Computational Modelling of Natural killer Cell Function in Vaccine Efficacy

使用先进的动物模型，最先进的成像和遗传分析提供了重要的见解，如何激活免疫反应，以响应模式识别受体的刺激。这导致了用于人类和动物健康应用的“第二代”佐剂的开发，其包括Toll样受体（TLR）激动剂。AS 01是GSK开发的一种新佐剂，用于VZV-带状疱疹和疟原虫（疟疾）疫苗。它含有两种关键的免疫增强剂，QS-21（皂苷）和TLR 4激动剂MPL，协同激活免疫反应。对AS 01的免疫应答的关键定义早期特征之一是细胞因子IFN的早期产生，其依赖于QS-21和MPL之间的协同作用。通过使用动物模型，已经显示早期干扰素产生是自然杀伤（NK）细胞依赖性的，并且这在用AS 01佐剂疫苗免疫后多功能T细胞的产生中具有关键作用。此外，IFN-γ在AS 01驱动的免疫应答中的作用在使用转录组学分析的疟疾疫苗接种/攻击研究中得到证实。虽然NK细胞是幼稚小鼠淋巴结中的一小部分细胞，但它们在被膜下区域中空间组织，在那里它们通过与被膜下巨噬细胞相互作用而对感染做出反应。它们可以通过含有佐剂的MPL在CCR 7依赖性过程中被募集到淋巴结。尽管传统上认为NK细胞参与保护以对抗肿瘤细胞和病毒感染的细胞，但最近已经显示细胞内细菌和寄生虫感染的控制依赖于NK细胞衍生的干扰素产生，其部分地通过引发树突细胞发育和成熟以及刺激巨噬细胞功能以及诱导强的TH 1免疫应答起作用。因此，理解NK细胞在免疫应答期间的动力学和作用机制对于使用合理设计过程产生更好的疫苗递送策略是重要的。在约克大学的Coles和Timms小组中，作为约克计算免疫学实验室（www.example.com）的一部分www.ycil.org，我们一直在开发基于智能体的免疫系统功能计算模型，以提供对控制免疫反应机制的新见解，透明的建模过程。计算机模拟可以捕获生物系统的复杂性（例如，对佐剂的局部应答），并且可以详细描述包括细胞、可溶性因子（例如，细胞因子、趋化因子）和局部组织微环境（例如，淋巴结或肌肉组织）在内的各个组分对引发高亲和力抗体应答和CTL应答的作用。使用三级淋巴组织（TLT）的计算机模拟，我们已经表明，根据TNF的炎症信号的强度，可以导致产生非常不同的病理结果，从弥漫性淋巴浸润到高度组织化的三级结构与滤泡树突状细胞网络。使用统计分析技术，我们已经能够解决为什么这种情况发生在模型中，并预测TLT将如何应对各种潜在的干预策略。我们最近一直在应用这项技术来了解不同的佐剂是如何工作的，2重点是淋巴结重塑和生发中心形成的过程，重点是不同免疫细胞之间的相互作用如何驱动这一过程。通过将关键系统工程原理应用于模拟，可以利用称为多目标优化的过程来确定在模拟中导致最佳免疫反应的关键参数，这些参数可以用作进一步实验的基础。

The use of advanced animal models, state-of-the art imaging and genetic analysis has provided significant insights into how the immune response is activated in response to pattern recognition receptor stimulation. This has led to the development of "second generation" adjuvants for applications in human and animal health that include Toll Like Receptor (TLR) agonists. AS01 is one such new adjuvant developed by GSK that is used in both VZV-Herpes Zoster and plasmodium (malaria) vaccines. It contains two key immune enhancers, QS-21 (saponin) and the TLR4 agonist MPL that synergistically activate the immune response. One of the key defining early features of immune responses to AS01 is the early production of the cytokine IFN which is dependent on the synergy between QS-21 and MPL. Through use of animal models it has been shown that early interferon production is natural killer (NK) cell dependent and this has a key role in the generation of polyfunctional T cells upon immunization with AS01-adjuvant vaccines. Moreover, a role for IFN- in AS01-driven immune responses was confirmed in a malaria vaccination/challenge study using transcriptomics analysis.Although NK cells are a small population of cells in naïve murine lymph nodes they are spatially-organised in the subcapsular region where they are primed to respond to infection through interacting with subcapsular macrophages. They can be recruited to lymph nodes in a CCR7 dependent process by MPL containing adjuvants. Although classically NK cells are thought to be involved in protection against tumour cells and virally infected cells, more recently control of intracellular bacterial and parasitic infection has been shown to depend on NK cell derived interferon production working in part through priming dendritic cell development and maturation and stimulating macrophage function in addition to inducing strong TH1 immune responses. Thus understanding the dynamics and mechanisms of action of NK cells during immune responses is important to generating better vaccine delivery strategies using a rational design process.In the Coles and Timms groups at the University of York, as part of the York Computational Immunology Laboratory (www.ycil.org), we have been developing agent based computational models of immune system function to provide new insights into mechanisms controlling immune responses using a transparent modelling process. In silico simulations can capture the complexity of the biological system (e.g. localised responses to adjuvants) and can detail the role of individual components including cells, soluble factors (e.g. cytokines, chemokines) and the localised tissue microenvironment (e.g. lymph node or muscle tissue) has on the priming of high affinity antibody responses and CTL responses. Using a computational simulation of tertiary lymphoid tissue (TLT) we have shown that depending on the strength of the inflammatory signal by TNF can lead to the generation of very different pathological outcomes from diffuse lymphoid infiltrates to highly organised tertiary structures with follicular dendritic cell networks. Using statistical analysis techniques we have been able to resolve why this occurs in the model and predicted how TLTs will respond to various potential intervention strategies. We have more recently been applying this technology to understand how different adjuvants work,2focusing on the process of lymph node remodelling and germinal centre formation focusing on how the interplay between different immune cells drives this process. Through application of critical systems engineering principles to the simulation it is possible to utilise a process called multi-objective optimisation to determine key parameters that lead to optimal immune responses within the simulation that can be used as a basis for further experimentation.