Pre-existing immunity to influenza viruses through infection and/or vaccination leads to viral mutational signatures associated with unique immune responses during a subsequent infection
Bibliographic record
Abstract
Abstract Our biggest challenge to reducing the burden of seasonal influenza is the constant antigen drift of circulating influenza viruses which then evades the protection of pre-existing immunity. Continual viral infection and influenza vaccination creates a layered immune history in people, however, how host preimmunity interacts with an antigenically divergent virus exposure is poorly understood. Here we investigated the influence of host immune histories on influenza viral mutations. Immune backgrounds were devised in mice similar to what is experienced in people: naive; previously infected (A/FM/1/1947); previously vaccinated (Sanofi quadrivalent vaccine); and previously infected and then vaccinated. Mice were challenged with the heterologous H1N1 strain A/Mexico/4108/2009 to assess protection, viral mutation, and host responses in respect to each immune background by RNAseq. Viral sequences were analyzed for antigenic changes using DiscoTope 2.0 and Immune Epitope Database (IEDB) Analysis Resource NetMHCpan EL 4.1 servers. The mock infected-vaccinated group consistently had the greatest number of viral mutations seen across several viral proteins, HA, NA, NP, and PB1 which was associated with strong antiviral responses and moderate T cell and B cell responses. In contrast, the preimmune-vaccinated mice were not associated with variant emergence and the host profiles were characterized by minimal antiviral immunity but strong T cell, B cell, and NK cell responses. This work suggests that the infection and vaccination history of the host dictates the capacity for viral mutation at infection through immune pressure. These results are important for developing next generation vaccination strategies. Importance Influenza is a continual public health problem. Due to constant virus circulation and vaccination efforts, people have complex influenza immune histories which may impact the outcome of future infections and vaccinations. How immune histories influence the emergence of new variants and the immune pressure stimulated at exposure is poorly understood. Our study addressed this knowledge gap by utilizing mice that are preimmune to influenza viruses and analyzing host responses as well as viral mutations associated with changes in antigenicity. Importantly, we found previous vaccination induced immune responses with moderate adaptive immunity and strong antiviral immunity which was associated with increased mutations in the influenza virus. Interestingly, animals that were previously infected with a heterologous virus and also vaccinated had robust adaptive responses with little to no antiviral induction which was associated with no emergence of viral variants. These results are important for the design of next generation influenza vaccines.
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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) | 0.001 | 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".