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Record W2953009674 · doi:10.1101/335281

Cross-protective immune responses induced by sequential influenza virus infection and by sequential vaccination with inactivated influenza vaccines

2018· preprint· en· W2953009674 on OpenAlexafffund
Wei Dong, Yoshita Bhide, Federica Sicca, Tjarko Meijerhof, Kate Guilfoyle, Othmar G. Engelhardt, Louis Boon, Cornelis A. M. de Haan, George Carnell, Nigel Temperton, Jacqueline de Vries‐Idema, David J. Kelvin, Anke Huckriede

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsInstitute of Infection and ImmunityDalhousie University
FundersInstitute of Infection and ImmunityShantou University Medical CollegeUniversiteit UtrechtCardiff UniversityUniversitair Medisch Centrum GroningenEuropean CommissionShantou University
KeywordsVirologyVaccinationVirusImmunizationBiologyImmune systemInfluenza A virusAntibodyAdoptive cell transferImmunologyCD8Neutralizing antibodyT cell

Abstract

fetched live from OpenAlex

Abstract Sequential infection with antigenically distinct influenza viruses induces cross-protective immune responses against heterologous virus strains in animal models. Here we investigated whether sequential immunization with antigenically distinct influenza vaccines can also provide cross-protection. To this end, we compared immune responses and protective potential against challenge with A(H1N1)pdm09 in mice infected sequentially with seasonal A(H1N1) virus followed by A(H3N2) virus or immunized sequentially with whole inactivated virus (WIV) or subunit (SU) vaccine derived from these viruses. Sequential infection provided solid cross-protection against A(H1N1)pdm09 infection while sequential vaccination with WIV, though not capable of preventing weight loss upon infection completely, protected the mice from reaching the humane endpoint. In contrast, sequential SU vaccination did not prevent rapid and extensive weight loss. Protection correlated with levels of cross-reactive but non-neutralizing antibodies of the IgG2a subclass, general increase of memory T cells and induction of influenza-specific CD4+ and CD8+ T cells. Adoptive serum transfer experiments revealed that despite lacking neutralizing activity, serum antibodies induced by sequential infection protected mice from weight loss and vigorous virus growth in the lungs upon A(H1N1)pdm09 virus challenge. Antibodies induced by WIV vaccination alleviated symptoms but could not control virus growth in the lung. Depletion of T cells prior to challenge revealed that CD8+ T cells, but not CD4+ T cells, contributed to cross-protection. These results imply that sequential immunization with WIV but not SU derived from antigenically distinct viruses could alleviate the severity of infection caused by a pandemic and may improve protection to unpredictable seasonal infection. Importance New influenza virus strains entering the human population may have large impact and therefore their emergence requires immediate action. Yet, since these strains are unpredictable, vaccines cannot be prepared in advance, at least not as long as there is no universal or broadly protective influenza vaccine available. It is therefore important to elucidate in how far immunization strategies based on currently available seasonal vaccines can provide at least some protection against newly emerging virus strains. Moreover, insight in the possible mechanisms of protection can guide the further development of pre-pandemic immunization strategies. Our study presents a vaccination strategy based on sequential administration of readily available seasonal whole inactivated virus vaccines which could be easily applied in case of a new pandemic. In addition, our study identifies immune mechanisms, in particular cross-reactive non-neutralizing antibodies and CD8+ T cells, which should be targeted by broadly protective influenza vaccines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.046
GPT teacher head0.338
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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