AUTHOR=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. TITLE=Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts JOURNAL=Frontiers in Immunology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1259197 DOI=10.3389/fimmu.2023.1259197 ISSN=1664-3224 ABSTRACT=Introduction

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.

Methods

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.