INTRODUCTION
Echinococcal disease is a cosmopolitan zoonosis caused by the taeniid tapeworm Echinococcusgranulosus. The disease is recognized as one of the world’s major zoonoses.[1] The dog is a definitive host and different ungulates (mainly sheep) are intermediate hosts. Humans are accidental hosts which acquire the disease by ingestion of parasite eggs released by the dog, which can later develop as a larval cyst in lungs, liver, and other tissues.[2] The disease constitutes an emerging public health problem, with a considerable impact in both human and animal health and causing important socioeconomic consequences in endemic areas, especially in regions with extensive livestock husbandry and nonsupervised slaughter.[3]
Early diagnosis of the disease is very important. Cysts generate few distinct symptoms, such as abdominal pain, nausea, jaundice, and a sense of weariness, and they develop slowly, which makes identification difficult and sometimes delayed. Surgery is still the best course of action for treating echinococcosis in its latter stages because the complications of cystic echinococcosis (CE2 and CE3b) may increase.[4] Up to 50% of alveolar echinococcosis and CE cases may remain asymptomatic and parasite lesions would incidentally be detected during examinations for other diseases. Current routine diagnosis of human echinococcosis is based on imaging procedures, i.e., ultrasound, X-ray, computed tomography, and magnetic resonance imaging.[5] Immunodiagnostic techniques such as enzyme-linked immunosorbent assay (ELISA) and immunoblotting are currently applied to confirm the presence of an Echinococcus cyst.[6]
Hydatid cyst fluid has been used as a main antigenic source for the primary immunodiagnosis of human CE.[7] ELISA using crude hydatid cyst fluid has a high sensitivity (75%–95%), but its specificity is often unsatisfactory.[8] The main problem is its cross-reactivity with sera from individuals infected by other helminths, mainly E. multilocularis and Taeniasolium.[6] So far, there is no standard, highly sensitive and specific test available for the diagnosis of the disease in humans.
It has been suggested that serodiagnosis of human echinococcosis may be improved by use of recombinant proteins or synthetic peptides that may enhance diagnostic specificity.[9] Through constant improvements of reagents, instruments, and synthesis protocols, it has now become possible to synthesize peptides of varying lengths.[10] There are already several studies reporting that the use of such peptides was proven suitable antigens for the diagnosis of various infectious diseases, not only of viral origin[11–13] but also of parasitic diseases like Toxoplasmagondii, Wuchereriabancrofti, Fasciolagigantica, P. falciparum, and Leishmaniadonovani.[14–19] In addition, Kouguchi et al.[20] revealed that the Emy162 recombinant antigen from E. multilocularis induced a 74.3% protective immune effect in rats. Similarly, Katoh et al.[21] cloned the Em95 antigen and generated a vaccine based on this antigen in order to protect against the larval-stage infection of E. multilocularis. These results suggested that the prevention of hydatid disease is quite feasible by a molecular vaccine. In the last few decades, an effort has been made to enhance diagnostic specificity of well-defined antigens using recombinant proteins, synthetic peptides, or combinations of both. However, in some cases, the use of such recombinant components still exhibit low diagnostic sensitivities. Thus, to improve the immunodiagnosis of CE, there is a need of characterizing new antigens as well as standardization of the techniques and antigen preparations currently available.
Thus, keeping these points in view, the aim of the study was to identify the immunogenic peptides of the candidate antigens which may be used for immunodiagnosis.
MATERIALS AND METHODS
Selection of proteins
The targets selected for the study were Hsp-90, Hsp-8, PEPC, Tsp-1, Ag 5, and Ag B. The amino acid sequences of proteins were retrieved from NCBI (http://www.ncbi.nih.gov/genbank/) with gene IDs: (i) Hsp-90: EUB54574.1, (ii) Hsp-8: EUB56318.1, (iii) PEPC: EUB57677.1, (iv) Tsp-1: EUB54938.1, (v) Ag 5: AFI71096.1, and (vi) Ag B: AAC47169.1. The sequences were subjected to blast to identify the similar sequences in other helminthic parasites like E. multilocularis, T. solium, and Schistosomamansoni. Further, multiple sequence alignment was carried out using ClustalW tool to analyze the similarity between the candidate proteins and other helminthic proteins.
Identification of T cell epitopes
For the identification of peptides of human leukocyte antigen (HLA) class-I T cell epitopes, the servers NetMHCpan 2.4, NetMHC3.4, IEDB-ANN, and IEDB-SMM servers were used.[21–24] The human leukocyte antigen has demonstrated significant genetic diversity throughout the global human population. HLA has been correlated with many various diseases, in which the profile of these alleles varies significantly between different populations throughout the diseased persons. Therefore, it is significant to study the relationship between different HLA alleles and promiscuous peptides predicted from the different antigens of Hydatid cyst to develop an effective subunit vaccine. Hence, the effectiveness of a subunit vaccine is compromised by the diversity of HLA alleles. The current study was carried out in the northern part of India, where the study’s selected HLA alleles—HLAA*01:01, HLAA*02:01, HLAA*11:01, and HLAA*30:01—are highly prevalent. The predicted peptide will be highly immunogenic, have a binding affinity for these alleles, and might be used to develop a subunit vaccine to protect against the pathogens.[20,24,25] T cell epitopes were classified based on their binding affinity for HLA alleles, using the half-maximal inhibitory concentration of a biological substance (IC50) as the unit of measure, as follows: high-affinity binding, IC50s of <50 nM; intermediate-affinity binding, IC50s of <500 nM; and low-affinity binding, IC50s of <5,000 nM.
Identification of B cell epitopes
The linear B cell epitopes were identified using ABCpred, BCPREDS, and BepiPred 1.0 online web servers.[25–27] The Ellipro and DiscoTope-2.0 servers were used to identify the conformational B cell epitopes.[28,29] In ABCpred, prediction was done at a window length of 16 and the threshold was set from 0.8 to 1.0. In BCPREDS, the classifier specificity was set at 80% and the epitope length was set 20. Using BepiPred server, the threshold for epitope assignment was set at 0.35. In Ellipro, prediction was made at a minimum level of 0.5 to the most stringent level of 1.0 and the maximum distance for residue clustering was kept at 6.0 Å. Selection of diagnostic proteins as B cell epitope candidates was based on the number of epitopes predicted with a minimum cutoff score of 0.8. In DiscoTope-2.0 server, the threshold for the epitope identification was set at − 3.7. The regions which were predicted to be B cell epitopes by at least three servers were selected.
Solvent accessibility and electrostatic potential
ASAView (http://www.abren.net/asaview/)[30] was used to calculate the solvent accessibility of all the residues in the generated models and electrostatic potential was analyzed by protein continuum electrostatics tool. The parameters such as dielectric constant of protein and surrounding solvent and ionic strength values were not altered.
Secondary structure prediction
The secondary structure of all the target proteins was predicted using the improved self-optimized prediction method (SOPMA) software (http://npsa-pbil. ibcp. fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html)[31] and PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/) fold servers. The four conformational states, including helices, sheets, turns, and coils, were analyzed in the protein sequence of the respective antigens.
Three-dimensional modeling of selected proteins
All the target proteins were modeled using I-Tasser, Ellipro, and the SWISS-MODEL Workspace server. A template model was obtained for each protein by submitting FASTA format protein sequences and modeling them. All the target proteins were modelled using I-Tasser, Ellipro and the SWISS-MODEL Workspace server.[32–34] A template model was obtained for each protein by submitting FASTA format protein sequences and modelling them.
Characterization of the proteins
Prediction of the secretory nature of the diagnostic candidates was performed and detection of the presence of signal sequences was performed using SignalP 4.1.[35] The TMHMM server was used to predict the transmembrane helices in the proteins.
Characterization of the predicted peptides
The ExPASy ProtParam tool (http://web.expasy.org/protparam/) was used to calculate the molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) of the predicted T and B cell epitopes.
RESULTS
T cell epitopes
Several publicly available tools trained on different datasets were employed for the determination of HLA Class I and Class II binders. Each protein was predicted to have a large number of low-affinity and a relatively small number of intermediate and high-affinity epitopes based on IC50 values. Hsp-90 was found to carry the largest numbers of HLA class I and class II T cell epitopes of intermediate and high-affinity binding followed by Tsp-1, Hsp-8, PEPC, Ag 5, and Ag B. The total number of T cell epitopes of intermediate and high affinity given in Table 1 is written as average values of all the predicted epitopes of different alleles using different servers.
;)
Table 1: Number of identified B cell and T cell (human leukocyte antigen class I and II) epitopes
B cell epitopes
The B cell epitopes were predicted using Ellipro, Discotope, BCEPREDS, BepiPred, and ABCpred online application tools. Here also, Hsp-90 was found to have the largest number of B cell epitopes, followed by Hsp-8, Ag 5, PEPC, Tsp-1, and Ag B [Table 1]. All the epitopes identified were present on the surface of their respective proteins. Hydrophobic content of the identified epitopes was also analyzed to identify regions with higher probability to interact with immunoglobulins. All residues of Ag B were hydrophobic, followed by 88.8% residues of Hsp-90, 70% residues of Ag 5, 65.6% of Hsp-8, 64.7% of Tsp-1, and 64.5% of PEPC. The polar, nonpolar, and charged residues of each predicted epitope are shown in Table 2.
Table 2: Overlapping human leukocyte antigen class I and II T cell epitopes
Overlapping residues of human leukocyte antigen class-I and class-II T cell epitopes
All the target proteins were found to have some T cell epitopes with binding affinity for both HLA class-I and class-II molecules. Some of the 9-mer HLA class I T cell epitopes were found to overlap with the 15-mer HLA class II T cell epitopes, offset at either the C-terminal or N-terminal end. The Tsp-1 and Hsp-90 protein had the largest number of overlapping epitopes [Table 2]. The identified overlapping T cell epitopes were also found to possess some conformational B cell epitopes as shown in Table 3. Such peptides could be of great value for vaccine development as they could induce the activation of both branches of immune system: humoral and adaptive immunity.
Table 3: Promising regions bearing linear B cell epitopes
Promising B cell epitopes for diagnostic use
The identification of linear B cell epitopes was carried out using four different servers. Only those regions which were identified by at least three different servers were predicted to be most promising B cell epitopes [Table 3]. Among the identified B cell epitopes, 5 epitopes from Hsp-90 and 1 from Tsp-1 were found to be specific in Echinococcus. In addition, 23 discontinuous B cell epitopes were also identified [Figure 1a-f]. The position of each of the predicted B cell epitopes was confirmed by visualizing them on their respective three-dimensional (3D) modeled proteins using Jmol viewer [Figures 1a-f and 2].
Figure 1: (a-f) Conformational B cell epitopes in yellow color on the 3D modeled proteins of E. granulosus predicted from the 3D structure templates. In the ball-and-stick model, yellow balls are the residues of predicted epitopes and white sticks are the structures for nonepitope and core residues. Each epitope is shown with predicted residues (abbreviated amino acids) and residue positions (superscript numerals): Antigen 5-A, MAR1-3; B, SRPL4-7; C, WIVF8-11; D, VCLFATAALGLELTLDPDELVKAQ12-35; E, YIAEE111-115; F,144-168 RGSFDKNTAKPSRRRWKDMDDDEAD; Antigen B-A, DDSK87-90; Hsp-8-A, EVD649-651, B, QEAGGAGGMPGGMPGGMPGGGGMGGASSGGRGPTIE613-648; C-KELESVCNPIITKMY598-612; D, HRQ595-597; Hsp-90-A, MADQVDTDVPMAQEVETFAFQAEIAQLMSLIINTFYSN; B, ELISNGSDALDK45-56; C, YLEE195-198; D, EDQSE190-194; E, KEIF39-42; F, RIK200-202; PEPC-A, MSPSL1-5; B, WEE6-8; Tsp-1-A, FNDKFVQNLLDKVSSQWSEQQVQDLVKFIHYL; B, GQ199-200. 3D: Three-dimensional
Figure 2: B cell epitopes on the 3D modeled proteins. (a) 3D model of Antigen-5 (Ag-5), (b) 3D model of Antigen B (Ag-B), (c) 3D model of Heat Shock Protein-8 (HsP-8), (d) 3D model of Heat Shock Protein-90 (HsP-90), (e) 3D model of Phosphoenolpyruvate carboxylase (PEPC), (f) 3D Model of Thrombospondin-1 (Tsp-1), B cell epitopes are shown yellow in colour. 3D: Three dimensional
Electrostatic potential
By assigning the values 1 to acidic residues (Asp, Glu) and +1 to basic residues (Lys, His, Arg), the charge on the respective proteins was calculated to be +6 for Ag 5, +6 for Ag B, 2 for Hsp8, 31 for Hsp90, +11 for PEPC, and +1 for Tsp1.
Secondary structure prediction
Secondary structure was predicted using self-optimized prediction method (SOPMA) software. We identified nine α helices and 23 β sheets in Ag 5, 4 α helices and 1 β sheet in Ag B, 28 α helices and 21 β sheets in Hsp-90, 27 α helices and 20 β sheets in Hsp-8, 15 α helices and 29 β sheets in PEPC, and 11 α helices and 13 β sheets in Tsp-1. Whereas, PSIPRED fold server identified 4 α helices and 20 β sheets in Ag 5, 4 α helices and 1 β sheets in Ag B, 23 α helices and 17 β sheets in Hsp-90, 13 α helices and 19 β sheets in Hsp-8, 15α helices and 20 β sheets in PEPC, and 9 α helices and 1 β sheets in Tsp-1.
Three-dimensional modeling of the selected proteins
All the target proteins were successfully modeled using Ellipro, the SWISS-MODEL Workspace, and I-Tasser server. The details of the template predicted by all the three servers are shown in Table 4. The templates identified by Ellipro server were quite different from the templates identified by I Tasser and Swiss Model Workspace. Of the six proteins, four (Ag 5, Hsp-8, Hsp-90, and PEPC) were identified to possess the same templates predicted by I Tasser and Swiss Model and were selected. However, the templates identified for Ag B and Tsp-1 were different. In this case, the template identified by I-Tasser server for both Ag B and Tsp-1 was selected. All the three servers identified the same template only for Hsp-90.
Table 4: Templates identified by different servers for the selected targets proteins
Characterization of proteins
SignalP 4.1 identified Ag 5 and Ag B as being secreted, with a score of 0.925 and 0.911, with cleavage sites between 21 and 22 for Ag 5 and between 20 and 21 for Ag B. Whereas, all other protein sequences submitted had low scores suggesting the absence of a signal sequence. The TMHMM server v. 2.0 was used to predict the transmembrane helices in proteins by searching hydrophobic regions. Tsp-1 protein was identified to possess three transmembrane regions at positions 11–33, 72–94, and 101–123, whereas Ag 5 with one transmembrane region at position 7–26. However, the rest of the proteins did not found to possess any transmembrane regions.
DISCUSSION
For the identification of immune epitopes and for the development of diagnostic kits against pathogens, knowledge of the interactions between HLA alleles, peptides, and host immune cells has immense immunological value. There are numerous databases of experimentally derived epitopes which have aided in development of algorithms for predicting epitopes of hitherto uncharacterized proteins. The quality of such computational tools for the prediction of epitopes for different proteins has been investigated.[36,37]
The serological tests used for the diagnosis of CE are not very sensitive due to the very poor positive predictive value.[38] The major problem is the cross-reaction of hydatid cyst fluid antigen with sera of individuals infected with other helminthic diseases.[6] For this reason, efforts have been made to find alternatives to biological antigens by using synthetic peptides.[39,40] Synthetic peptides are potentially ideal tools for dissecting the antigenicities of the native antigens. Using synthetic peptides mimicking B cell epitopes, it is possible to measure antibodies directed against very specific antigenic determinants.[41] There are several benefits of using synthetic peptides in contrast to biological products, like they are easily standardized, stable, and can be readily produced in large amounts.[42] It has been suggested that the diagnosis of CE can be improved by designing the peptides of its antigen.[9,42]
Keeping these points under consideration, the following study was designed to identify the T cell and B cell epitopes of the selected candidates. Only the high-affinity binding (IC50s of <50 nM) T cell epitopes to HLA alleles were further investigated. Linear and conformational B cell epitopes were identified using BcePred, Bepipred, ABCPred, Discotope, and Ellipro servers. The 3D structures of the selected proteins were matched to the most relevant templates in the PDB. Templates were available for all the proteins included in the study. It has been postulated that the prediction of conformational B cell epitopes is influenced by several factors like the degree of sequence alignment between the target protein and the modeled template, by sequence identity, and by the similarity of structural scaffolding.[43] Therefore, a template modeled with a longer range of sequence alignment and a higher percentage of sequence identity may have resulted in a more accurate epitope prediction. The proteins included in this study showed above 40% sequence identity, which is in accordance with the findings from other studies where sequence identity ≥40% proved to be significantly accurate.
The criteria for the selection of best predicted T cell epitopes were based on the following properties: should have overlapping class-I and class-II HLA epitopes, promiscuous, least IC50 value, and should be charged. The location of the identified epitopes was viewed on the 3D modeled templates of each protein. Most of the identified B cell epitopes were lying in the loop region of the proteins, whereas T cell epitopes were lying in the helix region. The identified epitopes were further investigated to possess the polar and nonpolar residues. The output showed various degrees of hydrophobic and polar residues implying high solvent accessibility of the identified epitopes. Brinda and Vishveshwara[44] demonstrated that the hydrophobic residues can function as weak interface hubs.[45] Further, solvent accessible arginine may interact with several amino acid residues facilitating subunit interaction. It has been suggested that arginine plays a major role as a central hub by interacting with residues from the same as well as other subunits. In our study, most of the identified epitopes were found to possess arginine residues as well. In the present work, we examined a number of E. granulosus proteins, and by homology modelling with the proteins of other Echinococcus species, we were able to determine the cross-reactivity of all antigenic proteins. We observed that the Hsp-8 protein of E. granulosus showed homology with Hsp-70 protein of E. multilocularis, putative heat shock protein 70 of S. mansoni, and small heat shock protein of T. solium. Similarly, PEPC protein of E. granulosus was found homologous to PEPC protein of E. multilocularis, S. mansoni, and T. solium. Furthermore, the amino acid sequence of Tsp-1 of E. granulosus showed homology with Tsp-1 of S. mansoni and moderate similarity to T24 protein of T. solium. Ag5 of E. granulosus was found homologous to glycoprotein antigen 5 of E. multilocularis and trypsin-like protein of T. solium. AgB was also homologous to AgB of E. multilocularis. Hsp-90 was the only protein found to be very much alike and quite specific for E. granulosus, showing rare homology with other helminthic parasites, thus suggesting it to be a promising marker for the diagnosis of E. granulosus, especially in terms of specificity. The overall study indicates that heat shock proteins and tetraspanin-1 seem to be associated with both cellular and humoral immune responses, thereby suggesting them to be promising candidates for diagnosis and in designing of subunit vaccine. There are several studies reporting the tetraspanins as a potential candidate for diagnosis and vaccine development in other helminthic parasites like Schistosomamansoni[44,45] and E. multilocularis.[46] Whereas, heat shock proteins are well known to play an important role in generating an immune response against malaria, schistosomiasis, trypanosomiasis, leishmaniasis, filariasis, syphilis, tuberculosis, leprosy, legionnaires’ disease, Lyme disease, Q fever, etc.,[47] thus indicating them to be an important marker in vaccine development.
In the present study, twelve peptides with overlapping HLA Class I and Class II epitopes were identified. In addition, they also possess some part of conformational B cell epitopes, thereby suggesting them to be highly immunogenic containing the property to induce both cellular and humoral immune responses. Such peptides could be very useful in designing a subunit vaccine targeting E. granulosus. In addition, we have identified six peptides (five from Hsp-90 and one from Tsp-1) specific for E. granulosus, which could be of great diagnostic value and may prevent the misdiagnosis encountered so far by decreasing the chances of cross-reactivity to other helminthic parasites.
CONCLUSION
The following insilico analysis led to the identification of potential immunogenic peptides, based on the presence of relatively large numbers of overlapping and promising epitopes. The identified peptides should further be tested for their immunoreactivity using in vitro and in vivo approaches to support the in silico findings. Immunogenicity analysis of these peptides may prove their value as diagnostics and/or vaccines. As the performance of prediction tools depends on the quality of databases, which are dynamic and evolving as new information is submitted, it is likely that the results of the predicted epitopes obtained in the following study may vary at later time points for the same set of proteins.
Ethical statement
The project was approved by Institute Ethical Committee (Shoolini University, solan) vide letter no SUIEC/10/01 Dated, 26/10/2010.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
The authors are thankful to the Indian Council of Medical Research, New Delhi, to provide with grant in aid to support the research work vide grant number 5/3/3/34/2010-ECD-1. The authors are also grateful to the Shoolini University, Solan, HP, India, to provide the laboratory and support the research work.
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