Computational Modelling of Natural killer Cell Function in Vaccine Efficacy

Lead Research Organisation: University of Oxford
Department Name: NDORMS

Abstract

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,
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focusing 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.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/N503770/1 01/10/2015 08/10/2017
1642710 Studentship BB/N503770/1 01/10/2015 30/09/2019 Paul Buckley
BB/N503770/2 09/10/2017 30/09/2019
1642710 Studentship BB/N503770/2 01/10/2015 30/09/2019 Paul Buckley
 
Description We have developed a framework to identify and capture current understanding and show how it was used to develop evidence-based hypotheses of how vaccines containing AS01 generate an immune response through integrating together key immunological processes, with AS01-specific experimental data. We have generated a model of the high-level interactions during the early stages of the immune response and we have also created state diagrams depicting the transition of states of entities and activity diagrams capturing cellular activity. Creation of these models has permitted development of an informed integration of current mechanistic knowledge, and evidence-informed hypotheses from various sources. This has led to the identification of knowledge gaps, and a consolidation of understanding between multi-disciplinary research teams. We have also developed a pipeline for automating experimental data structuring.
Exploitation Route Pipeline might be built upon. Framework for modelling and simulation in vaccine adjuvant understanding is new
Sectors Healthcare,Pharmaceuticals and Medical Biotechnology

 
Title Automated cleaning and structuring pipeline and methodology 
Description Pipeline for cleaning and structuring human readable data, for various formats, into a common hybrid human-machine readable format, ready for modelling and machine learning pipelines. We aim to publish soon. 
Type Of Material Improvements to research infrastructure 
Year Produced 2017 
Provided To Others? No  
Impact Only in the lab as of yet, but we have managed to restructure around 30 experiments worth of data into this common format.