Frontiers in Immunology (Nov 2023)
Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts
- Patrícia Conceição Gonzalez Dias Carvalho,
- Patrícia Conceição Gonzalez Dias Carvalho,
- Thiago Dominguez Crespo Hirata,
- Leandro Yukio Mano Alves,
- Isabelle Franco Moscardini,
- Ana Paula Barbosa do Nascimento,
- André G. Costa-Martins,
- André G. Costa-Martins,
- Sara Sorgi,
- Ali M. Harandi,
- Ali M. Harandi,
- Daniela M. Ferreira,
- Daniela M. Ferreira,
- Eleonora Vianello,
- Mariëlle C. Haks,
- Tom H. M. Ottenhoff,
- Francesco Santoro,
- Paola Martinez-Murillo,
- Angela Huttner,
- Angela Huttner,
- Claire-Anne Siegrist,
- Donata Medaglini,
- Helder I. Nakaya,
- Helder I. Nakaya
Affiliations
- Patrícia Conceição Gonzalez Dias Carvalho
- Oxford Vaccine Group, University of Oxford, Oxford, United Kingdom
- Patrícia Conceição Gonzalez Dias Carvalho
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
- Thiago Dominguez Crespo Hirata
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
- Leandro Yukio Mano Alves
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
- Isabelle Franco Moscardini
- Microbiotec Srl, Siena, Italy
- Ana Paula Barbosa do Nascimento
- Division of Infectious Diseases, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- André G. Costa-Martins
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
- André G. Costa-Martins
- Artificial Intelligence and Analytics Department, Institute for Technological Research, São Paulo, Brazil
- Sara Sorgi
- Laboratory of Molecular Microbiology and Biotechnology (LAMMB), Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Ali M. Harandi
- Department of Microbiology and Immunology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Ali M. Harandi
- Vaccine Evaluation Center, BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
- Daniela M. Ferreira
- Oxford Vaccine Group, University of Oxford, Oxford, United Kingdom
- Daniela M. Ferreira
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
- Eleonora Vianello
- 0Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
- Mariëlle C. Haks
- 0Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
- Tom H. M. Ottenhoff
- 0Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
- Francesco Santoro
- Laboratory of Molecular Microbiology and Biotechnology (LAMMB), Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Paola Martinez-Murillo
- 1Centre for Vaccinology, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Angela Huttner
- 1Centre for Vaccinology, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Angela Huttner
- 2Infectious Diseases Service, Geneva University Hospitals, Geneva, Switzerland
- Claire-Anne Siegrist
- 1Centre for Vaccinology, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Donata Medaglini
- 3Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Helder I. Nakaya
- 4Scientific Platform Pasteur-University of São Paulo, São Paulo, Brazil
- Helder I. Nakaya
- 5Hospital Israelita Albert Einstein, São Paulo, Brazil
- DOI
- https://doi.org/10.3389/fimmu.2023.1259197
- Journal volume & issue
-
Vol. 14
Abstract
IntroductionThe 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.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.
Keywords
- Ebola
- rVSVDG-ZEBOV-GP vaccine
- baseline gene signatures
- adverse events
- vaccine safety
- personalized vaccinology
