Environmental factors like temperature, pressure, and pH partly shaped the evolution of life. As life progressed, new stressors (e.g., poisons and antibiotics) arose as part of an arms race among organisms. Here we ask if cells co-opted existing mechanisms to respond to new stressors, or whether new responses evolved de novo. We use a network-clustering approach based purely on phenotypic growth measurements and interactions among the effects of stressors on population growth. We apply this method to two types of stressors-temperature and antibiotics-to discover the extent to which their cellular responses overlap in Escherichia coli. Our clustering reveals that responses to low and high temperatures are clearly separated, and each is grouped with responses to antibiotics that have similar effects to cold or heat, respectively. As further support, we use a library of transcriptional fluorescent reporters to confirm heat-shock and cold-shock genes are induced by antibiotics. We also show strains evolved at high temperatures are more sensitive to antibiotics that mimic the effects of cold. Taken together, our results strongly suggest that temperature stress responses have been co-opted to deal with antibiotic stress.
Objectives:Understanding how phenotypic traits vary has been a longstanding goal of evolutionary biologists. When examining antibiotic-resistance in bacteria, it is generally understood that the minimum inhibitory concentration (MIC) has minimal variation specific to each bacterial strain-antibiotic combination. However, there is a less studied resistance trait, the mutant prevention concentration (MPC), which measures the MIC of the most resistant sub-population. Whether and how MPC varies has been poorly understood. Here, we ask a simple, yet important question: How much does the MPC vary, within a single strain-antibiotic association? Using a Staphylococcus species and five antibiotics from five different antibiotic classes—ciprofloxacin, doxycycline, gentamicin, nitrofurantoin, and oxacillin—we examined the frequency of resistance for a wide range of concentrations per antibiotic, and measured the repeatability of the MPC, the lowest amount of antibiotic that would ensure no surviving cells in a 1010 population of bacteria.Results: We found a wide variation within the MPC and distributions that were rarely normal. When antibiotic resistance evolved, the distribution of the MPC changed, with all distributions becoming wider and some multi-modal.Conclusion: Unlike the MIC, there is high variability in the MPC for a given bacterial strain-antibiotic combination.
23Drug combinations are a promising strategy to increase killing efficiency and to decrease the 24 likelihood of evolving resistance. A major challenge is to gain a detailed understanding of how 25 drugs interact in a dose-specific manner, especially for interactions involving more than two 26 drugs. Here we introduce a direct and intuitive visual representation that we term "interaction 27 landscapes". We use these landscapes to clearly show that the interaction type of two drugs 28 typically transitions smoothly from antagonism to no interaction to synergy as drug doses 29 increase. This finding contradicts prevailing assumptions that interaction type is always the 30 same. Our results, from 56 interaction landscapes, are derived from all possible three-drug 31 combinations among 8 antibiotics, each varied across a range of 7 concentrations and applied 32 to a pathogenic Escherichia coli strain. Such comprehensive data and analysis are only recently 33 possible through implementation of an automated high-throughput drug-delivery system and 34 an explicit mathematical framework that disentangles pairwise versus three-way as well as net 35 (any effect) versus emergent (requiring all three drugs) interactions. Altogether, these 36 landscapes partly capture and encapsulate selective pressures that correspond to different 37 dose regions and could help optimize treatment strategies. Consequently, interaction 38 landscapes have profound consequences for choosing effective drug-dose combinations 39 because there are regions where small changes in dose can cause large changes in pathogen 40 killing efficiency and selective pressure. 41 42
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