Detection of T-cell T and their application to vaccine design

Transcription

1 QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture Detection of T-cell T epitopes and their application to vaccine design EpiVax Inc. 16 Bassett Street Providence Rhode Island Annie De Groot / / AnnieD@EpiVax.com

2 Outline for this Talk New approaches to vaccines Our approach Epitope Selection and Confirmation Tools Vaccine Design and Construction Tools Peering into the Future

3 Our Focus: Epitope mapping World of possible epitopes Immune system filter Immunogenic epitopes

4 Genome-derived Vaccines Genome Immunoinformatics Vaccine

5 Th cells Drive Ab and CTL Responses Epitope processing and Presentation by APC Th cell ALQDSGSEV epitope GIKPVVSTQL AVLSIVNRV AVLSIVNRV GRWPVKVl Antigen Presenting Cell Stimulates T cell help (IL-2, IL-4) - T Helper CTL response, and T-dependent T Ab are formed

6 B Cell Maturation In order for B Cells to mature into plasma cells, three events must occur; First, surface antibodies must recognize an antigen

7 B Cell Maturation The antigen is then internalized, degraded and presented on Class II MHC Presentation

8 B Cell Maturation Finally, helper T cells (CD4+) must recognize the bound peptide/mhc complex and initiate differentiation

9 Epitopes are defined by 9 AA sequence MHC Molecule GIKPVVSTQL ALQDSGSEV AVLSIVNRV AVLSIVNRV GRWPVKVl ALQDSGSEV ALQDSGSEV ALQDSGSEV T cell Epitopes Processing Binding HLA-restriction Clustering Sequence-dependent Antigen Presenting Cell

10 What an exciting time for designing vaccines! Thousands of genome sequences are available Proteomics can be used to discern proteome Immunome can also be uncovered... Epitopes can be used to make vaccines Epitopes can be used to determine whether the genes from which they re derived Interface with the immune system... (using epitopes to fish out antigens)

11 How to make a vaccine from a genome?... Select immunogenic regions of genome... Test host with a history of exposure to the pathogen... Identify epitopes - response means immunogenic!... pick genes!

12 Approach 1: Single genome available Humoral Theory (secreted/tm) Microarray (upregulated) Whole Genome Analysis Direct Sequencing

13 Approach 2: Comparing virulent/avirulent genomes Vaccinia Smallpox Variola Exclude housekeeping genes

14 Approach 3: Mapping Conserved Epitopes From Several Viral Subspecies HPV 16 ** HPV 18 Conserved epitopes in E6 Other HPV Conserved epitopes in E7

15 RPGNTKTVVPCKRPGNKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQAQIQQ RPGNTKTVVPCKRPGNKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQAQIQQEEQA EEQA QQEKEAMQ QQEKEAMQCTRPNNTRK CTRPNNTRKAMYELQKLNSWGTKNLQARYIQYGVYIVTVWGTKNLQRTVRFQTAIEK AMYELQKLNSWGTKNLQARYIQYGVYIVTVWGTKNLQRTVRFQTAIEK LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIP LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHA IHA LRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTA LRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACTRPNNTRK CTRPNNTRKIDRIRERKLTEDRWNKTACH IDRIRERKLTEDRWNKTACH NCYCKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQALATLITPKQLDCTHL NCYCKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQALATLITPKQLDCTHLEGKAVF EGKAVF IHNFKRKLVDFRELNKPGNTKTVVPCKRPGNKTVPGNKTVVPIGNKT YLKISLNKYYNLRPRQ IHNFKRKLVDFRELNKPGNTKTVVPCKRPGNKTVPGNKTVVPIGNKT YLKISLNKYYNLRPRQAWC AWC FHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHAPRQAWCWFKRQAWCWF FHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHAPRQAWCWFKRQAWCWFK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNKTAC TAC LKISLN LKISLNCTRPNNTRK CTRPNNTRKAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHA AWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHA RLRPGGKKKLARNCRAPPKQIIEQL RLRPGGKKKLARNCRAPPKQIIEQLCTRPNNTRK CTRPNNTRKTKTACNNCYCKRLIDRIRERKLTEDRWNKTAC TKTACNNCYCKRLIDRIRERKLTEDRWNKTAC LIFQWVQRRPNNYAKIKHTHTDIKQGPKEPSPRTLNAWVGGKKKYRLKQIIEQLIKKKILYQS LIFQWVQRRPNNYAKIKHTHTDIKQGPKEPSPRTLNAWVGGKKKYRLKQIIEQLIKKKILYQSNPYA NPYA IFQSSMTKPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSL IFQSSMTKPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLCTRPNNTRK CTRPNNTRKYLVSEFPIPHATVLD YLVSEFPIPHATVLD LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIP LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHA IHA LRP LRPCTRPNNTRK CTRPNNTRKNRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNKTACH NRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNKTACH NCYCKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQALATLITPKQLDCTHL NCYCKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQALATLITPKQLDCTHLEGKAVF EGKAVF RQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHAWTE RQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHAWTECTRPNNTRK CTRPNNTRK WFHSFNCGGEFTLFCASDA FHSFNCGGEFTLFCASDACTRPNNTRK CTRPNNTRKPSLYNVATYLVSEFPIPIHAPRQAWCWFKRQAWCWFK PSLYNVATYLVSEFPIPIHAPRQAWCWFKRQAWCWFK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNKTAC TAC LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIP LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHA IHA RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNKTAC TAC ELIFQWV ELIFQWVCTRPNNTRK CTRPNNTRKIKHTHTDIKQGPKEPSPRTLNAWVGGKKKYRLKQIIEQLIKKKILYQSNPYA IKHTHTDIKQGPKEPSPRTLNAWVGGKKKYRLKQIIEQLIKKKILYQSNPYA AIFQSSMTKPRQAWCWFHSFNCGGEFTLFCASDAKSLWD AIFQSSMTKPRQAWCWFHSFNCGGEFTLFCASDAKSLWDCTRPNNTRK CTRPNNTRKATYLVSEFPIPIHATVLD ATYLVSEFPIPIHATVLD LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIP LKISLNKYYNLRPRQAWCWFHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHA IHA RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNKTAC TAC NCYCKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQALATLITPKQLDCTHL NCYCKTVPGNKTVVPIGNKTKVVPITNKTVVPITLYIQYGVYIVLEQALATLITPKQLDCTHLEGKAVF EGKAVF IHNFKRKLVDFREL IHNFKRKLVDFRELCTRPNNTRK CTRPNNTRKPCKRPGNKTVPGNKTVVPIGNKT YLKISLNKYYNLRPRQAWC PCKRPGNKTVPGNKTVVPIGNKT YLKISLNKYYNLRPRQAWC FHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHAPRQAWCWFKRQAWCWF FHSFNCGGEFTLFCASDAKSLWDQSLKPSLYNVATYLVSEFPIPIHAPRQAWCWFKRQAWCWFK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNK RLRPGGKKKLARNCRAPPKQIIEQLIKKAIFQSSMTKTACNNCYCKRLIDRIRERKLTEDRWNKTAC TAC CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK Scanning multiple variants for conserved epitopes Conservatrix

16 Outline for this Talk New approaches to vaccines Our approach Epitope Selection and Confirmation Tools Vaccine Design and Construction Tools Peering into the Future

17 Genome to Vaccine Process Genome or proteome sequence EpiMatrix Conservatrix MHC ligands Immuno-informatics T cell epitopes T cell assays Back translation, cloning Vaccine construct Construct Design Preclinical Delivery vehicle / production Animal Model In vitro validation Vaccine Phase I

18 The TIGR TB Genome: CDC1551 QuickTime and a GIF decompressor are needed to see this picture. 4,000 genes x 1000 overlapping 10-mer potential epitopes = 4 million vaccine candidates

19 Identify Antigens/Regions of Interest within Pathogen eekikalveictemekegkiekigpenpyntpvfaikkkdstkwrklvdfrelnkrtqdfwevqlgiphpaglkkkksvtvldvgdayfsiplh edfrkytaftipsinnetpgiryqynvlpqgwkgspaifqssmtkilepfrkqnpevviyqymddlyvgsdleieqhrtkieelrehllrwgfttp dkkhqkerpdkkhqkerp Microarrays: : Identify proteins that are more likely to be produced in large quantities during infection Proteins that are very important for the pathogen s function (Seceted or Transmembrane) Protein fragments that are present in many strains of a given pathogen.

20 TB-Genome Genome-to-Vaccine Whole genome Bioinformatics tools for vaccine design Putative T cell Epitopes 95 % Reduction T-cell epitopes are mapped directly from the TB genome De Groot AS, et al. Vaccine, 2001

21 In vitro validation: ELISPOT Assays Antigen stimulated cell, producing IFN-γ Capture antibody, monoclonal anti-ifn IFN- γ Biotinylated detection antibody, anti-ifn IFN-γ Avidin bound enzyme, alkaline phosphatase Chromagen added, reacts with enzyme, entire complex seen as spot

22 Otitis media: Haemophilus influenza p5 Promiscuous epitopes P5 Analysis (All 11-Mers) Amino Acid Index

23 Overall GS EliSpot Results % Peptides Immunogenic / Genome Scans % 90% 80% 87% 76% 88% 82% 70% 60% 20/28 13/15 15/17 28/34 50% 40% 30% 20% 10% 0% GS_0 GS_1 GS_2 GS_1&2

24 Select TB Genome Epitopes Peptide J: hypothetical proteinase proteinase Peptide AO: upregulated transporter, LysE/YggA family Peptide AV: upregulated immunogenic protein MPT64 % PPD + and PPD - responding to GS1_J, GS2_AO, GS2_AV 100% 80% 60% 40% 20% 0% 100%100% 98% 83% 56% 0% 27% PPD + PPD Negative 0% 16% 0% PHA PPD GS1_J GS2_AO GS2_AV

25 A new approach: Immunoinformatics used to investigate host immune response Many of these proteins are hypothetical, function is unknown, not crystallized, not cloned, not expressed The implication is that T cell epitope mapping can be used to fish out genes that interface with the host Perhaps the interface is more extensive and varied than we previously believed.

26 Genes/Epitopes aligned in DNA construct Intended Protein Product: Many epitopes strung together in a String ring-of-beads Reverse Translation: Determines the DNA sequence necessary to code for the intended protein. This DNA is assembled for insertion into an expression vector. DNA insert DNA Vector Protein product (folded)

27 String-of of-epitopes ( Megatope Megatope ) TB vaccine: Epitopes Secreted Antigens of TB IC 38-2 IC 38-5 IC 38-6 IC 38-1 IC 38-7 IC 38-3 IC 14-1 IC 16-2 IC 19-1 IC 32-2 IC 16-1 IC MPT70-3 IC MPT70-4 IC MPT63-1 IC MPT64-2 IC MPT70-1 IC MPT64-1 IC MPT63-2 IC MPT59-3 IC MPT70-2 IC MPT59-4 IC MPT59-5 IC MPT64-3 GS1_J pcdna3-based plasmid CMV promoter Kanamycin R.

28 Evaluate for expression in targets Evaluate in animal model Vaccine construct In vitro analysis Transgenic mouse model Confirm (1) expression and (2) immunogenicity

29 Co-Immunization with IL-12 and TB plasmid Improves IFN-γ ELISPOT Combination of all TB Peptide Stimulants Medium TB Peptides

30 Outline for this Talk New approaches to vaccines Our approach Epitope Selection and Confirmation Tools Vaccine Design and Construction Tools Peering into the Future

31 Immuno-informatics : 15 years of validation Crystal Structure of MHC: 1989 Epitopes are Linear and Constrained therefore modeling is possible A B

32 To make a vaccine Need to find the immunogenic word that means TB or HIV HIV to the immune system MHC restriction: people hear different words: you may need to include more than just a few words, or perhaps a word that everyone recognizes? Perhaps a sentence or two would make a vaccine? Taking the mass of words that make up a language or a pathogen, one way of finding a word is to do a word search.

33 Humble Beginnings: using the find function to search for epitopes? L????? V?

34 1996: EpiMatrix, a matrix-based algorithm amino acid residue graphical representation graphical representation of A*0201 motif of (based A*0201 on list motif of actual peptides from Chicz) (based on list of actual peptides from Chicz) A C D Prediction of well-conserved HIV-1 1 ligands using a Matrix-based Algorithm, EpiMatrix, Schafer et al. Vaccine, 1998, Vol. 16, pp E F G H I From genome to vaccine: in silico predictions, ex vivo verification ion De Groot et al. Vaccine (2001). K L M N P Bioinformatics tools for identifying class I-restricted I epitopes. Bill Martin, et al., Methods (2003) Q R S T V amino acid position in peptide P R M S M V N H L V A C D E F G H I K L M N P Q R S T V W Y Bill Jesdale TB/HIV RL Modeling the immunogenicity W of therapeutic proteins using T cell epitope mapping De Groot et al. Developments Y in Biologicals (2003) Genome Derived Vaccines A. S. De Groot and R. Rappouli (2004)

35 Example of EpiMatrix Scoring: True ligands compared to random peptides Random sequence seeded with epitopes Random True *DR B*0101 Ligands and Epitopes

36 Prediction with EpiMatrix True ligands compared to random peptides 18.00% EPIMATRIX Classification of Known DRB1*0101 Epitope 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% Known Binders Random Random True 4.00% 2.00% 0.00% Known Binders Score

37 Scoring of known epitopes using new Class II matrices (pocket profile method) Random peptides True epitopes

38 Parsing Input Sequence Frame 1 Frame 2 Frame 3 Frame 4 Frame 5 ALCDETGHVPKL ALCDETGHV LCDETGHVP CDETGHVPK DETGHVPKL ETGHVPKLM

39 G9 G8 G7 G6 G5 G4 G3 G2 G1 G C9 c C8 C7 C6 C5 C4 C3 C2 C1 C P9 P8 P7 P6 P5 P4 P3 P2 P1 P D T E V H L D9 D8 D7 D6 D5 D4 D3 D2 D1 T9 T8 T7 T6 T5 T4 T3 T2 T1 E9 E8 E7 E6 E5 E4 E3 E2 E1 V9 V8 V7 V6 V5 V4 V3 V2 V1 H9 H8 H7 H6 H5 H4 H3 H2 H1 L9 L8 L7 L6 L5 L4 L3 L2 L1 P V H G T E D C L Raw Score = L1+C2+D3+E4+T5+G6+H7+V8+P9 Raw Score = L1+C2+D3+E4+T5+G6+H7+V8+P9 EpiMatrix

40 Class II approach: Pocket Profiles A B C D C B Sturniolo et al, Pocket Profiles Nature Biotechnology (Hammer)

41 FAQ s: Does it work? Does it predict published epitopes? Does it predict new epitopes? Does it predict epitopes in directly from genes? Yes Yes Yes Yes Do the predictions work in vitro/ in vivo? and Yes!

42 If we can do this for humans, why not cows and pigs? BOLA A Angus 0.3 Random Random True True 0 BOLA A-11 A Matrix

43 Class II approach: Pocket Profiles A B C D C B Sturniolo et al, Pocket Profiles Nature Biotechnology (Hammer)

44 Outline for this Talk New approaches to vaccines Our approach Epitope Selection and Confirmation Tools Vaccine Design and Construction Tools Peering into the Future

45 Putting Epitopes into a DNA (or other) Vaccine Intended Protein Product: Many epitopes strung together in a String ring-of-beads Reverse Translation: Determines the DNA sequence necessary to code for the intended protein. This DNA is assembled for insertion into an expression vector. DNA insert DNA Vector Protein product (folded)

46 Designing the String of Beads Construct Proteasome processing Junctional Epitopes Homology to Human

47 Junctional Epitopes

48 Vaccine-CAD Junctional epitopes Proteasome proc.

49 Outline for this Talk New approaches to vaccines Our approach Epitope Selection and Confirmation Tools Vaccine Design and Construction Tools Peering into the Future

50 Random True

51 New Matrix Development approach A B C D C B Sturniolo et al, Pocket Profiles Nature Biotechnology (Hammer)

52 Creating SLA Prediction Tools EpiVax determined the best fit between Swine and Human MHCs in a four-step process: 1. Align SLA and HLA sequences 2. Identify corresponding pocket residues in SLA based on alignments with HLA sequences 3. (a) Analyze homology between SLA and HLA alleles whole molecule match 4. (b) Analyze composite pocket profile matrices 5. Validate (retrospective)

53 Align SLA and HLA Sequences Each SLA was matched to the best corresponding sequence among human HLA SLA-2/2 Aligned to HLA B* HLA Sequence MRVRGPQAILILLSGALALTGTWAGPHSLSYFSTAVSRP l l l l l l l l l l l l l l MLVMAPRTVLLLLSAALALTETWAGSHSMRYFYTSVSRP SLA Sequence

54 Identify SLA Pocket Residues Once the SLA sequences were matched to a HLA sequence, the SLA pocket residues could be determined SLA-2/2 Aligned to HLA B* HLA Sequence MRVRGPQAILILLSGALALTGTWAGPHSLSYFSTAVSRP MLVMAPRTVLLLLSAALALTETWAGSHSMRYFYTSVSRP SLA Sequence

55 Retrospective Validation #1 To verify the predictive power of our SLA binding matrices, we compared c our predicted epitopes with those discovered experimentally in Porcine PseudoRabies Virus (J. Virology. June 1999; 22 (6) 559.) Comparison of Peptide Selection techniques Peptides Tested Epitopes Identified Number of 15-mers Tested Brute Force Composite Matrix Substitute Matrix 0 Negative Control Selection Method

56 Retrospective Validation #2 To verify the predictive power of our SLA binding matrices, we compared c our predicted epitopes with those discovered experimentally in Classical Swine fever virus J. General Virology, 1995; 76 (112): 3039 Comparison of Peptide Selection techniques Peptides Tested Epitopes Identified 179 Number of 15-mers Tested Brute Force Composite Matrix Substitute Matrix Negative Control Selection Method

57 Proposed Animal Pathogen Genome to Vaccine Genome or proteome sequence EpiMatrix Conservatrix MHC ligands Immuno-informatics T cell assays (ELISPOT) T cell epitopes Chose Genes/ Epitopes Back translation, cloning Vaccine construct Construct Design Preclinical Delivery vehicle / production Animal model In vitro validation Vaccine Phase I

58 Immunome to Vaccine Approaches Comparison of (virulent to avirulent) genomes and selection of unique proteins, exclusion of housekeeping genes Selection of proteins that are special (secreted, transmembrane) ) using proteomics Selection of antigens by above methods followed by cloning and serum screen Direct mapping by immunoinformatics of epitopes in orfs and screening of epitopes using immune-competent T cells

59 EpiVax: the Talent Annie De Groot M.D. CSO / CEO Immunology/Informatics MD U of Chicago / Postdoc Berzofsky (NIH) Assoc. Prof. of Medicine Brown U. William (Bill) Martin CIO / COO Informatics BA Economics and Business Administration Former Director at PAREXEL International Lee Shekter Ph.D Director, Informatics Proteomics PhD Informatics, University of Chicago

60 QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture Thank You! AnnieD@EpiVax.com EpiVax Inc. 16 Bassett Street Providence Rhode Island Annie De Groot / CEO and Bill Martin / CIO