Mapping antibody class switch mechanisms and function

绘制抗体类别转换机制和功能

• 批准号: BB/T002212/2
• 负责人: Franca Fraternali
• 金额: $ 200.47万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=BB%2FT002212%2F2
• 关键词: Mapping; antibody; class; switch; mechanisms

Antibodies are produced by a specialised immune cell (B cell) and act as important immune mediators, bridging between pathogens and effector cells to protect us from infection. Antibody molecules can also exist bound to the B cell surface where they act as receptor for detecting target molecules (antigens). Their versatility is immense, they are used to make diagnostics, research tools and therapeutics.One part of the antibody (variable region) is responsible for binding to the antigen, the other end of the molecule is responsible for activating/mediating different functions in the immune system. Uniquely, the antibody variable region can evolve within the organism within a short timescale, in response to infection or vaccination, to improve its binding to the antigen. The constant region does not evolve, but it can be changed to one of 9 different classes or subclasses in order to change the function of the antibody in a genetic process known as Class Switch Recombination (CSR). The (sub)classes of antibody are arranged in the genome in this order: IgM-IgD-IgG3-IgG1-IgA1-IgG2-IgG4-IgE-IgA2; a B cell starts life with IgM and IgD and after activation switches to another (sub)class. Until very recently it was thought that CSR had no effect on the binding abilities of the Variable region. Recent research in different areas (Ageing, Ebola, HIV infection, Cancer) indicates that we don't fully understand the processes that control which (sub)class will be used, the difference that CSR between subclasses makes to the outcome of an immune response or the exact molecular effects that CSR might have on the variable region binding properties. Therapeutic antibodies are a critical pharmaceutical resource, being the fastest growing class of pharmaceuticals, with thousands now in the development pipeline. Current products with regulatory approval/undergoing regulatory review are mostly IgG1 whilst none are IgA or IgE. As we understand more about the functions of these classes, we may find that the potential utility of antibodies can be increased, such as IgE in skin cancers or IgA in gut-related disorders.In this programme we propose to harness the unique expertise of a team of bioinformaticians and immunologists to determine what factors, both outside the cell and inside the cell, control CSR. We will monitor how CSR progresses with time on a daily basis for a fortnight after challenge with the flu vaccine and we will use computer modelling of antibody structures to investigate how changing one side of the antibody molecule may affect the other. Each of these three main objectives will produce a range of results, some of which will help to understand the work in the other objectives, although would not be critical for their success. All parts of the programme require input from all the team to varying levels. The methods we will use and develop are ground-breaking, in our preliminary data, we show pathways of class switching to different types of antibody in cell culture on a single cell basis. We will alter the conditions of these experiments, note the resulting changes and map the protein-protein and gene interactions to deduce what molecules are controlling CSR. These methods will be applicable in all cellular Bioscience disciplines and will transform cell biology research. The mapping of human CSR and the molecular modelling of antibody structure will also result in new tools for others to use, we have successfully done this before and have large global user groups in several areas. The interdisciplinary nature of our team means that we have insight into how to design tools that are user friendly and flexible, all data and tools will be made publicly available in a collective resource "BHive". We will also run our programme in such a way as to maximise the interdisciplinary familiarisation across all our teams and ensure our ECRs have a springboard into their future careers.

抗体由专门的免疫细胞（B 细胞）产生，充当重要的免疫介质，在病原体和效应细胞之间架起桥梁，保护我们免受感染。抗体分子也可以与 B 细胞表面结合，作为检测靶分子（抗原）的受体。它们的用途非常广泛，可用于制造诊断、研究工具和治疗方法。抗体的一部分（可变区）负责与抗原结合，分子的另一端负责激活/介导免疫系统中的不同功能。独特的是，抗体可变区可以在短时间内在生物体内进化，以响应感染或疫苗接种，以改善其与抗原的结合。恒定区不会进化，但可以改变为 9 个不同类别或亚类之一，以便在称为类别转换重组 (CSR) 的遗传过程中改变抗体的功能。抗体（亚）类在基因组中按以下顺序排列：IgM-IgD-IgG3-IgG1-IgA1-IgG2-IgG4-IgE-IgA2； B 细胞以 IgM 和 IgD 开始生命，激活后切换到另一个（子）类。直到最近，人们还认为 CSR 对可变区的结合能力没有影响。最近在不同领域（衰老、埃博拉、HIV 感染、癌症）的研究表明，我们并不完全了解控制使用哪个（子）类的过程、子类之间 CSR 对免疫反应结果的差异或 CSR 对可变区结合特性可能产生的确切分子效应。治疗性抗体是一种重要的药物资源，是增长最快的一类药物，目前有数千种药物正在开发中。目前已获得监管批准/正在进行监管审查的产品大多是 IgG1，而没有 IgA 或 IgE。随着我们对这些类别的功能了解更多，我们可能会发现抗体的潜在效用可以增加，例如皮肤癌中的 IgE 或肠道相关疾病中的 IgA。 在这个项目中，我们建议利用生物信息学家和免疫学家团队的独特专业知识来确定细胞外和细胞内的哪些因素控制 CSR。在注射流感疫苗后的两周内，我们将每天监测 CSR 如何随时间推移而进展，并且我们将使用抗体结构的计算机建模来研究改变抗体分子的一侧可能如何影响另一侧。这三个主要目标中的每一个都会产生一系列结果，其中一些结果将有助于理解其他目标中的工作，尽管对其成功并不至关重要。该计划的所有部分都需要不同级别的所有团队的投入。我们将使用和开发的方法是开创性的，在我们的初步数据中，我们展示了在单细胞基础上细胞培养中不同类型抗体的类别转换途径。我们将改变这些实验的条件，记录由此产生的变化，并绘制蛋白质-蛋白质和基因相互作用图，以推断哪些分子在控制 CSR。这些方法将适用于所有细胞生物科学学科，并将改变细胞生物学研究。人类企业社会责任图谱和抗体结构分子建模也将带来可供其他人使用的新工具，我们之前已经成功做到了这一点，并在多个领域拥有庞大的全球用户群体。我们团队的跨学科性质意味着我们深入了解如何设计用户友好且灵活的工具，所有数据和工具都将在集体资源“BHive”中公开提供。我们还将以最大限度地提高所有团队跨学科熟悉度的方式来运行我们的项目，并确保我们的 ECR 拥有进入未来职业生涯的跳板。

项目成果

B cell profiles, antibody repertoire and reactivity reveal dysregulated responses with autoimmune features in melanoma.

• DOI: 10.1038/s41467-023-39042-y
• 发表时间: 2023-06-08
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Crescioli, Silvia;Correa, Isabel;Ng, Joseph;Willsmore, Zena N.;Laddach, Roman;Chenoweth, Alicia;Chauhan, Jitesh;Di Meo, Ashley;Stewart, Alexander;Kalliolia, Eleni;Alberts, Elena;Adams, Rebecca;Harris, Robert J.;Mele, Silvia;Pellizzari, Giulia;Black, Anna B. M.;Bax, Heather J.;Cheung, Anthony;Nakamura, Mano;Hoffmann, Ricarda M.;Terranova-Barberio, Manuela;Ali, Niwa;Batruch, Ihor;Soosaipillai, Antoninus;Prassas, Ioannis;Ulndreaj, Antigona;Chatanaka, Miyo K.;Nuamah, Rosamund;Kannambath, Shichina;Dhami, Pawan;Geh, Jenny L. C.;Ross, Alastair D. MacKenzie;Healy, Ciaran;Grigoriadis, Anita;Kipling, David;Karagiannis, Panagiotis;Dunn-Walters, Deborah K.;Diamandis, Eleftherios P.;Tsoka, Sophia;Spicer, James;Lacy, Katie E.;Fraternali, Franca;Karagiannis, Sophia N.
• 通讯作者: Karagiannis, Sophia N.

An Integrated Analysis and Comparison of Serum, Saliva and Sebum for COVID-19 Metabolomics

COVID-19 代谢组学中血清、唾液和皮脂的综合分析和比较

• DOI: 10.21203/rs.3.rs-1337471/v1
• 发表时间: 2022
• 作者: Spick M