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

Decoding populations in the ocean microbiome

Understanding the characteristics and structure of populations is fundamental to comprehending ecosystem processes and evolutionary adaptations. While the study of animal and plant populations has spanned a few centuries, microbial populations have been under scientific scrutiny for a considerably shorter period. In the ocean, analyzing the genetic composition of microbial populations and their adaptations to multiple niches can yield important insights into ecosystem function and the microbiome's response to global change. However, microbial populations have remained elusive to the scientific community due to the challenges associated with isolating microorganisms in the laboratory. Today, advancements in large-scale metagenomics and metatranscriptomics facilitate the investigation of populations from many uncultured microbial species directly from their habitats. The knowledge acquired thus far reveals substantial genetic diversity among various microbial species, showcasing distinct patterns of population differentiation and adaptations, and highlighting the significant role of selection in structuring populations. In the coming years, population genomics is expected to significantly increase our understanding of the architecture and functioning of the ocean microbiome, providing insights into its vulnerability or resilience in the face of ongoing global change. Video Abstract.

Thermal acclimation uncovers a simple genetic basis of adaptation to high temperature in a cosmopolitan pest

Understanding a population's fitness heterogeneity and genetic basis of thermal adaptation is essential for predicting the responses to global warming. We examined the thermotolerance and genetic adaptation of Plutella xylostella to exposure to hot temperatures. The population fitness parameters of the hot-acclimated DBM strains varied in the thermal environments. Using genome scanning and transcription profiling, we find a number of genes potentially involved in thermal adaptation of DBM. Editing two ABCG transporter genes, PxWhite and PxABCG, confirmed their role in altering cuticle permeability and influencing thermal responses. Our results demonstrate that SNP mutations in genes and changes in gene expression can allow DBM to rapidly adapt to thermal environment. ABCG transporter genes play an important role in thermal adaptation of DBM. This work improves our understanding of genetic adaptation mechanisms of insects to thermal stress and our capacity to predict the effects of rising global temperatures on ectotherms.

The geologic history of primary productivity

The rate of primary productivity is a keystone variable in driving biogeochemical cycles today and has been throughout Earth's past.1 For example, it plays a critical role in determining nutrient stoichiometry in the oceans,2 the amount of global biomass,3 and the composition of Earth's atmosphere.4 Modern estimates suggest that terrestrial and marine realms contribute near-equal amounts to global gross primary productivity (GPP).5 However, this productivity balance has shifted significantly in both recent times6 and through deep time.7,8 Combining the marine and terrestrial components, modern GPP fixes ≈250 billion tonnes of carbon per year (Gt C year-1).5,9,10,11 A grand challenge in the study of the history of life on Earth has been to constrain the trajectory that connects present-day productivity to the origin of life. Here, we address this gap by piecing together estimates of primary productivity from the origin of life to the present day. We estimate that ∼1011-1012 Gt C has cumulatively been fixed through GPP (≈100 times greater than Earth's entire carbon stock). We further estimate that 1039-1040 cells have occupied the Earth to date, that more autotrophs than heterotrophs have ever existed, and that cyanobacteria likely account for a larger proportion than any other group in terms of the number of cells. We discuss implications for evolutionary trajectories and highlight the early Proterozoic, which encompasses the Great Oxidation Event (GOE), as the time where most uncertainty exists regarding the quantitative census presented here.

Interaction between mutation type and gene pleiotropy drives parallel evolution in the laboratory

What causes evolution to be repeatable is a fundamental question in evolutionary biology. Pleiotropy, i.e. the effect of an allele on multiple traits, is thought to enhance repeatability by constraining the number of available beneficial mutations. Additionally, pleiotropy may promote repeatability by allowing large fitness benefits of single mutations via adaptive combinations of phenotypic effects. Yet, this latter evolutionary potential may be reaped solely by specific types of mutations able to realize optimal combinations of phenotypic effects while avoiding the costs of pleiotropy. Here, we address the interaction of gene pleiotropy and mutation type on evolutionary repeatability in a meta-analysis of experimental evolution studies with Escherichia coli. We hypothesize that single nucleotide polymorphisms (SNPs) are principally able to yield large fitness benefits by targeting highly pleiotropic genes, whereas indels and structural variants (SVs) provide smaller benefits and are restricted to genes with lower pleiotropy. By using gene connectivity as proxy for pleiotropy, we show that non-disruptive SNPs in highly pleiotropic genes yield the largest fitness benefits, since they contribute more to parallel evolution, especially in large populations, than inactivating SNPs, indels and SVs. Our findings underscore the importance of considering genetic architecture together with mutation type for understanding evolutionary repeatability. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Evolutionary repeatability of emergent properties of ecological communities

Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Drivers of stability and transience in composition-functioning links during serial propagation of litter-decomposing microbial communities

Biotic factors that influence the temporal stability of microbial community functioning are an emerging research focus for control of natural and engineered systems. Discovery of common features within community ensembles that differ in functional stability over time is a starting point to explore biotic factors. We serially propagated a suite of soil microbial communities through five generations of 28 d microcosm incubations to examine microbial community compositional and functional stability during plant-litter decomposition. Using DOC abundance as a target function, we hypothesized that microbial diversity, compositional stability, and associated changes in interactions would explain the relative stability of the ecosystem function between generations. Communities with initially high DOC abundance tended to converge towards a "low DOC" phenotype within two generations, but across all microcosms, functional stability between generations was highly variable. By splitting communities into two cohorts based on their relative DOC functional stability, we found that compositional shifts, diversity, and interaction network complexity were associated with the stability of DOC abundance between generations. Further, our results showed that legacy effects were important in determining compositional and functional outcomes, and we identified taxa associated with high DOC abundance. In the context of litter decomposition, achieving functionally stable communities is required to utilize soil microbiomes to increase DOC abundance and long-term terrestrial DOC sequestration as 1 solution to reduce atmospheric carbon dioxide concentrations. Identifying factors that stabilize function for a community of interest may improve the success of microbiome engineering applications. Importance Microbial community functioning can be highly dynamic over time. Identifying and understanding biotic factors that control functional stability is of significant interest for natural and engineered communities alike. Using plant litter decomposing communities as a model system, this study examined the stability of ecosystem function over time following repeated community transfers. By identifying microbial community features that are associated with stable ecosystem functions, microbial communities can be manipulated in ways that promote the consistency and reliability of the desired function, improving outcomes and increasing the utility of microorganisms.

Drivers of stability and transience in composition-functioning links during serial propagation of litter-decomposing microbial communities

IMPORTANCE

Microbial community functioning can be highly dynamic over time. Identifying and understanding biotic factors that control functional stability is of significant interest for natural and engineered communities alike. Using plant litter decomposing communities as a model system, this study examined the stability of ecosystem function over time following repeated community transfers. By identifying microbial community features that are associated with stable ecosystem functions, microbial communities can be manipulated in ways that promote the consistency and reliability of the desired function, improving outcomes and increasing the utility of microorganisms.

Evolution of genome fragility enables microbial division of labor

Division of labor can evolve when social groups benefit from the functional specialization of its members. Recently, a novel means of coordinating the division of labor was found in the antibiotic-producing bacterium Streptomyces coelicolor, where specialized cells are generated through large-scale genomic re-organization. We investigate how the evolution of a genome architecture enables such mutation-driven division of labor, using a multiscale computational model of bacterial evolution. In this model, bacterial behavior-antibiotic production or replication-is determined by the structure and composition of their genome, which encodes antibiotics, growth-promoting genes, and fragile genomic loci that can induce chromosomal deletions. We find that a genomic organization evolves, which partitions growth-promoting genes and antibiotic-coding genes into distinct parts of the genome, separated by fragile genomic loci. Mutations caused by these fragile sites mostly delete growth-promoting genes, generating sterile, and antibiotic-producing mutants from weakly-producing progenitors, in agreement with experimental observations. This division of labor enhances the competition between colonies by promoting antibiotic diversity. These results show that genomic organization can co-evolve with genomic instabilities to enable reproductive division of labor.

At the threshold of symbiosis: the genome of obligately endosymbiotic 'Candidatus Nebulobacter yamunensis' is almost indistinguishable from that of a cultivable strain

Comparing obligate endosymbionts with their free-living relatives is a powerful approach to investigate the evolution of symbioses, and it has led to the identification of several genomic traits consistently associated with the establishment of symbiosis. 'Candidatus Nebulobacter yamunensis' is an obligate bacterial endosymbiont of the ciliate Euplotes that seemingly depends on its host for survival. A subsequently characterized bacterial strain with an identical 16S rRNA gene sequence, named Fastidiosibacter lacustris, can instead be maintained in pure culture. We analysed the genomes of 'Candidatus Nebulobacter' and Fastidiosibacter seeking to identify key differences between their functional traits and genomic structure that might shed light on a recent transition to obligate endosymbiosis. Surprisingly, we found almost no such differences: the two genomes share a high level of sequence identity, the same overall structure, and largely overlapping sets of genes. The similarities between the genomes of the two strains are at odds with their different ecological niches, confirmed here with a parallel growth experiment. Although other pairs of closely related symbiotic/free-living bacteria have been compared in the past, 'Candidatus Nebulobacter' and Fastidiosibacter represent an extreme example proving that a small number of (unknown) factors might play a pivotal role in the earliest stages of obligate endosymbiosis establishment.

Extracellular Vesicles from 50,000 Generation Clones of the Escherichia coli Long-Term Evolution Experiment

Extracellular vesicles (EVs) are critical elements of cell-cell communication. Here, we characterized the outer membrane vesicles (OMVs) released by specific clones of Escherichia coli isolated from the Long-Term Evolution Experiment after 50,000 generations (50K) of adaptation to glucose minimal medium. Compared with their ancestor, the evolved clones produce small OMVs but also larger ones which display variable amounts of both OmpA and LPS. Tracking ancestral, fluorescently labelled OMVs revealed that they fuse with both ancestral- and 50K-evolved cells, albeit in different proportions. We quantified that less than 2% of the cells from one 50K-evolved clone acquired the fluorescence delivered by OMVs from the ancestral strain but that one cell concomitantly fuses with several OMVs. Globally, our results showed that OMV production in E. coli is a phenotype that varies along bacterial evolution and question the contribution of OMVs-mediated interactions in bacterial adaptation.

A road map for in vivo evolution experiments with blood-borne parasitic microbes

Laboratory experiments in which blood-borne parasitic microbes evolve in their animal hosts offer an opportunity to study parasite evolution and adaptation in real time and under natural settings. The main challenge of these experiments is to establish a protocol that is both practical over multiple passages and accurately reflects natural transmission scenarios and mechanisms. We provide a guide to the steps that should be considered when designing such a protocol, and we demonstrate its use via a case study. We highlight the importance of choosing suitable ancestral genotypes, treatments, number of replicates per treatment, types of negative controls, dependent variables, covariates, and the timing of checkpoints for the experimental design. We also recommend specific preliminary experiments to determine effective methods for parasite quantification, transmission, and preservation. Although these methodological considerations are technical, they also often have conceptual implications. To this end, we encourage other researchers to design and conduct in vivo evolution experiments with blood-borne parasitic microbes, despite the challenges that the work entails.

Better under stress: Improving bacterial cellulose production by Komagataeibacter xylinus K2G30 (UMCC 2756) using adaptive laboratory evolution

Among naturally produced polymers, bacterial cellulose is receiving enormous attention due to remarkable properties, making it suitable for a wide range of industrial applications. However, the low yield, the instability of microbial strains and the limited knowledge of the mechanisms regulating the metabolism of producer strains, limit the large-scale production of bacterial cellulose. In this study, Komagataeibacter xylinus K2G30 was adapted in mannitol based medium, a carbon source that is also available in agri-food wastes. K. xylinus K2G30 was continuously cultured by replacing glucose with mannitol (2% w/v) for 210 days. After a starting lag-phase, in which no changes were observed in the utilization of mannitol and in bacterial cellulose production (cycles 1–25), a constant improvement of the phenotypic performances was observed from cycle 26 to cycle 30, accompanied by an increase in mannitol consumption. At cycle 30, the end-point of the experiment, bacterial cellulose yield increased by 38% in comparision compared to cycle 1. Furthermore, considering the mannitol metabolic pathway, D-fructose is an intermediate in the bioconversion of mannitol to glucose. Based on this consideration, K. xylinus K2G30 was tested in fructose-based medium, obtaining the same trend of bacterial cellulose production observed in mannitol medium. The adaptive laboratory evolution approach used in this study was suitable for the phenotypic improvement of K. xylinus K2G30 in bacterial cellulose production. Metabolic versatility of the strain was confirmed by the increase in bacterial cellulose production from D-fructose-based medium. Moreover, the adaptation on mannitol did not occur at the expense of glucose, confirming the versatility of K2G30 in producing bacterial cellulose from different carbon sources. Results of this study contribute to the knowledge for designing new strategies, as an alternative to the genetic engineering approach, for bacterial cellulose production.

Identification of the key functional genes in salt-stress tolerance of Cyanobacterium Phormidium tenue using in silico analysis

The development of artificial biocrust using cyanobacterium Phormidium tenue has been suggested as an effective strategy to prevent soil degradation. Here, a combination of in silico approaches with growth rate, photosynthetic pigment, morphology, and transcript analysis was used to identify specific genes and their protein products in response to 500 mM NaCl in P. tenue. The results show that 500 mM NaCl induces the expression of genes encoding glycerol-3-phosphate dehydrogenase (glpD) as a Flavoprotein, ribosomal protein S12 methylthiotransferase (rimO), and a hypothetical protein (sll0939). The constructed co-expression network revealed a group of abiotic stress-responsive genes. Using the Basic Local Alignment Search Tool (BLAST), the homologous proteins of rimO, glpD, and sll0939 were identified in the P. tenue genome. Encoded proteins of glpD, rimO, and DUF1622 genes, respectively, contain (DAO and DAO C), (UPF0004, Radical SAM and TRAM 2), and (DUF1622) domains. The predicted ligand included 22B and MG for DUF1622, FS5 for rimO, and FAD for glpD protein. There was no direct disruption in ligand-binding sites of these proteins by Na+, Cl-, or NaCl. The growth rate, photosynthetic pigment, and morphology of P. tenue were investigated, and the result showed an acceptable tolerance rate of this microorganism under salt stress. The quantitative real-time polymerase chain reaction (qRT-PCR) results revealed the up-regulation of glpD, rimO, and DUF1622 genes under salt stress. This is the first report on computational and experimental analyses of the glpD, rimO, and DUF1622 genes in P. tenue under salt stress to the best of our knowledge.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03050-w.

Multiple functions of flagellar motility and chemotaxis in bacterial physiology

Most swimming bacteria are capable of following gradients of nutrients, signaling molecules and other environmental factors that affect bacterial physiology. This tactic behavior became one of the most-studied model systems for signal transduction and quantitative biology, and underlying molecular mechanisms are well characterized in Escherichia coli and several other model bacteria. In this review, we focus primarily on less understood aspect of bacterial chemotaxis, namely its physiological relevance for individual bacterial cells and for bacterial populations. As evident from multiple recent studies, even for the same bacterial species flagellar motility and chemotaxis might serve multiple roles, depending on the physiological and environmental conditions. Among these, finding sources of nutrients and more generally locating niches that are optimal for growth appear to be one of the major functions of bacterial chemotaxis, which could explain many chemoeffector preferences as well as flagellar gene regulation. Chemotaxis might also generally enhance efficiency of environmental colonization by motile bacteria, which involves intricate interplay between individual and collective behaviors and trade-offs between growth and motility. Finally, motility and chemotaxis play multiple roles in collective behaviors of bacteria including swarming, biofilm formation and autoaggregation, as well as in their interactions with animal and plant hosts.

Symbiosis in Digital Evolution: Past, Present, and Future

Symbiosis, the living together of unlike organisms as symbionts, is ubiquitous in the natural world. Symbioses occur within and across all scales of life, from microbial to macro-faunal systems. Further, the interactions between symbionts are multimodal in both strength and type, can span from parasitic to mutualistic within one partnership, and persist over generations. Studying the ecological and evolutionary dynamics of symbiosis in natural or laboratory systems poses a wide range of challenges, including the long time scales at which symbioses evolve de novo, the limited capacity to experimentally control symbiotic interactions, the weak resolution at which we can quantify interactions, and the idiosyncrasies of current model systems. These issues are especially challenging when seeking to understand the ecological effects and evolutionary pressures on and of a symbiosis, such as how a symbiosis may shift between parasitic and mutualistic modes and how that shift impacts the dynamics of the partner population. In digital evolution, populations of computational organisms compete, mutate, and evolve in a virtual environment. Digital evolution features perfect data tracking and allows for experimental manipulations that are impractical or impossible in natural systems. Furthermore, modern computational power allows experimenters to observe thousands of generations of evolution in minutes (as opposed to several months or years), which greatly expands the range of possible studies. As such, digital evolution is poised to become a keystone technique in our methodological repertoire for studying the ecological and evolutionary dynamics of symbioses. Here, we review how digital evolution has been used to study symbiosis, and we propose a series of open questions that digital evolution is well-positioned to answer.

Running after your host: Stimulation of bacterial motility promotes colonization

In this issue of Cell Host and Microbe, Robinson et al. (2021) make elegant use of experimental evolution to demonstrate that increased motility promotes migration toward and colonization of zebrafish larvae by a commensal bacterium. Stimulation of motility depends on bacterial second messenger and on signals released by resident host microbiota.

Rapid evolution of trait correlation networks during bacterial adaptation to the rhizosphere

There is a growing awareness that traits do not evolve individually but rather are organized as modular networks of covarying traits. Although the importance of multi-trait correlation has been linked to the ability to evolve in response to new environmental conditions, the evolvability of the network itself has to date rarely been assessed experimentally. By following the evolutionary dynamics of a model bacterium adapting to plant roots, we demonstrate that the whole structure of the trait correlation network is highly dynamic. We experimentally evolved Pseudomonas protegens, a common rhizosphere dweller, on the roots of Arabidopsis thaliana. We collected bacteria at regular intervals and determined a range of traits linked to growth, stress resistance, and biotic interactions. We observed a rapid disintegration of the original trait correlation network. Ancestral populations showed a modular network, with the traits linked to resource use and stress resistance forming two largely independent modules. This network rapidly was restructured during adaptation, with a loss of the stress resistance module and the appearance of new modules out of previously disconnected traits. These results show that evolutionary dynamics can involve a deep restructuring of phenotypic trait organization, pointing to the emergence of novel life history strategies not represented in the ancestral phenotype.

pH Adaptation Drives Diverse Phenotypes in a Beneficial Bacterium-Host Mutualism

Abiotic variation can influence the evolution of specific phenotypes that contribute to the diversity of bacterial strains observed in the natural environment. Environmentally transmitted symbiotic bacteria are particularly vulnerable to abiotic fluctuations, given that they must accommodate the transition between the free-living state and the host's internal environment. This type of life history strategy can strongly influence the success of a symbiont, and whether adapting to changes outside the host will allow a greater capacity to survive in symbiosis with the host partner. One example of how environmental breadth is advantageous to the symbiosis is the beneficial association between Vibrio fischeri and sepiolid squids (Cephalopoda: Sepiolidae). Since Vibrio bacteria are environmentally transmitted, they are subject to a wide variety of abiotic variables prior to infecting juvenile squids and must be poised to survive in the host light organ. In order to better understand how a changing abiotic factor (e.g., pH) influences the diversification of symbionts and their eventual symbiotic competence, we used an experimental evolution approach to ascertain how pH adaptation affects symbiont fitness. Results show that low pH adapted Vibrio strains have more efficient colonization rates compared to their ancestral strains. In addition, growth rates had significant differences compared to ancestral strains (pH 6.5–6.8, and 7.2). Bioluminescence production (a marker for symbiont competence) of pH evolved strains also improved at pH 6.5–7.2. Results imply that the evolution and diversification of Vibrio strains adapted to low pH outside the squid improves fitness inside the squid by allowing a higher success rate for host colonization and symbiotic competence.

Prokaryotic life finds a way: insights from evolutionary experimentation in bacteria

While evolution proceeds through the generation of random variant alleles, the application of selective pressures can select for subsets of mutations that confer fitness-improving physiological benefits. This, in essence, defines the process of adaptive evolution. The rapid replication rate of bacteria has allowed for the design of experiments to study these processes over a reasonable timeframe within a laboratory setting. This has been greatly assisted by advances in tractability of diverse microorganisms, next generation sequencing technologies and bioinformatic analysis pipelines. Examining the processes by which organisms adapt their genetic code to cope with sub-optimal growth conditions has yielded a wealth of molecular insight into diverse biological processes. Here we discuss how the study of adaptive evolutionary trajectories in bacteria has allowed for improved understanding of stress responses, revealed important insight into microbial physiology, allowed for the production of highly optimised strains for use in biotechnology and increased our knowledge of the role of genomic plasticity in chronic infections.

Mutation-induced remodeling of the BfmRS two-component system in Pseudomonas aeruginosa clinical isolates

Genetic mutations are a primary driving force behind the adaptive evolution of bacterial pathogens. Multiple clinical isolates of Pseudomonas aeruginosa, an important human pathogen, have naturally evolved one or more missense mutations in bfmS, which encodes the sensor histidine kinase of the BfmRS two-component system (TCS). A mutant BfmS protein containing both the L181P and E376Q substitutions increased the phosphorylation and thus the transcriptional regulatory activity of its cognate downstream response regulator, BfmR. This reduced acute virulence and enhanced biofilm formation, both of which are phenotypic changes associated with a chronic infection state. The increased phosphorylation of BfmR was due, at least in part, to the cross-phosphorylation of BfmR by GtrS, a noncognate sensor kinase. Other spontaneous missense mutations in bfmS, such as A42E/G347D, T242R, and R393H, also caused a similar remodeling of the BfmRS TCS in P. aeruginosa This study highlights the plasticity of TCSs mediated by spontaneous mutations and suggests that mutation-induced activation of BfmRS may contribute to host adaptation by P. aeruginosa during chronic infections.

The Open Challenge of in vitro Modeling Complex and Multi-Microbial Communities in Three-Dimensional Niches

The comprehension of the underlying mechanisms of the interactions within microbial communities represents a major challenge to be faced to control their outcome. Joint efforts of in vitro, in vivo and ecological models are crucial to controlling human health, including chronic infections. In a broader perspective, considering that polymicrobial communities are ubiquitous in nature, the understanding of these mechanisms is the groundwork to control and modulate bacterial response to any environmental condition. The reduction of the complex nature of communities of microorganisms to a single bacterial strain could not suffice to recapitulate the in vivo situation observed in mammals. Furthermore, some bacteria can adapt to various physiological or arduous environments embedding themselves in three-dimensional matrices, secluding from the external environment. Considering the increasing awareness that dynamic complex and dynamic population of microorganisms (microbiota), inhabiting different apparatuses, regulate different health states and protect against pathogen infections in a fragile and dynamic equilibrium, we underline the need to produce models to mimic the three-dimensional niches in which bacteria, and microorganisms in general, self-organize within a microbial consortium, strive and compete. This review mainly focuses, as a case study, to lung pathology-related dysbiosis and life-threatening diseases such as cystic fibrosis and bronchiectasis, where the co-presence of different bacteria and the altered 3D-environment, can be considered as worst-cases for chronic polymicrobial infections. We illustrate the state-of-art strategies used to study biofilms and bacterial niches in chronic infections, and multispecies ecological competition. Although far from the rendering of the 3D-environments and the polymicrobial nature of the infections, they represent the starting point to face their complexity. The increase of knowledge respect to the above aspects could positively affect the actual healthcare scenario. Indeed, infections are becoming a serious threat, due to the increasing bacterial resistance and the slow release of novel antibiotics on the market.

Virulence Regulation and Innate Host Response in the Pathogenicity of Vibrio cholerae

The human pathogen Vibrio cholerae is the causative agent of severe diarrheal disease known as cholera. Of the more than 200 "O" serogroups of this pathogen, O1 and O139 cause cholera outbreaks and epidemics. The rest of the serogroups, collectively known as non-O1/non-O139 cause sporadic moderate or mild diarrhea and also systemic infections. Pathogenic V. cholerae circulates between nutrient-rich human gut and nutrient-deprived aquatic environment. As an autochthonous bacterium in the environment and as a human pathogen, V. cholerae maintains its survival and proliferation in these two niches. Growth in the gastrointestinal tract involves expression of several genes that provide bacterial resistance against host factors. An intricate regulatory program involving extracellular signaling inputs is also controlling this function. On the other hand, the ability to store carbon as glycogen facilitates bacterial fitness in the aquatic environment. To initiate the infection, V. cholerae must colonize the small intestine after successfully passing through the acid barrier in the stomach and survive in the presence of bile and antimicrobial peptides in the intestinal lumen and mucus, respectively. In V. cholerae, virulence is a multilocus phenomenon with a large functionally associated network. More than 200 proteins have been identified that are functionally linked to the virulence-associated genes of the pathogen. Several of these genes have a role to play in virulence and/or in functions that have importance in the human host or the environment. A total of 524 genes are differentially expressed in classical and El Tor strains, the two biotypes of V. cholerae serogroup O1. Within the host, many immune and biological factors are able to induce genes that are responsible for survival, colonization, and virulence. The innate host immune response to V. cholerae infection includes activation of several immune protein complexes, receptor-mediated signaling pathways, and other bactericidal proteins. This article presents an overview of regulation of important virulence factors in V. cholerae and host response in the context of pathogenesis.

Escherichia coli Has a Unique Transcriptional Program in Long-Term Stationary Phase Allowing Identification of Genes Important for Survival

Microbes live in complex and constantly changing environments, but it is difficult to replicate this in the laboratory. Escherichia coli has been used as a model organism in experimental evolution studies for years; specifically, we and others have used it to study evolution in complex environments by incubating the cells into long-term stationary phase (LTSP) in rich media. In LTSP, cells experience a variety of stresses and changing conditions. While we have hypothesized that this experimental system is more similar to natural environments than some other lab conditions, we do not yet know how cells respond to this environment biochemically or physiologically. In this study, we began to unravel the cells' responses to this environment by characterizing the transcriptome of cells during LTSP. We found that cells in LTSP have a unique transcriptional program and that several genes are uniquely upregulated or downregulated in this phase. Further, we identified two genes, cspB and cspI, which are most highly expressed in LTSP, even though these genes are primarily known to respond to cold shock. By competing cells lacking these genes with wild-type cells, we show that these genes are also important for survival during LTSP. These data can help identify gene products that may play a role in survival in this complex environment and lead to identification of novel functions of proteins.IMPORTANCE Experimental evolution studies have elucidated evolutionary processes, but usually in chemically well-defined and/or constant environments. Using complex environments is important to begin to understand how evolution may occur in natural environments, such as soils or within a host. However, characterizing the stresses that cells experience in these complex environments can be challenging. One way to approach this is by determining how cells biochemically acclimate to heterogenous environments. In this study, we began to characterize physiological changes by analyzing the transcriptome of cells in a dynamic complex environment. By characterizing the transcriptional profile of cells in long-term stationary phase, a heterogenous and stressful environment, we can begin to understand how cells physiologically and biochemically react to the laboratory environment, and how this compares to more-natural conditions.

Simulating the evolutionary trajectories of metabolic pathways for insect symbionts in the genus Sodalis

Insect-bacterial symbioses are ubiquitous, but there is still much to uncover about how these relationships establish, persist and evolve. The tsetse endosymbiont Sodalis glossinidius displays intriguing metabolic adaptations to its microenvironment, but the process by which this relationship evolved remains to be elucidated. The recent chance discovery of the free-living species of the genus Sodalis, Sodalis praecaptivus, provides a serendipitous starting point from which to investigate the evolution of this symbiosis. Here, we present a flux balance model for S. praecaptivus and empirically verify its predictions. Metabolic modelling is used in combination with a multi-objective evolutionary algorithm to explore the trajectories that S. glossinidius may have undertaken from this starting point after becoming internalized. The order in which key genes are lost is shown to influence the evolved populations, providing possible targets for future in vitro genetic manipulation. This method provides a detailed perspective on possible evolutionary trajectories for S. glossinidius in this fundamental process of evolutionary and ecological change.

In silico bacteria evolve robust cooperaion via complex quorum-sensing strategies

Many species of bacteria collectively sense and respond to their social and physical environment via 'quorum sensing' (QS), a communication system controlling extracellular cooperative traits. Despite detailed understanding of the mechanisms of signal production and response, there remains considerable debate over the functional role(s) of QS: in short, what is it for? Experimental studies have found support for diverse functional roles: density sensing, mass-transfer sensing, genotype sensing, etc. While consistent with theory, these results cannot separate whether these functions were drivers of QS adaption, or simply artifacts or 'spandrels' of systems shaped by distinct ecological pressures. The challenge of separating spandrels from drivers of adaptation is particularly hard to address using extant bacterial species with poorly understood current ecologies (let alone their ecological histories). To understand the relationship between defined ecological challenges and trajectories of QS evolution, we used an agent-based simulation modeling approach. Given genetic mixing, our simulations produce behaviors that recapitulate features of diverse microbial QS systems, including coercive (high signal/low response) and generalized reciprocity (signal auto-regulation) strategists - that separately and in combination contribute to QS-dependent resilience of QS-controlled cooperation in the face of diverse cheats. We contrast our in silico results given defined ecological challenges with bacterial QS architectures that have evolved under largely unknown ecological contexts, highlighting the critical role of genetic constraints in shaping the shorter term (experimental evolution) dynamics of QS. More broadly, we see experimental evolution of digital organisms as a complementary tool in the search to understand the emergence of complex QS architectures and functions.

Dynamics of genetic variation in transcription factors and its implications for the evolution of regulatory networks in Bacteria

The evolution of regulatory networks in Bacteria has largely been explained at macroevolutionary scales through lateral gene transfer and gene duplication. Transcription factors (TF) have been found to be less conserved across species than their target genes (TG). This would be expected if TFs accumulate mutations faster than TGs. This hypothesis is supported by several lab evolution studies which found TFs, especially global regulators, to be frequently mutated. Despite these studies, the contribution of point mutations in TFs to the evolution of regulatory network is poorly understood. We tested if TFs show greater genetic variation than their TGs using whole-genome sequencing data from a large collection of Escherichia coli isolates. TFs were less diverse than their TGs across natural isolates, with TFs of large regulons being more conserved. In contrast, TFs showed higher mutation frequency in adaptive laboratory evolution experiments. However, over long-term laboratory evolution spanning 60 000 generations, mutation frequency in TFs gradually declined after a rapid initial burst. Extrapolating the dynamics of genetic variation from long-term laboratory evolution to natural populations, we propose that point mutations, conferring large-scale gene expression changes, may drive the early stages of adaptation but gene regulation is subjected to stronger purifying selection post adaptation.

Multiple Roles of c-di-GMP Signaling in Bacterial Pathogenesis

The intracellular signaling molecule cyclic di-GMP (c-di-GMP) regulates the lifestyle of bacteria and controls many key functions and mechanisms. In the case of bacterial pathogens, a wide variety of virulence lifestyle factors have been shown to be regulated by c-di-GMP. Evidence of the importance of this molecule for bacterial pathogenesis has become so great that new antimicrobial agents are tested for their capacity of targeting c-di-GMP signaling. This review summarizes the current knowledge on this topic and reveals its application for the development of new antivirulence intervention strategies.

Experimental Evolution of Legume Symbionts: What Have We Learnt?

Rhizobia, the nitrogen-fixing symbionts of legumes, are polyphyletic bacteria distributed in many alpha- and beta-proteobacterial genera. They likely emerged and diversified through independent horizontal transfers of key symbiotic genes. To replay the evolution of a new rhizobium genus under laboratory conditions, the symbiotic plasmid of Cupriavidus taiwanensis was introduced in the plant pathogen Ralstonia solanacearum, and the generated proto-rhizobium was submitted to repeated inoculations to the C. taiwanensis host, Mimosa pudica L.. This experiment validated a two-step evolutionary scenario of key symbiotic gene acquisition followed by genome remodeling under plant selection. Nodulation and nodule cell infection were obtained and optimized mainly via the rewiring of regulatory circuits of the recipient bacterium. Symbiotic adaptation was shown to be accelerated by the activity of a mutagenesis cassette conserved in most rhizobia. Investigating mutated genes led us to identify new components of R. solanacearum virulence and C. taiwanensis symbiosis. Nitrogen fixation was not acquired in our short experiment. However, we showed that post-infection sanctions allowed the increase in frequency of nitrogen-fixing variants among a non-fixing population in the M. pudica-C. taiwanensis system and likely allowed the spread of this trait in natura. Experimental evolution thus provided new insights into rhizobium biology and evolution.

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

The effect of spatiotemporal antibiotic inhomogeneities on the evolution of resistance

Combating the evolution of widespread antibiotic resistance is one of the most pressing challenges facing modern medicine. Recent research has demonstrated that the evolution of pathogens with high levels of resistance can be accelerated by spatial and temporal inhomogeneities in antibiotic concentration, which frequently arise in patients and the environment. Strategies to predict and counteract the effects of such inhomogeneities will be critical in the fight against resistance. In this paper we develop a mechanistic framework for modelling the adaptive evolution of resistance in the presence of spatiotemporal antibiotic concentrations, which treats the adaptive process as an interaction between two mutually orthogonal forces; the first returns cells to their wild-type state in the absence of antibiotic selection, and the second selects for increased coping ability in the presence of an antibiotic. We apply our model to investigate laboratory adaptation experiments, and then extend it to consider the case in which multiple strategies for resistance undergo competitive evolution.

Trusting the hand that feeds: microbes evolve to anticipate a serial transfer protocol as individuals or collectives

BACKGROUND

Experimental evolution of microbes often involves a serial transfer protocol, where microbes are repeatedly diluted by transfer to a fresh medium, starting a new growth cycle. This has revealed that evolution can be remarkably reproducible, where microbes show parallel adaptations both on the level of the phenotype as well as the genotype. However, these studies also reveal a strong potential for divergent evolution, leading to diversity both between and within replicate populations. We here study how in silico evolved Virtual Microbe "wild types" (WTs) adapt to a serial transfer protocol to investigate generic evolutionary adaptations, and how these adaptations can be manifested by a variety of different mechanisms.

RESULTS

We show that all WTs evolve to anticipate the regularity of the serial transfer protocol by adopting a fine-tuned balance of growth and survival. This anticipation is done by evolving either a high yield mode, or a high growth rate mode. We find that both modes of anticipation can be achieved by individual lineages and by collectives of microbes. Moreover, these different outcomes can be achieved with or without regulation, although the individual-based anticipation without regulation is less well adapted in the high growth rate mode.

CONCLUSIONS

All our in silico WTs evolve to trust the hand that feeds by evolving to anticipate the periodicity of a serial transfer protocol, but can do so by evolving two distinct growth strategies. Furthermore, both these growth strategies can be accomplished by gene regulation, a variety of different polymorphisms, and combinations thereof. Our work reveals that, even under controlled conditions like those in the lab, it may not be possible to predict individual evolutionary trajectories, but repeated experiments may well result in only a limited number of possible outcomes.

Transcriptional Mutagenesis Prevents Ribosomal DNA Deterioration: The Role of Duplications and Deletions

Clashes between transcription and replication complexes can cause point mutations and chromosome rearrangements on heavily transcribed genes. In eukaryotic ribosomal RNA genes, the system that prevents transcription-replication conflicts also causes frequent copy number variation. Such fast mutational dynamics do not alter growth rates in yeast and are thus selectively near neutral. It was recently found that yeast regulates these mutations by means of a signaling cascade that depends on the availability of nutrients. Here, we investigate the long-term evolutionary effect of the mutational dynamics observed in yeast. We developed an in silico model of single-cell organisms whose genomes mutate more frequently when transcriptional load is larger. We show that mutations induced by high transcriptional load are beneficial when biased toward gene duplications and deletions: they decrease mutational load even though they increase the overall mutation rates. In contrast, genome stability is compromised when mutations are not biased toward gene duplications and deletions, even when mutations occur much less frequently. Taken together, our results show that the mutational dynamics observed in yeast are beneficial for the long-term stability of the genome and pave the way for a theory of evolution where genetic operators are themselves cause and outcome of the evolutionary dynamics.

A multihost bacterial pathogen overcomes continuous population bottlenecks to adapt to new host species

While many bacterial pathogens are restricted to single host species, some have the capacity to undergo host switches, leading to the emergence of new clones that are a threat to human and animal health. However, the bacterial traits that underpin a multihost ecology are not well understood. Following transmission to a new host, bacterial populations are influenced by powerful forces such as genetic drift that reduce the fixation rate of beneficial mutations, limiting the capacity for host adaptation. Here, we implement a novel experimental model of bacterial host switching to investigate the ability of the multihost pathogen Staphylococcus aureus to adapt to new species under continuous population bottlenecks. We demonstrate that beneficial mutations accumulated during infection can overcome genetic drift and sweep through the population, leading to host adaptation. Our findings highlight the remarkable capacity of some bacteria to adapt to distinct host niches in the face of powerful antagonistic population forces.

Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure

Background

Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental change. However in stable conditions, they suffer from the increased mutational load, hence being overcome by non-mutators. However, these results don’t take into account the fact that an elevated mutation rate can impact the genetic structure, hence changing the sensitivity of the population to mutations. Here we used Aevol, an in silico experimental evolution platform in which genomic structures are free to evolve, in order to study the fate of mutator populations evolving for a long time in constant conditions.

Results

Starting from wild-types that were pre-evolved for 300,000 generations, we let 100 mutator populations (point mutation rate ×100) evolve for 100,000 further generations in constant conditions. As expected all populations initially undergo a fitness loss. However, after that the mutator populations started to recover. Most populations ultimately recovered their ancestors fitness, and a significant fraction became even fitter than the non-mutator control clones that evolved in parallel. By analyzing the genomes of the mutators, we show that the fitness recovery is due to two mechanisms: i. an increase in robustness through compaction of the coding part of the mutator genomes, ii. an increase of the selection coefficient that decreases the mean-fitness of the population. Strikingly the latter is due to the accumulation of non-coding sequences in the mutators genomes.

Conclusion

Our results show that the mutational burden that is classically thought to be associated with mutator phenotype is escapable. On the long run mutators adapted their genomes and reshaped the distribution of mutation effects. Therewith the lineage is able to recover fitness even though the population still suffers the elevated mutation rate. Overall these results change our view of mutator dynamics: by being able to reduce the deleterious effect of the elevated mutation rate, mutator populations may be able to last for a very long time; A situation commonly observed in nature.

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.

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

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

Ribosome Provisioning Activates a Bistable Switch Coupled to Fast Exit from Stationary Phase

Observations of bacteria at the single-cell level have revealed many instances of phenotypic heterogeneity within otherwise clonal populations, but the selective causes, molecular bases, and broader ecological relevance remain poorly understood. In an earlier experiment in which the bacterium Pseudomonas fluorescens SBW25 was propagated under a selective regime that mimicked the host immune response, a genotype evolved that stochastically switched between capsulation states. The genetic cause was a mutation in carB that decreased the pyrimidine pool (and growth rate), lowering the activation threshold of a preexisting but hitherto unrecognized phenotypic switch. Genetic components surrounding bifurcation of UTP flux toward DNA/RNA or UDP-glucose (a precursor of colanic acid forming the capsules) were implicated as key components. Extending these molecular analyses-and based on a combination of genetics, transcriptomics, biochemistry, and mathematical modeling-we show that pyrimidine limitation triggers an increase in ribosome biosynthesis and that switching is caused by competition between ribosomes and CsrA/RsmA proteins for the mRNA transcript of a positively autoregulated activator of colanic acid biosynthesis. We additionally show that in the ancestral bacterium the switch is part of a program that determines stochastic entry into a semiquiescent capsulated state, ensures that such cells are provisioned with excess ribosomes, and enables provisioned cells to exit rapidly from stationary phase under permissive conditions.

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

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

Towards a systematic understanding of differences between archaeal and bacterial diversity

In this crystal ball, we discuss emerging methodologies that can help reaching a synthesis on the biodiversity of Archaea and Bacteria and thereby inform a central enigma in microbiology, i.e. the fundamental split between these primary domains of life and the apparent lower diversity of the Archaea.

Convergent Metabolic Specialization through Distinct Evolutionary Paths in Pseudomonas aeruginosa

Evolution by natural selection under complex and dynamic environmental conditions occurs through intricate and often counterintuitive trajectories affecting many genes and metabolic solutions. To study short- and long-term evolution of bacteria in vivo, we used the natural model system of cystic fibrosis (CF) infection. In this work, we investigated how and through which trajectories evolution of Pseudomonas aeruginosa occurs when migrating from the environment to the airways of CF patients, and specifically, we determined reduction of growth rate and metabolic specialization as signatures of adaptive evolution. We show that central metabolic pathways of three distinct Pseudomonas aeruginosa lineages coevolving within the same environment become restructured at the cost of versatility during long-term colonization. Cell physiology changes from naive to adapted phenotypes resulted in (i) alteration of growth potential that particularly converged to a slow-growth phenotype, (ii) alteration of nutritional requirements due to auxotrophy, (iii) tailored preference for carbon source assimilation from CF sputum, (iv) reduced arginine and pyruvate fermentation processes, and (v) increased oxygen requirements. Interestingly, although convergence was evidenced at the phenotypic level of metabolic specialization, comparative genomics disclosed diverse mutational patterns underlying the different evolutionary trajectories. Therefore, distinct combinations of genetic and regulatory changes converge to common metabolic adaptive trajectories leading to within-host metabolic specialization. This study gives new insight into bacterial metabolic evolution during long-term colonization of a new environmental niche.

Only a few examples of real-time evolutionary investigations in environments outside the laboratory are described in the scientific literature. Remembering that biological evolution, as it has progressed in nature, has not taken place in test tubes, it is not surprising that conclusions from our investigations of bacterial evolution in the CF model system are different from what has been concluded from laboratory experiments. The analysis presented here of the metabolic and regulatory driving forces leading to successful adaptation to a new environment provides an important insight into the role of metabolism and its regulatory mechanisms for successful adaptation of microorganisms in dynamic and complex environments. Understanding the trajectories of adaptation, as well as the mechanisms behind slow growth and rewiring of regulatory and metabolic networks, is a key element to understand the adaptive robustness and evolvability of bacteria in the process of increasing their in vivo fitness when conquering new territories.

Evolution of a Dominant Natural Isolate of Escherichia coli in the Human Gut over the Course of a Year Suggests a Neutral Evolution with Reduced Effective Population Size

In vitro and in vivo evolution experiments on Escherichia coli revealed several principles of bacterial adaptation. However, few data are available in the literature describing the behavior of E. coli in its natural environment. We attempted here to study the evolution in the human gut of a commensal dominant E. coli clone, ED1a belonging to the B2 phylogroup, through a longitudinal genomic study. We sequenced 24 isolates sampled at three different time points within a healthy individual over almost a year. We computed a mutation rate of 6.90 × 10^-7 mutations per base per year of the chromosome for E. coli ED1a in healthy human gut. We observed very limited genomic diversity and could not detect any evidence of selection, in contrast to what is observed in experimental evolution over a similar length of time. We therefore suggest that ED1a, being well adapted to the healthy human gut, evolves mostly neutrally with a low effective population size (N_e of ≈500 to 1,700).IMPORTANCE In this study, we follow the genomic fate of a dominant clone of Escherichia coli in the human gut of a healthy individual over about a year. We could compute a low annual mutation rate that supports low diversity, and we could not retrieve any clear signature of selection. These observations support a neutral evolution of E. coli in the human gut, compatible with a very limited effective population size that deviates drastically with the observations made previously in experimental evolution.

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Many believe that an essential component for the discovery of the tremendous diversity in natural organisms was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g., offspring tend to have similar-size legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization is rarely reported in computational simulations of evolution, which deprives us of in silico examples of canalization to study and raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally, and it could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this article, we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be more modular and hierarchical than expected by chance, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.

Evolutionary Remodeling of Bacterial Motility Checkpoint Control

Regulatory networks play a central role in the relationship between genotype and phenotype in all organisms. However, the mechanisms that underpin the evolutionary plasticity of these networks remain poorly understood. Here, we used experimental selection for enhanced bacterial motility in a porous environment to explore the adaptability of one of the most complex networks known in bacteria. We found that the resulting phenotypic changes are mediated by adaptive mutations in several functionally different proteins, including multiple components of the flagellar motor. Nevertheless, this evolutionary adaptation could be explained by a single mechanism, namely remodeling of the checkpoint regulating flagellar gene expression. Supported by computer simulations, our findings suggest that the specific “bow-tie” topology of the checkpoint facilitates evolutionary tuning of the cost-benefit trade-off between motility and growth. We propose that bow-tie regulatory motifs, which are widespread in cellular networks, play a general role in evolutionary adaptation.

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Multiple mutations enhance swimming behavior under selection

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A universal trade-off relationship between motility and growth is observed

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Checkpoint remodeling provides a mechanism of evolutionary adaptation

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Bow-tie topology facilitates evolvability of the motility network

Accumulation of Deleterious Mutations During Bacterial Range Expansions

Recent theory predicts that the fitness of pioneer populations can decline when species expand their range, due to high rates of genetic drift on wave fronts making selection less efficient at purging deleterious variants. To test these predictions, we studied the fate of mutator bacteria expanding their range for 1650 generations on agar plates. In agreement with theory, we find that growth abilities of strains with a high mutation rate (HMR lines) decreased significantly over time, unlike strains with a lower mutation rate (LMR lines) that present three to four times fewer mutations. Estimation of the distribution of fitness effect under a spatially explicit model reveals a mean negative effect for new mutations (-0.38%), but it suggests that both advantageous and deleterious mutations have accumulated during the experiment. Furthermore, the fitness of HMR lines measured in different environments has decreased relative to the ancestor strain, whereas that of LMR lines remained unchanged. Contrastingly, strains with a HMR evolving in a well-mixed environment accumulated less mutations than agar-evolved strains and showed an increased fitness relative to the ancestor. Our results suggest that spatially expanding species are affected by deleterious mutations, leading to a drastic impairment of their evolutionary potential.

Robustness of a model microbial community emerges from population structure among single cells of a clonal population

Microbial populations can withstand, overcome and persist in the face of environmental fluctuation. Previously, we demonstrated how conditional gene regulation in a fluctuating environment drives dilution of condition-specific transcripts, causing a population of Desulfovibrio vulgaris Hildenborough (DvH) to collapse after repeatedly transitioning from sulfate respiration to syntrophic conditions with the methanogen Methanococcus maripaludis. Failure of the DvH to successfully transition contributed to the collapse of this model community. We investigated the mechanistic basis for loss of robustness by examining whether conditional gene regulation altered heterogeneity in gene expression across individual DvH cells. We discovered that robustness of a microbial population across environmental transitions was attributable to the retention of cells in two states that exhibited different condition-specific gene expression patterns. In our experiments, a population with disrupted conditional regulation successfully alternated between cell states. Meanwhile, a population with intact conditional regulation successfully switched between cell states initially, but collapsed after repeated transitions, possibly due to the high energy requirements of regulation. These results demonstrate that the survival of this entire model microbial community is dependent on the regulatory system's influence on the distribution of distinct cell states among individual cells within a clonal population.