Immunoinformatics and Reverse Vaccinology, Potential Application to

Development of an ASF Vaccine

Nicholas SvitekPostdoctoral Scientist

ILRI-Kenya

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

The Project…

Phylogenetic Classification of T. parva

Adapted from PNAS

Alveolata

∼613mya

∼480mya

∼420mya

∼350mya

ApicomplexaPlasmodium

Babesia

Theileria

CoccidiaCiliophora

Dinoflagellata

Chromera velia

Gregarina

Cryotosporidium

Eimeria Sarcocystis

Toxoplasma

Neospora

Apicomplexans of medical and veterinary importance

ParasitePlasmodiumTheileria

HostsPrimates, birds, rodents, reptilesCattle, sheep, horses, buffalos

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($)

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

High priority to produce a recombinant vaccine

Nature Reviews Immunology

Goal

Use a reverse immunology approach for the identification of immunodominant peptides

from Theileria parva

Develop a better recombinant vaccine

Reverse ImmunologyWhole Genome Sequence

from T. parvaIn silico antigen predictions In vitro characterization of

predicted antigens

Selection of immunodominant peptides

VGYPKVKEEMLSHEELKKLGML

TGASIQTTLSKADVIAKY

Prime/BoostNaïve cattles

Challenge with T. parva

Computer algorithmsTrained on biological data

- Poxvirus175,000 peptides

- Proteolytic liberation35,000 peptides

- TAP transport30,000 peptides

- Class I binding150 peptides

- TCR recognition75 peptides

“Immunodomination”50 peptides

- Theileria parva4079 proteins

?

?

?

?

?

-Transmembrane/Secreted proteins200/738 (pred.) proteins

- Proteasomal degradation?? proteins

- TAP transport?? peptides

?

- Class I binding?? peptides

- TCR recognition?? peptides

“Immunodomination”?? peptides

Immunodominance

The MHC Class I Molecule

LeaderPeptide α1 domain α2 domain α3 domain Trans-

membraneCyto-

plasmic

Exon1Exon2

Exon3Exon4

Exon5Ex6

78

1 kb

360 aa

Ex1 Ex2 Ex3 Ex4 Ex5 Ex6 Ex7 Ex85 kb

MHC I Highly Polymorphic

HLA-B4001

HLA-C0110

HLA-A0101

HLA-B0702

Anchor Position

Sequencing Bovine MHC class I Genes

16 cattle

RNA isolation from PBMCs

454 pyrosequencing

RT-PCR

Full length cDNAExon 2- Exon 3

Exon2

Exon3

α1 α2

•High throughput •Rare variants

The Tools…

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.

Artificial Neural Network (ANN)Biological Neural Network Artificial Neural Network

Input neuronsPeptide/MHC seq

Hidden neurons

Output neuronsBinding affinity

Mathematical function whichdetermines the activation of

the neuron (weight)Computing units

NetMHCpan

45 SLA alleles

Enter your protein(s) sequence(s)

NetMHCpan Prediction Results

Published Manuscripts with NetMHCpan or NetCTL

Comparison Between Predicted and Actual Peptide Binding

NetMHCpan 2.0 (older version)

Hoof I, et al. Immunogenetics. 2009

Macaque Chimpanzee

Pig

Validating Peptide Binding with MHC-Tetramers…

Recombinant MHC Production

Complex formation

E. coli expression

Peptide-binding assaysRecombinant MHC class IHeavy chain

β2m

Peptide-MHC Tetramer StainingMHC-peptide

biotin

streptavidin

Fluorochrome: PEAntigen-specific

CD8+ T Cell

TCR

biotin

CD8+

CD8+/Tetramer +

• Allow for accurate and rapid enumeration of antigen-specific T cells• Specific• Sensitive

Peptide-MHC Tetramer Staining

Immunological Synapse

Immune Responses Towards AFSThe CTL Response

Nature Reviews Immunology

• Martins, CLV., et al. 1993• Ramiro-Ibanez, F., 1997• Jenson, JS., et al. 2000• Oura, CAL., et al. 2005

Reverse Immunology for ASFV ResearchWhole ASFV Genome Sequence In silico antigen predictions In vitro characterization of

predicted antigens

Selection of immunodominant peptides

VGYPKVKEEMLSHEELKKLGML

TGASIQTTLSKADVIAKY

Prime/BoostNaïve pigs

Challenge with virulent ASFV

Computer algorithmstrained on biological data

- Poxvirus175,000 peptides

- Proteolytic liberation35,000 peptides

- TAP transport30,000 peptides

- Class I binding150 peptides

- TCR recognition75 peptides

“Immunodomination”50 peptides

- African Swine Fever Virus150 proteins

- Class I binding?? peptides

- TCR recognition?? peptides

“Immunodomination”?? peptides

Immunodominance

- Proteolytic liberation?? peptides

- TAP transport?? peptides

Research Design1. 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 ASFVpeptides – MHC-peptide tetramer staining– ELIspot assay– CTL cell lysis assay (chromium release)

Successful Expression of Porcine MHC I

Pedersen LE, et al. Immunogenetics. 2011

Identification of FMDV CTL Epitopeswith 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.

• 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).

Basic Research to Enable Agricultural Development (BREAD)

Acknowledgments

Vish Nene (PI)Phil ToyeÉtienne de VilliersAnne FischerGeorge MichukiRoger PelléLucilla SteinaaNelson NdegwaFrederick MogebiRichard Bishop

John Barlow (PI)

William T. Golde (PI)

Søren Buus (Tetramers)

Morten Nielsen (NetMHCpan)

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

Peptide Binding in the MHC Class I Molecule

GSHSLRYFYTAVSRPGLGEPRFISVGYVDDTQFVRFDSDAPNPREEPRAPWIEKEGPEYWDRETRISKENTLVYRESLNNLRGYYNQSEAGSHTLQLMYGCDVGPDGRLLRGYRQDAYDSRDYIALNEELRSWTAADTAAQITKRKWEAEGYAESLRNYLEGRCVEWLRRYLENGKDALLRADPPMAHVTHHPSSEREVTLRCWALGFYPKEISLTWQREGEDQTQDMELVETRPSGDGTFQKWAALVVPSGEEQKYTCHVQHEGLQEPLILRWEPPQTSFLIMGIIVGLVLLVVAVVAGAVIWRKKRSGEKRQTHTQAASGDSDQGSDVSRMVPKA*

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.