro-inflammatory and vascular permeability-
enhancing activities. They are typically elevated in DHF patients although their exact
contribution to plasma leakage is unclear since there is a relative lack of tissue
inflammation during DV infection (108). TNF-α and IL-8 might serve as early markers
10
for disease severity, but more information is still needed on the kinetics of these
cytokines.
3.3 Cytokine pattern of the adaptive immune response
There is a general consensus that a more intense cell immune response occurs in
DHF than in DF (108). During a secondary infection with a heterologous DV serotype,
serotype cross-reactive T cells are activated. This causes an increase in pro-inflammatory
and Th1-type cytokine production such as IL-2, IFN-γ and TNF-α as has been observed in
severe DHF cases (10, 19, 20, 73). Serum levels of Th2-type cytokines may also be
elevated in DHF, such as IL-4 and IL-13, but their role in pathogenesis is still unclear (19,
90). Higher levels of serum IL-10 in DHF compared to DF raise the possibility of an
attempted regulatory response in the setting of more intense immune activation (46).
Serum levels of IL-10 measured 2 days prior to defervescence correlate with the size of
pleural effusion detected one day after defervescence. In contrast, Libraty et al. observed
peak levels of IL-10 after defervescence (81). Such inconsistencies may be due to
different sources of IL-10 such as infected macrophages (early disease) and T cells (later
phase).
3.4 Alterations in soluble receptors during infection
Soluble forms of receptors can be produced by differential splicing or enzymatic
cleavage of surface molecules. The presence of such markers in body fluids generally
suggests signaling activity but the available data are far from consistent. Alterations in
levels of several soluble receptors have been identified in patients with DV infections.
High levels of soluble CD4 and CD8 in plasma of patients with DHF have been measured
(73). In some studies, but not all, increased levels of soluble TNF-α receptors in the
serum of DHF patients have also been observed (20, 45, 81, 125). Increased levels of
serum IL-2R were found to correlate with an increase in liver enzymes in one study but
not another (81, 119). Moreover, serum levels of IL-1R in fatal DHS/DSS cases are
higher than in survivor cases (112). Increased levels of the soluble form of IL-1 receptor-
like protein (ST2) in serum correlate with a decrease of white blood cells and an increase
in serum transaminase levels (119). Whether these correlations reflect the roles of the
11
implicated signaling pathways (or the soluble receptors themselves) in the pathogenesis
of DHF is still unknown.
Vascular endothelial growth factor (VEGF) is a potent permeability-enhancing
cytokine. In various studies, it has been observed to increase in the circulation of patients
suffering DHF (108, 118). Associated with this elevation, is a decrease of circulating
soluble form of VEGFR-2, which in turn decreases the levels of soluble VEGFR-2/VEGF
complexes but increases the biologically active free form of VEGF. The degree of
decrease in plasma VEGFR-2 correlates with plasma leakage and viral load (108).
VEGFR-2 may be both an interesting biomarker of disease severity and provide an
interesting hint as to how DV may regulate permeability.
3.5 Markers associated with endothelial cell activation and injury
Activation of endothelial cells leads to changes in vascular permeability and to the
release of factors that activate the coagulation pathway. The frequency of circulating
endothelial cells is higher in DHF patients (26, 27). Also, elevated levels of soluble
endothelial surface molecules such as intercellular adhesion molecules (ICAM-1) and
vascular cell adhesion molecules (VCAM) are observed in patients with DHF (27, 69).
Elevated levels of soluble thrombomodulin, von Willebrand factor (vWF) antigen, tissue
factor and plasminogen activator have all been reported in dengue patients during the
acute phase and seem to be associated with disease severity (26, 107). At the same time,
the level of ADAMTS-13, a vWF-cleaving metalloproteinase, is decreased in DHF
patients compared to patients with DF. Endothelial cell activation markers may therefore
be helpful in identifying severe DV cases and provide some insight into the pathogenesis
of the disease. However, kinetic studies need to be conducted in order to determine the
time course of their expression and to evaluate whether these markers can be used to
predict disease severity.
12
Chapter 4: Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass
Spectrometry (SELDI-TOF MS)
First described in 1993, the SELDI platform has made great progress during the
last decade (59). This technique combines retention chromatography with TOF-MS
detection. SELDI-TOF MS permits the identification of the protein/peptide content of
complex biological fluids such as serum, plasma, urine, cell lysates and tissue extracts.
This system is comprised of three main components: the protein chip array, the mass
analyzer and the data analysis software.
4.1 ProteinChip arrays
Protein chip arrays are 10 mm wide x 80 mm long aluminum strips with 8 or 16
two-mm “spots” that constitute the active surface of the chip (127). These “spots” can
either be chemically or biochemically active (e.g. hydrophobic, hydrophilic, ion
exchange, immobilized metal or antibodies, receptors, oligonucleotides respectively) (99).
Each type of chip has affinity for a subset of specific proteins, implying that different
chips will produce different protein spectra from the same sample. In order to maximize
proteome coverage in any given experiment, different types of chips are often used on
multiple sample fractions.
The sample of interest is deposited on these chips. After binding to the active
surface, they are washed with buffers of increasing stringency which wash off low affinity
compounds while high affinity ones are enriched.
4.2 Desorption/ionization process
In mass spectrometry analysis, the most common ion sources are electrospray
ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). The former
produces a continuous source of ions from a solution while the latter sublimates and
ionizes the samples from a dry, crystalline matrix via discontinuous laser pulses (8).
SELDI functions on the same principles as MALDI except that the SELDI chip is
“enhanced” by being chemically active allowing for proteins with specific properties to
bind while the MALDI plate is inert. The bound proteins are then co-crystallized in the
13
presence of an energy-absorbing matrix (EAM) (α-cyano-4-hydroxy cinnamic acid or
sinapinic acid) in order to facilitate ionization/desorption (66). Once ionized by a laser of
a given energy, the ions can be separated according to their mass-to-charge ratio (m/z).
4.3 Ion separation and detection
The TOF analyzer of the SELDI apparatus is a pulsed analyzer and, as such, is
often coupled with a MALDI-like ion source (100). The sample is hit by a laser pulse of
specific energy to generate a burst of gaseous ions under vacuum, a phenomenon not yet
fully understood. The laser pulse therefore provides each ion with the same energy
(Figure 1). The ions are then accelerated by an electrical potential before entering the
electric field-free vacuum region (flight tube). As they enter the flight tube, each ion
travels at a velocity corresponding to their m/z. At the end of the tube, a detector
records the time of flight of each ion, producing the TOF spectrum. The time of flight
or drift time (td) can be estimated as , where D is the drift distance
(length of tube), m is the mass, KE is the initial kinetic energy, and z is the charge (88).
This means the ions drift at a velocity inversely proportional to the square root of their
m/z ratio. In other words, smaller ions have a higher velocity and will be detected
before larger ones.
Traditionally, ions were extracted in a continuous fashion. Since the ions are
desorbed/ionized in a strong electric field, a broad initial kinetic energy (KE) distribution
is obtained, where ions of the same m/z don’t have the exact same initial KE.
Consequently, ions of the same m/z reach the detector at slightly different times, which
results in poor resolution (or broadening of the MS peak). To remedy this issue of poor
resolution, the extraction of the ions is slightly “delayed” (i.e. the apparatus has a time-lag
focusing lens). The ions formed by SELDI are produced in a weak electric field and,
after a predetermined time delay, are extracted by a high voltage pulse followed by the
application of a potential gradient. Since the initial KE of ions correlate with their initial
position in the ion source, the potential gradient applied allows for initially slower ions to
acquire a slightly higher energy than the initially faster ones. This “time-lag energy
focusing” first described by Wiley and McLaren (124) enables to correct the dependence
td
= Dm
(2KE)z
14
of ion flight time on initial velocity so that tighter packets of ions reach the detector,
increasing resolution and mass accuracy (100).
4.4 Mass calibration and data processing
Each TOF mass spectrum describes a portion of the peptide/protein content of a
given sample where the x-axis represents the m/z detected and the y-axis the
corresponding intensity (in terms of ion current) of a given peak. Before further analysis,
mass calibration, baseline subtraction and normalization of the spectra need to be
performed. Mass calibration involves two steps: internal calibration in which a
calibration equation is generated followed by external calibration in which the calibration
equation is applied to experimental samples. In order to generate a calibration equation, a
mixture of known peptides is spotted onto the same type of SELDI ProteinChip and
analyzed under the same conditions as the samples of interest. The calibration constants
calculated for the standard peptides can then be used for calibration of the peaks from the
experimental samples. Mass calibration provides the experiment with maximal mass
accuracy, facilitates subsequent peak clustering (discussed in 3.5) and enables comparison
of biomarker candidates from different experimental conditions or across different
studies.
Mass spectrometers may not always deliver a flat baseline. To compensate, one
can apply a baseline subtraction algorithm to each spectrum that will subtract intensity
contributions from the ‘noise’ in order to calculate more accurate peak intensities. Total
ion current (TIC) normalization standardizes the intensities of a set of spectra to
compensate for any spectrum-to-spectrum variations due to minor differences in total
protein concentration, sample preparation, or data collection. This improves
reproducibility and helps the user to identify spectra of poor quality by generating
normalization factors. The normalization process first calculates the TIC by summing up
all the peak intensities (area under the curve) across a spectrum. The average TIC from
all selected spectra is then calculated and used to generate the normalization coefficient,
which will be used in turn to generate the normalization factors for each spectrum to
adjust the intensity scales (1).
15
4.5 Generation of peak clusters
The main objective of peak clustering is to create a list of all peaks and their
corresponding intensities across all spectra. The main parameters for clustering peaks are
the sensitivity settings for peak detection (e.g. signal-to-noise ratio, valley depth,
minimum peak threshold percentage) and the mass window setting for cluster completion
(peak width or percentage of mass) (1). In the first peak detection step, peaks are
identified for each single mass spectrum (Figure 4.1A). After peak clustering, single
mass spectra are reanalyzed, focusing with less strict parameters. Thereby, initially
missed peaks are found on the assumption that a peak is likely to exist in a spectrum if it
has already been found in many other spectra (Figure 4.1B) (126).
Figure 4.1 Pre-processing of mass spectrum data. (A) Peak detection on a single
spectrum. Three local intensity maxima are detected as peaks with a threshold above
signal-to-noise ratio (marked by arrows). At least one other local intensity maximum is
not detected as a peak (marked by *). (B) Peak clustering for four mass spectra: peaks of
four normalized and calibrated mass spectra are grouped to form peak clusters. Three
peak clusters correspond to the peaks already detected in (A). One additional peak cluster
corresponds to an intensity maximum not detected in (A) (second from left).
16
Chapter 5: ProteinChip Pattern Analysis Software
The rapid development of new biomarkers increasingly motivates multimarker
studies to assess the value of different biomarkers for diagnostic prediction. Several
different approaches exist for classifier generation (e.g., decision trees, artificial neural
networks, and support vector machines), each with their own strengths and weaknesses.
One of these analytical approaches is the classification and regression tree (CART)
methodology. Software such as ProteinChip Pattern Analysis Software (a.k.a. Biomarker
Pattern Software; BPS) provides users with an easy-to-use interface to perform CART
analysis.
5.1 Classification and regression trees (CART) methodology
Leo Breiman et al. (22) are the authors of the original CART monograph first
published in 1984 and the developers of its computational algorithms. CART is a binary
recursive partitioning method (2). It is binary because parent nodes are always split into
exactly two child nodes and recursive because the process can be repeated by treating
each child node as parent. Moreover, the method is non-parametric, making no
assumptions about the functional form of the data. It can be used to analyze either
categorical (classification) or continuous data (regression). The defining feature is that
this approach represents the results in the form of decision trees (Figure 5.1). The three
basic steps of CART involve (1) the split of the overall study group into two subgroups
using the most powerful predictor of the outcome, (2) splitting of the subgroups into two
until no further significant splits are found or the subgroups become too small, (3) results
displayed in a binary tree structure and tree pruning if necessary (89).
17
Figure 5.1 Example of classification and regression tree output. Classification and
regression trees begin with one “node” or group, containing the entire sample, called a
parent node (Node 1). The parent node branches into two descendent, or child, nodes
according to the independent variable that was selected. Each of the child nodes becomes
a parent node to the two groups into which it splits. At the point that no further split is
made, a terminal node is created.
Many different splitting criteria have been proposed, but all begin by defining the
impurity of a node. Impurity functions are symmetrical, concave functions with
maximum value at pi/j=0.5 and value zero when there is no impurity (i.e., when pi/j=0 or
1) (78). In other words, node impurity is minimal when all the variables at the node are
of the same category and reaches its maximum when all are equally likely. Impurity
functions include but are not limited to Gini, entropy, and minimum error. Regardless of
which impurity function is chosen, the splitting criterion selects the split that has the
largest difference between the impurity of the parent node and a weighted average of the
impurity of the two child nodes (78). One concern of this recursive splitting method is
determining when to stop growing the tree. Usually the user can define what are called
stopping rules in order to control how large the tree can become. Examples of stopping
rules involves defining the minimum number of individuals in the child nodes or in the
terminal nodes, or determining the maximum number of levels to which the tree can grow
(i.e. the maximum number of independent variable that can define a single terminal node)
(78). However, implementing stopping rules may cause some important associations to
18
be missed. Instead, BPS begins by growing a maximal tree, followed by a set of sub-trees
derived from it. The best tree is then determined by calculating the classification error
rate either by using an independent data set (i.e. a testing data set) or by cross validating
(e.g. v-fold cross-validation) (2).
19
Chapter 6: Contribution of the SELDI Technology to Dengue Virus Infection
Characterization
Beginning in the 1950s, the idea that plasma protein patterns might provide
important insight into the presence and activity of disease has been gaining momentum
(41). Such proteins, individually or as clusters or patterns are referred to as biomarkers.
The NIH definition of a biomarker is "a characteristic that is objectively measured and
evaluated as an indicator of normal biologic processes, pathogenic processes, or
pharmacologic responses to a therapeutic intervention" (3). Applying this concept to
infectious diseases such as DV, a biomarker may either be of viral or host origin. If
originating from the infected host, a biomarker may, for example, be measured in terms
of differential abundance compared to the healthy state of the host. Such variation may
be caused by various mechanisms such as sequestration, differential expression, secretion,
leakage, or unusual metabolism. All of these mechanisms may reflect the effects of the
pathogen on the host and give insight into the subtle changes of the body that contribute
to a disease state (41).
In a nutshell, what differentiates a biomarker from a simple measurement is its
ability to reliably distinguish between two or more biological states. A diagnostic
biomarker is a marker used to classify and/or determine the stage of a disease (e.g., DV
vs. OFI), while a prognostic biomarker predicts the outcome of disease and the prospect
of recovery (e.g., DF vs. DHF). Biomarkers associated with a disease of interest do not
need to be unique. In fact, a pattern of biomarkers might be more accurate in the context
of complex human illnesses. The identities of these protein markers do not even need to
be known since their pattern could reveal a diagnostic “fingerprint” in itself (127).
Recently, many groups have suggested that SELDI technology can contribute to
diagnostic test development (39, 53, 57, 99, 114, 127) and it has been used successfully to
identify biomarkers for a wide range of conditions. Many of the studies using SELDI
have focused on finding biomarkers for neoplastic conditions such as ovarian, breast and
prostate cancer (53, 57, 127, 131). Other applications have included inflammatory
diseases such as rheumatoid arthritis (33, 42, 47, 103, 109, 129), allergic (54) and
degenerative conditions such as Alzheimer’s (80). The microbiologic applications of this
20
technology have been mostly limited to the analysis of microbial products and in vitro
infections (36, 111, 113, 117). To date, there has been only limited research involving
clinical infectious diseases and SELDI. This platform has been used to study cognitive
impairment in HIV (85), to monitor clinical status of HBV (52) and HCV (43, 97), to
diagnose intrauterine infection (23, 24, 67) bacterial endocarditis (40) and to generate
“serum fingerprints” associated with poor outcome in SARS (95). And more recently,
our lab has used it to identify a panel of sensitive and specific biomarkers for chronic
Chagas’ disease (91).
The promising results obtained with SELDI in various conditions raised the
possibility that this technology could be applied to DV infection to develop novel
diagnostic test capacities. DHF/DSS typically occur after the virus has been cleared from
the circulation. The ADE hypothesis can partly explain the initiation of these
complications. However, it is virtually certain that other factors are involved in disease
progression in dengue infections, whether of human or viral origin. Therefore, we
postulated that there are serum/plasma biomarkers that can distinguish between DF and
DHF/DSS at a time before the symptoms associated with disease progression are
clinically obvious. Moreover, we postulated that the systematic study of serum
biomarkers in well-defined DV patients could give unique and unanticipated information
regarding the nature of the host-virus interaction.
21
Chapter 7: General Methods
General methods used to generate the results discussed throughout this thesis are
described in the Materials and methods section of chapter 8 (p.27). Methods not
described in chapter 8 that are relevant to the data presented in chapter 9 and in the
appendices are described in this chapter.
7.1 Purified vitronectin protein deglycosylation by PNGaseF
Purified human vitronectin (Vn) (Invitrogen, Camarillo, CA, USA) was
deglycosylated by PNGaseF (New England BioLabs, Ipswich, MA, USA) according to
the manufacturer’s instructions. Briefly, 10µL of purified human Vn was mixed with 1µL
of glycoprotein denaturing buffer (10X) and heated for 10 minutes at 100°C. Then 2µL
of G7 reaction buffer (10X), 2µL of 10% NP40, 2µL of PNGaseF, and 3µL of water was
added for a total solution volume of 20µL. The mixture was incubated at 37°C for 1
hour.
The control was treated in the same manner but no PNGaseF was added.
7.2 Vitronectin ELISA
Total vitronectin levels were measured for PR and Thai samples by enzyme-linked
immunosorbent assay (ELISA) kit (Technoclone, Vienna, Austria) according to the
manufacturer’s instructions.
7.3 Acquisition of MS of human purified vitronectin protein
7.3.1 On SELDI-TOF MS
2.5µg of human purified Vn (Invitrogen, Camarillo, CA, USA) was diluted into
90µL of CM Low Stringency Buffer (Ciphergen Biosystems, Fremont, CA, USA).
ProteinChip arrays were analyzed in the ProteinChip Biology System reader (model PBS
IIc, Ciphergen Biosystems). Arrays were read at 3,500nJ with a set mass range of 2,000-
100,000Da and a focus mass of 54,000Da. The spectra were externally calibrated using
Ciphergen Biosystems All-in-1 Protein Standard.
22
7.3.2 On MALDI-TOF MS
2.5µL (1.25µg) of human purified Vn (Invitrogen, Camarillo, CA, USA) were
cleaned using reversed-phase chromatography ZipTip (Millipore, Billerica, MA, USA)
according to the manufacturer’s instructions. Samples were eluted using 5µL sinapinic
acid (5mg in 50%ACN and 0.5%TFA).
7.4 Immunoprecipitation of vitronectin protein
A pool of 27µL OFI Thai plasma samples was diluted in 423µL PBS. The sample
was pre-cleared by incubating it with 50µL Dynabeads Protein G (Invitrogen, Camarillo,
CA, USA) for 10 minutes at room temperature. Immunoprecipitation was done according
to the manufacturer’s instructions with 1µl of sheep anti-human Vn purified IgG
antibody (Cedarlane Laboratories, Hornby, Ontario, Canada). Elution was performed
under denaturing conditions for analysis by Western blot and under non-denaturing
conditions for analysis by SELDI-TOF MS.
7.5 High-abundance serum protein depletion
Seppro IgY 14 Spin Columns (Sigma-Aldrich, St. Louis, MO, USA) were used to remove
14 highly abundant proteins from plasma samples. 15µL of pooled OFI Thai plasma
samples were diluted in 485µL Seppro dilution buffer. High-abundance proteins were
depleted according to the manufacturer’s instructions. The non-specifically bound
proteins were collected as the wash fraction and the proteins bound to the column were
eluted using the provided stripping buffer and collected as the elution fraction.
23
Chapter 8: Detection and Identification of Biomarkers for Dengue Fever and
Dengue Hemorrhagic Fever in Plasma Samples from Thailand and Puerto Rico
Using SELDI-TOF MS Technology
Alexa Gilbert1, Takol Chareonsirisuthigul2, Elizabeth Hunsperger3, Sukathida Ubol2,
Brian J. Ward1, Momar Ndao1
1 McGill University Health Center Research Institute, Montreal General Hospital, Quebec ,
Canada
2 Mahidol University, Bangkok, Thailand
3Centers for Disease Control and Prevention, Dengue Branch, San Juan, Puerto Rico
Abstract
Background. Surface-Enhanced, Laser-Desorption & Ionization, Time-Of-Flight Mass
Spectrometry (SELDI-TOF MS) permits the study of the protein/peptide content of
complex biological fluids such as serum, plasma, urine, cell lysates and tissue extracts.
This high throughput proteomic platform has been used to identify biomarkers for a wide
range of inflammatory, infectious and neoplastic conditions. The promising results
obtained in these varied conditions raised the possibility that SELDI could be applied to
dengue virus (DENV) infection to develop urgently needed diagnostic test capacities.
Methods. Plasma from Thai children with either confirmed primary (1°) or secondary
(2°) DF or DHF and samples from Thai children admitted to hospital with other febrile
illnesses (OFI) were analyzed and their proteomic profiles obtained by SELDI-TOF MS.
The same approach was performed using samples from Puerto Rican patients of different
ages with either 2° DF or DHF as well as suspected cases that were later determined as
OFI. Promising biomarkers that could discriminate between patients with DV infection
and those with OFI were found. Results. Thirty-three biomarkers were detected that
could distinguish between DV and OFI in samples from both geographical areas with p-
values ≤ 0.05 and a receiver operating characteristic (ROC) values ≥ 0.70. Combining
only five of those biomarkers, it was possible to construct a decision tree to distinguish
between DV and OFI test samples with a specificity of 100% and a sensitivity of 93%.
Four significant biomarkers were identified as able to distinguish between DF and DHF
24
cases in 2°DV infections in both populations. Sixteen promising candidate protein
biomarkers were identified using SDS-PAGE gels and MS/MS sequencing. One of the
candidate biomarkers, vitronectin (Vn) protein precursor, was further investigated. Vn
precursor seems to be able to differentiate between 1°DF and other severities of DV
infection. This precursor form was found in healthy humans as well as patients with OFI
or 1°DF but it was not detectable by Western blot in plasma samples from patients with
1°DHF, 2° DF, or 2° DHF. Finally, proteomics were strikingly different between primary
and secondary DENV infection (>500 candidate biomarkers) despite the clinical
similarities between these two conditions. Conclusions. This study demonstrates the
potential for high-throughput proteomics to develop useful diagnostic and prognostic tests
for DF/DHF. Moreover, these biomarkers may give unique insight into the mechanisms
that underlie the varied manifestations of DV infection.
8.1 Introduction
During the last decade, the spread of dengue worldwide has been fuelled by
unprecedented population growth, unplanned and uncontrolled urbanization, and the lack
of mosquito control among other things (18). It is estimated that 2.5 billion people are at
risk of a dengue virus (DV) infection in more than 100 countries across the world. There
are an estimated 50 million cases of dengue infection each year with 500,000 cases of
dengue hemorrhagic fever (the most severe form of the disease) and at least 12,000
deaths, mostly in children.
DV belongs to the Flavivirus genus of the Flaviviridae family. Four main
serotypes distinguishable by neutralizing antibodies exist, all of which can cause disease
ranging in severity from the mildest form of dengue fever (DF), characterised by frontal
headache, retroocular pain, muscle and joint pain, nausea, vomiting, and rash, to the most
severe forms, dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS) (9).
The main pathophysiological difference between DF and DHF is plasma leakage.
Moreover, DHF tends to follow a secondary DV infection with a heterologous strain,
supporting the antibody-dependent enhancement (ADE) model (6, 7, 10, 15, 23). DSS is
the most severe form of the disease characterized by circulatory failure and a narrowing
pulse range. Once shock begins, the fatality rate can be as high as 44% if adequate
25
medical support is not available (ie: intensive care) (15). There are no effective antiviral
drugs for DF/DHF or any interventions known to limit the plasma leakage. At the current
time, dengue treatment is only supportive, including analgesics and antipyretics
administration and careful fluid management (12, 18). This underscores the critical need
for improved diagnostic tools in dengue so that disease can be recognized early to
maximize the patient’s chance of survival and minimize morbidity. Unfortunately,
clinical findings alone are often not very helpful in distinguishing DF from other febrile
illnesses (OFI) such as malaria and yellow fever (infections often endemic in the same
regions as DV), making diagnosis difficult (18, 22, 24). Even the best tests (e.g. culture,
PCR) often fail to make a diagnosis of DV at the time that a child is hospitalized because
of critical ‘gap’ between the virological assays (eg. culture, PCR) and the development of
detectable antibodies.
Surface-enhanced laser desorption ionization-time-of-flight mass spectrometry
(SELDI-TOF MS) platform has made great progress during the last decade (8). This
technique combines retention chromatography with TOF-MS detection to provide protein
profiles of biological fluids. Many of the studies using SELDI have focused on finding
biomarkers for neoplastic conditions, but many groups including ours have used it to
study human infections, such as Chaga’s disease (14), cysticercosis (3), fasciolosis (19),
African sleeping sickness (16), hepatitis C (21), severe acute respiratory syndrome
(SARS) (25), and bacterial endocarditis (4). In this study, SELDI-TOF MS technology
was used to generate complex protein profiles specific to DV infection (diagnostic
biomarkers) as well as prognostic biomarkers able to distinguish between DF and DHF.
In order to achieve our goal, SELDI-TOF-MS analyses of DF, DHF and plasma obtained
from patients suffering of other febrile illnesses (OFI) from two distinct geographical
areas (Thailand and Puerto Rico) were compared using cluster recognition software
CiphergenExpressTM in a multiple pass-analysis that allowed identification of the most
discriminating candidate biomarkers. These biomarkers were specific for DV illness even
across the genetic variations and different virus strains associated with South-East Asia
and the Americas. Decision analysis software (BPS: Biomarker Pattern Software) was
then used to determine which combination biomarkers common to both geographical
areas gave the greatest accuracy in terms of specificity and sensitivity.
26
8.2 Materials and Methods
8.2.1 Patients and sample collection
Blood samples were collected and plasma was separated immediately and kept
frozen at -80°C until used. All cases were confirmed by RT-PCR, MAC ELISA, or IgG
ELISA. All cases were classified as having either primary or secondary infection by
hemeagglutination inhibition (HI) and IgM ELISA. Patients were further classified
according to WHO criteria at the University of Mahidol, Bangkok, Thailand, or the
Center for Disease Control and Prevention Dengue Branch in San Juan, Puerto Rico.
8.2.1.1 Thai samples
Blood samples were collected from pediatric patients who were enrolled in this
project after having completed an informed consent at the Queen Sirikit National Institute
of Child Health, Bangkok, Thailand. The investigation protocol was approved by the
committee on Human Rights Related to Human Experimentation, Mahidol University.
Blood samples from seventy-two patients infected with DV were collected at three time
points; on the first day of admission or fever day (t1), defervescence day (t2), and 30 days
after admission or convalescent day (t3). Twelve patients were defined as having primary
DF cases, 9 primary DHF/DSS cases, 24 secondary DF cases, and 27 secondary
DHF/DSS cases. Samples from 15 patients that presented with undifferentiated fever
were also collected at the same first two time points (t1 and t2) and used as controls
(other febrile illnesses: OFI). Thirteen of these patients were later determined as having
influenza, one as sinusitis, and one as bronchitis.
8.2.1.2 Puerto Rican samples
Blood samples from eighty-four patients (pediatric and adult) infected with DV
were collected on the first day of admission or fever day. Plasma was separated
immediately and kept frozen at -80°C until used. Thirty patients were defined as having
secondary DF, 54 secondary DHF cases. Samples from 30 patients suspected of being
infected with DV were also collected. These patients were later determined as having
OFI and were used as controls (lab negative).
27
8.2.2 Fractionation of plasma samples
Fractionation of plasma samples was performed with a Biomek 2000 Laboratory
Automation Workstation (Beckman Coulter, Brea, CA, USA) according to the protocols
provided by the Expression Difference Mapping Kit (Ciphergen Biosystems, Fremont,
CA, USA). Samples (20µL) were denatured and fractionated using anion-exchange
chromatographic beads and pH gradient elution. Six isoelectric fractions were obtained
and collected using different buffers provided in the Expression Difference Mapping Kit:
pH 9 (flow through), pH 7, pH5, pH 4, pH 3, and organic solvent (hereafter referred as to
F1, F2, F3, F4, F5, and F6). The fractions were stored at -80°C until further analysis.
8.2.3 Binding of fractions to ProteinChip arrays
Preliminary experiments revealed that F1, F5 and F6 bound to weak cation
exchange (CM10) and immobilized metal affinity capture (IMAC3) ProteinChip arrays
were the experimental conditions generating the richest protein-peak output and
differential protein expression. Therefore, aliquots (10µL) of F1, F5, and F6 were bound
by a randomized chip/spot allocation to avoid introduction of systematic bias. Sinapinic
acid matrix was applied.
Protein binding to ProteinChip Arrays was performed using the Biomek 2000
Laboratory Automation Workstation (Beckman Coulter) and protein binding software
protocols provided by Ciphergen Biosystems. Each bioprocessor included a QC sample
of pooled plasma samples of each group, blank spots as negative controls and reference
samples to monitor the intra-assay reproducibility.
8.2.4 Data acquisition and processing
ProteinChip arrays were analyzed in the ProteinChip Biology System reader
(model PBS IIc, Ciphergen Biosystems). Arrays were read at 2 settings, optimized either
for low molecular weight (LMW; 2,000-100,000 Da) or for high molecular weight
(HMW; 10,000-200,000 Da) ranges. The data were analyzed using ProteinChip software
(version 3.2.1) and Ciphergen Express Data Manager (version 2.1) (Ciphergen
Biosystems).
28
All data were imported into Ciphergen Express (CE) and grouped according to
condition (e.g., fraction 1 bound to a CM10 array, read at low laser intensity: F1CSL).
Spectra were externally calibrated using an equation generated from a spectrum of
protein standards with molecular weights ranging from 5733.6 Da (bovine insulin) to
147,300 Da (bovine IgG), which were collected at the same machine settings. The
spectra were further baseline subtracted, and normalized to total ion current within a
mass/charge (m/z) range corresponding to the optimized LMW or HMW ranges and with
an external normalization coefficient of 0.2 for both conditions. As a quality control
measure for the comparison of spectra processed on different days, the average
normalization factor was first calculated for all spectra. Any spectra that did not fall
within twice the overall average normalization factor were discarded from the analysis.
8.2.5 Data analysis
MS-based clinical proteomics and biomarker research may generate vast and
complex data sets. As a result, appropriate data pre-processing and analysis is critical in
achieving reliable results.
Using the integrated Biomarker Wizard software (Ciphergen Biosystems), a 2-step
analysis was performed on the spectra. In the first-pass peak detection, automatic peak
detection was used to determine qualified mass peaks (signal/noise (S/N) >3; cluster mass
window at 0.3%) and (S/N >3; cluster mass window at 2%) for LMW and HMW ranges
respectively. A peak cluster was determined as a peak found in at least 10% of the
spectra in one condition (e.g. F1 bound on CM10 chip read at LMW; F1CSL). The p-
values for these peaks were calculated for differences between different groups (DF,
DHF, and OFI) and peaks with a p-value ≤ 0.5 were visually inspected and manually
relabelled. The second-pass peak detection was performed on the user-defined peaks
only, with the same settings as the first-pass but the cluster mass window was increased to
2%. The p-values for differences in average peak intensity between groups (DF vs. OFI,
DHF vs. OFI, DF vs. DHF) were determined.
29
ProteinChip pattern analysis software (a.k.a. Biomarker Pattern software; BPS)
was used to perform supervised multivariate analysis and generate decision trees that use
a small panel of robust markers with defined splitting rules for classification.
A candidate biomarker was defined as a peak with a p-value≤0.05 and a
0.30≥ROC-value≥0.70 and an intensity ratio between the two groups being compared of
at least 2. These biomarkers were further considered only if they presented the same
pattern in the Puerto Rican (PR) and Thai samples. The PR sample set was used as the
learning set, which served to build a diagnostic model, and the Thai sample set as the
testing set that was used to validate it. The Gini method was used as the splitting criterion
to build the classification decision trees.
Reproducibility was tested by spotting a reference sample 7 times in a random
fashion across the bioprocessors. Across all 3 fractions (F1, F5, F6), the average
m/z%CV was 0.26% and the intensity %CV was 38%.
8.2.6 Protein Purification and identification
To facilitate protein identification, sample fractionation in the ZOOM® Isoelectric
Focusing (IEF) Fractionator was performed (Invitrogen, Carlsbad, California). OFI
samples at t1 and t2 were pooled as were samples of 1° DF and DHF at t1 and t2 from
Thailand. Samples from Puerto Rico were pooled in three groups: DF, DHF, and DV(-).
The pooled plasma samples were processed according to Invitrogen’s instructions.
Briefly, five ZOOM® Disks were used to cover a standard pH3.0-10.0 range (pH3.0,
pH4.6, pH5.4, pH7.0, pH9.1, pH10.0). 650 µl of the prepared samples were dispensed in
5 of the ZOOM® IEF Fractionator chambers. The ZOOM was run under standard
conditions (100V for 20min, 200V for 80min, and 600V for 80min). Once completed, the
five fractions obtained (pH3.0-4.6, pH4.6-5.4, pH5.4-7.0, pH7.0-9.1, pH9.1-10.0) were
kept at -20°C until further analysis. At the time of analysis, 40 µl of each fraction was
desalted using 3 volumes of methanol, 1 volume chloroform and 4 volumes of dH2O.
The precipitated protein samples were dissolved in SDS sample buffer and run on a
denaturing 4-12% Bis-Tris NuPAGE Gel Electrophoresis using Mark12 MW Marker 1x
(Invitrogen) as the molecular weight ladder. The gel was run at 200V for 45 min. The
30
gel was stained using colloidal Coomassie blue for 2 days and destained with MiliQ water
until band visualization was satisfactory.
The candidate biomarker bands were identified visually and were cut and kept in
2% acetic acid tubes for further analysis. The proteins of interest from Thailand were
sent to the McGill Sheldon Biotechnology Center (McGill University, Montreal), while
the ones from the PR samples were sent to Dr. Kurt Dejgaard (McGill University,
Montreal) for sequencing by tandem MS/MS. Both parties submitted the MS/MS spectra
to the database-mining tool MASCOT (Matrix Sciences) for identification.
8.2.7 Western Blot
The samples were classified in 14 different groups to find distinguishing
biomarkers. The groups consisted of samples from patients with a primary (1°) or
secondary (2°) infection, suffering either from classical dengue (DF) or dengue
hemorrhagic fever (DHF) at t1 and t2.
Vitronectin (Vn) precursor protein was assessed by Western blot analysis.
Briefly, 1µl of pooled plasma was run on 4-12% Bis-Tris polyacrylamide gels
(Invitrogen, Carlsbad, CA, USA), transferred, and incubated with sheep anti-human Vn
purified IgG antibody (1:500 dilution) (Cedarlane Laboratories, Hornby, Ontario,
Canada), followed by a preoxidase-conjugated rabbit anti-sheep secondary antibody
(1:10,000 dilution), and Vn was revealed by the enhanced chemiluminescence detection
method (ECL Kit, Amersham Pharmacia Biotech, Little Chalfont, Buckinghamshire,
UK).
8.3 Results
8.3.1 Discovery of protein biomarkers in the samples using CiphergenExpress software
Of the total number of 2646 sample spectra collected, the normalization
coefficients of 276 spectra did not respect normalization coefficient ≥ 2 µ and were
rejected from further analysis.
31
For the first-pass analysis, p-values were calculated for (1) DF versus OFI, (2)
DHF versus OFI, (3)DF_DHF (DV) vs. OFI, and (4) DF vs. DHF, in both data sets (at t1
for Thai samples). Significant peaks (p-value≤0.05) were manually relabelled and
reassessed during the second-pass analysis.
8.3.1.1 Diagnostic biomarkers at time of hospitalization (t1)
During the second-pass analysis, the p-value and ROC-value were recalculated
and 537 biomarkers in the Thai samples and 86 in the Puerto Rican samples were
determined to have a p-value≤0.05 and 0.30>ROC>0.70 that were able to discriminate
between patients with DV infection (DF or DHF) and OFI. Of these, 55 were common to
both geographical areas although only 33 followed the same pattern (up- or down-
regulated) in both sample sets, across all 3 fractions analyzed although more than half of
them were found in Fraction 1 (Table 8.1).
32
Table 8.1 Diagnostic biomarkers that can distinguish between secondary DV
infection and OFI at time of hospitalization (t1). Highlighted values indicate an
intensity ratio ≥2 between DV and OFI groups for both geographical areas. Only those
biomarkers that are highlighted were considered for CART analysis.
8.3.1.2 Diagnostic biomarkers that can distinguish between 2°DF and OFI at time of
hospitalization (t1)
A total of 539 biomarkers in the Thai set and 79 in the PR set were determined to
have a p-value≤0.05 and 0.30>ROC>0.70 that were able to discriminate between patients
with a 2°DF and OFI. Of these, 34 were common to both geographical areas but only 16
followed the same pattern in both sample sets across all 3 fractions analyzed although
more than half of them were found in Fraction 1 (Table 8.2).
33
Table 8.2 Biomarkers that can distinguish between DF and OFI at time of
hospitalization (t1). Highlighted values indicate an intensity ratio ≥2 between DF and
OFI groups for both geographical areas.
8.3.1.3 Biomarkers that can distinguish between 2°DHF and OFI
A total of 513 biomarkers in the Thai set and 231 in the PR set were determined to
have a p-value≤0.05 and 0.30>ROC>0.70 that were able to discriminate between patients
with a 2°DHF and OFI. Of these, 121 were common to both geographical areas and 60
followed the same pattern in both sample sets, across all 3 fractions analyzed (Table 8.3).
34
Table 8.3 Biomarkers that can distinguish between 2°DHF and OFI time of
hospitalization (t1). Highlighted values indicate an intensity ratio ≥2 between DHF and
OFI groups for both geographical areas.
35
8.3.1.4 Prognostic biomarkers that can distinguish between 2°-DF and –DHF at time of
hospitalization (t1)
A total of 43 biomarkers in the Thai set and 259 in the PR set were determined to
have a p-value≤0.05 and 0.30>ROC>0.70 that were able to discriminate between patients
with a 2°DF and 2°DHF. Of these, 7 were common to both geographical areas but only 4
followed the same pattern (up- or down-regulated) in both sample sets. All four of these
biomarkers were found in fraction 6 (Table 8.4).
Table 8.4 Prognostic Biomarkers that can distinguish between DF and DHF at time
of hospitalization (t1).
8.3.1.5 Biomarkers unique to each geographical area
In order to determine if there were distinct biomarkers found in both the Thai and
PR sample set that could distinguish between DF and/or DHF and OFI, and between DF
and DHF. We calculated the p-values for the above groups and 506 unique biomarkers
were detected to have a p-value≤0.05 and 0.30≥ROC≥0.70 that could distinguish between
2°DF and OFI in the Thai sample set while 48 were found in the PR sample set.
Moreover, 398, 573 and 36 unique Thai markers and 113, 31, 251 unique PR markers
were identified as able to distinguish between DHF and OFI, DV and OFI, and DF and
DHF, respectively (refer to Appendix A, table A1 and table A2).
8.3.1.6 Biomarkers common to both geographical area but demonstrating a different
expression pattern
Biomarkers were detected that were common to both geographical area but that
differed in their expression pattern, e.g. where a given marker is up-regulated in the Thai
samples, it is down-regulated in the PR samples, and vice versa. Once again, only
peptides/proteins achieving a p-value≤0.05 and 0.30≥ROC≥0.70 were determined as
36
potential biomarkers. In total, there were 18, 60, 23, and 3 markers that could distinguish
between DF and OFI, DHF and OFI, DV and OFI, and DF and DHF respectively (refer to
Appendix A, table A3) for distribution of biomarkers across fractions).
8.3.1.7 Proteomic differences between 1°- and 2°-DV infections
The p-value and ROC-value were calculated between primary (1°) and secondary
(2°) infections in the Thai samples. 487 differential peaks were detected when comparing
1°DF and 2°DF and 432 when comparing 1°DHF and 2°DHF that achieved a p-
value≤0.05 and 0.30>ROC>0.70 (refer to Appendix A, table A4) for distribution across
fractions), which suggests that the proteome of a 1° and 2° infection are significantly
different (Figure 8.1 and 8.2).
37
Figure 8.1 Heat map of biomarkers found in F1CSL that can differentiate between
1°DF and 2°DF. Each column in the heat map corresponds to a spectrum, each row to a
cluster (or a potential biomarker), and each cell to a peak, with the color indicating
intensity (black, calculated average intensity for each cluster; green, down-regulated; red,
up-regulated). F1CSL, fraction 1 using CM10 at low laser intensity.
38
Figure 8.2 Heat map of biomarkers found in F6CSH that can differentiate between
1°DHF and 2°DHF. Each column in the heat map corresponds to a spectrum, each row
to a cluster (or a potential biomarker), and each cell to a peak, with the color indicating
intensity (black, calculated average intensity for each cluster; green, down-‐regulated;
red, up-‐regulated). F6CSH, fraction 6 using CM10 at high laser intensity.
39
8.3.2 Building decision trees using Biomarker Pattern Software
The Biomarker Pattern Software (BPS) was used to generate decision trees that
would allow distinguishing between two different health states. To generate these trees,
peaks with p-value≤0.05 and 0.30≥ROC-values≥0.70 and ratio intensity between the two
groups being compared ≥2 were considered. Of these selected peaks, only the ones that
followed the same expression pattern in PR and Thai samples were further considered.
8.3.2.1 Diagnostic biomarkers to distinguish between 2°DV and OFI
Using the PR dataset as the learning set and the Thai dataset as the testing set, 7
trees were generated that could distinguish between DV and OFI with a sensitivity
ranging from 71-100% and a specificity from 73-100%. Figure 3 is a representative
example of a diagnostic algorithm generated using BPS. The PR data set was used as the
learning set (Figure 8.3A) and the Thai data set as the testing set (Figure 8.3B). The
learning tree (Figure 8.3A) achieves a specificity of 89% and a sensitivity of 90% while
the testing tree (Figure 8.3B) achieves a specificity of 100% and a sensitivity of 93%. In
this example, the intensities of the 4.6-, 5.1-, 133.7-, 134.2-, 4.4-m/z biomarkers establish
the splitting rules and are thus the candidate biomarkers able to distinguish between a
patient suffering from a DV infection and one with OFI.
40
Figure 8.3 BPS was used to generate candidate diagnostic algorithms. The CART procedure seeks to minimize a cost function that
balances prediction errors and total number of biomarkers used. Equal weight is given to false-positive and false-negative results. A
diagram of a learning (A) and its testing (B) decision classification tree are shown. The learning tree (PR samples) (A) achieves a
specificity of 89% and a sensitivity of 90% while the testing tree (Thai samples) (B) achieves 100% and 93% specificity and sensitivity
respectively. The intensities of the 4.6-, 5.1-, 133.7-, 134.2-, 4.4-m/z biomarkers establish the splitting rules. Samples that follow the
rule go to the left node, and samples that do not follow the rule go to the node to the right. The number of DV infected (0, red) or OFI
(1, blue) cases in each node is shown.
m/z 5,098≤0.19
m/z 4,568≤0.14 m/z 4,568>0.14
m/z 133,650≤0.36 m/z 133,650>0.36m/z 5,098>0.19
m/z 134,175≤0.02 m/z 134,175>0.02
m/z 4,433≤1.33 m/z 4,433>1.33
m/z 4,433≤0.33 m/z 4,433>0.33
m/z 5,098≤0.19
m/z 4,568≤0.14 m/z 4,568>0.14
m/z 133,650≤0.36 m/z 133,650>0.36m/z 5,098>0.19
m/z 134,175≤0.02 m/z 134,175>0.02
m/z 4,433≤1.33 m/z 4,433>1.33
m/z 4,433≤0.33 m/z 4,433>0.33
A B
41
8.3.2.2 Prognostic biomarkers to distinguish between 2°-DF and -DHF
Using the same approach where the PR dataset as the learning set and the Thai
dataset as the testing set, no trees were generated that could distinguish between DF and
DHF with a sensitivity and specificity of at least 70% in both data sets (data not shown).
8.3.3 Protein identification
A 4-12% Bis-Tris NuPAGE gel was run using PR samples. By visual inspection,
bands were determined as potential biomarkers if their intensity differed from one group
to another (Figure 8.4). The chosen bands were excised and trypsinized. An ESI-TRAP
was used to identify the proteins. The Mascot Search (Matrix Science) algorithm was
used to score the matched peptides. The Homo Sapiens (humans) taxonomy was searched
within the NCBInr 20100511 database. Carbamidomethyl (C) was selected as a fixed
modification and oxidation (O) as a variable one. A maximum of one missed cleavage
was allowed, and a peptide mass tolerance of ±1.3Da and a fragment mass tolerance of
±0.4Da was allowed. To determine which protein should be considered as identified, we
set the significant threshold to < 0.05 and the ion score or expect cut-off value to 20.
Only proteins with at least 2 peptides identified were considered. A total of 27 bands
were excised and 16 proteins were identified from the PR sample set (Table 8.5; refer to
Appendix A, table A5 for detailed results). Of these proteins, 6 corresponded to
previously identified proteins from a 4-12% Bis-Tris NuPAGE gel ran with Thai samples
following the same protocol (refer to Appendix A, table A6).
42
Figure 8.4 4-12% Bis-Tris NuPAGE gel of pooled PR samples. Lanes 1, 4, 8, 11, and
14 are pooled DV(-) samples; lanes 2, 5, 9, 12, and 15 are pooled DF samples; and lanes
3, 6, 10, 13, and 16 are pooled DHF samples; kDa lanes are the Marker 12 MW
(Invitrogen). All samples were ZOOM Fractionated and desalted using specific pI
ranges: F1 (pH3.0-4.6), F2 (pH4.6-5.4), F3 (pH5.4-7.0), F4 (pH7.0-9.1), and F5 (pH9.1-
10.0). The red boxes indicate the candidate biomarkers identified.
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Table 8.5 Non-redundant list of the 16 proteins identified. PR gel bands were excised
and MS/MS was performed using ESI-TRAP apparatus. Mascot Search (Matrix
Sciences) software was used to identify peptides/proteins using the NCBInr database.
Highlighted proteins were previously identified using the same protocol and Thai samples
(data not shown).
Protein Accession number
α1-antitrypsin gi177831
α2-macroglobulin precursor gi177870
Angiotensinogen gi532198
Apolipoprotein E gi178849
Apolipoprotein J precursor gi178855
Chain A complement component 3 gi78101267
Complement component C3 gi179665
Fibronectin precursor gi62198449
Ig α1 chain C region gi113584
Ig L-chain V-region gi443559
Ig λ heavy chain gi2765425
Plasma protease (C1) inhibitor precursor gi179619
Proapolipoprotein gi178775
Serotransferrin precursor gi4557871
Serum albumin gi23241675
Vitronectin precursor gi36575
8.3.3.1 Vitronectin precursor protein
One of the proteins identified was the heavily glycosylated vitronectin (Vn)
precursor protein. In order to confirm its presence in the samples, we performed a
Western blot on the samples (Figure 8.5). Vn precursor protein levels were undetectable
44
in 1°DHF, 2°DF, and 2°DHF, while they were pronounced in OFI and slightly present in
1°DF. As expected, DV(-) samples from PR were also found to have the low molecular
weight form of Vn (data not shown). However, DF and DHF samples also seemed to
show significant levels of the precursor form of Vn protein. Moreover, PR samples show
a lower expression of the mature form of the protein (75kDa) compared to the Thai
samples.
Figure 8.5 Assessment of vitronectin precursor protein in Thai samples by Western
blot analysis. Pooled samples of Thai 1° -DF and –DHF, 2° -DF and –DHF and of OFI
at t1 and t2. The arrow indicates the position of vitronectin precursor protein while the
two heavier bands are glycosylated isoforms of the mature protein.
8.4 Discussion
Although some of the current research methods to diagnose dengue are quite
sensitive and specific (e.g.: microneutralization, PCR), the high costs and/or the training
required to perform these tests are not appropriate for their use at all levels of the health
system - particularly in the poor regions of the world where dengue is endemic.
Furthermore, even the best tests often fail to make a diagnosis of dengue at the time that a
patient is hospitalized because of the critical ‘gap’ between the virologic assays (e.g.:
culture, PCR) and the development of detectable antibodies. At the current time, there is
no test that can predict which child will develop serious complications from DV infection
and which will recover uneventfully. The diagnosis of dengue is the first step in being
able to treat a patient and prevent or mitigate complications that can lead to death. The
development of diagnostic tests that are accurate (specific and sensitive), simple, and
cheap is thus of the uttermost importance.
45
The initial presentation of dengue is not clinically distinct and many infectious
diseases can present in an identical way (i.e.: undifferentiated fever ± headache ±
myalgias). Such infections include malaria, leptospirosis, thyphoid and many others.
Most of these infectious diseases are very common in the same areas of the world where
dengue is prevalent (18, 22, 24). The first step was to determine if biomarkers could be
found that discriminated between a disease caused by DV and OFI. As predicted, a total
of 55 significant biomarkers were present in both secondary DV infection Thai and PR
samples that could distinguish between DV infection and OFI in both geographical areas.
Of these, 33 followed the same expression pattern. Therefore, 22 of the markers may be
considered DV biomarkers but they are unique to each of the geographical areas studied.
This may provide support for the hypothesis that genetic variations among populations
are in fact involved in the pathogenesis of DV infections, although the manner in which
they do is unclear. Our study also demonstrated that not only did the same markers
behave differently depending on the origin of the patient but that completely different
markers may exist between the Thai and Puerto Rican people, as well as between
different age groups. Over 500 unique biomarkers were detected in the Thai samples that
could distinguish DV from OFI. In other words, 500 markers were present in the Thai
samples only but not in the PR samples. This highlights the importance of analyzing
various geographical areas affected by DV to ensure maximal efficacy of the selected
diagnostic biomarkers.
The next step was to determine how accurate these biomarkers are in diagnosing
samples with DV. But can the measurement of a single biomarker be sufficiently
sensitive and specific for screening populations worldwide? As explained above, larger
sample studies with patients of different ethnicities and different age groups would need
to be performed to confirm this. On the other hand, combining a set of biomarkers
instead of a unique one could theoretically increase both the specificity and sensitivity of
the assay. Many groups are in favour of using multiplex panels of biomarkers for
diagnosis. Such a strategy has been used mainly for carcinomas such as prostate cancer,
endometrial cancer and bladder cancer (1, 11, 13, 17, 26). MS technology has permitted
to measure more easily low molecular weight analytes as well as to perform highly
multiplexed analysis (1). Keeping this in mind, CART analysis was performed using
46
BPS in order to construct decision trees composed of the best biomarkers detected by the
SELDI-TOF MS analysis that could distinguish between DV and OFI. Seven such trees
were constructed that achieved an accuracy (specificity and sensitivity) ≥70%. For
example, by combining five biomarkers (4.4-, 4.6-, 5.1-, 133.7-, 134.2-kDa) from two
different fractions (F1CSL/ISL, F6CSH/ISH), a learning tree using data from PR samples
that achieved a specificity of 89% and a sensitivity of 93% was built. Since only the most
significant and common biomarkers were used, the test tree using the Thai samples was
able to reach a specificity of 100% and a sensitivity of 93%. Being able to attain such
high accuracy levels demonstrates the promise of high-throughput platforms, such as
SELDI, in the field of diagnostics.
The development of a prognostic test would also be a huge benefit to the rationale
provision of care in low resource settings. Sixteen biomarkers following the same pattern
expression were detected across both geographical areas that could distinguish between
DF and OFI, while 60 were detected when trying to differentiate between DHF and OFI.
However, only seven biomarkers common to both populations could distinguish between
DF and DHF diseases and not more than four followed the same pattern in samples from
both areas. No decision trees that could serve as prognostic tools could be built to
achieve an accuracy ≥70%. These results once again emphasize the importance of one’s
genetic background can have upon the development of a disease. Even more notable, the
lack of common biomarkers between these two groups suffering the same clinical
symptoms begs the question as to what other, perhaps external, factors are involved in
causing this disease.
In order to profit from the SELDI achievements in detecting candidate diagnostic
biomarkers, it is imperative to identify these markers. Once the identification is achieved,
these SELDI-derived markers will combine the great accuracy of the SELDI-TOF MS
platform to the much-needed practical aspect of multiplex immunoassays (such as
ELISA).
Guided by the SELDI-TOF MS results, an SDS-PAGE gel was performed using
PR samples and 16 proteins were identified. Of these, six proteins were also identified in
a gel done previously with Thai samples: α2-macroglobulin, complement component C3,
47
plasma protease (C1) inhibitor precursor, serotransferrin precursor, serum albumin, and
vitronectin (Vn) precursor. We decided to further investigate the latter one. Vitronectin
is a multifunctional glycoprotein present in blood and in the extracellular matrix. It binds
glycosaminoglycans, collagen, plasminogen and the urokinase-receptor, and also
stabilizes the inhibitory conformation of plasminogen activation inhibitor-1 (PAI-1) (20).
Upon performing a Western blot using pooled Thais samples, it is clear that Vn is either
involved in the pathology or is lost as a result of the DV infection. The precursor form of
Vn is present in OFI and in healthy specimens (data not shown). It is also present in
1°DF but it is surprisingly not detected in 2°DF, both disease states being clinically
undistinguishable. Furthermore, Vn precursor is not detectable in neither 1°- or 2°-DHF.
Along with Vn precursor proteins, these SELDI identified proteins may provide clues into
the physiopathology of dengue, but further studies need to be done to confirm and
understand the involvement of these molecules in disease progression.
The current literature assumes that primary and secondary dengue infections have
the same pathophysiology because, in most subjects, the dominant symptoms are
clinically indistinguishable. The antibody-dependent enhancement (ADE) model is one
of today’s most popular models to explain a host’s susceptibility to developing DHF
during a secondary infection. However, this phenomenon does not explain all aspects of
the pathogenesis. Many other factors still remain to be studied that might play a role such
as the strain’s virulence and serotype as well as the host susceptibility and the specific
role of T cells (2, 4). In favour of additional pathogenesis hypotheses, our data suggests
that the plasma proteome of a patient with a primary infection is markedly different than
that of plasma from a patient with a secondary infection. Indeed, it was found that almost
a 1000 significant biomarkers across all six fractions were present in Thai samples that
could distinguish between a primary and secondary infection in both DF and DHF
diseases. Such variation in the proteome content suggests important differences in
pathophysiology. These biomarkers may give more insights to the pathogenesis of 1° vs.
2° DV. They could also be useful in epidemiologic studies as well as epidemic
surveillance. In future studies, healthy controls would be required for analysis before
being able to make any physiopathological assumptions.
48
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immunodiagnostics of solid tumors. BioDrugs 18:387-98. 2. Chaturvedi, U., R. Nagar, and R. Shrivastava. 2006. Dengue and dengue
haemorrhagic fever: implications of host genetics. FEMS Immunol Med Microbiol
47:155-66. 3. Deckers, N., P. Dorny, K. Kanobana, J. Vercruysse, A. E. Gonzalez, B. Ward,
and M. Ndao. 2008. Use of ProteinChip technology for identifying biomarkers of
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Parasitol 120:320-9. 4. Fenollar, F., A. Goncalves, B. Esterni, S. Azza, G. Habib, J. P. Borg, and D.
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endocarditis: results from a study based on surface-enhanced laser
desorption/ionization time-of-flight mass spectrometry. J Infect Dis 194:1356-66. 5. Fink, J., F. Gu, and S. G. Vasudevan. 2006. Role of T cells, cytokines and
antibody in dengue fever and dengue haemorrhagic fever. Rev Med Virol 16:263-
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Innis, A. L. Rothman, A. Nisalak, and F. A. Ennis. 1997. Early clinical and
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49
11. Laxman, B., D. S. Morris, J. Yu, J. Siddiqui, J. Cao, R. Mehra, R. J. Lonigro,
A. Tsodikov, J. T. Wei, S. A. Tomlins, and A. M. Chinnaiyan. 2008. A first-
generation multiplex biomarker analysis of urine for the early detection of prostate
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P. Harnden, D. Thompson, I. Eardley, R. E. Banks, and M. A. Knowles. 2006.
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Santamaria, A. Ache, M. Duncan, M. R. Powell, and B. J. Ward. Identification
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Abanes, D. J. Cruz, R. R. Matias, H. Matsuura, F. Hasebe, S. Tanimura, A.
Kumatori, K. Morita, F. F. Natividad, and T. Nagatake. 2003. Correlation
between increased platelet-associated IgG and thrombocytopenia in secondary
dengue virus infections. J Med Virol 71:259-64. 16. Papadopoulos, M. C., P. M. Abel, D. Agranoff, A. Stich, E. Tarelli, B. A. Bell,
T. Planche, A. Loosemore, S. Saadoun, P. Wilkins, and S. Krishna. 2004. A
novel and accurate diagnostic test for human African trypanosomiasis. Lancet
363:1358-63. 17. Rhodes, D. R., M. G. Sanda, A. P. Otte, A. M. Chinnaiyan, and M. A. Rubin.
2003. Multiplex biomarker approach for determining risk of prostate-specific
antigen-defined recurrence of prostate cancer. J Natl Cancer Inst 95:661-8. 18. Rigau-Perez, J. G., G. G. Clark, D. J. Gubler, P. Reiter, E. J. Sanders, and A.
V. Vorndam. 1998. Dengue and dengue haemorrhagic fever. Lancet 352:971-7. 19. Rioux, M. C., C. Carmona, D. Acosta, B. Ward, M. Ndao, B. F. Gibbs, H. P.
Bennett, and T. W. Spithill. 2008. Discovery and validation of serum biomarkers
expressed over the first twelve weeks of Fasciola hepatica infection in sheep. Int J
50
Parasitol 38:123-36. 20. Schvartz, I., D. Seger, and S. Shaltiel. 1999. Vitronectin. Int J Biochem Cell Biol
31:539-44. 21. Schwegler, E. E., L. Cazares, L. F. Steel, B. L. Adam, D. A. Johnson, O. J.
Semmes, T. M. Block, J. A. Marrero, and R. R. Drake. 2005. SELDI-TOF MS
profiling of serum for detection of the progression of chronic hepatitis C to
hepatocellular carcinoma. Hepatology 41:634-42. 22. Senanayake, S. 2006. Dengue fever and dengue haemorrhagic fever--a diagnostic
challenge. Aust Fam Physician 35:609-12. 23. Stephenson, J. R. 2005. Understanding dengue pathogenesis: implications for
vaccine design. Bull World Health Organ 83:308-14. 24. Teles, F. R., D. M. Prazeres, and J. L. Lima-Filho. 2005. Trends in dengue
diagnosis. Rev Med Virol 15:287-302. 25. Yip, T. T., J. W. Chan, W. C. Cho, Z. Wang, T. L. Kwan, S. C. Law, D. N.
Tsang, J. K. Chan, K. C. Lee, W. W. Cheng, V. W. Ma, C. Yip, C. K. Lim, R.
K. Ngan, J. S. Au, A. Chan, and W. W. Lim. 2005. Protein chip array profiling
analysis in patients with severe acute respiratory syndrome identified serum
amyloid a protein as a biomarker potentially useful in monitoring the extent of
pneumonia. Clin Chem 51:47-55. 26. Yurkovetsky, Z., S. Ta'asan, S. Skates, A. Rand, A. Lomakin, F. Linkov, A.
Marrangoni, L. Velikokhatnaya, M. Winans, E. Gorelik, G. L. Maxwell, K. Lu,
and A. Lokshin. 2007. Development of multimarker panel for early detection of
endometrial cancer. High diagnostic power of prolactin. Gynecol Oncol 107:58-65.
51
Chapter 9: Vitronectin Precursor Protein as a New Lead in Dengue Physiopathology
The complete ORF sequence for vitronectin (Vn) consists of 459 amino acids
preceded by a cleaved leader peptide of 19 residues (61). The precursor protein has a
predicted molecular weight of 54kDa, while the mature form has a size of 75kDa. It
contains three glycosylation sites and owes almost a third of its weight to glycosylation.
Using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) under
reducing conditions, two major bands can be seen: a 75- and a 65-kDa band. The
presence of the 65kDa band is due to cleavage of a 10-kDa segment near the carboxyl
terminus; the two fragments are normally held by a disulfide bridge (Cys274–Cys453) (i.e.
under non-reducing conditions) (31). The presence of threonine rather than methionine at
position 381 has been proposed to be responsible for the susceptibility of Vn to cleavage
at Arg379–Ala380, by an as yet unidentified protease (104). The amino terminus of Vn
(amino acids 1-44) is identical to somatomedin B, which has been reported to be involved
in the binding of plasminogen activator inhibitor (PAI-1). At position 45-47, there is an
Arg-Gly-Asp (RGD) sequence through which it mediates the attachment and spreading of
cells to the extracellular matrix via specific integrin receptors. Following the RGD
sequence (residues 53-64), there is a stretch of acidic amino acids including two sulphated
tyrosine residues. This segment is most likely involved in the binding of the thrombin-
antithrombin III complex as well as stabilizing the three-dimensional structure of
vitronectin when forming multimers. Vn has also been shown to contain consensus
sequences for phosphorylation by various protein kinases such as camp-dependent protein
kinase, protein kinase C, and casein kinase II, most likely involved in Vn regulation
(104).
Vn is a major cell adhesion protein found in the plasma at concentrations of 200-
400 µg/ml. Other ‘adhesive’ glycoproteins in circulation include fibrinogen, fibronectin,
and von Willerbrand factor (vWF). Vn was demonstrated to be an inhibitor of formation
of the membrane lytic complex of complement. It may also regulate blood coagulation
by inhibiting the rapid inactivation of thrombin by antithrombin III in the presence of
heparin (31). Additionally, it has been shown to form a complex with plasminogen
activator inhibitor type 1 (PAI-1). This association stabilizes PAI-1’s inhibitory activity,
52
which in turn regulates fibrinolysis, cell migration, tumor growth and metastasis (98). In
addition to plasma, Vn has also been found in the extracellular matrix of tissues (31).
This suggests, in contrast to other adhesive proteins, that Vn may participate in localized
regulatory functions of blood coagulation as well as fibrinolysis in platelet-matrix
interactions and protection of this matrix against proteolysis. That is, approximately
0.08% of the plasma Vn pool is found in platelets which may be released upon
stimulation of the platelets in different molecular forms (i.e. as free protein and in
complex with PAI-1) (98).
The liver is the major source of plasma Vn which suggests that it may become
depleted during disseminated intravascular coagulation (DIC), a common severe
complication of DV infected patients. As predicted, Mosher et al. (31) observed that the
concentration of plasma Vn was remarkedly reduced in some patients with DIC,
especially in those with liver failure. Patients with Vn levels <40% normal invariably had
low fibrinogen and antithrombin III and a prolonged prothrombin time. The same group
demonstrated plasma Vn polymorphism where the ratio of the 75- and 65-kDa
polypeptides of reduced Vn differed in normal and patient plasmas (31). Yamada et al.’s
also observed a significant decrease in plasma Vn levels in chronic liver disease.
Moreover, the magnitude of the decrease seemed to correlate with the severity of the
disease (128). On the other hand, the hepatic Vn levels increase in chronic liver disease,
especially in the connective tissue around the portal and central veins and in the areas of
piecemeal and focal necrosis (128).
9.1 Protein identification from Thai samples
A 4-12% Bis-Tris NuPAGE gel was run using PR samples. By visual inspection,
bands were determined as potential biomarkers if their intensity differed from one group
to another (Figure 9.1). The chosen bands were excised and trypsinized and sent to Dr. B.
Gibbs (Sheldon Biotechnology Centre, McGill University) for MALDI analysis. The
Mascot Search (Matrix Science) algorithm was used to score the matched peptides. The
Homo Sapiens (humans) taxonomy was searched within the SwissProt 54.6x database. A
total of 35 bands were excised (refer to appendix A, table A6) and a non-redundant list of
25 proteins (Table 9.1) was identified from the Thai sample set. Of these proteins, 6
53
matched identified proteins from a 4-12% Bis-Tris NuPAGE gel run with Thai samples
following the same protocol (refer to section 2.6 of chapter 8).
Figure 9.1 4-12% Bis-Tris NuPAGE gel of pooled Thai samples. Thai samples of OFI
at t1 and t2 were pooled (C) and DF and DHF at t1 and t2 were pooled (D) All samples
were ZOOM Fractionated and desalted using specific pI ranges: F1 (pH3.0-4.6), F2
(pH4.6-5.4), F3 (pH5.4-7.0), F4 (pH7.0-9.1), and F5 (pH9.1-10.0). The red boxes indicate
the candidate biomarkers identified.
54
Table 9.1 Non-redundant list of the 25 proteins identified. Thai gel bands were
excised and MS/MS was performed. Mascot Search (Matrix Sciences) software was used
to identify peptides/proteins using the SwissProt 54.6 database.
Protein Accession
number
Afamin precursor AFAM_HUMAN
α1B-glycoprotein precursor A1BG_HUMAN
α2-macroglobulin precursor A2MG_HUMAN
Apolipoprotein A-I precursor APOA1_HUMAN
Apolipoprotein D precursor APOD_HUMAN
Carboxypeptidase N subunit 2 precursor CPN2_HUMAN
Ceruloplasmin precursor CERU_HUMAN
Complement C1q subcomponent subunit B precursor C1QB_HUMAN
Complement C3 precursor CO3_HUMAN
Complement C4-A precursor CO4A_HUMAN
Fibrinogen α chain precursor FIBA_HUMAN
Haptoglobin-related protein precursor HPTR_HUMAN
Hemoglobin subunit alpha HBA_HUMAN
Hemopexin precursor HEMO_HUMAN
Ig γ-1 chain C region IGHG1_HUMAN
Ig µ chain C region MUC_HUMAN
Insulin-like growth factor-binding protein complex acid labile ALS_HUMAN
Lumican precursor LUM_HUMAN
Mannose-binding protein C precursor MBL2_HUMAN
Plasma protease C1 inhibitor precursor IC1_HUMAN
Prothrombin precursor THRB_HUMAN
Serotransferrin precursor TRFE_HUMAN
Serum albumin precursor ALBU_HUMAN
Vitamin K-dependent protein S precursor PROS_HUMAN
Vitronectin precursor VTNC_HUMAN
Various acute-phase proteins were identified such as the mannose binding protein
C and complement proteins. Disruption of protein synthesis in hepatocytes could account
for these biomarkers, or they could simply be due to an inflammatory response.
55
Moreover, many proteins that we have identified as potential biomarkers in DV infections
are involved in iron transport, such as ceruloplasmin, serotransferin and hemopexin,
which may play a role in the hemorrhagic manifestations of DHF. Prothrombin and
fibrinogen α chain are proteins involved in coagulation which could also influence
bleeding in DHF. Four of the identified proteins were involved in homeostasis of plasma
lipid profile; apolipoprotein A-I, A-IV and D precursors and apolipoprotein B-100. This
observation is similar to the findings of van Gorp et al. who showed that levels of total
plasma cholesterol, high-density lipoprotein, and low-density lipoprotein were
significantly decreased in patients with the most severe manifestations compared with
patients with mild DHF and healthy controls (120). These proteins identified through
SDS-PAGE and MALDI may provide clues into the physiopathology of dengue, but
further studies are required to understand the involvement of these molecules in disease
progression.
9.2 Vitronectin protein characterization
One of the proteins found in both the Thai and PR samples was the heavily
glycosylated protein vitronectin (Vn). Further characterization of this protein was
performed to better understand the differential expression observed in different sample
types (depending on the geographical area, disease severity and sampling time).
Purified human vitronectin was deglycosylated with PNGaseF. Before treatment,
two bands are observed by SDS-PAGE under reducing conditions, corresponding to the
two predicted isoforms: 65- and 75-kDa proteins (Figure 9.2). After deglycosylation, a
third band was also observed at ~54kDa, corresponding to the approximate molecular
weight of the precursor form of Vn protein.
56
Figure 9.2 Deglycosylation of purified human vitronectin by PNGaseF. Purified
human vitronectin was deglycosylated for one hour at 37°C with PNGaseF. The arrow
points to the lighter deglycosylated form of Vn; it is absent in the control.
In order to confirm the nature and sequence of the 65kDa and 75kDa proteins, the
bands corresponding to these molecular weights were excised and analyzed using ESI-
TRAP MS/MS (Figure 10). The 75kDa protein (band#1) achieved a sequence coverage
of 22% compared to the 16% coverage achieved by the 65kDa (band#2). This 6%
coverage difference may be explained by the absence of the C-terminus in the protein of
band#2. In other words, amino acids 453-478 were not detected in the 65kDa isoforms,
as can be seen in the chromatogram (Figure 9.3), making the sequence coverage lesser
than that of the 75kDa protein.
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57
Figure 9.3 Chromatogram of two vitronectin isoforms. Purified human vitronectin was run on an SDS-PAGE gel and stained with
colloidal Coomassie blue. Two bands were excised and identified by ESI-TRAP MS/MS. Matched peptides are indicated in bold red
on the protein sequence. The heavier band #1 matches amino acids 453-478 while band #2 does not. Amino acids 453-478 match the
green chromatogram (band#1) peak at 835.46Da; the same peak is absent in the red chromatogram (band#2). The x-axis represents the
elution time and the y-axis the intensity.
58
9.3 Characterization of vitronectin protein in dengue samples
Once Vn was characterized, the samples were further analyzed by Western blot
and ELISA to determine its expression in different DV groups.
9.3.1 Western Blot
Western blotting for Vn revealed different expression patterns depending on the
type of disease (DF vs. DHF vs. OFI), time of sample collection and severity of DHF.
9.3.1.1 Thai samples
In 1°DF and 2°DF infection, t1 samples had generally lower levels of the Vn
precursor protein (Figure 9.4A). This reduction was most pronounced in 2° infection.
Levels, compared with OFI or healthy (hCt) samples, at t2 and t3 were generally
unchanged. In sharp contrast, Vn precursor levels in 1°DHF infection were undetectable
at t1 for grade I disease severity and almost undetectable in grade II (Figure 9.4B). While
the precursor protein seems to slowly return to normal at t2 in grade II DHF patients, it
does not in grade I samples. By t3, normal levels of Vn precursor protein were re-
established. Grade I patients suffering 2°DHF infection had undetectable levels of Vn
precursor at t1 and t2, but levels seemed to return to normal by t3 (Figure 9.4C). Grade
III patients also had undetectable levels of Vn precursor at t1, but normal levels returned
much more rapidly, beginning at t2. Grade II 2°DHF Vn precursor protein seemed to
follow the same pattern as that of grade II 1°DHF in which levels were low but detectable
at t1 and become progressively higher, reaching normalcy at t3.
59
Figure 9.4 Assessment of vitronectin precursor protein in Thai samples by Western
blot analysis. (A) Pooled samples of Thai 1° and 2°DF according to the three time points
(t1, t2, t3). Controls consisted of pooled OFI Thai samples as well as one healthy control
(hCt). (B) Pooled samples of Thai 1° DHF according to the three time points (t1, t2, t3)
as well as the WHO dengue severity classification system (Grade I-II) (4). No samples
were available for 1°DHF grade I t3. (C) Pooled samples of Thai 2° DHF according to the
three time points (t1, t2, t3) and three severity grades (Grade I, II, III). Arrows indicate
position of vitronectin precursor protein while the two heavier bands are the two
glycosylated isoforms of the mature protein.
Although 1°DF and 2°DF are clinically undistinguishable, there seems to be a
slight difference at t1 in the concentration of Vn precursor. This hints at the possibility
that primary DV infection may have long-term impact on the physiology of the host,
perhaps in the manner analogous to the ADE hypothesis that postulates prolonged
influence on the host's immunological status. These results are best considered
preliminary however and cannot be used to support firm conclusions. Nevertheless, the
kinetics of the Vn precursor protein were similar in 1°DHF and 2°DHF in which grade I
disease is depleted of precursor Vn for a longer period of time (i.e. up until t2) than
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60
patients suffering grade II disease. Surprisingly, it seems that the more severe the disease,
the quicker Vn precursor levels are recovered.
9.3.1.2 Puerto Rican samples
As expected, DV(-) samples from PR were also found to have the low molecular
weight form of Vn (Figure 9.5). However, in contrast to the Thai samples, detectable
levels of the Vn precursor protein were also present in DF and DHF samples . Moreover,
PR samples had a lower general expression of the mature form of the protein (75kDa)
compared to the Thai samples.
Figure 9.5 Assessment of vitronectin precursor protein in PR samples by Western
blot analysis. Pooled Puerto Rican samples of 2°DV infections. Controls consisted of
DV(-) sample from PR and one OFI sample from Thailand. Lane 1, pool of DV(-) PR
samples; lane 2, pool of DF PR samples; lane 3, pool of DHF PR samples; lane 4, OFI
Thai sample (t1).
Finally, Vn precursor protein kinetics in the PR samples did not follow the same
trends as the Thai samples. The PR samples were collected at hospitalization and should
therefore theoretically follow the same pattern as Thai samples at t1. However, samples
from the more severe form of the disease, DHF, seem to express more of the precursor
form when compared to DF samples. Moreover, they express less of the 75kDa isoform
compared to both PR DF and Thai OFI samples. Production of Vn may be modified due
to the infection, where Vn is not allowed to mature properly (i.e. is not glycosylated) but
is still secreted into the circulation.
This observation raises the possibility that DV infection may affect general
mechanisms of protein handling in the host tissues. Following the formation of the
nucleocapsid, DV particles acquire their envelopes inside the lumen of the rough
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61
endoplasmic reticulum (rER) and associated structures. Only some of the virus particles
are transferred to the Golgi system for maturation where they are delivered from the cell
by exocytosis. The majority of virus particles do not exit the Golgi system however, and
remain enclosed in rER-derived vesicles, even after cell and syncytial lysis (14). Because
of this intimate interaction with the rER, it is tempting to infer that the DV particles
directly hinder the protein maturation process of the host cells. Furthermore, it has been
shown that NS3 protein of DENV-2 interacts specifically with nuclear receptor binding
protein (NRBP), a host cellular protein that influences trafficking between the ER and
Golgi, and that interacts with a member of Rho-GTPase family, Rac3 (28). This suggests
that NS3 may subvert the role of NRBP in ER-Golgi trafficking, possibly causing the host
cell to export its proteins before their complete maturation. It is also possible that specific
mechanisms target different tissues such as the endothelium cells or the hepatocytes.
9.3.2 Total vitronectin concentration by ELISA
Total vitronectin levels were measured for both PR and Thai samples by enzyme-
linked immunosorbent assay (ELISA) kit (Technoclone, Vienna, Austria) in order to
determine if total levels of Vn were altered in DV infection.
The standard used in the assay is a pool of more than 100 normal plasma donors
and is assigned a value of 100% as there is no international standard to calibrate against.
The assay is therefore not calibrated against purified protein, as it is designed for
measuring Vn in plasma.
62
Figure 9.6. Total Vn levels in Thai samples at t1. *, p-value<0.01; w, p-value<0.05.
In 2° infection, the difference in Vn concentration in plasma samples from DF and
DHF specimens is significant, where the concentration is much lower in DHF Thai
patients, suggesting a hindrance in the synthesis of Vn or perhaps consumption of this
molecule during DHF process (Figure 9.6). There were no significant differences
between specimens infected with HCV (i.e. another flavivirus that also targets the liver)
and 2°DHF samples, suggesting liver involvement in Vn’s decreased plasma
concentration. In contrast, there were no such differences when comparing 1°DF samples
with 1°DHF Thai samples or OFI. Despite the differences between 1°DF and 2°DF
suggested by Western blot analysis, no significant differences were detected by ELISA
for total protein. Overall, these data suggest that differences are most likely explained by
changes in maturation of the protein rather than its synthesis.
63
Figure 9.7 Total Vn levels in Puerto Rican samples. *, p-value<0.001. Samples
below the detection limit with a negative O.D. value were converted to zero values (refer
to Appendix A, table A7).
All of the PR specimens were obtained from subjects with 2° infections and can
thus be compared with this subset of the Thai samples. Following the same trend as the
Thai samples, PR samples from DHF patients expressed significantly lower levels of
plasma Vn compared to DF patients (Figure 9.7). In the Western blots, DHF samples
were shown to be almost devoid of the 75kDa isoform in comparison to DF samples, but
seemed to have higher levels of the precursor form. Taken together with the ELISA
results, these data support the hypothesis that there may be defects in both the maturation
process and Vn synthesis.
Measured by ELISA, there were no significant differences in total Vn levels
between the DV(-) samples and DF or DHF samples obtained from PR.
9.4 Vitronectin and mass spectrometry
One of the goals of this project was, not only to use the SELDI platform to guide
biomarker discovery, but also to establish a protocol that would allow one to use this
platform to detect and confirm these biomarkers in real samples. In order to simplify this
ambitious endeavor, the next sections focus exclusively on Vn protein.
9.4.1 Experimental mass of vitronectin protein in SELDI-TOF MS and MALDI-TOF MS
64
The first step was to determine if Vn was detectable using the SELDI platform
and if so, what was its observed mass. Purified human Vn was analyzed by SELDI-TOF
MS (Figure 9.8). The three isoforms were detected, corresponding to peaks of MW 48-,
59-, and 66-kDa (vs. predicted 54-, 65-, and 75-kDa).
Figure 9.8 SELDI-TOF MS of purified human vitronectin. The three peaks (48-, 59-,
66-kDa) represent the three isoforms of vitronectin, where the lightest one is the precursor
one, the second one is the mature but truncated form and the third one is the mature form
of the protein.
In order to validate the results obtained with SELDI-TOF MS, human purified Vn
was analyzed by MALDI-TOF MS (Figure 9.9). Again, the three isoforms were detected
and the corresponding peaks matched the MW obtained with the SELDI platform.
65
Figure 9.9 MALDI-TOF MS of purified human vitronectin. The three peaks (49-, 59-,
66-kDa) represent the three isoforms of vitronectin, where the lightest one is the precursor
one, the second one is the mature truncated form and the third one is the mature form of
the protein.
Surprisingly, the three forms of the Vn protein did not “fly” at their expected
masses (i.e. 54-, 65-, and 75-kDa). Instead they were all detected at slightly lower masses
(i.e. 49-, 59-, and 66-kDa). It would be tempting to speculate that this was due to
degradation of the protein during the protocol. However, if this had been the case, some
of the intact protein would almost certainly have been detected as well as other various
smaller fragments. This was not the case. Moreover, upon independent repetition of the
protocol, the same results (masses) were obtained. This observation suggested instead
that the heavy glycosylation of Vn affects the total charge of the molecule in a way that
its flight pattern in the apparatus is modified.
9.4.2 Confirmation of vitronectin presence in dengue samples by SELDI-TOF MS by
immunoprecipitation
As per 9.4.1, Vn protein is detectable in high-throughput MS platforms. The next
step was to correlate the identification results with those obtained from the SELDI-TOF
MS analysis. In other words, could one or more of the Vn isoforms be detected in
patients’ plasma samples using SELDI-TOF MS?
66
OFI plasma from Thai patients was analyzed by SELDI-TOF MS (Figure 9.10A,
D). The main peaks observed are those of the doubly (33kDa) and singly charged
(66kDa) albumin. Since the signal intensity is only proportional to the total amount of
protein in the sample, the more abundant proteins may mask the less abundant ones,
causing only a few peaks to be detected. To overcome this phenomenon, samples can be
fractionated in order to reduce the complexity or the most abundant proteins can be
depleted. This latter method has been criticized mainly because some of the less
abundant proteins in association may be depleted from the sample at the same time that
the more abundant ones are. Since we did not observe many peaks/proteins, we
proceeded to deplete the samples of the 14 most abundant serum proteins using the
Seppro IgY 14 Spin Columns (Sigma-Aldrich, St. Louis, MO, USA). After depletion,
many more peaks are seen both at low and high energy, while the 66kDa peak associated
with serum albumin is greatly diminished in intensity (figure 9.10B, E). Figure 9.10C
and F show the eluted proteins, or the 14 abundant proteins depleted from the samples. In
light of these results, we continued working with depleted plasma samples.
67
Figure 9.10 Effect of treatment of plasma samples with Seppro IgY 14 Spin
Columns on mass spectra at high and low laser intensity. OFI samples from Thailand
were pooled and depleted of the 14 most abundant proteins using Seppro IgY 14 Spin
Columns (Sigma-Aldrich, St. Louis, MO, USA). Samples were bound to CM10 chips
and read at low (A,B,C) and high energy (D, E, F). A, non-depleted sample; B, depleted
sample; C, eluted proteins; D, non-depleted sample; E, depleted sample; F, eluted
proteins.
68
Immunoprecipitation was performed in order to establish which peak observed in
MS represented Vn (if any) rather than another molecule with interfering MW. To
confirm the success of the IP, a Western blot was done previous to the SELDI analysis
(Figure 9.11).
Lane 2 and lane 3 of Figure 9.11 show that Vn does not bind non-specifically to
the beads. Moreover, no Vn protein is detected in the supernatant fraction (lane 4), which
suggests complete Vn precipitation from the sample. Lane 5 confirms that the IP
successfully pulled out Vn from the sample. Figure 9.12 shows the IP results analyzed by
the SELDI platform.
Figure 9.11 Western blot of vitronectin immunoprecipitation from depleted Thai
plasma samples. A pool of OFI samples from Thailand was depleted of 14 of the most
abundant serum proteins with Seppro IgY 14 Spin Columns (Sigma-Aldrich, St. Louis,
MO, USA) and immunoprecipitated for Vn protein. The arrow points to the Vn precursor
protein. Lane 1, pre-treated OFI plasma samples; lane 2, sample after pre-clearing (to
minimize non-specific binding to the beads); lane 3, wash fraction; lane 4, supernatant
fraction; lane 5, elution fraction containing Vn protein.
!!!!"!!!!!!!!!!!!!!#!!!!!!!!!!!!!$!!!!!!!!!!!!%!!!!!!!!!!!!&!!
69
Figure 9.12 SELDI-TOF MS of vitronectin immunoprecipitation from OFI Thai
depleted plasma samples. Immunoprecipitation of Vn was performed on OFI Thai
plasma samples. A peak at 49-, 73-, and 146-kDa was detected by SELDI-TOF MS in
the elution fraction corresponding to Vn precursor protein, antibodies doubly charged and
antibodies singly charged respectively. A, pre-treated OFI depleted plasma sample; B,
supernatant fraction; C, elution fraction.
Considering figure 9.12, the 66kDa peak is present in both panels A (depleted
Thai plasma) and B (supernatant fraction), which means that the peak is most likely
serum albumin (as the depletion column is not 100% efficient) rather than the heavier
isoforms of Vn. In the elution fraction (panel C), the Vn precursor protein can be
observed at 49kDa along with the doubly and singly charged antibody used for the IP at
73- and 146-kDa respectively. However, the other two Vn mature forms cannot be seen
on the MS; there are no apparent peaks at 59- and 66-kDa. The reason for this is
uncertain but it is not due to the depletion as the same analysis was performed with non-
depleted samples and the same peak pattern was observed (refer to Appendix A, figure
1A).
70
9.4.3 Identification of the corresponding vitronectin precursor protein peak to our SELDI-
TOF MS analysis
In order to correlate Vn to our original SELDI-TOF MS biomarker discovery
analysis, we first needed to determine in which of the fractions Vn was observed. Thai
plasma samples were fractionated using anion-exchange chromatographic beads and pH
gradient elution; six isoelectric fractions were obtained. Each of these fractions was
probed for presence of Vn (Figure 9.13).
Figure 9.13 Western blot of vitronectin protein from fractionated Thai plasma
samples. A pool of 4 OFI Thai samples were denatured and fractionated using anion-
exchange chromatographic beads and pH gradient elution. Six isoelectric fractions were
obtained and collected using different buffers provided in the Expression Difference
Mapping Kit. Vitronectin precursor protein is detected in fractions 4 (pH4), 5 (pH3), and
6 (organic). Lane 1, non-fractionated pool; lane 2, human purified Vn; lane 3-8, F1-F6.
As can be seen from figure 9.13, the fractionation process is not absolute. Vn
protein was detected in fractions 4 through 6 but fraction 4 was not part of the initial
biomarker discovery protocol. No significant peaks corresponding to Vn’s apparent mass
(49-, 59-, or 66-kDa) and following the same expression pattern observed in the Western
blots and ELISA analyses could be seen in fractions 5 or 6 (data not shown). This could
be explained in part by the presence of other proteins, i.e. causing Vn to be observed at
different masses than the predicted ones or masking Vn protein. Spiking plasma samples
with Vn protein may give better insight as to why this is occurring.
9.5 Discussion
In industrialized countries, dengue virus infection tends to receive little attention;
attracting relatively little funding for research, prevention and control. However, DF and
DHF/DSS have a huge impact on public health internationally and cause social and
!"#$
$%$$$$$$$$$$"$$$$$$$$$$&$$$$$$$$'$$$$$$$$($$$$$$$$)$$$$$$$$$!$$$$$$$*$$
71
economic burdens comparable to, or greater than, many better known infectious diseases
(51). The molecular mechanism of DHF pathogenesis is not well understood and no
treatment is available. Since no vaccine is available, the control of the mosquito vector is
the only available preventive measure. Finally, diagnostic tests suitable for the conditions
where DV infection is endemic or that address the most pressing clinical questions are
simply not available.
Although some research methods to diagnose dengue virus infections are quite
sensitive and specific (e.g.: microneutralization, PCR), the high costs and/or the training
required to perform these tests preclude their use at all levels of the health system -
particularly in the poor regions of the world where dengue is endemic. Furthermore, even
the best of these tests often fail to make a diagnosis of dengue at the time that a child is
hospitalized because of the critical ‘gap’ between the virologic assays (e.g.: culture, PCR)
and the development of detectable antibodies. Furthermore, at the current time, there is no
test that can predict which patient will develop serious complications from DV infection
and which will recover uneventfully. The timely and accurate diagnosis of dengue is the
first step towards being able to treat a patient and prevent or mitigate complications that
can lead to death. The development of diagnostic tests that are accurate (specific and
sensitive), simple, and cheap is thus of the uttermost importance. The development of a
prognostic test would also be a huge benefit to the rational provision of care in low
resource settings. In 2006, Blacksell et al. evaluated 8 of the 20 commercially available
rapid immunochromatographic assays (RDTs) for diagnosis of acute DV infection. The
assay sensitivities ranged from as low as 6.4% to 65.3% and specificities ranged from
69.1% to 100%. Only 2 of the RDTs demonstrated sensitivity higher than 50% (the level
required to be considered clinically useful or better than the ‘flip of a coin’) (16). In this
light, our new-found diagnostic biomarkers seem quite promising. By combining five of
our biomarkers identified at t1, a learning decision tree was built and achieved a
specificity of 89% and a sensitivity of 90%. Even when tested with an independent
sample set from a different region of the world, this novel diagnostic ‘test’ achieved a
specificity of 100% and a sensitivity of 93%. This test is without a doubt superior to the
RDTs already on the market. However, SELDI equipment is both expensive and wholly
impractical as a field-testing device in developing countries. Nevertheless, our data
72
certainly raise the attractive possibility of developing a multiplex, point-of-care assay.
Many such new multiplex assay platforms have recently been developed for other
purposes. One of the most promising is a bead-based multiplex immunoassay that
permits simultaneous analysis of numerous analytes in one sample. The sensitivity of
such assays is comparable to that of a classical ELISA test without the requirement for
trained personnel (60). Minimal sample volume is required and the test can be performed
in a 96-well plate format with up to 100 tests/well (58). Reading of the results is based on
flow cytometry of panels of up to 100 labelled microspheres conjugated with capture
antibodies bound to the sample (each capture antibody is conjugated with a uniquely-
labelled microsphere) (21). This technology is highly attractive and is driven by the
availability of faster and cheaper computers, improved digital processing, and both
smaller and cheaper lasers. Given these trends, the eventual arrival of miniaturised field
equipment that operates on battery power seems inevitable. Such platforms would
theoretically allow multiplexed assays at low cost (60). A similar but distinct
technology, coupled particle light scattering (Copalis, DiaSorin USA, Stillwater, MN,
USA), has also been used for multiplexed immunoassays. The Coupled Particle Light
Scattering (Copalis) system measures changes in light-scattering properties of particles
when they form antibody-mediated complexes (15). This system is attractive due to the
low cost of its instrumentation since it uses a red iode laser as the light source. Moreover,
this technology has been applied to infectious diseases such as the T. gondii, rubella and
cytomegalovirus assay (ToRC), syphilis diagnosis, Epstein-Barr virus diagnosis, and a
hepatitis B surface antigen detection test in whole blood (60). Although reagents are not
readily available for this technology yet, monoclonal antibodies can be used, hence the
importance of identifying the candidate biomarkers. In the context of our current studies,
once identification has been accomplished and monoclonal reagents have been developed
against the most promising biomarkers, a SELDI-derived multiplex assays could
theoretically combine the great accuracy of the SELDI-TOF MS platform and the much
needed practical aspect of immunoassays.
Despite our promising results, it must be acknowledged that the SELDI platform
has been subjected to rigorous review and criticism. This technology has several, well-
established weaknesses: some that are general failings of all proteomic platforms and
73
others that are unique to SELDI platforms. In the former category, there is the question of
how much of the proteome is really detected. This caveat is well exemplified in our own
failure to correlate the Vn protein Western blot findings with the mass spectra produced
using the SELDI platform. One of the current challenges for this technology is the need
to develop high-capacity probes that are effective in the detection of low-abundance
proteins in complex biological media. The active surface of the SELDI protein chip plays
a large role in the ultimate detection limit for any individual compound for a given set of
experimental variables (114). To maximize the proteome coverage, we used two different
kinds of surface chemistry for our chip arrays, immobilized metal affinity capture
(IMAC) and weak-cation carboxymethylated (CM10) chips. Moreover, the plasma
samples were fractionated to minimize non-specific binding of high-abundance proteins,
which can otherwise hinder the binding of lower abundance proteins. However, only the
three fractions with the greatest number of peaks were used for our analysis: fractions 1,
5, and 6. There has also been much controversy about the reproducibility of SELDI
experiments (13, 17, 35, 66). To help determine if our standardized techniques were
reproducible, 45 randomly selected Thai samples were fractionated, bound to one chip
(CM10) and read (using same protocol as the original run) using a second and more
sensitive, SELDI platform: the PCS4000 reading system. The same candidate biomarkers
were detected by both machines (PBSIIc and PCS4000) although the latter had a wider
detection range (data not shown). This increased sensitivity raises the possibility that
there could be more biomarkers than we were actually able to detect with the PBSIIc but
provides us with some level of confidence regarding our screening methodology.
Moreover, our use of a robotic system to fractionate certainly decreased the variability
that can be generated by sample manipulation (9).
Aivado et al., has shown that the number of samples processed is key to increasing
reproducibility and ensuring the significance of the results (9). Indeed, one can never
have too many samples. Overall, we believe that the number of specimens used for our
analysis was sufficient to have reasonable confidence in our conclusions: 72 DV and 15
OFI Thai cases, and 84 DV and 30 OFI PR cases. We specifically chose to study samples
from 2 distinct populations in two very distinct regions of the world to address the
possible influences of host genetic background and DV strain on disease manifestations
74
and biomarker expression. It was therefore enormously reassuring that many of our
candidate biomarkers were confirmed using samples collected from patients with very
different ethnicities (Puerto Rican and Thai origin).
In this study, we chose not to include control plasma from healthy patients. The
rationale behind this decision was simple; a diagnostic test is not needed to differentiate
someone presenting with febrile symptoms and someone in good health. We chose to
compare samples from patients registering at hospitals and/or clinics suffering from other
undifferentiated febrile illnesses rather than samples from a healthy population.
However, it may still be quite informative to compare candidate DV biomarkers with
plasma/serum from healthy subjects to better understand the possible role that these
biomarkers may play in the pathology of the disease. Finally, we assumed in these studies
that there were no significant differences between the host-virus responses to the different
DV serotypes based upon a limited previous study performed in our lab (data not shown).
However, contrary to current literature, we detected marked differences in the serum
proteomes derived from primary and secondary DV infection, we chose to focus the
current analysis on secondary DV infection between the Thai and Puerto Rican samples.
Typical symptoms (fever, headache, eye pain, myalgia, arthralagias and rash) that
may be observed in dengue infection as well as clinical or laboratory parameters such as
anorexia, nausea, vomiting, positive tourniquet test, thrombocytopenia, leukopenia and
liver enzymes are routinely used to help diagnose dengue (63, 72, 93, 122). Moreover,
various molecules that may be involved in dengue pathology have been identified as
elevated in subjects with infection. Specific cytokines (TNF-α, IL-10, IL-18, TGF-β1),
soluble intercellular adhesion molecules (sICAM-1), vascular cell adhesion molecules
(sVCAM-1) and the presence of circulating endothelial cells (CECs) have been shown to
be related to the disease severity and outcome (12, 27, 45, 46, 56, 65, 69, 76, 101, 125).
Of these aforementioned cytokines/chemokines, it is somewhat surprising that none were
identified using our SELDI screening approach. This may be due to the fact that most
cytokines are small molecules, often poorly-detected by SELDI and irresolvable by gel
electrophoresis. We did not measure the levels of these other candidate prognostic
markers in our current study (insufficient sample material) so we do not know if one or
75
more of them could be combined with our biomarkers to achieve even higher sensitivity
and specificity. However, none of these published clinical or laboratory biomarkers are
exclusive to DV infection, highlighting once more the urgent need for novel DV
biomarkers.
Since the only treatment of DF and DHF is supportive, early diagnosis is crucial to
prevent or mitigate complications. Prognostic biomarkers that could predict whether a
patient with undifferentiated fever will develop DHF/DSS or not would be of a great
advantage. This is particularly true in low resource settings where children generally
cannot be kept in hospital for prolonged periods. It is quite typical for a child with
‘presumed dengue’ to be discharged from the hospital once the fever has disappeared (i.e.
after defervescence). This is precisely the time that some of them will go on to develop
DHF. Our results show that there are four candidate prognostic biomarkers capable of
distinguishing between those who have uncomplicated 2°DF and those who are likely to
progress to 2°DHF in both Thai and Puerto Rican samples at the time of hospitalization.
These findings are very promising since they suggest that some idea of a patient’s
prognosis could be determined early in the disease. As described earlier, since the
diagnosis is the first step in preventing complications, knowing whether a patient is at risk
of developing DHF/DSS is crucial in giving him/her optimal treatment to reduce
morbidity and mortality. Biomarkers that can, at the same time, distinguish between DV
infection and OFI as well as provide prognostic information would have enormous
clinical implications. Such an assay would greatly simplify testing algorithms for
subjects with undifferentiated fever (a very common occurrence in most of the developing
world). Such a multiplexed test might also minimize both the time needed to make a
diagnosis and the costs. Unfortunately, of the 33 diagnostic biomarkers discovered (i.e.
DF vs. OFI), none were considered to be significant prognostic biomarkers.
76
Chapter 10: Summary and Conclusion
The urgent need for improved diagnostic tools that are both accurate and timely in
dengue cannot be over-emphasized. The disease needs to be recognized early to
maximize the patient’s chance of survival and minimize morbidity. Here we have used
the high-throughput proteomic platform SELDI-TOF MS followed by CART analysis to
discover a protein signature in plasma samples specific to DV infected patients. Using
mass spectra from Puerto Rican samples as the ‘learning set’ and mass spectra from Thai
samples as an independent ‘testing set’, we determine the best decision tree (i.e.
diagnostic algorithm) to differentiate between 2°DV and OFI. We achieved a specificity
and sensitivity ≥89% in both the learning and testing sets. Even more impressive, this
level of specificity was achieved using only 5 MS peaks (m/z ranging from 4.4-
134.2kDa). Other recent serum protein profiling studies in infectious diseases using the
SELDI platform have required much larger numbers of MS peaks to achieve similar
accuracy. For example, an artificial neural network (ANN) developed by Poon et al. to
predict liver fibrosis in HBV patients used 30 peaks along with age, sex and an additional
12 routine clinical parameters in order to arrive at a predictive value of 90% (97).
Schwegler et al. also had to use a disconcerting number of peaks (n = 38) in HCV
infected patients to clearly differentiate between subjects with only HCV from those with
HCV and hepatocellular carcinoma (HCC). In this latter study, the decision tree
algorithm that was developed only achieved a sensitivity and specificity of 61 and 76%
respectively (105). Because we used samples collected in two distinct geographical areas
and from subjects over a wide age range to build our decision trees, we were able to
diminish the risk of over-fitting the data by keeping the model’s complexity to a
minimum. In this manner, we were able to ensure that our signature protein profile was
applicable to DV-infected patients worldwide. It is also worth emphasizing how much
more accurate our developed diagnostic algorithm is compared to available serological
testing. Mantke et al. initiated an external quality assurance (QA) program for
77
serological diagnosis in Germany. The participants reported concurrent and correct
results for 71% of the IgG-positive samples, 89% of the IgG-negative samples, 58% of
the IgM-positive samples, and 97% of the IgM-negative samples. Important
disagreement rates (range) were also found in the 18 participant laboratories and the
reference center in a quality control (QC) exercise for serologic diagnosis in the Americas
(38). High discrepancy rates have also been observed between laboratories in the nucleic
acid amplification techniques in Europe, while most hyperendemic Asian countries/areas
lack reports on QA programs (38).
In contrast, we were not able to define a proteomic signature of prognostic value
(i.e.: accuracy ≥70% for DHF) across geographic regions. Although potential prognostic
biomarkers were detected separately in each region, there was very little overlap between
the two sample sets studied. We qualified a peak as being a potential biomarker only if it
could differentiate between two groups (e.g. DV vs. OFI, DF vs. DHF) with a
significance of p-value ≤ 0.50 and 0.30 ≥ ROC-value ≥ 0.70. To be further considered for
our algorithm, the intensity ratio between the two groups being compared needed to be of
at least 2 and the same pattern (up- or down-regulated) in PR and Thai samples had to be
observed. Only four peaks that could distinguish between 2°DF and 2°DHF fulfilled
these requirements and were used in an attempt to establish a prognostic decision tree
algorithm. Putting aside the reproducibility challenges that some may reproach to the
SELDI technology, this paucity of prognostic biomarkers between the two countries
could be due to the fact that the age range of patients in both groups was not the same
(pediatric Thai samples vs. wide-range of age PR samples). This is however improbable.
Although the data is not shown, we did try to identify significant pattern differences
between different age ranges using the PR samples and obtained no conclusive results
suggesting that in this case age did not affect the analysis and that pooling the samples in
a same group was appropriate. Moreover, we established rigorous criteria in order to
qualify a peak as a candidate biomarker. Although the significance (p-value) is an
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obvious important criterion in any experiment, we emphasized on the difference in signal
intensity (ratio between the two groups being compared), silencing the critics that may
want to attribute the up- or down-regulation of a biomarker to mere reproducibility
artifacts (since the sample sets were not processed on the same day). To further insure
that the accuracy of our model was not due to over fitting our data by using the same
sample set for learning and testing purposes, we used two independent data sets (i.e. a
learning set from PR, and the testing set from Thailand). And so any differences that
might have been due to reproducibility issues became irrelevant since the same “artifacts”
would unlikely be carried from one sample set to another, processed on different
bioprocessors, on different days. This lack of common prognostic biomarkers between
the PR and Thai samples of DF and DHF patients suffering the same clinical symptoms
suggests that there may be external factors involved in the pathogenesis of the more
severe forms of disease (i.e. DHF). Such factors might include the genetic background of
the subjects studied, nutritional influences or exposure to other endemic diseases, among
others. Today still, there are no prognostic tests that allow one to determine who will
recover uneventfully and who will develop DHF/DSS. Our tentative prognostic
algorithm seems to be a good starting point to develop a more robust and accurate
prognostic test. By combining clinical data/symptoms to our current prognostic decision
tree, we might be able to increase both the sensitivity and specificity of the test without
increasing the amount of sample needed, the time required to obtain results, the amount of
manipulations required, or the cost. The use of proteomic signature and MS analysis for
routine diagnostic of infectious disease such as DV is likely to be small due to practical
and cost considerations, the endemic regions being often remote and/or impoverished.
On the other hand, there will most likely be an increase in the area of “basic” research
using this technology, leading to a greater understanding of infectious disease and further
advances in diagnostics development. In other words, the SELDI and MALDI platforms
will guide the scientist in his ‘top-down’ approach to discover novel biomarkers. To
profit from SELDI it is however imperative to identify the candidate biomarkers. Once
79
identification of key biomarkers is achieved and suitable reagents developed (e.g.
antibodies against the novel biomarkers), these SELDI-derived markers will combine the
great accuracy of SELDI to much needed practical aspects of multiplex immunoassays
(e.g. ELISA).
At the outset, we believed that the systematic study of serum biomarkers in well-
defined DV patients would not only enable us to develop novel diagnostic tools but would
also give us unique and unanticipated information regarding the nature of host-virus
interactions. Many other groups have also tried to identify serum biomarkers for DV
infection using a wide range of techniques. For example, Bozza et al. studied sera from
59 dengue patients using a 17-cytokine fluorescent bead array and found that MIP-β was
a good prognostic marker while IFN-γ was associated with disease severity (19).
However, like most such studies, these sera were collected at only one time point
(between 3 and 10 days after disease onset). Moreover, they did not formally assess
whether or not these markers had any predictive value on test and validation sample sets
but simply correlated the measured cytokine concentrations with dengue disease severity.
In fact, similar cytokine studies have been repeated many times in the past. As described
in Chapter 3, such data are often conflicting, rendering kinetic studies essential to
determine the time course of the expression of markers and to evaluate whether or not
these markers can be used to predict disease severity. Another group used 2-D gels
followed by MALDI-TOF/MS to detect and identify markers able to discriminate
between DF and DHF groups (116). Unfortunately, they identified only two proteins: α1-
antitrypsin (also identified by our group in the PR sample set) and dengue protein NS1,
both of which were 2-fold higher in DHF samples compared to DF ones. Highlighting
the impreciseness of these studies (where no validation steps are taken), Yoksan et el.
tested the use of dengue NS1 antigen as an early diagnosis during the febrile stage in
patients with DV infection. A total of 165 with DF and DHF were tested and although
80
the positive rates of patients with DF were higher than those with DHF, there was no
statistical difference found, rendering NS1 antigen a useless prognostic tool (29).
Keeping this in mind, we performed SDS-PAGE gels and sequenced the potential
biomarkers detected between DV and OFI groups. Various proteins were detected but the
following five proteins were identified in both the Thai and PR samples: α2-
macroglobulin, complement component C3, plasma protease (C1) inhibitor,
serotransferrin, and serum albumin. Since the hemorrhagic manifestations in the more
severe cases of DV remain the main problem when trying to treat patients and that the
cause of such phenomena remain unclear, we decided to focus our attention on Vn protein
due to its involvement in the coagulation system (among other roles) by stabilizing PAI-I.
Systemic inflammation (e.g.: upon DV infection) results in activation of coagulation due
to TF-mediated thrombin generation, down-regulation of physiological anticoagulant
mechanisms, and inhibition of fibrinolysis. It is characterized by widespread
intravascular fibrin deposition, which appears to be a result of enhanced fibrin formation
and impaired fibrin degradation. Enhanced fibrin formation is caused by TF-mediated
thrombin generation and simultaneously occurring depression of inhibitory mechanisms,
such as the protein C and S system. The impairment of endogenous thrombolysis is
mainly due to high circulating levels of PAI-1 (stabilized by Vn), the principal inhibitor
of plasminogen activation (79).
Our data suggest that there is almost a three-fold decrease in serum Vn
concentration in DHF patients at the time of hospitalization (t1) compared to DF
specimen, OFI, or healthy patients (p-value < 0.01). Previous studies have revealed an
increase of PAI in DHF patients compared to patients with uncomplicated DF (86).
Although our data cannot conclusively establish a causal relationship between these two
observations, the decrease in Vn we observed could plausibly explain why the body
produces more PAI. In other words, since Vn is not present to stabilize PAI it is degraded
more rapidly, causing the body to produce more of it. It would be of interest to determine
81
the kinetic curves of Vn and PAI in DV patient to determine if the concentrations of these
two molecules are inversely correlated in individual children and/or how quickly they
return to normal levels through the course of infection.
In fact, it is the precursor form of Vn that seems to be primarily affected by DV
infection as described in chapter 9. Moreover, the three detected isoforms of Vn are not
expressed in the same proportions in PR and Thai samples; the concentration of the
mature form seems to be generally lower in the PR samples compared to the Thai
samples. Additionally, there were subtle differences in Vn isoform levels between the two
study populations: in the PR samples, our data suggest that the maturation process is most
affected by DV infection, while the results from the Thai samples suggest that it is, in
fact, the synthesis process that is altered in DV infection. Despite these differences in
SELDI results, the total Vn levels in plasma samples measured by ELISA follows the
same pattern in both sample sets (DHF < DF, OFI) suggesting that maturation and
synthesis of Vn may compensate for one another. If the maturation process is stunted,
more protein is secreted/synthesized but when synthesis is affected, maturation of protein
already present is favored. To better understand the role (if any) of Vn in DV pathology,
it is imperative to clearly determine whether it is the synthesis or the maturation that is
altered during infection. To do so, the impact of DV infection on Vn production should
be characterized in an in vivo hepatocyte cell culture model using both the Asian and
American strains (liver being the main organ producing Vn). Other acute-phase proteins
produced by the liver may also behave in a fashion similar to Vn. Thus it may also be
useful to test whether or not the production/maturation of other glycosylated and/or acute-
phase liver proteins are inhibited in DHF. All four DV existing serotypes should be
tested since different strains are known to exhibit different virulence patterns (49). The
serotypes known to be more virulent (i.e. DV-2) may have a more noticeable impact on
liver proteins. Such in vitro experiments would likely be complicated by differing rates of
cell survival which means DV strains will need to be carefully chosen in order to
82
maximize infection while minimizing cell death. If these experiments show that synthesis
is blocked, subsequent experiments could determine how the virus manages to divert the
cellular machinery. On the other hand, if this approach shows that maturation is the
primary target of DV, one would need to determine if this effect is due to overloading of
the ER by DV proteins or if DV is controlling maturation in the hepatocytes in more
subtle ways.
Poorly understood physiopathology underlying the variable presentation of DV
infection remains the main bottleneck in developing more than just supportive treatments
for this infection. In identifying novel biomarkers, we have the opportunity to gain new
insights into the disease mechanisms as exemplified by our observations with the Vn
protein. It is therefore important to continue the discovery of novel biomarkers but also to
characterize them. Considering the different roles that a biomarker may have in a given
disease/symptom, it is critical to determine whether a given molecular marker is the cause
or instead, the consequence (direct or indirect) of the physiopathology. For example, Vn
would be considered to be a primary biomarker if it was involved in the “creation” of DV
infections symptoms such as plasma leakage, thrombocytopenia, etc. But if on the other
hand it is but a product of those molecules (e.g. endogenous proteases) causing the
disease, then it may be considered as a secondary biomarker. In other words, key
differential markers may also be peptide fragments of native proteins generated by the
action of endogenous proteases, themselves differentially expressed as a result of the
disease process (i.e. secondary markers). The distinction between biomarkers of an
infection and those of a disease process secondary to an infection is important, especially
when trying to understand the pathology of a given disease. Given the highly interactive
nature of proteins, candidate biomarkers that are most altered in an infection may not be
directly involved at the pathogen-host interface. Thus, biomarkers that are seemingly
unrelated to infection, but predictably change levels in response to infection can most
certainly be used as surrogate biomarkers.
83
Continuing the endeavor of identifying the most promising candidate biomarkers,
the next step in our research should be to identify the markers determined by our CART
analysis. Since only the molecular weights of the determined biomarkers are known,
MW filters could be used on the appropriate serum fraction followed by MS/MS or a
simpler, and often more accurate, on-line method such as LC/MS. Once the proteins are
identified, the model can be further validated using specific antibodies against these
markers. Guided by the cut-off intensity values obtained for each of the five peaks used
in our diagnostic algorithm, we could determine the key concentration of each of the five
biomarkers (e.g. using an ELISA) that would allow discriminating between patients with
DF from ones with OFI. Adapting the SELDI results to a more practical assay such as an
ELISA will ensure that this potential diagnostic test is suitable for DV endemic regions
while hopefully retaining the accuracy of the former technology.
In 1977, Kuberski et al. investigated clinical and laboratory measures in patients
with primary or secondary DV-1 infections with hemorrhagic manifestations in Fiji (70).
With the exception of virologic and serologic findings, no important differences between
these two groups were noted. Thirty years later, serological data (IgM/IgG ratio) is still
the method of choice - and only one available - to determine if a patient is suffering a
primary or secondary DV infection (32, 34, 123). Indeed, clinically, a primary DV
infection is indistinguishable from a secondary one. Yet knowing which patient has a
secondary infection would help the physician and healthcare personnel to focus their time
and resources on the patients most at risk of developing the more severe form of the
disease (i.e. DHF/DSS). Although measuring the antibody titers is the standard, many
investigators challenge its use in regions where two or more flaviviruses are cocirculating
because IgG antibodies measured are broadly flavivirus reactive. Another downside for
this test is that it requires paired sera samples and cannot give early diagnosis (87). Using
an IgM/IgG ratio overcomes the paired sera sample necessity but since IgM can persist
for more than 8 months and can be produced in a nonspecific manner, many controls are
84
required to render this test credible. Needless to say, a rapid and reliable test able to
recognize patients with a secondary DV infection at an early stage would be very
valuable, not only for the treating physician and staff but also for epidemiological
purposes. Although the focus of our study was not to develop such test, we did observe
significant differences between the proteomes of primary versus secondary DV infections
in Thai samples. When comparing 1°DF with 2°DF and 1°DHF with 2°DHF, 487 and
432 biomarkers were found, respectively, with a p-value ≤ 0.05 and 0.30 > ROC-value >
0.70. This suggests that the plasma proteome of patients with a 1° infection is markedly
different than that of a patient with a 2° infection and that perhaps the underlying disease
mechanism might differ in subtle ways. These results would need to be tested with
samples from other parts of the world and an algorithm could be developed in the same
way that we developed one for diagnostic purposes. These biomarkers should also be
tested for their specificity to DV infection to ensure that the proteomic differences
observed are due to DV rather than to other infectious agents (e.g. other flaviviruses
infections, OFI).
Finally, this research paves the way for a new biomarker discovery pipeline. We
demonstrated that SELDI can be used to generate algorithms that discriminate between
DV and OFI patients from two distinct regions of the world using a simple CART model.
Moreover, characterization of Vn protein’s serum levels and expression patterns show
significant differences between DV disease severities and OFI. What may seem as an
obvious shortcoming to our study is the fact that Vn protein was not determined by our
CART model to be a candidate biomarker. There are multiple explanations for this.
When purified Vn was analyzed using both SELDI and MALDI technologies, its three
isoforms were not detected at there theoretical MW (i.e. 54-, 65-, 75-kDa) but instead at
slightly lower masses (i.e. 49-, 59-, 66-kDa). Despite taking these findings into
consideration, Vn remains invisible to our CART analysis. This is most likely due to the
discrepancy between the Thai and PR samples as described by the Western blots. The
85
expression of all three Vn isoforms did not follow the same pattern across both countries,
making it impossible to determine a single cut-off intensity value for any of the isoforms
that would allow discriminating DV from OFI. In other words, although total Vn protein
may be a relevant biomarker (DHF, HCV<DF, OFI, healthy), its behavior at the
molecular level (i.e. all three isoforms) is not uniform across different regions of the
world. Samples from other countries should be assessed to determine if it is the genetic
background of the patients or the strain of DV that is most responsible for these
differences and how this links to the underlying disease mechanism of DV infection.
86
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APPENDIX A. Supplementary data
101
Table A1. Distribution across fractions of Thai unique biomarkers. DF vs. OFI DHF vs. OFI DV vs. OFI DF vs. DHF
F1CSL 90 69 87 10
F1CSH 62 51 59 1
F1ISL 34 21 35 5
F1ISH 61 50 60 0
F5CSL 34 29 33 4
F5CSH 41 28 42 1
F6CSL 29 33 109 2
F6CSH 92 67 88 3
F6ISH 63 50 60 10
Total: 506 398 573 36
Table A2. Distribution across fractions of PR unique biomarkers.
DF vs. OFI DHF vs. OFI DV vs. OFI DF vs. DHF
F1CSL 20 22 11 58
F1CSH 1 5 2 6
F1ISL 15 22 8 37
F1ISH 1 2 0 9
F5CSL 4 11 1 25
F5CSH 0 2 0 15
F6CSL 6 21 9 39
F6CSH 1 6 0 34
F6ISH 0 22 0 28
Total: 48 113 31 251
102
Table A3. Distribution across all fractions of common biomarkers between Thai and
PR samples but expressing a unique pattern (up- or down-regulated) in both
geographical areas.
DF vs. OFI DHF vs. OFI DV vs. OFI DF vs. DHF
F1CSL 15 12 10 0
F1CSH 0 8 4 1
F1ISL 0 4 2 0
F1ISH 0 6 3 0
F5CSL 0 2 0 0
F5CSH 1 5 1 0
F6CSL 1 2 0 0
F6CSH 1 15 2 0
F6ISH 0 6 1 2
Total: 18 60 23 3
Table A4. Distribution across all fractions of biomarkers that can distinguish between 1° and 2° infections (DF or DHF) in Thai samples.
1°DF vs. 2°DF 1°DHF vs. 2°DHF
F1CSL 118 47
F1CSH 65 65
F1ISL 41 35
F1ISH 54 53
F5CSL 19 25
F5CSH 33 36
F6CSL 25 27
F6CSH 82 92
F6ISH 50 52
Total: 487 432
103
Table A5. Proteins identified from PR samples 4-12% Bis-Tris NuPAGE gel
analysis.
Band Protein matched Predicted MW (Da)
Calculated pI value
Mascot score
Sequence coverage
(%)
# Queries matched
1a plasma protease (C1) inhibitor precursor
55375 6.09 165 8 3
1b serum albumin 46442 5.77 396 16 10 1c serum albumin 46442 5.77 246 9 5 1d apolipoprotein J precursor 49342 6.27 130 17 7
alpha-1-antitrypsin 46848 5.43 125 16 11 2a alpha-1-antitrypsin 46787 5.42 280 18 7 angiotensinogen 53407 5.78 263 8 6 vitronectin precursor 55165 5.61 187 8 3 Ig alpha-1 chain C region 38486 6.08 136 13 4
2b alpha-1-antitrypsin 44356 5.43 535 33 27 2c apolipoprotein J precursor 49342 6.27 417 21 9 complement component C3 188585 6.02 409 26 6
2d proapolipoprotein 28944 5.45 538 30 14 Ig L-chain V-region 23020 6.69 201 25 6
2e proapolipoprotein 28944 5.45 540 30 10 2f proapolipoprotein 28944 5.45 831 46 32 3a alpha-2-macroglobulin
precursor 164600 6.00 657 12 13
3b alpha-2-macroglobulin precursor
164600 6.00 661 11 12
3c fibronectin precursor 246196 5.48 934 13 22 3d alpha-2-macroglobulin
precursor 164600 6.00 760 13 17
complement component C3 188585 6.02 168 3 5 3e fibronectin precursor 246196 5.48 482 5 8 3f serum albumin 46442 5.77 241 9 5 3g macroglobulin apha2 162072 5.95 1034 18 26
serotreansferrin precursor 79280 6.81 122 8 5 3h apolipoprotein E 36302 5.65 507 35 13
complement component C3 144417 8.24 223 4 6 4a Ig lambda heavy chain 53379 8.74 245 17 5 4b n/a 4c Ig lambda heavy chain 53379 8.74 364 20 13 4d chain A complement
component 3 71317 6.82 976 37 39
4e Ig lambda heavy chain 53379 8.74 412 22 13
104
5a alpha-2-macroglobulin precursor
164600 6.00 268 6 6
5b n/a 5c serum albumin 46442 5.77 347 16 7 5d chain A complement
component 3 71317 6.82 874 28 20
Table A6. Proteins identified from Thai samples 4-12% Bis-Tris NuPAGE gel
analysis.
Band Protein matched Predicted MW (Da)
Calculated pI value
Mascot score
Sequence coverage
(%)
# Queries matched
2 n/a 3 alpha-2-macroglobulin
precursor 164614 6 390 4 7
4 Plasma protease C1 inhibitor precursor
55347 6.09 311 11 6
carboxypeptidase N subunit 2 precursor
61431 5.63 189 6 4
5 carboxypeptidase N subunit 2 precursor
61431 5.63 220 9 5
Serotransferrin precursor 79280 6.81 99 3 3 lumican precursor 38747 6.16 60 5 2
6 Serum albumin precursor 71317 5.92 223 6 4 lumican precursor 38747 6.16 130 7 3
7 Serum albumin precursor 71317 5.92 1236 32 30 lumican precursor 38747 6.16 92 5 2 hemopexin precursor 52385 6.55 73 4 2
8 Apolipoprotein D precursor 21547 5.06 229 28 6 9 Apolipoprotein D precursor 21547 5.06 161 20 5
10 n/a 11 n/a 12 n/a 13 n/a 14 Serum albumin precursor 71317 5.92 257 7 5
Ceruloplasmin precursor 122983 5.44 228 4 4 15 Ceruloplasmin precursor 122983 5.44 529 12 12
Serum albumin precursor 71317 5.92 396 12 9 Apolipoprotein A-I precursor 30759 5.56 112 12 3 Plasma protease C1 inhibitor
precursor 55347 6.09 65 4 2
16 Prothrombin precursor 71475 5.64 611 22 11
105
lumican precursor 38747 6.16 225 14 5 insulin-like growth factor-
binding protein complex acid labile
66735 6.33 195 5 4
alpha-1B-glycoprotein precursor
54809 5.58 187 11 5
Afamin precursor 70963 5.64 154 10 6 Haptoglobin-related protein
precursor 39496 n/a 99 n/a 2
Ig mu chain C region 50210 n/a 74 n/a 2 17 Prothrombin precursor 71475 5.64 1187 42 27
insulin-like growth factor-binding protein complex acid labile
66735 6.33 299 9 5
Afamin precursor 70963 5.64 219 9 7 Serum albumin precursor 71317 5.92 203 9 6 lumican precursor 38747 6.16 184 11 4 Vitamin K-dependent protein S
precursor 77127 5.48 184 5 4
Apolipoprotein A-I precursor 30759 5.48 139 8 2 alpha-1B-glycoprotein
precursor 54809 5.58 123 5 3
Hemopexin precursor 52385 52385 95 4 2 Vitronectin precursor 55069 5.55 92 3 2
18 Apolipoprotein A-I precursor 30759 5.48 213 19 5 Haptoglobin-related protein
precursor 39496 6.63 117 3 2
19 Haptoglobin-related protein precursor
39496 6.63 137 3 2
Serum albumin precursor 71317 5.92 136 4 3 Apolipoprotein A-I precursor 30759 5.48 127 11 4
20 Serum albumin precursor 71317 5.92 719 26 16 Ig gamma-1 chain C region 36596 36596 359 32 8 Mannose-binding protein C
precursor 26526 5.39 291 26 6
Complement C4-A precursor 194247 6.65 277 4 8 Prothrombin precursor 71475 5.64 154 5 2
21 Serum albumin precursor 71317 5.92 715 23 17 Mannose-binding protein C
precursor 26526 5.39 292 26 6
Ig gamma-1 chain C region 36596 8.46 200 16 5 22 Serum albumin precursor 71317 5.92 729 28 16
Complement C3 precursor 188569 6.02 639 10 11 Complement C4-A precursor 194247 6.65 515 6 10 Ig gamma-1 chain C region 36596 8.46 355 29 9 Serotransferrin precursor 79280 6.81 207 8 5
106
Ig gamma-2 chain C region 36489 7.66 135 11 4 23 Fibrinogen alpha chain
precursor 95656 5.7 818 24 25
Complement C3 precursor 188569 6.02 438 8 8 Ig gamma-1 chain C region 36596 8.46 418 31 8 Serum albumin precursor 71317 5.92 346 12 8
24 Ig gamma-1 chain C region 36596 8.46 347 26 8 Serum albumin precursor 71317 5.92 206 6 4 Complement C1q
subcomponent subunit B precursor
26670 8.83 139 14 3
Serotransferrin precursor 79280 6.81 120 4 3 Ig gamma-2 chain C region 36489 7.66 111 8 4
25 Ig gamma-1 chain C region 36596 8.46 426 32 10 Complement C4-A precursor 194247 6.65 319 5 8 Fibrinogen alpha chain
precursor 95656 5.7 314 10 9
Complement C3 precursor 188569 6.02 268 3 6 Complement C1q
subcomponent subunit B precursor
26670 8.83 216 19 5
Ig gamma-2 chain C region 36489 7.66 146 11 5 Serum albumin precursor 71317 5.92 102 3 2 Ig gamma-4 chain C region 36431 7.18 94 13 3 Serotransferrin precursor 79280 6.81 76 4 3
26 Serum albumin precursor 71317 5.92 228 8 6 Hemoglobin subunit alpha 15305 8.72 221 25 4 Haptoglobin-related protein
precursor 39496 6.63 127 3 2
Ig gamma-1 chain C region 36596 8.46 98 6 2 27 Ig gamma-1 chain C region 36596 8.46 136 10 4
Serum albumin precursor 71317 5.92 96 3 2 28 n/a 29 n/a 30 Ig gamma-1 chain C region 36596 8.46 110 8 3 31 Ig gamma-1 chain C region 36596 8.46 47 3 2 32 Ig gamma-1 chain C region 36596 8.46 268 19 9
Complement C3 precursor 188569 6.02 266 4 5 Ig gamma-2 chain C region 36489 7.66 167 12 6
33 Ig gamma-1 chain C region 36596 8.46 103 6 4 34 n/a 35 n/a
107
Table A7. Total Vn levels in Puerto Rican samples in percentage (%) of normal
plasma. Blue values indicate values that were below the detection level and were
converted to zero values.
DF DHF DV(-) 83.27624 17.16418 30.7881 61.0038 -48.88852 20.04128 -18.585 95.12544 77.82492 99.3186 -35.2334 56.79852 -18.585 -39.52048 47.6092
82.34172 50.98444 11.16346 60.69232 19.38711 17.082 19.26252 54.16004 6.023676 68.32412 259.1458 37.32965 105.3929 -35.39219 61.62684 33.43587 112.115 93.08856 123.1485 -35.39219 77.51344 65.83212 -9.510952 89.66204 113.1804 -29.04096 -24.81505 110.9999 49.23788 78.60368 112.5574 -49.84124 86.39124 11.31922 36.37663 75.0214 162.4484 5.09686 5.24492 46.85616 -22.68974 6.179424 52.09592 91.15592 25.33681 104.0172 -42.85488 -20.45401 93.06128 26.37345 -29.33183 27.00858 -51.11148 70.50464 38.59956 -42.85488 -1.296627 100.0476 -27.9295 61.31532 -1.413145 -51.27024 68.0126 61.1464 -45.2366 82.2642 -23.64242 74.1664 -33.80438
67.97396 -48.09464
108
Figure A1. SELDI-TOF MS analysis of Vn IP from OFI Thai non-depleted plasma
samples. Immunoprecipitation of Vn was performed on OFI Thai plasma samples. A
peak at 49-, 73-, and 146-kDa was detected by SELDI-TOF MS in the elution fraction
corresponding to Vn precursor protein, antibodies doubly charged and antibodies singly
charged respectively. A, pre-treated OFI non-depleted plasma sample; B, elution fraction;
C, supernatant fraction.