A systems approach to manipulate microbial adaptation to structured environments

操纵微生物适应结构化环境的系统方法

• 批准号: 10159858
• 负责人: Nitin S Baliga
• 金额: $ 88.87万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-07 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10159858
• 关键词: Acids; Antimicrobial Resistance; Biological Assay; Caffeine; Cells; Cessation of life; Complex; Cost-Benefit Analysis; Costs and Benefits; Coupled; Cues; Dose; Drug Tolerance; Environment; Environmental Risk Factor; Escherichia coli; Evaluation; Evolution; Frequencies; Future; Generations; Genes; Genetic; Genetic Transcription; Genetic Variation; Genome; Genome engineering; Genomics; Heterogeneity; Immune Tolerance; Immunotherapeutic agent; Knowledge; Laboratories; Libraries; Life; Link; Metabolic; Modeling; Monitor; Mutation; Mycobacterium tuberculosis; Nature; Nutrient; Organism; Pharmaceutical Preparations; Phenotype; Population Analysis; Population Heterogeneity; Race; Readiness; Resources; Role; Saccharomyces cerevisiae; Sorting - Cell Movement; Starvation; Stress; Structure; System; Systems Analysis; Technology; Temperature; Testing; Yeasts; arm; base; cost; environmental change; experimental study; fitness; high throughput technology; microbial; network models; novel; pathogen; predictive modeling; preempt; pressure; prevent; programs; response; success; theories; tool; transcriptome; transcriptomics

PROJECT SUMMARY (30 lines) Adaptive prediction (AP) is a strategy utilized by all organisms to predict and prepare for a future selective pressure. E. coli and M. tuberculosis (MTB), for instance, utilize neutral cues such as a rise in temperature or nutrient starvation to prepare in advance for a hostile host environment. There is growing evidence that the drug/immune tolerant phenotype resulting from AP gives pathogens a window of opportunity to evolve antimicrobial resistance (AMR)—a catastrophic problem that could cause >10 million deaths by 2050. Knowledge of how AP is encoded within the genome and gene networks of an organism will enable strategies to disrupt and prevent drug tolerance to potentiate complete killing by frontline drugs. We’ve demonstrated proof-of-concept for this strategy by potentiating bedaquiline killing of MTB through rational disruption of the starvation-induced, bedaquiline-specific tolerance network with a second drug—pretomanid (Peterson et al, Nature Micro 2016). To further advance this approach, we established a laboratory evolution framework to dissect dynamics and mechanisms of AP (Lomana et al, Genome Biol Evol 2017). Using this set up we have demonstrated that when subjected to laboratory evolution in an artificially structured environment, novel AP emerges within 50 generations to enable Saccharomyces cerevisiae (yeast) to use caffeine as a cue to anticipate and elicit a protective response to subsequent challenge with a sub-lethal dose of 5-fluoroorotic acid. Based on evolutionary dynamics, genetic variation, and phenotypic heterogeneity of evolved lines, we hypothesize that three factors govern emergence and retention of AP: (1) cost vs. benefit of AP vis-à-vis frequency and predictability of coupled environmental changes, including period between exposures, energy required for advanced preparedness, and overall fitness benefit; (2) coordinated changes in metabolic and regulatory networks to adaptively trigger a tolerant state upon sensing a cue; and (3) evolutionary game strategies (bet-hedging) arising from population heterogeneity. The two specific aims to test these hypotheses will make use of a systems approach to study and manipulate complex phenotypes, including, (i) an integrated network model for predicting phenotypic consequences of regulatory and metabolic mutations; (ii) a technology for phenotyping >10,000 colonies, (iii) a technology to sort translationally active and dormant sub-populations; and (iv) laboratory evolution and genome engineering capabilities to generate and manipulate AP. Through iterative computational prediction and experimentation, we will characterize how structure and dynamics of environmental change influences emergence and retention of AP (Aim 1); and elucidate and rationally manipulate interplay of metabolic, regulatory, and evolutionary game strategies for AP (Aim 2). This project will advance theory of AP with implications on strategies to preempt AMR; advance tools to predict and manipulate complex phenotypes; and track and isolate rare strains within heterogeneous populations. 1

项目摘要（30行） 自适应预测（AP）是所有生物体使用的一种策略，以预测和准备未来的选择性 压力。例如 营养饥饿，以预先为敌对的主机环境做准备。越来越多的证据表明 AP产生的药物/免疫耐受的表型为病原体提供了进化的机会窗口 抗菌耐药性（AMR） - 到2050年，可能导致1000万死亡的灾难性问题。 了解AP在生物体的基因组和基因网络中如何编码的知识将实现 破坏和防止药物对一线药物杀死可能完全杀死的策略。我们已经 通过理性地将MTB杀死MTB来证明该策略的概念证明 使用第二种药物的饥饿诱导的，贝塔奎林特异性耐受性网络的破坏 （Peterson等，Nature Micro 2016）。为了进一步推进这种方法，我们建立了实验室进化 剖析AP动力学和机制的框架（Lomana等，基因组Biol Evol 2017）。使用此组 我们已经证明，在人工结构化的环境中经过实验室进化时， 新型AP在50代内出现，以使酿酒酵母（酵母）使用咖啡馆作为提示 预测并引起对随后挑战的受保护反应，并以5-氟毒素的亚致死剂量 酸。根据进化线的进化动力学，遗传变异和表型异质性，我们 假设三个因素控制着AP的出现和保留。 耦合环境变化的频率和可预测性，包括暴露，能量之间的周期 高级准备和整体健身益处所需的； （2）代谢和代谢的协调变化 调节网络在感应提示时适应触发宽容状态； （3）进化游戏 人口异质性引起的策略（赌注）。这两个特定的目的是检验这些假设 将利用系统方法来研究和操纵复杂的表型，包括（i）集成 用于预测调节和代谢突变的表型后果的网络模型； （ii）一项技术 对于> 10,000个菌落的表型，（iii）一种用于对翻译活跃和休眠子群体进行分类的技术； （iv）实验室进化和基因组工程能力，以产生和操纵AP。通过 迭代计算预测和实验，我们将表征如何结构和动力学 环境变化影响AP的紧急情况和保留（AIM 1）；并阐明和理性 操纵AP的代谢，调节和进化游戏策略的相互作用（AIM 2）。这个项目将 AP的提前理论具有对抢先AMR的策略的影响；预测和 操纵复杂表型；在异质种群中跟踪和分离稀有菌株。 1