Understanding the Spread of Antibiotic Resistance Through Mobile Genetic Elements in Bacterial Populations

预计到2050年，抗菌素耐药性（AMR）的威胁日益严重，每年将导致1000万人死亡。细菌迅速获得抗生素耐药性的显著能力是医疗保健专业人员和制药行业面临的主要挑战。即使有可能治疗由多重耐药病原体引起的感染，也可能是困难和昂贵的，而且由于长期开发和测试导致耐药性迅速出现，市场上缺乏新的抗微生物药物。因此，防止潜在病原体中出现AMR是进化微生物学的一个重要目标。移动遗传因子（MGEs）如质粒和转座因子是细菌群落内传播抗菌素耐药性的关键驱动力。当存在低选择压力或没有选择压力时，质粒预计会丢失，但已知含有AMR基因的质粒在低甚至没有抗生素选择压力（低抗生素浓度）的环境中持续存在。据认为，抗菌素耐药性质粒在群落中持续存在的能力是由于细菌的存在，这些细菌可以作为质粒的储存库，因为不同的细菌对获取、窝藏和传播耐药MGEs的易感性各不相同。然而，使细菌容易成为质粒储存库的特征尚不清楚，这阻碍了我们预测这种行为和开发有效解决抗菌素耐药性的治疗方法的能力。我将致力于研究细菌对MGEs的易感性，并提出问题：哪些遗传和分子因素能稳定宿主体内的质粒？是什么导致了差异获取质粒的能力？是什么进化力量稳定了这些相互作用？我的项目将集中在不同菌株的机会性病原体铜绿假单胞菌之间的相互作用，以及它们相关的MGEs。我将把湿实验室实验与机器学习和生物信息学工具结合起来，研究质粒动力学作为质粒和宿主基因组之间遗传相互作用的功能。这项工作将有助于确定允许质粒在细菌群落成员之间持续存在和转移的潜在机制。最终，我们将能够预测AMR质粒传播可能加剧的微生物群，使我们能够更好地控制抗菌素耐药性的传播。

The growing threat of Antimicrobial Resistance (AMR) is predicted to cause the death of 10 million people annually by 2050. The remarkable ability of bacteria to rapidly acquire resistance to antibiotics is a major challenge for healthcare professionals and the pharmaceutical industry. Even when it is possible to treat infections caused by multi-drug resistant pathogens, it can be difficult and expensive, and there is a dearth of new antimicrobials coming to market owing to rapid emergence of resistance in the face of prolonged development and testing. Preventing the emergence of AMR in potential pathogens is therefore an important goal for evolutionary microbiology. Mobile genetic elements (MGEs) such as plasmids and transposable elements are key driving forces that transmit AMR within bacterial communities. While plasmids are expected to be lost when there is low or no selection pressures, plasmids containing AMR genes are known to persist in environments with low, or even no, antibiotic selection pressure (low antibiotic concentration). It is thought that the ability of AMR plasmids to persist in communities is due to the presence of bacteria that can act as reservoirs for plasmids, because different bacteria vary in their susceptibility to acquiring, harbouring, and disseminating resistance MGEs. However, the features of bacteria that predispose them to become plasmid reservoirs is unclear, hampering our ability to predict such behaviour and develop therapeutic treatments to efficiently tackle AMR.I will be working on studying the susceptibility of bacteria towards MGEs, and asking: what genetic and molecular factors stabilise plasmids in the host? What causes the ability to differentially acquire plasmids? And what evolutionary forces stabilise these interactions? My project will focus on the interactions between diverse strains of the opportunistic pathogen Pseudomonas aeruginosa, and their associated MGEs. I will be combining wet-lab experiments with Machine Learning and Bioinformatics tools to study plasmid dynamics as a function of the genetic interaction between the plasmid and the host genome. This work will help identify the underlying mechanisms that allow plasmids to persist and transfer among members of bacterial communities. Ultimately, we will be able to predict microbiomes in which transmission of AMR plasmids are likely to be heightened, allowing us to better control the spread of antimicrobial resistance.