A systems approach to manipulate microbial adaptation to structured environments

项目摘要（30 行） 适应性预测（AP）是所有生物体利用的一种策略，用于预测未来的选择性并为其做好准备。 压力。例如，大肠杆菌和结核分枝杆菌 (MTB) 利用中性线索，例如温度升高或 营养饥饿，为恶劣的宿主环境提前做好准备。越来越多的证据表明 AP 产生的药物/免疫耐受表型为病原体提供了进化的机会之窗 抗菌素耐药性 (AMR)——一个灾难性问题，到 2050 年可能导致超过 1000 万人死亡。 了解 AP 如何在生物体的基因组和基因网络中编码将有助于 破坏和防止药物耐受性的策略，以加强前线药物的彻底杀死。我们已经 通过通过合理的方法增强贝达奎林对 MTB 的杀灭作用，证明了该策略的概念验证 使用第二种药物 pretomanid 破坏饥饿诱导的贝达喹啉特异性耐受网络 （Peterson 等人，Nature Micro 2016）。为了进一步推进这种方法，我们建立了一个进化实验室 剖析 AP 动力学和机制的框架（Lomana 等人，Genome Biol Evol 2017）。使用这套 我们已经证明，当在人工结构的环境中进行实验室进化时， 新型 AP 在 50 代内出现，使酿酒酵母（酵母）能够使用咖啡因作为提示 预测并引发对亚致死剂量的 5-氟乳清蛋白后续攻击的保护性反应 酸。基于进化动力学、遗传变异和进化系的表型异质性，我们 假设三个因素控制 AP 的出现和保留：(1) AP 的成本与收益 耦合环境变化的频率和可预测性，包括暴露间隔时间、能量 高级准备和整体健身效益所需的； (2) 新陈代谢的协调变化 调节网络在感知到提示后自适应地触发容忍状态； (3)进化博弈 群体异质性产生的策略（下注对冲）。检验这些假设的两个具体目标 将利用系统方法来研究和操纵复杂的表型，包括：（i） 用于预测调节和代谢突变的表型后果的网络模型； (二) 一项技术 对于超过 10,000 个菌落的表型分析，(iii) 一种对翻译活跃和休眠亚群进行分类的技术； (iv) 生成和操纵 AP 的实验室进化和基因组工程能力。通过 迭代计算预测和实验，我们将描述结构和动力学如何 环境变化影响AP的出现和滞留（目标1）；并加以阐释和合理 操纵 AP 的代谢、调节和进化博弈策略的相互作用（目标 2）。该项目将 推进 AP 理论，对预防 AMR 的策略具有影响；先进的工具来预测和 操纵复杂的表型；追踪和分离异质群体中的稀有菌株。 1

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