TY - JOUR
T1 - Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts
AU - Gonzalez Dias Carvalho, Patrícia Conceição
AU - Dominguez Crespo Hirata, Thiago
AU - Mano Alves, Leandro Yukio
AU - Moscardini, Isabelle Franco
AU - do Nascimento, Ana Paula Barbosa
AU - Costa-Martins, André G.
AU - Sorgi, Sara
AU - Harandi, Ali M.
AU - Ferreira, Daniela
AU - Vianello, Eleonora
AU - Haks, Mariëlle C.
AU - Ottenhoff, Tom H.M.
AU - Santoro, Francesco
AU - Martinez-Murillo, Paola
AU - VSV-EBOVAC Consortia, Consortia
AU - VSV-EBOPLUS Consortia, Consortia
AU - Huttner, Angela
AU - Siegrist, Claire Anne
AU - Medaglini, Donata
AU - Nakaya, Helder I.
PY - 2023/11/8
Y1 - 2023/11/8
N2 - 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.
AB - 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.
KW - adverse events
KW - baseline gene signatures
KW - data integration
KW - Ebola
KW - machine learning
KW - personalized vaccinology
KW - rVSVDG-ZEBOV-GP vaccine
KW - vaccine safety
U2 - 10.3389/fimmu.2023.1259197
DO - 10.3389/fimmu.2023.1259197
M3 - Article
SN - 1664-3224
VL - 14
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 1259197
ER -
