Oxford Vaccine Group, University of Oxford, Oxford, United Kingdom.
Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Front Immunol. 2023 Nov 8;14:1259197. doi: 10.3389/fimmu.2023.1259197. eCollection 2023.
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 reactogenicity.
rVSVDG-ZEBOV-GP（Ervebo）疫苗具有免疫原性，可预防埃博拉病毒。然而，该疫苗会引起广泛的短暂不良反应，从头痛到关节炎。确定基线反应原性特征可以推进个性化疫苗学，并提高我们对与这些不良反应相关的分子因素的理解。
在这项研究中，我们开发了一种机器学习方法，将接种前的基因表达数据与接种后 14 天内发生的不良反应进行整合。
我们分析了来自瑞士、美国、加蓬和肯尼亚 4 个 I 期临床试验队列的 343 个血液样本中 144 个基因的表达。我们的机器学习方法揭示了 22 个关键基因与不良反应相关，如局部反应、疲劳、头痛、肌痛、发热、寒战、关节痛、恶心和关节炎，为与疫苗反应原性相关的潜在生物学机制提供了见解。
