Long-term Evolution Experiment Research Articles

Neuronal Function and Dopamine Signaling Evolve at High Temperature in Drosophila

Neuronal activity is temperature sensitive and affects behavioral traits important for individual fitness, such as locomotion and courtship. Yet, we do not know enough about the evolutionary response of neuronal phenotypes in new temperature environments. Here, we use long-term experimental evolution of Drosophila simulans populations exposed to novel temperature regimes. Here, we demonstrate a direct relationship between thermal selective pressure and the evolution of neuronally expressed molecular and behavioral phenotypes. Several essential neuronal genes evolve lower expression at high temperatures and higher expression at low temperatures, with dopaminergic neurons standing out by displaying the most consistent expression change across independent replicates. We functionally validate the link between evolved gene expression and behavioral changes by pharmacological intervention in the experimentally evolved D.simulans populations as well as by genetically triggered expression changes of key genes in D.melanogaster. As natural temperature clines confirm our results for Drosophila and Anopheles populations, we conclude that neuronal dopamine evolution is a key factor for temperature adaptation.

Prolonged culture in aerobic environments alters Escherichia coli H2 production capacity.

Growing interest in renewable energy continues to motivate new work on microbial biohydrogen production and in particular utilizing Escherichia coli a well-studied, facultative anaerobe. Here we characterize, for the first time the H2 production rate and capacity, of E coli isolates from the 50 000th generation of the Long-Term Evolution Experiment. Under these reaction conditions, peak production rates near or above 5 mL per hour for 100 mL of lysogeny broth (LB media) was established for the ancestral strains and batch efficiencies between 0.15 and 0.22 mL H2 produced per 1 mL LB media were achieved. All 11 isolates studied, which had been aerobically cultured in minimal media since 1988, exhibited a decreased H2 production rate or capacity with many strains unable to grow under anaerobic conditions at all. The genomes of these strains have been sequenced and a preliminary analysis of the correlations between genotype and phenotype shows that mutations in gene ydjO are exclusively observed in the two isolates which produce H2, potentially suggesting a role for this gene in the maintenance of wild type metabolic pathways in the context of diverse mutational backgrounds. These results provide hints towards uncovering new genetic targets for the pursuit of bacterial strains with increased capacity for H2 production as well as a case study in speciation and the control of phenotypic switching.

Dynamics of adaptation in an anisotropic phenotype-fitness landscape

We study the dynamics of adaptation of a large asexual population in a n-dimensional phenotypic space, under anisotropic mutation and selection effects. When n=1 or under isotropy assumptions, the ‘replicator-mutator’ equation is a standard model to describe these dynamics. However, the n-dimensional anisotropic case remained largely unexplored.We prove here that the equation admits a unique solution, which is interpreted as the phenotype distribution, and we propose a new and general framework to the study of the quantitative behaviour of this solution. Our method builds upon a degenerate nonlocal parabolic equation satisfied by the distribution of the ‘fitness components’, and a nonlocal transport equation satisfied by the cumulant generating function of the joint distribution of these components. This last equation can be solved analytically and we then get a general formula for the trajectory of the mean fitness and all higher cumulants of the fitness distribution, over time. Such mean fitness trajectory is the typical outcome of empirical studies of adaptation by experimental evolution, and can thus be compared to empirical data.In sharp contrast with the known results based on isotropic models, our results show that the trajectory of mean fitness may exhibit (n−1) plateaus before it converges. It may thus appear ‘non-saturating’ for a transient but possibly long time, even though a phenotypic optimum exists. To illustrate the empirical relevance of these results, we show that the anisotropic model leads to a very good fit of Escherichia coli long-term evolution experiment, one of the most famous experimental dataset in experimental evolution. The two ‘evolutionary epochs’ that have been observed in this experiment have long puzzled the community: we propose that the pattern may simply stem from a climbing hill process, but in an anisotropic fitness landscape.

Resource-dependent evolution of female resistance responses to sexual conflict.

Sexual conflict can promote the evolution of dramatic reproductive adaptations as well as resistance to its potentially costly effects. Theory predicts that responses to sexual conflict will vary significantly with resource levels—when scant, responses should be constrained by trade‐offs, when abundant, they should not. However, this can be difficult to test because the evolutionary interests of the sexes align upon short‐term exposure to novel environments, swamping any selection due to sexual conflict. What is needed are investigations of populations that are well adapted to both differing levels of sexual conflict and resources. Here, we used this approach in a long‐term experimental evolution study to track the evolution of female resistance to sexual conflict in the fruit fly Drosophila melanogaster. In resource‐rich regimes, high‐conflict females evolved resistance to continual exposure to males. There was no difference in baseline survival, consistent with the idea that responses evolving under nutritional abundance experienced no trade‐offs with resistance. In the poor resource regimes, the ability of high‐conflict females to evolve resistance to males was severely compromised and they also showed lower baseline survival than low‐conflict females. This suggested high‐conflict females traded off somatic maintenance against any limited resistance they had evolved in response to sexual conflict. Overall, these findings provide experimental support for the hypothesis that evolutionary responses to sexual conflict are critically dependent upon resource levels.

Molecular signatures of aneuploidy-driven adaptive evolution

Alteration of normal ploidy (aneuploidy) can have a number of opposing effects, such as unbalancing protein abundances and inhibiting cell growth but also accelerating genetic diversification and rapid adaptation. The interplay of these detrimental and beneficial effects remains puzzling. Here, to understand how cells develop tolerance to aneuploidy, we subject disomic (i.e. with an extra chromosome copy) strains of yeast to long-term experimental evolution under strong selection, by forcing disomy maintenance and daily population dilution. We characterize mutations, karyotype alterations and gene expression changes, and dissect the associated molecular strategies. Cells with different extra chromosomes accumulated mutations at distinct rates and displayed diverse adaptive events. They tended to evolve towards normal ploidy through chromosomal DNA loss and gene expression changes. We identify genes with recurrent mutations and altered expression in multiple lines, revealing a variant that improves growth under genotoxic stresses. These findings support rapid evolvability of disomic strains that can be used to characterize fitness effects of mutations under different stress conditions.

Adaptation of Lactobacillus plantarum to Ampicillin Involves Mechanisms That Maintain Protein Homeostasis.

The widespread use of antibiotics has caused great concern in the biosafety of probiotics. In this study, we conducted a 12-month adaptive laboratory evolution (ALE) experiment to select for antibiotics-adapted Lactobacillus plantarum P-8, a dairy-originated probiotic bacterium. During the ALE process, the ampicillin MIC for the parental L. plantarum P-8 strain increased gradually and reached the maximum level of bacterial fitness. To elucidate the molecular mechanisms underlying the ampicillin-resistant phenotype, we comparatively analyzed the genomes and proteomes of the parental strain (L. plantarum P-8) and two adapted lines (L. plantarum 400g and L. plantarum 1600g). The adapted lines showed alterations in their carbon, amino acid, and cell surface-associated metabolic pathways. Then, gene disruption mutants were created to determine the role of six highly expressed genes in contributing to the enhanced ampicillin resistance. Inactivation of an ATP-dependent Clp protease/the ATP-binding subunit ClpL, a small heat shock protein, or a hypothetical protein resulted in partial but significant phenotypic reversion, confirming their necessary roles in the bacterial adaptation to ampicillin. Genomic analysis confirmed that none of the ampicillin-specific differential expressed genes were flanked by any mobile genetic elements; thus, even though long-term exposure to ampicillin upregulated their expression, there is low risk of spread of these genes and adapted drug resistance to other bacteria via horizontal gene transfer. Our study has provided evidence of the biosafety of probiotics even when used in the presence of antibiotics.IMPORTANCE Antibiotic resistance acquired by adaptation to certain antibiotics has led to growing public concerns. Here, a long-term evolution experiment was used together with proteomic analysis to identify genes/proteins responsible for the adaptive phenotype. This work has provided novel insights into the biosafety of new probiotics with high tolerance to antibiotics.

Quantification of Plasmid-Mediated Antibiotic Resistance in an Experimental Evolution Approach.

Plasmids play a major role in microbial ecology and evolution as vehicles of lateral gene transfer and reservoirs of accessory gene functions in microbial populations. This is especially the case under rapidly changing environments such as fluctuating antibiotics exposure. We recently showed that plasmids maintain antibiotic resistance genes in Escherichia coli without positive selection for the plasmid presence. Here we describe an experimental system that allows following both the plasmid genotype and phenotype in long-term evolution experiments. We use molecular techniques to design a model plasmid that is subsequently introduced to an experimental evolution batch system approach in an E. coli host. We follow the plasmid frequency over time by applying replica plating of the E. coli populations while quantifying the antibiotic resistance persistence. In addition, we monitor the conformation of plasmids in host cells by analyzing the extent of plasmid multimer formation by plasmid nicking and agarose gel electrophoresis. Such an approach allows us to visualize not only the genome size of evolving plasmids but also their topological conformation-a factor highly important for plasmid inheritance. Our system combines molecular strategies with traditional microbiology approaches and provides a set-up to follow plasmids in bacterial populations over a long time. The presented approach can be applied to study a wide range of mobile genetic elements in the future.

Clonal reconstruction from time course genomic sequencing data

BackgroundBacterial cells during many replication cycles accumulate spontaneous mutations, which result in the birth of novel clones. As a result of this clonal expansion, an evolving bacterial population has different clonal composition over time, as revealed in the long-term evolution experiments (LTEEs). Accurately inferring the haplotypes of novel clones as well as the clonal frequencies and the clonal evolutionary history in a bacterial population is useful for the characterization of the evolutionary pressure on multiple correlated mutations instead of that on individual mutations.ResultsIn this paper, we study the computational problem of reconstructing the haplotypes of bacterial clones from the variant allele frequencies observed from an evolving bacterial population at multiple time points. We formalize the problem using a maximum likelihood function, which is defined under the assumption that mutations occur spontaneously, and thus the likelihood of a mutation occurring in a specific clone is proportional to the frequency of the clone in the population when the mutation occurs. We develop a series of heuristic algorithms to address the maximum likelihood inference, and show through simulation experiments that the algorithms are fast and achieve near optimal accuracy that is practically plausible under the maximum likelihood framework. We also validate our method using experimental data obtained from a recent study on long-term evolution of Escherichia coli.ConclusionWe developed efficient algorithms to reconstruct the clonal evolution history from time course genomic sequencing data. Our algorithm can also incorporate clonal sequencing data to improve the reconstruction results when they are available. Based on the evaluation on both simulated and experimental sequencing data, our algorithms can achieve satisfactory results on the genome sequencing data from long-term evolution experiments.AvailabilityThe program (ClonalTREE) is available as open-source software on GitHub at https://github.com/COL-IU/ClonalTREE.

Historical contingency in the evolution of antibiotic resistance after decades of relaxed selection.

Populations often encounter changed environments that remove selection for the maintenance of particular phenotypic traits. The resulting genetic decay of those traits under relaxed selection reduces an organism’s fitness in its prior environment. However, whether and how such decay alters the subsequent evolvability of a population upon restoration of selection for a previously diminished trait is not well understood. We addressed this question using Escherichia coli strains from the long-term evolution experiment (LTEE) that independently evolved for multiple decades in the absence of antibiotics. We first confirmed that these derived strains are typically more sensitive to various antibiotics than their common ancestor. We then subjected the ancestral and derived strains to various concentrations of these drugs to examine their potential to evolve increased resistance. We found that evolvability was idiosyncratic with respect to initial genotype; that is, the derived strains did not generally compensate for their greater susceptibility by “catching up” to the resistance level of the ancestor. Instead, the capacity to evolve increased resistance was constrained in some backgrounds, implying that evolvability depended upon prior mutations in a historically contingent fashion. We further subjected a time series of clones from one LTEE population to tetracycline and determined that an evolutionary constraint arose early in that population, corroborating the role of contingency. In summary, relaxed selection not only can drive populations to increased antibiotic susceptibility, but it can also affect the subsequent evolvability of antibiotic resistance in an unpredictable manner. This conclusion has potential implications for public health, and it underscores the need to consider the genetic context of pathogens when designing drug-treatment strategies.

Genetics of Adaptation of the Ascomycetous Fungus Podospora anserina to Submerged Cultivation.

Podospora anserina is a model ascomycetous fungus which shows pronounced phenotypic senescence when grown on solid medium but possesses unlimited lifespan under submerged cultivation. In order to study the genetic aspects of adaptation of P. anserina to submerged cultivation, we initiated a long-term evolution experiment. In the course of the first 4 years of the experiment, 125 single-nucleotide substitutions and 23 short indels were fixed in eight independently evolving populations. Six proteins that affect fungal growth and development evolved in more than one population; in particular, in the G-protein alpha subunit FadA, new alleles fixed in seven out of eight experimental populations, and these fixations affected just four amino acid sites, which is an unprecedented level of parallelism in experimental evolution. Parallel evolution at the level of genes and pathways, an excess of nonsense and missense substitutions, and an elevated conservation of proteins and their sites where the changes occurred suggest that many of the observed fixations were adaptive and driven by positive selection.

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER.

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring fitness phenotypes and improving our understanding of how genotypes are shaped by selection. Extensive recent efforts to develop and apply niche continuous culture devices have revealed the benefits of conducting new forms of cell culture control. This includes defining custom selection pressures and increasing throughput for studies ranging from long-term experimental evolution to genome-wide library selections and synthetic gene circuit characterization. The eVOLVER platform was recently developed to meet this growing demand: a continuous culture platform with a high degree of scalability, flexibility, and automation. eVOLVER provides a single standardizing platform that can be (re)-configured and scaled with minimal effort to perform many different types of high-throughput or multi-dimensional growth selection experiments. Here, a protocol is presented to provide users of the eVOLVER framework a description for configuring the system to conduct a custom, large-scale continuous growth experiment. Specifically, the protocol guides users on how to program the system to multiplex two selection pressures - temperature and osmolarity - across many eVOLVER vials in order to quantify fitness landscapes of Saccharomyces cerevisiae mutants at fine resolution. We show how the device can be configured both programmatically, through its open-source web-based software, and physically, by arranging fluidic and hardware layouts. The process of physically setting up the device, programming the culture routine, monitoring and interacting with the experiment in real-time over the internet, sampling vials for subsequent offline analysis, and post experiment data analysis are detailed. This should serve as a starting point for researchers across diverse disciplines to apply eVOLVER in the design of their own complex and high-throughput cell growth experiments to study and manipulate biological systems.

Bacterial genome architecture shapes global transcriptional regulation by DNA supercoiling.

DNA supercoiling acts as a global transcriptional regulator in bacteria, that plays an important role in adapting their expression programme to environmental changes, but for which no quantitative or even qualitative regulatory model is available. Here, we focus on spatial supercoiling heterogeneities caused by the transcription process itself, which strongly contribute to this regulation mode. We propose a new mechanistic modeling of the transcription-supercoiling dynamical coupling along a genome, which allows simulating and quantitatively reproducing in vitro and in vivo transcription assays, and highlights the role of genes’ local orientation in their supercoiling sensitivity. Consistently with predictions, we show that chromosomal relaxation artificially induced by gyrase inhibitors selectively activates convergent genes in several enterobacteria, while conversely, an increase in DNA supercoiling naturally selected in a long-term evolution experiment with Escherichia coli favours divergent genes. Simulations show that these global expression responses to changes in DNA supercoiling result from fundamental mechanical constraints imposed by transcription, independently from more specific regulation of each promoter. These constraints underpin a significant and predictable contribution to the complex rules by which bacteria use DNA supercoiling as a global but fine-tuned transcriptional regulator.

Modelling and simulating Lenski’s long-term evolution experiment

We revisit the model by Wiser et al. (2013), which describes how the mean fitness increases over time due to beneficial mutations in Lenski’s long-term evolution experiment. We develop the model further both conceptually and mathematically. Conceptually, we describe the experiment with the help of a Cannings model with mutation and selection, where the latter includes diminishing returns epistasis. The analysis sheds light on the growth dynamics within every single day and reveals a runtime effect, that is, the shortening of the daily growth period with increasing fitness; and it allows to clarify the contribution of epistasis to the mean fitness curve. Mathematically, we explain rigorous results in terms of a law of large numbers (in the limit of infinite population size and for a certain asymptotic parameter regime), and present approximations based on heuristics and supported by simulations for finite populations.

Changes in Intrinsic Antibiotic Susceptibility during a Long-Term Evolution Experiment with Escherichia coli.

High-level resistance often evolves when populations of bacteria are exposed to antibiotics, by either mutations or horizontally acquired genes. There is also variation in the intrinsic resistance levels of different bacterial strains and species that is not associated with any known history of exposure. In many cases, evolved resistance is costly to the bacteria, such that resistant types have lower fitness than their progenitors in the absence of antibiotics. Some longer-term studies have shown that bacteria often evolve compensatory changes that overcome these tradeoffs, but even those studies have typically lasted only a few hundred generations. In this study, we examine changes in the susceptibilities of 12 populations of Escherichia coli to 15 antibiotics after 2,000 and 50,000 generations without exposure to any antibiotic. On average, the evolved bacteria were more susceptible to most antibiotics than was their ancestor. The bacteria at 50,000 generations tended to be even more susceptible than after 2,000 generations, although most of the change occurred during the first 2,000 generations. Despite the general trend toward increased susceptibility, we saw diverse outcomes with different antibiotics. For streptomycin, which was the only drug to which the ancestral strain was highly resistant, none of the evolved lines showed any increased susceptibility. The independently evolved lineages often exhibited correlated responses to the antibiotics, with correlations usually corresponding to their modes of action. On balance, our study shows that bacteria with low levels of intrinsic resistance often evolve to become even more susceptible to antibiotics in the absence of corresponding selection.IMPORTANCE Resistance to antibiotics often evolves when bacteria encounter antibiotics. However, bacterial strains and species without any known exposure to these drugs also vary in their intrinsic susceptibility. In many cases, evolved resistance has been shown to be costly to the bacteria, such that resistant types have reduced competitiveness relative to their sensitive progenitors in the absence of antibiotics. In this study, we examined changes in the susceptibilities of 12 populations of Escherichia coli to 15 antibiotics after 2,000 and 50,000 generations without exposure to any drug. The evolved bacteria tended to become more susceptible to most antibiotics, with most of the change occurring during the first 2,000 generations, when the bacteria were undergoing rapid adaptation to their experimental conditions. On balance, our findings indicate that bacteria with low levels of intrinsic resistance can, in the absence of relevant selection, become even more susceptible to antibiotics.

Computational Complexity as an Ultimate Constraint on Evolution.

Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima-thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semismooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics-even ones that do not follow adaptive paths-can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question.

Plasticity of Promoter-Core Sequences Allows Bacteria to Compensate for the Loss of a Key Global Regulatory Gene

Transcription regulatory networks (TRNs) are of central importance for both short-term phenotypic adaptation in response to environmental fluctuations and long-term evolutionary adaptation, with global regulatory genes often being targets of natural selection in laboratory experiments. Here, we combined evolution experiments, whole-genome resequencing, and molecular genetics to investigate the driving forces, genetic constraints, and molecular mechanisms that dictate how bacteria can cope with a drastic perturbation of their TRNs. The crp gene, encoding a major global regulator in Escherichia coli, was deleted in four different genetic backgrounds, all derived from the Long-Term Evolution Experiment (LTEE) but with different TRN architectures. We confirmed that crp deletion had a more deleterious effect on growth rate in the LTEE-adapted genotypes; and we showed that the ptsG gene, which encodes the major glucose-PTS transporter, gained CRP (cyclic AMP receptor protein) dependence over time in the LTEE. We then further evolved the four crp-deleted genotypes in glucose minimal medium, and we found that they all quickly recovered from their growth defects by increasing glucose uptake. We showed that this recovery was specific to the selective environment and consistently relied on mutations in the cis-regulatory region of ptsG, regardless of the initial genotype. These mutations affected the interplay of transcription factors acting at the promoters, changed the intrinsic properties of the existing promoters, or produced new transcription initiation sites. Therefore, the plasticity of even a single promoter region can compensate by three different mechanisms for the loss of a key regulatory hub in the E. coli TRN.

Single-cell copy number variant detection reveals the dynamics and diversity of adaptation.

Copy number variants (CNVs) are a pervasive source of genetic variation and evolutionary potential, but the dynamics and diversity of CNVs within evolving populations remain unclear. Long-term evolution experiments in chemostats provide an ideal system for studying the molecular processes underlying CNV formation and the temporal dynamics with which they are generated, selected, and maintained. Here, we developed a fluorescent CNV reporter to detect de novo gene amplifications and deletions in individual cells. We used the CNV reporter in Saccharomyces cerevisiae to study CNV formation at the GAP1 locus, which encodes the general amino acid permease, in different nutrient-limited chemostat conditions. We find that under strong selection, GAP1 CNVs are repeatedly generated and selected during the early stages of adaptive evolution, resulting in predictable dynamics. Molecular characterization of CNV-containing lineages shows that the CNV reporter detects different classes of CNVs, including aneuploidies, nonreciprocal translocations, tandem duplications, and complex CNVs. Despite GAP1’s proximity to repeat sequences that facilitate intrachromosomal recombination, breakpoint analysis revealed that short inverted repeat sequences mediate formation of at least 50% of GAP1 CNVs. Inverted repeat sequences are also found at breakpoints at the DUR3 locus, where CNVs are selected in urea-limited chemostats. Analysis of 28 CNV breakpoints indicates that inverted repeats are typically 8 nucleotides in length and separated by 40 bases. The features of these CNVs are consistent with origin-dependent inverted-repeat amplification (ODIRA), suggesting that replication-based mechanisms of CNV formation may be a common source of gene amplification. We combined the CNV reporter with barcode lineage tracking and found that 102–104 independent CNV-containing lineages initially compete within populations, resulting in extreme clonal interference. However, only a small number (18–21) of CNV lineages ever constitute more than 1% of the CNV subpopulation, and as selection progresses, the diversity of CNV lineages declines. Our study introduces a novel means of studying CNVs in heterogeneous cell populations and provides insight into their dynamics, diversity, and formation mechanisms in the context of adaptive evolution.

Innovation in an E. coli evolution experiment is contingent on maintaining adaptive potential until competition subsides.

Key innovations are disruptive evolutionary events that enable a species to escape constraints and rapidly diversify. After 15 years of the Lenski long-term evolution experiment with Escherichia coli, cells in one of the twelve populations evolved the ability to utilize citrate, an abundant but previously untapped carbon source in the environment. Descendants of these cells became dominant in the population and subsequently diversified as a consequence of invading this vacant niche. Mutations responsible for the appearance of rudimentary citrate utilization and for refining this ability have been characterized. However, the complete nature of the genetic and/or ecological events that set the stage for this key innovation is unknown. In particular, it is unclear why it took so long for citrate utilization to evolve and why it still has evolved in only one of the twelve E. coli populations after 30 years of the Lenski experiment. In this study, we recapitulated the initial mutation needed to evolve citrate utilization in strains isolated from throughout the first 31,500 generations of the history of this population. We found that there was already a slight fitness benefit for this mutation in the original ancestor of the evolution experiment and in other early isolates. However, evolution of citrate utilization was blocked at this point due to competition with other mutations that improved fitness in the original niche. Subsequently, an anti-potentiated genetic background evolved in which it was deleterious to evolve rudimentary citrate utilization. Only later, after further mutations accumulated that restored the benefit of this first-step mutation and the overall rate of adaptation in the population slowed, was citrate utilization likely to evolve. Thus, intense competition and the types of mutations that it favors can lead to short-sighted evolutionary trajectories that hide a stepping stone needed to access a key innovation from many future generations.

An incoherent feedforward loop facilitates adaptive tuning of gene expression.

We studied adaptive evolution of gene expression using long-term experimental evolution of Saccharomyces cerevisiae in ammonium-limited chemostats. We found repeated selection for non-synonymous variation in the DNA binding domain of the transcriptional activator, GAT1, which functions with the repressor, DAL80 in an incoherent type-1 feedforward loop (I1-FFL) to control expression of the high affinity ammonium transporter gene, MEP2. Missense mutations in the DNA binding domain of GAT1 reduce its binding to the GATAA consensus sequence. However, we show experimentally, and using mathematical modeling, that decreases in GAT1 binding result in increased expression of MEP2 as a consequence of properties of I1-FFLs. Our results show that I1-FFLs, one of the most commonly occurring network motifs in transcriptional networks, can facilitate adaptive tuning of gene expression through modulation of transcription factor binding affinities. Our findings highlight the importance of gene regulatory architectures in the evolution of gene expression.

The Odyssey of the Ancestral Escherich Strain through Culture Collections: an Example of Allopatric Diversification.

More than a century ago, Theodor Escherich isolated the bacterium that was to become Escherichia coli, one of the most studied organisms. Not long after, the strain began an odyssey and landed in many laboratories across the world. As laboratory culture conditions could be responsible for major changes in bacterial strains, we conducted a genome analysis of isolates of this emblematic strain from different culture collections (England, France, the United States, Germany). Strikingly, many discrepancies between the isolates were observed, as revealed by multilocus sequence typing (MLST), the presence of virulence-associated genes, core genome MLST, and single nucleotide polymorphism/indel analyses. These differences are correlated with the phylogeographic history of the strain and were due to an unprecedented number of mutations in coding DNA repair functions such as mismatch repair (MutL) and oxidized guanine nucleotide pool cleaning (MutT), conferring a specific mutational spectrum and leading to a mutator phenotype. The mutator phenotype was probably acquired during subculturing and corresponded to second-order selection. Furthermore, all of the isolates exhibited hypersusceptibility to antibiotics due to mutations in efflux pump- and porin-encoding genes, as well as a specific mutation in the sigma factor-encoding gene rpoS. These defects reflect a self-preservation and nutritional competence tradeoff allowing survival under the starvation conditions imposed by storage. From a clinical point of view, dealing with such mutator strains can lead microbiologists to draw false conclusions about isolate relatedness and may impact therapeutic effectiveness. IMPORTANCE Mutator phenotypes have been described in laboratory-evolved bacteria, as well as in natural isolates. Several genes can be impacted, each of them being associated with a typical mutational spectrum. By studying one of the oldest strains available, the ancestral Escherich strain, we were able to identify its mutator status leading to tremendous genetic diversity among the isolates from various collections and allowing us to reconstruct the phylogeographic history of the strain. This mutator phenotype was probably acquired during the storage of the strain, promoting adaptation to a specific environment. Other mutations in rpoS and efflux pump- and porin-encoding genes highlight the acclimatization of the strain through self-preservation and nutritional competence regulation. This strain history can be viewed as unintentional experimental evolution in culture collections all over the word since 1885, mimicking the long-term experimental evolution of E.coli of Lenski et al. (O. Tenaillon, J. E. Barrick, N. Ribeck, D. E. Deatherage, J. L. Blanchard, A. Dasgupta, G. C. Wu, S. Wielgoss, S. Cruveiller, C. Médigue, D. Schneider, and R. E. Lenski, Nature 536:165-170, 2016, https://doi.org/10.1038/nature18959) that shares numerous molecular features.