The Durability of immune Responses to vaccination against SARS-CoV-2 and its Variants.

这项建议是由一个团队提出的，他们一直在研究的最前沿工作，以了解在整个大流行期间对SARS-COV-2病毒的免疫力。随着时间的推移，这些问题的焦点已经发生了变化。随着越来越多的人接受COVID-19疫苗接种，一个关键的问题是需要了解保护性免疫的细节，以及在全球可用的许多不同疫苗的“现实生活环境”中通常可能保持保护性多长时间。此外，与最初进行临床试验时的简化条件相比，我们现在面临的情况是，许多接受疫苗的人以前曾感染过SARS-CoV-2或其α-δ变体。我们发现，先前的感染大大增强了对疫苗接种的反应。考虑到这些要点，我们的目标是生成详细的免疫学数据集，使我们能够了解保护性免疫持续多久，从而了解何时可能需要加强免疫。为此，我们汇集了我们在过去一年中与合作者一起开发和表征的不同纵向COVID-19队列的工作。我们的队列包括12097名个体，他们在英国、南非和巴西有不同的SARS-CoV-2及其变体暴露。我们将能够研究接受不同疫苗的人，无论是否感染SARS-CoV-2和相关的α，β，γ和δ变体。我们的工作旨在了解免疫保护的持久性和性质，包括对再感染和突破性感染的易感性。为了跟踪纵向免疫力，我们将使用现有的协议来测量连续血液样本中免疫力的各个方面。测试将考虑抗体对病毒的中和作用，以及评估B细胞（产生抗体）和T细胞（协调抗病毒反应）中的“免疫记忆”（引发的免疫反应“记忆”病毒并因此保护的能力）。简而言之，我们的问题可以被定义为：一些研究表明血清中的保护性抗体迅速减弱，因此可能需要相对频繁的加强剂，而另一些研究表明免疫力在接种疫苗后逐渐提高，称为“亲和力成熟”，并且B和T细胞记忆是长期存在的，这意味着加强免疫可能会延迟更长时间。通过建立一个详细的，纵向的数据集的许多措施的免疫力，我们将模拟从接种疫苗的时间过程中预测的保护性免疫力的损失。这将使我们能够为有关免疫监测和促进战略的政策制定提供准确的投入，并为在日常生活中应用“免疫认证”措施的现实提供信息-评估认证可能有意义的时间。分析需要注意的一点是，人们是多样化的。我们知道，疫苗接种反应包括一个谱，在每一端都有高反应者和低反应者。我们将在性别、种族、年龄和肥胖等因素的背景下分析大样本数据。从过去的研究来看，有些人会因为他们服用的药物而产生次优的免疫反应。我们在这里包括两个这样的同伙。在一个，我们考虑的影响，最常见的药物之一，给人的自身免疫性疾病，如炎症性肠病和类风湿性关节炎（抗肿瘤坏死因子生物制剂）。另一方面，我们考虑药物治疗对慢性粒细胞白血病患者的影响。总之，我们旨在填补的知识空白是保护性免疫的定义及其在疫苗接种后一年内的纵向分析中的稳定性。这将提供数据集和预测模型，为加强剂的时机，变异突破，人群中保护性反应的多样性和免疫抑制方案的优化提供决策信息。

This proposal has been developed by a team who have worked at the forefront of studies to understand immunity to the SARS-COV-2 virus throughout the pandemic. With time, the focus of these questions has changed. As more people receive COVID-19 vaccination, a key concern becomes the need to understand the details of protective immunity and how long it is typically likely to remain protective in the 'real-life settings' of the many different vaccines available globally. Furthermore, compared to the reductionist conditions under which initial clinical trials were conducted, we now face a situation whereby many people receiving vaccines having been previously infected by SARS-CoV-2 or its alpha to delta variants. We showed that prior infection substantially boosts the response to vaccination. These points in mind, our aim is to generate the detailed immunological datasets enabling us to understand how long protective immunity lasts and thus, when boosts are likely to be needed. To do this, we bring together our work on diverse, longitudinal, COVID-19 cohorts, already developed and characterised with our collaborators over the past year. Our cohorts encompass 12097 individuals, with different exposures to SARS-CoV-2 and its variants, in the UK, South Africa and Brazil. We will be able to study people receiving different vaccines, with or without infection by SARS-CoV-2 and the alpha, beta, gamma and delta variants of concern. Our work aims to understand the durability and nature of immune protection, including susceptibility to reinfection and breakthrough infections. To track longitudinal immunity we will use existing protocols to measure diverse aspects of immunity in sequential blood samples. Tests will consider virus neutralisation by antibody, as well as assessing 'immune memory' (-ability of a primed immune response to 'remember' the virus and so protect) in B cells (which make antibody) and T cells (which orchestrate the antiviral response). Put simply, our question can be framed as: some studies indicate that protective antibodies in serum wane rapidly, so that relatively frequent boosters may be needed, while others show that immunity progressively improves after vaccination, termed 'affinity maturation', and that B and T cell memory are long-lived, implying the possibility of longer delays to boosting. By building a detailed, longitudinal dataset of many measures of immunity, we will model the timecourse from vaccination to the predicted loss of protective immunity. This will enable us to offer precise input to policy-making about immune-monitoring and boosting strategy, as well as informing the realities of applying 'immune certification' measures in everyday life - assessing how long certification is likely to be meaningful. Analysis needs to be mindful of the point that people are diverse. We know that vaccination responses encompass a spectrum, with high-responders and poor-responders at each end. We will analyse data from our large sample in the context of factors such as gender, ethnicity, age and obesity. From past studies, some will make a sub-optimal immune response because of the medicines they are taking. We here include two such cohorts. In one, we consider the impact of one of the most common drugs given to people with autoimmune conditions such as inflammatory bowel disease and rheumatoid arthritis (anti-TNF biologics). In another, we consider the impact of drug treatment in people with chronic myelogenous leukaemia. In summary, the knowledge-gap we aim to fill is the definition of protective immunity and its stability in longitudinal analysis during the year following vaccination. This will supply the dataset and predictive models to inform decision-making on timing of boosters, variant breakthrough, diversity of protective responses within the population and optimisation of protocols for the immunosuppressed.