Disentangling TB epidemiology: The effects of immune gene diversity, gut microbiota and social networks on disease susceptibility in a natural meerkat model

结核病（TB）是一种毁灭性的疾病，是人类和许多其他哺乳动物物种，包括猫鼬（Suricata suricatta）的地方病。个体之间存在个体对结核病感染的易感性和耐药性的差异，确定这种差异的根本原因对病原体流行病学和疾病控制具有重要意义。然而，支撑个体对传染病（包括结核病）的易感性和抵抗力的生物驱动因素尚未得到很好的理解。实验室研究有令人信服的证据表明，宿主遗传学和肠道微生物可以相互作用，介导宿主免疫反应，但这些相互作用在自然人群中的程度及其对宿主-病原体动力学的影响尚不清楚。在这个项目中，我们将探索免疫基因和肠道微生物群组成共同塑造自然动物群体中个体结核病易感性的程度，并整合社会网络信息，以模拟结核病爆发期间研究人群的关键传播途径。我们将应用一个非常高的分辨率数据集2300野生猫鼬在卡拉哈里沙漠，南非，收集了25年的时间。这个多层次的数据集包括行为和运动的准确记录，遗传数据，以及来自研究人群中几乎所有个体的纵向粪便样本。具体而言，我们将能够解开宿主-结核病相互作用，终生肠道微生物群动态，免疫基因多样性（MHC）在生活史决策和结核病耐药性中的作用。我们将首先研究感染前的微生物群落如何与宿主遗传学相互作用，以预测生命后期的感染结果。随后，我们将把易感性数据与社会网络模式相结合，以建立一个流行病学模型，旨在确定传播的主要驱动因素。该项目不仅将大大增加我们对结核病流行病学的生物和社会驱动因素的理解，而且将产生对宿主-微生物相互作用的新见解，为未来微生物和疾病生态学的研究奠定基础。

Tuberculosis (TB) is a devastating disease that is endemic to humans and many other mammal species, including meerkats (Suricata suricatta). Variation in individual susceptibility and resistance to TB infection exists between individuals, and identifying the underlying causes of this variation has major implications for pathogen epidemiology and disease control. However, the biological drivers that underpin an individual’s susceptibility and resistance to infectious disease (including TB) are not well understood. There is compelling evidence from laboratory studies that host genetics and gut microbes can interact to mediate host immune responses, but the extent of these interactions in natural populations and their consequences for host-pathogen dynamics are unknown. In this project, we will explore the extent to which immune genes and gut microbiota composition together shape individual TB susceptibility in a natural animal population, and integrate social network information to model key transmission routes across the study population during TB outbreaks. We will apply an exceptionally high resolution dataset on 2300 wild meerkats in the Kalahari desert, South Africa, collected over a 25 year period. This multi-layered dataset includes accurate records of behavior and movement, genetic data, and longitudinal fecal samples from virtually all individuals within the study population. Specifically, we will be able to unravel host-TB interactions, lifetime gut microbiota dynamics, the role of immune gene diversity (MHC) in life history decisions and TB resistance. We will start by examining how microbial communities prior to infection interact with host genetics to predict infection outcomes later in life. Later, we will integrate susceptibility data with social network patterns in order to build an epidemiological model aiming at identifying the main drivers of transmission. This project will not only significantly add to our understanding of the biological and social drivers of TB epidemiology, but will generate novel insights into host-microbe interactions that will provide the foundation for future research on microbial and disease ecology.