A novel B- and helper T-cell epitopes-based prophylactic vaccine against Echinococcus granulosus

Introduction

The hydatid disease is one of the most important zoonotic parasitic infections worldwide. The main part of Echinococcus granulosus life cycle depends upon the growth and differentiation of protoscoleces (PSCs) within the intestine of definitive hosts.¹ The survival of an organism is mainly dependent on its indirect transmission cycle from the definitive hosts (particularly dogs) to the intermediate hosts, including sheep and human. According to the latest updated report of the World Health Organization (WHO) in March 2017, there may be over one million infected cases with echinococcosis at any time.² A hydatid cyst (HC) encompasses numerous PSCs and cyst fluid and is formed in visceral organs (liver and lung) of the infected intermediate hosts.³ Morphogenesis of PSCs from the cystic viscera into worm’s scolex (or head) occurs on the surface of the small intestine of the definitive host, and then the head-like structure attaches to the gut epithelial lining and develops into an adult worm within 32-80 days post-infection.⁴

The currently used treatment modalities against echinococcosis, as multi-stage parasitic infection, are based on the anthelmintic drugs such as albendazole, praziquantel, and ivermectin. However, these medicaments are often associated with some issues such as inadvertent side effects.^5-7 Therefore, to control this parasitic infection, it is necessary to improve preventive interventions such as vaccination in addition to hygiene practices. Several constraining factors may influence the vaccine development against such multi-stage pathogens, including economic, socio-cultural/political issues and technical hurdles.⁸ Thus, to overcome the issues, it is necessary to implement a rationalized approach towards the construction of multipotent vaccines through targeting a different aspect of the infection. In silico modeling of vaccines provides a cost- and time-effective approach that can improve such endeavor towards designing and developing effective vaccines.⁹ Further, such approach favors the identification and selection of key vaccine candidate antigens (VCAs) with high immunogenicity. Ideally, a vaccine construct, to be highly effective, should encompass several parts, including epitopes of one/more VCAs, B-cells epitope and T-cells epitopes (BEs and TEs, respectively).¹⁰ Nevertheless, even a well-designed vaccine may fail to elicit the immune system's responses because of the complexity of various cascades involved in the immunity against parasitic infections. Altogether, epitope-based vaccines (EVs) seem to be one of the most effective vaccines. However, despite being a tedious and laborious approach, designing effective EV type vaccines depends upon several key parameters, including the selection of the best epitopes of a right VCA among other factors.¹¹

Of various VCAs involved in echinococcosis, the E. granulosus (Eg) 14-3-3 as a secretory Ag plays key roles in parasite’s survival, cell cycle, growth and differentiation regulation and apoptotic cell death, as well as the interaction between the adult worm and definitive host.^12,13 The main immune response to the echinococcus infection is based on the activation of humoral immunity, mainly through production of the specific antibodies (Abs).^14,15

In this study, to design an effective prophylactic EV, we capitalized on the protein sequence of Eg14-3-3 isolated from the infected cases in Iran and used it for 3D structure homology modeling and prediction of BEs and TEs. This dog-specific vaccine construct, to the best of our knowledge, is the first EV designed for the immunization of definitive hosts against the adult worm of E. granulosus.

Materials and Methods

Results

PCR-based DNA amplification

The double-stranded PCR product of Eg14-3-3 (approximately 744 bp) was visualized on a cyber-green stained agarose gel electrophoresis (Fig. 1a).

Fig. 1. Amplification of genomic DNA of E. granulosus protoscoleces and structure-based representation of the predicted post-translational modifications. a) The gene was amplified by polymerase chain reaction (PCR) in two repeats (1 & 2). M: 1 kb DNA marker. b) Shows four potential mucin type O-glycosylation regions and their prediction scores. c) The N-glycolysation residues and prediction values. d) The potential phosphorylation of serine, threonine and tyrosine residues as the 3D structure of the Eg14-3-3 protein.

Accessibility and hydrophobicity

The accessibility and hydrophobicity plots of residues in the Eg14-3-3 protein and their positions within the 3D structure are shown in Fig. 2 (a, and c). The residues between aa169-182 were predicted as a highly hydrophobic segment (Fig. 2b). In five amino acid fragments (amino acids 16-23, 132-134, 166-171 and 181-184 and 221-224), the surface accessibility scores were less than 5.0. Hence, they were predicted as highly non-accessible regions (Fig. 2d). Such hydrophobic and non-accessible residues will be ignored during the BE prediction because they are unlikely to interact with the binding site of specific Abs.

Figure 2

Fig. 2. Solvent accessible and hydrophilic regions of the Eg14-3-3 protein. a) The orange residues show the hydrophilic regions of Eg14-3-3 protein as 3D structure. b) Indicates ProtScale hydrophobicity plot. One highly hydrophobic region (aa^169-182) was predicted as brown. c) The surface-accessible regions are illustrated as 3D structure. d) Accessible residues are illustrated as the ProtScale plot. The residues above the threshold (6.0) are highlighted as surface accessible residues. The 3D structure of Eg14-3-3 was visualized as transparency and ribbon style via UCSF Chimera software (freely available at: http://www.rbvi.ucsf.edu/chimera).

Assessment of Eg14-3-3 homology modeling quality

The 3D structure of modeled Eg14-3-3 is shown in Fig. 3. The EasyModeller software provided two types of validation scores, including the GA341 score that is ranged from zero to 1.0, and the normalized DOPE score as an energy profile for each residue. In this regard, the model with correct folding showed GA341 scores near 1.0 and the model with more negative DOPE values counted as a stable model. The energetic values of the GA341 score and DOPE score relevant to the modeled Eg14-3-3 were 1.0, and -19088.43 kcal/mole, respectively. The DOPE score was altered to -26746.53 after loop refinement. Therefore, the relative conformational stability of the predicted model was increased. The DOPE score profile for the initially generated model and the loop-refined model are displayed in Fig. 3 (a). The Eg14-3-3 model was re-arranged after energy minimization (EM) and the EM value was measured -12650.81 kJ/mol. Two main structural rearrangements occurred at the Gly¹³⁸ and Cys²⁰ amino acids (Fig. 3c).

Figure 3

Fig. 3. The model loop-refinement and energy minimization. a) The DOPE score values of each residue were imported to Excel program and their related profiles before (red) and after (green) loop refinement were plotted. b) The 3D structure of the loop-refined model (green structure) and the newly built model (blue structure) were compared through MatchMaker tool of Chimera software. Two main changes after loop-refinement are marked as arrows. c) The structural superimposed image of the initially predicted model (green color) and geometrically minimized model. Alpha helix of glycine 138 (left) and cysteine 20 (right) in initial model were converted to strand after the first and second energy refinements, respectively.

C-alpha atoms of the Eg14-3-3 were analyzed by ProSA-web server and exhibited the overall model quality by the z-score value of -7.21 (Fig. S7, Electronic Supplementary Data). The Verify3D-1D profile showed that 83.81% of the model residues has an averaged 3D-1D score over 0.2 (Fig. S8, Electronic Supplementary Data).

Ramachandran plot of the predicted model before and after energy minimization was also assessed (Fig. S9, Electronic Supplementary Data). After structure optimization, the number of residues within the favored region was increased from 94.7% (232 amino acids) to 98.8% (242 amino acids). Before energy minimization, seven residues (2.9%) were within the outlier region, however, after energy optimization, all of these residues were distributed to the favored and/or allowed regions.

B-cell epitope prediction and analysis of their mutative/conservative

We used two types of algorithms for increasing accuracy of the BE prediction, including (i) the physicochemical-based algorithms (e.g., Bcepred and IEDB-based linear epitope prediction) and (ii) the machine learning based algorithms (e.g., ABCpred, BepiPred, and LBtope). The residue-based scores for different predictor web-servers were imported into the Microsoft Excel for plotting and selection of the best BEs (Figs. 4 and 5).

Figure 4

Fig. 4. The BE prediction scores for LBTope, IEDB, SEPPA and BepiPred web-servers. a) The plot obtained by the scores of LBtope server. In six positions the values are more than the threshold (60%). b) The result of five different physicochemical algorithms of IEDB online server (flexibility, antigenicity, surface accessibility, beta-turn, and hydrophilicity) and the consensus result (black curve) plotted for prediction of linear BEs. c) The residue-based scores of the SEPPA and BepiPred online tools. Six high-score peptides and their average values that were predicted by means of BepiPred server were exhibited top of the red curve.

Figure 5

Fig. 5. The residue-based scores for different BE predictor web-servers. a) The graphical panel of BcePred prediction result. The curve of six different methods and result of the consensus method (black curve) are indicated simultaneously. b) The sequence of four high-rank epitopes that were predicted by means of ABCPred and ElliPro servers. c) The residue-based values of DiscoTope web-server. Three positions are observed as possible discontinuous BEs (threshold: -3.7). Avg: Average.

Mean Shannon’s entropy value for Eg14-3-3 isolates was 0.584, implying that the Eg14-3-3 isolates are semi-conserved among different E. granulosus strains (Fig. S10, Electronic Supplementary Data). The entropy values at each residue position in the alignment were ranged from zero (high conservation) to 1.242 (hyper-variable).

The computationally mapped BEs and their mean entropy (Hx) values were ranked based on their scores (Table 1). These data reflect the fact that the predicted epitopes were conserved in some, but not all, of the isolated of E. granulosus. The mean entropy values for each predicted epitope provide some information in terms of the vaccine coverage. The epitopes 1 (LTLWNSDAGDTDAAEPPKAD) and 4 (FCTGDERKQASDNS) exhibited a lower sequence variability, thus can cover more variety of E. granulosus strains.

Table 1. The consensus predicted B-cell epitopes
No.	Consensus B-cell Epitope*	Position	Length	Mean entropy values
1	LTLWNSDAGDTDAAEPPKAD	228–247	20	0.376
2	MSSLSKREENVYMAKLCEQCERYDE	1–25	25	0.845
3	RKAFDDAVAELDTLPEESYKD	196–216	21	0.508
4	FCTGDERKQASDNS	135–148	14	0.372
5	GARRSSWRVISSIEQKHDGDAKMQIAKKVREE	57–88	32	0.427
*The predicted epitopes were sorted based on their score.

Validation of homology modeling of the MHC alleles and energy minimization

The modeling validation parameters, including RMSD, C-score, TM-score (template modeling score) and ProSA Z-score is shown in Table S1. The zero and negative values in ProSA plots were found to be related to the stabilized models. Therefore, the 3D predicted models were found to be structurally acceptable (Fig. S11, Electronic Supplementary Data). The topological stereochemistry of DLA-DRB1*01501 model after energy minimization was -7270.35 kJ/mol. The energy minimization for DRB1*01101 model resulted in some residual dislocations, while the percentage of favored residues in Ramachandran plot was decreased. Therefore, the prepared DRB1*01501 model and initially generated DRB1*01101 model were used for the subsequent docking simulation (Fig. 6).

Figure 6

Fig. 6. Visualization of the 3D structure of DLA-DRB1*01501 allele, and the DLA-epitope docking. a) The docking analysis of DLA-DRB1*01101 and the peptide fragment of Eg14-3-3 from 200 to 212. The LigPlot software was used to plot the left image in the panel a, showing a two-dimensional diagram of the intra-molecular hydrophobic (red spline curves) and hydrogen (green dotted lines) binding interactions. The UCSF Chimera program was used to plot the right image in the panel a. In two-dimensional diagrams, the peptide bonds, as well as MHC allele bonds, are shown as purple and brown lines, respectively. b) The docking between DLA-DRB1*01501 and the peptide fragment of Eg14-3-3 from 60 to 72. c) Initially modeled DLA-DRB1*01501 allele and its Ramachandran plot. Phe³⁵, Gly⁴⁹ and Arg⁸⁸ were located into the outlier region of the plot. d) Tertiary structure and Ramachandran plot of DRB1*01501 allele after energy minimization. E-total for the energy-minimized model was -7270.35 kJ/mol. e) The structural superposition of tertiary structure of DRB1*01501 allele before and after geometry optimization. MatchMaker tool of Chimera (Needleman-Wunsch algorithm, BLOSUM-62 matrix) was conducted for structural comparison. Iterative pruning of atom pairs exceeding 2.0 Å RMSD was used to obtain the best-matching core regions.

Computational epitope-scaffold designing

To enhance the bioactivity of vaccine construct, it is so important to assemble the epitopes together via appropriate linker amino acids based on their antigenicity and immune-dominancy. Some important epitopes can be intentionally repeated in the vaccine construct to obtain maximal immune system responses. For this reason, the selected TEs were inserted into the vaccine construct with two repeats, and HEYGAEALERAG sequence was added between the repeated TEs. We used KFERQ linker sequence at the both NH2– and –COOH termini. This specific linker can enzymatically and/or chemically be cleaved by proteasomal degradation machinery, making a rational distance between the minigene construct and expression plasmid. Additionally, due to their sensitivity to proteasomes, the linker can promote the peptides cleavage efficiency.^51,52 A four-residue spacer, Gly-Pro-Ser-Leu (GPSL), was served between the T and B cell epitopes. This type of linkers acts as a flexible spacer and can provide a free folding potential for T and B cell epitopes independently.⁵³ A short linker peptide, GGSSGG, was used between the BEs to minimize the interference between the adjacent epitopes and prevent formation of the new epitope. A six-glycine linker was conjugated upstream of six histidine-tag in order to minimize the possible effect of the tag on other epitopes.^54,55 The Shine-Dalgarno sequence (AGGAGG) was added to the upstream of the start codon to improve the mRNA stability, ribosomal orientation, and translation initiation. The scaffold of the vaccine construct is displayed in Fig. 7. The C-score, TM-score and estimated RMSD of the construct 3D structure were -2.30, 0.44±0.14 and 10.9±4.6 Å, respectively.

Figure 7

Fig. 7. Flowchart for scaffolding the vaccine construct and 3D structure of the minigene construct. The helper T- and B-cell epitopes are separated by the specific molecular linkers (KFERQ, HEYGAEALERAG, GPSL, GGSSGG, and GGGGGG). The amino acid sequence corresponding to each peptide was written on the 3D structure of the peptides.

Back-translation, codon usage, and mRNA secondary structure optimization

One of the approaches for improvement in the gene expression level is the optimization of codons and the secondary structure of mRNA. In this sense, the amino acid sequence of the multi-epitope construct was reversely translated to the DNA sequence using Visual Gene Developer v1.7. Further, E. coli K12 codon frequency table was used to replace each amino acid with the corresponding and most frequently occurring codon. Table S2 represents several key parameters, including the overall G/C content, G/C content at the first, second and third places, tRNA adaptation index (tAI), the effective number of codons (Nc) and CAI values before and after the optimization. Synonymous codons substitution reduced the percentage of G/C content at the third codon site. The part of unfavorable hairpin stem-loops at the beginning of the mRNA was optimized to stabilize the mRNA structure and ribosomal binding site. The mRNA secondary structure, their overall Gibbs free energy and their residue-based plots before and after the optimization are shown in Fig. 8 and Fig. S12 (Electronic Supplementary Data). The Gibbs free energy (ΔG) of mRNA secondary structure before, and after the optimization was -279.0 and -285.30 kcal/mol, respectively. The overall negative Gibbs free energy indicates favorable mRNA folding process. The stop codon (TAA) was finally added at the 3ꞌ-end of the optimized construct.

Figure 8

Fig. 8. The mRNA secondary structure before and after optimization. a) The secondary structure of mRNA before energy optimization. b) The effects of energy optimization on the secondary structure of mRNA, in particular on the beginning of the sequence. c) The diagram of Gibbs energy profile for each nucleotide sequence, before (blue curve) and after (yellow curve) optimization.

Discussion

The human health is often related to the animals’ health, in large part, because there are several human diseases (e.g., hydatid cyst) that are derived from the domestic and/or wild animals – categorized as the zoonotic diseases. For instance, dogs with echinococcus infection are the main cause of the human hydatidosis. Therefore, the aim of this study was to design an effective dog-specific prophylactic vaccine construct considering the 3D structure, post-translational modifications (PTMs) and hydrophobic/hydrophilic regions of the Eg14-3-3 used as an Ag isolated from the infected cases in Iran (Figs. 1 and 2). This designed minigene construct was comprised of BEs and TEs that were predicted based on Eg14-3-3 Ag. In this minigene vaccine, two most frequent DLA class II alleles were included in order to increase the probability of major histocompatibility (MHC) epitope binding in various dog breeds. We believe that such design, based on DLA class II alleles together with the multilateral computational assays (Figs. 3-6), might improve the coverage rate of the vaccination among different dog population.

E. granulosus infection in dogs is mainly mediated by the production of Ag-specific B-cell Abs.^14,15,56 However, differentiation of B-lymphocytes usually requires activation of naive helper T-cells. Therefore, we inserted the predicted CD4⁺ T-helper epitopes at the 3ꞌ-end of the construct. It should be noted that, in some bioinformatics investigations, vaccines were simply concentrated based upon the prediction of BEs from different VCAs, regardless of TEs.^57,58 In some other studies, TEs were predicted based on the major MHC alleles of animal models,^59,60 while these cannot thoroughly be substituted for the natural hosts of the parasite. All of these issues were considered for designing of the current multi-epitopic vaccine (Table 1; Figs. 6 and 7). Additionally, sequence-based TE prediction for the vaccine design against HC disease is associated with some technical limitations. Unfortunately, little is known about the canine MHC alleles. Therefore, the sequence-based TE prediction algorithms cannot be used in the prediction of specific TEs for dog vaccination. Given such facts, the structure-based approaches, especially computational molecular docking techniques, might be recruited for the prediction of either the helper or the cytotoxic T-cell epitopes.⁶¹

In the face of extracellularly associated parasites such as nematodes and cestodes, MHC class II molecules are mainly involved in the binding and the epitope presenting to the CD4⁺ T-helper cells.⁶² As reported previously, the DLA-DRB1 genotype is mainly associated with levels of anti-disease Abs in some of the canine diseases such as chronic hepatitis,⁶³ visceral leishmaniasis,⁶⁴ and rheumatoid arthritis.⁶⁵ MHC-DRB1 in canine has been suggested as a key factor for the resistance or susceptibility to infectious diseases. Moreover, among different studies on dog populations, DRB1 allele is a highly frequent allele than the DLA-DQA1 and DLA-DQB1 alleles.⁶⁴ Generally, two points have been considered in the selection of the most suitable MHC alleles for the TE prediction, including (i) the frequency of DLA-DRB1 alleles between diverse dog breeds^63,66-69 and (ii) the allele(s) associated with the parasitic diseases. Thereby, two high frequent DLA class II alleles (DRB1*01501 and DRB1*01101) were served for the peptide-MHC docking and finally for the prediction of MHC class II-restricted TEs (Table S1; Figs. 6 and S11).

The Eg14-3-3 is a secretory component from the rostellar glands of E. granulosus adult worms. Immunolocalization studies have revealed some localized areas of Eg14-3-3 in 35-day-old worms. In this developmental stage, the Eg14-3-3 was reported to be discernible within the periphery of testicle cells, apical of rostellum and around tegument.¹³ Further, the capability of Eg14-3-3 as an immunotherapeutic Ag has been recently studied,⁷⁰ in which the recombinant Eg14-3-3 was extracted and purified from E. coli BL21, and subcutaneously administered to the ICR mice. This vaccination trial exhibited a high binding potential (84.7%) to the anti-Eg14-3-3 IgG subclasses (IgG1, IgG2a, and IgG2b), and significant enhancement in the production of IL-2 and INFγ against E. granulosus. Given that the parasite secretory products play key roles in the survival of the worms within the definitive host's intestine; therefore, it might count as an ideal Ag for the vaccination against E. granulosus.⁷⁰

As a part of this study, for the first time, we modeled the secondary and 3D structure of Eg14-3-3 Ag (Figs. 1 and S6). In this regard, we validated the accuracy of the protein modeling by means of different computational tools such as Verify3D, ProSA z-score and ProSA energy plot (Figs. S7 and S8). Given that the zero and negative values of z-score are related to the stabilized models, the predicted 3D model appeared to be structurally favorable. The ProSA energy plot represents the local model quality (Fig. S7). The Verify3D was used to calculate the compatibility of the 3D structure of the predicted model with its own amino acid sequence (1D), and also to evaluate various structural regions of the model. This server gives 3D-1D structural profile to assess the compatibility of each residue with their local environment.^32,71

Shannon's entropy analysis demonstrated that BEs of the designed vaccine are conserved and semi-conserved and can cover the majority of E. granulosus strains. Further, the Eg14-3-3 isolates were found to be semi-conserved among different E. granulosus strains (Fig. S10), and hence it is not necessary to consider all the isolate of this Ag for the vaccine design. The CD4⁺ T helper epitopes were predicted based on the molecular docking between the 13-mer peptide residues of the whole Ag sequence and the high frequent dog MHC (Canine Leukocyte Antigen, CLA/DLA) class II alleles. The physicochemical and immunological properties of the designed minigene vaccine revealed that this immunogenic peptide was not sufficiently stable at the different temperatures. Furthermore, we used the protease specificity prediction server (PROSPER) as an on line web-server used for the prediction of potential endogenous proteases activity of E. coli. The potential cleavage sites into the minigene construct were found (Fig. S13 and Table S3, Electronic Supplementary Data). It should be stated that such results upon the assessment of proteases activities would be favorable for the extraction and purification of this immunogenic peptide from E. coli.

As reported previously, an ideal vaccine should be safe, effective and broad-spectrum. That is why we recruited several steps for designing this rational EV (Fig. 7), including (i) identifying and selecting a key immunogenic Ag, (ii) using bioinformatics tools for analysis of different parameters of the selected Ag, Eg14-3-3, (iii) predicting BEs and TEs computationally, and (iv) assembling the selected epitopes with each other using the suitable molecular linkers.⁴¹ To revalidate such approaches, we also looked at the mRNA secondary structure before and after optimization of the construct (Table S2; Figs. 8 and S12).

The expression level of many of E. granulosus VCAs are dependent upon the stage of the parasite life cycle. Therefore, for designing a strong poly-epitopic vaccine, we need to pay a close attention to several indices of the selected Ag, including the role of Ag in the parasite survival, antigenicity, immunogenicity and allergenicity, stage of Ag expression, and its localization in the parasite body [e.g., intra- and/or extracellular, somatic and/or excretory-secretory].⁷² The hydrophobic interactions are the main parameters for the Ag-Ab binding energy in most of Ags.⁷³ Because of some known correlations between BEs and surface exposed regions of Ags,⁷⁴ we focused on the surface-exposed and accessible regions of Eg14-3-3 where there are more possibilities for interaction with the Ab binding sites.⁷⁵

Transmembrane helices need several continuous hydrophobic residues to spin accurately within the cell membrane. Accordingly, the hydrophobic transmembrane domains of the Ag cannot be detected by the immune system’s Abs. Moreover, signal sequences act as key players in terms of protein secretion onto the extracellular membrane as well as periplasmic space. Signal peptidases can cleave the signal peptide off (as co-translationally or post-translationally) to produce a mature protein.⁷⁶ Therefore, the signal peptide cannot also be as an appropriate segment for the epitope-paratope interaction (Figs. S1, S2, S3 and S4). The minigene protein processing during post-translational modifications (PTM), especially N-glycosylation, can block the accessibility of some epitopes for neutralizing Abs and affect the proteolytic process by specific immunoproteasome components.⁷⁷ It should be highlighted that the PTMs (e.g., phosphorylated and glycosylated regions) of the epitopes used for the selection of BEs need to be taken into consideration (Fig. S5), in large part because such epitopes are usually located on the unexposed regions.⁷⁸ In fact, some of the PTMs can partially/fully mask epitopes, in particular, those that are linear.⁷⁹

To the best of our knowledge, conclusively, there is no similar in silico Eg14-3-3 based multi-epitope vaccine, and this is the first report on the design of a multimodal dog-specific vaccine. In addition, for designing of this multi-epitope construct, we capitalized on various computational software and web-servers to address all issues related to the improved functionality of such multi-epitope vaccine, which is envisioned to be effective for a diverse population of dog breeds. It should be noted that the predicted epitopes of Eg14-3-3 can be used in combination with the conserved epitopes of some other Ags to produce a universal vaccine with high-rate successful vaccination.

Conclusion

The current in silico study is the first part of the multi-antigenic vaccine designing project that resulted in promising outcomes for immunization of dogs against E. granulosus. In this current work, we reported on a rationalized in silico methodology to design this novel prophylactic vaccine. The undertaken in silico approach may provide vaccinologists a robust tool for designing host-specific multi-epitopic and/or multi-antigenic vaccines against different pathogens. Our vaccine construct designed by use of the local isolate of E. granulosus from the infected cases in Iran. The TEs parts of the vaccine were predicted based on two highly frequent canine MHC alleles through several MHC-peptide docking procedures. Notwithstanding, for more accurate in silico BE identification, we modeled 3D structure of Eg14-3-3 using homology modeling methods. Taken all, the designed vaccine construct comprised of four CD4⁺ MHC-II epitopes, which can elicit protective MHC class II, restricted responses based on DLA-DRB1*01101 and 01501 alleles, and five BEs from E. granulosus 14-3-3 antigen. Codon usage of the construct was comprehensively optimized in terms of codon usage table of Escherichia coli k-12. Based on our findings, conclusively, we envision that the constructed dog-specific multi-epitope vaccine against echinococcosis might provide an improved treatment modality. These data may encourage the vaccinologists to focus on such multi-epitopic vaccines to fight against multi-host and genetically variable pathogens.

Ethical approval

There is none to be declared.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgments

This work is part of the Ph.D. thesis project that was financially supported by the University of Tabriz and the Research Center for Pharmaceutical Nanotechnology (RCPN) at Tabriz University of Medical Sciences (grant No.: RCPN-95002).

Supplementary Materials

The online version of this article (doi: 10.15171/bi.2018.06) contains supplementary material, which is available to authorized users.

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