Defining high-resolution T-cell correlates of protection in abortive versus seropositive SARS-CoV-2 infection

定义流产与血清阳性 SARS-CoV-2 感染中的高分辨率 T 细胞保护相关性

• 批准号: 2720649
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2720649
• 关键词: Defining; resolution; cell; correlates; protection

We have recently demonstrated that exposure to SARS-CoV-2 without detectable infection (repeatedly PCR- antibody-) expands T-cell responses in seronegative health care workers (HCW) who have an innate signature compatible with a subclinical/abortive infection (Swadling.Nature.2022). Importantly, we show in this unique setting of HCW who resist overt infection, that their T-cells preferentially target the non-structural regions of SARS-CoV-2, in particular essential proteins within the replication-transcription complex (RTC; NSP12 polymerase, NSP7 cofactor, NSP13 helicase). In contrast, T-cell responses in HCW who have overt seropositive infection predominantly target structural regions of the virus. Having newly identified an association between expansion of RTC-specific T-cells and early viral control, we will now characterise these T-cells at greater resolution to determine if there are specific qualities, other than specificity, which could explain their ability to mediate early viral control. We will investigate both pre-existing cross-reactive T-cells, recruited into the immune response, and T-cells generated de novo during abortive infection, and contrast these with T-cells responses during overt SARS-CoV-2 infection. Part-I: This will include functional assessment of SARS-CoV-2-reactive T-cells via spectral cytometry, which allows multiparameter single cell analysis, maximising insights from rare samples. MHC class I/II multimers and Activation-induced marker assay (AIM) will be used to investigate T-cell phenotypes, including: memory subset, trafficking, transcription factor profile, coinhibitory and costimulatory receptors. High-dimensional data will require dimension reduction and emerging data visualisation tools, such as UMAP/tSNE/SPADE/Flowsom. Part-II: In addition to comprehensive assessment of the quality of the T-cell response during abortive infection, we will further refine the specificity of the T-cells correlating with protection from detectable SARS-COV-2 infection. Epitopes within the RTC region will be identified using cutting-edge machine learning tools, such as those develop by collaborator Dr Bravi (Imperial Mathematics), RBM-MHC. These epitopes will be validated and their association with abortive infection determined through cohort screening (COVIDSortium, n=731 ~70 abortive infections ~140 lab-confirmed infections). Part-III: Finally, immunodominant epitopes will be identified using TCR-repertoire analysis, through the creation of an RTC-T-cell receptor library, generated by expanding T-cells using RTC peptides and sequencing TCRs. Importantly, TCRs will be clustered into metaclonotypes according to sequenced similarity, determined by machine-learning approach with Dr Tiffeau-Mayer (UCL, IIT), allowing inter-individual comparisons of the TCRs targeting the RTC. Publicly available TCR repertoires will also be mined to see if which RTC-specific TCRs are expanded in pre-pandemic repertoires, showing they are cross-reactive with coronaviruses circulating before the pandemic, and repertoires from bronchoalveolar lavage samples, showing enrichment at the site of coronavirus control. This work will ultimately identify if specific TCR sequence motifs or TCR-epitope pairs correlate with protection from detectable SARS-CoV-2 infection. Overall, we will take a holistic approach, examining the fine epitope and TCR specificity and functional and phenotypic characteristics of T-cells targeting the RTC of SARS-CoV-2 to refine this correlate of protection, leading to a greater understanding of the role of T cells in viral control and informing the type of T-cell immunity that should be aimed for with next generation vaccines.

我们最近已经证明，暴露于SARS-CoV-2而没有可检测的感染（重复PCR-抗体-）会扩大血清阴性的卫生保健工作者（HCW）的T细胞应答，HCW具有与亚临床/流产感染相容的先天特征（Swadling.Nature.2022）。重要的是，我们在HCW的这种独特的环境中显示，他们的T细胞优先靶向SARS-CoV-2的非结构区域，特别是复制-转录复合物（RTC; NSP 12聚合酶，NSP 7辅因子，NSP 13解旋酶）中的必需蛋白。相比之下，HCW中的T细胞应答主要针对病毒的结构区域，HCW具有明显的血清阳性感染。在新发现RTC特异性T细胞的扩增与早期病毒控制之间的关联后，我们现在将以更高的分辨率对这些T细胞进行分析，以确定是否存在特异性以外的特定品质，这可以解释它们介导早期病毒控制的能力。我们将研究预先存在的交叉反应性T细胞，招募到免疫反应，和T细胞产生的从头在流产感染，并将这些与T细胞反应在明显的SARS-CoV-2感染。第一部分：这将包括通过光谱细胞术对SARS-CoV-2反应性T细胞进行功能评估，这允许进行多参数单细胞分析，最大限度地提高对罕见样本的了解。将使用MHC I/II类多聚体和活化诱导标记物测定（AIM）研究T细胞表型，包括：记忆亚群、运输、转录因子谱、共抑制和共刺激受体。高维数据需要降维和新兴的数据可视化工具，例如UMAP/tSNE/SPADE/Flowsom。第二部分：除了全面评估流产感染期间T细胞应答的质量外，我们还将进一步完善与保护免受可检测的SARS-COV-2感染相关的T细胞特异性。RTC区域内的表位将使用尖端的机器学习工具进行识别，例如由合作者Bravi博士（帝国数学）开发的RBM-MHC。将验证这些表位，并通过队列筛选确定其与流产感染的相关性（COVIDSortium，n=731 ~70例流产感染~140例实验室确认的感染）。第三部分：最后，将使用TCR库分析，通过创建RTC-T细胞受体文库来鉴定免疫显性表位，所述RTC-T细胞受体文库通过使用RTC肽扩增T细胞并对TCR进行测序而产生。重要的是，TCR将根据测序相似性被聚类为元克隆型，这是由Tiffeau-Mayer博士（UCL，IIT）通过机器学习方法确定的，允许对靶向RTC的TCR进行个体间比较。公开可用的TCR库也将被挖掘，以查看哪些RTC特异性TCR在大流行前库中扩增，显示它们与大流行前流行的冠状病毒交叉反应，以及来自支气管肺泡灌洗样本的库，显示在冠状病毒控制部位富集。这项工作将最终确定特定的TCR序列基序或TCR-表位对是否与保护免受可检测的SARS-CoV-2感染相关。总的来说，我们将采取一种整体的方法，检查靶向SARS-CoV-2 RTC的T细胞的精细表位和TCR特异性以及功能和表型特征，以完善这种保护的相关性，从而更好地理解T细胞在病毒控制中的作用，并告知下一代疫苗应针对的T细胞免疫类型。