Genome dynamics during experimental evolution

Evolution of satellite plasmids can prolong the maintenance of newly acquired accessory genes in bacteria

Transmissible plasmids spread genes encoding antibiotic resistance and other traits to new bacterial species. Here we report that laboratory populations of Escherichia coli with a newly acquired IncQ plasmid often evolve 'satellite plasmids' with deletions of accessory genes and genes required for plasmid replication. Satellite plasmids are molecular parasites: their presence reduces the copy number of the full-length plasmid on which they rely for their continued replication. Cells with satellite plasmids gain an immediate fitness advantage from reducing burdensome expression of accessory genes. Yet, they maintain copies of these genes and the complete plasmid, which potentially enables them to benefit from and transmit the traits they encode in the future. Evolution of satellite plasmids is transient. Cells that entirely lose accessory gene function or plasmid mobility dominate in the long run. Satellite plasmids also evolve in Snodgrassella alvi colonizing the honey bee gut, suggesting that this mechanism may broadly contribute to the importance of IncQ plasmids as agents of bacterial gene transfer in nature.

Fail-safe genetic codes designed to intrinsically contain engineered organisms

One challenge in engineering organisms is taking responsibility for their behavior over many generations. Spontaneous mutations arising before or during use can impact heterologous genetic functions, disrupt system integration, or change organism phenotype. Here, we propose restructuring the genetic code itself such that point mutations in protein-coding sequences are selected against. Synthetic genetic systems so-encoded should fail more safely in response to most spontaneous mutations. We designed fail-safe codes and simulated their expected effects on the evolution of so-encoded proteins. We predict fail-safe codes supporting expression of 20 or 15 amino acids could slow protein evolution to ∼30% or 0% the rate of standard-encoded proteins, respectively. We also designed quadruplet-codon codes that should ensure all single point mutations in protein-coding sequences are selected against while maintaining expression of 20 or more amino acids. We demonstrate experimentally that a reduced set of 21 tRNAs is capable of expressing a protein encoded by only 20 sense codons, whereas a standard 64-codon encoding is not expressed. Our work suggests that biological systems using rationally depleted but otherwise natural translation systems should evolve more slowly and that such hypoevolvable organisms may be less likely to invade new niches or outcompete native populations.

Influenza Viruses in Mice: Deep Sequencing Analysis of Serial Passage and Effects of Sialic Acid Structural Variation

Influenza A viruses have regularly jumped to new host species to cause epidemics or pandemics, an evolutionary process that involves variation in the viral traits necessary to overcome host barriers and facilitate transmission. Mice are not a natural host for influenza virus but are frequently used as models in studies of pathogenesis, often after multiple passages to achieve higher viral titers that result in clinical disease such as weight loss or death. Here, we examine the processes of influenza A virus infection and evolution in mice by comparing single nucleotide variations of a human H1N1 pandemic virus, a seasonal H3N2 virus, and an H3N2 canine influenza virus during experimental passage. We also compared replication and sequence variation in wild-type mice expressing N-glycolylneuraminic acid (Neu5Gc) with those seen in mice expressing only N-acetylneuraminic acid (Neu5Ac). Viruses derived from plasmids were propagated in MDCK cells and then passaged in mice up to four times. Full-genome deep sequencing of the plasmids, cultured viruses, and viruses from mice at various passages revealed only small numbers of mutational changes. The H3N2 canine influenza virus showed increases in frequency of sporadic mutations in the PB2, PA, and NA segments. The H1N1 pandemic virus grew well in mice, and while it exhibited the maintenance of some minority mutations, there was no clear evidence for adaptive evolution. The H3N2 seasonal virus did not establish in the mice. Finally, there were no clear sequence differences associated with the presence or absence of Neu5Gc.IMPORTANCE Mice are commonly used as a model to study the growth and virulence of influenza A viruses in mammals but are not a natural host and have distinct sialic acid receptor profiles compared to humans. Using experimental infections with different subtypes of influenza A virus derived from different hosts, we found that evolution of influenza A virus in mice did not necessarily proceed through the linear accumulation of host-adaptive mutations, that there was variation in the patterns of mutations detected in each repetition, and that the mutation dynamics depended on the virus examined. In addition, variation in the viral receptor, sialic acid, did not affect influenza virus evolution in this model. Overall, our results show that while mice provide a useful animal model for influenza virus pathology, host passage evolution will vary depending on the specific virus tested.

The Genomic Basis for Short-Term Evolution of Environmental Adaptation in Maize

Understanding the evolutionary capacity of populations to adapt to novel environments is one of the major pursuits in genetics. Moreover, for plant breeding, maladaptation is the foremost barrier to capitalizing on intraspecific variation in order to develop new breeds for future climate scenarios in agriculture. Using a unique study design, we simultaneously dissected the population and quantitative genomic basis of short-term evolution in a tropical landrace of maize that was translocated to a temperate environment and phenotypically selected for adaptation in flowering time phenology. Underlying 10 generations of directional selection, which resulted in a 26-day mean decrease in female-flowering time, [Formula: see text] of the heritable variation mapped to [Formula: see text] of the genome, where, overall, alleles shifted in frequency beyond the boundaries of genetic drift in the expected direction given their flowering time effects. However, clustering these non-neutral alleles based on their profiles of frequency change revealed transient shifts underpinning a transition in genotype-phenotype relationships across generations. This was distinguished by initial reductions in the frequencies of few relatively large positive effect alleles and subsequent enrichment of many rare negative effect alleles, some of which appear to represent allelic series. With these genomic shifts, the population reached an adapted state while retaining [Formula: see text] of the standing molecular marker variation in the founding population. Robust selection and association mapping tests highlighted several key genes driving the phenotypic response to selection. Our results reveal the evolutionary dynamics of a finite polygenic architecture conditioning a capacity for rapid environmental adaptation in maize.

Exploiting Substrate Promiscuity of Ectoine Hydroxylase for Regio- and Stereoselective Modification of Homoectoine

Extant enzymes are not only highly efficient biocatalysts for a single, or a group of chemically closely related substrates but often have retained, as a mark of their evolutionary history, a certain degree of substrate ambiguity. We have exploited the substrate ambiguity of the ectoine hydroxylase (EctD), a member of the non-heme Fe(II)-containing and 2-oxoglutarate-dependent dioxygenase superfamily, for such a task. Naturally, the EctD enzyme performs a precise regio- and stereoselective hydroxylation of the ubiquitous stress protectant and chemical chaperone ectoine (possessing a six-membered pyrimidine ring structure) to yield trans-5-hydroxyectoine. Using a synthetic ectoine derivative, homoectoine, which possesses an expanded seven-membered diazepine ring structure, we were able to selectively generate, both in vitro and in vivo, trans-5-hydroxyhomoectoine. For this transformation, we specifically used the EctD enzyme from Pseudomonas stutzeri in a whole cell biocatalyst approach, as this enzyme exhibits high catalytic efficiency not only for its natural substrate ectoine but also for homoectoine. Molecular docking approaches with the crystal structure of the Sphingopyxis alaskensis EctD protein predicted the formation of trans-5-hydroxyhomoectoine, a stereochemical configuration that we experimentally verified by nuclear-magnetic resonance spectroscopy. An Escherichia coli cell factory expressing the P. stutzeri ectD gene from a synthetic promoter imported homoectoine via the ProU and ProP compatible solute transporters, hydroxylated it, and secreted the formed trans-5-hydroxyhomoectoine, independent from all currently known mechanosensitive channels, into the growth medium from which it could be purified by high-pressure liquid chromatography.

Learning dynamical information from static protein and sequencing data

Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations, and phylogenetic trees, respectively. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein-sequencing datasets, and future cryo-electron microscopy (cryo-EM) data.

Population Genetics in the Human Microbiome

While the human microbiome's structure and function have been extensively studied, its within-species genetic diversity is less well understood. However, genetic mutations in the microbiome can confer biomedically relevant traits, such as the ability to extract nutrients from food, metabolize drugs, evade antibiotics, and communicate with the host immune system. The population genetic processes by which these traits evolve are complex, in part due to interacting ecological and evolutionary forces in the microbiome. Advances in metagenomic sequencing, coupled with bioinformatics tools and population genetic models, facilitate quantification of microbiome genetic variation and inferences about how this diversity arises, evolves, and correlates with traits of both microbes and hosts. In this review, we explore the population genetic forces (mutation, recombination, drift, and selection) that shape microbiome genetic diversity within and between hosts, as well as efforts towards predictive models that leverage microbiome genetics.

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.

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.

Rapid adaptation of signaling networks in the fungal pathogen Magnaporthe oryzae

Background

One fundamental question in biology is how the evolution of eukaryotic signaling networks has taken place. “Loss of function” (lof) mutants from components of the high osmolarity glycerol (HOG) signaling pathway in the filamentous fungus Magnaporthe oryzae are viable, but impaired in osmoregulation.

Results

After long-term cultivation upon high osmolarity, stable individuals with reestablished osmoregulation capacity arise independently from each of the mutants with inactivated HOG pathway. This phenomenon is extremely reproducible and occurs only in osmosensitive mutants related to the HOG pathway – not in other osmosensitive Magnaporthe mutants. The major compatible solute produced by these adapted strains to cope with high osmolarity is glycerol, whereas it is arabitol in the wildtype strain. Genome and transcriptome analysis resulted in candidate genes related to glycerol metabolism, perhaps responsible for an epigenetic induced reestablishment of osmoregulation, since these genes do not show structural variations within the coding or promotor sequences.

Conclusion

This is the first report of a stable adaptation in eukaryotes by producing different metabolites and opens a door for the scientific community since the HOG pathway is worked on intensively in many eukaryotic model organisms.

Strain-Specific Metabolic Requirements Revealed by a Defined Minimal Medium for Systems Analyses of Staphylococcus aureus

Staphylococcus aureus is a Gram-positive pathogenic bacterium that colonizes an estimated one-third of the human population and can cause a wide spectrum of disease, ranging from superficial skin infections to life-threatening sepsis. The adaptive mechanisms that contribute to the success of this pathogen remain obscure partially due to a lack of knowledge of its metabolic requirements. Systems biology approaches can be extremely useful in predicting and interpreting metabolic phenotypes; however, such approaches rely on a chemically defined minimal medium as a basis to investigate the requirements of the cell. In this study, a chemically defined minimal medium formulation, termed synthetic minimal medium (SMM), was investigated and validated to support growth of three S. aureus strains: LAC and TCH1516 (USA300 lineage), as well as D592 (USA100 lineage). The formulated SMM was used in an adaptive laboratory evolution experiment to probe the various mutational trajectories of all three strains leading to optimized growth capabilities. The evolved strains were phenotypically characterized for their growth rate and antimicrobial susceptibility. Strains were also resequenced to examine the genetic basis for observed changes in phenotype and to design follow-up metabolite supplementation assays. Our results reveal evolutionary trajectories that arose from strain-specific metabolic requirements. SMM and the evolved strains can also serve as important tools to study antibiotic resistance phenotypes of S. aureus IMPORTANCE As researchers try to understand and combat the development of antibiotic resistance in pathogens, there is a growing need to thoroughly understand the physiology and metabolism of the microbes. Staphylococcus aureus is a threatening pathogen with increased antibiotic resistance and well-studied virulence mechanisms. However, the adaptive mechanisms used by this pathogen to survive environmental stresses remain unclear, mostly due to the lack of information about its metabolic requirements. Defining the minimal metabolic requirements for S. aureus growth is a first step toward unraveling the mechanisms by which it adapts to metabolic stresses. Here, we present the development of a chemically defined minimal medium supporting growth of three S. aureus strains, and we reveal key genetic mutations contributing to improved growth in minimal medium.

Evolutionary pathways to antibiotic resistance are dependent upon environmental structure and bacterial lifestyle

Bacterial populations vary in their stress tolerance and population structure depending upon whether growth occurs in well-mixed or structured environments. We hypothesized that evolution in biofilms would generate greater genetic diversity than well-mixed environments and lead to different pathways of antibiotic resistance. We used experimental evolution and whole genome sequencing to test how the biofilm lifestyle influenced the rate, genetic mechanisms, and pleiotropic effects of resistance to ciprofloxacin in Acinetobacter baumannii populations. Both evolutionary dynamics and the identities of mutations differed between lifestyle. Planktonic populations experienced selective sweeps of mutations including the primary topoisomerase drug targets, whereas biofilm-adapted populations acquired mutations in regulators of efflux pumps. An overall trade-off between fitness and resistance level emerged, wherein biofilm-adapted clones were less resistant than planktonic but more fit in the absence of drug. However, biofilm populations developed collateral sensitivity to cephalosporins, demonstrating the clinical relevance of lifestyle on the evolution of resistance.

A bacterium known as Acinetobacter baumannii causes serious lung infections in people with weakened immune systems. These illnesses are becoming more common largely because A. baumannii is increasingly developing resistance to antibiotics.

Inside the airways, individual A. baumannii cells can stick together and coat themselves in a slimy substance to form a structure called biofilm, which physically protects bacteria from antibiotics. This may be one of the reasons why it is often harder to treat bacterial infections associated with biofilms.

Another possibility is that bacteria may evolve differently in biofilms compared with cells living independently. For example, A. baumannii may colonize several regions of the lungs during an infection, leading to distinct groups of bacteria that experience different conditions and evolve separately. Each population may therefore respond differently to an antibiotic. In contrast, bacteria living independently in a well-mixed population – such as in the bloodstream of their host – would be more likely to all evolve in the same way.

Santos-Lopez, Marshall et al. tested this theory by exposing populations of A. baumannii that lived either independently or in biofilms to increasing levels of an antibiotic called ciprofloxacin. The genetic information of these cells was examined as the populations were evolving, and the bacteria were also put in contact with other types of antibiotics.

The analyses revealed that bacteria in well-mixed populations shared the same limited number of mutations: these gave the bacteria high levels of resistance to the antibiotic’s primary target, an enzyme involved in DNA processes. The bacteria had also become resistant to other classes of antibiotics.

In contrast, the bacteria in biofilm populations evolved to be more genetically diverse, exhibiting different types of mutations that helped the cells to pump out the drug. These bacteria were less resistant to ciprofloxacin and more sensitive to other types of antibiotics.

Further experiments looked into the fitness of the bacteria – their ability to survive, reproduce and compete with each other. High levels of antibiotic resistance came with lower fitness: biofilm bacteria had evolved to become being fitter than those from well-mixed population. Even in the absence of drugs, these populations were in fact fitter than the original cells.

Overall, understanding how the lifestyles of bacteria affect the way they respond to drugs may help researchers to develop new approaches that limit the spread of antibiotic resistance and improve treatment.

Family matters: Variation in the physiology of brooded Porites astreoides larvae is driven by parent colony effects

The planktonic larval phase of scleractinian coral life-history represents a crucial stage when dispersal takes place and genetic diversity among populations is maintained. Understanding the dynamics influencing larval survival is especially relevant in the context of climate change, as larvae may be more vulnerable to environmental disturbances than adults. Several physiological parameters of coral larvae have been shown to vary by release time and past environmental history. However, the contribution of parental or genetic effects is largely unknown. To investigate these potential familial effects, we collected adult Porites astreoides colonies in April 2018 from two reef zones in the lower Florida Keys and quantified physiological traits and thermal tolerance of the newly released larvae. Family accounted for more variation than day of release and reef origin, with >60% of the variation in chlorophyll a and protein content explained by family. The survivorship of larvae under 36 °C acute temperature stress was also tightly linked to what parent colony they were released from. During a 32 °C moderate temperature stress experiment, inshore larvae tended to bleach less than offshore larvae, mirroring the enhanced bleaching resistance previously observed in inshore adult coral populations. The significant familial effects identified in the present study suggest that researchers should be cautious when interpreting results of studies which pool larvae among families, and that future studies should take care to account for this variation.

What are the costs of learning? Modest trade-offs and constitutive costs do not set the price of fast associative learning ability in a parasitoid wasp

Learning ability has been associated with energetic costs that typically become apparent through trade-offs in a wide range of developmental, physiological, and life-history traits. Costs associated with learning ability can be either constitutive or induced, depending on whether they are always incurred or only when information is actively learned and memorized. Using lines of the parasitoid wasp Nasonia vitripennis that were selected for fast associative learning ability, we assessed a range of traits that have previously been identified as potential costs associated with learning. No difference in longevity, lipid reserves, tibia length, egg load, or fecundity was observed between the selected and control lines. All of these traits are considered to potentially lead to constitutive costs in the setup of this study. A gradual reversal to baseline learning after two forms of relaxed selection was indicative of a small constitutive cost of learning ability. We also tested for a trade-off with other memory types formed at later stages, but found no evidence that the mid-term memory that was selected for caused a decrease in performance of other memory types. In conclusion, we observe only one minor effect of a constitutive cost and none of the other costs and trade-offs that are reported in the literature to be of significant value in this case. We, therefore, argue for better inclusion of ecological and economic costs in studies on costs and benefits of learning ability.

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.

Microbial Experimental Evolution - a proving ground for evolutionary theory and a tool for discovery

Microbial experimental evolution uses controlled laboratory populations to study the mechanisms of evolution. The molecular analysis of evolved populations enables empirical tests that can confirm the predictions of evolutionary theory, but can also lead to surprising discoveries. As with other fields in the life sciences, microbial experimental evolution has become a tool, deployed as part of the suite of techniques available to the molecular biologist. Here, I provide a review of the general findings of microbial experimental evolution, especially those relevant to molecular microbiologists that are new to the field. I also relate these results to design considerations for an evolution experiment and suggest future directions for those working at the intersection of experimental evolution and molecular biology.

Changes detected in the genome sequences of Escherichia coli, Listeria monocytogenes, Vibrio parahaemolyticus, and Salmonella enterica after serial subculturing

Whole genome sequencing (WGS) is rapidly replacing other molecular techniques for identifying and subtyping bacterial isolates. The resolution or discrimination offered by WGS is significantly higher than that offered by other molecular techniques, and WGS readily allows infrequent differences that occur between 2 closely related strains to be found. In this investigation, WGS was used to identify the changes that occurred in the genomes of 13 strains of bacterial foodborne pathogens after 100 serial subcultures. Pure cultures of Shiga-toxin-producing Escherichia coli, Salmonella enterica, Listeria monocytogenes, and Vibrio parahaemolyticus were subcultured daily for 100 successive days. The 1st and 100th subcultures were whole-genome sequenced using short-read sequencing. Single nucleotide polymorphisms (SNPs) were identified between the 1st and final culture using 2 different approaches, and multilocus sequence typing of the whole genome was also performed to detect any changes at the allelic level. The number of observed genomic changes varied by strain, species, and the SNP caller used. This study provides insight into the genomic variation that can be detected using next-generation sequencing and analysis methods after repeated subculturing of 4 important bacterial pathogens.

IAMBEE: a web-service for the identification of adaptive pathways from parallel evolved clonal populations

IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement.

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.

Evolutionary Dynamics in the RNA Bacteriophage Qβ Depends on the Pattern of Change in Selective Pressures

The rate of change in selective pressures is one of the main factors that determines the likelihood that populations can adapt to stress conditions. Generally, the reduction in the population size that accompanies abrupt environmental changes makes it difficult to generate and select adaptive mutations. However, in systems with high genetic diversity, as happens in RNA viruses, mutations with beneficial effects under new conditions can already be present in the population, facilitating adaptation. In this work, we have propagated an RNA bacteriophage (Qβ) at temperatures higher than the optimum, following different patterns of change. We have determined the fitness values and the consensus sequences of all lineages throughout the evolutionary process in order to establish correspondences between fitness variations and adaptive pathways. Our results show that populations subjected to a sudden temperature change gain fitness and fix mutations faster than those subjected to gradual changes, differing also in the particular selected mutations. The life-history of populations prior to the environmental change has great importance in the dynamics of adaptation. The conclusion is that in the bacteriophage Qβ, the standing genetic diversity together with the rate of temperature change determine both the rapidity of adaptation and the followed evolutionary pathways.

Emergence of plasmid stability under non-selective conditions maintains antibiotic resistance

Plasmid acquisition is an important mechanism of rapid adaptation and niche expansion in prokaryotes. Positive selection for plasmid-coded functions is a major driver of plasmid evolution, while plasmids that do not confer a selective advantage are considered costly and expected to go extinct. Yet, plasmids are ubiquitous in nature, and their persistence remains an evolutionary paradox. Here, we demonstrate that non-mobile plasmids persist over evolutionary timescales without selection for the plasmid function. Evolving a minimal plasmid encoding for antibiotics resistance in Escherichia coli, we discover that plasmid stability emerges in the absence of antibiotics and that plasmid loss is determined by transcription-replication conflicts. We further find that environmental conditions modulate these conflicts and plasmid persistence. Silencing the transcription of the resistance gene results in stable plasmids that become fixed in the population. Evolution of plasmid stability under non-selective conditions provides an evolutionary explanation for the ubiquity of plasmids in nature.

It is expected that plasmids are costly and therefore that selection is required to maintain them within bacterial populations. Here, Wein et al. show that plasmid stability can emerge even in the absence of positive selection and that loss may be determined by transcription-replication conflict.

A Double-Strand Break Does Not Promote Neisseria gonorrhoeae Pilin Antigenic Variation

The major subunit of the type IV pilus (T4p) of Neisseria gonorrhoeae undergoes antigenic variation (AV) dependent on a guanine quadruplex (G4) DNA structure located upstream of the pilin gene. Since the presence of G4 DNA induces genome instability in both eukaryotic and prokaryotic chromosomes, we tested whether a double-strand break (DSB) at the site of the pilE G4 sequence could substitute for G4-directed pilin AV. The G4 motif was replaced by an I-SceI cut site, and the cut site was also introduced to locations near the origin of replication and the terminus. Expression of the I-SceI endonuclease from an irrelevant chromosomal site confirmed that the endonuclease functions to induce double-strand breaks at all three locations. No antigenic variants were detected when the G4 was replaced with the I-SceI cut site, but there was a growth defect from having a DSB in the chromosome, and suppressor mutations that were mainly deletions of the cut site and/or the entire pilE gene accumulated. Thus, the pilE G4 does not act to promote pilin AV by generating a DSB but requires either a different type of break, a nick, or more complex interactions with other factors to stimulate this programmed recombination system.IMPORTANCE Neisseria gonorrhoeae, the causative agent of gonorrhea, possesses a DNA recombination system to change one of its surface-exposed antigens. This recombination system, known as antigenic variation, uses an alternate DNA structure to initiate variation. The guanine quadruplex DNA structure is known to cause nicks or breaks in DNA; however, much remains unknown about how this structure functions in cells. We show that inducing a break by different means does not allow antigenic variation, indicating that the DNA structure may have a more complicated role.

Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism

Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.

This study reconciles multiple existing microbial rate-yield tradeoff theories with experimental data. There is great interest in developing quantitative descriptions of the relationship between growth rate and growth yield [1]. However, some reported experiments [2–4] in the literature do not agree with existing theories [5–7]. Specifically, overflow metabolism in E. coli can either be coupled [5, 8] or decoupled [2–4] from growth rate. We found that adaptive laboratory evolution (ALE) experiments of E. coli reveal a two-dimensional rate-yield tradeoff in adapted strains where the dimensions are (i) a tradeoff between growth rate and growth yield, previously reported by [5], and (ii) a tradeoff between substrate uptake rate and growth yield. The appearance of this two-dimensional tradeoff during adaptation suggests that microorganisms adapting to new environments are subject to a more complex set of rate-yield tradeoffs than previously reported [5, 6]. In this study, the two-dimensional rate-yield tradeoff is quantitatively explained through our multi-scale modeling approach, combining a previously reported small-scale proteome allocation model [5] with a genome-scale model of metabolism and gene-expression (ME-model) [9]. The modeling approach is also instrumental to future studies.

Genomic signatures accompanying the dietary shift to phytophagy in polyphagan beetles

BACKGROUND

The diversity and evolutionary success of beetles (Coleoptera) are proposed to be related to the diversity of plants on which they feed. Indeed, the largest beetle suborder, Polyphaga, mostly includes plant eaters among its approximately 315,000 species. In particular, plants defend themselves with a diversity of specialized toxic chemicals. These may impose selective pressures that drive genomic diversification and speciation in phytophagous beetles. However, evidence of changes in beetle gene repertoires driven by such interactions remains largely anecdotal and without explicit hypothesis testing.

RESULTS

We explore the genomic consequences of beetle-plant trophic interactions by performing comparative gene family analyses across 18 species representative of the two most species-rich beetle suborders. We contrast the gene contents of species from the mostly plant-eating suborder Polyphaga with those of the mainly predatory Adephaga. We find gene repertoire evolution to be more dynamic, with significantly more adaptive lineage-specific expansions, in the more speciose Polyphaga. Testing the specific hypothesis of adaptation to plant feeding, we identify families of enzymes putatively involved in beetle-plant interactions that underwent adaptive expansions in Polyphaga. There is notable support for the selection hypothesis on large gene families for glutathione S-transferase and carboxylesterase detoxification enzymes.

CONCLUSIONS

Our explicit modeling of the evolution of gene repertoires across 18 species identifies putative adaptive lineage-specific gene family expansions that accompany the dietary shift towards plants in beetles. These genomic signatures support the popular hypothesis of a key role for interactions with plant chemical defenses, and for plant feeding in general, in driving beetle diversification.

Adaptive Evolution within Gut Microbiomes of Healthy People

Natural selection shapes bacterial evolution in all environments. However, the extent to which commensal bacteria diversify and adapt within the human gut remains unclear. Here, we combine culture-based population genomics and metagenomics to investigate the within-microbiome evolution of Bacteroides fragilis. We find that intra-individual B. fragilis populations contain substantial de novo nucleotide and mobile element diversity, preserving years of within-person history. This history reveals multiple signatures of within-person adaptation, including parallel evolution in sixteen genes. Many of these genes are implicated in cell-envelope biosynthesis and polysaccharide utilization. Tracking evolutionary trajectories using near-daily metagenomic sampling, we find evidence for years-long coexistence in one subject despite adaptive dynamics. We used public metagenomes to investigate one adaptive mutation common in our cohort and found that it emerges frequently in Western, but not Chinese, microbiomes. Collectively, these results demonstrate that B. fragilis adapts within individual microbiomes, pointing to factors that promote long-term gut colonization.

Chaperonin overproduction and metabolic erosion caused by mutation accumulation in Escherichia coli

Bacterial cells adapting to a constant environment tend to accumulate mutations in portions of their genome that are not maintained by selection. This process has been observed in bacteria evolving under strong genetic drift, and especially in bacterial endosymbionts of insects. Here, we study this process in hypermutable Escherichia coli populations evolved through 250 single-cell bottlenecks on solid rich medium in a mutation accumulation experiment that emulates the evolution of bacterial endosymbionts. Using phenotype microarrays monitoring metabolic activity in 95 environments distinguished by their carbon sources, we observe how mutation accumulation has decreased the ability of cells to metabolize most carbon sources. We study if the chaperonin GroEL, which is naturally overproduced in bacterial endosymbionts, can ameliorate the process of metabolic erosion, because of its known ability to buffer destabilizing mutations in metabolic enzymes. Our results indicate that GroEL can slow down the negative phenotypic consequences of genome decay in some environments.

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

Quantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 235 mean expression QTLs (eQTLs) at 10% FDR, of which 79% replicate in bulk RNA-seq data from the same individuals. We further identified 5 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs) which could alter the variance of expression independently of the mean can have larger fold changes, but explain less phenotypic variance than eQTLs. We estimate 4,015 individuals as a lower bound to achieve 80% power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.

Common genetic variation can alter the level of average gene expression in human tissues, and through changes in gene expression have downstream consequences on cell function, human development, and human disease. However, human tissues are composed of many cells, each with its own level of gene expression. With advances in single cell sequencing technologies, we can now go beyond simply measuring the average level of gene expression in a tissue sample and directly measure cell-to-cell variance in gene expression. We hypothesized that genetic variation could also alter gene expression variance, potentially revealing new insights into human development and disease. To test this hypothesis, we used single cell RNA sequencing to directly measure gene expression variance in multiple individuals, and then associated the gene expression variance with genetic variation in those same individuals. Our results suggest that effects on gene expression variance are smaller than effects on mean expression, relative to how much the phenotypes vary between individuals, and will require much larger studies than previously thought to detect.

Enzyme promiscuity shapes adaptation to novel growth substrates

Evidence suggests that novel enzyme functions evolved from low‐level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems‐level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non‐native substrates in Escherichia coli K‐12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non‐native substrates—D‐lyxose, D‐2‐deoxyribose, D‐arabinose, m‐tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high‐efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome‐scale model simulations of metabolism with enzyme promiscuity.

SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes

Motivation

Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution.

Results

To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution.

Availability and implementation

Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows.

Supplementary information

Supplementary data are available at Bioinformatics online.

Recalibrating the molecular clock for Arctic marine invertebrates based on DNA barcodes

Divergence times for species assemblages of Arctic marine invertebrates have often been estimated using a standard rate (1.4%/MY) of molecular evolution calibrated using a single sister pair of tropical crustaceans. Because rates of molecular evolution vary among taxa and environments, it is essential to obtain clock calibrations from northern lineages. The recurrent opening and closure of the Bering Strait provide an exceptional opportunity for clock calibration. Here, we apply the iterative calibration approach to investigate patterns of molecular divergence among lineages of northern marine molluscs and arthropods using publicly available sequences of the cytochrome c oxidase subunit I (COI) gene and compare these results with previous estimates of trans-Bering divergences for echinoderms and polychaetes. The wide range of Kimura two-parameter (K2P) divergences among 73 trans-Bering sister pairs (0.12%–16.89%) supports multiple pulses of migration through the Strait. Overall, the results indicate a rate of K2P divergence of 3.2%/MY in molluscs, 5%–5.2%/MY in arthropods, and 3.5%–4.7%/MY in polychaetes. While these rates are considerably higher than the often-adopted 1.4%/MY rate, they are similar to calibrations (3%–5%/MY) in several other studies of marine invertebrates. This upward revision in rates means there is a need both to reevaluate the evolutionary history of marine lineages and to reexamine the impact of prior climatic changes upon the diversification of marine life.

Effects of life history and ecology on virus evolutionary potential

The life history traits of viruses pose many consequences for viral population structure. In turn, population structure may influence the evolutionary trajectory of a virus. Here we review factors that affect the evolutionary potential of viruses, including rates of mutation and recombination, bottlenecks, selection pressure, and ecological factors such as the requirement for hosts and vectors. Mutation, while supplying a pool of raw genetic material, also results in the generation of numerous unfit mutants. The infection of multiple host species may expand a virus' ecological niche, although it may come at a cost to genetic diversity. Vector-borne viruses often experience a diminished frequency of positive selection and exhibit little diversity, and resistance against vector-borne viruses may thus be more durable than against non-vectored viruses. Evidence indicates that adaptation to a vector is more evolutionarily difficult than adaptation to a host. Overall, a better understanding of how various factors influence viral dynamics in both plant and animal pathosystems will lead to more effective anti-viral treatments and countermeasures.

Evolved Biofilm: Review on the Experimental Evolution Studies of Bacillus subtilis Pellicles

For several decades, laboratory evolution has served as a powerful method to manipulate microorganisms and to explore long-term dynamics in microbial populations. Next to canonical Escherichia coli planktonic cultures, experimental evolution has expanded into alternative cultivation methods and species, opening the doors to new research questions. Bacillus subtilis, the spore-forming and root-colonizing bacterium, can easily develop in the laboratory as a liquid-air interface colonizing pellicle biofilm. Here, we summarize recent findings derived from this tractable experimental model. Clonal pellicle biofilms of B. subtilis can rapidly undergo morphological and genetic diversification creating new ecological interactions, for example, exploitation by biofilm non-producers. Moreover, long-term exposure to such matrix non-producers can modulate cooperation in biofilms, leading to different phenotypic heterogeneity pattern of matrix production with larger subpopulation of "ON" cells. Alternatively, complementary variants of biofilm non-producers, each lacking a distinct matrix component, can engage in a genetic division of labor, resulting in superior biofilm productivity compared to the "generalist" wild type. Nevertheless, inter-genetic cooperation appears to be evanescent and rapidly vanquished by individual biofilm formation strategies altering the amount or the properties of the remaining matrix component. Finally, fast-evolving mobile genetic elements can unpredictably shift intra-species interactions in B. subtilis biofilms. Understanding evolution in clonal biofilm populations will facilitate future studies in complex multispecies biofilms that are more representative of nature.

Contingency in the convergent evolution of a regulatory network: Dosage compensation in Drosophila

The repeatability or predictability of evolution is a central question in evolutionary biology and most often addressed in experimental evolution studies. Here, we infer how genetically heterogeneous natural systems acquire the same molecular changes to address how genomic background affects adaptation in natural populations. In particular, we take advantage of independently formed neo-sex chromosomes in Drosophila species that have evolved dosage compensation by co-opting the dosage-compensation male-specific lethal (MSL) complex to study the mutational paths that have led to the acquisition of hundreds of novel binding sites for the MSL complex in different species. This complex recognizes a conserved 21-bp GA-rich sequence motif that is enriched on the X chromosome, and newly formed X chromosomes recruit the MSL complex by de novo acquisition of this binding motif. We identify recently formed sex chromosomes in the D. melanica and D. robusta species groups by genome sequencing and generate genomic occupancy maps of the MSL complex to infer the location of novel binding sites. We find that diverse mutational paths were utilized in each species to evolve hundreds of de novo binding motifs along the neo-X, including expansions of microsatellites and transposable element (TE) insertions. However, the propensity to utilize a particular mutational path differs between independently formed X chromosomes and appears to be contingent on genomic properties of that species, such as simple repeat or TE density. This establishes the "genomic environment" as an important determinant in predicting the outcome of evolutionary adaptations.

Microevolution of the pathogenic yeasts Candida albicans and Candida glabrata during antifungal therapy and host infection

Infections by the pathogenic yeasts Candida albicans and Candida glabrata are among the most common fungal diseases. The success of these species as human pathogens is contingent on their ability to resist antifungal therapy and thrive within the human host. C. glabrata is especially resilient to azole antifungal treatment, while C. albicans is best known for its wide array of virulence features. The core mechanisms that underlie antifungal resistance and virulence in these pathogens has been continuously addressed, but the investigation on how such mechanisms evolve according to each environment is scarcer. This review aims to explore current knowledge on micro-evolution experiments to several treatment and host-associated conditions in C. albicans and C. glabrata. The analysis of adaptation strategies that evolve over time will allow to better understand the mechanisms by which Candida species are able to achieve stable phenotypes in real-life scenarios, which are the ones that should constitute the most interesting drug targets.

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.

Domestication of Local Microbial Consortia for Efficient Recovery of Gold Through Top-Down Selection in Airlift Bioreactors

Extreme acidophiles play central roles in the geochemical cycling of diverse elements in low pH environments. This has been harnessed in biotechnologies such as biomining, where microorganisms facilitate the recovery of economically important metals such as gold. By generating both extreme acidity and a chemical oxidant (ferric iron) many species of prokaryotes that thrive in low pH environments not only catalyze mineral dissolution but also trigger both community and individual level adaptive changes. These changes vary in extent and direction depending on the ore mineralogy, water availability and local climate. The use of indigenous versus introduced microbial consortia in biomining practices is still a matter of debate. Yet, indigenous microbial consortia colonizing sulfidic ores that have been domesticated, i.e., selected for their ability to survive under specific polyextreme conditions, are claimed to outperform un-adapted foreign consortia. Despite this, little is known on the domestication of acidic microbial communities and the changes elicited in their members. In this study, high resolution targeted metagenomic techniques were used to analyze the changes occurring in the community structure of local microbial consortia acclimated to growing under extreme acidic conditions and adapted to endure the conditions imposed by the target mineral during biooxidation of a gold concentrate in an airlift reactor over a period of 2 years. The results indicated that operative conditions evolving through biooxidation of the mineral concentrate exerted strong selective pressures that, early on, purge biodiversity in favor of a few Acidithiobacillus spp. over other iron oxidizing acidophiles. Metagenomic analysis of the domesticated consortium present at the end of the adaptation experiment enabled reconstruction of the RVS1-MAG, a novel representative of Acidithiobacillus ferrooxidans from the Andacollo gold mineral district. Comparative genomic analysis performed with this genome draft revealed a net enrichment of gene functions related to heavy metal transport and stress management that are likely to play a significant role in adaptation and survival to adverse conditions experienced by these acidophiles during growth in presence of gold concentrates.

Experimental evolution: its principles and applications in developing stress-tolerant yeasts

Stress tolerance and resistance in industrial yeast strains are important attributes for cost-effective bioprocessing. The source of stress-tolerant yeasts ranges from extremophilic environments to laboratory engineered strains. However, industrial stress-tolerant yeasts are very rare in nature as the natural environment forces them to evolve traits that optimize survival and reproduction and not the ability to withstand harsh habitat-irrelevant industrial conditions. Experimental evolution is a frequent method used to uncover the mechanisms of evolution and microbial adaption towards environmental stresses. It optimizes biological systems by means of adaptation to environmental stresses and thus has immense power of development of robust stress-tolerant yeasts. This mini-review briefly outlines the basics and implications of evolution experiments and their applications to industrial biotechnology. This work is meant to serve as an introduction to those new to the field of experimental evolution, and as a guide to biologists working in the field of yeast stress response. Future perspectives of experimental evolution for potential biotechnological applications have also been elucidated.

Improvement of thermotolerance in Lachancea thermotolerans using a bacterial selection pressure

The use of thermotolerant yeast strains is an important attribute for a cost-effective high temperature biofermentation processes. However, the availability of thermotolerant yeast strains remains a major challenge. Isolation of temperature resistant strains from extreme environments or the improvements of current strains are two major strategies known to date. We hypothesised that bacteria are potential “hurdles” in the life cycle of yeasts, which could influence the evolution of extreme phenotypes, such as thermotolerance. We subjected a wild-type yeast, Lachancea thermotolerans to six species of bacteria sequentially for several generations. After coevolution, we observed that three replicate lines of yeasts grown in the presence of bacteria grew up to 37 °C whereas the controls run in parallel without bacteria could only grow poorly at 35 °C retaining the ancestral mesophilic trait. In addition to improvement of thermotolerance, our results show that the fermentative ability was also elevated, making the strains more ideal for the alcoholic fermentation process because the overall productivity and ethanol titers per unit volume of substrate consumed during the fermentation process was increased. Our unique method is attractive for the development of thermotolerant strains or to augment the available strain development approaches for high temperature industrial biofermentation.

Deep scanning lysine metabolism in Escherichia coli

Our limited ability to predict genotype-phenotype relationships has called for strategies that allow testing of thousands of hypotheses in parallel. Deep scanning mutagenesis has been successfully implemented to map genotype-phenotype relationships at a single-protein scale, allowing scientists to elucidate properties that are difficult to predict. However, most phenotypes are dictated by several proteins that are interconnected through complex and robust regulatory and metabolic networks. These sophisticated networks hinder our understanding of the phenotype of interest and limit our capabilities to rewire cellular functions. Here, we leveraged CRISPR-EnAbled Trackable genome Engineering to attempt a parallel and high-resolution interrogation of complex networks, deep scanning multiple proteins associated with lysine metabolism in Escherichia coli We designed over 16,000 mutations to perturb this pathway and mapped their contribution toward resistance to an amino acid analog. By doing so, we identified different routes that can alter pathway function and flux, uncovering mechanisms that would be difficult to rationally design. This approach sets a framework for forward investigation of complex multigenic phenotypes.

Adaptive Evolution under Extreme Genetic Drift in Oxidatively Stressed Caenorhabditis elegans

A mutation-accumulation (MA) experiment with Caenorhabditis elegans nematodes was conducted in which replicate, independently evolving lines were initiated from a low-fitness mitochondrial electron transport chain mutant, gas-1. The original intent of the study was to assess the effect of electron transport chain dysfunction involving elevated reactive oxygen species production on patterns of spontaneous germline mutation. In contrast to results of standard MA experiments, gas-1 MA lines evolved slightly higher mean fitness alongside reduced among-line genetic variance compared with their ancestor. Likewise, the gas-1 MA lines experienced partial recovery to wildtype reactive oxygen species levels. Whole-genome sequencing and analysis revealed that the molecular spectrum but not the overall rate of nuclear DNA mutation differed from wildtype patterns. Further analysis revealed an enrichment of mutations in loci that occur in a gas-1-centric region of the C. elegans interactome, and could be classified into a small number of functional-genomic categories. Characterization of a backcrossed four-mutation set isolated from one gas-1 MA line revealed this combination to be beneficial on both gas-1 mutant and wildtype genetic backgrounds. Our combined results suggest that selection favoring beneficial mutations can be powerful even under unfavorable population genetic conditions, and agree with fitness landscape theory predicting an inverse relationship between population fitness and the likelihood of adaptation.

Mutation and Selection in Bacteria: Modelling and Calibration

Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection–mutation model has unobservable fitness parameters, and, to estimate them, we use an Approximate Bayesian Computation algorithm. The algorithms are illustrated using in vitro data for phase variable genes of Campylobacter jejuni.

Prophage-Driven Genomic Structural Changes Promote Bartonella Vertical Evolution

Bartonella is a genetically diverse group of vector-borne bacteria. Over 40 species have been characterized to date, mainly from mammalian reservoirs and arthropod vectors. Rodent reservoirs harbor one of the largest Bartonella diversity described to date, and novel species and genetic variants are continuously identified from these hosts. Yet, it is still unknown if this significant genetic diversity stems from adaptation to different niches or from intrinsic high mutation rates. Here, we explored the vertical occurrence of spontaneous genomic alterations in 18 lines derived from two rodent-associated Bartonella elizabethae-like strains, evolved in nonselective agar plates under conditions mimicking their vector- and mammalian-associated temperatures, and the transmission cycles between them (i.e., 26 °C, 37 °C, and alterations between the two), using mutation accumulation experiments. After ∼1,000 generations, evolved genomes revealed few point mutations (average of one-point mutation per line), evidencing conserved single-nucleotide mutation rates. Interestingly, three large structural genomic changes (two large deletions and an inversion) were identified over all lines, associated with prophages and surface adhesin genes. Particularly, a prophage, deleted during constant propagation at 37 °C, was associated with an increased autonomous replication at 26 °C (the flea-associated temperature). Complementary molecular analyses of wild strains, isolated from desert rodents and their fleas, further supported the occurrence of structural genomic variations and prophage-associated deletions in nature. Our findings suggest that structural genomic changes represent an effective intrinsic mechanism to generate diversity in slow-growing bacteria and emphasize the role of prophages as promoters of diversity in nature.

Transcriptome Analysis of a Spray Drying-Resistant Subpopulation Reveals a Zinc-Dependent Mechanism for Robustness in L. lactis SK11

The viability of starter cultures is essential for an adequate contribution to the fermentation process and end-product. Therefore, robustness during processing and storage is an important characteristic of starter culture strains. For instance, during spray drying cells are exposed to heat and oxidative stress, generally resulting in loss of viability. In this study, we exposed the industrially relevant but stress-sensitive Lactococcus lactis strain SK11 to two cycles of heat stress, with intermediate recovery and cultivation at moderate temperatures. After these two cycles of heat exposure, the abundance of robust derivatives was increased as compared with the original culture, which enabled isolation of heat-resistant subpopulations displaying up to 1,000-fold enhanced heat stress survival. Moreover, this heat-resistant subpopulation demonstrated an increased survival during spray drying. Derivatives from two independent lineages displayed different transcriptome changes as compared with the wild type strain, indicating that the increased robustness within these lineages was established by different adaptive strategies. Nevertheless, an overlap in differential gene expression in all five derivatives tested in both lineages included three genes in an operon involved in zinc transport. The link between zinc homeostasis and heat stress survival in L. lactis was experimentally established by culturing of the wild type strain SK11 in medium with various levels of zinc ions, which resulted in alterations in heat stress survival phenotypes. This study demonstrates that robust derivatives of a relatively sensitive L. lactis strain can be isolated by repeated exposure to heat stress. Moreover, this work demonstrates that transcriptome analysis of these robust derivatives can provide clues for improvement of the robustness of the original strain. This could boost the industrial application of strains with specific desirable traits but inadequate robustness characteristics.