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Improved prediction of antigenic relationships among RNA viruses
Richard ReeveBoyd Orr Centre for Population and Ecosystem
HealthUniversity of Glasgow
Acknowledgements
Boyd Orr Centre forPopulation 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
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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
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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
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
importance2. Predict antigenic phenotype of novel/emerging
viruses 3. Predict coverage of potential vaccines/reference
strains
FMDV data
Serotype
SAT1 O A
Antigenic data
(VN test)
Reference strains
(antisera)5 5 7
Test viruses 42 77 56
Antisera-virus pairs
153 308 371
Measurements 1809 740 929
Genetic data Full 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)
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Human Influenza A data
Subtype
H1N1(1995-2009)
H3N2(1968-2013)
Antigenic data
(HI assay)
Reference strains
(antisera)43 169
Test viruses 506 229
Antisera-virus pairs
3,734 2,738
Measurements 19,905 7,315
Genetic data HA1 sequences
Generated by The Crick Worldwide Influenza Centre, Francis Crick Institute, UK
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
Identifying antigenic determinants
SAT1
O
Tracing antigenic evolution
Virus Protein
aa position
FMDV A VP1VP2VP3
VP1 81, 138, 148†, 159†
VP2 74, 79†
VP3 132
FMDV O VP1VP2
VP1 142, 169, 211†
VP2 74†, 193*VP3 56
FMDV SAT1 VP1VP2VP3
VP1 144†, 149†, 164, 209VP2 72†
VP3 72†, 77†, 138†
H1N1 HA1 36, 72†, 74† or 120, 130*, 141*†, 142†, 153*†, 163†, 183, 184†, 187*†, 190†, 252, 274, 313
H3N2 HA1 62†, 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 determinantsExperimentally validated aa positions
Identifying antigenic determinantsExperimentally validated aa positions
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Identifying antigenic determinantsIncluded aa positions
Identifying antigenic determinantsIncluded aa positions
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Identifying antigenic determinantsIncluded aa positions
Identifying antigenic determinantsIncluded aa positions
Predicting cross-reactivity of existing vaccines with new strains
Predicting coverage of potential new vaccine seed strains
Predicting coverage of potential new vaccine seed strainsAntiserum variability dominates
SAT1
A
O
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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
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Thanks!
John Boyd Orr
Boyd Orr Centre forPopulation and Ecosystem Health
Validating antigenic determinantsInfluenza A(H1N1) – experimental validation of estimated antigenic impacts using reverse genetics and HI assay for testing
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Predicting the futureH1
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Predicting the futureH3