Adaptive evolution of the lactose utilization network in experimentally evolved populations of Escherichia coli

Optimizing a CRISPR-Cas13d Gene Circuit for Tunable Target RNA Downregulation with Minimal Collateral RNA Cutting

The invention of RNA-guided DNA cutting systems has revolutionized biotechnology. More recently, RNA-guided RNA cutting by Cas13d entered the scene as a highly promising alternative to RNA interference to engineer cellular transcriptomes for biotechnological and therapeutic purposes. Unfortunately, "collateral damage" by indiscriminate off-target cutting tampered enthusiasm for these systems. Yet, how collateral activity, or even RNA target reduction depends on Cas13d and guide RNA abundance has remained unclear due to the lack of expression-tuning studies to address this question. Here we use precise expression-tuning gene circuits to show that both nonspecific and specific, on-target RNA reduction depend on Cas13d and guide RNA levels, and that nonspecific RNA cutting from trans cleavage might contribute to on-target RNA reduction. Using RNA-level control techniques, we develop new Multi-Level Optimized Negative-Autoregulated Cas13d and crRNA Hybrid (MONARCH) gene circuits that achieve a high dynamic range with low basal on-target RNA reduction while minimizing collateral activity in human kidney cells and green monkey cells most frequently used in human virology. MONARCH should bring RNA-guided RNA cutting systems to the forefront, as easily applicable, programmable tools for transcriptome engineering in biotechnological and medical applications.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Long-term ecological and evolutionary dynamics in the gut microbiomes of carbapenemase-producing Enterobacteriaceae colonized subjects

Long-term colonization of the gut microbiome by carbapenemase-producing Enterobacteriaceae (CPE) is a growing area of public health concern as it can lead to community transmission and rapid increase in cases of life-threatening CPE infections. Here, leveraging the observation that many subjects are decolonized without interventions within a year, we used longitudinal shotgun metagenomics (up to 12 timepoints) for detailed characterization of ecological and evolutionary dynamics in the gut microbiome of a cohort of CPE-colonized subjects and family members (n = 46; 361 samples). Subjects who underwent decolonization exhibited a distinct ecological shift marked by recovery of microbial diversity, key commensals and anti-inflammatory pathways. In addition, colonization was marked by elevated but unstable Enterobacteriaceae abundances, which exhibited distinct strain-level dynamics for different species (Escherichia coli and Klebsiella pneumoniae). Finally, comparative analysis with whole-genome sequencing data from CPE isolates (n = 159) helped identify substrain variation in key functional genes and the presence of highly similar E. coli and K. pneumoniae strains with variable resistance profiles and plasmid sharing. These results provide an enhanced view into how colonization by multi-drug-resistant bacteria associates with altered gut ecology and can enable transfer of resistance genes, even in the absence of overt infection and antibiotic usage.

Quantifying rapid bacterial evolution and transmission within the mouse intestine

Due to limitations on high-resolution strain tracking, selection dynamics during gut microbiota colonization and transmission between hosts remain mostly mysterious. Here, we introduced hundreds of barcoded Escherichia coli strains into germ-free mice and quantified strain-level dynamics and metagenomic changes. Mutations in genes involved in motility and metabolite utilization are reproducibly selected within days. Even with rapid selection, coprophagy enforced similar barcode distributions across co-housed mice. Whole-genome sequencing of hundreds of isolates revealed linked alleles that demonstrate between-host transmission. A population-genetics model predicts substantial fitness advantages for certain mutants and that migration accounted for ∼10% of the resident microbiota each day. Treatment with ciprofloxacin suggests interplay between selection and transmission. While initial colonization was mostly uniform, in two mice a bottleneck reduced diversity and selected for ciprofloxacin resistance in the absence of drug. These findings highlight the interplay between environmental transmission and rapid, deterministic selection during evolution of the intestinal microbiota.

The cost of evolved constitutive lac gene expression is usually, but not always, maintained during evolution of generalist populations

Beneficial mutations can become costly following an environmental change. Compensatory mutations can relieve these costs, while not affecting the selected function, so that the benefits are retained if the environment shifts back to be similar to the one in which the beneficial mutation was originally selected. Compensatory mutations have been extensively studied in the context of antibiotic resistance, responses to specific genetic perturbations, and in the determination of interacting gene network components. Few studies have focused on the role of compensatory mutations during more general adaptation, especially as the result of selection in fluctuating environments where adaptations to different environment components may often involve trade-offs. We examine whether costs of a mutation in lacI, which deregulated the expression of the lac operon in evolving populations of Escherichia coli bacteria, were compensated. This mutation occurred in multiple replicate populations selected in environments that fluctuated between growth on lactose, where the mutation was beneficial, and on glucose, where it was deleterious. We found that compensation for the cost of the lacI mutation was rare, but, when it did occur, it did not negatively affect the selected benefit. Compensation was not more likely to occur in a particular evolution environment. Compensation has the potential to remove pleiotropic costs of adaptation, but its rarity indicates that the circumstances to bring about the phenomenon may be peculiar to each individual or impeded by other selected mutations.

Exploring a Local Genetic Interaction Network Using Evolutionary Replay Experiments

Understanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments, we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, shedding light on an underexplored region of the yeast genetic interaction network.

Historical Contingency Causes Divergence in Adaptive Expression of the lac Operon

Populations of Escherichia coli selected in constant and fluctuating environments containing lactose often adapt by substituting mutations in the lacI repressor that cause constitutive expression of the lac operon. These mutations occur at a high rate and provide a significant benefit. Despite this, eight of 24 populations evolved for 8,000 generations in environments containing lactose contained no detectable repressor mutations. We report here on the basis of this observation. We find that, given relevant mutation rates, repressor mutations are expected to have fixed in all evolved populations if they had maintained the same fitness effect they confer when introduced to the ancestor. In fact, reconstruction experiments demonstrate that repressor mutations have become neutral or deleterious in those populations in which they were not detectable. Populations not fixing repressor mutations nevertheless reached the same fitness as those that did fix them, indicating that they followed an alternative evolutionary path that made redundant the potential benefit of the repressor mutation, but involved unique mutations of equivalent benefit. We identify a mutation occurring in the promoter region of the uspB gene as a candidate for influencing the selective choice between these paths. Our results detail an example of historical contingency leading to divergent evolutionary outcomes.

Extreme Antagonism Arising from Gene-Environment Interactions

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.

Environment-dependent costs and benefits of recombination in independently evolved populations of Escherichia coli

Understanding of the causes by which reproductive isolation arises remains limited. We examine the role of adaptation in driving reproductive isolation among 12 Escherichia coli populations evolved in two different environments. We found that, regardless of whether parents were selected in the same or different environments, the average fitness of recombinants was lower than the expected, consistent with a prevailing influence of incompatibility between independently accumulated mutations. Exceptions to this pattern occurred among recombinants of some parents evolved in different environments. These recombinants were less fit than expected in the selective environment of one parent, but more fit than expected in the selective environment of the other parent. Our results indicate that both parallel and divergent adaptation can quickly lead to intrinsic genetic barriers contributing to the initial stages of speciation and show that these barriers can be complex, for example, depending on the environment in which recombinant offspring are tested.

Evolutionary regain of lost gene circuit function

Natural or synthetic genetic modules can lose their function over long-term evolution if the function is costly. How populations can evolve to restore such broken function is poorly understood. To test the reversibility of evolutionary breakdown, we use yeast cell populations with a chromosomally integrated synthetic gene circuit. In previous evolution experiments the gene circuit lost its costly function through various mutations. By exposing such mutant populations to conditions where regaining gene circuit function would be beneficial, we find adaptation scenarios with or without repairing lost gene circuit function. These results are important for drug resistance or future synthetic biology applications where evolutionary loss and regain of function play a significant role.

Evolutionary reversibility—the ability to regain a lost function—is an important problem both in evolutionary and synthetic biology, where repairing natural or synthetic systems broken by evolutionary processes may be valuable. Here, we use a synthetic positive-feedback (PF) gene circuit integrated into haploid Saccharomyces cerevisiae cells to test if the population can restore lost PF function. In previous evolution experiments, mutations in a gene eliminated the fitness costs of PF activation. Since PF activation also provides drug resistance, exposing such compromised or broken mutants to both drug and inducer should create selection pressure to regain drug resistance and possibly PF function. Indeed, evolving 7 PF mutant strains in the presence of drug revealed 3 adaptation scenarios through genomic, PF-external mutations that elevate PF basal expression, possibly by affecting transcription, translation, degradation, and other fundamental cellular processes. Nonfunctional mutants gained drug resistance without ever developing high expression, while quasifunctional and dysfunctional PF mutants developed high expression nongenetically, which then diminished, although more slowly for dysfunctional mutants where revertant clones arose. These results highlight how intracellular context, such as the growth rate, can affect regulatory network dynamics and evolutionary dynamics, which has important consequences for understanding the evolution of drug resistance and developing future synthetic biology applications.

Diversity in lac Operon Regulation among Diverse Escherichia coli Isolates Depends on the Broader Genetic Background but Is Not Explained by Genetic Relatedness

The lac operon of Escherichia coli is a classic model for studying gene regulation. This study has uncovered features such as the environmental input logic controlling gene expression, as well as gene expression bistability and hysteresis. Most lac operon studies have focused on a few lab strains, and it is not known how generally those findings apply to the diversity of E. coli strains. We examined the environmental dependence of lac gene regulation in 20 natural isolates of E. coli and found a wide range of regulatory responses. By transferring lac genes from natural isolate strains into a common reference strain, we found that regulation depends on both the lac genes themselves and on the broader genetic background, indicating potential for still-greater regulatory diversity following horizontal gene transfer. Our results reveal that there is substantial natural variation in the regulation of the lac operon and indicate that this variation can be ecologically meaningful.

Transcription of bacterial genes is controlled by the coordinated action of cis- and trans-acting regulators. The activity and mode of action of these regulators can reflect different requirements for gene products in different environments. A well-studied example is the regulatory function that integrates the environmental availability of glucose and lactose to control the Escherichia colilac operon. Most studies of lac operon regulation have focused on a few closely related strains. To determine the range of natural variation in lac regulatory function, we introduced a reporter construct into 23 diverse E. coli strains and measured expression with combinations of inducer concentrations. We found a wide range of regulatory functions. Several functions were similar to the one observed in a reference lab strain, whereas others depended weakly on the presence of cAMP. Some characteristics of the regulatory function were explained by the genetic relatedness of strains, indicating that differences varied on relatively short time scales. The regulatory characteristics explained by genetic relatedness were among those that best predicted the initial growth of strains following transition to a lactose environment, suggesting a role for selection. Finally, we transferred the lac operon, with the lacI regulatory gene, from five natural isolate strains into a reference lab strain. The regulatory function of these hybrid strains revealed the effect of local and global regulatory elements in controlling expression. Together, this work demonstrates that regulatory functions can be varied within a species and that there is variation within a species to best match a function to particular environments.

Long-term evolution of the natural isolate of Escherichia coli 536 in the mouse gut colonized after maternal transmission reveals convergence in the constitutive expression of the lactose operon

In vitro experimental evolution has taught us many lessons on the molecular bases of adaptation. To move towards more natural settings, evolution in the mice gut has been successfully performed. Yet, these experiments suffered from the use of laboratory strains as well as the use of axenic or streptomycin-treated mice to maintain the inoculated strains. To circumvent these limitations, we conducted a one-year experimental evolution in vivo using a natural isolate of E. coli, strain 536, in conditions mimicking as much as possible natural environment with mother-to-offspring microbiota transmission. Mice were then distributed in 24 independent cages and separated into two different diets: a regular one (chow diet, CD) and high-fat and high-sugar one (Western Diet, WD). Genome sequences revealed an early and rapid selection during the breastfeeding period that selected the constitutive expression of the well-characterized lactose operon. E. coli was lost significantly more in CD than WD; however, we could not detect any genomic signature of selection, nor any diet specificities during the later part of the experiments. The apparently neutral evolution presumably due to low population size maintained nevertheless at high frequency the early selected mutations affecting lactose regulation. The rapid loss of lactose operon regulation challenges the idea that plastic gene expression is both optimal and stable in the wild.

Inactivation of a Mismatch-Repair System Diversifies Genotypic Landscape of Escherichia coli During Adaptive Laboratory Evolution

Adaptive laboratory evolution (ALE) is used to find causal mutations that underlie improved strain performance under the applied selection pressure. ALE studies have revealed that mutator populations tend to outcompete their non-mutator counterparts following the evolutionary trajectory. Among them, mutS-inactivated mutator cells, characterize d by a dysfunctional methyl-mismatch repair system, are frequently found in ALE experiments. Here, we examined mutS inactivation as an approach to facilitate ALE of Escherichia coli. The wild-type E. coli MG1655 and mutS knock-out derivative (ΔmutS) were evolved in parallel for 800 generations on lactate or glycerol minimal media in a serial-transfer experiment. Whole-genome re-sequencing of each lineage at 100-generation intervals revealed that (1) mutations emerge rapidly in the ΔmutS compared to in the wild-type strain; (2) mutations were more than fourfold higher in the ΔmutS strain at the end-point populations compared to the wild-type strain; and (3) a significant number of random mutations accumulated in the ΔmutS strains. We then measured the fitness of the end-point populations on an array of non-adaptive carbon sources. Interestingly, collateral fitness increases on non-adaptive carbon sources were more pronounced in the ΔmutS strains than the parental strain. Fitness measurement of single mutants revealed that the collateral fitness increase seen in the mutator lineages can be attributed to a pool of random mutations. Together, this study demonstrates that short-term mutator ALE extensively expands possible genotype space, resulting in versatile bacteria with elevated fitness levels across various carbon sources.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

Genome Plasticity of agr-Defective Staphylococcus aureus during Clinical Infection

Therapy for bacteremia caused by Staphylococcus aureus is often ineffective, even when treatment conditions are optimal according to experimental protocols. Adapted subclones, such as those bearing mutations that attenuate agr-mediated virulence activation, are associated with persistent infection and patient mortality.

Therapy for bacteremia caused by Staphylococcus aureus is often ineffective, even when treatment conditions are optimal according to experimental protocols. Adapted subclones, such as those bearing mutations that attenuate agr-mediated virulence activation, are associated with persistent infection and patient mortality. To identify additional alterations in agr-defective mutants, we sequenced and assembled the complete genomes of clone pairs from colonizing and infected sites of several patients in whom S. aureus demonstrated a within-host loss of agr function. We report that events associated with agr inactivation result in agr-defective blood and nares strain pairs that are enriched in mutations compared to pairs from wild-type controls. The random distribution of mutations between colonizing and infecting strains from the same patient, and between strains from different patients, suggests that much of the genetic complexity of agr-defective strains results from prolonged infection or therapy-induced stress. However, in one of the agr-defective infecting strains, multiple genetic changes resulted in increased virulence in a murine model of bloodstream infection, bypassing the mutation of agr and raising the possibility that some changes were selected. Expression profiling correlated the elevated virulence of this agr-defective mutant to restored expression of the agr-regulated ESAT6-like type VII secretion system, a known virulence factor. Thus, additional mutations outside the agr locus can contribute to diversification and adaptation during infection by S. aureus agr mutants associated with poor patient outcomes.

Increasing Extracellular Free Fatty Acid Production in Escherichia coli by Disrupting Membrane Transport Systems

Transposon mutagenesis was used to identify three mutants of E. coli that exhibited increased free fatty acid (FFA) production, which resulted from the disruption of genes related to membrane transport. Deletion of envR, gusC, and mdlA individually in a recombinant E. coli strain resulted in 1.4-, 1.8-, and 1.2-fold increases in total FFA production, respectively. In particular, deletion of envR increased the percentage of extracellular FFA to 46%, compared with 29% for the control strain. Multiple deletion of envR, gusC, mdlA, ompF, and fadL had a synergistic effect on FFA production, resulting in high extracellular FFA production, comprising up to 50% of total FFA production. This study has identified new membrane proteins involved in FFA production and showed that genetic engineering targeting these membrane transporters is important to increase both total FFA and extracellular FFA production.

Tuning of the Lethal Response to Multiple Stressors with a Single-Site Mutation during Clinical Infection by Staphylococcus aureus

The agr system of Staphylococcus aureus promotes invasion of host tissues, and as expected, agents that block agr quorum sensing have anti-infective properties. Paradoxically, agr-defective mutants are frequently recovered from patients, especially those persistently infected with S. aureus We found that an agr deficiency increased survival of cultured bacteria during severe stress, such as treatment with gentamicin, ciprofloxacin, heat, or low pH. With daptomycin, deletion of agr decreased survival. Therefore, agr activity can be either detrimental or protective, depending on the type of lethal stress. Deletion of agr had no effect on the ability of the antimicrobials to block bacterial growth, indicating that agr effects are limited to lethal action. Thus, the effect of an agr deletion is on bacterial tolerance, not resistance. For gentamicin and daptomycin, activity can be altered by agr-regulated secreted factors. For ciprofloxacin, a detrimental function was downregulation of glutathione peroxidase (bsaA), an enzyme responsible for defense against oxidative stress. Deficiencies in agr and bsaA were epistatic for survival, consistent with agr having a destructive role mediated by reactive oxygen species. Enhanced susceptibility to lethal stress by wild-type agr, particularly antimicrobial stress, helps explain why inactivating mutations in S. aureus agr commonly occur in hospitalized patients during infection. Moreover, the agr quorum-sensing system of S. aureus provides a clinically relevant example in which a single-step change in the response to severe stress alters the evolutionary path of a pathogen during infection.IMPORTANCE When phenotypes produced in response to an environmental stress are inadequate to buffer against that stress, changes that do buffer may become genetically encoded by natural selection. A clinically relevant example is seen with S. aureus mutants that are deficient in the key virulence regulator agr Paradoxically, defects in agr are selected during serious hospital infection and have been associated with worse outcome. The current work helps resolve this paradox: agr mutants are often less readily killed by lethal stressors without affecting MIC, a phenomenon known as tolerance. Our results indicate that tolerance, which would not be detected as resistance, can be selected in clinical settings. The data also support the ideas that (i) S. aureus broadly hedges against environmental change and stress through genome plasticity, (ii) reactive oxygen can be involved in the self-destructive response in bacteria, and (iii) therapeutic targeting of agr and virulence can be counterproductive.

Hitchhiking and epistasis give rise to cohort dynamics in adapting populations

Beneficial mutations are the driving force of adaptive evolution. In asexual populations, the identification of beneficial alleles is confounded by the presence of genetically linked hitchhiker mutations. Parallel evolution experiments enable the recognition of common targets of selection; yet these targets are inherently enriched for genes of large target size and mutations of large effect. A comprehensive study of individual mutations is necessary to create a realistic picture of the evolutionarily significant spectrum of beneficial mutations. Here we use a bulk-segregant approach to identify the beneficial mutations across 11 lineages of experimentally evolved yeast populations. We report that nearly 80% of detected mutations have no discernible effects on fitness and less than 1% are deleterious. We determine the distribution of driver and hitchhiker mutations in 31 mutational cohorts, groups of mutations that arise synchronously from low frequency and track tightly with one another. Surprisingly, we find that one-third of cohorts lack identifiable driver mutations. In addition, we identify intracohort synergistic epistasis between alleles of hsl7 and kel1, which arose together in a low-frequency lineage.

Escherichia coli DNA ligase B may mitigate damage from oxidative stress

Escherichia coli encodes two DNA ligases, ligase A, which is essential under normal laboratory growth conditions, and ligase B, which is not. Here we report potential functions of ligase B. We found that across the entire Enterobacteriaceae family, ligase B is highly conserved in both amino acid identity and synteny with genes associated with oxidative stress. Deletion of ligB sensitized E. coli to specific DNA damaging agents and antibiotics resulted in a weak mutator phenotype, and decreased biofilm formation. Overexpression of ligB caused a dramatic extension of lag phase that eventually resumed normal growth. The ligase function of ligase B was not required to mediate the extended lag phase, as overexpression of a ligase-deficient ligB mutant also blocked growth. Overexpression of ligB during logarithmic growth caused an immediate block of cell growth and DNA replication, and death of about half of cells. These data support a potential role for ligase B in the base excision repair pathway or the mismatch repair pathway.

The Response to 2-Aminoacrylate Differs in Escherichia coli and Salmonella enterica, despite Shared Metabolic Components

The metabolic network of an organism includes the sum total of the biochemical reactions present. In microbes, this network has an impeccable ability to sense and respond to perturbations caused by internal or external stimuli. The metabolic potential (i.e., network structure) of an organism is often drawn from the genome sequence, based on the presence of enzymes deemed to indicate specific pathways. Escherichia coli and Salmonella enterica are members of the Enterobacteriaceae family of Gram-negative bacteria that share the majority of their metabolic components and regulatory machinery as the "core genome." In S. enterica, the ability of the enamine intermediate 2-aminoacrylate (2AA) to inactivate a number of pyridoxal 5'-phosphate (PLP)-dependent enzymes has been established in vivo In this study, 2AA metabolism and the consequences of its accumulation were investigated in E. coli The data showed that despite the conservation of all relevant enzymes, S. enterica and E. coli differed in both the generation and detrimental consequences of 2AA. In total, these findings suggest that the structure of the metabolic network surrounding the generation and response to endogenous 2AA stress differs between S. enterica and E. coliIMPORTANCE This work compared the metabolic networks surrounding the endogenous stressor 2-aminoacrylate in two closely related members of the Enterobacteriaceae The data showed that despite the conservation of all relevant enzymes in this metabolic node, the two closely related organisms diverged in their metabolic network structures. This work highlights how a set of conserved components can generate distinct network architectures and how this can impact the physiology of an organism. This work defines a model to expand our understanding of the 2-aminoacrylate stress response and the differences in metabolic structures and cellular milieus between S. enterica and E. coli.

Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies

Adaptive laboratory evolution (ALE) experiments are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the observed distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications.IMPORTANCE Evolution and natural selection inexorably lead to an organism's improved fitness in a given environment, whether in a laboratory or natural setting. However, despite the frequent natural occurrence of complex and dynamic growth environments, laboratory evolution experiments typically maintain simple, static culturing environments so as to reduce selection pressure complexity. In this study, we investigated the adaptive strategies underlying evolution to fluctuating environments by evolving Escherichia coli to conditions of frequently switching growth substrate. Characterization of evolved strains via a number of different data types revealed the various genetic and phenotypic changes implemented in pursuit of growth optimality and how these differed across the different growth substrates and switching protocols. This work not only helps to establish general principles of adaptation to complex environments but also suggests strategies for experimental design to achieve desired evolutionary outcomes.

Inactivation of Transcriptional Regulators during Within-Household Evolution of Escherichia coli

We analyzed the within-household evolution of two household-associated Escherichia coli strains from pandemic clonal group ST131-H30, using isolates recovered from five individuals within two families, each of which had a distinct strain. Family 1's strain was represented by a urine isolate from the index patient (older sister) with recurrent cystitis and a blood isolate from her younger sister with fatal urosepsis. Family 2's strain was represented by a urine isolate from the index patient (father) with pyelonephritis and renal abscesses, blood and kidney drainage isolates from the daughter with emphysematous pyelonephritis, and urine and fecal isolates from the mother with cystitis. Collectively, the several variants of each family's strain had accumulated a total of 8 (family 1) and 39 (family 2) point mutations; no two isolates were identical. Of the 47 total mutations, 36 resulted in amino acid changes or truncation of coded proteins. Fourteen such mutations (39%) targeted genes encoding transcriptional regulators, and 9 (25%) involved DNA-binding transcription factors (TFs), which significantly exceeded the relative contribution of TF genes to the isolates' genomes (∼6%). At least one-half of the transcriptional regulator mutations were inactivating, based on phenotypic and/or transcriptional analysis. In particular, inactivating mutations in the global regulator LrhA (repressor of type 1 fimbriae and flagella) occurred in the blood isolates from both households and increased the virulence of E. coli strains in a murine sepsis model. The results indicate that E. coli undergoes adaptive evolution between and/or within hosts, generating subpopulations with distinctive phenotypes and virulence potential.IMPORTANCE The clonal evolution of bacterial strains associated with interhost transmission is poorly understood. We characterized the genome sequences of clonal descendants of two Escherichia coli strains, recovered at different time points from multiple individuals within two households who had different types of urinary tract infection. We found evidence that the E. coli strains underwent extensive mutational diversification between and within these individuals, driven disproportionately by inactivation of transcriptional regulators. In urosepsis isolates, the mutations observed in the global regulator LrhA increased bacterial virulence in a murine sepsis model. Our findings help in understanding the adaptive dynamics and strategies of E. coli during short-term natural evolution.

Positive-feedback, ratiometric biosensor expression improves high-throughput metabolite-producer screening efficiency in yeast

Biosensors are valuable and versatile tools in synthetic biology that are used to modulate gene expression in response to a wide range of stimuli. Ligand responsive transcription factors are a class of biosensor that can be used to couple intracellular metabolite concentration with gene expression to enable dynamic regulation and high-throughput metabolite producer screening. We have established the Saccharomyces cerevisiae WAR1 transcriptional regulator and PDR12 promoter as an organic acid biosensor that can be used to detect varying levels of para-hydroxybenzoic acid (PHBA) production from the shikimate pathway and output green fluorescent protein (GFP) expression in response. The dynamic range of GFP expression in response to PHBA was dramatically increased by engineering positive-feedback expression of the WAR1 transcriptional regulator from its target PDR12 promoter. In addition, the noise in GFP expression at the population-level was controlled by normalising GFP fluorescence to constitutively expressed mCherry fluorescence within each cell. These biosensor modifications increased the high-throughput screening efficiency of yeast cells engineered to produce PHBA by 5,000-fold, enabling accurate fluorescence activated cell sorting isolation of producer cells that were mixed at a ratio of 1 in 10,000 with non-producers. Positive-feedback, ratiometric transcriptional regulator expression is likely applicable to many other transcription-factor/promoter pairs used in synthetic biology and metabolic engineering for both dynamic regulation and high-throughput screening applications.

Single-molecule imaging reveals modulation of cell wall synthesis dynamics in live bacterial cells

The peptidoglycan cell wall is an integral organelle critical for bacterial cell shape and stability. Proper cell wall construction requires the interaction of synthesis enzymes and the cytoskeleton, but it is unclear how the activities of individual proteins are coordinated to preserve the morphology and integrity of the cell wall during growth. To elucidate this coordination, we used single-molecule imaging to follow the behaviours of the two major peptidoglycan synthases in live, elongating Escherichia coli cells and after perturbation. We observed heterogeneous localization dynamics of penicillin-binding protein (PBP) 1A, the synthase predominantly associated with cell wall elongation, with individual PBP1A molecules distributed between mobile and immobile populations. Perturbations to PBP1A activity, either directly through antibiotics or indirectly through PBP1A's interaction with its lipoprotein activator or other synthases, shifted the fraction of mobile molecules. Our results suggest that multiple levels of regulation control the activity of enzymes to coordinate peptidoglycan synthesis.

The bacterial cell wall is important for cell shape and stability, but how the activities of the biosynthetic machinery are coordinated are not clear. Here the authors use single-molecule imaging and chemical perturbations to determine factors that affect the localization dynamics of penicillin-binding proteins (PBP)1A and PBP1B.

Frequency-dependent selection: a diversifying force in microbial populations

The benefits of “bet‐hedging” strategies have been assumed to be the main cause of phenotypic diversity in biological populations. However, in their recent work, Healey et al (2016) provide experimental support for negative frequency‐dependent selection (NFDS) as an alternative driving force of diversity. NFDS favors rare phenotypes over common ones, resulting in an evolutionarily stable mixture of phenotypes that is not necessarily optimal for population growth.

Complex Interplay of Physiology and Selection in the Emergence of Antibiotic Resistance

Emergence of antibiotic resistance, an evolutionary process of major importance for human health [1], often occurs under changing levels of antibiotics. Selective sweeps, in which resistant cells become dominant in the population, are a critical step in this process [2]. While resistance emergence has been studied in laboratory experiments [3-8], the full progression of selective sweeps under fluctuating stress, from stochastic events in single cells to fixation in populations, has not been characterized. Here, we study fluctuating selection using Escherichia coli populations engineered with a stochastic switch controlling tetracycline resistance. Using microfluidics and live-cell imaging, we treat multiple E. coli populations with the same total amount of tetracycline but administered in different temporal patterns. We find that populations exposed to either short or long antibiotic pulses are likely to develop resistance through selective sweeps, whereas intermediate pulses allow higher growth rates but suppress selective sweeps. On the basis of single-cell measurements and a dynamic growth model, we identify the major determinants of population growth and show that both physiological memory and environmental durations can strongly modulate the emergence of resistance. Our detailed quantification in a model synthetic system provides key lessons on the interaction between single-cell physiology and selection that should inform the design of treatment regimens [9-12] and the analysis of phenotypically diverse populations adapting under fluctuating selection [13-17].

The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate

Bi-phasic or diauxic growth is often observed when microbes are grown in a chemically defined medium containing two sugars (for example glucose and lactose). Typically, the two growth stages are separated by an often lengthy phase of arrested growth, the so-called lag-phase. Diauxic growth is usually interpreted as an adaptation to maximise population growth in multi-nutrient environments. However, the lag-phase implies a substantial loss of growth during the switch-over. It therefore remains unexplained why the lag-phase is adaptive. Here we show by means of a stochastic simulation model based on the bacterial PTS system that it is not possible to shorten the lag-phase without incurring a permanent growth-penalty. Mechanistically, this is due to the inherent and well established limitations of biological sensors to operate efficiently at a given resource cost. Hence, there is a trade-off between lost growth during the diauxic switch and the long-term growth potential of the cell. Using simulated evolution we predict that the lag-phase will evolve depending on the distribution of conditions experienced during adaptation. In environments where switching is less frequently required, the lag-phase will evolve to be longer whereas, in frequently changing environments, the lag-phase will evolve to be shorter.

Cellular Growth Arrest and Persistence from Enzyme Saturation

Metabolic efficiency depends on the balance between supply and demand of metabolites, which is sensitive to environmental and physiological fluctuations, or noise, causing shortages or surpluses in the metabolic pipeline. How cells can reliably optimize biomass production in the presence of metabolic fluctuations is a fundamental question that has not been fully answered. Here we use mathematical models to predict that enzyme saturation creates distinct regimes of cellular growth, including a phase of growth arrest resulting from toxicity of the metabolic process. Noise can drive entry of single cells into growth arrest while a fast-growing majority sustains the population. We confirmed these predictions by measuring the growth dynamics of Escherichia coli utilizing lactose as a sole carbon source. The predicted heterogeneous growth emerged at high lactose concentrations, and was associated with cell death and production of antibiotic-tolerant persister cells. These results suggest how metabolic networks may balance costs and benefits, with important implications for drug tolerance.

In bacteria, changes in gene expression, with resulting changes in protein concentration, can drastically change how fast cells and cellular populations grow. This fact has big implications for how we treat infectious disease, which types of organisms make up our microbiomes, and what patterns of gene regulation have undergone evolutionary selection. Here, we show how, in principle, the expression level of a single enzyme can affect bacterial population growth by creating a threshold where cells grow optimally fast just below it, but rapidly reach a state of no growth just above it because metabolic byproducts build up and halt growth. The narrow margin between these two states makes entering either of them possible for the same bacterium because of intrinsic uncertainty, or "noise", in gene expression. The predicted result is a variety of growth rates in a single population of genetically identical cells, manifested as a mix of fast- and slow-growing cells. We created laboratory conditions that reproduce the effect in the model organism E. coli, and showed that there may be a benefit to having slower growing cells, because they can survive antibiotic exposure for longer.

Stress-response balance drives the evolution of a network module and its host genome

Stress response genes and their regulators form networks that underlie drug resistance. These networks often have an inherent tradeoff: their expression is costly in the absence of stress, but beneficial in stress. They can quickly emerge in the genomes of infectious microbes and cancer cells, protecting them from treatment. Yet, the evolution of stress resistance networks is not well understood. Here, we use a two-component synthetic gene circuit integrated into the budding yeast genome to model experimentally the adaptation of a stress response module and its host genome in three different scenarios. In agreement with computational predictions, we find that: (i) intra-module mutations target and eliminate the module if it confers only cost without any benefit to the cell; (ii) intra- and extra-module mutations jointly activate the module if it is potentially beneficial and confers no cost; and (iii) a few specific mutations repeatedly fine-tune the module's noisy response if it has excessive costs and/or insufficient benefits. Overall, these findings reveal how the timing and mechanisms of stress response network evolution depend on the environment.

Constraints on adaptation of Escherichia coli to mixed-resource environments increase over time

Can a population evolved in two resources reach the same fitness in both as specialist populations evolved in each of the individual resources? This question is central to theories of ecological specialization, the maintenance of genetic variation, and sympatric speciation, yet relatively few experiments have examined costs of generalism over long-term adaptation. We tested whether selection in environments containing two resources limits a population's ability to adapt to the individual resources by comparing the fitness of replicate Escherichia coli populations evolved for 6000 generations in the presence of glucose or lactose alone (specialists), or in varying presentations of glucose and lactose together (generalists). We found that all populations had significant fitness increases in both resources, though the magnitude and rate of these increases differed. For the first 4000 generations, most generalist populations increased in fitness as quickly in the individual resources as the corresponding specialist populations. From 5000 generations, however, a widespread cost of adaptation affected all generalists, indicating a growing constraint on their abilities to adapt to two resources simultaneously. Our results indicate that costs of generalism are prevalent, but may influence evolutionary trajectories only after a period of cost-free adaptation.

Populations adapt to fluctuating selection using derived and ancestral allelic diversity

Populations can adapt to changing environments by using allelic diversity, yet whether diversity is recently derived or ancestral is often debated. Although evolution could productively use both types of diversity in a changing environment, their relative frequency has not been quantified. We address this question experimentally using budding yeast strains that harbor a tandem repeat containing URA3 gene, which we expose to cyclical selection and counterselection. We characterize and quantify the dynamics of frameshift events in the URA3 gene in eight populations over 12 cycles of selection and find that ancestral alleles account for 10-20% of all adaptive events. Using a general model of fluctuating selection, we determine how these results depend on mutation rates, population sizes, and fluctuation timescales. We quantify the contribution of derived alleles to the adaptation process using the de novo mutation rate along the population's ancestral lineage, a novel measure that is applicable in a wide range of settings. We find that the adaptive dynamics undergoes a sharp transition from selection on ancestral alleles to selection on derived alleles as fluctuation timescales increase. Our results demonstrate that fluctuations can select between different modes of adaptation over evolutionary timescales.

Interactions between genotype and environment drive the metabolic phenotype within Escherichia coli isolates

To gain insights into the adaptation of the Escherichia coli species to different environments, we monitored protein abundances using quantitative proteomics and measurements of enzymatic activities of central metabolism in a set of five representative strains grown in four contrasted culture media including human urine. Two hundred and thirty seven proteins representative of the genome-scale metabolic network were identified and classified into pathway categories. We found that nutrient resources shape the general orientation of metabolism through coordinated changes in the average abundances of proteins and in enzymatic activities that all belong to the same pathway category. For example, each culture medium induces a specific oxidative response whatever the strain. On the contrary, differences between strains concern isolated proteins and enzymes within pathway categories in single environments. Our study confirms the predominance of genotype by environment interactions at the proteomic and enzyme activity levels. The buffering of genetic variation when considering life-history traits suggests a multiplicity of evolutionary strategies. For instance, the uropathogenic isolate CFT073 shows a deregulation of iron demand and increased oxidative stress response.

Systematic perturbation of cytoskeletal function reveals a linear scaling relationship between cell geometry and fitness

Diversification of cell size is hypothesized to have occurred through a process of evolutionary optimization, but direct demonstrations of causal relationships between cell geometry and fitness are lacking. Here, we identify a mutation from a laboratory-evolved bacterium that dramatically increases cell size through cytoskeletal perturbation and confers a large fitness advantage. We engineer a library of cytoskeletal mutants of different sizes and show that fitness scales linearly with respect to cell size over a wide physiological range. Quantification of the growth rates of single cells during the exit from stationary phase reveals that transitions between "feast-or-famine" growth regimes are a key determinant of cell-size-dependent fitness effects. We also uncover environments that suppress the fitness advantage of larger cells, indicating that cell-size-dependent fitness effects are subject to both biophysical and metabolic constraints. Together, our results highlight laboratory-based evolution as a powerful framework for studying the quantitative relationships between morphology and fitness.

Use of adaptive laboratory evolution to discover key mutations enabling rapid growth of Escherichia coli K-12 MG1655 on glucose minimal medium

Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated using Escherichia coli K-12 MG1655 on glucose minimal media at 37°C. By keeping E. coli in constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription gene rpoB, an 82-bp deletion between the metabolic pyrE gene and rph, and an IS element between the DNA structural gene hns and tdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

A dynamically assembled cell wall synthesis machinery buffers cell growth

Assembly of protein complexes is a key mechanism for achieving spatial and temporal coordination in processes involving many enzymes. Growth of rod-shaped bacteria is a well-studied example requiring such coordination; expansion of the cell wall is thought to involve coordination of the activity of synthetic enzymes with the cytoskeleton via a stable complex. Here, we use single-molecule tracking to demonstrate that the bacterial actin homolog MreB and the essential cell wall enzyme PBP2 move on timescales orders of magnitude apart, with drastically different characteristic motions. Our observations suggest that PBP2 interacts with the rest of the synthesis machinery through a dynamic cycle of transient association. Consistent with this model, growth is robust to large fluctuations in PBP2 abundance. In contrast to stable complex formation, dynamic association of PBP2 is less dependent on the function of other components of the synthesis machinery, and buffers spatially distributed growth against fluctuations in pathway component concentrations and the presence of defective components. Dynamic association could generally represent an efficient strategy for spatiotemporal coordination of protein activities, especially when excess concentrations of system components are inhibitory to the overall process or deleterious to the cell.

Optimal performance of the tryptophan operon of E. coli: a stochastic, dynamical, mathematical-modeling approach

In this work, we develop a detailed, stochastic, dynamical model for the tryptophan operon of E. coli, and estimate all of the model parameters from reported experimental data. We further employ the model to study the system performance, considering the amount of biochemical noise in the trp level, the system rise time after a nutritional shift, and the amount of repressor molecules necessary to maintain an adequate level of repression, as indicators of the system performance regime. We demonstrate that the level of cooperativity between repressor molecules bound to the first two operators in the trp promoter affects all of the above enlisted performance characteristics. Moreover, the cooperativity level found in the wild-type bacterial strain optimizes a cost-benefit function involving low biochemical noise in the tryptophan level, short rise time after a nutritional shift, and low number of regulatory molecules.

Experimental evolution of protein-protein interaction networks

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks.

Evolution in microbes

This review presents a broad survey of experimental microbial evolution, covering diverse topics including trade-offs, epistasis, fluctuating conditions, spatial dynamics, cooperation, aging, and stochastic switching. Emphasis is placed on examples that highlight key conceptual points or address theoretical predictions. Experimental evolution is discussed from two points of view. First, population trajectories are described as adaptive walks on a fitness landscape, whose genetic structure can be probed by experiments. Second, populations are viewed from a physiological perspective, and their nongenetic heterogeneity is examined. Bringing together these two viewpoints remains a major challenge for the future.

Adaptive laboratory evolution -- principles and applications for biotechnology

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.

Experimental interrogation of the path dependence and stochasticity of protein evolution using phage-assisted continuous evolution

To what extent are evolutionary outcomes determined by a population's recent environment, and to what extent do they depend on historical contingency and random chance? Here we apply a unique experimental system to investigate evolutionary reproducibility and path dependence at the protein level. We combined phage-assisted continuous evolution with high-throughput sequencing to analyze evolving protein populations as they adapted to divergent and then convergent selection pressures over hundreds of generations. Independent populations of T7 RNA polymerase genes were subjected to one of two selection histories ("pathways") demanding recognition of distinct intermediate promoters followed by a common final promoter. We observed distinct classes of solutions with unequal phenotypic activity and evolutionary potential evolve from the two pathways, as well as from replicate populations exposed to identical selection conditions. Mutational analysis revealed specific epistatic interactions that explained the observed path dependence and irreproducibility. Our results reveal in molecular detail how protein adaptation to different environments, as well as stochasticity among populations evolved in the same environment, can both generate evolutionary outcomes that preclude subsequent convergence.

Somewhat in control--the role of transcription in regulating microbial metabolic fluxes

The most common way for microbes to control their metabolism is by controlling enzyme levels through transcriptional regulation. Yet recent studies have shown that in many cases, perturbations to the transcriptional regulatory network do not result in altered metabolic phenotypes on the level of the flux distribution. We suggest that this may be a consequence of cells protecting their metabolism against stochastic fluctuations in expression as well as enabling a fast response for those fluxes that may need to be changed quickly. Furthermore, it is impossible for a regulatory program to guarantee optimal expression levels in all conditions. Several studies have found examples of demonstrably suboptimal regulation of gene expression, and improvements to the regulatory network have been investigated in laboratory evolution experiments.

Experimental evolution of Legionella pneumophila in mouse macrophages leads to strains with altered determinants of environmental survival

The Gram-negative bacterium, Legionella pneumophila, is a protozoan parasite and accidental intracellular pathogen of humans. We propose a model in which cycling through multiple protozoan hosts in the environment holds L. pneumophila in a state of evolutionary stasis as a broad host-range pathogen. Using an experimental evolution approach, we tested this hypothesis by restricting L. pneumophila to growth within mouse macrophages for hundreds of generations. Whole-genome resequencing and high-throughput genotyping identified several parallel adaptive mutations and population dynamics that led to improved replication within macrophages. Based on these results, we provide a detailed view of the population dynamics of an experimentally evolving bacterial population, punctuated by frequent instances of transient clonal interference and selective sweeps. Non-synonymous point mutations in the flagellar regulator, fleN, resulted in increased uptake and broadly increased replication in both macrophages and amoebae. Mutations in multiple steps of the lysine biosynthesis pathway were also independently isolated, resulting in lysine auxotrophy and reduced replication in amoebae. These results demonstrate that under laboratory conditions, host restriction is sufficient to rapidly modify L. pneumophila fitness and host range. We hypothesize that, in the environment, host cycling prevents L. pneumophila host-specialization by maintaining pathways that are deleterious for growth in macrophages and other hosts.

Legionella pneumophila is an accidental pathogen of humans, responsible for the severe, often-fatal pneumonia known as Legionnaires' disease. In the environment, L. pneumophila survives and replicates within protozoa by co-opting the intracellular machinery of these microbial predators. These freshwater encounters between bacteria and protozoa likely provided L. pneumophila with the selective pressures required to evolve into an intracellular pathogen. Many of the host pathways that L. pneumophila manipulates during infection are highly conserved and this is presumably what allows L. pneumophila to infect human cells. It is likely that L. pneumophila is suboptimally adapted to replication within mammalian cells, however, as replication within human cells is thought to be an evolutionary dead end. In this study, we developed an experimental evolution approach to determine what unique selective pressures might be present within mammalian hosts and how these pressures might modify this pathogen. We subjected L. pneumophila to continuous passage within mouse macrophages for several months, selecting for spontaneous mutations that resulted in improved fitness within these cells. We sequenced the genomes of each of the adapted strains, measured the population dynamics of each evolving population, and identified mutations that improve replication in mammalian cells and alter bacterial fitness in amoebae.