1. Improved prediction of antigenic relationships among RNA viruses Richard Reeve Boyd Orr Centre for Population and Ecosystem Health University of Glasgow

2. Acknowledgements Boyd Orr Centre for Population and Ecosystem Health University of Glasgow (UK) -Will Harvey, Dan Haydon The Pirbright Institute (UK) - Daryl Borley, Fufa Bari, Sasmita Upadhyaya, Mana Mahapatra, David Paton, Satya Parida Onderstepoort Veterinary Institute (South Africa) - Francois Maree, Azwidowi Lukhwareni, Jan Esterhuysen, Belinda Blignaut MRC National Institute for Medical Research (UK) -John McCauley, Alan Hay, Rod Daniels, Victoria Gregory, Donald Benton

3. 3 Background Antigenic variability presents a significant challenge for vaccination against various diseases of livestock, poultry and humans Foot-and-mouth disease virus and Influenza A -Vaccines can offer good levels of protection against antigenically similar viruses (response is antibody dominated) -Diversification within FMDV serotypes – distinct antigenic variants continue to emerge -Antigenic drift in influenza A subtypes – requires regular updates to vaccine strains

4. 4 Background Characterising antigenic phenotype -Serological assays such as virus neutralisation test (VNT), liquid phase blocking ELISA (LPBE) or haemagglutination inhibition (HI) -Measure antigenic similarity of two strains -antiserum from vaccine or reference strain and virus sample from a second strain -Challenges: interpretability, variability, unwanted sources of variation in titre (e.g. variability in receptor-binding avidity) Increased knowledge of the genetic variation underlying antigenic variability -> rational vaccine design

5. Aims How can modelling approaches aid traditional approaches and help us understand within-serotype antigenic relationships? 1. Sequence-based approach Assess ability to: 1. Identify antigenic determinants and quantify importance 2. Predict antigenic phenotype of novel/emerging viruses 3. Predict coverage of potential vaccines/reference strains

6. FMDV data Serotype SAT1OA Antigenic data (VN test) Reference strains (antisera) 557 Test viruses427756 Antisera-virus pairs 153308371 Measurements1809740929 Genetic dataFull capsid sequences (P1) Generated by Pirbright Institute, UK (Daryl Borley, Sasmita Upadhyaya - O, Fufa Bari - A) and Onderstepoort Veterinary Institute, South Africa (Francois Maree et al. - SAT1)

7. 7 Human Influenza A data Subtype H1N1 (1995-2009) H3N2 (1968-2013) Antigenic data (HI assay) Reference strains (antisera) 43169 Test viruses506229 Antisera-virus pairs 3,7342,738 Measurements19,9057,315 Genetic dataHA1 sequences Generated by The Crick Worldwide Influenza Centre, Francis Crick Institute, UK

8. Methodology Regression-based modelling with titre from antigenic assay (VNT or HI) as response variable -Identify aa positions at which variation can explain antigenic differences (drops in titre) Structural information used where possible -Surface-exposed aa positions identified to limit search space for modelling Phylogeny is taken into account -Greater statistical weight given to aa positions associated with antigenic differences in multiple branches -Reduces false positive detection rate Antigenic impact of specific aa substitutions at identified positions quantified -The regression coefficients in the model

9. Identifying antigenic determinants

10. SAT1 O Tracing antigenic evolution

11. VirusProtei n aa position FMDV AVP1 VP2 VP3 VP1 81, 138, 148 †, 159 † VP2 74, 79 † VP3 132 FMDV OVP1 VP2 VP1 142, 169, 211 † VP2 74 †, 193* VP3 56 FMDV SAT1VP1 VP2 VP3 VP1 144 †, 149 †, 164, 209 VP2 72 † VP3 72 †, 77 †, 138 † H1N1HA136, 72 †, 74 † or 120, 130*, 141* †, 142 †, 153* †, 163 †, 183, 184 †, 187* †, 190 †, 252, 274, 313 H3N2HA162 †, 83 †, 124 †, 133 †, 135 †, 138 †, 144 †, 145 †, 155 †, 156 †, 157 †, 158 †, 159 †, 164 †, 172 †, 183, 189 †, 193 †, 197 †, 212, 214, 217 †, 262 †, 276 † * Reverse genetics study carried out as part of collaboration † MAb escape study for this serotype from literature Identifying antigenic determinants Experimentally validated aa positions Identifying antigenic determinants Experimentally validated aa positions

12. 12 Identifying antigenic determinants Included aa positions Identifying antigenic determinants Included aa positions

13. 13 Identifying antigenic determinants Included aa positions Identifying antigenic determinants Included aa positions

14. Predicting cross-reactivity of existing vaccines with new strains

15. Predicting coverage of potential new vaccine seed strains

16. Predicting coverage of potential new vaccine seed strains Antiserum variability dominates SAT1 A O

17. 17 Conclusions Identifying antigenic determinants -Sequence-based approach can directly identify important aa positions in epitopes and quantify importance -Allows us to trace antigenic evolution of viruses Allows prediction of titres for new viruses -Aid targeting of antigenic analyses prior to lab testing Generality of modelling approach -But potential for further extension Predicting coverage of potential vaccine seed strains -Need to be able to predict immunogenicity and avidity of viruses

18. 18 Thanks! John Boyd Orr Boyd Orr Centre for Population and Ecosystem Health

19. Validating antigenic determinants Influenza A(H1N1) – experimental validation of estimated antigenic impacts using reverse genetics and HI assay for testing

20. 20 Predicting the future H1

21. 21 Predicting the future H3