Yuan Lifang, Li Xu, Li Minchao, Bi Rongjun, Li Yingrui, Song Jiaping, Li Wei, Yan Mingchen, Luo Huanle, Sun Caijun, Shu Yuelong
School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China.
School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; Department of Pathogenic Biology and Immunology, School of Basic Medicine, Xiangnan University, Chenzhou, Hunan, PR China.
Int J Biol Macromol. 2024 Jan;254(Pt 3):128071. doi: 10.1016/j.ijbiomac.2023.128071. Epub 2023 Nov 14.
Influenza remains a global health concern due to its potential to cause pandemics as a result of rapidly mutating influenza virus strains. Existing vaccines often struggle to keep up with these rapidly mutating flu viruses. Therefore, the development of a broad-spectrum peptide vaccine that can stimulate an optimal antibody response has emerged as an innovative approach to addressing the influenza threat. In this study, an immunoinformatic approach was employed to rapidly predict immunodominant epitopes from different antigens, aiming to develop an effective multiepitope influenza vaccine (MEV). The immunodominant B-cell linear epitopes of seasonal influenza strains hemagglutinin (HA) and neuraminidase (NA) were predicted using an antibody-peptide microarray, involving a human cohort including vaccinees and infected patients. On the other hand, bioinformatics tools were used to predict immunodominant cytotoxic T-cell (CTL) and helper T-cell (HTL) epitopes. Subsequently, these epitopes were evaluated by various immunoinformatic tools. Epitopes with high antigenicity, high immunogenicity, non-allergenicity, non-toxicity, as well as exemplary conservation were then connected in series with appropriate linkers and adjuvants to construct a broad-spectrum MEV. Moreover, the structural analysis revealed that the MEV candidates exhibited good stability, and the docking results demonstrated their strong affinity to Toll-like receptors 4 (TLR4). In addition, molecular dynamics simulation confirmed the stable interaction between TLR4 and MEVs. Three injections with MEVs showed a high level of B-cell and T-cell immune responses according to the immunological simulations in silico. Furthermore, in-silico cloning was performed, and the results indicated that the MEVs could be produced in considerable quantities in Escherichia coli (E. coli). Based on these findings, it is reasonable to create a broad-spectrum MEV against different subtypes of influenza A and B viruses in silico.
由于流感病毒株快速变异，流感有引发大流行的可能性，因此它仍是一个全球健康问题。现有的疫苗往往难以跟上这些快速变异的流感病毒。因此，开发一种能刺激最佳抗体反应的广谱肽疫苗已成为应对流感威胁的一种创新方法。在本研究中，采用免疫信息学方法从不同抗原中快速预测免疫显性表位，旨在开发一种有效的多表位流感疫苗（MEV）。使用抗体-肽微阵列预测季节性流感毒株血凝素（HA）和神经氨酸酶（NA）的免疫显性B细胞线性表位，该微阵列涉及包括疫苗接种者和感染患者的人类队列。另一方面，利用生物信息学工具预测免疫显性细胞毒性T细胞（CTL）和辅助性T细胞（HTL）表位。随后，通过各种免疫信息学工具对这些表位进行评估。然后，将具有高抗原性、高免疫原性、无致敏性、无毒性以及良好保守性的表位与合适的连接子和佐剂串联连接，构建广谱MEV。此外，结构分析表明，MEV候选物具有良好的稳定性，对接结果表明它们与Toll样受体4（TLR4）具有很强的亲和力。此外，分子动力学模拟证实了TLR4与MEV之间的稳定相互作用。根据计算机模拟的免疫结果，三次注射MEV显示出高水平的B细胞和T细胞免疫反应。此外，进行了计算机克隆，结果表明MEV可以在大肠杆菌中大量生产。基于这些发现，在计算机上创建一种针对甲型和乙型流感病毒不同亚型的广谱MEV是合理的。
