Understanding The Eco-evolutionary Drivers Of Antifungal Resistance In Opportunistic Fungal Pathogens

了解机会性真菌病原体抗真菌耐药性的生态进化驱动因素

• 批准号: NE/X00550X/1
• 负责人: Toni Gladding
• 金额: $ 30.89万
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX00550X%2F1
• 关键词: Understanding; Eco; evolutionary; Drivers; Antifungal

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 statisticalmethods 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.

环境中的微生物暴露于不断变化的条件，这些条件选择最合适的变体。随着最适量突变的变化，这种持续的适应过程导致人群在时空转移的遗传组成。不幸的是，当被设计为杀死微生物的化学物质进行选择时，那些遗传抗性的频率上升。这导致了全球抗菌抗性在环境中进化的问题。尽管新兴的抗菌抗性在细菌中得到了广泛认可，但对抗真菌化学物质的抗性抗真菌化学物质的出现却不足，但却却损害了我们不受欢迎的斑点和对严重的人类疾病的疾病，因此造成了疾病的疾病，却呈现了疾病的疾病。我们项目的核心重点是曲霉物种，这是所有人类在空中无处不在的常见环境模具。值得注意的是，烟曲霉会影响全球数百万个易感个体（包括患有COVID-19的人），并且越来越多地引起疾病，该疾病对前线甲唑抗真菌药物具有抗药性，用于治疗它。至关重要的是，这是农民用作杀真菌剂的同一类化学物质，它正在推动抗唑杆菌烟曲霉的激增，因为这些霉菌在其自然环境中被这些霉菌所接受。但是，目前，我们对景观尺度途径的了解很少，这些途径导致杀菌剂化学残留物积累到霉菌中选择的浓度并扩大耐药性。我们对不同杀菌剂对抵抗的出现的后果组合的了解甚至更少，或者与更广泛的微生物群落的相互作用可能会阻碍（或帮助）抗性的出现。您的项目将研究嵌套的人为驱动因素 - 农业实践 - 农用野生型和绿色 - 循环循环 - 以了解如何创造出抗性的抗剂，以理解如何创建抗剂。我们将要关注的模具嵌入了复杂的微生物生态系统中，我们将通过在富含杀菌剂的中核环境中看到的生态继承来确定量表的影响。为此，我们将将现场和实验室研究与基于贝叶斯的统计信息相结合，这些统计信息在空间解释的框架内考虑了进化和生态复杂性。通过这样做，我们将能够识别，理解和链接导致抗杀菌剂霉菌热点的关键因素。我们测量的变量 - 土地使用，杀菌剂，真菌遗传学和微生物群落生态学 - 将集成到系统网络分析中，该分析将杀菌剂在环境中使用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”是一种强大的工具，它将使我们可以预测真菌药物抗性的热点，并使我们能够通过将杀真菌剂输入减少到特定的“固定点”中，以减轻这种风险来减轻这种风险。我们可以通过将其识别为扩展的（内部意外）的拟合型拟合方法来识别，从而识别出这些风险。阻力，我们的项目将在其更大的“健康”背景下解决这个问题。迫切需要我们的方法来发展知识库，以了解当前的风险以及减轻选择压力的选择，从而推动了真菌抗菌抗性在环境中的未来出现。