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