基于2011年德国O104:H4爆发菌株的肠道致病性大肠杆菌多表位疫苗的反向疫苗学和免疫信息学方法的芯片设计

IF 3 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Eman G Youssef, Khaled Elnesr, Amro Hanora
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引用次数: 0

摘要

背景:虽然大多数大肠杆菌菌株是胃肠道微生物群中无害的成员,但某些致病性变异可引起严重的肠道和肠外疾病。欧洲发生了一次引人注目的O104:H4大肠杆菌暴发,涉及肠聚集(EAEC)和肠出血性(EHEC)菌株,导致从血性腹泻到危及生命的结肠炎和溶血性尿毒症综合征(HUS)等症状。由于治疗选择仍然有限,而且在过去40年中变化不大,因此迫切需要一种有效的疫苗。这种疫苗将通过预防严重感染和减少与疫情有关的费用,提供重大的公共卫生和经济效益。由于免疫信息学的进步,多表位疫苗方法提供了一种有希望的策略,用于靶向引起胡斯综合征的大肠杆菌(O104:H4和O157:H7血清型),同时对正常微生物群的破坏最小。本研究旨在利用生物信息学和免疫信息学工具设计免疫原性多表位疫苗(MEV)结构。方法与结果:比较蛋白质组学分析鉴定出672个大肠杆菌O104:H4特有的蛋白,不包括与非致病性大肠杆菌K-12-MG1655株共有的蛋白和短于100个氨基酸的蛋白。亚细胞定位(P-SORTb)鉴定了17个细胞外或外膜蛋白。根据跨膜结构域(TMHMM)、抗原性(VaxiJen)和肠出血性大肠杆菌菌株之间的保守性,选择4种蛋白作为候选疫苗。表位预测显示10个b细胞、4个细胞毒性t细胞和3个辅助t细胞表位。设计了四种不同佐剂的mev,并对其溶解度、稳定性和抗原性进行了评估。结构细化(GALAXY)和对接研究证实了它与toll样受体4 (TLR4)的强相互作用。硅免疫模拟(C-ImmSim)显示了强大的体液和细胞免疫反应。综上所述,MEV结构具有良好的免疫原性,值得在实验模型中进一步验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In Silico Design of a Multiepitope Vaccine Against Intestinal Pathogenic Escherichia coli Based on the 2011 German O104:H4 Outbreak Strain Using Reverse Vaccinology and an Immunoinformatic Approach.

Background: While most Escherichia coli strains are harmless members of the gastrointestinal microbiota, certain pathogenic variants can cause severe intestinal and extraintestinal diseases. A notable outbreak of E. coli O104:H4, involving both enteroaggregative (EAEC) and enterohemorrhagic (EHEC) strains, occurred in Europe, resulting in symptoms ranging from bloody diarrhea to life-threatening colitis and hemolytic uremic syndrome (HUS). Since treatment options remain limited and have changed little over the past 40 years, there is an urgent need for an effective vaccine. Such a vaccine would offer major public health and economic benefits by preventing severe infections and reducing outbreak-related costs. A multiepitope vaccine approach, enabled by advances in immunoinformatics, offers a promising strategy for targeting HUS-causing E. coli (O104:H4 and O157:H7 serotypes) with minimal disruption to normal microbiota. This study aimed to design an immunogenic multiepitope vaccine (MEV) construct using bioinformatics and immunoinformatic tools.

Methods and results: Comparative proteomic analysis identified 672 proteins unique to E. coli O104:H4, excluding proteins shared with the nonpathogenic E. coli K-12-MG1655 strain and those shorter than 100 amino acids. Subcellular localization (P-SORTb) identified 17 extracellular or outer membrane proteins. Four proteins were selected as vaccine candidates based on transmembrane domains (TMHMM), antigenicity (VaxiJen), and conservation among EHEC strains. Epitope prediction revealed ten B-cell, four cytotoxic T-cell, and three helper T-cell epitopes. Four MEVs with different adjuvants were designed and assessed for solubility, stability, and antigenicity. Structural refinement (GALAXY) and docking studies confirmed strong interaction with Toll-Like Receptor 4 (TLR4). In silico immune simulations (C-ImmSim) indicated robust humoral and cellular immune responses. In Conclusions, the proposed MEV construct demonstrated promising immunogenicity and warrants further validation in experimental models.

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