Ecological and Evolutionary Drivers of Antibiotic Resistance in Patients

致病菌的抗生素耐药性对人类健康构成根本性威胁。该项目的总体目标是确定驱动住院患者抗生素耐药性出现和丧失的生态和进化过程。该项目的第一个目标是发现耐药感染的驱动因素。具体来说，我们将确定(i)新生变异，（ii）预先存在变异和（iii）重复感染作为抗生素治疗的功能对耐药性出现的相对贡献。该项目的第二个目标是确定影响患者耐药性稳定性的关键因素。具体来说，我们将(i)测量耐药性的成本，（ii）检验耐药种群的代偿进化，（iii）检验在没有抗生素治疗的情况下，高适应成本加速抗生素耐药性丧失的假设。该项目的第三个目标是确定抗生素治疗对基因组进化的影响。具体来说，我们将(i)测量抗生素治疗对分子进化速度的影响，（ii）系统地识别耐药性的遗传驱动因素。为了实现这些目标，我们将使用在ASPIRE-ICU期间收集的样本和数据，这是一项大规模的由机会致病菌铜绿假单胞菌引起的感染临床试验。从大量不同抗生素治疗方案的纵向取样患者中分离出的菌株的特征将使我们能够研究对抗生素治疗的表型和基因组反应。临床分离株的实验进化将使我们能够直接测试预测形成抗生素耐药性的关键变量的作用。临床采样，实验进化和基因组分析的新组合将使我们能够产生前所未有的见解。在这个项目中，我们将研究抗生素耐药性感染的生态和进化驱动因素。

Antibiotic resistance in pathogenic bacteria poses a fundamental threat to human health. The overarching aim of this project is todetermine the ecological and evolutionary processes that drive the emergence and loss of antibiotic resistance in hospitalizedpatients. The first objective of this project is to uncover drivers of resistant infections. Specifically, we will determine the relativecontribution of (i) de novo variation, (ii) pre-existing variation and (iii) superinfection to the emergence of resistance as a function ofantibiotic treatment. The second objective of the project is to determine key factors that shape the stability of resistance in patients.Specifically, we will (i) measure the cost of resistance, (ii) test for compensatory evolution in resistant populations and (iii) test the hypothesis that high fitness costs accelerate the loss of antibiotic resistance in the absence of antibiotic treatment. The third objective of this project is to determine the impact of antibiotic treatment on genome evolution. Specifically, we will (i) measure the impact of antibiotic treatment on the rate of molecular evolution and (ii) systematically identify genetic drivers of resistance. To achieve these objectives we will use samples and data collected during ASPIRE-ICU, a large scale clinical trial of infections caused by the opportunistic pathogen P.aeruginosa. Characterization of isolates from large numbers of longitudinally sampled patients with varying antibiotic treatment regimes will allow us to study the phenotypic and genomic responses to antibiotic treatment. Experimental evolution with clinical isolates will let us directly test the role of key variables that are predicted to shape antibiotic resistance. The novel combination of clinical sampling, experimental evolution and genomic analyses will allow us to generate unprecedented insights intIn this project we will investigate the ecological and evolutionary drivers of antibiotic resistant infection.