Influenza preimmunity increases vaccination efficacy by influencing antibody longevity, neutralization activity, and epitope specificity
Bibliographic record
Abstract
ABSTRACT Influenza virus infections are a recurrent public health problem causing millions of hospitalizations each year despite vaccination efforts. The well-known yearly cycling of influenza viruses is the result of the reciprocal and coevolutionary relationship between the host and virus. Together, from the frequent infections and yearly vaccinations humans build a complex immune history over their lifetimes. Despite the prominence of immune history, vaccines are rarely evaluated in the imprinted (preimmune) host. We developed a ferret model for this purpose where ferrets were imprinted with a sublethal dose of the historical seasonal H1N1 strain A/USSR/90/1977 (USSR/77). A +60 day recovery period was given to build immune memory prior to vaccination with a split virion QIV vaccine. To evaluate protection, the ferrets were challenged with a 2009 H1N1 pandemic virus matching the vaccine antigens. The preimmune-vaccinated ferrets did not experience significant disease during challenge while the naïve-vaccinated group were the most severe. Hemagglutination inhibition (HAI) assays showed that preimmune ferrets had a faster and longer antibody response post vaccination for all vaccine antigens compared to minimal HAI responses in the naïve-vaccinated group. To investigate the immune mechanisms leading to disease protection in the preimmune ferrets, we performed microneutralization and isotype ELISA assays. Microneutralization suggested preimmune ferrets developed antibodies that were more functional for virus neutralization. Antibody isotype profiling indicated that virus specific antibodies in the preimmune-vaccinated ferrets was dominated by the IgG isotype suggesting B cell maturity and possible plasticity in a pre-existing B cell. Surprisingly, the naïve-vaccinated ferrets developed a more severe disease with less virus neutralization suggesting improper immunological processing of vaccine antigens. Together, these results showed the preimmune host had greater responses to vaccination, and the predominant IgG virus specific antibodies suggested a flexible long-lived B cell response. These results are important and should be considered for vaccine design. AUTHOR SUMMARY The influenza virus is a significant threat to human health and the economy despite large-scale vaccination efforts. The low effectiveness of the seasonal influenza vaccine is attributed to the frequently mutating virus enabling people to have several influenza virus infections throughout their lifetimes. As people are susceptible to multiple infections, they build a complex immune history. Despite this, vaccines are often not evaluated in animals with an immune history. Here we developed a ferret model that had previously been infected with a historical influenza virus to evaluate vaccine responses to current vaccines. Ferrets were infected with a sublethal does of a historical virus, A/USSR/90/1977, to develop a preimmune background. Preimmune ferrets were vaccinated with the Sanofi quadrivalent influenza vaccine and the antibody responses were investigated after vaccination. Our results showed that preimmune ferrets had a stronger antibody response following vaccination and the antibodies developed were older and better at neutralizing influenza virus at a virus challenge. Clinically, preimmune-vaccinated ferrets developed a milder disease during challenge compared to naïve-vaccinated ferrets. This work indicates that the host responses to vaccination are dependent on the host background and that influenza vaccine development and evaluation should take host influenza background into account.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".