Multi-epitope Models Explain How Pre-existing Antibodies Affect the Generation of Broadly Protective Responses to Influenza

Abstract

The development of next-generation influenza vaccines that elicit strain-transcendent immunity against both seasonal and pandemic viruses is a key public health goal. Targeting the evolutionarily conserved epitopes on the stem of influenza’s major surface molecule, hemagglutinin, is an appealing prospect, and novel vaccine formulations show promising results in animal model systems. However, studies in humans indicate that natural infection and vaccination result in limited boosting of antibodies to the stem of HA, and the level of stem-specific antibody elicited is insufficient to provide broad strain-transcendent immunity. Here, we use mathematical models of the humoral immune response to explore how pre-existing immunity affects the ability of vaccines to boost antibodies to the head and stem of HA in humans, and, in particular, how it leads to the apparent lack of boosting of broadly cross-reactive antibodies to the stem epitopes. We consider hypotheses where binding of antibody to an epitope: (i) results in more rapid clearance of the antigen; (ii) leads to the formation of antigen-antibody complexes which inhibit B cell activation through Fcγ receptor-mediated mechanism; and (iii) masks the epitope and prevents the stimulation and proliferation of specific B cells. We find that only epitope masking but not the former two mechanisms to be key in recapitulating patterns in data. We discuss the ramifications of our findings for the development of vaccines against both seasonal and pandemic influenza.

Successes and failures of the live-attenuated influenza vaccine, can we do better?

“…This has been historically used to measure the immune response to IIV, but there is increasing evidence that other assays measuring mucosal humoral responses might be better indicators of the LAIV immunogeniticy [61][62][63][64]. As more data becomes available on the antibody responses to different epitopes on the hemagglutinin and other proteins of influenza it may be possible to develop strategies that focus on the generation of responses to conserved eptiopes such as those on the stem of hemagglutinin and thus generate a universal influenza vaccine which avoids problems associated with strain variation [65][66][67][68]. Another simplification of our model is that we focus on antibody responses and do not consider T cell responses to influenza [69][70][71].…”

Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

“…Mechanistic host response models have been built to examine the activation and production of cells or cytokines and the efficacy of different factors (ie, cells or antibodies) in removing virus or infected cells . The models range in complexity with some attempting to incorporate several pro‐inflammatory cytokines, anti‐inflammatory cytokines, and cell populations .…”

“…14,34 Mechanistic host response models have been built to examine the activation and production of cells or cytokines and the efficacy of different factors (ie, cells or antibodies) in removing virus or infected cells. 15,18,[27][28][29][30][31][32][33][34][35]49,50,72 The models range in complexity with some attempting to incorporate several pro-inflammatory cytokines, anti-inflammatory cytokines, and cell populations. 15,35,49,50 The most common responses modeled are type I IFNs, CD8 + T cells, and antibodies because of their profound influence during influenza virus infection.…”

“…have made it possible to dissect critical mechanisms that drive the infection. The models have successfully quantified and predicted the viral load kinetics from clinical and experimental infections, [12][13][14][15][16][17][18][19][20][21][22][23][24][25] the symptoms that arise during infection, 12,13 the dynamics and efficiency of different host immune responses, 14,17,18,[25][26][27][28][29][30][31][32][33][34] the effect of different viral and host factors, 15,16,[20][21][22]35 the efficacy and design of vaccines and antiviral therapies, 14,[19][20][21][22][36][37][38][39][40] and the mechanisms of coinfection between influenza viruses and other viruses or bacteria. [41][42]…”

The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

“…152,218,219 Perhaps the closest analysis of actual sequences are models of population-level immunity in which the fitness of a given influenza sequence is in part determined by its similarity to existing sequences to which the population is already presumably immune. 220,221 There are also controlled experiments and mathematical models working to understand the impact of antibody feedback, 151,188,189,222,223 although this work has not been generalized to an inferential framework that can be used to understand individual repertoire datasets. "Mutational antigenic profiling," [224][225][226] which reveals how mutating an antigen can change antibody binding, and "deep mutational scanning," used to understand the impact of antibody sequence variation on binding, 227,228 may be helpful in these efforts.…”