A systems approach to manipulate microbial adaptation to structured environments

PROJECT SUMMARY (30 lines)Adaptive prediction (AP) is a strategy utilized by all organisms to predict and prepare for a future selectivepressure. E. coli and M. tuberculosis (MTB), for instance, utilize neutral cues such as a rise in temperature ornutrient starvation to prepare in advance for a hostile host environment. There is growing evidence that thedrug/immune tolerant phenotype resulting from AP gives pathogens a window of opportunity to evolveantimicrobial 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 enablestrategies to disrupt and prevent drug tolerance to potentiate complete killing by frontline drugs. We?vedemonstrated proof-of-concept for this strategy by potentiating bedaquiline killing of MTB through rationaldisruption 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 evolutionframework to dissect dynamics and mechanisms of AP (Lomana et al, Genome Biol Evol 2017). Using this setup 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 cueto anticipate and elicit a protective response to subsequent challenge with a sub-lethal dose of 5-fluorooroticacid. Based on evolutionary dynamics, genetic variation, and phenotypic heterogeneity of evolved lines, wehypothesize that three factors govern emergence and retention of AP: (1) cost vs. benefit of AP vis--visfrequency and predictability of coupled environmental changes, including period between exposures, energyrequired for advanced preparedness, and overall fitness benefit; (2) coordinated changes in metabolic andregulatory networks to adaptively trigger a tolerant state upon sensing a cue; and (3) evolutionary gamestrategies (bet-hedging) arising from population heterogeneity. The two specific aims to test these hypotheseswill make use of a systems approach to study and manipulate complex phenotypes, including, (i) an integratednetwork model for predicting phenotypic consequences of regulatory and metabolic mutations; (ii) a technologyfor 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. Throughiterative computational prediction and experimentation, we will characterize how structure and dynamics ofenvironmental change influences emergence and retention of AP (Aim 1); and elucidate and rationallymanipulate interplay of metabolic, regulatory, and evolutionary game strategies for AP (Aim 2). This project willadvance theory of AP with implications on strategies to preempt AMR; advance tools to predict andmanipulate complex phenotypes; and track and isolate rare strains within heterogeneous populations.1