Using strain history to improve prediction of the evolution of antimicrobial resistance in Acinetobacter baumannii

项目摘要 本建议的目的是为了更全面地了解进化的影响， 历史对进化的可预测性。理解进化的可预测性和可重复性一直是 进化生物学家的目标。将这些问题应用于抗菌素耐药性（AMR）领域， 获得临床相关结果的好处，同时也研究的问题是根本， 进化生物学AMR是一个迅速恶化的全球健康问题， 大多数药物无法治疗的感染。值得严重关注的是鲍曼不动杆菌， 医院感染和高度多药耐药（MDR）病原体。尽管抵抗力通常是通过 常见的途径，如增加药物外排和修改药物靶点，目前尚不清楚如何 进化史影响AMR的进化，以及耐药性进化到何种程度或水平是可预测的。 病史（如遗传相关性和既往抗生素暴露）是一个未被充分认识的方面， 影响进化，并可能对基因组产生持久的影响，改变或限制可用于 适应抗生素压力。 为了弥补这些知识上的差距，我们建议使用一个预先存在的A。鲍曼不动杆菌临床 分离株该提案的第一个目的是了解遗传和表型差异， 这些菌株，特别关注毒力和抗性。通过比较基因组学，我们将确定 序列相似性以及泛基因组组成。我们将使用已知的抵抗原因， 库珀实验室和其他人先前的研究，以创建基于基因组的多位点抗性预测因子 表型我们将联合收割机的比较基因组学分析与生物膜的表型测定结果相结合 形成，在不同条件下的生长，和持久性，以评估任何系统发育表型模式。我们 将通过分子工程验证选择的基因-表型关联，帮助解开基因型- 表型图。鲍曼不动杆菌。第二个拟议目标将进一步加强我们对 抗性进化的可预测性。我们将在核糖体存在的情况下进化出20个临床分离株- 靶向药物替加环素，并监测相应的耐药倍数增加，预测 初始抗性水平将决定最终进化抗性水平。通过整个人群，整个基因组 通过测序，我们将能够评估抗性进化在遗传上发生的可预测性水平。 这个项目的完成不仅将为广泛的进化问题提供答案， 这是对抗抗生素耐药性危机的关键一步。我们将得出结论， AMR是可预测的机制。更好地理解可预测性将具有直接的临床意义 并允许用于治疗和预防MDR感染的个性化治疗策略。

PROJECT SUMMARY The objective of this proposal is to gain a more complete understanding of the influence of evolutionary history on the predictability of evolution. Understanding predictability and repeatability of evolution has long been a goal of evolutionary biologists. Applying these questions to the field of antimicrobial resistance (AMR) provides the benefit of obtaining clinically relevant results while also studying questions that are fundamental to evolutionary biology. AMR is a rapidly worsening global health issue, with an increasing number of bacterial infections becoming impossible to treat with most drugs. Of serious concern is Acinetobacter baumannii, a nosocomial and highly multidrug resistant (MDR) pathogen. Although resistance is often attained through common pathways, such as increased drug efflux and modifications to the drug target, it remains unclear how evolutionary history affects the evolution of AMR, and to what degree or level resistance evolution is predictable. History (such as genetic relatedness and previous antibiotic exposure) is an underappreciated aspect that influences evolution and can have lasting effects on the genome that alter or constrain paths available for adaptation to antibiotic stress. To address these gaps in knowledge, we propose to use a preexisting collection of A. baumannii clinical isolates. The first aim of this proposal focuses on understanding the genetic and phenotypic differences within these isolates, with special focus on virulence and resistance. Through comparative genomics we will identify sequence similarity as well as pan-genome composition. We will use known causes of resistance, compiled from previous studies in the Cooper Lab and others, to create a genome based multi-locus predictor of resistance phenotypes. We will combine the comparative genomics analysis with results of phenotypic assays for biofilm formation, growth in different conditions, and persistence, to assess any phylogenetic phenotypic patterns. We will verify select gene-phenotype associations through molecular engineering, helping to detangle the genotype- phenotype map of A. baumannii. The second proposed aim will further enhance our knowledge regarding the predictability of resistance evolvability. We will evolve 20 clinical isolates in the presence of the ribosome- targeting drug, tigecycline, and monitor the corresponding fold increase of resistance, with the prediction that initial resistance level will dictate final evolved resistance level. Through whole population, whole genome sequencing, we will be able to assess the level of predictability to which resistance evolution occurs genetically. Completion of this project will not only provide answers to broad evolutionary questions but will also be a crucial step in the fight against the antimicrobial resistance crisis. We will make conclusions as to what level AMR is predictable mechanistically. A greater understanding of predictability will have direct clinical importance and allow for personalized therapeutic strategies for the treatment and prevention of MDR infections.