Taofeek Oluwaseun Oluwatosin, Alile Solomon Osarumwense, Evans Elcanah Mauta, Ezediuno Louis Odinakaose, George Ifeoluwa Adeniyi, Oyewole Olawale Moses, Owiti Peter Ngo'la, Sulaimon Lateef Adegboyega
Department of Chemical Sciences, Crescent University, Abeokuta, Nigeria.
Department of Computer Science, Pan-Atlantic University, Lekki, Nigeria.
Clin Exp Vaccine Res. 2025 Jul;14(3):210-228. doi: 10.7774/cevr.2025.14.e21. Epub 2025 Mar 31.
Tuberculosis (TB) claims around 1.5 million lives annually. The M72/AS01E vaccine candidate is an innovative effort demonstrating a 50% reduction in the incidence of active TB in adults. However, optimization and effective immunization strategies against TB depends heavily on precise identification of specific molecular signatures active in vaccine protection.
In this study, we employed weighted gene co-expression network analysis, machine learning, and network biology to investigate the gene expression patterns of peripheral blood mononuclear cells, identifying transcriptomic markers of vaccine protection.
Our comprehensive exploration of publicly available gene expression dataset comprising samples from subjects vaccinated twice with 10 μg of M72/AS01E vaccine one day post-second dose (D31) and one week post-second dose (D37) in a phase IIA clinical trial revealed intense induction of multiple gene modules, indicative of acute/immediate immune response at D31 that subsided by D37. Thirty-one hub genes with significant elevation/correlation with immune protection were identified significantly mediating key events in immunity to TB. The more selective profile at D37 involved additional adaptive immunity pathways including T helper (Th) 1/Th2/Th17 differentiation, T cell receptor and cytokine signaling. The functional relevance of these biomarkers in predicting vaccine response was further analyzed using the Random Forest classifier demonstrating high accuracy in distinguishing between vaccinated and non-vaccinated samples. Additionally, the study pinpointed a miRNAs-transcription factors (TF)-target regulatory network excavating key TF, miRNA, mRNAs mediating vaccine protection.
Our results provided new insights into M72/AS01E immunity, warranting further study to optimize and guide future TB vaccine development.
结核病每年夺去约150万人的生命。M72/AS01E候选疫苗是一项创新性成果，已证明可使成人活动性结核病发病率降低50%。然而，结核病的优化和有效免疫策略在很大程度上依赖于精确识别疫苗保护中活跃的特定分子特征。
在本研究中，我们采用加权基因共表达网络分析、机器学习和网络生物学方法来研究外周血单核细胞的基因表达模式，以确定疫苗保护的转录组学标志物。
我们对公开可用的基因表达数据集进行了全面探索，该数据集包含在一项IIA期临床试验中接受两次10μg M72/AS01E疫苗接种的受试者样本，分别在第二次接种后一天（D31）和第二次接种后一周（D37）。结果显示多个基因模块被强烈诱导，表明在D31时出现急性/即时免疫反应，到D37时减弱。我们鉴定出31个与免疫保护显著升高/相关的枢纽基因，它们显著介导了结核病免疫中的关键事件。D37时更具选择性的特征涉及包括辅助性T细胞（Th）1/Th2/Th17分化、T细胞受体和细胞因子信号传导在内的额外适应性免疫途径。使用随机森林分类器进一步分析了这些生物标志物在预测疫苗反应中的功能相关性，结果表明其在区分接种和未接种样本方面具有很高的准确性。此外，该研究还确定了一个miRNA-转录因子（TF）-靶标调控网络，挖掘出介导疫苗保护的关键TF、miRNA和mRNA。
我们的结果为M72/AS01E免疫提供了新的见解，值得进一步研究以优化和指导未来的结核病疫苗开发。
