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Immunoinformatics and ReverseVaccinology, Potential Application to   Development of an ASF Vaccine            Nicholas Svi...
Presentation Outline• My postdoctoral project: “Develop an  immunoinformatic approach for the  identification of immunodom...
The Project…
Phylogenetic Classification of T. parva                                             Chromera velia                Plasmodi...
Theileria parva Pathogenesis• Transformation of leucocytes   – T lymphocytes       • Macrophages can be infected but are n...
The Need for a Better Vaccine                                    • “Infection and treatment”                              ...
Goal           Develop a better         recombinant vaccineUse a reverse immunology approach for theidentification of immu...
Reverse ImmunologyWhole Genome Sequence     In silico antigen predictions    In vitro characterization of     from T. parv...
Immunodominance - Poxvirus                      - Theileria parva   175,000 peptides                4079 proteins         ...
The MHC Class I Molecule         Ex1        Ex2               Ex3               Ex4    Ex5        Ex6   Ex7       Ex85 kb1...
MHC I Highly Polymorphic                     Anchor                     Position                                HLA-B4001 ...
Sequencing Bovine MHC class I Genes                                                           RT-PCR                   RNA...
The Tools…
NetMHCpan• Predicts binding of peptides to any known MHC  molecule using artificial neural networks (ANN).• Trained on mor...
Artificial Neural Network (ANN)Biological Neural Network             Artificial Neural Network                            ...
NetMHCpan   Enter your protein(s) sequence(s)    45 SLA alleles
NetMHCpan Prediction Results
Published Manuscripts with  NetMHCpan or NetCTL
Comparison Between Predicted and     Actual Peptide Binding Macaque       Chimpanzee                                      ...
Validating Peptide Binding with       MHC-Tetramers…
Recombinant MHC Production  E. coli expression                                     β2mRecombinant MHC class I   Complex fo...
Peptide-MHC Tetramer Staining               MHC-peptide                                                                   ...
Peptide-MHC Tetramer Staining                     Immunological Synapse
Immune Responses Towards AFS The CTL Response                             • Martins, CLV., et al. 1993                    ...
Reverse Immunology for ASFV ResearchWhole ASFV Genome Sequence       In silico antigen predictions    In vitro characteriz...
Immunodominance - Poxvirus                 - African Swine Fever Virus   175,000 peptides           150 proteins - Proteol...
Research Design1. Sequencing SLA class 1 cDNA   – Expression profile   – Number of variants2. Predict ASFV peptide binding...
Successful Expression of Porcine MHC I        Pedersen LE, et al. Immunogenetics. 2011
Identification of FMDV CTL Epitopes          with NetMHCpan
Tetramer Staining of FMDV   Specific Swine T Cells      SLA-1*0401/MVTAHITVPY tetramer
Summary and Perspectives• Tools (NetMHCpan, tetramers) are available and  functional to identify CTL epitopes in ASFV.• Un...
AcknowledgmentsVish Nene (PI)                                John Barlow (PI)Phil ToyeÉtienne de VilliersAnne Fischer     ...
Known T. parva Antigens Among Top 6%T. parva Ag   Bovine MHC I   Peptide       Length   Immunodominance   Percentage (%)Tp...
Peptide Binding in theMHC Class I Molecule       GSHSLRYFYTAVSRPGLGEPRFISVGYVDDTQFVRFDSD       APNPREEPRAPWIEKEGPEYWDRETRI...
Automated High-Throughput System       to assay for Peptide BindingHigh throughput setup    • Hamilton liquid handling rob...
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Immunoinformatics and Reverse Vaccinology, Potential Application to Development of an ASF Vaccine

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Presented by Nicholas Svitek at the African Swine Fever Diagnostics, Surveillance, Epidemiology and Control Workshop, Nairobi, Kenya, 20-21 July 2011

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Immunoinformatics and Reverse Vaccinology, Potential Application to Development of an ASF Vaccine

  1. 1. Immunoinformatics and ReverseVaccinology, Potential Application to Development of an ASF Vaccine Nicholas Svitek Postdoctoral Scientist ILRI-Kenya
  2. 2. Presentation Outline• My postdoctoral project: “Develop an immunoinformatic approach for the identification of immunodominant peptides from Theileria parva”• Tools: ANN, Peptide-MHC tetramer• How immunoinformatics can be used for ASF vaccine research
  3. 3. The Project…
  4. 4. Phylogenetic Classification of T. parva Chromera velia Plasmodium Apicomplexa Babesia Dinoflagellata Theileria Ciliophora ∼350 Coccidia mya Neospora ∼420 ∼480 mya Toxoplasma mya Eimeria Sarcocystis ∼613 Gregarina mya Cryotosporidium Alveolata Apicomplexans of medical and veterinary importance Parasite Hosts Adapted from PNAS Plasmodium Primates, birds, rodents, reptiles Theileria Cattle, sheep, horses, buffalos
  5. 5. Theileria parva Pathogenesis• Transformation of leucocytes – T lymphocytes • Macrophages can be infected but are not transformed – NF-kB – Anti-apopotic c-Myc/Mcl-1 – Increased Tgf-b2• Invasion of lymphoid and non- lymphoid tissues with proliferating infected lymphoblasts – Susceptible animals die within 3-4 weeks of infection• 1 million die each year• Annual losses of more than 300 million USD($)
  6. 6. The Need for a Better Vaccine • “Infection and treatment” immunization method (ITM): induction of long-term immunity based on CD8+ (cytotoxic) T cell responses. • Variable protection against heterologous strains. • Economic and logistic disadvantages: •Difficult to produce •Delivery requires a cold chainNature Reviews Immunology High priority to produce a recombinant vaccine
  7. 7. Goal Develop a better recombinant vaccineUse a reverse immunology approach for theidentification of immunodominant peptides from Theileria parva
  8. 8. Reverse ImmunologyWhole Genome Sequence In silico antigen predictions In vitro characterization of from T. parva predicted antigens Computer algorithms Trained on biological data Selection ofChallenge with T. parva immunodominant peptides VGYPKVKEEML Prime/Boost SHEELKKLGML Naïve cattles TGASIQTTL SKADVIAKY
  9. 9. Immunodominance - Poxvirus - Theileria parva 175,000 peptides 4079 proteins -Transmembrane/Secreted - Proteolytic liberation ? proteins 35,000 peptides 200/738 (pred.) proteins - TAP transport - Proteasomal degradation ? 30,000 peptides ?? proteins ? - TAP transport ?? peptides - Class I binding ? - Class I binding 150 peptides ?? peptides - TCR recognition - TCR recognition ? 75 peptides ?? peptides “Immunodomination” ? “Immunodomination” 50 peptides ?? peptides
  10. 10. The MHC Class I Molecule Ex1 Ex2 Ex3 Ex4 Ex5 Ex6 Ex7 Ex85 kb1 kb Exon1 Exon2 Exon3 Exon4 Exon5 Ex6 7 8360 aa Leader Trans- Cyto- α1 domain α2 domain α3 domain Peptide membrane plasmic
  11. 11. MHC I Highly Polymorphic Anchor Position HLA-B4001 HLA-B0702 HLA-C0110 HLA-A0101
  12. 12. Sequencing Bovine MHC class I Genes RT-PCR RNA isolation from PBMCs16 cattle Exon 2- Exon 3 Full length cDNA Exon•High throughput 2 Exon 3•Rare variants α1 α2 454 pyrosequencing
  13. 13. The Tools…
  14. 14. NetMHCpan• Predicts binding of peptides to any known MHC molecule using artificial neural networks (ANN).• Trained on more than 115,000 quantitative binding data covering more than 120 different MHC molecules.• MHC class I: humans, non-human primates (chimpanzee, rhesus macaque, gorilla), mice, pigs, and cattle.• Includes the newest MHC allele releases from the IMGT/HLA & IPD-MHC databases.
  15. 15. Artificial Neural Network (ANN)Biological Neural Network Artificial Neural Network Input neurons Peptide/MHC seq Mathematical function which Computing units determines the activation of the neuron (weight) Hidden neurons Output neurons Binding affinity
  16. 16. NetMHCpan Enter your protein(s) sequence(s) 45 SLA alleles
  17. 17. NetMHCpan Prediction Results
  18. 18. Published Manuscripts with NetMHCpan or NetCTL
  19. 19. Comparison Between Predicted and Actual Peptide Binding Macaque Chimpanzee Pig NetMHCpan 2.0 (older version) Hoof I, et al. Immunogenetics. 2009
  20. 20. Validating Peptide Binding with MHC-Tetramers…
  21. 21. Recombinant MHC Production E. coli expression β2mRecombinant MHC class I Complex formation Peptide-binding assaysHeavy chain
  22. 22. Peptide-MHC Tetramer Staining MHC-peptide CD8+/Tetramer + biotin biotin TCR streptavidin Antigen-specific CD8+Fluorochrome: PE CD8+ T Cell • Allow for accurate and rapid enumeration of antigen-specific T cells • Specific • Sensitive
  23. 23. Peptide-MHC Tetramer Staining Immunological Synapse
  24. 24. Immune Responses Towards AFS The CTL Response • Martins, CLV., et al. 1993 • Ramiro-Ibanez, F., 1997 • Jenson, JS., et al. 2000 • Oura, CAL., et al. 2005 Nature Reviews Immunology
  25. 25. Reverse Immunology for ASFV ResearchWhole ASFV Genome Sequence In silico antigen predictions In vitro characterization of predicted antigens Computer algorithms trained on biological data Selection of Challenge with virulent ASFV immunodominant peptides VGYPKVKEEML Prime/Boost SHEELKKLGML Naïve pigs TGASIQTTL SKADVIAKY
  26. 26. Immunodominance - Poxvirus - African Swine Fever Virus 175,000 peptides 150 proteins - Proteolytic liberation - Proteolytic liberation 35,000 peptides ?? peptides - TAP transport - TAP transport 30,000 peptides ?? peptides - Class I binding - Class I binding 150 peptides ?? peptides - TCR recognition - TCR recognition 75 peptides ?? peptides “Immunodomination” “Immunodomination” 50 peptides ?? peptides
  27. 27. Research Design1. Sequencing SLA class 1 cDNA – Expression profile – Number of variants2. Predict ASFV peptide binding in MHC I (in silico) NetMHCpan 2.4 server3. Identify in vitro the “true” immunodominant ASFV peptides – MHC-peptide tetramer staining – ELIspot assay – CTL cell lysis assay (chromium release)
  28. 28. Successful Expression of Porcine MHC I Pedersen LE, et al. Immunogenetics. 2011
  29. 29. Identification of FMDV CTL Epitopes with NetMHCpan
  30. 30. Tetramer Staining of FMDV Specific Swine T Cells SLA-1*0401/MVTAHITVPY tetramer
  31. 31. Summary and Perspectives• Tools (NetMHCpan, tetramers) are available and functional to identify CTL epitopes in ASFV.• Understanding more precisely the immune response elicited towards ASFV.• Develop vaccines• Vaccinogenomics. – Integrating pathogen and host genomics in vaccine research (delivreing specific peptide mix to pigs with particular MHC class I expression).
  32. 32. AcknowledgmentsVish Nene (PI) John Barlow (PI)Phil ToyeÉtienne de VilliersAnne Fischer William T. Golde (PI)George MichukiRoger PelléLucilla Steinaa Søren Buus (Tetramers)Nelson NdegwaFrederick MogebiRichard Bishop Morten Nielsen (NetMHCpan) Basic Research to Enable Agricultural Development (BREAD)
  33. 33. Known T. parva Antigens Among Top 6%T. parva Ag Bovine MHC I Peptide Length Immunodominance Percentage (%)Tp1 N*01301 VGYPKVKEEML 11-mer 30th / 514 5.84Tp2 N*04001 SHEELKKLGML 11-mer 11th /1 41 7.80Tp2 N*01201 KSSHGMGKVGK 11-mer 4th / 141 2.84Tp2 N*01201 QSLVCVLMK 9-mer 3rd / 141 2.13Tp2 T2c FAQSLVCVL 9-mer 3rd / 141 2.13Tp4 N*00101 TGASIQTTL 9-mer 42nd / 571 7.36Tp5 N*00902 SKADVIAKY 9-mer 1st / 141 0.71Tp7 N*04701 EFISFPISL 9-mer 73rd /7 13 10.24Tp8 N*00101 CGAELNHFL 9-mer 44th / 432 10.19Tp9 N*02301 AKFPGMKKSK 10-mer 38th / 325 11.69
  34. 34. Peptide Binding in theMHC Class I Molecule GSHSLRYFYTAVSRPGLGEPRFISVGYVDDTQFVRFDSD APNPREEPRAPWIEKEGPEYWDRETRISKENTLVYRES LNNLRGYYNQSEAGSHTLQLMYGCDVGPDGRLLRGY RQDAYDSRDYIALNEELRSWTAADTAAQITKRKWEAE GYAESLRNYLEGRCVEWLRRYLENGKDALLRADPPMA HVTHHPSSEREVTLRCWALGFYPKEISLTWQREGEDQT QDMELVETRPSGDGTFQKWAALVVPSGEEQKYTCHVQ HEGLQEPLILRWEPPQTSFLIMGIIVGLVLLVVAVVAGAVI WRKKRSGEKRQTHTQAASGDSDQGSDVSRMVPKA*
  35. 35. Automated High-Throughput System to assay for Peptide BindingHigh throughput setup • Hamilton liquid handling robot • 96 peptides • CORE 96 HEAD dilutes 96 peptides at the time. • addition of MHC and β2m, one specific MHC predicted peptide • Duplicate dilution.

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