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

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）。多种细胞因子信号参与了每一种免疫反应--一个由正反馈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间滞后，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的数据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应答的方式。在这个项目中，我建议将宿主视为一个封闭的生态系统，在这个系统中，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？细胞因子的吸引力是三方面的：-细胞因子是免疫相关的-例如，几乎所有的免疫学家都测量细胞因子以帮助他们推断细胞的功能，无论研究的是哪种宿主细胞类型，也无论是哪种感染或自身免疫状况。- 细胞因子是易于分析的--相同的<10种细胞因子涉及所有描述的感染性和自身免疫性疾病。涉及哪些细胞和膜结合分子（以及不太丰富或不太好描述的细胞因子）的细节确实在这些系统中发生变化，但相同的细胞因子总是重要的事实是惊人的。对这些<10的细胞群进行建模比对许多细胞群进行建模更好，例如，在不同的背景下。- （对于进化研究），因为细胞因子选择杀死寄生虫的机制，它们强烈影响宿主的健康和生存。拟议的项目旨在利用基于优化和概率的数学方法，将关于共感染小鼠的多种细胞因子的真实的数据整合到一个网络框架中。最优性方法是合适的，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，优化管理原生动物-蠕虫共感染。同时，概率方法也是合适的，因为细胞因子形成概率网络，具有大量的可变性和偶然事件。我建议使用的概率统计方法已经成功地应用于环境科学（例如，在给定多个相互作用的环境因素的情况下，预测珊瑚礁的损害）。测试最优性预测大大加深了我们对进化生物学的理解，从鸟类的鸣叫到抗生素耐药性的发展。将这些预测分析方法结合应用于免疫分子也将取得成果：对与宿主健康相关的免疫系统功能的综合理解。

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.