Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
Front Immunol. 2024 May 22;15:1356314. doi: 10.3389/fimmu.2024.1356314. eCollection 2024.
Outbreaks of coronaviruses and especially the recent COVID-19 pandemic emphasize the importance of immunological research in this area to mitigate the effect of future incidents. Bioinformatics approaches are capable of providing multisided insights from virus sequencing data, although currently available software options are not entirely suitable for a specific task of mutation surveillance within immunogenic epitopes of SARS-CoV-2.
Here, we describe the development of a mutation tracker, EpitopeScan, a Python3 package with command line and graphical user interface tools facilitating the investigation of the mutation dynamics in SARS-CoV-2 epitopes via analysis of multiple-sequence alignments of genomes over time. We provide an application case by examining three Spike protein-derived immunodominant CD4 T-cell epitopes restricted by HLA-DRB1*04:01, an allele strongly associated with susceptibility to rheumatoid arthritis (RA). Mutations in these peptides are relevant for immune monitoring of CD4 T-cell responses against SARS-CoV-2 spike protein in patients with RA. The analysis focused on 2.3 million SARS-CoV-2 genomes sampled in England.
We detail cases of epitope conservation over time, partial loss of conservation, and complete divergence from the wild type following the emergence of the N969K Omicron-specific mutation in November 2021. The wild type and the mutated peptide represent potential candidates to monitor variant-specific CD4 T-cell responses. EpitopeScan is available via GitHub repository https://github.com/Aleksandr-biochem/EpitopeScan.
冠状病毒的爆发,尤其是最近的 COVID-19 大流行,强调了在该领域进行免疫学研究的重要性,以减轻未来事件的影响。生物信息学方法能够从病毒测序数据中提供多方面的见解,尽管目前可用的软件选项并不完全适合 SARS-CoV-2 免疫原性表位中突变监测的特定任务。
在这里,我们描述了突变追踪器 EpitopeScan 的开发,这是一个 Python3 包,具有命令行和图形用户界面工具,通过随时间分析基因组的多序列比对,方便研究 SARS-CoV-2 表位中的突变动态。我们通过检查三个 Spike 蛋白衍生的免疫显性 CD4 T 细胞表位来提供一个应用案例,这些表位受 HLA-DRB1*04:01 限制,该等位基因与类风湿关节炎(RA)的易感性密切相关。这些肽中的突变与 RA 患者针对 SARS-CoV-2 刺突蛋白的 CD4 T 细胞反应的免疫监测有关。该分析侧重于在英格兰采样的 230 万 SARS-CoV-2 基因组。
我们详细介绍了随着时间的推移,表位保守性的情况,部分保守性丧失以及在 2021 年 11 月出现 N969K 奥密克戎特异性突变后的完全发散。野生型和突变型肽代表了监测变异特异性 CD4 T 细胞反应的潜在候选物。EpitopeScan 可通过 GitHub 存储库 https://github.com/Aleksandr-biochem/EpitopeScan 获得。