A systems approach to manipulate microbial adaptation to structured environments

项目总结(30行) 自适应预测(AP)是一种被所有生物体用来预测和准备未来选择性 压力。例如，大肠杆菌和结核分枝杆菌(MTB)利用中性信号，如温度上升或 营养饥饿，为敌对的寄主环境提前做好准备。越来越多的证据表明， AP引起的药物/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)--这是一个灾难性的问题，到2050年可能导致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使 破坏和预防药物耐受性的战略，以加强一线药物的完全杀戮。我们已经 通过增强贝达奎兰对结核分枝杆菌的杀灭作用，论证了这一策略的概念验证 用第二种药物前甘露醇破坏饥饿诱导的贝达奎兰特异性耐受网络 (Peterson等人，《自然微电子》2016)。为了进一步推进这一方法，我们建立了一个实验室进化 分析AP的动力学和机制的框架(Lomana等人，Genome Biol Evol 2017)。使用此集合 我们已经证明，当在人工结构的环境中进行实验室进化时， 新的AP在50代内出现，使酿酒酵母(酵母)能够使用咖啡因作为线索 预测和诱导对亚致死剂量5-氟化钠后续攻击的保护性反应 酸。基于进化动力学、遗传变异和进化品系的表型异质性，我们 假设有三个因素支配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 提前准备和整体健康益处所需；(2)代谢和健康的协调变化 调节网络在感知到提示后自适应地触发容忍状态；以及(3)进化博弈 源于种群异质性的策略(更好的对冲)。这两个具体的目的是为了检验这些假设 将利用系统方法来研究和操纵复杂的表型，包括：(I)综合 用于预测调节和代谢突变的表型后果的网络模型；(Ii)一种技术 对于10,000个菌落的表型鉴定，(Iii)一种对翻译活跃和休眠亚群进行分类的技术； 以及(Iv)产生和操纵AP的实验室进化和基因组工程能力。穿过 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的出现和保留(目标1)；并理性地阐明 为AP操纵代谢、调节和进化博弈策略的相互作用(目标2)。这个项目将 AP的先进理论及其对抢占AMR战略的影响；先进的预测工具和 操纵复杂的表型；跟踪和分离不同种群中的稀有菌株。 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