Manuscript 1 Adaptive tuning of mutation rates allows fast response to lethal stress in 2 Escherichia coli 3 4 a a a a a,b 5 Toon Swings , Bram Van den Bergh , Sander Wuyts , Eline Oeyen , Karin Voordeckers , Kevin J. a,b a,c a a,* 6 Verstrepen , Maarten Fauvart , Natalie Verstraeten , Jan Michiels 7 8 a 9 Centre of Microbial and Plant Genetics, KU Leuven - University of Leuven, Kasteelpark Arenberg 20, 10 3001 Leuven, Belgium b 11 VIB Laboratory for Genetics and Genomics, Vlaams Instituut voor Biotechnologie (VIB) Bioincubator 12 Leuven, Gaston Geenslaan 1, 3001 Leuven, Belgium c 13 Smart Systems and Emerging Technologies Unit, imec, Kapeldreef 75, 3001 Leuven, Belgium * 14 To whom correspondence should be addressed: Jan Michiels, Department of Microbial and 2 15 Molecular Systems (M S), Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, box 16 2460, 3001 Leuven, Belgium, [email protected], Tel: +32 16 32 96 84 1 Manuscript 17 Abstract 18 19 While specific mutations allow organisms to adapt to stressful environments, most changes in an 20 organism's DNA negatively impact fitness. The mutation rate is therefore strictly regulated and often 21 considered a slowly-evolving parameter. In contrast, we demonstrate an unexpected flexibility in 22 cellular mutation rates as a response to changes in selective pressure. We show that hypermutation 23 independently evolves when different Escherichia coli cultures adapt to high ethanol stress. 24 Furthermore, hypermutator states are transitory and repeatedly alternate with decreases in mutation 25 rate. Specifically, population mutation rates rise when cells experience higher stress and decline again 26 once cells are adapted. Interestingly, we identified cellular mortality as the major force driving the 27 quick evolution of mutation rates. Together, these findings show how organisms balance robustness 28 and evolvability and help explain the prevalence of hypermutation in various settings, ranging from 29 emergence of antibiotic resistance in microbes to cancer relapses upon chemotherapy. 2 Manuscript 30 Theory predicts that the optimal mutation rate depends on several different factors, including 31 genome size and effective population size. For example, unicellular organisms, such as viruses and 32 bacteria, exhibit per-base mutation rates that are inversely correlated with their population and 33 genome sizes, whereas multicellular organisms with much larger genomes and smaller effective 1,2 34 population sizes may have higher per-base mutation rates . While mutations are necessary to adapt 3,4 35 to new and stressful environments , random changes in an organism’s DNA are rarely beneficial, but 5–7 36 more often neutral or slightly deleterious . Consequently, mutation rates are strictly balanced by the 8–11 37 trade-off between the need for mutations to adapt and the concomitant increase in genetic load . 38 This trade-off between adaptability and adaptedness is believed to be responsible for the low genomic 39 mutation rates usually observed in organisms, while a further decrease in mutation rate is restricted by 12–15 40 the energy needed to increase and maintain high replication fidelity . Furthermore, the power of 41 random drift will limit selection on even lower mutation rates when additional increases in replication 42 fidelity are insufficiently advantageous. Due to this drift-barrier, mutation rates are believed to evolve 2 43 to an equilibrium where the strength of selection matches the power of drift . 1,16 44 An organism’s cellular mutation rate is generally considered to be near-constant , yet the optimal 17,18 45 mutation rate has been reported to depend on the environment . Wild-type bacteria grown under -3 46 optimal conditions typically have low mutation rates in the order of 10 mutations per genome per 19,20 47 generation . Under these conditions, hypermutators with weakly (10-fold) or strongly (100-10.000- 12,21 48 fold) increased mutation rates occur only sporadically . Despite low frequencies of hypermutators in 22 23,24 49 laboratory populations , a much higher prevalence is observed in natural bacterial populations , 25–27 28–31 27 50 such as clinical isolates of pathogenic E. coli , Pseudomonas aeruginosa , Salmonella , 32 33–35 51 Staphylococcus aureus among others and in nearly all A. baumannii strains adapting to severe 36 52 tigecycline stress . In addition, high frequency of hypermutation is also documented in eukaryotic 37,38 53 pathogens including the malaria-causing parasite Plasmodium falciparum and the fungal pathogen 39 54 Candida glabrata . Moreover, hypermutation plays an important role in cancer development and 40–42 55 proliferation, as it helps to overcome different barriers to tumor progression . These observations 56 suggest the natural occurrence of situations in which higher mutation rates confer a selectable 57 advantage. This is especially obvious in harsh environments, where near-lethal stress requires swift 43 58 adaptation of at least some individuals to avoid complete extinction of the population . Adaptation 59 sufficiently rapid to save a population from extinction is called evolutionary rescue. This phenomenon 3 Manuscript 60 is widely studied in the light of climate change and adaptation of declining populations to new 44 61 environments . It occurs when a population under stress lacks sufficient phenotypic plasticity and can 45 62 only avoid extinction through genetic change . Evolutionary rescue depends on different factors such 63 as the population size, genome size, mutation rate, degree of environmental change and history of the 45,46 64 stress . By increasing the supply of mutations, hypermutation might also be crucial to enable 65 evolutionary rescue for populations under near-lethal stress. 66 Despite the high prevalence of hypermutation in clinical settings, current knowledge is lacking on 67 the long-term fate of mutators and their specific role in survival under near-lethal stress conditions. 68 Previous studies exploring the costs and benefits of mutators mostly focused on mild stresses. Both 69 experimental evidence and theory show that mutators can readily increase in frequency in a 70 population through second-order selection. In this case, a mutator hitchhikes along with a sporadically 47–52 71 occurring, beneficial mutation that thrives under natural selection . This process relies on different 53 72 elements, such as initial mutator frequency , the relative timing of the emergence of one or multiple 54 51,55 73 beneficial mutations , the degree of environmental change or selection strength , the mutational 56 57 74 spectrum and the strength of the specific mutator . Although hypermutation can readily spread in a 75 population by means of hitchhiking when adaptation is required, long-term evolution experiments also 9,58 76 show selection against hypermutation . These results demonstrate that the actual mutation rate of a 77 population is prone to change by evolution. However, our current knowledge on the long-term 78 dynamics of hypermutation and the mechanisms underlying changes in mutation rate remains 79 fragmentary. Specifically, conditions under which the spread of mutators is inhibited or the increased 59 80 mutation rate is reversed, remain largely unexplored . 81 The aim of the current study was to better understand the dynamics of hypermutation under near- 82 lethal, complex stress. Therefore, we used E. coli exposed to high ethanol stress as a model 60,61 83 system . Here, multiple mutations epistatically interact and diverse evolutionary trajectories can 62 84 lead to adaptation to high ethanol concentrations . In our study, we found an unexpected flexibility in 85 cellular mutation rates as a response to changes in selective pressure. First, we used a defined 86 collection of mutators with distinct mutation rates to identify a range of optimal mutation rates to 87 enable rapid growth under high ethanol stress. Next, experimental evolution revealed an essential role 88 for hypermutation for de novo adaptation to high ethanol stress. While hypermutation quickly and 89 recurrently arose concurrent with increases in ethanol concentrations, mutation rates rapidly declined 4 Manuscript 90 again once cells were adapted to the stress. Interestingly, we identified cellular mortality as the major 91 force that drives fast evolution of mutation rates. In summary, our results shed new light on the 92 dynamics of mutation rate evolution and help explain why maintaining high mutation rates is limited in 93 time. 94 95 Results 96 Hypermutation enables rapid growth under high ethanol stress 97 Little is known on the role of hypermutators under complex, near-lethal stress conditions. In these 98 conditions growth rates are low and the probability to accumulate an adaptive mutation is strongly 99 limited. We postulate that mutator mutants yield variable benefits under these conditions, depending 100 on their mutation rates. To verify this hypothesis, a collection of E. coli mutants displaying a range of 101 mutation rates (Figure 1 - Figure Supplement 1) was grown in 5% EtOH. At this concentration, 102 ethanol almost completely inhibits growth and drastically reduces the carrying capacity of a wild-type 103 culture, indicating extreme stress (Figure 1 - Figure Supplement 2). 63,64 104 Growth rate and lag time reflect the fitness of a strain in a specific environment . These growth 105 parameters are contingent upon the initial population size. On the one hand, the effect of a rare 51 106 beneficial