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Keywords:

  • C. albicans;
  • HLA-DRB1 restriction;
  • secretory aspartyl proteinase;
  • T cell epitope prediction

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Sap2 is the most abundant virulence factor expressed during Candida infection, and the principal protein known to induce antibody response during Candida infection in humans. Its role in T-cell activation however, has not yet been determined. Sequence analysis revealed that Sap2 contains two variable regions: Var1 and Var2. Computational predictions by the Hotspot Hunter program identified that Var1 contains three candidate T-cell epitopes, whereas Var2 contains four. Thirty-nine overlapping peptides of Sap2 were then synthesized, and tested for their ability to induce proliferation of PBMC from 12 donors. Peptides P11, P17 and P31 exhibited significantly higher proliferative indices when compared with those of other peptides or controls. P17 and P31 are located in the areas of prediction, while P11 is not. There were other peptides outside the prediction areas that could stimulate PBMC proliferation at low levels. Nevertheless, the proliferative noise caused by such peptides was ruled out by IL-2 ELISpot analysis. Only P17 and P31 were shown to induce clonal proliferation of IFN-γ producing lymphocytes, suggesting that these two peptides contain T cell epitopes. P11, which stimulated IL-2 producing clones, contains a known B-cell epitope. Interestingly, P17 and P31 elicited both Th1 and Th2 cell responses with significant numbers of IL-13 secreting clones in response to stimulation. Taken together, the computer-based T cell epitope prediction method could identify the immunogenic T cell epitopes of C. albicans Sap2 that promiscuously bind to the HLA-DRB1 supertype.

List of Abbreviations: 
Ab

antibody

ANN

artificial neural network

BCIP/NBT

5-bromo-4-chloro-3′-indoylphosphate p-toluidine salt/nitro blue tetrazolium chloride

C. albicans

Candida albicans

CandiVF database

C. albicans virulence factor database

DMSO

dimethyl sulfoxide

ELISpot

enzyme-linked immunospot

fmoc

9-fluorenylmethoxycarbonyl

HLA

human leukocyte antigen

HMM

hidden Markov model

IFN-γ

interferon gamma

IL-13

interleukin 13

IL-2

interleukin 2

PBMC

peripheral blood mononuclear cells

PBS

phosphate buffered solution

PCR-SSP

polymerase chain reaction with specific sequence primers

PHA

phytohemagglutinin

PI

proliferative index

PVDF

polyvinylidene difluoride

Sap2

secretory aspartyl proteinase 2

SSS

successive state splitting

Th

T-helper cell

Var1

variant 1

Var2

variant 2

C. albicans is an opportunistic dimorphic fungus responsible for serious fungal infections in both immunocompetent and immunocompromised individuals (1). This fungus is normally present as a commensal saprophyte residing on human mucosal surfaces such as those of the mouth, gut, and vagina (2, 3). This endogenous host-fungal relationship is responsible for a higher infection rate with C. albicans than with other medically important fungi (4). Several factors predispose to C. albicans infection, including age, sex, immune status, and microflora density (3). Candida infection commonly occurs in the newborn, especially in premature babies where oral and systemic candidiasis cause high morbidity and mortality rates. It has been estimated that more than 75% of women are susceptible to Candida vulvovaginitis (5). In addition, patients with long-term catheterization, and those who have undergone organ transplantation or brain surgery, are also threatened by drug-resistant candidal biofilm (6). Candidiasis also occurs with increased frequency in people with AIDS (7, 8). Oral and vaginal infections are the most common form the disease takes in HIV patients (1, 9). In severe cases, the fungus invades deep tissue, enters the blood stream, and spreads to all tissues in the host's body causing systemic candidiasis (3).

Sap2 is one of the common antigens found in the vast majority of C. albicans strains. People with candidiasis have high titers of antibodies to aspartyl proteinases, especially Sap2, with soluble antigens present in their serum (10). Mice immunized with C. albicans extracts enriched with Sap2 have shown a decrease in mucosal tissue fungal burden (10). Intradermal administration of purified Sap2 with alum to BALB/c mice which have been infected intraperitoneally with C. albicans has resulted in a 20-fold decrease in kidney colonization (11). Hence, this protein is considered to be a candidate for a vaccine against systemic candidiasis. Several B cell epitopes of Sap2 have been characterized (12); however, its role in T-cell activation has not yet been clarified.

Identification of specific antigenic determinants recognized by lymphocytes, B and T cells is important for understanding immune responses against infectious diseases, autoimmune diseases, and cancer. Conventionally, characterization of antigenic epitopes is achieved by isolating molecules to which lymphocytes react strongly. For the T-cell epitope, the characterization process includes synthesis of a set of overlapping peptides, then performing experiments to validate their role in T cell activation. This type of conventional method is costly and time consuming (13). Thus, computational prediction is used for pre-screening of B- and T-cell epitopes, thereby minimizing the number of necessary experiments (14). The combination of bioinformatics and conventional immunological experimentation is known as immunomics (15). TEPITOPE (16) and MULTIPRED (17) are examples of algorithms developed for predicting peptide binding to multiple HLA-DR molecules (17). Immunomics assisted by those algorithms have been employed for the analysis of microbial antigens to provide insights into the host-pathogen interaction and new avenues for vaccine development (6, 18–22).

Even though epitope informatics has been widely used in the study of viruses, bacteria, parasites, and tumors (23), the approach has not been extensively used for studying fungal pathogens (24). In this study, we employed immunomics to identify promiscuous T-cell epitopes in C. albicans Sap2. Conventional immunological methods were subsequently performed to assess the ability of the peptides to stimulate T cell proliferation and cytokine production. The novel immunogenic peptides of C. albicans Sap2 thus characterized may assist future design and development of vaccines and diagnostic tools.

MATERIALS AND METHODS

  1. Top of page
  2. ABSTRACT
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Prediction of promiscuous Class II HLA-DRB1 binding peptides

Amino acid sequences of Sap2 were collected from the CandiVF database (25). There were 11 Sap2 entries annotated in this database, namely D000001–D000008, D000023, D000033, and D000034. Nine entries (D000001–D000008 and D000023) have full-length peptide sequences while the other two sequences (D000033 and D000034) are fragments. Sap2 sequence analysis revealed two main sequence variants that we termed Var1 (D000001-D000005, D000008, D000023, D000033 and D000034) and Var2 (D000006 and D000007). These two variants differ mainly in amino acid composition at the C-terminal positions 214–236 and 370–396 (Fig. 1). Promiscuous CD4+ restricted T-cell epitopes were predicted by the Hotspot Hunter program through the CandiVF website (http://antigen.i2r.a-star.edu.sg/Templar/DB/CandiVF/) (25). The program identified HLA-DRB1 peptides predicted to be highly promiscuous which bind to eight supertypes of HLA-DRB1, namely 0101, 0301, 0401, 0701, 0801, 1101, 1301, and 1501 (17). Sequences of Sap2 Var1 and Var2 were submitted to the Hotspot Hunter program for prediction with the threshold for binding prediction set at 75. Peptides with binding scores of 75 of higher were predicted as HLA-DR binders, whereas other peptides were predicted as non-binders. From the prediction of HLA-DRB1 binding peptides, there were three T cell epitopes in Var1 (MFLKNIFIGLAIALLVDATPTT, LSGDVVFNFSKNAKISVPA and ILGDNFLRSAYIVYD) and four T cell epitopes in Var2 (MFLKNIFIGLAIALLVDATPTT, NAYSLILILQMLPRDKSFSVGLIMLNIVGSLIALPVTSDRE, LSGDVVFNFSKNAKISVPA and NFLRSLILFMIWMIMKFLWLKSNILLFQY). Among these epitopes, only one peptide was found in both variables (LSGDVVFNFSKNAKISVPA).

image

Figure 1. Multiple sequences of Sap2 protein collected from CandiVF database. Amino acid sequences in bold represent the predicted T cell epitopes.

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Overlapping peptides

Because Var 1 of Sap2 protein is most commonly found during infection, synthetic peptides were ordered based on the full-length sequence of Var1 (D000001), as well as the Var2 209–249 (D000006), which had a prediction score of 81. Short peptides of 20mer in length, overlapping by 10 amino acids, were synthesized from the Sap2 mature protein sequence (lacking the signal sequence with 56 amino acids). Synthesis of Sap2 overlapping peptides was performed by Chiron Mimotope (Melbourne, Australia). Synthesis relied on the solid phase peptide synthesis fmoc strategy with purity of ≥90%. Of the 40 overlapping peptides synthesized (P1–P40), 36 peptides covered the full-length mature sequence of Var1 (57–398) while four peptides (P17, P19, P21 and P23) covered one predicted epitope of Var2 (209–249). Thirty-nine peptides were synthesized, while one P3 peptide was not successfully recovered from the synthesis due to the complexity of the sequence physical properties. Nineteen peptides had purity ≥90%, while the rest had a purity ≥80%. Each synthetic peptide was reconstituted in concentrated DMSO as a stock solution at 1 mg/ml (Sigma-Aldrich, St. Louis, MO, USA) and stored at −20°C.

Specimen collection

Fifty milliliters of peripheral blood was obtained by venipuncture from 12 healthy volunteers according to the guidelines of the Ethical Clearance Committee on Human Rights related to research involving humans of the Faculty of Medicine, Ramathibodi Hospital, Mahidol University (Protocol Number ID 05-48-29), Thailand. Informed consent was obtained from all participants before they joined this study. Information on gender and age of donors is provided in Table 1.

Table 1. Information on donors and types of HLA-DRB1
DonorGenderAgeHLA-Allele 1HLA-Allele 2
D1Male28DRB1*12DRB1*13
D2Female26DRB1*04DRB1*15
D3Male27DRB1*03DRB1*08
D4Female29DRB1*04DRB1*15
D5Female35DRB1*04DRB1*0701
D6Female29DRB1*03DRB1*15
D7Male47DRB1*03DRB1*12
D8Male39DRB1*12DRB1*12
D9Female28DRB1*04DRB1*14
D10Female33DRB1*04DRB1*04
D11Male24DRB1*04DRB1*0701
D12Male42DRB1*03DRB1*15

Genomic DNA extraction of peripheral blood leukocytes and HLA typing

Extraction of genomic DNA from peripheral blood leukocytes was performed as described elsewhere (26). Genomic DNA was precipitated and resuspended in distilled water and stored at −20°C. DNA concentration was determined by UV spectrophotometry (OD260/OD280) and run through agarose gel for quality checking. HLA-typing was done by PCR-SSP as previously described (27).

Isolation of PBMC

Human PBMC were isolated by density gradient centrifugation. Thirty milliliters of peripheral blood were mixed with equal volume of 1× PBS. The diluted blood (30 ml) was then carefully overlayered on the top of 15 ml Ficoll-hypaque (Isoprep, Nycomed Pharma, Oslo, Norway) in a non-pyrogenic conical tube (CORNING, Corning, NY, USA). After separation, PBMC were transferred into a new pyrogen free conical tube and washed three times with 1× PBS and collected by centrifugation. Contaminated red blood cells were osmotically lysed by adding sterile distilled water. When washing was complete, the PBMC pellet was resuspended in complete RPMI-1640 (GIBCO/BRL, Grand Island, NY, USA). Cell numbers were determined using the Bright-Line Improved Newbauer Haemacytometer (American Optical, Buffalo, NY, USA) and the concentration of the cells was adjusted to 2 × 106 cells/ml.

PBMC proliferation assay

PBMC at 2 × 105 cells were seeded in individual wells of a 96-well plate. Sap2 synthetic peptides at a final concentration of 10 μg/ml were used to stimulate the PBMC. PBMC stimulated with 5 μg/ml PHA (Sigma-Aldrich) served as positive controls while PBMC cultured in complete RPMI-1640 alone served as negative controls. Cultures were incubated at 37°C in 5% CO2 for 24 hours. At the end of the incubation period, proliferation of PBMC was determined by enumeration of proliferative colonies under an inverted microscope (CK2, Olympus, Tokyo, Japan). Proliferative colonies were determined for five microscopic fields under a 20× magnifying objective lens. The proliferative index was determined using the following equations:

  • image([1])

In which: N= Number of proliferative colonies

Xi= Summation of proliferative colonies from five microscopic fields under a 20× magnifying objective lens (colonies/mm2)

Y= 32.2 mm2 (surface area of bottom of well)

Proliferative response expressed as PI:

  • image([2])

In which: Ns = Number of proliferative colonies in the presence of stimuli

Nr = Number of proliferative colonies in RPMI medium alone

ELISpot assay

Proliferation of IL-2, IFN-γ and IL-13 producing clones in response to Sap2 synthetic peptides was analyzed using ELISpot human IL-2, IFN-γ and IL-13 kits (R&D systems, Minneapolis, MN, USA) following the manufacturer's instructions. In brief, PVDF membrane in each well was pre-wet and 2 × 105 cells immediately added. For the PHA control, the number of PBMC used in the culture was 2 × 104. Subsequently, Sap2 synthetic peptide was added into each assigned well at a final concentration of 10 μg/ml. The culture was incubated in humidified 5% CO2 at 37°C for 18 hours. After washing four times using a semi-autowasher (Nunclonâ, Nalge Nunc International, Rochester, NY, USA), 100 μl of diluted detection antibody were added to each well and the reaction allowed to continue overnight. Excess antibody was then removed, alkaline phosphatase-conjugated streptavidin added and the plate incubated for 2 hr at room temperature. Color development due to the addition of BCIP/NBT chromogen was carried out at room temperature in a dark chamber. The plate was finally rinsed with deionized water and completely dried at room temperature. The number of spots was assessed by automated KS ELISpot reader model ZEISS Stemi 2000-C (Carl Zeiss, Jena, Germany). Two duplicate experiments were performed for each peptide.

Statistical analysis

SPSS statistical software package Version 9.0 (SPSS, 1998) was used for data analysis of peptide stimulation. Paired T-test was used to determine the significance of differences between groups. P values of ≤ 0.05 were considered to be statistically significant.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

HLA typing and PBMC proliferation assay

Since the Hotspot Hunter program predicted the promiscuity of peptides that bind to eight multiple HLA-DRB1 supertypes, blood samples of individuals bearing HLA backgrounds of these eight supertypes were collected for immunogenicity validation. In this study, the identification of HLA genotype focused on HLA supertypes rather than subtypes. The 12 volunteers had eight types of HLA-DRB1 identified by HLA-typing, namely HLA-DRB1*03, 04, 07, 08, 12, 13, 14 and 15. Donors expressing HLA-DRB1*04/15 (D2), HLA-DRB1*03/08 (D3), HLA-DRB1*04/15 (D4), HLA-DRB1*04/0701 (D5), HLA-DRB1*03/15 (D6), HLA-DRB1*04/04 (D10), HLA-DRB1*04/0701 (D11) and HLA-DRB1*03/15 (D12) were subjects who had two alleles of HLA-DRB1 match up with the Hotspot Hunter HLA-DRB1 predicted supertypes. Those expressing only a single allele of HLA-DRB1 that matched with Hotspot Hunter HLA-DRB1 predictions were D1 (HLA-DRB1*12/13), D7 (HLA-DRB1*03/12) and D9 (HLA-DRB1*04/14). There was only one blood donor, D8 (HLA-DRB1*12/*12), whose HLA-DRB1 did not match any of the Hotspot Hunter HLA-DRB1 supertypes.

The degree of peptide stimulation was expressed as PI. After stimulation of PBMC with PHA, the cells started to proliferate and colonies were observable after 6–8 hours. For the RPMI control, an insignificant level of spontaneous cell proliferation could be seen. During the same period, PBMC stimulated with peptides demonstrated variable levels of cell proliferation. After 16 hours of incubation, peptides P11 (YKDTVGFGGVSIKNQVLADV), P17 (KKQGVIAKNAYSLILILQMS), and P31 (DCNLSGDVVFNFSKNAKISV) showed strong induction of PBMC proliferation as judged by colony size and the number of proliferative colonies.

Peptides from two regions of Sap2, namely Sap2 145–184 (P17-P21) and 255–342 (P31-P40), induced strong PBMC proliferation. Different proliferative profiles were observed when Sap2 synthetic peptides were used to stimulate PBMC of blood donors with two HLA-DRB1 genes allele-matched to those covered by Hotspot Hunter. PBMC from the majority of donors displayed strong responses to peptides P11, P17 and P31. Only D2 and D3 showed no response to either P11 or P17. Some donors, namely, D4 (DRB1*04/15), D6 (DRB1*03/15), D10 (DRB1*04/04), D11 (DRB1*04/0701), and D12 (DRB1*03/15) showed responses to other peptides located at the carboxyl terminal of the protein, including P35, P36 and P39 (Fig. 2).

image

Figure 2. Degree of PBMC proliferation of 12 donors with two allele-matched [(a) D10: HLA-DRB1*04/04, (b) D5: HLA-DRB1*04/0701, (c) D11: HLA-DRB1*04/0701, (d) D3: HLA-DRB1*03/08, (e) D4: HLA-DRB1*04/15, (f) D2: HLA-DRB1*04/15, (g) D6: HLA-DRB1*03/15 and (h) D12: HLA-DRB1*03/15], one allele-matched donors [(i) D7: HLA-DRB1*03/12, (j) D9: HLA-DRB1*04/14 and (k) D1: HLA-DRB1*12/13] and unmatched donor [(l) D8: HLA-DRB1*12/12]. Values are means ± S.D. of three determinations. *Significantly different from controls (P < 0.05).

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PBMC from donors with single allele-matches also showed strong proliferative responses to P11, P17 and P31 as seen in both D7 (DRB1*03/12) and D9 (DRB1*04/14) (Fig. 2). However, P17 did not induce a significant level of stimulation in D1 (DRB1*12/13). It is important to note that D9 had a history of vulvovaginitis, and the proliferative profile of her PBMC was quite different from those of the other donors. In this case, the peptides that conferred significant levels of stimulation were located at the N-terminal region of Sap2. P11, P17 and P31 also induced significant proliferative profiles in individual D8 (DRB1*12/12), who had no matched HLA allele. However, in contrast to most donors with allele-matches, peptides at the C-terminal of Sap2 also stimulated proliferation (Fig. 2).

IL-2, IFN-γ and IL-13 ELISpot assays

With PBMC stimulation by peptides, clonal proliferation can arise from both T and B cells. To confirm that the peptide stimulation was specific to CD4+ T cell response, ELISpot assay was used to detect the clones producing IL-2 and IFN-γ. Peptides P11, P17, P31 and their overlapping neighbors (P10, P12, P16, P18, P30, and P32) were selected for experimental verification. Two volunteers with two HLA-DRB1 alleles which matched up with predictions (D4 and D6), a volunteer with a single allele-match who had experienced chronic Candida vulvovaginitis (D9), and another without any matching allele (D8) were selected for the assessment. Donor D4 showed the highest number of IL-2 producing cells in response to P11, P17, and P31 while D6, D9, and D8 showed response to only P17 and P31 (Fig. 3a). In summary, all donors showed strong responses to P17 and P31. Other peptides which had demonstrated an ability to induce significant PBMC proliferation earlier showed insignificant numbers of IL-2 producing clones.

image

Figure 3. The numbers of IL-2 (a) and IFN-γ (b) producing clones in response to Sap2 synthetic peptides. PBMC from four representative donors D4: HLA-DRB1*04/15, D6: HLA-DRB1*03/15, D9: HLA-DRB1*04/14 (person with history of vulvovaginitis) and D8: HLA-DRB1* 12/12 were assessed for the ability to respond to peptide stimulation by ELISpot assay. The results are derived from two independent determinations. *Significantly different from controls (P < 0.05).

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P17 and P31 activated proliferation of PBMC producing IFN-γ in every volunteer tested. Interestingly, P11 and their neighbor peptides P10 and P12 did not induce proliferation of IFN-γ producing colonies (Fig. 3b). Collectively, the results suggest that P17 and P31 contain T-cell epitopes while P11 carries a B-cell epitope of Sap2 protein.

Since Sap2 has been reported to induce IgE-mediated allergic reactions in atopic individuals (12), we further assessed IL-13 production of PBMC in response to P17 and P31. Fig. 4 shows that these peptides could induce IL-13 production, and that the number of IL-13 producing clones from P31 stimulation was significant higher than from P17 stimulation. We also found that donors who had previously been exposed to C. albicans, or those with high titers of anti-candida antibody displayed a similar profile, having high numbers of proliferative IL-13 producing cells in response to P17 and 31. These donors include D6 (anti-candidal Ab titer = 15 100), D9 (anti-candidal Ab titer = 25 000) and D8 (anti-candidal Ab titer = 7500). With regard to P11 and the other peptides of the flanking sequences, IL-13 production occurred only at background concentrations.

image

Figure 4. Comparison between the numbers of IL-13 and IFN-γ producing clones stimulated by P17 and P31 of 4 representative donors. The results are derived from two independent determinations.

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DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Antigenic prediction of C. albicans Sap2

In this study, in silico experiments (dry lab) were performed to stimulate Class II HLA antigen presentation of C. albicans virulent antigens. Unlike viruses, C. albicans is an extracellular pathogen. Thus, after the fungal antigen has been processed, the digested peptides would be presented in the context of Class II HLA molecules to CD4+ T cells (28). Hence, the Hotspot Hunter program, which is a suitable prediction tool for Class II HLA-DRB1, was chosen as a computational method and attached to CandiVF (25). The program uses an ANN method and HMM as predictive engines for identifying antigenic clusters of peptides that are able to fit into the groove of Class II HLA molecules. ANN has been used for the prediction of peptides that bind to both multiple Class I and Class II HLA molecules, with a sensitivity and specificity close to 80% (29).

HMM is a novel predictive engine for T cell epitope prediction (30). The use of HMM in the prediction of peptide antigenicity has been demonstrated in Class I HLA-A2, but not in Class II HLA. The model gave a high accuracy of prediction (30). Notably, difficulty in Class II HLA epitope prediction is due to the length of peptides presented in the groove of HLA, which are approximately 11–30 residues. Therefore, the predictions were combined and selected as longer peptides containing a cluster of predicted peptides. Noguchi et al. were the first group to combine HMM with the SSS algorithm for optimization of HMM structure, and use this for the prediction of peptides that bind to Class II HLA-DRB1*0101 (30). They have demonstrated that S-HMM prediction accuracy is comparable to fully connected HMM and ANN methods.

Immunogenicity of predicted peptide epitopes

Validation of epitope prediction demonstrated that stimulation of PBMC by P11, P17 and P31 consistently led to the strongest proliferative indices and the highest number of IL-2 producing clones. Sequence comparison between these three peptides using in silico prediction results revealed that 50% of the amino acid sequence at C-terminal of P17, and 85% of the amino acid sequence at C-terminal of P31, were within the predicted areas. P11, on the contrary, lay outside the predicted region. Since there are mixtures of macrophages, B and T cells in PBMC, and these cells can interact with one another resulting in production of IL-2 by T cells and macrophages, the ELISpot assay for IFN-γ was performed to identify only T cell proliferative clones. The IFN-γ assay suggested that P17 and P31 were T-cell epitopes, because insignificant numbers of IFN-γ producing colonies were detected in P11 stimulated samples. When the P11 sequence was compared to previously published results (12), the peptide contained a B-cell epitope in the middle of the sequence (Fig. 5). Only a truncated form of B-cell epitope is present in the flanking P10 and P12 overlapping peptides, which show a lower degree of proliferative responses upon stimulation. It has previously been found that T and B cells recognize the same or very closely overlapping sites on a protein (31–33). Such a relationship between T helper specificity and B cell specificity for the same protein antigen can be explained by the T-B reciprocity hypothesis (34). These relationships play a possible role in regulating both arms of immune responses.

image

Figure 5. Sequence of synthetic peptides, P17 and P31 (upper panel) in comparison to the predicted Sap2 T cell epitope (highlighted). Lower panel displays Sap2 B-cell epitope (highlighted) lays within the sequence of P10–12.

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It is important to note that even though IL-4 is the important cytokine in the regulation of IgE synthesis, our analysis of IL-4 levels in in vitro conditions showed no significant correlation with Th1 or Th2 response. Unlike IL-13, there was an abnormal balance of IL-4/IFN-γ production in the tested samples. This may be due to the fact that IL-13 expresses for longer periods than IL-4, as has previously been stated by Katagari et al. (35).

A three dimensional structure analysis of Sap2 showed that P31 lies on the surface of the Sap2 molecule while P17 is less accessible to external molecules (Fig. 6). Indeed, a general perception of B cell binding sites is that they tend to be exposed or protrude out of the molecule, or be assembled as topographic antigenic sites. In contrast, T cells specific for processed antigens are limited to recognizing short segments of continuous structures thereby limiting to primary and secondary structures. The structural analysis showed that P17 and P31 possess β-sheets at the central part of the peptides. The β-sheets carry hydrophobic amino acids whose side chains are pointed towards the inside of the Sap2 molecule (Fig. 6). P17 has one β-sheet which contains -AYSL-(β154-β157) (Fig. 6) while P31 shows two β-sheets with -DVVFNFS-(β261-β267) and -AKIS-(β270-β273) sequences. These β-sheets are enriched with the hydrophobic amino acids Y, L, V, F and I that are common anchor residues at the peptide position 1 for HLA-DRB1 binding (Fig. 6) (36).

image

Figure 6. The three dimensional map of P17 and P31 peptides (signified in orange) on Sap2 molecule. Amino acid residues (signified in blue) represent N-terminal region of peptides which are outside the predicted area.

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The experimental validation showed that Hotspot Hunter can identify some promiscuous peptides that enable stimulation of T cell activation from volunteers of eight HLA-DRB1 supertypes. The reported allele frequency of the eight HLA-DRB1 supertypes among 140 Thais and Singaporean Chinese was approximately 34.2% and 38.9%, respectively (37). In the case of C. albicans Sap2, P31 was an example of a peptide within the predicted areas that could stimulate T cells from all blood donors. Nevertheless, its predictive binding score did not correlate with the immunogenicity of the peptide. This peptide consists of an amino acid sequence that spans 90% of the predicted peptide and is present in all Sap2 varieties. Additionally, both P17 and P31 could stimulate T cells of a donor that carries the HLA-DRB1 allele (*12/12) other than the eight predicted supertypes. Thus it is possible that P17 and P31 could promiscuously stimulate the T cell response of a broader population than previously expected.

PBMC proliferation and ELISpot assays do not verify the actual binding of a peptide to HLA-DRB1, but rather measure the response of T helper cells from HLA-DRB1 allele-matched individuals. In addition, there is a possibility that the stimulated peptides, such as P17 and P31, could be presented in the context of HLA-DQ and HLA-DP. However, it has previously been reported that most fungal infections on skin and mucosa induce HLA-DR, but not HLA-DQ, expression on the surface of the cells (38). A marked expression of HLA-DR antigens has been shown throughout the epithelium of patients with oral candidiasis by indirect immunohistochemistry (38). The only epithelial type that has been shown to display HLA-DQ is that of the oral cavity (38). Another study, on T cell reactivity to allergic bronchopulmonary aspergillosis, has demonstrated that 19 of 21 T cell clones specific to the Asp F1 antigen of Aspergillus fumigatus were restricted by HLA-DR molecules, and only the two remaining clones by HLA-DP molecules (39). Interestingly, HLA class II involvement in Asp f1 presentation in all patients studied has been restricted to one or a few of the alleles of a given DRB1 genotype (39). All in all, it appears that HLA-DR plays a more important role than other HLA molecules in fungal antigen presentation.

In summary, we have demonstrated the utility of immunomics, which is the database construction and prediction of T-cell epitopes using a computational approach, followed by experimental validation. With the genome and proteome data of many pathogens growing rapidly, such an approach significantly improves the efficacy of extraction and analysis of biological research. This study has also demonstrated the use of bioinformatics tools to accelerate immunological research through a concept of “reverse vaccinology” which represents a paradigm shift as compared to conventional approaches to vaccine development (40). The latter approach is time-consuming and can identify only antigens that can be purified. The combination of large-scale screening by informatics and targeted validation experiments defines a knowledge-based approach to epitope-driven vaccine discovery and design.

ACKNOWLEDGMENTS

  1. Top of page
  2. ABSTRACT
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

This work was supported by BIOTEC, the National Science and Technology Development Agency (NSTDA), Bangkok, Thailand. Songsak Tongchusak was supported by the Thailand Research Fund through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/021/46). We are grateful to Drs. Pokrath Hansasuta and Kiat Ruxrungthum for their technical assistance on ELISpot assay. We thank Dr. Yutaka Sagara for his correction of English grammer.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES
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