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，他们工作在研究的前沿，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，这些问题的焦点也发生了变化。随着越来越多的人接种新冠肺炎疫苗，一个关键的问题变成了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“现实环境”中，保护性免疫通常可能保持多久。此外，与最初进行临床试验的简化论条件相比，我们现在面临的情况是，许多接种疫苗的人以前感染了SARS-CoV-2或其阿尔法到三角洲变种。我们表明，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长时间，从而了解何时可能需要加强免疫。为了做到这一点，我们将我们在不同的、纵向的新冠肺炎队列上的工作汇集在一起，这些队列已经在过去一年里与我们的合作者一起开发和表征了。我们的队列包括英国、南非和巴西的12097人，他们对SARS-CoV-2及其变种有不同的接触。我们将能够研究接种不同疫苗的人，无论是否感染SARS-CoV-2以及令人担忧的阿尔法、贝塔、伽马和德尔塔变种。我们的工作旨在了解免疫保护的持久性和性质，包括对再次感染和突破性感染的易感性。为了跟踪纵向免疫，我们将使用现有的方案来测量连续血液样本中免疫的不同方面。测试将考虑抗体对病毒的中和作用，以及评估B细胞(制造抗体)和T细胞(协调抗病毒反应)的“免疫记忆”(启动免疫反应‘记住’病毒从而保护病毒的能力)。简而言之，我们的问题可以概括为：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增强剂，而另一些研究表明，免疫接种后免疫力逐渐提高，被称为“亲和力成熟”，而且B和T细胞的记忆是长期的，这意味着增强可能会延迟更长时间。通过建立一个详细的、纵向的许多免疫措施的数据集，我们将对从接种疫苗到预计失去保护性免疫的时间过程进行建模。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信息，并告知在日常生活中应用‘免疫认证’措施的现实--评估认证可能有多长时间有意义。分析需要注意的一点是，人是多样化的。我们知道，疫苗接种反应包括一个光谱，两端都有高应答者和低应答者。我们将在性别、种族、年龄和肥胖等因素的背景下分析我们的大样本数据。从过去的研究来看，一些人会因为服用的药物而产生次优免疫反应。我们这里包括两个这样的群体。在一项研究中，我们考虑了一种最常见的药物对患有自身免疫性疾病的人的影响，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)。在另一项研究中，我们考虑了药物治疗对慢性粒细胞白血病患者的影响。总而言之，我们要填补的知识空白是保护性免疫的定义及其在接种疫苗后一年的纵向分析中的稳定性。这将提供数据集和预测模型，为加强免疫的时机、变种突破、人群中保护反应的多样性以及免疫抑制患者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.