Chen Zhili, Jiang Yongxin, Cui Jiazhen, Li Wannan, Han Weiwei, Liu Gang
Academy of Military Medical Sciences, Beijing 100850, China.
Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
Int J Mol Sci. 2025 Jan 30;26(3):1203. doi: 10.3390/ijms26031203.
The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunodeficiencies, and cancers. The vaccinia virus Tiantan strain (VVTT) has been instrumental as an irreplaceable vaccine strain in the eradication of smallpox in China; however, it still presents significant adverse toxic effects. After the WHO recommended that routine smallpox vaccination be discontinued, the Chinese government stopped the national smallpox vaccination program in 1981. The outbreak of monkeypox in 2022 has focused people's attention on the . However, there are limited reports on the safety and toxic side effects of VVTT. In this study, we employed a combination of transcriptomic analysis and machine learning-based feature selection to identify key genes implicated in the VVTT infection process. We utilized four machine learning algorithms, including random forest (RF), minimum redundancy maximum relevance (MRMR), eXtreme Gradient Boosting (XGB), and least absolute shrinkage and selection operator cross-validation (LASSOCV), for feature selection. Among these, XGB was found to be the most effective and was used for further screening, resulting in an optimal model with an ROC curve of 0.98. Our analysis revealed the involvement of pathways such as spinocerebellar ataxia and the p53 signaling pathway. Additionally, we identified three critical targets during VVTT infection-ARC, JUNB, and EGR2-and further validated these targets using qPCR. Our research elucidates the mechanism by which VVTT infects cells, enhancing our understanding of the smallpox vaccine. This knowledge not only facilitates the development of new and more effective vaccines but also contributes to a deeper comprehension of viral pathogenesis. By advancing our understanding of the molecular mechanisms underlying VVTT infection, this study lays the foundation for the further development of VVTT. Such insights are crucial for strengthening global health security and ensuring a resilient response to future pandemics.
痘苗病毒（VV）在各种传染病、心血管疾病、免疫缺陷和癌症的治疗中被广泛用作疫苗载体。痘苗病毒天坛株（VVTT）在中国根除天花的过程中作为一种不可替代的疫苗株发挥了重要作用；然而，它仍然存在显著的不良毒性作用。在世卫组织建议停止常规天花疫苗接种后，中国政府于1981年停止了全国天花疫苗接种计划。2022年猴痘的爆发使人们的注意力集中在了……。然而，关于VVTT安全性和毒副作用的报道有限。在本研究中，我们采用转录组分析和基于机器学习的特征选择相结合的方法，以识别与VVTT感染过程相关的关键基因。我们利用了四种机器学习算法，包括随机森林（RF）、最小冗余最大相关性（MRMR）、极端梯度提升（XGB）和最小绝对收缩和选择算子交叉验证（LASSOCV）进行特征选择。其中，发现XGB最有效，并用于进一步筛选，得到了一个ROC曲线为0.98的最优模型。我们的分析揭示了脊髓小脑共济失调和p53信号通路等途径的参与。此外，我们在VVTT感染过程中确定了三个关键靶点——ARC、JUNB和EGR2——并使用qPCR进一步验证了这些靶点。我们的研究阐明了VVTT感染细胞的机制，加深了我们对天花疫苗的理解。这些知识不仅有助于开发新的、更有效的疫苗，还有助于更深入地理解病毒发病机制。通过推进我们对VVTT感染潜在分子机制的理解，本研究为VVTT的进一步开发奠定了基础。这些见解对于加强全球卫生安全和确保对未来大流行的弹性应对至关重要。
