Understanding host-pathogen interactions using a new synthetic theoretical framework for organismal nutrition

健康的饮食是至关重要的，原因有很多，但最近越来越受到重视的是饮食如何影响抵抗传染病的能力。这对人类和其他动物都是如此，但我们目前还不知道这是如何工作的。换句话说，我们不知道动物吃的食物如何改变其免疫系统抵抗病原体的能力，科普感染对其身体的负面影响，或者它如何直接影响病原体在动物体内生长的能力。最后一种影响特别有趣，因为研究人员认为它比前两种选择少得多，但当然，动物吃的食物会成为病原体“吃”的食物，以便它生长。这导致了一种有趣的可能性，即通过改变饮食，动物可以操纵其身体对病原体生长的适应程度。在这项研究中，我们将仔细挑选4种细菌病原体和1种宿主的饮食需求，棉花叶虫是一种对欧洲和非洲农业造成破坏的毛虫作物害虫。除了本身是一种重要的动物外，这种毛毛虫很容易在实验室中饲养，并且可以用作了解其他动物反应的模型。我们将饲养宿主的饮食，不同的卡路里含量和蛋白质与碳水化合物的比例。我们将测量生长，蛋白质周转，呼吸和粪便中碳和氮的排泄，告诉我们动物如何使用饮食中的营养物质来构建其身体以及排出的营养物质。我们还将测量免疫反应和血液的其他生化特性。在之前的一项研究中，我们分析了以这些饮食饲养的毛毛虫的血液营养成分，并创造了“营养血液”，模仿毛毛虫血液的营养特性，但没有任何免疫分子。我们将使用这些营养血液来培养细菌，至于宿主，测量它们的生长，蛋白质周转，呼吸以及碳和氮的排泄。这些信息将使我们能够为宿主和4种病原体创建代谢预算。然后，我们将使用一个名为“几何化学计量学”（GS）的建模系统来预测病原体如何在宿主中生长，因为我们了解宿主和病原体如何使用和排泄营养物质。这是令人兴奋的，因为我们可以模拟宿主的营养状态在感染过程中如何变化，这将对病原体的生长产生什么影响，反过来病原体的生长将对宿主的营养状态产生什么影响，因为我们期望两个系统之间不断反馈。然后，我们可以通过依次感染4种病原体并测量宿主和细菌如何随时间变化来测试GS模型的预测。如果GS能准确预测感染过程中发生的事情，这将告诉我们，宿主和病原体的营养需求是决定谁将赢得生存竞争的最重要因素。如果GS不能完全解释相互作用的结果，我们可以使用我们实验中的其他信息（免疫反应和其他血液特性）来确定这些其他元素在控制感染中的重要性。这项研究的信息可能对控制作物害虫有价值，或者更广泛地说，它可以帮助我们了解牲畜饮食如何影响感染，或者我们自己的饮食如何影响我们感染疾病的风险。

A healthy diet is vital for many reasons, but one that has come to be appreciated more recently is how diet impacts the ability to fight off infectious diseases. This is true for humans and other animals but we currently don't really understand how this works. In other words we don't understand exactly how the food an animal eats changes its ability to either fight off a pathogen with its immune system, cope with the negative effects of infection on its body or indeed, how it directly affects the ability of the pathogen to grow in the animal's body. This last effect is particularly interesting as it has been considered much less by researchers than the first two options, but of course, the food an animal eats becomes the food the pathogen 'eats' in order for it to grow. This leads to the intriguing possibility that by changing a diet, an animal can manipulate how suitable its body is for a pathogen to grow in it. In this study we will carefully unpick the dietary requirements of 4 bacterial pathogens and 1 host, the cotton leafworm a caterpillar crop pest that causes devastation to agriculture across Europe and Africa. As well as being an important animal in its own right, this caterpillar is easy to rear in the lab and can be used as a model to understand the responses of other animals. We will rear the host on diets that differ in their calorie content and the ratio of proteins to carbohydrates. We will measure growth, protein turnover, respiration and the excretion of carbon and nitrogen in the faeces, telling us exactly how the animal uses the nutrients in the diet to build its body and those that are expelled. We will also measure immune responses and other biochemical properties of the blood. In a previous study we analysed the blood nutrients of caterpillars reared on these diets and created 'NutriBloods' that mimic the nutritional properties of caterpillar blood, but without any immune molecules. We will use these NutriBloods to grow the bacteria, and as for the host, measure their growth, protein turnover, respiration and the excretion of carbon and nitrogen. This information will allow us to create metabolic budgets for the host and the 4 pathogens. We will then use a modelling system called 'Geometric Stoichiometry' (GS) to predict how the pathogens should grow in the host, given that we understand how both host and pathogens use and excrete nutrients. This is exciting as we can model how the host's nutritional state will change during infection, what impact this will have on the growth of the pathogen and in turn what effects the growth of the pathogen will have on the host's nutritional state, as we expect constant feedback between the two systems. We can then test the predictions from the GS models by carrying out infections with the 4 pathogens in turn and measuring how the host and bacteria change over time. If GS predicts closely what happens during infection this will tell us that the nutritional requirements of the host and pathogen are the most important element in determining who will win the race for survival. If GS does not completely explain the outcome of the interaction we can use other information from our experiments (immune response and other blood properties) to determine how important these other elements are in controlling infection. The information from this study could be valuable for controlling crop pests, or more widely, it could help us, for example, to understand how livestock diet impacts infections, or indeed how our own diet impacts our risk of infectious disease.