New insights into bacterial adaptation through in vivo and in silico experimental evolution

The evolution of bacterial cell size: the internal diffusion-constraint hypothesis

Size is one of the most important biological traits influencing organismal ecology and evolution. However, we know little about the drivers of body size evolution in unicellulars. A long-term evolution experiment (Lenski's LTEE) in which Escherichia coli adapts to a simple glucose medium has shown that not only the growth rate and the fitness of the bacterium increase over time but also its cell size. This increase in size contradicts prominent 'external diffusion' theory (EDC) predicting that cell size should have evolved toward smaller cells. Among several scenarios, we propose and test an alternative 'internal diffusion-constraint' (IDC) hypothesis for cell size evolution. A change in cell volume affects metabolite concentrations in the cytoplasm. The IDC states that a higher metabolism can be achieved by a reduction in the molecular traffic time inside of the cell, by increasing its volume. To test this hypothesis, we studied a population from the LTEE. We show that bigger cells with greater growth and CO₂ production rates and lower mass-to-volume ratio were selected over time in the LTEE. These results are consistent with the IDC hypothesis. This novel hypothesis offers a promising approach for understanding the evolutionary constraints on cell size.

Beware batch culture: Seasonality and niche construction predicted to favor bacterial adaptive diversification

Metabolic cross-feeding interactions between microbial strains are common in nature, and emerge during evolution experiments in the laboratory, even in homogeneous environments providing a single carbon source. In sympatry, when the environment is well-mixed, the reasons why emerging cross-feeding interactions may sometimes become stable and lead to monophyletic genotypic clusters occupying specific niches, named ecotypes, remain unclear. As an alternative to evolution experiments in the laboratory, we developed Evo²Sim, a multi-scale model of in silico experimental evolution, equipped with the whole tool case of experimental setups, competition assays, phylogenetic analysis, and, most importantly, allowing for evolvable ecological interactions. Digital organisms with an evolvable genome structure encoding an evolvable metabolic network evolved for tens of thousands of generations in environments mimicking the dynamics of real controlled environments, including chemostat or batch culture providing a single limiting resource. We show here that the evolution of stable cross-feeding interactions requires seasonal batch conditions. In this case, adaptive diversification events result in two stably co-existing ecotypes, with one feeding on the primary resource and the other on by-products. We show that the regularity of serial transfers is essential for the maintenance of the polymorphism, as it allows for at least two stable seasons and thus two temporal niches. A first season is externally generated by the transfer into fresh medium, while a second one is internally generated by niche construction as the provided nutrient is replaced by secreted by-products derived from bacterial growth. In chemostat conditions, even if cross-feeding interactions emerge, they are not stable on the long-term because fitter mutants eventually invade the whole population. We also show that the long-term evolution of the two stable ecotypes leads to character displacement, at the level of the metabolic network but also of the genome structure. This difference of genome structure between both ecotypes impacts the stability of the cross-feeding interaction, when the population is propagated in chemostat conditions. This study shows the crucial role played by seasonality in temporal niche partitioning and in promoting cross-feeding subgroups into stable ecotypes, a premise to sympatric speciation.

Stable bacterial cross-feeding interactions, where one strain feeds on the waste of the other, are important to understand, as they can be a first step toward bacterial speciation. Their emergence is commonly observed in laboratory experiments using Escherichia coli as a model organism. Yet it is not clear how cross-feeding interactions can resist the invasion of a fitter mutant when the environment contains a single resource since there seems to be a single ecological niche. Here, we used another kind of “model organism”, a digital one, allowing for detailed and fast investigations, and providing a way to disentangle generic evolutionary mechanisms from specificities associated with E. coli. Digital organisms with evolvable genomes and metabolic networks compete for resources in conditions mimicking laboratory evolution experiments. In chemostat simulations, although cross-feeding interactions regularly emerged, selective sweeps regularly purged the population of its diversity. By contrast, batch culture allowed for much more stable cross-feeding interactions, because it includes seasons and thus distinct temporal niches, thereby favoring the adaptive diversification of proto-species.

Mechanism for microbial population collapse in a fluctuating resource environment

Managing trade-offs through gene regulation is believed to confer resilience to a microbial community in a fluctuating resource environment. To investigate this hypothesis, we imposed a fluctuating environment that required the sulfate-reducer Desulfovibrio vulgaris to undergo repeated ecologically relevant shifts between retaining metabolic independence (active capacity for sulfate respiration) and becoming metabolically specialized to a mutualistic association with the hydrogen-consuming Methanococcus maripaludis Strikingly, the microbial community became progressively less proficient at restoring the environmentally relevant physiological state after each perturbation and most cultures collapsed within 3-7 shifts. Counterintuitively, the collapse phenomenon was prevented by a single regulatory mutation. We have characterized the mechanism for collapse by conducting RNA-seq analysis, proteomics, microcalorimetry, and single-cell transcriptome analysis. We demonstrate that the collapse was caused by conditional gene regulation, which drove precipitous decline in intracellular abundance of essential transcripts and proteins, imposing greater energetic burden of regulation to restore function in a fluctuating environment.

The genotype-phenotype map of an evolving digital organism

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10¹⁴¹ genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

The phenotype of an organism comprises the set of morphological and functional traits encoded by its genome. In natural evolving systems, phenotypes are organized into mutationally connected networks of genotypes, which increase the likelihood for an evolving population to encounter novel adaptive phenotypes (i.e., its evolvability). We do not know whether artificial systems, such as self-replicating and evolving computer programs—digital organisms—are more or less evolvable than natural systems. By studying how genotypes map onto phenotypes in digital organisms, we characterize many commonalities between natural and artificial evolving systems. In addition, we show that phenotypic complexity can both facilitate and constrain evolution, which harbors lessons not only for designing evolvable artificial systems, but also for synthetic biology.

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Isolated populations derived from a common ancestor are expected to diverge genetically and phenotypically as they adapt to different local environments. To examine this process, 30 populations of Escherichia coli were evolved for 2,000 generations, with six in each of five different thermal regimes: constant 20 °C, 32 °C, 37 °C, 42 °C, and daily alternations between 32 °C and 42 °C. Here, we sequenced the genomes of one endpoint clone from each population to test whether the history of adaptation in different thermal regimes was evident at the genomic level. The evolved strains had accumulated ∼5.3 mutations, on average, and exhibited distinct signatures of adaptation to the different environments. On average, two strains that evolved under the same regime exhibited ∼17% overlap in which genes were mutated, whereas pairs that evolved under different conditions shared only ∼4%. For example, all six strains evolved at 32 °C had mutations in nadR, whereas none of the other 24 strains did. However, a population evolved at 37 °C for an additional 18,000 generations eventually accumulated mutations in the signature genes strongly associated with adaptation to the other temperature regimes. Two mutations that arose in one temperature treatment tended to be beneficial when tested in the others, although less so than in the regime in which they evolved. These findings demonstrate that genomic signatures of adaptation can be highly specific, even with respect to subtle environmental differences, but that this imprint may become obscured over longer timescales as populations continue to change and adapt to the shared features of their environments.

Enhanced in planta Fitness through Adaptive Mutations in EfpR, a Dual Regulator of Virulence and Metabolic Functions in the Plant Pathogen Ralstonia solanacearum

Experimental evolution of the plant pathogen Ralstonia solanacearum, where bacteria were maintained on plant lineages for more than 300 generations, revealed that several independent single mutations in the efpR gene from populations propagated on beans were associated with fitness gain on bean. In the present work, novel allelic efpR variants were isolated from populations propagated on other plant species, thus suggesting that mutations in efpR were not solely associated to a fitness gain on bean, but also on additional hosts. A transcriptomic profiling and phenotypic characterization of the efpR deleted mutant showed that EfpR acts as a global catabolic repressor, directly or indirectly down-regulating the expression of multiple metabolic pathways. EfpR also controls virulence traits such as exopolysaccharide production, swimming and twitching motilities and deletion of efpR leads to reduced virulence on tomato plants after soil drenching inoculation. We studied the impact of the single mutations that occurred in efpR during experimental evolution and found that these allelic mutants displayed phenotypic characteristics similar to the deletion mutant, although not behaving as complete loss-of-function mutants. These adaptive mutations therefore strongly affected the function of efpR, leading to an expanded metabolic versatility that should benefit to the evolved clones. Altogether, these results indicated that EfpR is a novel central player of the R. solanacearum virulence regulatory network. Independent mutations therefore appeared during experimental evolution in the evolved clones, on a crucial node of this network, to favor adaptation to host vascular tissues through regulatory and metabolic rewiring.

Among plant pathogens of major economic and food crops, Ralstonia solanacearum, the causal agent of bacterial wilt, is recognized as one of the most destructive plant bacterial diseases. In addition, the emergence of new pathotypes, more aggressive and adapted to new hosts, has been reported. During an evolution experiment of R. solanacearum, where bacteria were maintained on plant lineages for more than 300 generations, we demonstrated that several single mutations in the regulatory gene efpR were associated with fitness gain on plants. However, the function of the EfpR regulator was totally unknown. In this work, we provided evidence that EfpR controls several metabolic pathways and important virulence traits of R. solanacearum. We then demonstrated that the single mutations selected in the efpR gene during the evolution experiment strongly alter the efpR expression, and thus enlarge the metabolic capacities of the bacterial cell. Altogether, our study reveals that EfpR is a novel key component of the complex regulatory network of the R. solanacearum cell, tightly linking the bacterial metabolism to virulence in response to multiple environmental signals.

Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms

A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large-but not intermediate-sized-populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.

Using long-term experimental evolution to uncover the patterns and determinants of molecular evolution of an Escherichia coli natural isolate in the streptomycin-treated mouse gut

Although microbial ecology of the gut is now a major focus of interest, little is known about the molecular determinants of microbial adaptation in the gut. Experimental evolution coupled with whole-genome sequencing can provide insights of the adaptive process. In vitro experiments have revealed some conserved patterns: intermediate convergence, and epistatic interactions between beneficial mutations and mutations in global regulators. To test the relevance of these patterns and to identify the selective pressures acting in vivo, we have performed a long-term adaptation of an E. coli natural isolate, the streptomycin-resistant strain 536, in the digestive tract of streptomycin-treated mice. After a year of evolution, a clone from 15 replicates was sequenced. Consistently with in vitro observations, the identified mutations revealed a strong pattern of convergence at the mutation, gene, operon and functional levels. Yet, the rate of molecular evolution was lower than in in vitro, and no mutations in global regulators were recovered. More specific targets were observed: the dgo operon, involved in the galactonate pathway that improved growth on D-galactonate, and rluD and gidB, implicated in the maturation of the ribosomes, which mutations improved growth only in the presence of streptomycin. As in vitro, the nonrandom associations of mutations within the same pathways suggested a role of epistasis in shaping the adaptive landscape. Overall, we show that 'evolve and sequence' approach coupled with an analysis of convergence, when applied to a natural isolate, can be used to study adaptation in vivo and uncover the specific selective pressures of that environment.

Heterozygote Advantage Is a Common Outcome of Adaptation in Saccharomyces cerevisiae

Adaptation in diploids is predicted to proceed via mutations that are at least partially dominant in fitness. Recently, we argued that many adaptive mutations might also be commonly overdominant in fitness. Natural (directional) selection acting on overdominant mutations should drive them into the population but then, instead of bringing them to fixation, should maintain them as balanced polymorphisms via heterozygote advantage. If true, this would make adaptive evolution in sexual diploids differ drastically from that of haploids. The validity of this prediction has not yet been tested experimentally. Here, we performed four replicate evolutionary experiments with diploid yeast populations (Saccharomyces cerevisiae) growing in glucose-limited continuous cultures. We sequenced 24 evolved clones and identified initial adaptive mutations in all four chemostats. The first adaptive mutations in all four chemostats were three copy number variations, all of which proved to be overdominant in fitness. The fact that fitness overdominant mutations were always the first step in independent adaptive walks supports the prediction that heterozygote advantage can arise as a common outcome of directional selection in diploids and demonstrates that overdominance of de novo adaptive mutations in diploids is not rare.

Contrasting effects of historical contingency on phenotypic and genomic trajectories during a two-step evolution experiment with bacteria

BACKGROUND

The impact of historical contingency, i.e. the past evolutionary history of a population, on further adaptation is mostly unknown at both the phenotypic and genomic levels. We addressed this question using a two-step evolution experiment. First, replicate populations of Escherichia coli were propagated in four different environmental conditions for 1000 generations. Then, all replicate populations were transferred and propagated for further 1000 generations to a single new environment.

RESULTS

Using this two-step experimental evolution strategy, we investigated, at both the phenotypic and genomic levels, whether and how adaptation in the initial historical environments impacted evolutionary trajectories in a new environment. We showed that both the growth rate and fitness of the evolved populations obtained after the second step of evolution were contingent upon past evolutionary history. In contrast however, the genes that were modified during the second step of evolution were independent from the previous history of the populations.

CONCLUSIONS

Our work suggests that historical contingency affects phenotypic adaptation to a new environment. This was however not reflected at the genomic level implying complex relationships between environmental factors and the genotype-to-phenotype map.

Adaptive engineering of a hyperthermophilic archaeon on CO and discovering the underlying mechanism by multi-omics analysis

The hyperthermophilic archaeon Thermococcus onnurineus NA1 can grow and produce H₂ on carbon monoxide (CO) and its H₂ production rates have been improved through metabolic engineering. In this study, we applied adaptive evolution to enhance H₂ productivity. After over 150 serial transfers onto CO medium, cell density, CO consumption rate and H₂ production rate increased. The underlying mechanism for those physiological changes could be explained by using multi-omics approaches including genomic, transcriptomic and epigenomic analyses. A putative transcriptional regulator was newly identified to regulate the expression levels of genes related to CO oxidation. Transcriptome analysis revealed significant changes in the transcript levels of genes belonging to the categories of transcription, translation and energy metabolism. Our study presents the first genome-scale methylation pattern of hyperthermophilic archaea. Adaptive evolution led to highly enhanced H₂ productivity at high CO flow rates using synthesis gas produced from coal gasification.

Expanding the Limits of Thermoacidophily in the Archaeon Sulfolobus solfataricus by Adaptive Evolution

Extremely thermoacidophilic Crenarchaeota belonging to the order Sulfolobales flourish in hot acidic habitats that are strongly oxidizing. The pH extremes of these habitats, however, often exceed the acid tolerance of type species and strains. Here, adaptive laboratory evolution was used over a 3-year period to test whether such organisms harbor additional thermoacidophilic capacity. Three distinct cell lines derived from a single type species were subjected to high-temperature serial passage while culture acidity was gradually increased. A 178-fold increase in thermoacidophily was achieved after 29 increments of shifted culture pH resulting in growth at pH 0.8 and 80°C. These strains were named super-acid-resistant Crenarchaeota (SARC). Mathematical modeling using growth parameters predicted the limits of acid resistance, while genome resequencing and transcriptome resequencing were conducted for insight into mechanisms responsible for the evolved trait. Among the mutations that were detected, a set of eight nonsynonymous changes may explain the heritability of increased acid resistance despite an unexpected lack of transposition. Four multigene components of the SARC transcriptome implicated oxidative stress as a primary challenge accompanying growth at acid extremes. These components included accelerated membrane biogenesis, induction of the mer operon, and an increased capacity for the generation of energy and reductant.

Population-reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population-reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population-reaction model. We also show that population-reaction models can be applied to various ecological concepts, such as predator-prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms.

In Silico Gene-Level Evolution Explains Microbial Population Diversity through Differential Gene Mobility

Microbial communities can show astonishing ecological and phylogenetic diversity. What is the role of pervasive horizontal gene transfer (HGT) in shaping this diversity in the presence of clonally expanding “killer strains”? Does HGT of antibiotic production and resistance genes erase phylogenetic structure? To answer these questions, we study a spatial eco-evolutionary model of prokaryotes, inspired by recent findings on antagonistic interactions in Vibrionaceae populations. We find toxin genes evolve to be highly mobile, whereas resistance genes minimize mobility. This differential gene mobility is a requirement to maintain a diverse and dynamic ecosystem. The resistance gene repertoire acts as a core genome that corresponds to the phylogeny of cells, whereas toxin genes do not follow this phylogeny and have a patchy distribution. We also show that interstrain HGT makes the emergent phylogenetic structure robust to selective sweeps. Finally, in this evolved ecosystem we observe antagonistic interactions between, rather than within, spatially structure subpopulations, as has been previously observed for prokaryotes in soils and oceans. In contrast to ascribing the diversification and evolution of microbial communities to clonal dynamics, we show that multilevel evolution can elegantly explain the observed phylogenetic structure and ecosystem diversity.

First-Step Mutations during Adaptation Restore the Expression of Hundreds of Genes

The temporal change of phenotypes during the adaptive process remains largely unexplored, as do the genetic changes that affect these phenotypic changes. Here we focused on three mutations that rose to high frequency in the early stages of adaptation within 12 Escherichia coli populations subjected to thermal stress (42 °C). All the mutations were in the rpoB gene, which encodes the RNA polymerase beta subunit. For each mutation, we measured the growth curves and gene expression (mRNAseq) of clones at 42 °C. We also compared growth and gene expression with their ancestor under unstressed (37 °C) and stressed conditions (42 °C). Each of the three mutations changed the expression of hundreds of genes and conferred large fitness advantages, apparently through the restoration of global gene expression from the stressed toward the prestressed state. These three mutations had a similar effect on gene expression as another single mutation in a distinct domain of the rpoB protein. Finally, we compared the phenotypic characteristics of one mutant, I572L, with two high-temperature adapted clones that have this mutation plus additional background mutations. The background mutations increased fitness, but they did not substantially change gene expression. We conclude that early mutations in a global transcriptional regulator cause extensive changes in gene expression, many of which are likely under positive selection for their effect in restoring the prestress physiology.

Elucidating the molecular architecture of adaptation via evolve and resequence experiments

Evolve and resequence (E&R) experiments use experimental evolution to adapt populations to a novel environment, then next-generation sequencing to analyse genetic changes. They enable molecular evolution to be monitored in real time on a genome-wide scale. Here, we review the field of E&R experiments across diverse systems, ranging from simple non-living RNA to bacteria, yeast and the complex multicellular organism Drosophila melanogaster. We explore how different evolutionary outcomes in these systems are largely consistent with common population genetics principles. Differences in outcomes across systems are largely explained by different starting population sizes, levels of pre-existing genetic variation, recombination rates and adaptive landscapes. We highlight emerging themes and inconsistencies that future experiments must address.

A Model of Genome Size Evolution for Prokaryotes in Stable and Fluctuating Environments

Temporal variability in ecosystems significantly impacts species diversity and ecosystem productivity and therefore the evolution of organisms. Different levels of environmental perturbations such as seasonal fluctuations, natural disasters, and global change have different impacts on organisms and therefore their ability to acclimatize and adapt. Thus, to understand how organisms evolve under different perturbations is a key for predicting how environmental change will impact species diversity and ecosystem productivity. Here, we developed a computer simulation utilizing the individual-based model approach to investigate genome size evolution of a haploid, clonal and free-living prokaryotic population across different levels of environmental perturbations. Our results show that a greater variability of the environment resulted in genomes with a larger number of genes. Environmental perturbations were more effectively buffered by populations of individuals with relatively large genomes. Unpredictable changes of the environment led to a series of population bottlenecks followed by adaptive radiations. Our model shows that the evolution of genome size is indirectly driven by the temporal variability of the environment. This complements the effects of natural selection directly acting on genome optimization. Furthermore, species that have evolved in relatively stable environments may face the greatest risk of extinction under global change as genome streamlining genetically constrains their ability to acclimatize to the new environmental conditions, unless mechanisms of genetic diversification such as horizontal gene transfer will enrich their gene pool and therefore their potential to adapt.

Adaptive Evolution of Thermotoga maritima Reveals Plasticity of the ABC Transporter Network

Thermotoga maritima is a hyperthermophilic anaerobe that utilizes a vast network of ABC transporters to efficiently metabolize a variety of carbon sources to produce hydrogen. For unknown reasons, this organism does not metabolize glucose as readily as it does glucose di- and polysaccharides. The leading hypothesis implicates the thermolability of glucose at the physiological temperatures at which T. maritima lives. After a 25-day laboratory evolution, phenotypes were observed with growth rates up to 1.4 times higher than and glucose utilization rates exceeding 50% those of the wild type. Genome resequencing revealed mutations in evolved cultures related to glucose-responsive ABC transporters. The native glucose ABC transporter, GluEFK, has more abundant transcripts either as a result of gene duplication-amplification or through mutations to the operator sequence regulating this operon. Conversely, BglEFGKL, a transporter of beta-glucosides, is substantially downregulated due to a nonsense mutation to the solute binding protein or due to a deletion of the upstream promoter. Analysis of the ABC2 uptake porter families for carbohydrate and peptide transport revealed that the solute binding protein, often among the transcripts detected at the highest levels, is predominantly downregulated in the evolved cultures, while the membrane-spanning domain and nucleotide binding components are less varied. Similar trends were observed in evolved strains grown on glycerol, a substrate that is not dependent on ABC transporters. Therefore, improved growth on glucose is achieved through mutations favoring GluEFK expression over BglEFGKL, and in lieu of carbon catabolite repression, the ABC transporter network is modulated to achieve improved growth fitness.

Shape matters: lifecycle of cooperative patches promotes cooperation in bulky populations

Natural cooperative systems take many forms, ranging from one-dimensional cyanobacteria arrays to fractal-like biofilms. We use in silico experimental systems to study a previously overlooked factor in the evolution of cooperation, physical shape of the population. We compare the emergence and maintenance of cooperation in populations of digital organisms that inhabit bulky (100 × 100 cells) or slender (4 × 2500) toroidal grids. Although more isolated subpopulations of secretors in a slender population could be expected to favor cooperation, we find the opposite: secretion evolves to higher levels in bulky populations. We identify the mechanistic explanation for the shape effect by analyzing the lifecycle and dynamics of cooperator patches, from their emergence and growth, to invasion by noncooperators and extinction. Because they are constrained by the population shape, the cooperator patches expand less in slender than in bulky populations, leading to fewer cooperators, less public good secretion, and generally lower cooperation. The patch dynamics and mechanisms of shape effect are robust across several digital cooperation systems and independent of the underlying basis for cooperation (public good secretion or a cooperation game). Our results urge for a greater consideration of population shape in the study of the evolution of cooperation across experimental and modeling systems.

Acid-adapted strains of Escherichia coli K-12 obtained by experimental evolution

Enteric bacteria encounter a wide range of pHs throughout the human intestinal tract. We conducted experimental evolution of Escherichia coli K-12 to isolate clones with increased fitness during growth under acidic conditions (pH 4.5 to 4.8). Twenty-four independent populations of E. coli K-12 W3110 were evolved in LBK medium (10 g/liter tryptone, 5 g/liter yeast extract, 7.45 g/liter KCl) buffered with homopiperazine-N,N'-bis-2-(ethanosulfonic acid) and malate at pH 4.8. At generation 730, the pH was decreased to 4.6 with HCl. By 2,000 generations, all populations had achieved higher endpoint growth than the ancestor at pH 4.6 but not at pH 7.0. All evolving populations showed a progressive loss of activity of lysine decarboxylase (CadA), a major acid stress enzyme. This finding suggests a surprising association between acid adaptation and moderation of an acid stress response. At generation 2,000, eight clones were isolated from four populations, and their genomes were sequenced. Each clone showed between three and eight missense mutations, including one in a subunit of the RNA polymerase holoenzyme (rpoB, rpoC, or rpoD). Missense mutations were found in adiY, the activator of the acid-inducible arginine decarboxylase (adiA), and in gcvP (glycine decarboxylase), a possible acid stress component. For tests of fitness relative to that of the ancestor, lacZ::kan was transduced into each strain. All acid-evolved clones showed a high fitness advantage at pH 4.6. With the cytoplasmic pH depressed by benzoate (at external pH 6.5), acid-evolved clones showed decreased fitness; thus, there was no adaptation to cytoplasmic pH depression. At pH 9.0, acid-evolved clones showed no fitness advantage. Thus, our acid-evolved clones showed a fitness increase specific to low external pH.

Genomic analysis reveals distinct concentration-dependent evolutionary trajectories for antibiotic resistance in Escherichia coli

Evolution of bacteria under sublethal concentrations of antibiotics represents a trade-off between growth and resistance to the antibiotic. To understand this trade-off, we performed in vitro evolution of laboratory Escherichia coli under sublethal concentrations of the aminoglycoside kanamycin over short time durations. We report that fixation of less costly kanamycin-resistant mutants occurred earlier in populations growing at lower sublethal concentration of the antibiotic, compared with those growing at higher sublethal concentrations; in the latter, resistant mutants with a significant growth defect persisted longer. Using deep sequencing, we identified kanamycin resistance-conferring mutations, which were costly or not in terms of growth in the absence of the antibiotic. Multiple mutations in the C-terminal end of domain IV of the translation elongation factor EF-G provided low-cost resistance to kanamycin. Despite targeting the same or adjacent residues of the protein, these mutants differed from each other in the levels of resistance they provided. Analysis of one of these mutations showed that it has little defect in growth or in synthesis of green fluorescent protein (GFP) from an inducible plasmid in the absence of the antibiotic. A second class of mutations, recovered only during evolution in higher sublethal concentrations of the antibiotic, deleted the C-terminal end of the ATP synthase shaft. This mutation confers basal-level resistance to kanamycin while showing a strong growth defect in the absence of the antibiotic. In conclusion, the early dynamics of the development of resistance to an aminoglycoside antibiotic is dependent on the levels of stress (concentration) imposed by the antibiotic, with the evolution of less costly variants only a matter of time.

Large chromosomal rearrangements during a long-term evolution experiment with Escherichia coli

Large-scale rearrangements may be important in evolution because they can alter chromosome organization and gene expression in ways not possible through point mutations. In a long-term evolution experiment, twelve Escherichia coli populations have been propagated in a glucose-limited environment for over 25 years. We used whole-genome mapping (optical mapping) combined with genome sequencing and PCR analysis to identify the large-scale chromosomal rearrangements in clones from each population after 40,000 generations. A total of 110 rearrangement events were detected, including 82 deletions, 19 inversions, and 9 duplications, with lineages having between 5 and 20 events. In three populations, successive rearrangements impacted particular regions. In five populations, rearrangements affected over a third of the chromosome. Most rearrangements involved recombination between insertion sequence (IS) elements, illustrating their importance in mediating genome plasticity. Two lines of evidence suggest that at least some of these rearrangements conferred higher fitness. First, parallel changes were observed across the independent populations, with ~65% of the rearrangements affecting the same loci in at least two populations. For example, the ribose-utilization operon and the manB-cpsG region were deleted in 12 and 10 populations, respectively, suggesting positive selection, and this inference was previously confirmed for the former case. Second, optical maps from clones sampled over time from one population showed that most rearrangements occurred early in the experiment, when fitness was increasing most rapidly. However, some rearrangements likely occur at high frequency and may have simply hitchhiked to fixation. In any case, large-scale rearrangements clearly influenced genomic evolution in these populations.

Bacterial chromosomes are dynamic structures shaped by long histories of evolution. Among genomic changes, large-scale DNA rearrangements can have important effects on the presence, order, and expression of genes. Whole-genome sequencing that relies on short DNA reads cannot identify all large-scale rearrangements. Therefore, deciphering changes in the overall organization of genomes requires alternative methods, such as optical mapping. We analyzed the longest-running microbial evolution experiment (more than 25 years of evolution in the laboratory) by optical mapping, genome sequencing, and PCR analyses. We found multiple large genome rearrangements in all 12 independently evolving populations. In most cases, it is unclear whether these changes were beneficial themselves or, alternatively, hitchhiked to fixation with other beneficial mutations. In any case, many genome rearrangements accumulated over decades of evolution, providing these populations with genetic plasticity reminiscent of that observed in some pathogenic bacteria.

Evolution of Escherichia coli to 42 °C and subsequent genetic engineering reveals adaptive mechanisms and novel mutations

Adaptive laboratory evolution (ALE) has emerged as a valuable method by which to investigate microbial adaptation to a desired environment. Here, we performed ALE to 42 °C of ten parallel populations of Escherichia coli K-12 MG1655 grown in glucose minimal media. Tightly controlled experimental conditions allowed selection based on exponential-phase growth rate, yielding strains that uniformly converged toward a similar phenotype along distinct genetic paths. Adapted strains possessed as few as 6 and as many as 55 mutations, and of the 144 genes that mutated in total, 14 arose independently across two or more strains. This mutational recurrence pointed to the key genetic targets underlying the evolved fitness increase. Genome engineering was used to introduce the novel ALE-acquired alleles in random combinations into the ancestral strain, and competition between these engineered strains reaffirmed the impact of the key mutations on the growth rate at 42 °C. Interestingly, most of the identified key gene targets differed significantly from those found in similar temperature adaptation studies, highlighting the sensitivity of genetic evolution to experimental conditions and ancestral genotype. Additionally, transcriptomic analysis of the ancestral and evolved strains revealed a general trend for restoration of the global expression state back toward preheat stressed levels. This restorative effect was previously documented following evolution to metabolic perturbations, and thus may represent a general feature of ALE experiments. The widespread evolved expression shifts were enabled by a comparatively scant number of regulatory mutations, providing a net fitness benefit but causing suboptimal expression levels for certain genes, such as those governing flagellar formation, which then became targets for additional ameliorating mutations. Overall, the results of this study provide insight into the adaptation process and yield lessons important for the future implementation of ALE as a tool for scientific research and engineering.

Robustness and plasticity of metabolic pathway flux among uropathogenic isolates of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a human pathogen that frequently causes urinary tract and catheter-associated urinary tract infections. Here, using ¹³C-metabolic flux analysis, we conducted quantitative analysis of metabolic fluxes in the model strain P. aeruginosa PAO1 and 17 clinical isolates. All P. aeruginosa strains catabolized glucose through the Entner-Doudoroff pathway with fully respiratory metabolism and no overflow. Together with other NADPH supplying reactions, this high-flux pathway provided by far more NADPH than needed for anabolism: a benefit for the pathogen to counteract oxidative stress imposed by the host. P. aeruginosa recruited the pentose phosphate pathway exclusively for biosynthesis. In contrast to glycolytic metabolism, which was conserved among all isolates, the flux through pyruvate metabolism, the tricarboxylic acid cycle, and the glyoxylate shunt was highly variable, likely caused by adaptive processes in individual strains during infection. This aspect of metabolism was niche-specific with respect to the corresponding flux because strains isolated from the urinary tract clustered separately from those originating from catheter-associated infections. Interestingly, most glucose-grown strains exhibited significant flux through the glyoxylate shunt. Projection into the theoretical flux space, which was computed using elementary flux-mode analysis, indicated that P. aeruginosa metabolism is optimized for efficient growth and exhibits significant potential for increasing NADPH supply to drive oxidative stress response.

Evolutionary history and genetic parallelism affect correlated responses to evolution

We investigated the relationship between genomic and phenotypic evolution among replicate populations of Escherichia coli evolved for 1000 generations in four different environments. By resequencing evolved genomes, we identified parallel changes in genes encoding transcription regulators within and between environments. Depending on both the environment and the altered gene, genetic parallelism at the gene level involved mutations that affected identical codons, protein domains or were widely distributed across the gene. Evolved clones were characterized by parallel phenotypic changes in their respective evolution environments but also in the three alternative environments. Phenotypic parallelism was high for clones that evolved in the same environment, even in the absence of genetic parallelism. By contrast, clones that evolved in different environments revealed a higher parallelism in correlated responses when they shared mutated genes. Altogether, this work shows that after an environmental change or the colonization of a new habitat, similar ecological performance might be expected from individuals that share mutated genes or that experienced similar past selective pressures.

Epistasis and allele specificity in the emergence of a stable polymorphism in Escherichia coli

Ecological opportunities promote population divergence into coexisting lineages. However, the genetic mechanisms that enable new lineages to exploit these opportunities are poorly understood except in cases of single mutations. We examined how two Escherichia coli lineages diverged from their common ancestor at the outset of a long-term coexistence. By sequencing genomes and reconstructing the genetic history of one lineage, we showed that three mutations together were sufficient to produce the frequency-dependent fitness effects that allowed this lineage to invade and stably coexist with the other. These mutations all affected regulatory genes and collectively caused substantial metabolic changes. Moreover, the particular derived alleles were critical for the initial divergence and invasion, indicating that the establishment of this polymorphism depended on specific epistatic interactions.

Evolution of extreme resistance to ionizing radiation via genetic adaptation of DNA repair

By directed evolution in the laboratory, we previously generated populations of Escherichia coli that exhibit a complex new phenotype, extreme resistance to ionizing radiation (IR). The molecular basis of this extremophile phenotype, involving strain isolates with a 3-4 order of magnitude increase in IR resistance at 3000 Gy, is now addressed. Of 69 mutations identified in one of our most highly adapted isolates, functional experiments demonstrate that the IR resistance phenotype is almost entirely accounted for by only three of these nucleotide changes, in the DNA metabolism genes recA, dnaB, and yfjK. Four additional genetic changes make small but measurable contributions. Whereas multiple contributions to IR resistance are evident in this study, our results highlight a particular adaptation mechanism not adequately considered in studies to date: Genetic innovations involving pre-existing DNA repair functions can play a predominant role in the acquisition of an IR resistance phenotype.

DOI:http://dx.doi.org/10.7554/eLife.01322.001

X-rays and other forms of ionizing radiation can damage DNA and proteins inside cells. The radiation interacts with aqueous solutions to produce reactive forms of oxygen, which then cause the damage. A range of mechanisms exist to moderate and/or repair this damage, with certain species being able to tolerate extraordinary levels of radiation. The bacterium D. radiodurans, for example, can survive radiation levels that are over 1000 times higher than the levels that can kill human cells.

The molecular basis of high-level resistance to ionizing radiation is not well understood, and several mechanisms have been proposed. Recent work has focused on passive mechanisms that are based on changes in cellular levels of certain small molecules that prevent damage by reactive forms of oxygen molecules.

Now, based on experiments on E. coli, Byrne et al. demonstrate that active mechanisms, involving adaptations in the cellular DNA repair systems, can bring about dramatic increases in radiation resistance. The experiments were performed on populations of E. coli cells that had been subjected to an evolutionary selection for extremely high resistance to ionizing radiation. This involved exposing the E. coli cells to ionizing radiation that killed most of the population, and then growing up the survivors. Many repetitions of this process led to a population of cells with a resistance that was comparable to that of the bacterium D. radiodurans. The same evolution experiment was carried out four times, generating four separate populations of bacteria that were resistant to ionizing radiation.

Byrne et al. sequenced the genomes of the E. coli after 20, 40 or 50 rounds of the selection process, and compared mutations found in the four separate evolved populations. This showed that nine genes were particularly prone to mutations. Together, these genes had roles in repairing and copying DNA sequences, in decreasing damage caused by reactive forms of oxygen, and in manufacturing the molecular wall that shields cells.

To assess the importance of the mutations in the nine genes, Byrne et al. took Founder cells from the initial population of E. coli cells–which were not resistant to ionizing radiation–and introduced the very same mutations, one at a time. Then the mutations that had the largest positive effects on resistance to ionizing radiation were combined. Introducing particular mutations into three DNA repair genes resulted in the highest aggregate levels of resistance. Finally, evolved E. coli cells that were already resistant were made more sensitive to radiation by repairing the same individual mutations. Again, the biggest change was observed with the DNA repair genes. Indeed, repairing the mutations in just the three DNA repair genes completely removed the radiation resistance.

The next step is to determine how the properties of the mutated proteins change, and how those changes lead to radiation resistance. Also, there are clues in the work that suggest the presence of additional ways for cells to become radiation resistant, and these remain to be explored.

DOI:http://dx.doi.org/10.7554/eLife.01322.002

Fisher's geometrical model emerges as a property of complex integrated phenotypic networks

Models relating phenotype space to fitness (phenotype-fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher's geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model "from first principles" is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher's model's assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts.

A network characteristic that correlates environmental and genetic robustness

As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation.

Advances in the ways that living systems can be perturbed in order to study how they function and sharp reductions in the cost of computer resources have allowed the collection of large amounts of data. The aim of biological system modeling is to analyze this data in order to pin down the precise interactions of molecules that underlie the observed functions. This is made difficult due to two features of biological systems: (1) Living things do not show an appreciable loss of function across large ranges of environmental factors. (2) Their function is inherited from parent to child more or less unchanged in spite of random mutations in genetic sequences. We find that these two features are more correlated in a specific subset of networks and show how to use this observation to find networks in which these two features appear together. Working within this smaller space of networks may make it easier to find suitable underlying models from data.

The reproducibility of adaptation in the light of experimental evolution with whole genome sequencing

A key question in evolutionary biology is the reproducibility of adaptation. This question can now be quantitatively analyzed using experimental evolution coupled to whole genome sequencing (WGS). With complete sequence data, one can assess convergence among replicate populations. In turn, convergence reflects the action of natural selection and also the breadth of the field of possible adaptive solutions. That is, it provides insight into how many genetic solutions or adaptive paths may lead to adaptation in a given environment. Convergence is both a property of an adaptive landscape and, reciprocally, a tool to study that landscape. In this chapter we present the links between convergence and the properties of adaptive landscapes with respect to two types of microbial experimental evolution. The first tries to reconstruct a full adaptive landscape using a handful of carefully identified mutations (the reductionist approach), while the second uses WGS of replicate experiments to infer properties of the adaptive landscape. Reductionist approaches have highlighted the importance of epistasis in shaping the adaptive landscape, but have also uncovered a wide diversity of landscape architectures. The WGS approach has uncovered a very high diversity of beneficial mutations that affect a limited set of genes or functions and also suggests some shortcomings of the reductionist approach. We conclude that convergence may be better defined at an integrated level, such as the genic level or even at a phenotypic level, and that integrated mechanistic models derived from systems biology may offer an interesting perspective for the analysis of convergence at all levels.

Induced adaptation of Bacillus sp. to antimicrobial nanosilver

The natural ability of Bacillus sp. to adapt to nanosilver cytotoxicity upon prolonged exposure is reported for the first time. The combined adaptive effects of nanosilver resistance and enhanced growth are induced under various intensities of nanosilver-stimulated cellular oxidative stress, ranging from only minimal cellular redox imbalance to the lethal levels of cellular ROS stimulation. An important implication of the present work is that such adaptive effects lead to the ultimate domination of nanosilver-resistant Bacillus sp. in the microbiota, to which nanosilver cytotoxicity is continuously applied.

In silico experimental evolution: a tool to test evolutionary scenarios

Comparative genomics has revealed that some species have exceptional genomes, compared to their closest relatives. For instance, some species have undergone a strong reduction of their genome with a drastic reduction of their genic repertoire. Deciphering the causes of these atypical trajectories can be very difficult because of the many phenomena that are intertwined during their evolution (e.g. changes of population size, environment structure and dynamics, selection strength, mutation rates...). Here we propose a methodology based on synthetic experiments to test the individual effect of these phenomena on a population of simulated organisms. We developed an evolutionary model--aevol--in which evolutionary conditions can be changed one at a time to test their effects on genome size and organization (e.g. coding ratio). To illustrate the proposed approach, we used aevol to test the effects of a strong reduction in the selection strength on a population of (simulated) bacteria. Our results show that this reduction of selection strength leads to a genome reduction of ~35% with a slight loss of coding sequences (~15% of the genes are lost--mainly those for which the contribution to fitness is the lowest). More surprisingly, under a low selection strength, genomes undergo a strong reduction of the noncoding compartment (~55% of the noncoding sequences being lost). These results are consistent with what is observed in reduced Prochlorococcus strains (marine cyanobacteria) when compared to close relatives.

Adaptive divergence in experimental populations of Pseudomonas fluorescens. V. Insight into the niche specialist fuzzy spreader compels revision of the model Pseudomonas radiation

Pseudomonas fluorescens is a model for the study of adaptive radiation. When propagated in a spatially structured environment, the bacterium rapidly diversifies into a range of niche specialist genotypes. Here we present a genetic dissection and phenotypic characterization of the fuzzy spreader (FS) morphotype-a type that arises repeatedly during the course of the P. fluorescens radiation and appears to colonize the bottom of static broth microcosms. The causal mutation is located within gene fuzY (pflu0478)-the fourth gene of the five-gene fuzVWXYZ operon. fuzY encodes a β-glycosyltransferase that is predicted to modify lipopolysaccharide (LPS) O antigens. The effect of the mutation is to cause cell flocculation. Analysis of 92 independent FS genotypes showed each to have arisen as the result of a loss-of-function mutation in fuzY, although different mutations have subtly different phenotypic and fitness effects. Mutations within fuzY were previously shown to suppress the phenotype of mat-forming wrinkly spreader (WS) types. This prompted a reinvestigation of FS niche preference. Time-lapse photography showed that FS colonizes the meniscus of broth microcosms, forming cellular rafts that, being too flimsy to form a mat, collapse to the vial bottom and then repeatably reform only to collapse. This led to a reassessment of the ecology of the P. fluorescens radiation. Finally, we show that ecological interactions between the three dominant emergent types (smooth, WS, and FS), combined with the interdependence of FS and WS on fuzY, can, at least in part, underpin an evolutionary arms race with bacteriophage SBW25Φ2, to which mutation in fuzY confers resistance.

Comparative genomics and transcriptomics analysis of experimentally evolved Escherichia coli MC1000 in complex environments

It has recently become feasible to study the basis and nature of evolutionary changes in bacteria in an experimental setting using defined media. However, assessment of adaptive changes in complex environments has been scarce. In an effort to describe the responses in such environments, we unravel, in a comparative approach, the transcriptional and genetic profiles of 19 Escherichia coli strains that evolved in Luria Bertani medium under three different oxygen regimes over 1000 generations. A positive relationship between upregulation of gene expression and the number of mutations was observed, suggesting that a number of metabolic pathways were activated. Phenotypic polymorphisms were observed in parallel cultures, of which some were related with mutations at the regulatory level. Non-parallel responses were observed at the intrapopulational level, which is indicative of diversifying selection. Parallel responses encompassed transcriptome diversity, and their effects were directly affected by differing genomic backgrounds. A fluctuating selective force produced higher phenotypic diversity compared with constant forces. This study demonstrates how phenotypic innovations may depend on the relationship between genomic changes and local ecological conditions. Using both comparative genomics and transcriptomics approaches, the results help elucidating various adaptive responses in cultures in unexplored complex environments.

Tracing evolutionary footprints to identify novel gene functional linkages

Systematic determination of gene function is an essential step in fully understanding the precise contribution of each gene for the proper execution of molecular functions in the cell. Gene functional linkage is defined as to describe the relationship of a group of genes with similar functions. With thousands of genomes sequenced, there arises a great opportunity to utilize gene evolutionary information to identify gene functional linkages. To this end, we established a computational method (called TRACE) to trace gene footprints through a gene functional network constructed from 341 prokaryotic genomes. TRACE performance was validated and successfully tested to predict enzyme functions as well as components of pathway. A so far undescribed chromosome partitioning-like protein ro03654 of an oleaginous bacteria Rhodococcus sp. RHA1 (RHA1) was predicted and verified experimentally with its deletion mutant showing growth inhibition compared to RHA1 wild type. In addition, four proteins were predicted to act as prokaryotic SNARE-like proteins, and two of them were shown to be localized at the plasma membrane. Thus, we believe that TRACE is an effective new method to infer prokaryotic gene functional linkages by tracing evolutionary events.

A case of adaptation through a mutation in a tandem duplication during experimental evolution in Escherichia coli

Background

DNA duplications constitute important precursors for genome variation. Here we analyzed an unequal duplication harboring a beneficial mutation that may provide alternative evolutionary outcomes.

Results

We characterized this evolutionary event during experimental evolution for only 100 generations of an Escherichia coli strain under glucose limitation within chemostats. By combining Insertion Sequence based Restriction Length Polymorphism experiments, pulsed field gel electrophoresis and two independent genome re-sequencing experiments, we identified an evolved lineage carrying a 180 kb duplication of the 46’ region of the E. coli chromosome. This evolved duplication revealed a heterozygous state, with one copy harboring a 2668 bp deletion that included part of the ogrK gene and both the yegR and yegS genes. By genetically manipulating ancestral and evolved strains, we showed that the single yegS inactivation was sufficient to confer a frequency dependent fitness increase under the chemostat selective conditions in both the ancestor and evolved genetic contexts, implying that the duplication itself was not a direct fitness contributor. Nonetheless, the heterozygous duplicated state was relatively stable in the conditions prevailing during evolution in chemostats, in striking contrast to non selective conditions in which the duplication resolved at high frequency into either its ancestral or deleted copy.

Conclusions

Our results suggest that the duplication state may constitute a second order selection process providing higher evolutionary potential. Moreover, its heterozygous nature may provide differential evolutionary opportunities in alternating environments. Our results also highlighted how careful analyses of whole genome data are needed to identify such complex rearrangements.

Long-term diversity and genome adaptation of Acinetobacter baylyi in a minimal-medium chemostat

Laboratory-based evolution experiments on microorganisms that do not recombine frequently show two distinct phases: an initial rapid increase in fitness followed by a slower regime. To explore the population structure and the evolutionary tree in the later stages of adaptation, we evolved a very large population (∼3 × 10) of Acinetobacter baylyi bacteria for approximately 2,800 generations from a single clone. The population was maintained in a chemostat at a high dilution rate. Nitrate in limiting amount and as the sole nitrogen source was used as a selection pressure. Analysis via resequencing of genomes extracted from populations at different generations provides evidence that long-term diversity can be established in the chemostat in a very simple medium. To find out which biological parameters were targeted by adaptation, we measured the maximum growth rate, the nitrate uptake, and the resistance to starvation. Overall, we find that maximum growth rate could be a reasonably good proxy for fitness. The late slow adaptation is compatible with selection coefficients spanning a typical range of 10–10 per generation as estimated by resequencing, pointing to a possible subpopulations structuring.

From in vitro to in vivo Models of Bacterial Biofilm-Related Infections

The influence of microorganisms growing as sessile communities in a large number of human infections has been extensively studied and recognized for 30–40 years, therefore warranting intense scientific and medical research. Nonetheless, mimicking the biofilm-life style of bacteria and biofilm-related infections has been an arduous task. Models used to study biofilms range from simple in vitro to complex in vivo models of tissues or device-related infections. These different models have progressively contributed to the current knowledge of biofilm physiology within the host context. While far from a complete understanding of the multiple elements controlling the dynamic interactions between the host and biofilms, we are nowadays witnessing the emergence of promising preventive or curative strategies to fight biofilm-related infections. This review undertakes a comprehensive analysis of the literature from a historic perspective commenting on the contribution of the different models and discussing future venues and new approaches that can be merged with more traditional techniques in order to model biofilm-infections and efficiently fight them.

Evolutionary remodeling of global regulatory networks during long-term bacterial adaptation to human hosts

The genetic basis of bacterial adaptation to a natural environment has been investigated in a highly successful Pseudomonas aeruginosa lineage (DK2) that evolved within the airways of patients with cystic fibrosis (CF) for more than 35 y. During evolution in the CF airways, the DK2 lineage underwent substantial phenotypic changes, which correlated with temporal fixation of specific mutations in the genes mucA (frame-shift), algT (substitution), rpoN (substitution), lasR (deletion), and rpoD (in-frame deletion), all encoding regulators of large gene networks. To clarify the consequences of these genetic changes, we moved the specific mutations, alone and in combination, to the genome of the reference strain PAO1. The phenotypes of the engineered PAO1 derivatives showed striking similarities with phenotypes observed among the DK2 isolates. The phenotypes observed in the DK2 isolates and PAO1 mutants were the results of individual, additive and epistatic effects of the regulatory mutations. The mutations fixed in the σ factor encoding genes algT, rpoN, and rpoD caused minor changes in σ factor activity, resulting in remodeling of the regulatory networks to facilitate generation of unexpected phenotypes. Our results suggest that adaptation to a highly selective environment, such as the CF airways, is a highly dynamic and complex process, which involves continuous optimization of existing regulatory networks to match the fluctuations in the environment.

Evolution of Escherichia coli rifampicin resistance in an antibiotic-free environment during thermal stress

BACKGROUND

Beneficial mutations play an essential role in bacterial adaptation, yet little is known about their fitness effects across genetic backgrounds and environments. One prominent example of bacterial adaptation is antibiotic resistance. Until recently, the paradigm has been that antibiotic resistance is selected by the presence of antibiotics because resistant mutations confer fitness costs in antibiotic free environments. In this study we show that it is not always the case, documenting the selection and fixation of resistant mutations in populations of Escherichia coli B that had never been exposed to antibiotics but instead evolved for 2000 generations at high temperature (42.2°C).

RESULTS

We found parallel mutations within the rpoB gene encoding the beta subunit of RNA polymerase. These amino acid substitutions conferred different levels of rifampicin resistance. The resistant mutations typically appeared, and were fixed, early in the evolution experiment. We confirmed the high advantage of these mutations at 42.2°C in glucose-limited medium. However, the rpoB mutations had different fitness effects across three genetic backgrounds and six environments.

CONCLUSIONS

We describe resistance mutations that are not necessarily costly in the absence of antibiotics or compensatory mutations but are highly beneficial at high temperature and low glucose. Their fitness effects depend on the environment and the genetic background, providing glimpses into the prevalence of epistasis and pleiotropy.

Expression divergence between Escherichia coli and Salmonella enterica serovar Typhimurium reflects their lifestyles

Escherichia coli K12 is a commensal bacteria and one of the best-studied model organisms. Salmonella enterica serovar Typhimurium, on the other hand, is a facultative intracellular pathogen. These two prokaryotic species can be considered related phylogenetically, and they share a large amount of their genetic material, which is commonly termed the "core genome." Despite their shared core genome, both species display very different lifestyles, and it is unclear to what extent the core genome, apart from the species-specific genes, plays a role in this lifestyle divergence. In this study, we focus on the differences in expression domains for the orthologous genes in E. coli and S. Typhimurium. The iterative comparison of coexpression methodology was used on large expression compendia of both species to uncover the conservation and divergence of gene expression. We found that gene expression conservation occurs mostly independently from amino acid similarity. According to our estimates, at least more than one quarter of the orthologous genes has a different expression domain in E. coli than in S. Typhimurium. Genes involved with key cellular processes are most likely to have conserved their expression domains, whereas genes showing diverged expression are associated with metabolic processes that, although present in both species, are regulated differently. The expression domains of the shared "core" genome of E. coli and S. Typhimurium, consisting of highly conserved orthologs, have been tuned to help accommodate the differences in lifestyle and the pathogenic potential of Salmonella.

Cumulative impact of chaperone-mediated folding on genome evolution

Molecular chaperones support protein folding and unfolding along with assembly and translocation of protein complexes. Chaperones have been recognized as important mediators between an organismal genotype and phenotype as well as important maintainers of cellular fitness under environmental conditions that induce high mutational loads. Here we review recent studies revealing that the folding assistance supplied by chaperones is evident in genomic sequences implicating chaperone-mediated folding as an influential factor during protein evolution. Interaction of protein with chaperones ensures a proper folding and function, yet an adaptation to obligatory dependence on such assistance may be irreversible, representing an evolutionary trap. A correlation between the requirement for a chaperone and protein expression level indicates that the evolution of substrate-chaperone interaction is bounded by the required substrate abundance within the cell. Accumulating evidence suggests that the utility of chaperones is governed by a delicate balance between their help in mitigating the risks of protein misfolding and aggregate formation on one hand and the slower rate of protein maturation and the energetic cost of chaperone synthesis on the other.