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Cytokine network ecology: towards a dynamic understanding of immune responses to co-infection

细胞因子网络生态学：动态理解对共感染的免疫反应

基本信息

批准号：
BB/D01977X/1
负责人：
Andrea Graham
金额：
$ 135.12万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2006
资助国家：
英国
起止时间：
2006 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FD01977X%2F1
关键词：
Cytokine network ecology towards dynamic

项目摘要

Cytokines are important molecules for the organisation of immune responses: they activate cells to divide and to attack infectious agents, and they sometimes act directly against pathogens. Cytokines thus strongly influence how (& how quickly) parasites are killed. Multiple cytokine signals are involved in every immune response -- an intricate network of positive and negative feedback loops that determine how well the host manages to fight infection. These feedback loops, and the time lags inherent in such a signalling system, can make the immune system difficult to study. Data collected at any one time point or analysed only one variable at a time simply cannot reveal the way that different cytokines must interact to generate an observed system-wide immune response. With this project, I propose to treat the host as a closed ecological system in which ecological and evolutionary analyses can identify how cytokines work together to generate effective versus pathological immune responses. Why study cytokines to learn about the whole immune system? The appeal of cytokines is three-fold: - cytokines are immunologically relevant -- e.g., nearly all immunologists measure cytokines to help them infer the function of cells, no matter which host cell type nor which infection or autoimmune condition is under study. - cytokines are analytically tractable -- the same <10 cytokines are implicated in all described infectious and autoimmune diseases. Details of which cells and membrane bound molecules (& less abundant or less well described cytokines) are involved does change across these systems, but the fact that the same cytokines are always important is striking. Better to model these <10 than to model the many cell populations, for example, that vary across context. - and (for evolutionary studies), because cytokines choose parasite-killing mechanisms, they strongly influence host health and survival. The proposed project aims to integrate real data on multiple cytokines of co-infected mice into a network framework, making use of optimality-based and probabilistic mathematical methods. Optimality methods are appropriate because we expect that there has been natural selection on immune systems, particularly to optimise their ability to multi-task / for example, to optimally manage protozoan-helminth co-infection. At the same time, probabilistic methods are also appropriate because cytokines form a probabilistic network, with a lot of variability and chance events. The probabilistic statistical methods that I propose to use have been successfully applied to environmental science (for example, to predict damage to coral reefs given multiple interacting environmental factors). Testing optimality predictions has greatly deepened our understanding of the evolutionary biology of everything from bird song to the development of antibiotic resistance. The combined application of these predictive analytical methods to immunological molecules will also bear fruit: an integrated understanding of immune system functioning that links to the health of hosts.

细胞因子是组织免疫反应的重要分子：它们激活细胞分裂并攻击感染因子，有时直接对抗病原体。因此，细胞因子强烈影响寄生虫被杀死的方式（以及速度）。每一种免疫反应都涉及多种细胞因子信号——这是一个由正反馈回路和负反馈回路组成的复杂网络，决定了宿主抵抗感染的能力。这些反馈回路以及这种信号系统固有的时滞可能使免疫系统难以研究。在任何一个时间点收集的数据或一次仅分析一个变量根本无法揭示不同细胞因子必须相互作用以产生观察到的全系统免疫反应的方式。通过这个项目，我建议将宿主视为一个封闭的生态系统，其中生态和进化分析可以确定细胞因子如何共同作用以产生有效的与病理性的免疫反应。为什么要研究细胞因子来了解整个免疫系统？细胞因子的吸引力有三方面： - 细胞因子与免疫学相关——例如，几乎所有免疫学家都会测量细胞因子以帮助他们推断细胞的功能，无论正在研究哪种宿主细胞类型、哪种感染或自身免疫性疾病。 - 细胞因子易于分析处理——相同的<10个细胞因子与所有描述的传染性和自身免疫性疾病有关。涉及哪些细胞和膜结合分子（以及不太丰富或描述不太清楚的细胞因子）的细节在这些系统中确实发生了变化，但相同的细胞因子始终很重要的事实是惊人的。例如，对这些小于 10 的细胞进行建模比对许多细胞群进行建模更好，这些细胞群因环境而异。 - 并且（对于进化研究），由于细胞因子选择杀死寄生虫的机制，因此它们强烈影响宿主的健康和生存。该项目旨在利用基于最优性和概率的数学方法，将共感染小鼠的多种细胞因子的真实数据整合到网络框架中。最优方法是合适的，因为我们期望免疫系统存在自然选择，特别是优化其多任务能力/例如，最佳地管理原生动物-蠕虫双重感染。同时，概率方法也是合适的，因为细胞因子形成一个概率网络，具有很多可变性和偶然事件。我建议使用的概率统计方法已成功应用于环境科学（例如，在多种相互作用的环境因素下预测珊瑚礁的损害）。测试最优性预测极大地加深了我们对从鸟鸣到抗生素耐药性发展等一切进化生物学的理解。这些预测分析方法在免疫分子上的综合应用也将取得成果：对与宿主健康相关的免疫系统功能的综合理解。