Variability in mRNA SARS-CoV-2 BNT162b2 vaccine immunogenicity is associated with differences in the gut microbiome and habitual dietary fibre intake
Notice bibliographique
Résumé
ABSTRACT Objective Little is known about the interplay between gut microbiome and SARS-CoV-2 vaccine immunogenicity. In this prospective observational study, we investigated associations between the gut microbiome, habitual dietary fibre intake, and mRNA vaccine-elicited immune responses, including anti-Spike IgG, avidity, and ACE-2 competition (surrogate neutralization). Design 16S rRNA sequencing and short-chain fatty acid analyses were undertaken using stool samples collected from 48 healthy individuals at baseline and twelve-weeks after 1 st BNT162b2 SARS-CoV-2 vaccine dose. Associations between gut microbiome data and SARS-CoV-2 spike and RBD IgG levels, competitive binding antibodies, and anti-SARS-CoV-2 spike total relative fractional avidity assays were evaluated. A validated dietary fibre intake food frequency questionnaire was also used to correlate habitual dietary fibre intakes with vaccine responses. Results Our data revealed several baseline bacterial taxa, including Prevotella, Haemophilus and Veillonella (p<0.01), associated with BNT162b2 vaccine responses. Several Bacteroides spp. (p<0.01) as well as Bifidobacterium animalis , (p=0.003), amongst others, were positively associated with antibody avidity. Conversely, concentrations of isovaleric and isobutyric acid were higher in individuals with the lowest SARS-CoV-2 vaccine responses (p<0.01). Classifying participants based on habitual dietary fibre intake identified distinct avidity responses. Conclusion We showed associations between baseline gut microbiota composition and immunogenicity of BNT162b2 vaccine responses, particularly avidity maturation. We also demonstrate that branched-chain fatty acids and habitual dietary fibre intakes are associated with BNT162b2 vaccine immunogenicity. Together these findings indicate a link between gut microbiome, diet and antibody immunity to SARS-CoV-2 spike protein, suggesting interventions which modulate the gut microbiome could enhance COVID-19 vaccine responses. SIGNIFICANCE OF THIS STUDY What is already known on this subject? Strength and persistence of the SARS-CoV-2 BNT162b2 vaccine is variable between individuals. To date, only one study has demonstrated that baseline gut microbiota can predict SARS-CoV-2 vaccine response. What are the new findings? For the first time we showed that the higher concentrations of branched-chain fatty acids, isovaleric and isobutyric acids, are negatively associated with SARS-CoV-2 BNT162b2 vaccine responses. We revealed that habitual dietary fibre intake led to variability in the strength of antibody binding after the BNT162b2 vaccine. Specifically, high dietary fibre consumers displayed a significant increase in antibody avidity between in their 1 st and 2 nd dose. How this study might affect research, practice, or policy Our data suggests that therapeutic interventions which target the gut microbiome, including dietary modification, as well as pre-, pro-, and post-biotics, could enhance BNT162b2 vaccine immunogenicity, thus helping in the fight against COVID-19.
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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 | 0,001 |
| 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) | 0,002 | 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 ».