Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts

The rVSVDG-ZEBOV-GP (Ervebo®) vaccine is both immunogenic and protective against Ebola. However, the vaccine can cause a broad range of transient adverse reactions, from headache to arthritis. Identifying baseline reactogenicity signatures can advance personalized vaccinology and increase our unders...

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Bibliographic Details
Published in:Frontiers in immunology 2023, Vol.14, p.1259197-1259197
Main Authors: Gonzalez Dias Carvalho, Patrícia Conceição, Dominguez Crespo Hirata, Thiago, Mano Alves, Leandro Yukio, Moscardini, Isabelle Franco, do Nascimento, Ana Paula Barbosa, Costa-Martins, André G, Sorgi, Sara, Harandi, Ali M, Ferreira, Daniela M, Vianello, Eleonora, Haks, Mariëlle C, Ottenhoff, Tom H M, Santoro, Francesco, Martinez-Murillo, Paola, Huttner, Angela, Siegrist, Claire-Anne, Medaglini, Donata, Nakaya, Helder I
Format: Article
Language:English
Subjects:
adverse events
Antibodies, Viral
Arthritis - etiology
baseline gene signatures
Clinical Trials, Phase I as Topic
data integration
Ebola
Ebola Vaccines - adverse effects
Ebolavirus - genetics
Headache
Hemorrhagic Fever, Ebola
Humans
Immunologi inom det medicinska området
Immunology in the medical area
machine learning
personalized vaccinology
rVSVDG-ZEBOV-GP vaccine
Vaccination - adverse effects
Vaccination - methods
vaccine safety
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Summary:The rVSVDG-ZEBOV-GP (Ervebo®) vaccine is both immunogenic and protective against Ebola. However, the vaccine can cause a broad range of transient adverse reactions, from headache to arthritis. Identifying baseline reactogenicity signatures can advance personalized vaccinology and increase our understanding of the molecular factors associated with such adverse events. In this study, we developed a machine learning approach to integrate prevaccination gene expression data with adverse events that occurred within 14 days post-vaccination. We analyzed the expression of 144 genes across 343 blood samples collected from participants of 4 phase I clinical trial cohorts: Switzerland, USA, Gabon, and Kenya. Our machine learning approach revealed 22 key genes associated with adverse events such as local reactions, fatigue, headache, myalgia, fever, chills, arthralgia, nausea, and arthritis, providing insights into potential biological mechanisms linked to vaccine reactogenicity.
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2023.1259197