Department of Clinical and Toxicological Analyses, Faculty of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, Brazil.
Interunit Postgraduate Program on Bioinformatics, Institute of Mathematics and Statistics (IME), University of Sao Paulo (USP), Sao Paulo, SP, Brazil.
Front Immunol. 2024 Feb 20;15:1282754. doi: 10.3389/fimmu.2024.1282754. eCollection 2024.
Dengue virus infection is a global health problem lacking specific therapy, requiring an improved understanding of DENV immunity and vaccine responses. Considering the recent emerging of new dengue vaccines, here we performed an integrative systems vaccinology characterization of molecular signatures triggered by the natural DENV infection (NDI) and attenuated dengue virus infection models (DVTs).
We analyzed 955 samples of transcriptomic datasets of patients with NDI and attenuated dengue virus infection trials (DVT1, DVT2, and DVT3) using a systems vaccinology approach. Differential expression analysis identified 237 common differentially expressed genes (DEGs) between DVTs and NDI. Among them, 28 and 60 DEGs were up or downregulated by dengue vaccination during DVT2 and DVT3, respectively, with 20 DEGs intersecting across all three DVTs. Enriched biological processes of these genes included type I/II interferon signaling, cytokine regulation, apoptosis, and T-cell differentiation. Principal component analysis based on 20 common DEGs (overlapping between DVTs and our NDI validation dataset) distinguished dengue patients by disease severity, particularly in the late acute phase. Machine learning analysis ranked the ten most critical predictors of disease severity in NDI, crucial for the anti-viral immune response.
This work provides insights into the NDI and vaccine-induced overlapping immune response and suggests molecular markers (e.g., and ) for anti-dengue-specific therapies and effective vaccination development.
登革热病毒感染是一个全球性的健康问题,缺乏特效疗法,因此需要深入了解 DENV 免疫和疫苗反应。鉴于新的登革热疫苗最近出现,我们在这里对自然登革热病毒感染(NDI)和减毒登革热病毒感染模型(DVT)引发的分子特征进行了综合系统疫苗学分析。
我们使用系统疫苗学方法分析了 955 份 NDI 和减毒登革热病毒感染试验(DVT1、DVT2 和 DVT3)的转录组数据集样本。差异表达分析确定了 DVTs 和 NDI 之间的 237 个共同差异表达基因(DEGs)。其中,DVT2 和 DVT3 期间登革热疫苗接种分别上调或下调了 28 和 60 个 DEGs,有 20 个 DEGs 与所有三个 DVT 都有交集。这些基因的富集生物过程包括 I/II 型干扰素信号、细胞因子调节、细胞凋亡和 T 细胞分化。基于 20 个共同 DEGs(与 DVTs 和我们的 NDI 验证数据集重叠)的主成分分析区分了登革热患者的疾病严重程度,特别是在急性后期。机器学习分析对 NDI 中疾病严重程度的十个最重要预测因子进行了排序,这些预测因子对抗病毒免疫反应至关重要。
这项工作深入了解了 NDI 和疫苗诱导的重叠免疫反应,并提出了分子标记物(例如和),用于抗登革热的特异性治疗和有效的疫苗开发。