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

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Carvalho, Patricia Conceicao Gonzalez Dias ; Hirata, Thiago Dominguez Crespo ; Alves, Leandro Yukio Mano ; Moscardini, Isabelle Franco ; do Nascimento, Ana Paula Barbosa ; Costa-Martins, Andre G. ; Sorgi, Sara ; Harandi, Ali M. ; Ferreira, Daniela M. ; Vianello, Eleonora ; Haks, Marielle C. ; Ottenhoff, Tom H. M. ; Santoro, Francesco ; Martinez-Murillo, Paola ; Huttner, Angela ; Siegrist, Claire-Anne ; Medaglini, Donata ; Nakaya, Helder I.

• Tipo de documento: Artigo Científico
• Fonte: FRONTIERS IN IMMUNOLOGY; v. 14, p. 10-pg., 2023-11-08.

Resumo

IntroductionThe rVSVDG-ZEBOV-GP (Ervebo (R)) 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.MethodsIn 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 DiscussionWe 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. (AU)

• Processo FAPESP: 18/14933-2 - Biologia integrativa aplicada à saúde humana
• Beneficiário: Helder Takashi Imoto Nakaya
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2