Defining Evolutionary Trajectories: Molecular adaptation to antibiotic resistance

描述（由申请人提供）：细菌病原体之间的抗生素耐药性仍然是当今世界公共卫生面临的巨大挑战之一。尽管抗生素取得了显著的成功，但细菌感染仍然是导致死亡的主要原因之一。抗生素的持续和广泛使用越来越多地选择了多重耐药细菌，这些细菌迅速适应新一代抗生素并缩短其临床疗效。我们开发了一种可扩展的整体方法，我们称之为“定量进化动力学”（QED）来研究临床分离的万古霉素耐药肠球菌（VRE）对达托霉素和替加环素的耐药性，以及鲍曼不动杆菌对替加环素的耐药性。QED可以应用于许多生物和抗生素，以提供：1)概念和机制见解，2)药物设计的新靶点，以及3)揭示细胞适应性变化的潜在生物物理基础，从而在选择过程中产生更大的抗性。为了进行QED，我们结合了浊化器（保持细菌种群以最快生长速度增长的发酵罐）的实验进化、基因组测序、DNA条形码测量等位基因频率（FREQ-SEQ）、RNA-Seq和物理化学表征（包括x射线晶体学），为鉴定和表征耐药靶点和机制提供了一种综合方法。QED使用实验进化来识别适应的中间体，以重建负责抗性的适应网络。我们使用进化生物学的原理对人群中这些变化的可能重要性进行排序，并为更耗时的物理研究优先考虑最重要的目标。QED与抗生素耐药性的体内临床观察结果非常吻合。我们不仅深入了解耐药性的临床相关策略，还了解耐药性的特定生化机制，负责这些生化变化的特定候选基因，以及在这些变化与病原体对特定药物的适应性（例如耐药性）之间建立定量联系的基础。QED是一种强大而新颖的方法，可以补充体内和临床研究，并揭示抗生素耐药性的进化动力学。

DESCRIPTION (provided by applicant): Antibiotic resistance among bacterial pathogens remains one of the great challenges confronting public health in the world today. Despite the remarkable success of antibiotics, bacterial infections remain one of the leading causes for mortality. Increasingly, sustained and broad use of antibiotics has selected for multi-drug resistant bacteria that adapt rapidly to newer generation antibiotics and shorten their clinical efficacy. We have developed a scalable and holistic approach that we call 'Quantitative Evolutionary Dynamics' (QED) to study daptomycin and tigecycline resistance in clinical isolates of vancomycin-resistant enterococci (VRE) and to tigecycline resistance in Acinetobacter baumannii. QED can be applied across many organisms and antibiotics to provide: 1) conceptual and mechanistic insights, 2) new targets for drug design, and 3) reveal the underlying biophysical basis for changes in cellular fitness leading to greater resistance during selection. To conduct QED, we use a combination of experimental evolution in turbidostats (fermentors that maintain bacterial populations at their fastest growth rate), genomic sequencing, DNA bar-coding to measure allelic frequencies (FREQ-SEQ), RNA-Seq and physicochemical characterization, including X-ray crystallography, to provide an integrative approach to the identification and characterization of drug resistance targets and mechanisms. QED uses experimental evolution to identify the intermediates of adaptation to reconstruct the adaptive networks responsible for resistance. We use principles from evolutionary biology to rank the likely importance of such changes within the population and prioritize the most important targets for the more time consuming physical studies. QED shows excellent correspondence to in vivo clinical observations of antibiotic resistance. We produce insights not just into the clinically relevant strategies for resistance, but also the specific biochemical mechanisms of resistance, the specific candidate genes responsible for those biochemical changes, and the basis for developing a quantitative link between those changes and the fitness (e.g. resistance) of the pathogen towards a specific drug. QED is a powerful and novel approach that can complement in vivo and clinical studies as well as reveal the evolutionary dynamics of antibiotic resistance.