Computational Modelling of Natural killer Cell Function in Vaccine Efficacy

先进的动物模型、最先进的成像和遗传分析的使用为免疫反应如何在模式识别受体刺激下被激活提供了重要的见解。这导致了用于人类和动物健康的“第二代”佐剂的开发，其中包括Toll样受体(TLR)激动剂。AS01是葛兰素史克开发的一种新佐剂，用于带状疱疹和疟疾疫苗。它含有两种关键的免疫增强剂，QS-21(皂苷)和TLR4激动剂MPL，它们协同激活免疫反应。AS01早期免疫反应的关键特征之一是早期产生细胞因子干扰素，这依赖于QS-21和MPL之间的协同作用。通过动物模型的使用，已经证明早期干扰素的产生是自然杀伤(NK)细胞依赖的，这在AS01佐剂疫苗免疫后多功能T细胞的产生中起着关键作用。此外，利用转录组学分析在疟疾疫苗/挑战研究中证实了干扰素在AS01驱动的免疫反应中的作用。尽管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有空间组织，在那里它们通过与被膜下巨噬细胞的相互作用来对感染做出反应。它们可以通过含有佐剂的MPL在CCR7依赖的过程中被招募到淋巴结。虽然传统上认为NK细胞参与了对肿瘤细胞和病毒感染细胞的保护，但最近的研究表明，细胞内细菌和寄生虫感染的控制依赖于NK细胞来源的干扰素的产生，这在一定程度上是通过启动树突状细胞的发育、成熟和刺激巨噬细胞功能以及诱导强大的TH1免疫反应来发挥作用的。因此，了解NK细胞在免疫反应中的动态和作用机制对于使用合理的设计过程生成更好的疫苗递送策略非常重要。在约克大学的Coles和TIMMS小组中，作为约克计算免疫实验室(www.ycil.org)的一部分，我们一直在开发基于试剂的免疫系统功能计算模型，以使用透明的建模过程为控制免疫反应的机制提供新的见解。在计算机模拟中，可以捕捉生物系统的复杂性(例如，对佐剂的局部反应)，并可以详细说明包括细胞、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)在内的单个组件在启动高亲和力抗体反应和CTL反应方面所起的作用。利用对三级淋巴组织(TLT)的计算机模拟，我们已经证明，根据肿瘤坏死因子的炎症信号的强度，可以导致非常不同的病理结果，从弥漫性淋巴浸润物到具有滤泡树突状细胞网络的高度组织化的第三级结构。使用统计分析技术，我们已经能够解决模型中发生这种情况的原因，并预测TLTS将如何对各种潜在的干预策略做出反应。我们最近一直在应用这项技术来了解不同的佐剂如何发挥作用，2重点放在淋巴结重塑和生发中心形成的过程中，重点放在不同免疫细胞之间的相互作用如何驱动这一过程。通过将关键的系统工程原理应用到模拟中，可以利用称为多目标优化的过程来确定关键参数，这些关键参数导致模拟中的最佳免疫反应，可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.