MUSIC: MGE Uptake and Spread In microbial Communities

移动的遗传元件（MGEs）通过在细菌菌株和种之间移动基因而在细菌进化中起关键作用，其可以显著改变细菌的表型，例如它们对抗菌剂的抗性和毒力性状。鉴于新出现的抗生素耐药性危机，迫切需要更好地了解MGE如何在复杂的微生物群落中传播和进化。我建议结合联合收割机实验，生物信息学和理论的方法来研究不同的细菌防御限制MGE物种之间的传播和跨社区的相对重要性。我将重点介绍“ESKAPEE”病原体，由于它们快速获得由不同类型的MGE携带的抗生素耐药基因，因此难以治疗感染。在我的生物信息学分析中，我将分析超过62，000个ESKAPEE基因组序列，以确定其存在与MGE负载减少相关的宿主防御基因。然后，我将用大量临床分离株及其MGE进行荧光激活细胞分选实验，以直接测量宿主防御基因在MGE感染成功和维持中驱动变异的贡献。我将应用反向和正向遗传学方法来明确证明防御基因和MGE摄取和稳定性之间的因果关系。最后，我将测量数百个成对组合的分离株之间的MGE的传播，无论是在物种内，以及物种之间，以了解宿主防御基因如何塑造一个社区内的感染网络。我将使用机器学习来预测网络结构如何塑造MGE在社区中的传播，这将使用合成社区进行实验测试。这项研究计划将为我们理解复杂环境中不同MGE通过微生物群落的传播做出关键贡献。

Mobile genetic elements (MGEs) play a key role in bacterial evolution by moving genes between bacterial strains and species, which can dramatically alter bacterial phenotypes, such as their resistance to antimicrobials and virulence traits. In view of the emerging antibiotic resistance crisis, there is a pressing need to better understand how MGEs spread and evolve in complex microbial communities. I propose to combine experimental, bioinformatics and theoretical approaches to examine the relative importance of different bacterial defences in restricting MGE transmission between species and across communities. I will focus on the "ESKAPEE" pathogens, which cause hard to treat infections due to their rapid acquisition of antibiotic resistance genes that are vectored by different types of MGEs. In my bioinformatics analyses, I will analyse >62,000 ESKAPEE genome sequences to identify host defence genes whose presence correlates with reduced MGE loads. I will then carry out fluorescence-activated cell sorting experiments with a large collection of clinical isolates and their MGEs to directly measure the contribution of host defence genes in driving variation in MGE infection success and maintenance. I will apply both reverse and forward genetics approaches to unambiguously demonstrate causality between defence genes and MGE uptake and stability. Finally, I will measure the spread of MGEs between hundreds of pairwise combinations of isolates, both within species as well as between species, to understand how host defence genes shape the infection network within a community. I will use machine learning to predict how the network structure shapes the spread of MGEs through the community, which will be tested experimentally using synthetic communities. This research program will make key contributions to our understanding of the spread of different MGEs through microbial communities in complex environments.