Article Versions
Export Article
Cite this article
  • Normal Style
  • MLA Style
  • APA Style
  • Chicago Style
Research Article
Open Access Peer-reviewed

Multi Epitope Based Peptide Vaccine against Marek’s Disease Virus Serotype 1 Glycoprotein H and B

Sanaa Bashir, Khoubieb Ali Abd-elrahman, Mohammed A. Hassan, Yassir A. Almofti
American Journal of Microbiological Research. 2018, 6(4), 124-139. DOI: 10.12691/ajmr-6-4-2
Received July 01, 2018; Revised August 01, 2018; Accepted August 12, 2018

Abstract

Background: Marek’s disease (MD) is a highly contagious disease of chickens caused by Marek’s disease virus (MDV). It causes economic losses in poultry industry estimated to be more than 1 billion per year. The aim of this study was to design a peptide vaccine against Marek’s disease virus serotype 1 (MDV-1) by targeting the Glycoproteins H and B as an immunogens to stimulate protective immune response. A total of 43 Glycoprotein H and 33 glycoprotein B of Gallid alphaherpesvirus 2 (MDV-1) were retrieved from the National Center for Biotechnology Information database (NCBI) in the 13th of October 2017. Several tests at Immune Epitope Database (IEDB) were used to detect the highly conserved immunogenic epitopes that elicit B and T cells and could be used as efficient vaccine candidates. In our results three epitopes from glycoprotein H namely; 91-FYKRPVSKLL-100, 255-LKPYEPVDKF-264, and 684-PRPL-687 and three epitopes of glycoprotein B; 162- EKQV-165, 234-YGLSPPE-240, and 363-YNDSHVK-369 were fulfilled the criteria of surface accessibility, antigenicity for becoming the most probable B cell epitope. While Four epitopes of glycoprotein H; 425-YVLRSAYAF-433, 175-LTSELTGTY-183, 476-LYYAFASIF-484, and 367-MITETLSTF-375 were addressed as potentially promising epitopes as they bound the highest number of both MHC-I and MHC-II alleles with a high binding affinity to chickens MHC-I molecule (BF2*2101) haplotype in the structural level. Also two epitopes of glycoprotein B; 598-FLFGSGYAL-606, 727-FMSNPFGAL-735 were bound with the highest number of both MHC-I and MHC-II with high binding affinity. Taken together Marek’s disease is a significant disease of poultry. We addressed epitopes from glycoprotein H and B that could act as candidates’ vaccine. To our knowledge there is no in silico epitope based vaccine for Marek’s disease virus serotype 1 (MDV-1). An in vitro and in vivo application is required to prove the efficacy of the predicted epitopes as peptide vaccine.

1. Introduction

Marek’s disease (MD) is a highly contagious disease of chickens caused by Marek’s disease virus (MDV) leads to the formation of T-cell tumors in various body tissues and neurological manifestations 1, 2, 3, 4, 5. The disease transmitted by inhalation 6 and classified as a B-list infectious disease in Office International des Epizooties (OIE) with important economic losses in poultry industry estimated at more than 1 billion per year 7, 8, 9. The morbidity and mortality rate depending on host genetic susceptibility and virulence of the MDV strain. The Mortality rates were commonly 10% to 30% and on incident could reach 60% to 80% 10, 11, 12. Marek’s disease (MD) is widespread in the worldwide. Recently, OIE estimates that about half of the world countries have reported cases of MDV infection such as the United States, the central Ethiopia, Saudi Arabia, Egypt, India 13, 14, 15, 16, 17, 18, 19, and China is an endemic area with outbreaks reported 20, 21.

Marek’s disease virus (MDV) is double-standard DNA that belongs to the Herpesviridae family, subfamily Alphaherpesvirinae in the genus Mardivirus. It has three serotypes different in their virulence in chicken or their susceptibility to induce T-cell lymphomas. These serotypes are Marek’s disease virus serotype 1 (MDV-1) (Gallid herpesvirus 2), serotype 2 (Gallid herpesvirus 3), and serotype 3 (herpesvirus of turkeys) (HVT or MDV3) 22, 23. Among these serotypes, serotype 1 MDVs are oncogenic 24, 25, 26, 27, while other serotypes are non-oncogenic chicken viruses 28, 29, 30. MDV-1 genome is 175 to 180 kb, depending on the strain and is predicted to encode 103 glycoproteins 31, 32. Glycoproteins are virion surface components and represent potent immunogens. Among them are glycoprotein B, C, D and H 33. Glycoproteins H (gH) and B (gB) were the main target of immune response to the virus 4, 34, 35, 36.

The MD is still good controlled by vaccinations as the first successful live vaccine to control MD is herpesvirus of turkey (HVT) since the 1970s 37. HVT is a highly cell-associated virus but vaccine preparations require special handling and storage 38. MDV strain required the introduction of new generations of MD vaccine such as CVI988 (Rispens) vaccine, a naturally attenuated MDV-1 to induce protection against very virulent plus MDV strains 39, 40. But these vaccines prevented the tumor development but not the MDV infection 41. In spite of that, the recombinant DNA vaccines overcome these problems associated with MD vaccine 42, 43 but it provided partial protection against MDV 38, 44. Therefore, it is important to consider alternative approaches for (MDV-1) vaccine development.

Peptide –based vaccine is a one of immunoinformatics applications. It is based on identification and chemical synthesis of B-cell epitopes which mainly induce antibody production and the T-cell epitopes that induce cellular response and cytokine secretion as cytotoxic T-cells 45, 46, 47, 48, 49. Peptides have become more desirable vaccine candidates owing to their relatively easy production and construction, chemical stability, and absence of infectious potential, which lessens the time and reduce cost 45, 46, 47, 48, 49. Moreover these approaches serve as therapeutic and vaccine candidates for many infectious diseases 50, 51, 52, 53, 54.

Therefore aim of this study was to design a peptide vaccine against Marek’s disease virus serotype 1 (MDV-1) using immunoinformatics approach. Glycoprotein H and B were used as an immunogens to stimulate protective immune response.

2. Materials and Methods

To identify the best likely B- and T-cell peptides which could be used to design an effective vaccine, different methods were taken into attention in this study and an outline of the methodology was shown in Figure 1.

2.1. Protein Sequence Retrieval

A total of 43 Protein sequences of glycoprotein H and 33 of glycoprotein B of Gallid alphaherpesvirus (MDV-1) were retrieved from the National Center for Biotechnology Information database (NCBI) in the 13th of October 2017. The retrieved sequences and their accession numbers as well as the collection area were listed in Table 1.

2.2. Conserved Regions Determination

Retrieval sequences of Glycoprotein H and Glycoprotein B of MDV-1 were aligned to obtain the conserved regions using Multiple Sequence Alignment (MSA) by Clustal-W as applied in the Bio-Edit program (version 7.2.5.0) 55. Which were considered as candidate epitopes were further analyzed by different prediction tools from Immune Epitope Database IEDB analysis resource (http: //www.iedb.org/)

2.3. B-cell Epitope Prediction

B-cell epitope is defined as part of antigen that is recognized by B-cell receptor to elicit antibody in humeral response 56. The epitopes of a native protein must be accessible at the antigen surface and possess antigenic reactivity with antibodies. To predict these physicochemical and structural parameters are usually calculated based on propensity scales for each of the 20 amino acids 57, 58. The most epitope prediction tools available in immune epitope database IEDB. The reference sequences of glycoprotein H and glycoprotein B of MDV-1 were subjected to different B cell tools from immune epitope database (http://tools.iedb.org/bcell/) to predict linear B-cell epitopes using BepiPred linear epitope prediction, surface accessibility by Emini surface accessibility prediction tool, and antigenic reactivity using Kolaskar and Tongaonkar antigenicity tool.

2.4. T-cell Epitope Prediction

A functional T-cell response requires MHC–peptide binding and a suitable interaction of the MHC–peptide ligand with a specific T-cell receptor (TR) 45. Therefore this requires the prediction of peptides bind to MHC class I &II.


2.4.1. Major Histocompatibility Complex (MHC) Class I Binding Predictions

For prediction of peptides bind to MHC class I; the reference sequence of Glycoprotein H and Glycoprotein B of MDV-1 were submitted in MHC-I Binding prediction tool (http: //tools.iedb.org/mhci/) in IEDB. Prediction method of Artificial Neural Network (ANN) 59 was used to calculate IC50 values of peptide binding to MHC1 molecules. Analysis was done using human leucocyte antigen (HLA) alleles 60. For all the alleles, peptide length was set to 9 amino acids prior to the prediction 61. The alleles having binding affinity IC50 less than 100 nm were suggested to be promising candidate epitopes.


2.4.2. Major Histocompatibility Complex (MHC) Class II Binding Predictions

For prediction of peptides bind to MHC class II; the reference sequence of Glycoprotein H Glycoprotein B of MDV-1 were assessed by the IEDB MHC-II prediction tool at (http: //tools.iedb.org/mhcii/). For the screening of promising epitopes, human allele reference set (HLA DR, DQ, DP) were used 62, 63. NN-align prediction method 64 in IEDB was used with (IC50) 0f 1000 nm. Peptides less or equal to the (IC50) value were chosen for further consideration.

2.5. Homology Modeling

For creation of the 3D structure of the reference sequence of Glycoprotein H (YP_001033950.1) and glycoprotein B (YP_001033956) using Raptor X protein structure prediction server available at (http: // raptorx.uchicago.edu/Structure Prediction/predict/). Chimera 1.8 65 was used to visualize the selected epitopes belonging to the B cell, MHC-I, and MHC-II.

2.6. Molecular Docking of the Proposed Epitopes with MHC Class 1 Alleles

The MHC class I of Chickens BF2*2101 haplotype (CAK54661.1 ) was retrieved in FASTA format in the 15th of October 2017 from the National Center for Biotechnology Information (NCBI) and uploaded to the Raptor X (http: //raptorx.uchicago.edu/StructurePrediction/predict/) to obtain the 3D structure. Four peptides of glycoprotein H and two peptides of glycoprotein B are bound the largest number of alleles were selected to predict 3D peptide structure using Pep fold available at the mobyle web server 66. The proposed models for each of the selected MHC class I binding peptides were docked with the MHC class I protein (BF2*2101) using Patch dock server 67 and visualized using Chimera 1.8.

3. Results

3.1. Conserved Regions Determination

Figure 2 showed the conserved regions in the retrieval sequences of Glycoprotein H and Glycoprotein B of MDV-1. Moreover the thresholds for B- Cells for the linear epitopes, surface accessibility and antigenicity were shown in Figure 3.

3.2. B-cell Epitope Prediction

The reference sequence of glycoprotein H and glycoprotein B of MDV-1 were subjected to different tools in B-cell epitope prediction at IEDB. Several epitopes were predicted to interact against B cells for both glycoproteins H and B. Glycoprotein H epitopes were shown in Table 2 while the best three epitopes of glycoprotein H were shown in Table 3 with their scores. Table 4 showed the total epitopes that obtained from glycoprotein B and Table 5 demonstrated the best three epitopes from Glycoprotein B with their scores. These selected epitopes fulfilled the criteria of surface accessibility, antigenicity. Moreover the 3D structures of the selected epitopes from glycoprotein H and B were shown in Figure 4.

3.3. T-cell Epitope Prediction
3.3.1. MHC Class I Binding Predictions

MHC class-I of various HLA alleles with interaction of T-cell epitopes of glycoprotein H and glycoprotein B were predicted. Successful candidate’s epitopes had a half maximal inhibitory concentration (IC50) < 100 nM, were listed in Table 6 and Table 7. The 3D structure of these epitopes was demonstrated in Figure 5.


3.3.2. MHC Class II Binding Predictions

T- Cell epitopes and interaction with MHC Class II were predicted based on the NN-align method with half maximal inhibitory concentration (IC50) ≤ 1000 nm. The most promising four epitopes of glycoprotein H and two of glycoprotein B that had binding affinity with human MHC class II alleles were shown in Table 8 and Table 9 repectively. The 3D structure of these epitopes was depicted in Figure 5.


3.3.3. Molecular Docking of the Proposed Epitopes with MHC Class 1 Alleles

Four docked epitopes; 425-YVLRSAYAF-433, 175-LTSELTGTY-183, 476-LYYAFASIF-484 and 367-MITETLSTF-375 of glycoprotein H and two epitopes; 598-FLFGSGYAL-606 and 727-FMSNPFGAL-735 of glycoprotein B were found to have binding affinity to chickens MHC I molecule (BF2*2101) haplotype which the binding energy score for four epitopes as shown in Table 10 as obtained from patch dock. Figure 6 visualize the binding interactions between MHC I receptor and epitopes in the structural level.

4. Discussion

Vaccination is usually considered to be the most effective method of preventing infectious diseases. Inactivated vaccines live attenuated vaccines, Subunit vaccines, and DNA vaccines were shown with drawbacks. They were characterized by time-consuming process and active infectious particles can be administered together with the vaccine 46. The increase of incidence of viral infections in animals and human provided the need of new available technologies. For instance the peptides based vaccines approach enables achieving effective, cost-efficient vaccines development. The process is based on identification and chemical synthesis of B-cell and T-cell epitopes that can induce specific immune responses 45.

Therefore, in this study, we aimed to design epitopes based vaccine for Marek’s disease virus serotype 1 (MDV-1). Marek’s disease (MD) caused by Marek’s disease virus (MDV) serotype 1 (MDV-1) is oncogenic which causes economic losses in poultry industry estimated to be more than 1$ billion per year 10, 26, 68. In this report both glycoprotein H and B of Marek’s disease were targeted as an immunogens and tested for their efficacy in eliciting immunity against B-cell and T-cell. In our results three epitopes of glycoprotein H (91-FYKRPVSKLL-100, 255-LKPYEPVDKF-264, and 684-PRPL-687) and three epitopes of glycoprotein B (162-EKQV-165, 234-YGLSPPE-240, and 363-YNDSHVK-369) were shown to elicit the B cells. They fulfilled the criteria of surface accessibility, antigenicity. Therefore they were proposed as B cell epitopes since they got scores above thresholds in Bepipred, Emini surface accessibility and Kolaskar and Tongaonkar antigenicity prediction methods and showed 100% conservancy.

During virus infection, the importance of MHC I and II in fighting the infection is crucial 69. Most importantly since there were no data available in the IEDB concerning chicken alleles, the human alleles were used to predict the allelic interaction with MHC1 and MHC11. Several studies concluded the highly similarities between human and chickens MHC molecules 60, 62, 63, 70. Interestingly four epitopes namely (425-YVLRSAYAF-433, 175-LTSELTGTY-183, 476-LYYAFASIF-484 and 367-MITETLSTF-375) from glycoprotein H and two epitopes namely (598-FLFGSGYAL-606 and 727-FMSNPFGAL-735) from glycoprotein B were found to interact with high binding affinity to both MHC1 and MHC11 alleles. Therefore these epitopes were selected as promising peptides vaccine against Marek’s disease. Furthermore for critical binding the predicted epitopes were further docked against MHC1 molecule. Strikingly the predicted epitopes from the glycoprotein H and B demonstrated low binding energy score to chickens MHC class I molecule (BF2*2101) haplotype in the structural level. This result could further solidify the ability of the predicted epitopes to act as strong vaccine epitopes

To our knowledge there is no epitope based vaccine for the Marek’s disease virus serotype 1 (MDV-1) using in silico approach. The advantages of this approach is focusing in immune response, enhancing immunity and reducing costs 47, 71. It is noteworthy the peptide based vaccine approach have successfully used in therapeutic and designing peptide-based vaccine in a considerable number of human and animal viruses and diseases such as Influenza virus, Paratuberculosis, HIV, Ebola virus, cancer and others 72, 73, 74, 75, 76, 77, 78.

This study proposed an interesting epitopes of glycoprotein H and glycoprotein B that have very strong binding affinity to both B and T cells (MHC1 and MHC11 alleles) which indicates strong potential to formulate peptide vaccine for Marek’s disease virus serotype (MDV-1). An in vitro and in vivo application is required to prove the efficacy of the predicted epitopes as peptide vaccine.

5. Conclusion

In conclusion, this study indicated that immunoinformatics wide screening of vaccine targets of emerging highly pathogenic pathogens is a promising strategy to accelerate their vaccine development, which lessens the time and cost required for laboratory analysis of pathogen gene products.

Acknowledgments

Authors would like to thank the staff members of College of Veterinary Medicine, University of Bahri, Khartoum/ Sudan for their cooperation and support.

Competing Interest

The authors declare that they have no competing interests.

References

[1]  Faiz NM, Cortes AL, Guy JS, Fletcher OJ, Cimino T, Gimeno IM. Evaluation of factors influencing the development of late Marek's disease virus-induced immunosuppression: virus pathotype and host sex. Avian pathology : journal of the WVPA. 2017: 1-10.
In article      View Article
 
[2]  Abdul-Careem MF, Hunter BD, Sarson AJ, Parvizi P, Haghighi HR, Read L, et al. Host responses are induced in feathers of chickens infected with Marek's disease virus. Virology. 2008; 370(2): 323-32.
In article      View Article  PubMed
 
[3]  Haesendonck R, Garmyn A, Dorrestein GM, Hellebuyck T, Antonissen G, Pasmans F, et al. Marek's disease virus associated ocular lymphoma in Roulroul partridges (Rollulus rouloul). Avian pathology : journal of the WVPA. 2015; 44(5): 347-51.
In article      View Article  PubMed
 
[4]  Hu X, Qin A, Qian K, Shao H, Yu C, Xu W, et al. Analysis of protein expression profiles in the thymus of chickens infected with Marek's disease virus. Virology journal. 2012; 9: 256.
In article      View Article  PubMed
 
[5]  Jarosinski KW. Marek's disease virus late protein expression in feather follicle epithelial cells as early as 8 days postinfection. Avian diseases. 2012; 56(4): 725-31.
In article      View Article  PubMed
 
[6]  Abdul-Careem MF, Haq K, Shanmuganathan S, Read LR, Schat KA, Heidari M, et al. Induction of innate host responses in the lungs of chickens following infection with a very virulent strain of Marek's disease virus. Virology. 2009; 393(2): 250-7.
In article      View Article  PubMed
 
[7]  Couteaudier M, Denesvre C. Marek's disease virus and skin interactions. Veterinary research. 2014; 45: 36.
In article      View Article  PubMed
 
[8]  Perumbakkam S, Muir WM, Black-Pyrkosz A, Okimoto R, Cheng HH. Comparison and contrast of genes and biological pathways responding to Marek's disease virus infection using allele-specific expression and differential expression in broiler and layer chickens. BMC genomics. 2013; 14: 64.
In article      View Article  PubMed
 
[9]  Atkins KE, Read AF, Walkden-Brown SW, Savill NJ, Woolhouse ME. The effectiveness of mass vaccination on Marek's disease virus (MDV) outbreaks and detection within a broiler barn: a modeling study. Epidemics. 2013; 5(4): 208-17.
In article      View Article  PubMed
 
[10]  Atkins KE, Read AF, Savill NJ, Renz KG, Walkden-Brown SW, Woolhouse ME. Modelling Marek's disease virus (MDV) infection: parameter estimates for mortality rate and infectiousness. BMC veterinary research. 2011; 7: 70.
In article      View Article  PubMed
 
[11]  Biggs PM, Nair V. The long view: 40 years of Marek's disease research and Avian Pathology. Avian pathology: journal of the WVPA. 2012; 41(1): 3-9.
In article      View Article  PubMed
 
[12]  Gong Z, Zhang L, Wang J, Chen L, Shan H, Wang Z, et al. Isolation and analysis of a very virulent Marek's disease virus strain in China. Virology journal. 2013; 10: 155.
In article      View Article  PubMed
 
[13]  Nitish Boodhoo AG, Shayan Sharif and Shahriar Behboudi. Marek’s disease in chickens: a review with focus on immunology. Veterinary research. 2016: 1-19.
In article      View Article
 
[14]  Demeke B, Jenberie S, Tesfaye B, Ayelet G, Yami M, Lamien CE, et al. Investigation of Marek's disease virus from chickens in central Ethiopia. Tropical animal health and production. 2017; 49(2): 403-8.
In article      View Article  PubMed
 
[15]  Mohamed MH, El-Sabagh IM, Al-Habeeb MA, Al-Hammady YM. Diversity of Meq gene from clinical Marek's disease virus infection in Saudi Arabia. Veterinary world. 2016; 9(6): 572-8.
In article      View Article  PubMed
 
[16]  Dunn JR, Gimeno IM. Current status of Marek's disease in the United States and worldwide based on a questionnaire survey. Avian diseases. 2013; 57(2 Suppl): 483-90.
In article      View Article  PubMed
 
[17]  Kennedy DA, Dunn JR, Dunn PA, Read AF. An observational study of the temporal and spatial patterns of Marek's-disease-associated leukosis condemnation of young chickens in the United States of America. Preventive veterinary medicine. 2015; 120(3-4): 328-35.
In article      View Article  PubMed
 
[18]  Ola Hassanin FA, and Iman E. El-Araby. Molecular Characterization and Phylogenetic Analysis of Marek’s Disease Virus from Clinical Cases of Marek’s Disease in Egypt. Avian diseases. 2013; 57: 555–61.
In article      View Article  PubMed
 
[19]  Raja A, Dhinakar Raj G, Bhuvaneswari P, Balachandran C, Kumanan K. Detection of virulent Marek's disease virus in poultry in India. Acta virologica. 2009; 53(4): 255-60.
In article      View Article  PubMed
 
[20]  Zhuang X, Zou H, Shi H, Shao H, Ye J, Miao J, et al. Outbreak of Marek's disease in a vaccinated broiler breeding flock during its peak egg-laying period in China. BMC veterinary research. 2015; 11: 157.
In article      View Article  PubMed
 
[21]  Cui N, Su S, Sun P, Zhang Y, Han N, Cui Z. Isolation and pathogenic analysis of virulent Marek's disease virus field strain in China. Poultry science. 2016; 95(7): 1521-8.
In article      View Article  PubMed
 
[22]  Angamuthu R, Baskaran S, Gopal DR, Devarajan J, Kathaperumal K. Rapid detection of the Marek's disease viral genome in chicken feathers by loop-mediated isothermal amplification. Journal of clinical microbiology. 2012; 50(3): 961-5.
In article      View Article  PubMed
 
[23]  Matsuyama-Kato A, Murata S, Isezaki M, Kano R, Takasaki S, Ichii O, et al. Molecular characterization of immunoinhibitory factors PD-1/PD-L1 in chickens infected with Marek's disease virus. Virology journal. 2012; 9: 94.
In article      View Article  PubMed
 
[24]  Suresh P, Johnson Rajeswar J, Sukumar K, Harikrishnan TJ, Srinivasan P. Complete nucleotide sequence analysis of the oncogene "Meq" from serotype 1 Marek's disease virus isolates from India. British poultry science. 2017; 58(2): 111-5.
In article      View Article  PubMed
 
[25]  Chen C, Li H, Xie Q, Shang H, Ji J, Bai S, et al. Transcriptional profiling of host gene expression in chicken liver tissues infected with oncogenic Marek's disease virus. The Journal of general virology. 2011; 92(Pt 12): 2724-33.
In article      View Article  PubMed
 
[26]  Tai SS, Hearn C, Umthong S, Agafitei O, Cheng HH, Dunn JR, et al. Expression of Marek's Disease Virus Oncoprotein Meq During Infection in the Natural Host. Virology. 2017; 503: 103-13.
In article      View Article  PubMed
 
[27]  Suchodolski PF, Izumiya Y, Lupiani B, Ajithdoss DK, Lee LF, Kung HJ, et al. Both homo and heterodimers of Marek's disease virus encoded Meq protein contribute to transformation of lymphocytes in chickens. Virology. 2010; 399(2): 312-21.
In article      View Article  PubMed
 
[28]  Luo J, Yu Y, Mitra A, Chang S, Zhang H, Liu G, et al. Genome-wide copy number variant analysis in inbred chickens lines with different susceptibility to Marek's disease. G3. 2013; 3(2): 217-23.
In article      View Article  PubMed
 
[29]  Rong S, Wheeler D, Weber F. Efficient Marek's disease virus (MDV) and herpesvirus of turkey infection of the QM7 cell line that does not contain latent MDV genome. Avian pathology : journal of the WVPA. 2014; 43(5): 414-9.
In article      View Article  PubMed
 
[30]  Witter RL, Calnek BW, Buscaglia C, Gimeno IM, Schat KA. Classification of Marek's disease viruses according to pathotype: philosophy and methodology. Avian pathology: journal of the WVPA. 2005; 34(2): 75-90.
In article      View Article  PubMed
 
[31]  Tulman ER, Afonso CL, Lu Z, Zsak L, Rock DL, Kutish GF. The genome of a very virulent Marek's disease virus. Journal of virology. 2000; 74(17): 7980-8.
In article      View Article  PubMed
 
[32]  McPherson MC, Delany ME. Virus and host genomic, molecular, and cellular interactions during Marek's disease pathogenesis and oncogenesis. Poultry science. 2016; 95(2): 412-29.
In article      View Article  PubMed
 
[33]  Shamblin CE, Greene N, Arumugaswami V, Dienglewicz RL, Parcells MS. Comparative analysis of Marek's disease virus (MDV) glycoprotein-, lytic antigen pp38- and transformation antigen Meq-encoding genes: association of meq mutations with MDVs of high virulence. Veterinary microbiology. 2004; 102(3-4): 147-67.
In article      View Article  PubMed
 
[34]  Scott SD, Smith GD, Ross NL, Binns MM. Identification and sequence analysis of the homologues of the herpes simplex virus type 1 glycoprotein H in Marek's disease virus and the herpesvirus of turkeys. The Journal of general virology. 1993; 74 (Pt 6): 1185-90.
In article      View Article  PubMed
 
[35]  Spatz SJ, Zhao Y, Petherbridge L, Smith LP, Baigent SJ, Nair V. Comparative sequence analysis of a highly oncogenic but horizontal spread-defective clone of Marek's disease virus. Virus genes. 2007; 35(3): 753-66.
In article      View Article  PubMed
 
[36]  Chi XJ, Lu YX, Zhao P, Li CG, Wang XJ, Wang M. Interaction domain of glycoproteins gB and gH of Marek's disease virus and identification of an antiviral peptide with dual functions. PloS one. 2013; 8(2): e54761.
In article      View Article  PubMed
 
[37]  Baigent SJ, Petherbridge LJ, Smith LP, Zhao Y, Chesters PM, Nair VK. Herpesvirus of turkey reconstituted from bacterial artificial chromosome clones induces protection against Marek's disease. The Journal of general virology. 2006; 87(Pt 4): 769-76.
In article      View Article  PubMed
 
[38]  Reddy SM, Izumiya Y, Lupiani B. Marek's disease vaccines: Current status, and strategies for improvement and development of vector vaccines. Veterinary microbiology. 2016.
In article      View Article
 
[39]  Gimeno IM, Cortes AL. Evaluation of factors influencing replication of serotype 1 Marek's disease vaccines in the chicken lung. Avian pathology : journal of the WVPA. 2010; 39(2): 71-9.
In article      View Article  PubMed
 
[40]  Islam T, Walkden Brown SW, Renz KG, Fakhrul Islam AF, Ralapanawe S. Vaccination-challenge interval markedly influences protection provided by Rispens CVI988 vaccine against very virulent Marek's disease virus challenge. Avian pathology: journal of the WVPA. 2013; 42(6): 516-26.
In article      View Article  PubMed
 
[41]  Mwangi WN, Smith LP, Baigent SJ, Smith AL, Nair V. Induction of lymphomas by inoculation of Marek's disease virus-derived lymphoblastoid cell lines: prevention by CVI988 vaccination. Avian pathology : journal of the WVPA. 2012; 41(6): 589-98.
In article      View Article  PubMed
 
[42]  Li K, Liu Y, Liu C, Gao L, Zhang Y, Cui H, et al. Recombinant Marek's disease virus type 1 provides full protection against very virulent Marek's and infectious bursal disease viruses in chickens. Scientific reports. 2016; 6: 39263.
In article      View Article  PubMed
 
[43]  Zhang X, Wu Y, Huang Y, Liu X. Protection conferred by a recombinant Marek's disease virus that expresses the spike protein from infectious bronchitis virus in specific pathogen-free chicken. Virology journal. 2012; 9: 85.
In article      View Article  PubMed
 
[44]  Cui H, Gao H, Cui X, Zhao Y, Shi X, Li Q, et al. Avirulent Marek's disease virus type 1 strain 814 vectored vaccine expressing avian influenza (AI) virus H5 haemagglutinin induced better protection than turkey herpesvirus vectored AI vaccine. PloS one. 2013; 8(1): e53340.
In article      View Article  PubMed
 
[45]  De NTaRK. Immunoinformatics: an integrated scenario. Immunology. 2010: 153-68.
In article      PubMed  PubMed
 
[46]  Doytchinova APaI. T-cell epitope vaccine design by immunoinformatics. Open Biology. 2013: 1-13.
In article      View Article
 
[47]  Ruth E. Soria-Guerra RN-G, Dania O. Govea-Alonso , Sergio Rosales-Mendoza. An overview of bioinformatics tools for epitope prediction: Implications on vaccine development. Journal of Biomedical Informatics. 2014: 1-9.
In article      View Article
 
[48]  Kohlbacher LBaO. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Medicine 2015: 1-12.
In article      PubMed  PubMed
 
[49]  Bette Korber ML, Karina Yusim. Immunoinformatics Comes of Age. PLoS Computational Biology. 2006; 2(6): 484-92.
In article      View Article  PubMed
 
[50]  Groot ASD. Immunomics: discovering new targets for vaccines and therapeutics. Drug Discovery Today. 2006; 11: 203-9.
In article      View Article
 
[51]  Mawadda Abd-Elraheem Awad-Elkareem SAO, Hanaa Abdalla Mohamed, Hadeel Abd-Elrahman Hassan, Ahmed Hamdi Abu-haraz, Khoubieb Ali Abd-elrahman and Mohamed Ahmed Salih. Prediction and Conservancy Analysis of Multiepitope Based Peptide Vaccine Against Merkel Cell Polyomavirus: An Immunoinformatics Approach. Immunome Research. 2017; 13(2): 1-16.
In article      View Article
 
[52]  Malaz Abdelbagi TH, Mohammed Shihabeldin, Sanaa Bashir, Elkhaleel Ahmed, Elmoez Mohamed, Shawgi Hafiz, Abdah Abdelmonim, Tassneem Hamid, Shimaa Awad, Ahmed Hamdi, Khoubieb Ali and Mohammed A. Hassan. Immunoinformatics Prediction of Peptide-Based Vaccine Against African Horse Sickness Virus. Immunome Research. 2017; 13(2): 1-14.
In article      View Article
 
[53]  Jiandong Shi, Jing Zhang,, Sijin Li, Jing Sun, Yumei Teng, Meini Wu, Jianfan Li, Yanhan Li, Ningzhu Hu, Haixuan Wang, Yunzhang Hu. Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases. PloS one. 2015: 1-16.
In article      View Article
 
[54]  Tahirah Yasmin SA, Mouly Debnath, Akio Ebihara, Tsutomu Nakagawa and A. H. M. Nurun Nabi. In silico proposition to predict cluster of B- and T-cell epitopes for the usefulness of vaccine design from invasive, virulent and membrane associated proteins of C. jejuni. In Silico Pharmacology. 2016: 1-10.
In article      View Article
 
[55]  Tom Hall Ib, Carlsbad, Ca. BioEdit: An important software for molecular biology. GERF Bulletin of Biosciences. 2011: 60-1.
In article      View Article
 
[56]  Pingping Sun HJ, Zhenbang Liu, Qiao Ning, Jian Zhang, Xiaowei Zhao, Yanxin Huang, Zhiqiang Ma, and Yuxin Li. Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction. Computational and Mathematical Methods in Medicine. 2013.
In article      PubMed  PubMed
 
[57]  John Wiley & Sons L. Immunoinformatics may lead to a reappraisal of the nature of B cell epitopes and of the feasibility of synthetic peptide vaccines. J Mol Recognit 2006; 19: 183-7.
In article      View Article  PubMed
 
[58]  Chun-Hung Su NRP, Ken-Li Lin, I-Fang Chung. Identification of Amino Acid Propensities That Are Strong Determinants of Linear B-cell Epitope Using Neural Networks. PloS one. 2013.
In article      View Article
 
[59]  MORTEN NIELSEN CL, PEDER WORNING, SANNE LISE LAUEMØLLER, KASPER LAMBERTH, SØREN BUUS SØREN BRUNAK, AND OLE LUND. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Science. 2003: 12: 1007-17.
In article      View Article  PubMed
 
[60]  Michael Koch SC, Trevor Collen, David Avila, Jan Salomonsen, Hans-Joachim Wallny, Andrew van Hateren, Lawrence Hunt, Jansen P. Jacob, Fiona Johnston, Denise A. Marston, Iain Shaw, P. Rod Dunbar, Vincenzo Cerundolo, , E. Yvonne Jones aJK. Structures of an MHC Class I Molecule from B21 Chickens Illustrate Promiscuous Peptide Binding. Immunity. 2007: 885-99.
In article      PubMed
 
[61]  Claus Lundegaard OLaMN. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. bioinformatics. 2008; 24: 1397-8.
In article      View Article  PubMed
 
[62]  Bjørn Bremnes MR, Merete Gedde-Dahl, Tommy W. Nordeng, Jorunn Jacobsen, Scott A. Ness, and Oddmund Bakke. The MHC Class II-Associated Chicken Invariant Chain Shares Functional Properties with Its Mammalian Homologs. Experimental cell research. 2000: 360-9.
In article      View Article
 
[63]  F. Chen LP, W. Chao , Y. Dai , and W. Yu. Character of chicken polymorphic major histocompatibility complex class II alleles of 3 Chinese local breeds. Poultry science. 2012 91 1097-104.
In article      View Article  PubMed
 
[64]  Lund MNaO. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics. 2009: 1-10.
In article      PubMed  PubMed
 
[65]  ERIC F. PETTERSEN TDG, CONRAD C. HUANG, GREGORY S. COUCH, DANIEL M. GREENBLATT, ELAINE C. MENG, THOMAS E. FERRIN. UCSF Chimera-A Visualization System for Exploratory Research and Analysis. J Comput Chem. 2004; 25: 1605-12.
In article      View Article  PubMed
 
[66]  Maupetit J DP, Tuffery P. PEP-FOLD: an online resource for de novo peptide structure prediction. Nucleic Acids Res. 2009: 498-503.
In article      View Article  PubMed
 
[67]  Schneidman-Duhovny D IY, Nussinov R, Wolfson HJ, et al. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005; 33: 363-7.
In article      View Article  PubMed
 
[68]  Brown AC, Smith LP, Kgosana L, Baigent SJ, Nair V, Allday MJ. Homodimerization of the Meq viral oncoprotein is necessary for induction of T-cell lymphoma by Marek's disease virus. Journal of virology. 2009; 83(21): 11142-51.
In article      View Article  PubMed
 
[69]  Niikura M, Kim T, Hunt HD, Burnside J, Morgan RW, Dodgson JB, et al. Marek's disease virus up-regulates major histocompatibility complex class II cell surface expression in infected cells. Virology. 2007; 359(1): 212-9.
In article      View Article  PubMed
 
[70]  Isolation of chicken major histocompatibility complex class 11 (B-L) chain sequences: comparison with mammalian chains and expression in lymphoid organs. The EMBO journal. 1988; 7 1031 -9.
In article      PubMed  PubMed
 
[71]  Jonathan M. Gershoni AR-B, Dror D. Siman-Tov, Natalia Tarnovitski Freund and, Weiss Y. Epitope Mapping The First Step in Developing Epitope-Based Vaccines. Biodrugs. 2007: 145-56.
In article      
 
[72]  Navid Nezafat ZK, Mahboobeh Eslami , Milad Mohkama, Sanam Zandian , Younes Ghasemi. Designing an efficient multi-epitope peptide vaccine against Vibrio cholerae via combined immunoinformatics and protein interaction based approaches. Computational Biology and Chemistry. 2016; 62: 82–95.
In article      View Article
 
[73]  Ahmed Hamdi Abu-haraz KAA-e, Mojahid Salah Ibrahim, Waleed Hassan Hussien, Mohammed Siddig Mohammed, Marwan Mustafa Badawi and Mohamed Ahmed Salih. Multi Epitope Peptide Vaccine Prediction against Sudan Ebola Virus Using Immuno-Informatics Approaches. Advanced Techniques in Biology & Medicine. 2017; 5(1): 1-21.
In article      View Article
 
[74]  Marwan Mustafa Badawi AAFA, Salma Sleak Alam, Wafa Aljack Mohamed, Duaa Adil Nasr-Eldin Osman, Samar Ali Abd Alrazig Ali, Entissar Mohamed Elhassan Ahmed, Abdah AbdElmonim Adam, Ranya Omar Abdullh, Mohamed Ahmed Salih. Immunoinfomatics Predication and in silico Modeling of Epitope-Based Peptide Vaccine Against virulent Newcastle Disease Viruses. American Journal of Infectious Diseases and Microbiology. 2016; 4: 61-71.
In article      View Article
 
[75]  Anne S. De Groot MA, Elizabeth M. McClaine, Leonard Moise,William D. Martin. Immunoinformatic comparison of T-cell epitopes contained in novel swine-origin influenza A (H1N1) virus with epitopes in 2008–2009 conventional influenza vaccine. Vaccine. 2009; 27 5740-7.
In article      View Article
 
[76]  Sandra Iurescia DF, Vito Michele Fazio, Monica Rinaldi. Epitope-driven DNA vaccine design employing immunoinformatics against B-cell lymphoma: A biotech's challenge. Biotechnology Advances. 2012: 372-83.
In article      View Article  PubMed
 
[77]  PerlaCarlos V, SébastienHolbert , Felipe Ascencio , KrisHuygen, GraciaGomez-Anduro, MaximeBranger , MarthaReyes-Becerril, CarlosAngulo. In silico epitope analysis of unique and membrane associated proteins from Mycobacteriumavium subsp. paratuberculosis for immunogenicity and vaccine evaluation. Journal of TheoreticalBiology. 2015: 1-9.
In article      PubMed
 
[78]  De Groot AS SH, Aubin CS, McMurry J, Martin W,. Immunoinformatics: Mining genomes for vaccine components. Immunol Cell Biol. 2002; 80: 255-69.
In article      View Article
 

Published with license by Science and Education Publishing, Copyright © 2018 Sanaa Bashir, Khoubieb Ali Abd-elrahman, Mohammed A. Hassan and Yassir A. Almofti

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Cite this article:

Normal Style
Sanaa Bashir, Khoubieb Ali Abd-elrahman, Mohammed A. Hassan, Yassir A. Almofti. Multi Epitope Based Peptide Vaccine against Marek’s Disease Virus Serotype 1 Glycoprotein H and B. American Journal of Microbiological Research. Vol. 6, No. 4, 2018, pp 124-139. http://pubs.sciepub.com/ajmr/6/4/2
MLA Style
Bashir, Sanaa, et al. "Multi Epitope Based Peptide Vaccine against Marek’s Disease Virus Serotype 1 Glycoprotein H and B." American Journal of Microbiological Research 6.4 (2018): 124-139.
APA Style
Bashir, S. , Abd-elrahman, K. A. , Hassan, M. A. , & Almofti, Y. A. (2018). Multi Epitope Based Peptide Vaccine against Marek’s Disease Virus Serotype 1 Glycoprotein H and B. American Journal of Microbiological Research, 6(4), 124-139.
Chicago Style
Bashir, Sanaa, Khoubieb Ali Abd-elrahman, Mohammed A. Hassan, and Yassir A. Almofti. "Multi Epitope Based Peptide Vaccine against Marek’s Disease Virus Serotype 1 Glycoprotein H and B." American Journal of Microbiological Research 6, no. 4 (2018): 124-139.
Share
  • Figure 2. Multiple Sequences Alignment (MSA), A; for glycoprotein H and B; for glycoprotein B. The boxes within the sequences showed position of mutations among the retrieved strains
  • Figure 3. Prediction of B-cell epitopes by different IEDB scales (1- Bepipred linear epitope prediction, 2- Emini surface accessibility, 3- Kolaskar and Tongaonkar antigenicity prediction). Regions above threshold (red line) are proposed to be a part of B cell epitope while regions below the threshold (red line) are not
  • Figure 4. Structural position of the most promising conserved B cell epitopes; A: for glycoprotein H and B: for glycoprotein B of MDV-1
  • Figure 5. Structural position of the most promising conserved T- cell epitopes. A: glycoprotein H and B: glycoprotein B of MDV-1 that Interacts with both MHC-I and MHC-II alleles
  • Figure 6. Interaction of proposed epitopes with MHC I allele of Chickens (BF2*2101). A: Epitopes of glycoprotein H. B: epitopes of glycoprotein B
  • Table 1. Glycoprotein H and glycoprotein B of MDV-1 retrieved strains, their accession numbers and their area of collection
  • Table 2. B- Cells conserved peptides of glycoprotein H with their surface accessibility score and antigenicity score
  • Table 3. The best three B- Cell epitopes of glycoprotein H that gave score above threshold in Emini & Antigenicity
  • Table 4. B- Cells conserved peptides of glycoprotein B with their surface accessibility score and antigenicity score
  • Table 5. The best three B- Cell epitopes from glycoprotein B that gave score above threshold in Emini & Antigenicity
  • Table 6. The best epitopes of glycoprotein H that had binding affinity with the human MHC class I alleles
  • Table 7. The best epitopes of glycoprotein B that had binding affinity with the human MHC class I alleles
  • Table 8. The best four epitopes of glycoprotein H that had binding affinity with the human MHC class II alleles
  • Table 9. The best two epitopes of glycoprotein B that had binding affinity with the human MHC class II alleles
  • Table 10. Docking results of most promising predicted and modeled MHC-I binding epitopes with the binding energy score
[1]  Faiz NM, Cortes AL, Guy JS, Fletcher OJ, Cimino T, Gimeno IM. Evaluation of factors influencing the development of late Marek's disease virus-induced immunosuppression: virus pathotype and host sex. Avian pathology : journal of the WVPA. 2017: 1-10.
In article      View Article
 
[2]  Abdul-Careem MF, Hunter BD, Sarson AJ, Parvizi P, Haghighi HR, Read L, et al. Host responses are induced in feathers of chickens infected with Marek's disease virus. Virology. 2008; 370(2): 323-32.
In article      View Article  PubMed
 
[3]  Haesendonck R, Garmyn A, Dorrestein GM, Hellebuyck T, Antonissen G, Pasmans F, et al. Marek's disease virus associated ocular lymphoma in Roulroul partridges (Rollulus rouloul). Avian pathology : journal of the WVPA. 2015; 44(5): 347-51.
In article      View Article  PubMed
 
[4]  Hu X, Qin A, Qian K, Shao H, Yu C, Xu W, et al. Analysis of protein expression profiles in the thymus of chickens infected with Marek's disease virus. Virology journal. 2012; 9: 256.
In article      View Article  PubMed
 
[5]  Jarosinski KW. Marek's disease virus late protein expression in feather follicle epithelial cells as early as 8 days postinfection. Avian diseases. 2012; 56(4): 725-31.
In article      View Article  PubMed
 
[6]  Abdul-Careem MF, Haq K, Shanmuganathan S, Read LR, Schat KA, Heidari M, et al. Induction of innate host responses in the lungs of chickens following infection with a very virulent strain of Marek's disease virus. Virology. 2009; 393(2): 250-7.
In article      View Article  PubMed
 
[7]  Couteaudier M, Denesvre C. Marek's disease virus and skin interactions. Veterinary research. 2014; 45: 36.
In article      View Article  PubMed
 
[8]  Perumbakkam S, Muir WM, Black-Pyrkosz A, Okimoto R, Cheng HH. Comparison and contrast of genes and biological pathways responding to Marek's disease virus infection using allele-specific expression and differential expression in broiler and layer chickens. BMC genomics. 2013; 14: 64.
In article      View Article  PubMed
 
[9]  Atkins KE, Read AF, Walkden-Brown SW, Savill NJ, Woolhouse ME. The effectiveness of mass vaccination on Marek's disease virus (MDV) outbreaks and detection within a broiler barn: a modeling study. Epidemics. 2013; 5(4): 208-17.
In article      View Article  PubMed
 
[10]  Atkins KE, Read AF, Savill NJ, Renz KG, Walkden-Brown SW, Woolhouse ME. Modelling Marek's disease virus (MDV) infection: parameter estimates for mortality rate and infectiousness. BMC veterinary research. 2011; 7: 70.
In article      View Article  PubMed
 
[11]  Biggs PM, Nair V. The long view: 40 years of Marek's disease research and Avian Pathology. Avian pathology: journal of the WVPA. 2012; 41(1): 3-9.
In article      View Article  PubMed
 
[12]  Gong Z, Zhang L, Wang J, Chen L, Shan H, Wang Z, et al. Isolation and analysis of a very virulent Marek's disease virus strain in China. Virology journal. 2013; 10: 155.
In article      View Article  PubMed
 
[13]  Nitish Boodhoo AG, Shayan Sharif and Shahriar Behboudi. Marek’s disease in chickens: a review with focus on immunology. Veterinary research. 2016: 1-19.
In article      View Article
 
[14]  Demeke B, Jenberie S, Tesfaye B, Ayelet G, Yami M, Lamien CE, et al. Investigation of Marek's disease virus from chickens in central Ethiopia. Tropical animal health and production. 2017; 49(2): 403-8.
In article      View Article  PubMed
 
[15]  Mohamed MH, El-Sabagh IM, Al-Habeeb MA, Al-Hammady YM. Diversity of Meq gene from clinical Marek's disease virus infection in Saudi Arabia. Veterinary world. 2016; 9(6): 572-8.
In article      View Article  PubMed
 
[16]  Dunn JR, Gimeno IM. Current status of Marek's disease in the United States and worldwide based on a questionnaire survey. Avian diseases. 2013; 57(2 Suppl): 483-90.
In article      View Article  PubMed
 
[17]  Kennedy DA, Dunn JR, Dunn PA, Read AF. An observational study of the temporal and spatial patterns of Marek's-disease-associated leukosis condemnation of young chickens in the United States of America. Preventive veterinary medicine. 2015; 120(3-4): 328-35.
In article      View Article  PubMed
 
[18]  Ola Hassanin FA, and Iman E. El-Araby. Molecular Characterization and Phylogenetic Analysis of Marek’s Disease Virus from Clinical Cases of Marek’s Disease in Egypt. Avian diseases. 2013; 57: 555–61.
In article      View Article  PubMed
 
[19]  Raja A, Dhinakar Raj G, Bhuvaneswari P, Balachandran C, Kumanan K. Detection of virulent Marek's disease virus in poultry in India. Acta virologica. 2009; 53(4): 255-60.
In article      View Article  PubMed
 
[20]  Zhuang X, Zou H, Shi H, Shao H, Ye J, Miao J, et al. Outbreak of Marek's disease in a vaccinated broiler breeding flock during its peak egg-laying period in China. BMC veterinary research. 2015; 11: 157.
In article      View Article  PubMed
 
[21]  Cui N, Su S, Sun P, Zhang Y, Han N, Cui Z. Isolation and pathogenic analysis of virulent Marek's disease virus field strain in China. Poultry science. 2016; 95(7): 1521-8.
In article      View Article  PubMed
 
[22]  Angamuthu R, Baskaran S, Gopal DR, Devarajan J, Kathaperumal K. Rapid detection of the Marek's disease viral genome in chicken feathers by loop-mediated isothermal amplification. Journal of clinical microbiology. 2012; 50(3): 961-5.
In article      View Article  PubMed
 
[23]  Matsuyama-Kato A, Murata S, Isezaki M, Kano R, Takasaki S, Ichii O, et al. Molecular characterization of immunoinhibitory factors PD-1/PD-L1 in chickens infected with Marek's disease virus. Virology journal. 2012; 9: 94.
In article      View Article  PubMed
 
[24]  Suresh P, Johnson Rajeswar J, Sukumar K, Harikrishnan TJ, Srinivasan P. Complete nucleotide sequence analysis of the oncogene "Meq" from serotype 1 Marek's disease virus isolates from India. British poultry science. 2017; 58(2): 111-5.
In article      View Article  PubMed
 
[25]  Chen C, Li H, Xie Q, Shang H, Ji J, Bai S, et al. Transcriptional profiling of host gene expression in chicken liver tissues infected with oncogenic Marek's disease virus. The Journal of general virology. 2011; 92(Pt 12): 2724-33.
In article      View Article  PubMed
 
[26]  Tai SS, Hearn C, Umthong S, Agafitei O, Cheng HH, Dunn JR, et al. Expression of Marek's Disease Virus Oncoprotein Meq During Infection in the Natural Host. Virology. 2017; 503: 103-13.
In article      View Article  PubMed
 
[27]  Suchodolski PF, Izumiya Y, Lupiani B, Ajithdoss DK, Lee LF, Kung HJ, et al. Both homo and heterodimers of Marek's disease virus encoded Meq protein contribute to transformation of lymphocytes in chickens. Virology. 2010; 399(2): 312-21.
In article      View Article  PubMed
 
[28]  Luo J, Yu Y, Mitra A, Chang S, Zhang H, Liu G, et al. Genome-wide copy number variant analysis in inbred chickens lines with different susceptibility to Marek's disease. G3. 2013; 3(2): 217-23.
In article      View Article  PubMed
 
[29]  Rong S, Wheeler D, Weber F. Efficient Marek's disease virus (MDV) and herpesvirus of turkey infection of the QM7 cell line that does not contain latent MDV genome. Avian pathology : journal of the WVPA. 2014; 43(5): 414-9.
In article      View Article  PubMed
 
[30]  Witter RL, Calnek BW, Buscaglia C, Gimeno IM, Schat KA. Classification of Marek's disease viruses according to pathotype: philosophy and methodology. Avian pathology: journal of the WVPA. 2005; 34(2): 75-90.
In article      View Article  PubMed
 
[31]  Tulman ER, Afonso CL, Lu Z, Zsak L, Rock DL, Kutish GF. The genome of a very virulent Marek's disease virus. Journal of virology. 2000; 74(17): 7980-8.
In article      View Article  PubMed
 
[32]  McPherson MC, Delany ME. Virus and host genomic, molecular, and cellular interactions during Marek's disease pathogenesis and oncogenesis. Poultry science. 2016; 95(2): 412-29.
In article      View Article  PubMed
 
[33]  Shamblin CE, Greene N, Arumugaswami V, Dienglewicz RL, Parcells MS. Comparative analysis of Marek's disease virus (MDV) glycoprotein-, lytic antigen pp38- and transformation antigen Meq-encoding genes: association of meq mutations with MDVs of high virulence. Veterinary microbiology. 2004; 102(3-4): 147-67.
In article      View Article  PubMed
 
[34]  Scott SD, Smith GD, Ross NL, Binns MM. Identification and sequence analysis of the homologues of the herpes simplex virus type 1 glycoprotein H in Marek's disease virus and the herpesvirus of turkeys. The Journal of general virology. 1993; 74 (Pt 6): 1185-90.
In article      View Article  PubMed
 
[35]  Spatz SJ, Zhao Y, Petherbridge L, Smith LP, Baigent SJ, Nair V. Comparative sequence analysis of a highly oncogenic but horizontal spread-defective clone of Marek's disease virus. Virus genes. 2007; 35(3): 753-66.
In article      View Article  PubMed
 
[36]  Chi XJ, Lu YX, Zhao P, Li CG, Wang XJ, Wang M. Interaction domain of glycoproteins gB and gH of Marek's disease virus and identification of an antiviral peptide with dual functions. PloS one. 2013; 8(2): e54761.
In article      View Article  PubMed
 
[37]  Baigent SJ, Petherbridge LJ, Smith LP, Zhao Y, Chesters PM, Nair VK. Herpesvirus of turkey reconstituted from bacterial artificial chromosome clones induces protection against Marek's disease. The Journal of general virology. 2006; 87(Pt 4): 769-76.
In article      View Article  PubMed
 
[38]  Reddy SM, Izumiya Y, Lupiani B. Marek's disease vaccines: Current status, and strategies for improvement and development of vector vaccines. Veterinary microbiology. 2016.
In article      View Article
 
[39]  Gimeno IM, Cortes AL. Evaluation of factors influencing replication of serotype 1 Marek's disease vaccines in the chicken lung. Avian pathology : journal of the WVPA. 2010; 39(2): 71-9.
In article      View Article  PubMed
 
[40]  Islam T, Walkden Brown SW, Renz KG, Fakhrul Islam AF, Ralapanawe S. Vaccination-challenge interval markedly influences protection provided by Rispens CVI988 vaccine against very virulent Marek's disease virus challenge. Avian pathology: journal of the WVPA. 2013; 42(6): 516-26.
In article      View Article  PubMed
 
[41]  Mwangi WN, Smith LP, Baigent SJ, Smith AL, Nair V. Induction of lymphomas by inoculation of Marek's disease virus-derived lymphoblastoid cell lines: prevention by CVI988 vaccination. Avian pathology : journal of the WVPA. 2012; 41(6): 589-98.
In article      View Article  PubMed
 
[42]  Li K, Liu Y, Liu C, Gao L, Zhang Y, Cui H, et al. Recombinant Marek's disease virus type 1 provides full protection against very virulent Marek's and infectious bursal disease viruses in chickens. Scientific reports. 2016; 6: 39263.
In article      View Article  PubMed
 
[43]  Zhang X, Wu Y, Huang Y, Liu X. Protection conferred by a recombinant Marek's disease virus that expresses the spike protein from infectious bronchitis virus in specific pathogen-free chicken. Virology journal. 2012; 9: 85.
In article      View Article  PubMed
 
[44]  Cui H, Gao H, Cui X, Zhao Y, Shi X, Li Q, et al. Avirulent Marek's disease virus type 1 strain 814 vectored vaccine expressing avian influenza (AI) virus H5 haemagglutinin induced better protection than turkey herpesvirus vectored AI vaccine. PloS one. 2013; 8(1): e53340.
In article      View Article  PubMed
 
[45]  De NTaRK. Immunoinformatics: an integrated scenario. Immunology. 2010: 153-68.
In article      PubMed  PubMed
 
[46]  Doytchinova APaI. T-cell epitope vaccine design by immunoinformatics. Open Biology. 2013: 1-13.
In article      View Article
 
[47]  Ruth E. Soria-Guerra RN-G, Dania O. Govea-Alonso , Sergio Rosales-Mendoza. An overview of bioinformatics tools for epitope prediction: Implications on vaccine development. Journal of Biomedical Informatics. 2014: 1-9.
In article      View Article
 
[48]  Kohlbacher LBaO. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Medicine 2015: 1-12.
In article      PubMed  PubMed
 
[49]  Bette Korber ML, Karina Yusim. Immunoinformatics Comes of Age. PLoS Computational Biology. 2006; 2(6): 484-92.
In article      View Article  PubMed
 
[50]  Groot ASD. Immunomics: discovering new targets for vaccines and therapeutics. Drug Discovery Today. 2006; 11: 203-9.
In article      View Article
 
[51]  Mawadda Abd-Elraheem Awad-Elkareem SAO, Hanaa Abdalla Mohamed, Hadeel Abd-Elrahman Hassan, Ahmed Hamdi Abu-haraz, Khoubieb Ali Abd-elrahman and Mohamed Ahmed Salih. Prediction and Conservancy Analysis of Multiepitope Based Peptide Vaccine Against Merkel Cell Polyomavirus: An Immunoinformatics Approach. Immunome Research. 2017; 13(2): 1-16.
In article      View Article
 
[52]  Malaz Abdelbagi TH, Mohammed Shihabeldin, Sanaa Bashir, Elkhaleel Ahmed, Elmoez Mohamed, Shawgi Hafiz, Abdah Abdelmonim, Tassneem Hamid, Shimaa Awad, Ahmed Hamdi, Khoubieb Ali and Mohammed A. Hassan. Immunoinformatics Prediction of Peptide-Based Vaccine Against African Horse Sickness Virus. Immunome Research. 2017; 13(2): 1-14.
In article      View Article
 
[53]  Jiandong Shi, Jing Zhang,, Sijin Li, Jing Sun, Yumei Teng, Meini Wu, Jianfan Li, Yanhan Li, Ningzhu Hu, Haixuan Wang, Yunzhang Hu. Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases. PloS one. 2015: 1-16.
In article      View Article
 
[54]  Tahirah Yasmin SA, Mouly Debnath, Akio Ebihara, Tsutomu Nakagawa and A. H. M. Nurun Nabi. In silico proposition to predict cluster of B- and T-cell epitopes for the usefulness of vaccine design from invasive, virulent and membrane associated proteins of C. jejuni. In Silico Pharmacology. 2016: 1-10.
In article      View Article
 
[55]  Tom Hall Ib, Carlsbad, Ca. BioEdit: An important software for molecular biology. GERF Bulletin of Biosciences. 2011: 60-1.
In article      View Article
 
[56]  Pingping Sun HJ, Zhenbang Liu, Qiao Ning, Jian Zhang, Xiaowei Zhao, Yanxin Huang, Zhiqiang Ma, and Yuxin Li. Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction. Computational and Mathematical Methods in Medicine. 2013.
In article      PubMed  PubMed
 
[57]  John Wiley & Sons L. Immunoinformatics may lead to a reappraisal of the nature of B cell epitopes and of the feasibility of synthetic peptide vaccines. J Mol Recognit 2006; 19: 183-7.
In article      View Article  PubMed
 
[58]  Chun-Hung Su NRP, Ken-Li Lin, I-Fang Chung. Identification of Amino Acid Propensities That Are Strong Determinants of Linear B-cell Epitope Using Neural Networks. PloS one. 2013.
In article      View Article
 
[59]  MORTEN NIELSEN CL, PEDER WORNING, SANNE LISE LAUEMØLLER, KASPER LAMBERTH, SØREN BUUS SØREN BRUNAK, AND OLE LUND. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Science. 2003: 12: 1007-17.
In article      View Article  PubMed
 
[60]  Michael Koch SC, Trevor Collen, David Avila, Jan Salomonsen, Hans-Joachim Wallny, Andrew van Hateren, Lawrence Hunt, Jansen P. Jacob, Fiona Johnston, Denise A. Marston, Iain Shaw, P. Rod Dunbar, Vincenzo Cerundolo, , E. Yvonne Jones aJK. Structures of an MHC Class I Molecule from B21 Chickens Illustrate Promiscuous Peptide Binding. Immunity. 2007: 885-99.
In article      PubMed
 
[61]  Claus Lundegaard OLaMN. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. bioinformatics. 2008; 24: 1397-8.
In article      View Article  PubMed
 
[62]  Bjørn Bremnes MR, Merete Gedde-Dahl, Tommy W. Nordeng, Jorunn Jacobsen, Scott A. Ness, and Oddmund Bakke. The MHC Class II-Associated Chicken Invariant Chain Shares Functional Properties with Its Mammalian Homologs. Experimental cell research. 2000: 360-9.
In article      View Article
 
[63]  F. Chen LP, W. Chao , Y. Dai , and W. Yu. Character of chicken polymorphic major histocompatibility complex class II alleles of 3 Chinese local breeds. Poultry science. 2012 91 1097-104.
In article      View Article  PubMed
 
[64]  Lund MNaO. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics. 2009: 1-10.
In article      PubMed  PubMed
 
[65]  ERIC F. PETTERSEN TDG, CONRAD C. HUANG, GREGORY S. COUCH, DANIEL M. GREENBLATT, ELAINE C. MENG, THOMAS E. FERRIN. UCSF Chimera-A Visualization System for Exploratory Research and Analysis. J Comput Chem. 2004; 25: 1605-12.
In article      View Article  PubMed
 
[66]  Maupetit J DP, Tuffery P. PEP-FOLD: an online resource for de novo peptide structure prediction. Nucleic Acids Res. 2009: 498-503.
In article      View Article  PubMed
 
[67]  Schneidman-Duhovny D IY, Nussinov R, Wolfson HJ, et al. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005; 33: 363-7.
In article      View Article  PubMed
 
[68]  Brown AC, Smith LP, Kgosana L, Baigent SJ, Nair V, Allday MJ. Homodimerization of the Meq viral oncoprotein is necessary for induction of T-cell lymphoma by Marek's disease virus. Journal of virology. 2009; 83(21): 11142-51.
In article      View Article  PubMed
 
[69]  Niikura M, Kim T, Hunt HD, Burnside J, Morgan RW, Dodgson JB, et al. Marek's disease virus up-regulates major histocompatibility complex class II cell surface expression in infected cells. Virology. 2007; 359(1): 212-9.
In article      View Article  PubMed
 
[70]  Isolation of chicken major histocompatibility complex class 11 (B-L) chain sequences: comparison with mammalian chains and expression in lymphoid organs. The EMBO journal. 1988; 7 1031 -9.
In article      PubMed  PubMed
 
[71]  Jonathan M. Gershoni AR-B, Dror D. Siman-Tov, Natalia Tarnovitski Freund and, Weiss Y. Epitope Mapping The First Step in Developing Epitope-Based Vaccines. Biodrugs. 2007: 145-56.
In article      
 
[72]  Navid Nezafat ZK, Mahboobeh Eslami , Milad Mohkama, Sanam Zandian , Younes Ghasemi. Designing an efficient multi-epitope peptide vaccine against Vibrio cholerae via combined immunoinformatics and protein interaction based approaches. Computational Biology and Chemistry. 2016; 62: 82–95.
In article      View Article
 
[73]  Ahmed Hamdi Abu-haraz KAA-e, Mojahid Salah Ibrahim, Waleed Hassan Hussien, Mohammed Siddig Mohammed, Marwan Mustafa Badawi and Mohamed Ahmed Salih. Multi Epitope Peptide Vaccine Prediction against Sudan Ebola Virus Using Immuno-Informatics Approaches. Advanced Techniques in Biology & Medicine. 2017; 5(1): 1-21.
In article      View Article
 
[74]  Marwan Mustafa Badawi AAFA, Salma Sleak Alam, Wafa Aljack Mohamed, Duaa Adil Nasr-Eldin Osman, Samar Ali Abd Alrazig Ali, Entissar Mohamed Elhassan Ahmed, Abdah AbdElmonim Adam, Ranya Omar Abdullh, Mohamed Ahmed Salih. Immunoinfomatics Predication and in silico Modeling of Epitope-Based Peptide Vaccine Against virulent Newcastle Disease Viruses. American Journal of Infectious Diseases and Microbiology. 2016; 4: 61-71.
In article      View Article
 
[75]  Anne S. De Groot MA, Elizabeth M. McClaine, Leonard Moise,William D. Martin. Immunoinformatic comparison of T-cell epitopes contained in novel swine-origin influenza A (H1N1) virus with epitopes in 2008–2009 conventional influenza vaccine. Vaccine. 2009; 27 5740-7.
In article      View Article
 
[76]  Sandra Iurescia DF, Vito Michele Fazio, Monica Rinaldi. Epitope-driven DNA vaccine design employing immunoinformatics against B-cell lymphoma: A biotech's challenge. Biotechnology Advances. 2012: 372-83.
In article      View Article  PubMed
 
[77]  PerlaCarlos V, SébastienHolbert , Felipe Ascencio , KrisHuygen, GraciaGomez-Anduro, MaximeBranger , MarthaReyes-Becerril, CarlosAngulo. In silico epitope analysis of unique and membrane associated proteins from Mycobacteriumavium subsp. paratuberculosis for immunogenicity and vaccine evaluation. Journal of TheoreticalBiology. 2015: 1-9.
In article      PubMed
 
[78]  De Groot AS SH, Aubin CS, McMurry J, Martin W,. Immunoinformatics: Mining genomes for vaccine components. Immunol Cell Biol. 2002; 80: 255-69.
In article      View Article