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

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 ORCID:, Vianello, Eleonora, Haks, Mariëlle C., Ottenhoff, Tom H. M., Santoro, Francesco, Martinez-Murillo, Paola, Huttner, Angela, Siegrist, Claire-Anne, Medaglini, Donata and Nakaya, Helder I. (2023) 'Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts'. Frontiers in Immunology, Vol 14.

fimmu-14-1259197.pdf - Published Version
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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.

Results and Discussion:
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.

Item Type: Article
Subjects: QU Biochemistry > Genetics > QU 450 General Works
QW Microbiology and Immunology > Immunotherapy and Hypersensitivity > QW 806 Vaccination
WC Communicable Diseases > WC 20 Research (General)
Faculty: Department: Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI):
SWORD Depositor: JISC Pubrouter
Depositing User: JISC Pubrouter
Date Deposited: 23 Nov 2023 14:16
Last Modified: 23 Nov 2023 14:16


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