Understanding the eco-evolutionary drivers of emerging antifungal resistance

他们环境中的微生物暴露在不断变化的条件下，选择最适合的变种。这种持续的适应过程导致种群的遗传组成在空间和时间上发生变化，因为最适合的突变跟踪变化。不幸的是，当选择是由旨在杀死微生物的化学物质强加的时候，那么那些具有基因抵抗力的化学物质的频率就会上升；这导致了全球的抗菌素耐药性问题在环境中不断演变。虽然新出现的抗菌素耐药性在细菌中得到了广泛的承认，但对抗真菌化学物质具有抗药性的真菌的出现却被低估了，但它正在损害我们种植无枯萎病作物和治疗严重的人类真菌疾病的能力--因此，这是一个经典的健康困境。我们项目的核心焦点是曲霉，这是一种常见的环境霉菌，由于它们在空气中无处不在，所有人都会接触到它们。值得注意的是，烟曲霉菌影响着全世界数以百万计的易感人群(包括新冠肺炎携带者)，并越来越多地导致对用于治疗它的一线唑类抗真菌药物产生耐药性的疾病。至关重要的是，这与农民使用的杀菌剂是同一类化学物质，随着这种霉菌在其自然环境中受到这些化学物质的选择，这种化学物质正在推动耐唑烟曲霉的激增。然而，我们目前对导致杀菌剂化学残留积累到霉菌中选择并放大抗药性的浓度的景观尺度途径知之甚少。我们更不了解不同杀菌剂的组合对耐药性出现的后果，也不知道如何与更广泛的微生物群落相互作用，这可能会阻碍(或帮助)耐药性的出现。我们的项目将研究嵌套的人为驱动因素--农业做法和绿色废物回收--目的是了解它们如何为抗真菌耐药性病原体创造进化热点。我们将关注的霉菌嵌入复杂的微生物生态系统中，我们将从真菌在全国范围内的分布、通过富含杀菌剂的中微体环境中的生态演替以及向下到单个模型微观世界的模型来确定规模的影响。为此，我们将把实地和实验室研究与基于贝叶斯的统计方法结合起来，这些方法在空间上明确的框架内同时考虑到进化和生态的复杂性。通过这样做，我们将能够识别、了解和联系导致抗菌剂霉菌形成热点的关键因素。我们测量的变量--土地利用、杀菌剂、真菌遗传学和微生物群落生态--将被整合到一个系统网络分析中，该分析将环境中杀菌剂的使用与选择抗药性的生态环境联系起来。这些“贝叶斯概率网络”是一个强大的工具，它将允许我们预测真菌耐药性的热点，并允许我们建立方法，通过减少进入我们识别的特定“夹点”的杀菌剂输入来缓解这种风险。最终，通过剖析杀菌剂使用的延长(无意)后果，因为这些化学物质推动真菌抗菌素耐药性的演变，我们的项目将在其更大的“一个健康”的背景下解决这个问题。迫切需要我们的方法来开发所需的知识库，以了解当前的风险，以及减轻导致环境中未来出现真菌抗菌素耐药性的选择压力。

Microbes in their environment are exposed to changing conditions, which select for the most fit variants. This continual process of adaptation leads to the genetic composition of populations shifting in space and time as the fittest mutations track change. Unfortunately, when selection is imposed by chemicals that are designed to kill microbes, then those that are genetically resistant rise in frequency; this results in the global problem of antimicrobial resistance evolving in the environment.While emerging antimicrobial resistance is widely recognised in bacteria, the emergence of fungi that are resistant to antifungal chemicals is underappreciated yet is compromising our ability to grow blight-free crops and to treat serious human fungal diseases -therefore presenting a classic One Health dilemma. The core focus of our project is Aspergillus species, common environmental moulds to which all humans are exposed due to their ubiquitous presence in the air. Of note, A. fumigatus affects millions of susceptible individuals worldwide (including those with COVID-19) and is increasingly causing disease that is resistant to the frontline azole antifungal drugs that are used to treat it. Crucially, this is the same class of chemicals is used by farmers as fungicides, which is driving a surge in azole-resistant A. fumigatus as this mould comes under selection by these chemicals in its natural environment. However, we currently have very little understanding of the landscape-scale pathways that lead to fungicide chemical residues accumulating to the concentrations that select for, and amplify, resistance in moulds. We understand even less about the consequences combinations of different fungicides on the emergence of resistance, or how interactions with the wider microbial community that may hinder (or help) the emergence of resistance.Our project will examine the nested anthropogenic drivers - agricultural practices and green-waste recycling - with the aim of understanding how they create hotspots of evolution for antifungal resistant pathogens. The moulds on which we will focus are embedded in complex microbial ecosystems and we will determine the impact of scale from country-wide distributions of the fungus, through the ecological succession seen in fungicide-rich mesocosm environments, and down to individual model microcosm models. To do this, we will couple field and laboratory studies with Bayesian-based statistical methods that take into account both evolutionary and ecological complexity within a spatially-explicit framework. In doing so, we will be able to identify, understand and link the key factors that lead to hotspots of fungicide-resistant moulds forming. The variables that we measure - landuse, fungicides, fungal genetics and microbial community ecology - will be integrated into a systems network analysis that links the usage of fungicides in the environment to ecological settings where resistance is selected for. These 'Bayesian probabilistic networks' are a powerful tool which will allow us predict hotspots for fungal drug-resistance, as well as allowing us to model methods to mitigate against this risk by reducing fungicide-inputs into specific 'pinch-points' that we identify. Ultimately, by dissecting the extended (unintentional) consequence of fungicide use as these chemicals drive the evolution of fungal antimicrobial resistance, our project will address this problem within its greater 'One Health' context. Our approach is urgently needed to develop the knowledge-base that is needed to understand the current risk as well as to mitigate the selection-pressure driving future emergence of fungal antimicrobial resistance in the environment.