Adaptation of Escherichia coli to long-term batch culture in various rich media

Single-cell genomics reveals population structures from in vitro evolutionary studies of Salmonella

Single-cell DNA sequencing has the potential to reveal detailed hierarchical structures in evolving populations of cells. Single cell approaches are increasingly used to study clonal evolution in human ageing and cancer but have not yet been deployed to study evolving clonal microbial populations. Here, we present an approach for single bacterial genomic analysis for in vitro evolution experiments using FACS isolation of individual bacteria followed by whole-genome amplification and sequencing. We apply this to the experimental evolution of a hypermutator strain of Salmonella in response to antibiotic stress (ciprofloxacin). By analysing sequence polymorphisms in individual cells from populations we identified the presence and prevalence of sub-populations which have acquired polymorphisms in genes previously demonstrated to be associated with ciprofloxacin susceptibility. We were also able to identify that the population exposed to antibiotic stress was able to develop resistance whilst maintaining diversity. This population structure could not be resolved from bulk sequence data, and our results show how high-throughput single-cell sequencing can enhance experimental studies of bacterial evolution.

Parallel Evolution towards Increased Motility in Long-Term Cultures of Escherichia coli, Even Though Motility was Not Required for Long-Term Survival

Escherichia coli can survive for long periods in batch culture in the laboratory, where they experience a stressful and heterogeneous environment. During this incubation, E. coli acquires mutations that are selected in response to this environment, ultimately leading to evolved populations that are better adapted to these complex conditions, which can lead to a better understanding of evolutionary mechanisms. Mutations in regulatory genes often play a role in adapting to heterogeneous environments. To identify such mutations, we examined transcriptional differences during log phase growth in unaged cells compared to those that had been aged for 10 days and regrown. We identified expression changes in genes involved in motility and chemotaxis after adaptation to long-term cultures. We hypothesized that aged populations would also have phenotypic changes in motility and that motility may play a role in survival and adaptation to long-term cultures. While aged populations did show an increase in motility, this increase was not essential for survival in long-term cultures. We identified mutations in the regulatory gene sspA and other genes that may contribute to the observed differences in motility. Taken together, these data provide an overall picture of the role of mutations in regulatory genes for adaptation while underscoring that all changes that occur during evolution in stressful environments are not necessarily adaptive. IMPORTANCE Understanding how bacteria adapt in long-term cultures aids in both better treatment options for bacterial infections and gives insight into the mechanisms involved in bacterial evolution. In the past, it has been difficult to study these organisms in their natural environments. By using experimental evolution in heterogeneous and stressful laboratory conditions, we can more closely mimic natural environments and examine evolutionary mechanisms. One way to observe these mechanisms is to look at transcriptomic and genomic data from cells adapted to these complex conditions. Here, we found that although aged cells increase motility, this increase is not essential for survival in these conditions. These data emphasize that not all changes that occur due to evolutionary processes are adaptive, but these observations could still lead to hypotheses about the causative mutations. The information gained here allow us to make inferences about general mechanisms underlying phenotypic changes due to evolution.

The role of sigma factor competition in bacterial adaptation under prolonged starvation

The study of adaptive microbial evolution in the laboratory can illuminate the genetic mechanisms of gaining fitness under a pre-defined set of selection factors. Laboratory evolution of bacteria under long-term starvation has gained importance in recent years because of its ability to uncover adaptive strategies that overcome prolonged nutrient limitation, a condition often encountered by natural microbes. In this evolutionary paradigm, bacteria are maintained in an energy-restricted environment in a growth phase called long-term stationary phase (LTSP). This phase is characterized by a stable, viable population size and highly dynamic genetic changes. Multiple independent iterations of LTSP evolution experiments have given rise to mutants that are slow-growing compared to the ancestor. Although the antagonistic regulation between rapid growth and the stress response is well-known in bacteria (especially Escherichia coli), the growth deficit of many LTSP-adapted mutants has not been explored in detail. In this review, I pinpoint the trade-off between growth and stress response as a dominant driver of evolutionary strategies under prolonged starvation. Focusing on mainly E. coli-based research, I discuss the various affectors and regulators of the competition between sigma factors to occupy their targets on the genome, and assess its effect on growth advantage in stationary phase (GASP). Finally, I comment on some crucial issues that hinder the progress of the field, including identification of novel metabolites in nutrient-depleted media, and the importance of using multidisciplinary research to resolve them.

Endurance of extremely prolonged nutrient prevention across kingdoms of life

Numerous observations demonstrate that microorganisms can survive very long periods of nutrient deprivation and starvation. Moreover, it is evident that prolonged periods of starvation are a feature of many habitats, and many cells in all kingdoms of life are found in prolonged starvation conditions. Bacteria exhibit a range of responses to long-term starvation. These include genetic adaptations such as the long-term stationary phase and the growth advantage in stationary phase phenotypes characterized by mutations in stress-signaling genes and elevated mutation rates. Here, we suggest using the term "endurance of prolonged nutrient prevention" (EPNP phase), to describe this phase, which was also recently described in eukaryotes. Here, we review this literature and describe the current knowledge about the adaptations to very long-term starvation conditions in bacteria and eukaryotes, its conceptual and structural conservation across all kingdoms of life, and point out possible directions that merit further research.

Experimental Evolution in Plant-Microbe Systems: A Tool for Deciphering the Functioning and Evolution of Plant-Associated Microbial Communities

In natural environments, microbial communities must constantly adapt to stressful environmental conditions. The genetic and phenotypic mechanisms underlying the adaptive response of microbial communities to new (and often complex) environments can be tackled with a combination of experimental evolution and next generation sequencing. This combination allows to analyse the real-time evolution of microbial populations in response to imposed environmental factors or during the interaction with a host, by screening for phenotypic and genotypic changes over a multitude of identical experimental cycles. Experimental evolution (EE) coupled with comparative genomics has indeed facilitated the monitoring of bacterial genetic evolution and the understanding of adaptive evolution processes. Basically, EE studies had long been done on single strains, allowing to reveal the dynamics and genetic targets of natural selection and to uncover the correlation between genetic and phenotypic adaptive changes. However, species are always evolving in relation with other species and have to adapt not only to the environment itself but also to the biotic environment dynamically shaped by the other species. Nowadays, there is a growing interest to apply EE on microbial communities evolving under natural environments. In this paper, we provide a non-exhaustive review of microbial EE studies done with systems of increasing complexity (from single species, to synthetic communities and natural communities) and with a particular focus on studies between plants and plant-associated microorganisms. We highlight some of the mechanisms controlling the functioning of microbial species and their adaptive responses to environment changes and emphasize the importance of considering bacterial communities and complex environments in EE studies.

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.

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.

Microfluidic Synchronizer Using a Synthetic Nanoparticle-Capped Bacterium

Conventional techniques to synchronize bacterial cells often require manual manipulations and lengthy incubation lacking precise temporal control. An automated microfluidic device was recently developed to overcome these limitations. However, it exploits the stalk property of Caulobacter crescentus that undergoes asymmetric stalked and swarmer cell cycle stages and is therefore restricted to this species. To address this shortcoming, we have engineered Escherichia coli cells to adhere to microchannel walls via a synthetic and inducible "stalk". The pole of E. coli is capped by magnetic fluorescent nanoparticles via a polar-localized outer membrane protein. A mass of cells is immobilized in a microfluidic chamber by an externally applied magnetic field. Daughter cells are formed without the induced stalk and hence are flushed out, yielding a synchronous population of "baby" cells. The stalks can be tracked by GFP and nanoparticle fluorescence; no fluorescence signal is detected in the eluted cell population, indicating that it consists solely of daughters. The collected daughter cells display superb synchrony. The results demonstrate a new on-chip method to synchronize the model bacterium E. coli and likely other bacterial species, and also foster the application of synthetic biology to the study of the bacterial cell cycle.

Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa

Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To study this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. Our data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. A genome-scale metabolic network reconstruction of P. aeruginosa was paired with whole-genome sequencing data to predict genes contributing to observed changes in metabolism. We experimentally validated computational predictions to identify mutations in resistant P. aeruginosa affecting loss of catabolic function. Finally, we found a shared metabolic phenotype between lab-evolved P. aeruginosa and clinical isolates with similar mutational landscapes. Our results build upon previous knowledge of antibiotic-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens.

Acute biotoxicity assessment of heavy metals in sewage sludge based on the glucose consumption of Escherichia coli

As a simple and feasible method for acute biotoxicity assessment, personal glucose meter (PGM) can be successfully applied in the early warning of environmental pollutants in sewage. In this paper, the acute biotoxicity of single and joint heavy metals in sewage and real sludge samples was systematically described based on the glucose metabolism of Escherichia coli (E. coli). Results indicated that the biotoxicity order of five single heavy metals in sewage was Hg2+ > As3+ > Cu2+ > Zn2+ > Cd2+. The joint heavy metals of Cu2+ + Zn2+, Cu2+ + Cd2+, and Cu2+ + Hg2+ produced synergistic effects, while Cu2+ + As3+ and Cd2+ + Zn2+ possessed antagonistic effects for the combined biotoxicity. In spiked sludge, Cd2+ and Zn2+ owned higher biotoxicity than Cu2+ and As3+. Notably, the electroplate factory and housing estate sludge respectively showed the highest and lowest inhibition rates as 57.4% and 17.7% under the real sludge biotoxicity assessment. These results demonstrated that PGM was a sensitive and portable method, which could be widely used for acute biotoxicity assessment of heavy metals in sewage sludge.