Repeated vaccination with homologous influenza hemagglutinin broadens human antibody responses to unmatched flu viruses
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Curated by eLife
eLife Assessment
This valuable study provides outlines the mechanism by which repeated vaccination broadens the breadth of antibody responses against epitope unmatched virus strains. The authors' mathematical model is solid and incorporates various parameters that regulate B cell activation and antibody response.
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Abstract
The on-going diversification of influenza virus necessicates annual vaccine updating. The vaccine antigen, the viral spike protein hemagglutinin (HA), tends to elicit strain-specific neutralizing activity, predicting that sequential immunization with the same HA strain will boost antibodies with narrow coverage. However, repeated vaccination with homologous SARS-CoV-2 vaccine eventually elicits neutralizing activity against highly unmatched variants, questioning this immunological premise. We evaluated a longitudinal influenza vaccine cohort, where each year the subjects received the same, novel H1N1 2009 pandemic vaccine strain. Repeated vaccination gradually enhanced receptor-blocking antibodies (HAI) to highly unmatched H1N1 strains within individuals with no initial memory recall against these historical viruses. An in silico model of affinity maturation in germinal centers integrated with a model of differentiation and expansion of memory cells provides insight into the mechanisms underlying these results and shows how repeated exposure to the same immunogen can broaden the antibody response against diversified targets.
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eLife Assessment
This valuable study provides outlines the mechanism by which repeated vaccination broadens the breadth of antibody responses against epitope unmatched virus strains. The authors' mathematical model is solid and incorporates various parameters that regulate B cell activation and antibody response.
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Reviewer #1 (Public Review):
In this study, Deng et al. investigate the antibody response against HA antigen following repeated vaccination with the H1N1 2009 pandemic influenza vaccine strain, using in silico modeling. The proposed model provides valuable mechanistic insights into how the broadening of the antibody response takes place upon repeated vaccination.
Overall, the authors' model effectively explains the mechanistic principles underlying antibody responses against the viral antigens harboring epitope immunodominancy.
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Reviewer #2 (Public Review):
The authors have been studying the mechanism of breadth expansion in antibody responses with repeated vaccinations using their own mathematical model. In this study, they applied this mathematical model to a cohort data analyzing anti-HA antibody responses after multiple influenza virus vaccination and investigated the mechanism of antibody breadth expansion to diversified target viral strains.
The manuscript is well written, and the mathematical model is well built that incorporates various parameters related to B cell activation in GC and EGC based on experimental data.Strengths:
By carefully reanalyzing the published cohort data (Nunez IA et al 2017 PLoS One), they have clearly demonstrated that the repeated influenza virus vaccinations result in an expansion of the breadth to unmatched viral strains.
Usi…
Reviewer #2 (Public Review):
The authors have been studying the mechanism of breadth expansion in antibody responses with repeated vaccinations using their own mathematical model. In this study, they applied this mathematical model to a cohort data analyzing anti-HA antibody responses after multiple influenza virus vaccination and investigated the mechanism of antibody breadth expansion to diversified target viral strains.
The manuscript is well written, and the mathematical model is well built that incorporates various parameters related to B cell activation in GC and EGC based on experimental data.Strengths:
By carefully reanalyzing the published cohort data (Nunez IA et al 2017 PLoS One), they have clearly demonstrated that the repeated influenza virus vaccinations result in an expansion of the breadth to unmatched viral strains.
Using their mathematical model, they have determined the major factors for the breadth expansion following multiple immunizations.
Weaknesses:
The overall concept of their model has already been published (Yang L et al 2023 Cell Reports) with a SRAS-CoV-2 vaccine model, and they have applied it to influenza virus vaccine in this study, with the conclusions being largely the same.
It is unclear how the re-evaluation of public data in the first half part is related to the validation of their model in the later part.
Other points:
In the original data by Nurez LA et al., HAI (the inhibitory effect of anti-HA antibodies on the binding of HA to sialic acid on erythrocytes) was used as the lead-out. The authors conclude that the breadth expansion with repeated vaccinations is primarily due to the activation of B cells with BCRs that recognize minor common epitopes, induced by covering up of strain specific major epitopes by pre-existing antibodies. However, as they themselves show in Fig 1, once the sialic acid-binding region is covered, it seems difficult for another BCR to bind to this region. When the target epitope is limited like this, the effect of increasing antigen supply to DCs by pre-existing antibodies and the effect of increasing the presentation of minor epitopes appears to compete with each other. Could the author please explain this point? In relation to this point, please explain the meaning of analysis of the entire ectodomain when the original data's lead-out is HAI.
Minor point:
The description "The purpose of this model is ...." starting at line 171 and the description of "we obtain results in harmony with the clinical findings ...." starting at line 478 sound to be contradictory. As the authors themselves state at line 171, if the purpose of this model is not to fit the data but to demonstrate the principle, then the prudent sampling and reanalyzing data itself seems to have less meaning.
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Author response:
Reviewer #1 (Public Review):
In this study, Deng et al. investigate the antibody response against HA antigen following repeated vaccination with the H1N1 2009 pandemic influenza vaccine strain, using in silico modeling. The proposed model provides valuable mechanistic insights into how the broadening of the antibody response takes place upon repeated vaccination.
Overall, the authors' model effectively explains the mechanistic principles underlying antibody responses against the viral antigens harboring epitope immunodominancy.
We thank the Reviewer for their positive and thoughtful assessment of the work. We address issues raised in the revised manuscript and in the point-by-point responses below.
Reviewer #2 (Public Review):
The authors have been studying the mechanism of breadth expansion in antibody responses …
Author response:
Reviewer #1 (Public Review):
In this study, Deng et al. investigate the antibody response against HA antigen following repeated vaccination with the H1N1 2009 pandemic influenza vaccine strain, using in silico modeling. The proposed model provides valuable mechanistic insights into how the broadening of the antibody response takes place upon repeated vaccination.
Overall, the authors' model effectively explains the mechanistic principles underlying antibody responses against the viral antigens harboring epitope immunodominancy.
We thank the Reviewer for their positive and thoughtful assessment of the work. We address issues raised in the revised manuscript and in the point-by-point responses below.
Reviewer #2 (Public Review):
The authors have been studying the mechanism of breadth expansion in antibody responses with repeated vaccinations using their own mathematical model. In this study, they applied this mathematical model to a cohort data analyzing anti-HA antibody responses after multiple influenza virus vaccination and investigated the mechanism of antibody breadth expansion to diversified target viral strains.
The manuscript is well written, and the mathematical model is well built that incorporates various parameters related to B cell activation in GC and EGC based on experimental data.
We thank the reviewer for their positive and thoughtful review and address issues raised in a revised version of the manuscript and in the point-by-point below.
Strengths:
By carefully reanalyzing the published cohort data (Nunez IA et al 2017 PLoS One), they have clearly demonstrated that the repeated influenza virus vaccinations result in an expansion of the breadth to unmatched viral strains.
Using their mathematical model, they have determined the major factors for the breadth expansion following multiple immunizations.
We thank the reviewer for pointing out the strengths of our study.
Weaknesses
The overall concept of their model has already been published (Yang L et al 2023 Cell Reports) with a SARS-CoV-2 vaccine model, and they have applied it to influenza virus vaccine in this study, with the conclusions being largely the same.
It is unclear how the re-evaluation of public data in the first half part is related to the validation of their model in the later part.
The reviewer is correct in that we build directly on our model published previously to study related phenomena for SARS-CoV-2. However, a critical advance of the work was to now ask whether antibody broadening following repeated homologous antigen exposure is a general feature of human humoral immunity. As we point out in the introduction of our manuscript, repeated exposure to the same antigen has long been assumed to predominantly boost strain limited humoral immunity, necessitating rational design of vaccines that re-orient antibody responses to target otherwise immune-subdominant targets. Hence, antibody broadening in response to homologous SARS-CoV-2 antigen points to reconsideration of that basic premise in immunology; and if we are to now define this as general feature of human antibody responses, then evaluation of the principle using a different vaccine protocol and antigen is necessitated. Accordingly, we took advantage of the influenza vaccine space where, within the immediate years following the 2009 H1N1 pandemic, the 2009 H1N1 strain was repeatedly applied as the seasonal vaccine strain. This HA was also novel (as it was from a pandemic virus pHA), meaning that traditional back-boosting to historical strains would be limited. We then re-evaluated the longitudinal HAI data of Nurez et al. to define whether a broadening to increasingly divergent vaccine-unmatched strains is observed upon repeated exposure to pHA. This was not done before and was enabled by incorporating our amino acid relatedness parameter and our structure-based definition of the RBS patch. To then query mechanistic origins of the broadening effect, we adapted and extended our previous computational model to: (1) better reflect HA epitope diversity and overlap within the RBS patch; and (2) to better reflect the influenza immunization regimens that are used clinically. The differences between the modeling done in this paper and that in Yang et al. 2023 are described in the Methods section separately. Taken together, our analyses of data in Nunez et al and our simulations strengthen the emerging view that repeated boosting with the same antigen enables the humoral immune system to diversify immune responses because of feedback regulation which leads to enhanced antigen on FDCs, persistent GCs, and epitope masking. This, in turn, enables the immune system to generalize to recognize and respond to unseen variant antigens that harbor mutations in the immunodominant epitopes. Our results point to a new and emerging paradigm regarding booster immunizations and fundamental features of the humoral immune system.
Other points:
In the original data by Nurez LA et al., HAI (the inhibitory effect of anti-HA antibodies on the binding of HA to sialic acid on erythrocytes) was used as the lead-out. The authors conclude that the breadth expansion with repeated vaccinations is primarily due to the activation of B cells with BCRs that recognize minor common epitopes, induced by covering up of strain specific major epitopes by pre-existing antibodies. However, as they themselves show in Fig 1, once the sialic acid-binding region is covered, it seems difficult for another BCR to bind to this region. When the target epitope is limited like this, the effect of increasing antigen supply to DCs by pre-existing antibodies and the effect of increasing the presentation of minor epitopes appears to compete with each other. Could the author please explain this point?
We agree that accounting for epitope overlap is important when the target is limited, as the reviewer indicates. In Figure 6C vs 6D we assess steric effects of possible spatial overlap between dominant and subdominant epitopes. Under overlapping conditions, we find evidence for steric-based constrainment of broadening, as predicted by the reviewer. Depending upon the degree of overlap between the epitopes and differences in germline characteristics in the B cells targeting dominant and subdominant epitopes, this effect could be compensated during subsequent shots, as described by our results (see lines 392-406).
We also now incorporate the following sentence into our discussion (lines 448-453):
“Epitope masking will also be constrained by the dimensions of the RBS and our simulations do report attenuation of titers against historical influenza strains when we introduce epitope overlap. Depending upon the degree of overlap between the epitopes and differences in germline characteristics in the B cells targeting dominant and subdominant epitopes, this effect could be compensated during subsequent shots.”
In relation to this point, please explain the meaning of analysis of the entire ectodomain when the original data's lead-out is HAI.
We include side-by-side full length ectodomain versus RBS patch (sialic acid binding residues + antibody epitope ring) to demonstrate relatedness differences in the lead-out data. But it is precisely because of the point raised by the reviewer that we focus on using the RBS patch as the relatedness values to assess antibody broadening as defined by HAI activity (see Figure 3 and S2).
Minor point:
The description "The purpose of this model is ...." starting at line 171 and the description of "we obtain results in harmony with the clinical findings ...." starting at line 478 sound to be contradictory. As the authors themselves state at line 171, if the purpose of this model is not to fit the data but to demonstrate the principle, then the prudent sampling and reanalyzing data itself seems to have less meaning.
We respectfully disagree. Please see above point as to how the clinical data is more than just “reanalyzing” but to first discover the previously unreported broadening effect across highly divergent strains following sequential immunization with homologous antigen in the influenza vaccine space; we then extended and adapted our computational model for the influenza vaccination paradigm to gain mechanistic insight on how such antibody broadening may occur. The word “harmony” was not meant to imply quantitative agreement, and apologize if it caused confusion.
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