Understanding the eco-evolutionary drivers of emerging antifungal resistance

环境中的微生物暴露在不断变化的条件下，从而选择最适合的变体。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，因为最适合的突变会跟踪变化。不幸的是，当选择是由旨在杀死微生物的化学物质强加的，那么那些具有遗传抗性的频率就会上升;这导致了环境中抗生素耐药性的全球性问题。虽然细菌中出现的抗生素耐药性被广泛认识，对抗真菌化学物质有抗性的真菌的出现被低估了，但却损害了我们种植枯萎病的能力，免费作物和治疗严重的人类真菌疾病-因此提出了一个典型的一个健康困境。我们项目的核心重点是曲霉菌，这是一种常见的环境霉菌，由于它们在空气中无处不在，所有人类都暴露在其中。值得注意的是，A。烟曲霉影响着全球数百万易感人群（包括COVID-19患者），并且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，这与农民使用的杀真菌剂是同一类化学品，这导致了耐唑类烟曲霉的激增。烟曲霉，因为这种霉菌在其自然环境中受到这些化学品的选择。然而，我们目前对导致杀真菌剂化学残留物积累到选择和放大霉菌抗性的浓度的微生物规模途径知之甚少。我们更不了解不同杀菌剂的组合对抗药性出现的影响，或者与更广泛的微生物群落的相互作用如何可能阻碍（或帮助）抗药性的出现。我们的项目将研究嵌套的人为驱动因素-农业实践和绿色废物回收-旨在了解它们如何为抗药性病原体创造进化热点。我们将关注的模具嵌入在复杂的微生物生态系统中，我们将确定真菌在全国范围内分布的规模的影响，通过在富含杀真菌剂的中生态环境中看到的生态演替，并下降到单个模型微观模型。要做到这一点，我们将耦合现场和实验室研究与贝叶斯为基础的统计方法，考虑到进化和生态的复杂性在一个空间明确的框架。在这样做的过程中，我们将能够识别，理解和链接导致抗真菌剂霉菌形成热点的关键因素。我们测量的变量-土地利用，杀真菌剂，真菌遗传学和微生物群落生态学-将被整合到一个系统网络分析中，该分析将环境中杀真菌剂的使用与选择抗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，它将使我们能够预测真菌耐药性的热点，并使我们能够通过将杀真菌剂输入减少到我们确定的特定“夹点”来建模方法以减轻这种风险。最终，通过剖析杀真菌剂使用的扩展（无意）后果，因为这些化学品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”背景下解决这个问题。迫切需要我们的方法来开发了解当前风险所需的知识基础，并减轻推动环境中真菌抗菌素耐药性未来出现的选择压力。

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.