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

1. Immunoinformatics and Reverse
Vaccinology, Potential Application to
Development of an ASF Vaccine
Nicholas Svitek
Postdoctoral Scientist
ILRI-Kenya

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. The Project…

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. 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. 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 chain
Nature Reviews Immunology
High priority to produce a recombinant vaccine

7. Goal
Develop a better
recombinant vaccine
Use a reverse immunology approach for the
identification of immunodominant peptides
from Theileria parva

8. Reverse Immunology
Whole Genome Sequence In silico antigen predictions In vitro characterization of
from T. parva predicted antigens
Computer algorithms
Trained on biological data
Selection of
Challenge with T. parva immunodominant peptides
VGYPKVKEEML
Prime/Boost SHEELKKLGML
Naïve cattles TGASIQTTL
SKADVIAKY

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. The MHC Class I Molecule
Ex1 Ex2 Ex3 Ex4 Ex5 Ex6 Ex7 Ex8
5 kb
1 kb Exon1
Exon2
Exon3
Exon4
Exon5
Ex6
7
8
360 aa Leader Trans- Cyto-
α1 domain α2 domain α3 domain
Peptide membrane plasmic

11. MHC I Highly Polymorphic
Anchor
Position
HLA-B4001
HLA-B0702
HLA-C0110
HLA-A0101

12. Sequencing Bovine MHC class I Genes
RT-PCR
RNA isolation from PBMCs
16 cattle
Exon 2- Exon 3 Full length cDNA
Exon
•High throughput 2
Exon
3
•Rare variants
α1 α2
454 pyrosequencing

13. The Tools…

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. 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. NetMHCpan
Enter your protein(s) sequence(s)
45 SLA alleles

17. NetMHCpan Prediction Results

18. Published Manuscripts with
NetMHCpan or NetCTL

19. Comparison Between Predicted and
Actual Peptide Binding
Macaque Chimpanzee
Pig
NetMHCpan 2.0 (older version)
Hoof I, et al. Immunogenetics. 2009

20. Validating Peptide Binding with
MHC-Tetramers…

21. Recombinant MHC Production
E. coli expression
β2m
Recombinant MHC class I Complex formation Peptide-binding assays
Heavy chain

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. Peptide-MHC Tetramer Staining
Immunological Synapse

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. Reverse Immunology for ASFV Research
Whole 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. 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. Research Design
1. Sequencing SLA class 1 cDNA
– Expression profile
– Number of variants
2. Predict ASFV peptide binding in MHC I (in silico)
NetMHCpan 2.4 server
3. Identify in vitro the “true” immunodominant ASFV
peptides
– MHC-peptide tetramer staining
– ELIspot assay
– CTL cell lysis assay (chromium release)

28. Successful Expression of Porcine MHC I
Pedersen LE, et al. Immunogenetics. 2011

29. Identification of FMDV CTL Epitopes
with NetMHCpan

30. Tetramer Staining of FMDV
Specific Swine T Cells
SLA-1*0401/MVTAHITVPY tetramer

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. Acknowledgments
Vish Nene (PI) John Barlow (PI)
Phil Toye
Étienne de Villiers
Anne Fischer
William T. Golde (PI)
George Michuki
Roger Pellé
Lucilla Steinaa Søren Buus (Tetramers)
Nelson Ndegwa
Frederick Mogebi
Richard Bishop Morten Nielsen (NetMHCpan)
Basic Research to Enable Agricultural
Development (BREAD)

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 5.84
Tp2 N*04001 SHEELKKLGML 11-mer 11th /1 41 7.80
Tp2 N*01201 KSSHGMGKVGK 11-mer 4th / 141 2.84
Tp2 N*01201 QSLVCVLMK 9-mer 3rd / 141 2.13
Tp2 T2c FAQSLVCVL 9-mer 3rd / 141 2.13
Tp4 N*00101 TGASIQTTL 9-mer 42nd / 571 7.36
Tp5 N*00902 SKADVIAKY 9-mer 1st / 141 0.71
Tp7 N*04701 EFISFPISL 9-mer 73rd /7 13 10.24
Tp8 N*00101 CGAELNHFL 9-mer 44th / 432 10.19
Tp9 N*02301 AKFPGMKKSK 10-mer 38th / 325 11.69

34. Peptide Binding in the
MHC Class I Molecule
GSHSLRYFYTAVSRPGLGEPRFISVGYVDDTQFVRFDSD
APNPREEPRAPWIEKEGPEYWDRETRISKENTLVYRES
LNNLRGYYNQSEAGSHTLQLMYGCDVGPDGRLLRGY
RQDAYDSRDYIALNEELRSWTAADTAAQITKRKWEAE
GYAESLRNYLEGRCVEWLRRYLENGKDALLRADPPMA
HVTHHPSSEREVTLRCWALGFYPKEISLTWQREGEDQT
QDMELVETRPSGDGTFQKWAALVVPSGEEQKYTCHVQ
HEGLQEPLILRWEPPQTSFLIMGIIVGLVLLVVAVVAGAVI
WRKKRSGEKRQTHTQAASGDSDQGSDVSRMVPKA*

35. Automated High-Throughput System
to assay for Peptide Binding
High 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.