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Spatio-temporal dynamics of mutation avoidance and antimicrobial resistance

突变避免和抗菌素耐药性的时空动态

基本信息

批准号：
MR/T021225/1
负责人：
Rok Krasovec
金额：
$ 128.23万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT021225%2F1
关键词：
Spatio temporal dynamics mutation avoidance

项目摘要

Spontaneous mutations are the basis of evolutionary innovation. They are also central to diseases in higher organisms and the root of some of the most pressing medical problems that we face: antimicrobial resistance and cancer. Mutations are usually detrimental for microbial cells. Therefore, cells have evolved to control mutations very tightly and DNA mutation rates are remarkably low. Yet mutations can be beneficial, for instance, a single point mutation in the right gene helps a cell to gain antibiotic resistance. Cells thus need mutations but just not too many. We need deep understanding of how the number of mutations fluctuate with the internal and external cellular environment. Addressing such dynamical aspects of mutations in the natural environment is vital for understanding the survival, adaptation and evolution of all cells. My previous work found that the rate of mutations is regulated by the environment associated with the cell-density: mutation rates decrease in dense populations up to 20-fold. Specifically, I discovered that crucial effectors of this environmental dependence of mutations are enzymes that enable cells to avoid mutations. In this Fellowship I shall observe mutations in individual microbes growing in their native environment, community. Only studying individual cells growing in a dense community will give us an understanding of mutation dynamics in the real-world. To accomplish my aims, I will combine live fluorescence microscopy, microfluidics, statistical modelling and interact with outstanding researchers from other disciplines. I shall use the Escherichia coli K-12 model system and observe mobility of DNA repair proteins, which will enable me to count mutations in individual cells. This will tell us about cell-to-cell variation in the number of mutations and how such a heterogeneity depends on micro-environments generated in the community. I will also quantify the molecular diffusion of mutation avoidance protein MutT in these micro-environments. I will establish the potential link between MutT dynamics and downstream processes involved in the generation of mutations. I will do all this not only in cells during normal community growth, but also in cells that survive the antibiotic treatment without obtaining the genetic resistance. These tolerant and persistent cells start to divide again, after the antibiotic is removed. My study will determine dynamics of mutations in these survivor cells and how micro-environments, generated in the aftermath of the antibiotic treatment, affect their fate. Understanding factors that affect a cell's capability to avoid and repair mutations is essential to better predict the development of mutation-based resistance in microbial communities and to exploit that understanding to combat antimicrobial resistance.Generated knowledge, in years 1-4, will be used in years 5-7 to test how various drug candidates impact the mutation dynamics and fate of cells that survive antibiotic treatment. I will also apply these developed experimental approach to more clinically relevant strains.

自发突变是进化创新的基础。它们也是高等生物体疾病的核心，也是我们面临的一些最紧迫的医学问题的根源：抗菌素耐药性和癌症。突变通常对微生物细胞有害。因此，细胞已经进化到非常严格地控制突变，DNA突变率非常低。然而，突变可能是有益的，例如，正确基因的单点突变有助于细胞获得抗生素耐药性。因此，细胞需要突变，但不能太多。我们需要深入了解突变的数量如何随着细胞内外环境的变化而波动。解决自然环境中突变的这些动态方面对于理解所有细胞的生存、适应和进化至关重要。我之前的工作发现，突变的速度受到与细胞密度相关的环境的调节：在密度高的人群中，突变率最高可降低20倍。具体地说，我发现这种突变环境依赖性的关键效应物是使细胞避免突变的酶。在这个团契中，我将观察在它们的原生环境和群落中生长的单个微生物的突变。只有研究在密集群落中生长的单个细胞，才能让我们了解现实世界中的突变动态。为了实现我的目标，我将结合实时荧光显微镜、微流体学、统计建模，并与来自其他学科的杰出研究人员互动。我将使用大肠杆菌K-12模型系统，观察DNA修复蛋白的流动性，这将使我能够计算单个细胞中的突变。这将告诉我们突变数量的细胞间差异，以及这种异质性如何依赖于群落中产生的微环境。我还将量化突变避免蛋白MUT在这些微环境中的分子扩散。我将建立突变动力学和涉及突变产生的下游过程之间的潜在联系。我将不仅在正常社区生长的细胞中这样做，而且在那些在抗生素治疗中幸存下来但没有获得遗传耐药性的细胞中这样做。在抗生素被移除后，这些耐受和持久的细胞开始再次分裂。我的研究将确定这些存活细胞中突变的动态，以及抗生素治疗后产生的微环境如何影响它们的命运。了解影响细胞避免和修复突变能力的因素对于更好地预测微生物群落中基于突变的耐药性的发展并利用这种了解来对抗抗菌素耐药性是至关重要的。1-4年中的遗传知识将在5-7年中用于测试各种候选药物如何影响抗生素治疗后存活的细胞的突变动态和命运。我还将把这些开发的实验方法应用于更多临床相关的菌株。