Long term intrinsic cycling in human life course antibody responses to influenza A(H3N2): an observational and modeling study

Measures of Population Immunity Can Predict the Dominant Clade of Influenza A (H3N2) in the 2017-2018 Season and Reveal Age-Associated Differences in Susceptibility and Antibody-Binding Specificity

BACKGROUND

For antigenically variable pathogens such as influenza, strain fitness is partly determined by the relative availability of hosts susceptible to infection with that strain compared with others. Antibodies to the hemagglutinin (HA) and neuraminidase (NA) confer substantial protection against influenza infection. We asked if a cross-sectional antibody-derived estimate of population susceptibility to different clades of influenza A (H3N2) could predict the success of clades in the following season.

METHODS

We collected sera from 483 healthy individuals aged 1 to 90 years in the summer of 2017 and analyzed neutralizing responses to the HA and NA of representative strains using focus reduction neutralization tests (FNRT) and enzyme-linked lectin assays (ELLA). We estimated relative population-average and age-specific susceptibilities to circulating viral clades and compared those estimates to changes in clade frequencies in the following 2017-2018 season.

RESULTS

The clade to which neutralizing antibody titers were lowest, indicating greater population susceptibility, dominated the next season. Titer correlations between viral strains varied by age, suggesting age-associated differences in epitope targeting driven by shared past exposures. Yet substantial unexplained variation remains within age groups.

CONCLUSIONS

This study indicates how representative measures of population immunity might improve evolutionary forecasts and inform selective pressures on influenza.

Measures of population immunity can predict the dominant clade of influenza A (H3N2) in the 2017-2018 season and reveal age-associated differences in susceptibility and antibody-binding specificity

Background

For antigenically variable pathogens such as influenza, strain fitness is partly determined by the relative availability of hosts susceptible to infection with that strain compared to others. Antibodies to the hemagglutinin (HA) and neuraminidase (NA) confer substantial protection against influenza infection. We asked if a cross-sectional antibody-derived estimate of population susceptibility to different clades of influenza A (H3N2) could predict the success of clades in the following season.

Methods

We collected sera from 483 healthy individuals aged 1 to 90 years in the summer of 2017 and analyzed neutralizing responses to the HA and NA of representative strains using Focus Reduction Neutralization Tests (FNRT) and Enzyme-Linked Lectin Assays (ELLA). We estimated relative population-average and age-specific susceptibilities to circulating viral clades and compared those estimates to changes in clade frequencies in the following 2017-18 season.

Results

The clade to which neutralizing antibody titers were lowest, indicating greater population susceptibility, dominated the next season. Titer correlations between viral strains varied by age, suggesting age-associated differences in epitope targeting driven by shared past exposures. Yet substantial unexplained variation remains within age groups.

Conclusions

This study indicates how representative measures of population immunity might improve evolutionary forecasts and inform selective pressures on influenza.

Sequence-based detection of emerging antigenically novel influenza A viruses

The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.

A speed limit on serial strain replacement from original antigenic sin

Many pathogens evolve to escape immunity, yet it remains difficult to predict whether immune pressure will lead to diversification, serial replacement of one variant by another, or more complex patterns. Pathogen strain dynamics are mediated by cross-protective immunity, whereby exposure to one strain partially protects against infection by antigenically diverged strains. There is growing evidence that this protection is influenced by early exposures, a phenomenon referred to as original antigenic sin (OAS) or imprinting. In this paper, we derive constraints on the emergence of the pattern of successive strain replacements demonstrated by influenza, SARS-CoV-2, seasonal coronaviruses, and other pathogens. We find that OAS implies that the limited diversity found with successive strain replacement can only be maintained if [Formula: see text] is less than a threshold set by the characteristic antigenic distances for cross-protection and for the creation of new immune memory. This bound implies a "speed limit" on the evolution of new strains and a minimum variance of the distribution of infecting strains in antigenic space at any time. To carry out this analysis, we develop a theoretical model of pathogen evolution in antigenic space that implements OAS by decoupling the antigenic distances required for protection from infection and strain-specific memory creation. Our results demonstrate that OAS can play an integral role in the emergence of strain structure from host immune dynamics, preventing highly transmissible pathogens from maintaining serial strain replacement without diversification.

A speed limit on serial strain replacement from original antigenic sin

Many pathogens evolve to escape immunity, yet it remains difficult to predict whether immune pressure will lead to diversification, serial replacement of one variant by another, or more complex patterns. Pathogen strain dynamics are mediated by cross-protective immunity, whereby exposure to one strain partially protects against infection by antigenically diverged strains. There is growing evidence that this protection is influenced by early exposures, a phenomenon referred to as original antigenic sin (OAS) or imprinting. In this paper, we derive new constraints on the emergence of the pattern of successive strain replacements demonstrated by influenza, SARS-CoV-2, seasonal coronaviruses, and other pathogens. We find that OAS implies that the limited diversity found with successive strain replacement can only be maintained if R 0 is less than a threshold set by the characteristic antigenic distances for cross-protection and for the creation of new immune memory. This bound implies a "speed limit" on the evolution of new strains and a minimum variance of the distribution of infecting strains in antigenic space at any time. To carry out this analysis, we develop a theoretical model of pathogen evolution in antigenic space that implements OAS by decoupling the antigenic distances required for protection from infection and strain-specific memory creation. Our results demonstrate that OAS can play an integral role in the emergence of strain structure from host immune dynamics, preventing highly transmissible pathogens from maintaining serial strain replacement without diversification.

Using interpretable machine learning to extend heterogeneous antibody-virus datasets

A central challenge in biology is to use existing measurements to predict the outcomes of future experiments. For the rapidly evolving influenza virus, variants examined in one study will often have little to no overlap with other studies, making it difficult to discern patterns or unify datasets. We develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We validate this method using hemagglutination inhibition data from seven studies and predict 2,000,000 new values ± uncertainties. Our analysis quantifies the transferability between vaccination and infection studies in humans and ferrets, shows that serum potency is negatively correlated with breadth, and provides a tool for pandemic preparedness. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."