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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».