Abstract:
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.
Referência:
CARVALHO, Patricia Conceição Gonzales; HIRATA, Thiago Domingues Crespo; ALVES, Leandro Yukio Mano; MOSCARDINI, Isabelle Franco; NASCIMENTO, Ana Paula Barbosa; MARTINS, André G. Costa; SORGI, Sara; HARANDI, Ali M.; FERREIRA, Daniela M.; VIANELLO, Eleonora; HAKS, Marielle; OTTENHOFF, Tom H.M.; SANTORO, Francesco; MURILLO, Paola Martinez; HUTTNER, Angela; SIEGRIST, Claire-Anne; MEDAGLINI, Donata; NAKAYA, Helder I. Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts. Fontiers in Immunology, v.14, 10 p., 2023.
Acesso ao artigo no site do Periódico:
https://www.frontiersin.org/articles/10.3389/fimmu.2023.1259197/full
