Spontaneous mutation rate is a plastic trait associated with population density across domains of life

Harnessing microbial heterogeneity for improved biosynthesis fueled by synthetic biology

Metabolic engineering-driven microbial cell factories have made great progress in the efficient bioproduction of biochemical and recombinant proteins. However, the low efficiency and robustness of microbial cell factories limit their industrial applications. Harnessing microbial heterogeneity contributes to solving this. In this review, the origins of microbial heterogeneity and its effects on biosynthesis are first summarized. Synthetic biology-driven tools and strategies that can be used to improve biosynthesis by increasing and reducing microbial heterogeneity are then systematically summarized. Next, novel single-cell technologies available for unraveling microbial heterogeneity and facilitating heterogeneity regulation are discussed. Furthermore, a combined workflow of increasing genetic heterogeneity in the strain-building step to help in screening highly productive strains - reducing heterogeneity in the production process to obtain highly robust strains (IHP-RHR) facilitated by single-cell technologies was proposed to obtain highly productive and robust strains by harnessing microbial heterogeneity. Finally, the prospects and future challenges are discussed.

Collective peroxide detoxification determines microbial mutation rate plasticity in E. coli

Mutagenesis is responsive to many environmental factors. Evolution therefore depends on the environment not only for selection but also in determining the variation available in a population. One such environmental dependency is the inverse relationship between mutation rates and population density in many microbial species. Here, we determine the mechanism responsible for this mutation rate plasticity. Using dynamical computational modelling and in culture mutation rate estimation, we show that the negative relationship between mutation rate and population density arises from the collective ability of microbial populations to control concentrations of hydrogen peroxide. We demonstrate a loss of this density-associated mutation rate plasticity (DAMP) when Escherichia coli populations are deficient in the degradation of hydrogen peroxide. We further show that the reduction in mutation rate in denser populations is restored in peroxide degradation-deficient cells by the presence of wild-type cells in a mixed population. Together, these model-guided experiments provide a mechanistic explanation for DAMP, applicable across all domains of life, and frames mutation rate as a dynamic trait shaped by microbial community composition.

Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli

Spontaneous mutations are the ultimate source of novel genetic variation on which evolution operates. Although mutation rate is often discussed as a single parameter in evolution, it comprises multiple distinct types of changes at the level of DNA. Moreover, the rates of these distinct changes can be independently influenced by genomic background and environmental conditions. Using fluctuation tests, we characterized the spectrum of spontaneous mutations in Escherichia coli grown in low and high glucose environments. These conditions are known to affect the rate of spontaneous mutation in wild-type MG1655, but not in a ΔluxS deletant strain - a gene with roles in both quorum sensing and the recycling of methylation products used in E. coli's DNA repair process. We find an increase in AT>GC transitions in the low glucose environment, suggesting that processes relating to the production or repair of this mutation could drive the response of overall mutation rate to glucose concentration. Interestingly, this increase in AT>GC transitions is maintained by the glucose non-responsive ΔluxS deletant. Instead, an elevated rate of GC>TA transversions, more common in a high glucose environment, leads to a net non-responsiveness of overall mutation rate for this strain. Our results show how relatively subtle changes, such as the concentration of a carbon substrate or loss of a regulatory gene, can substantially influence the amount and nature of genetic variation available to selection.

Mutation bias and adaptation in bacteria

Genetic mutation, which provides the raw material for evolutionary adaptation, is largely a stochastic force. However, there is ample evidence showing that mutations can also exhibit strong biases, with some mutation types and certain genomic positions mutating more often than others. It is becoming increasingly clear that mutational bias can play a role in determining adaptive outcomes in bacteria in both the laboratory and the clinic. As such, understanding the causes and consequences of mutation bias can help microbiologists to anticipate and predict adaptive outcomes. In this review, we provide an overview of the mechanisms and features of the bacterial genome that cause mutational biases to occur. We then describe the environmental triggers that drive these mechanisms to be more potent and outline the adaptive scenarios where mutation bias can synergize with natural selection to define evolutionary outcomes. We conclude by describing how understanding mutagenic genomic features can help microbiologists predict areas sensitive to mutational bias, and finish by outlining future work that will help us achieve more accurate evolutionary forecasts.

Determinants of genetic diversity in Neotropical salamanders (Plethodontidae: Bolitoglossini)

Genetic diversity is the raw material of evolution, yet the reasons why it varies among species remain poorly understood. While studies at deeper phylogenetic scales point to the influence of life history traits on genetic diversity, it appears to be more affected by population size but less predictable at shallower scales. We used proxies for population size, mutation rate, direct selection, and linked selection to test factors affecting genetic diversity within a diverse assemblage of Neotropical salamanders, which vary widely for these traits. We estimated genetic diversity of noncoding loci using ddRADseq and coding loci using RNAseq for an assemblage of Neotropical salamanders distributed from northern Mexico to Costa Rica. Using ddRADseq loci, we found no significant association with genetic diversity, while for RNAseq data we found that environmental heterogeneity and proxies of population size predict a substantial portion of the variance in genetic diversity across species. Our results indicate that diversity of coding loci may be more predictable than that of noncoding loci, which appears to be mostly unpredictable at shallower phylogenetic scales. Our results suggest that coding loci may be more appropriate for genetic diversity estimates used in conservation planning because of the lack of any association between the variables we used and genetic diversity of noncoding loci.

Noise driven phase transitions in eco-evolutionary systems

In complex ecosystems such as microbial communities, there is constant ecological and evolutionary feedback between the residing species and the environment occurring on concurrent timescales. Species respond and adapt to their surroundings by modifying their phenotypic traits, which in turn alters their environment and the resources available. To study this interplay between ecological and evolutionary mechanisms, we develop a consumer-resource model that incorporates phenotypic mutations. In the absence of noise, we find that phase transitions require finely-tuned interaction kernels. Additionally, we quantify the effects of noise on frequency dependent selection by defining a time-integrated mutation current, which accounts for the rate at which mutations and speciation occurs. We find three distinct phases: homogeneous, patterned, and patterned traveling waves. The last phase represents one way in which co-evolution of species can happen in a fluctuating environment. Our results highlight the principal roles that noise and non-reciprocal interactions between resources and consumers play in phase transitions within eco-evolutionary systems.

The role of microbial interactions on rhizobial fitness

Rhizobia are soil bacteria that can establish a nitrogen-fixing symbiosis with legume plants. As horizontally transmitted symbionts, the life cycle of rhizobia includes a free-living phase in the soil and a plant-associated symbiotic phase. Throughout this life cycle, rhizobia are exposed to a myriad of other microorganisms that interact with them, modulating their fitness and symbiotic performance. In this review, we describe the diversity of interactions between rhizobia and other microorganisms that can occur in the rhizosphere, during the initiation of nodulation, and within nodules. Some of these rhizobia-microbe interactions are indirect, and occur when the presence of some microbes modifies plant physiology in a way that feeds back on rhizobial fitness. We further describe how these interactions can impose significant selective pressures on rhizobia and modify their evolutionary trajectories. More extensive investigations on the eco-evolutionary dynamics of rhizobia in complex biotic environments will likely reveal fascinating new aspects of this well-studied symbiotic interaction and provide critical knowledge for future agronomical applications.

Eco-evolution from deep time to contemporary dynamics: The role of timescales and rate modulators

Eco-evolutionary dynamics, or eco-evolution for short, are often thought to involve rapid demography (ecology) and equally rapid heritable phenotypic changes (evolution) leading to novel, emergent system behaviours. We argue that this focus on contemporary dynamics is too narrow: Eco-evolution should be extended, first, beyond pure demography to include all environmental dimensions and, second, to include slow eco-evolution which unfolds over thousands or millions of years. This extension allows us to conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the 'theatre' in which ecology and evolution are two interacting 'players'. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change. Our perspective attempts to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from today to deep time.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Mutators can drive the evolution of multi-resistance to antibiotics

Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with 'mutators', organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic-under conditions where purely wild-type populations cannot. In populations of Escherichia coli subjected to combination treatment, we detected a diverse array of acquired mutations, including multiple alleles in the canonical targets of resistance for the two drugs, as well as mutations in multi-drug efflux pumps and genes involved in DNA replication and repair. Unexpectedly, mutators not only allowed multi-resistance to evolve under combination treatment where it was favoured, but also under single-drug treatments. Using simulations, we show that the increase in mutation rate of the two canonical resistance targets is sufficient to permit multi-resistance evolution in both single-drug and combination treatments. Under both conditions, the mutator allele swept to fixation through hitch-hiking with single-drug resistance, enabling subsequent resistance mutations to emerge. Ultimately, our results suggest that mutators may hinder the utility of combination therapy when mutators are present. Additionally, by raising the rates of genetic mutation, selection for multi-resistance may have the unwanted side-effect of increasing the potential to evolve resistance to future antibiotic treatments.

Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions

Genetically identical bacterial cells commonly display different phenotypes. This phenotypic heterogeneity is well known for stress responses, where it is often explained as bet hedging against unpredictable environmental threats. Here, we explore phenotypic heterogeneity in a major stress response of Escherichia coli and find it has a fundamentally different basis. We characterize the response of cells exposed to hydrogen peroxide (H2O2) stress in a microfluidic device under constant growth conditions. A machine-learning model reveals that phenotypic heterogeneity arises from a precise and rapid feedback between each cell and its immediate environment. Moreover, we find that the heterogeneity rests upon cell-cell interaction, whereby cells shield each other from H2O2 via their individual stress responses. Our work shows how phenotypic heterogeneity in bacterial stress responses can emerge from short-range cell-cell interactions and result in a collective phenotype that protects a large proportion of the population.

Efflux-linked accelerated evolution of antibiotic resistance at a population edge

Efflux is a common mechanism of resistance to antibiotics. We show that efflux itself promotes accumulation of antibiotic-resistance mutations (ARMs). This phenomenon was initially discovered in a bacterial swarm where the linked phenotypes of high efflux and high mutation frequencies spatially segregated to the edge, driven there by motility. We have uncovered and validated a global regulatory network connecting high efflux to downregulation of specific DNA-repair pathways even in non-swarming states. The efflux-DNA repair link was corroborated in a clinical "resistome" database: genomes with mutations that increase efflux exhibit a significant increase in ARMs. Accordingly, efflux inhibitors decreased evolvability to antibiotic resistance. Swarms also revealed how bacterial populations serve as a reservoir of ARMs even in the absence of antibiotic selection pressure. High efflux at the edge births mutants that, despite compromised fitness, survive there because of reduced competition. This finding is relevant to biofilms where efflux activity is high.

Global coordination of the mutation and growth rates across the genetic and nutritional variety in Escherichia coli

Fitness and mutability are the primary traits of living organisms for adaptation and evolution. However, their quantitative linkage remained largely deficient. Whether there is any general relationship between the two features and how genetic and environmental variables influence them remained unclear and were addressed here. The mutation and growth rates of an assortment of Escherichia coli strain collections, including the wild-type strains and the genetically disturbed strains of either reduced genomes or deletion of the genes involved in the DNA replication fidelity, were evaluated in various media. The contribution of media to the mutation and growth rates was differentiated depending on the types of genetic disturbance. Nevertheless, the negative correlation between the mutation and growth rates was observed across the genotypes and was common in all media. It indicated the comprehensive association of the correlated mutation and growth rates with the genetic and medium variation. Multiple linear regression and support vector machine successfully predicted the mutation and growth rates and the categories of genotypes and media, respectively. Taken together, the study provided a quantitative dataset linking the mutation and growth rates, genotype, and medium and presented a simple and successful example of predicting bacterial growth and mutability by data-driven approaches.

Selection in a growing colony biases results of mutation accumulation experiments

Mutations provide the raw material for natural selection to act. Therefore, understanding the variety and relative frequency of different type of mutations is critical to understanding the nature of genetic diversity in a population. Mutation accumulation (MA) experiments have been used in this context to estimate parameters defining mutation rates, distribution of fitness effects (DFE), and spectrum of mutations. MA experiments can be performed with different effective population sizes. In MA experiments with bacteria, a single founder is grown to a size of a colony (~ 108). It is assumed that natural selection plays a minimal role in dictating the dynamics of colony growth. In this work, we simulate colony growth via a mathematical model, and use our model to mimic an MA experiment. We demonstrate that selection ensures that, in an MA experiment, fraction of all mutations that are beneficial is over-represented by a factor of almost two, and that the distribution of fitness effects of beneficial and deleterious mutations are inaccurately captured in an MA experiment. Given this, the estimate of mutation rates from MA experiments is non-trivial. We then perform an MA experiment with 160 lines of E. coli, and show that due to the effect of selection in a growing colony, the size and sector of a colony from which the experiment is propagated impacts the results. Overall, we demonstrate that the results of MA experiments need to be revisited taking into account the action of selection in a growing colony.

Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments

Stress is thought to increase mutation rate and thus to accelerate evolution. In the context of antibiotic resistance, sub-inhibitory treatments could then lead to enhanced evolvability, thereby fuelling the adaptation of pathogens. Combining wet-lab experiments, stochastic simulations and a meta-analysis of the literature, we found that the increase in mutation rates triggered by antibiotic treatments is often cancelled out by reduced population size, resulting in no overall increase in genetic diversity. A careful analysis of the effect of ecological factors on genetic diversity showed that the potential for regrowth during recovery phase after treatment plays a crucial role in evolvability, being the main factor associated with increased genetic diversity in experimental data.

Localized pmrB hypermutation drives the evolution of colistin heteroresistance

Colistin has emerged as an important last line of defense for the treatment of infections caused by antibiotic-resistant gram-negative pathogens, but colistin resistance remains poorly understood. Here, we investigate the responses of ≈1,000 populations of a multi-drug-resistant (MDR) strain of P. aeruginosa to a high dose of colistin. Colistin exposure causes rapid cell death, but some populations eventually recover due to the growth of sub-populations of heteroresistant cells. Heteroresistance is unstable, and resistance is rapidly lost under culture in colistin-free medium. The evolution of heteroresistance is primarily driven by selection for heteroresistance at two hotspot sites in the PmrAB regulatory system. Localized hypermutation of pmrB generates colistin resistance at 10³–10⁴ times the background resistance mutation rate (≈2 × 10^-5 per cell division). PmrAB provides resistance to antimicrobial peptides that are involved in host immunity, suggesting that this pathogen may have evolved a highly mutable pmrB as an adaptation to host immunity.

•

Pseudomonas populations recover from colistin due to the growth of heteroresistant cells

•

Heteroresistance is driven by pre-existing mutations in the PmrAB regulatory system

•

pmrB mutations arise at 10³–10⁴ times the background mutation rate

•

Heteroresistance is unstable and is rapidly lost in the absence of colistin

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.

Frequency, composition and mobility of Escherichia coli-derived transposable elements in holdings of plasmid repositories

By providing the scientific community with uniform and standardized resources of consistent quality, plasmid repositories play an important role in enabling scientific reproducibility. Plasmids containing insertion sequence elements (IS elements) represent a challenge from this perspective, as they can change the plasmid structure and function. In this study, we conducted a systematic analysis of a subset of plasmid stocks distributed by plasmid repositories (The Arabidopsis Biological Resource Center and Addgene) which carry unintended integrations of bacterial mobile genetic elements. The integration of insertion sequences was most often found in, but not limited to, pBR322-derived vectors, and did not affect the function of the specific plasmids. In certain cases, the entire stock was affected, but the majority of the stocks tested contained a mixture of the wild-type and the mutated plasmids, suggesting that the acquisition of IS elements likely occurred after the plasmids were acquired by the repositories. However, comparison of the sequencing results of the original samples revealed that some plasmids already carried insertion mutations at the time of donation. While an extensive BLAST analysis of 47 877 plasmids sequenced from the Addgene repository uncovered IS elements in only 1.12%, suggesting that IS contamination is not widespread, further tests showed that plasmid integration of IS elements can propagate in conventional Escherichia coli hosts over a few tens of generations. Use of IS-free E. coli hosts prevented the emergence of IS insertions as well as that of small indels, suggesting that the use of IS-free hosts by donors and repositories could help limit unexpected and unwanted IS integrations into plasmids.

Obligate mutualistic cooperation limits evolvability

Cooperative mutualisms are widespread and play fundamental roles in many ecosystems. Given that these interactions are often obligate, the Darwinian fitness of the participating individuals is not only determined by the information encoded in their own genomes, but also the traits and capabilities of their corresponding interaction partners. Thus, a major outstanding question is how obligate cooperative mutualisms affect the ability of organisms to adapt evolutionarily to changing environmental conditions. Here we address this issue using a mutualistic cooperation between two auxotrophic genotypes of Escherichia coli that reciprocally exchanged costly amino acids. Amino acid-supplemented monocultures and unsupplemented cocultures were exposed to stepwise increasing concentrations of different antibiotics. This selection experiment reveals that metabolically interdependent bacteria are generally less able to adapt to environmental stress than autonomously growing strains. Moreover, obligate cooperative mutualists frequently regain metabolic autonomy, resulting in a collapse of the mutualistic interaction. Together, our results identify a limited evolvability as a significant evolutionary cost that individuals have to pay when entering into an obligate mutualistic cooperation.

One Cell, Two Gears: Extensive Somatic Genome Plasticity Accompanies High Germline Genome Stability in Paramecium

Mutation accumulation (MA) experiments are conventionally employed to study spontaneous germline mutations. However, MA experiments can also shed light on somatic genome plasticity in a habitual and genetic drift-maximizing environment. Here, we revisit an MA experiment that uncovered extraordinary germline genome stability in Paramecium tetraurelia, a single-celled eukaryote with nuclear dimorphism. Our re-examination of isogenic P. tetraurelia MA lines propagated in nutrient-rich medium for >40 sexual cycles reveals that their polyploid somatic genome accrued hundreds of intervening DNA segments (IESs), which are normally eliminated during germline-soma differentiation. These IESs frequently occupy a fraction of the somatic DNA copies of a given locus, producing IES excision/retention polymorphisms, and preferentially fall into a class of epigenetically controlled sequences. Relative to control lines, retained IESs are flanked by stronger cis-acting signals and interrupt an excess of highly expressed coding exons. These findings suggest that P. tetraurelia’s elevated germline DNA replication fidelity is associated with pervasive somatic genome plasticity. They show that MA regimes are powerful tools for investigating the role that developmental plasticity, somatic mutations, and epimutations have in ecology and evolution.

Dictyostelium discoideum as a Model to Assess Genome Stability Through DNA Repair

Preserving genome integrity through repair of DNA damage is critical for human health and defects in these pathways lead to a variety of pathologies, most notably cancer. The social amoeba Dictyostelium discoideum is remarkably resistant to DNA damaging agents and genome analysis reveals it contains orthologs of several DNA repair pathway components otherwise limited to vertebrates. These include the Fanconi Anemia DNA inter-strand crosslink and DNA strand break repair pathways. Loss of function of these not only results in malignancy, but also neurodegeneration, immune-deficiencies and congenital abnormalities. Additionally, D. discoideum displays remarkable conservations of DNA repair factors that are targets in cancer and other therapies, including poly(ADP-ribose) polymerases that are targeted to treat breast and ovarian cancers. This, taken together with the genetic tractability of D. discoideum, make it an attractive model to assess the mechanistic basis of DNA repair to provide novel insights into how these pathways can be targeted to treat a variety of pathologies. Here we describe progress in understanding the mechanisms of DNA repair in D. discoideum, and how these impact on genome stability with implications for understanding development of malignancy.

Mitochondrial mutations in Caenorhabditis elegans show signatures of oxidative damage and an AT-bias

Rapid mutation rates are typical of mitochondrial genomes (mtDNAs) in animals, but it is not clear why. The difficulty of obtaining measurements of mtDNA mutation that are not biased by natural selection has stymied efforts to distinguish between competing hypotheses about the causes of high mtDNA mutation rates. Several studies which have measured mtDNA mutations in nematodes have yielded small datasets with conflicting conclusions about the relative abundance of different substitution classes (i.e., the mutation spectrum). We therefore leveraged Duplex Sequencing, a high-fidelity DNA sequencing technique, to characterize de novo mtDNA mutations in Caenorhabditis elegans. This approach detected nearly an order of magnitude more mtDNA mutations than documented in any previous nematode mutation study. Despite an existing extreme AT bias in the C. elegans mtDNA (75.6% AT), we found that a significant majority of mutations increase genomic AT content. Compared to some prior studies in nematodes and other animals, the mutation spectrum reported here contains an abundance of CG→AT transversions, supporting the hypothesis that oxidative damage may be a driver of mtDNA mutations in nematodes. Furthermore, we found an excess of G→T and C→T changes on the coding DNA strand relative to the template strand, consistent with increased exposure to oxidative damage. Analysis of the distribution of mutations across the mtDNA revealed significant variation among protein-coding genes and as well as among neighboring nucleotides. This high-resolution view of mitochondrial mutations in C. elegans highlights the value of this system for understanding relationships among oxidative damage, replication error, and mtDNA mutation.

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Mutability of mononucleotide repeats, not oxidative stress, explains the discrepancy between laboratory-accumulated mutations and the natural allele-frequency spectrum in C. elegans

Important clues about natural selection can be gleaned from discrepancies between the properties of segregating genetic variants and of mutations accumulated experimentally under minimal selection, provided the mutational process is the same in the laboratory as in nature. The base-substitution spectrum differs between C. elegans laboratory mutation accumulation (MA) experiments and the standing site-frequency spectrum, which has been argued to be in part owing to increased oxidative stress in the laboratory environment. Using genome sequence data from C. elegans MA lines carrying a mutation (mev-1) that increases the cellular titer of reactive oxygen species (ROS), leading to increased oxidative stress, we find the base-substitution spectrum is similar between mev-1, its wild-type progenitor (N2), and another set of MA lines derived from a different wild strain (PB306). Conversely, the rate of short insertions is greater in mev-1, consistent with studies in other organisms in which environmental stress increased the rate of insertion–deletion mutations. Further, the mutational properties of mononucleotide repeats in all strains are different from those of nonmononucleotide sequence, both for indels and base-substitutions, and whereas the nonmononucleotide spectra are fairly similar between MA lines and wild isolates, the mononucleotide spectra are very different, with a greater frequency of A:T → T:A transversions and an increased proportion of ±1-bp indels. The discrepancy in mutational spectra between laboratory MA experiments and natural variation is likely owing to a consistent (but unknown) effect of the laboratory environment that manifests itself via different modes of mutability and/or repair at mononucleotide loci.

Transposable elements and introgression introduce genetic variation in the invasive ant Cardiocondyla obscurior

Introduced populations of invasive organisms have to cope with novel environmental challenges, while having reduced genetic variation caused by founder effects. The mechanisms associated with this "genetic paradox of invasive species" has received considerable attention, yet few studies have examined the genomic architecture of invasive species. Populations of the heart node ant Cardiocondyla obscurior belong to two distinct lineages, a New World lineage so far only found in Latin America and a more globally distributed Old World lineage. In the present study, we use population genomic approaches to compare populations of the two lineages with apparent divergent invasive potential. We find that the strong genetic differentiation of the two lineages began at least 40,000 generations ago and that activity of transposable elements (TEs) has contributed significantly to the divergence of both lineages, possibly linked to the very unusual genomic distribution of TEs in this species. Furthermore, we show that introgression from the Old World lineage is a dominant source of genetic diversity in the New World lineage, despite the lineages' strong genetic differentiation. Our study uncovers mechanisms underlying novel genetic variation in introduced populations of C. obscurior that could contribute to the species' adaptive potential.

Intra-Population Competition during Adaptation to Increased Temperature in an RNA Bacteriophage

Evolution of RNA bacteriophages of the family Leviviridae is governed by the high error rates of their RNA-dependent RNA polymerases. This fact, together with their large population sizes, leads to the generation of highly heterogeneous populations that adapt rapidly to most changes in the environment. Throughout adaptation, the different mutants that make up a viral population compete with each other in a non-trivial process in which their selective values change over time due to the generation of new mutations. In this work we have characterised the intra-population dynamics of a well-studied levivirus, Qβ, when it is propagated at a higher-than-optimal temperature. Our results show that adapting populations experienced rapid changes that involved the ascent of particular genotypes and the loss of some beneficial mutations of early generation. Artificially reconstructed populations, containing a fraction of the diversity present in actual populations, fixed mutations more rapidly, illustrating how population bottlenecks may guide the adaptive pathways. The conclusion is that, when the availability of beneficial mutations under a particular selective condition is elevated, the final outcome of adaptation depends more on the occasional occurrence of population bottlenecks and how mutations combine in genomes than on the selective value of particular mutations.

Deuterium-depletion has no significant impact on the mutation rate of Escherichia coli, deuterium abundance therefore has a probabilistic, not deterministic effect on spontaneous mutagenesis

Deuterium (D), the second most abundant isotope of hydrogen is present in natural waters at an approximate concentration of 145-155 ppm (ca. 1.5E-4 atom/atom). D is known to influence various biological processes due to its physical and chemical properties, which significantly differ from those of hydrogen. For example, increasing D-concentration to >1000-fold above its natural abundance has been shown to increase the frequency of genetic mutations in several species. An interesting deterministic hypothesis, formulated with the intent of explaining the mechanism of D-mutagenicity is based on the calculation that the theoretical probability of base pairs to comprise two adjacent D-bridges instead of H-bridges is 2.3E-8, which is equal to the mutation rate of certain species. To experimentally challenge this hypothesis, and to infer the mutagenicity of D present at natural concentrations, we investigated the effect of a nearly 100-fold reduction of D concentration on the bacterial mutation rate. Using fluctuation tests, we measured the mutation rate of three Escherichia coli genes (cycA, ackA and galK) in media containing D at either <2 ppm or 150 ppm concentrations. Out of 15 pair-wise fluctuation analyses, nine indicated a significant decrease, while three marked the significant increase of the mutation/culture value upon D-depletion. Overall, growth in D-depleted minimal medium led to a geometric mean of 0.663-fold (95% confidence interval: 0.483-0.911) change in the mutation rate. This falls nowhere near the expected 10,000-fold reduction, indicating that in our bacterial systems, the effect of D abundance on the formation of point mutations is not deterministic. In addition, the combined results did not display a statistically significant change in the mutation/culture value, the mutation rate or the mutant frequency upon D-depletion. The potential mutagenic effect of D present at natural concentrations on E. coli is therefore below the limit of detection using the indicated methods.

Competition for nutritional resources masks the true frequency of bacterial mutants

Background

It is widely assumed that all mutant microorganisms present in a culture are able to grow and form colonies, provided that they express the features required for selection. Unlike wild-type Escherichia coli, PHO-constitutive mutants overexpress alkaline phosphatase and hence can hydrolyze glycerol-2-phosphate (G2P) to glycerol and form colonies on plates having G2P as the sole carbon source. These mutations mostly occur in the pst operon. However, the frequency of PHO-constitutive colonies on the G2P selective plate is exceptionally low.

Results

We show that the rate in which spontaneous PHO-constitutive mutations emerge is about 8.0 × 10⁻⁶/generation, a relatively high rate, but the growth of most existing mutants is inhibited by their neighboring wild-type cells. This inhibition is elicited only by non-mutant viable bacteria that can take up and metabolize glycerol formed by the mutants. Evidence indicates that the few mutants that do form colonies derive from microclusters of mutants on the selective plate. A mathematical model that describes the fate of the wild-type and mutant populations under these circumstances supports these results.

Conclusion

This scenario in which neither the wild-type nor the majority of the mutants are able to grow resembles an unavoidable “tragedy of the commons” case which results in the collapse of the majority of the population. Cooperation between rare adjacent mutants enables them to overcome the competition and eventually form mutant colonies. The inhibition of PHO-constitutive mutants provides an example of mutant frequency masked by orders of magnitude due to a competition between mutants and their ancestral wild-type cells. Similar “tragedy of the commons-like” cases may occur in other settings and should be taken into consideration while estimating true mutant frequencies and mutation rates.

Supplementary Information

The online version contains supplementary material available at (doi:10.1186/s12915-020-00913-1).

Competition delays multi-drug resistance evolution during combination therapy

Combination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

How industrial bacterial cultures can be kept stable over time

The tremendous variation that exists between bacterial species illustrates the power of evolution, which is the continuous process of mutation and selection over time. Even within a bacterial species, individual members can harbour an impressive degree of genetic variation, depending on the species. The question then arises how similar the offspring of a given bacterial cell over time is, and how long it takes before differences are noticeable? Here we show that on the one hand one can expect random mutations to arise, as a result of various mechanisms. On the other hand, there are forces at play that keep the offspring of a cell genetically relatively constant, unless there is selection for a particular characteristic. The most common mechanisms behind mutations that can appear in a bacterial population are briefly introduced. Next, it is explained why nevertheless such mutations are rarely observed, as long as single colonies are randomly selected, unless selective pressures apply. Since quality control of industrial bacterial cultures is likely to depend heavily on genome sequencing in the near future, the accuracy of whole‐genomic sequencing technologies is also discussed. It can be concluded that the bacteriologists who started picking single colonies from agar plates more than hundred years ago were unknowingly ingeneous, as their practice maintains a bacterial culture stable over time.

Significance and Impact of Study

The questions addressed here are relevant for industries that depend on live bacteria for (manufacturing of) their products, as they have to guard their bacterial cultures that remain unchanged over time. The explanation why randomly selection of single colonies keeps a population stable can be of use in bacteriology courses. The limitations of whole‐genome sequencing are relevant to legislators to avoid overinterpretation of those data.

Mutation-rate plasticity and the germline of unicellular organisms

The mutation rate is a fundamental factor in evolutionary genetics. Recently, mutation rates were found to be strongly reduced at high density in a wide range of unicellular organisms, prokaryotic and eukaryotic. Independently, cell division was found to become more asymmetrical at increasing density in diverse organisms; some 'mother' cells continue dividing, while their 'offspring' cells do not divide further. Here, we investigate how this increased asymmetry in cell division at high density can be reconciled with reduced mutation-rate estimates. We calculated the expected number of mutant cells due to replication errors under various modes of segregation of template-DNA strands and copy-DNA strands, both under symmetrical (exponential) and asymmetrical (linear) growth. We show that the observed reduction in the mutation rate at high density can be explained if mother cells preferentially retain the template-DNA strands, since new mutations are then confined to non-dividing daughter cells, thus reducing the spread of mutant cells. Any other inheritance mode results in an increase in the number of mutant cells at higher density. The proposed hypothesis that patterns of DNA-strand segregation are density-dependent fundamentally challenges our current understanding of mutation-rate estimates and extends the distinction between germline and soma to unicellular organisms.

Diffusion-driven enhancement of the antibiotic resistance selection window

The current crisis of antimicrobial resistance in clinically relevant pathogens has highlighted our limited understanding of the ecological and evolutionary forces that drive drug resistance adaptation. For instance, although human tissues are highly heterogeneous, most of our mechanistic understanding about antibiotic resistance evolution is based on constant and well-mixed environmental conditions. A consequence of considering spatial heterogeneity is that, even if antibiotics are prescribed at high dosages, the penetration of drug molecules through tissues inevitably produces antibiotic gradients, exposing bacterial populations to a range of selective pressures and generating a dynamic fitness landscape that changes in space and time. In this paper, we will use a combination of mathematical modelling and computer simulations to study the population dynamics of susceptible and resistant strains competing for resources in a network of micro-environments with varying degrees of connectivity. Our main result is that highly connected environments increase diffusion of drug molecules, enabling resistant phenotypes to colonize a larger number of spatial locations. We validated this theoretical result by culturing fluorescently labelled Escherichia coli in 3D-printed devices that allow us to control the rate of diffusion of antibiotics between neighbouring compartments and quantify the spatio-temporal distribution of resistant and susceptible bacterial cells.

Conditional Approximate Bayesian Computation: A New Approach for Across-Site Dependency in High-Dimensional Mutation-Selection Models

A key question in molecular evolutionary biology concerns the relative roles of mutation and selection in shaping genomic data. Moreover, features of mutation and selection are heterogeneous along the genome and over time. Mechanistic codon substitution models based on the mutation-selection framework are promising approaches to separating these effects. In practice, however, several complications arise, since accounting for such heterogeneities often implies handling models of high dimensionality (e.g., amino acid preferences), or leads to across-site dependence (e.g., CpG hypermutability), making the likelihood function intractable. Approximate Bayesian Computation (ABC) could address this latter issue. Here, we propose a new approach, named Conditional ABC (CABC), which combines the sampling efficiency of MCMC and the flexibility of ABC. To illustrate the potential of the CABC approach, we apply it to the study of mammalian CpG hypermutability based on a new mutation-level parameter implying dependence across adjacent sites, combined with site-specific purifying selection on amino-acids captured by a Dirichlet process. Our proof-of-concept of the CABC methodology opens new modeling perspectives. Our application of the method reveals a high level of heterogeneity of CpG hypermutability across loci and mild heterogeneity across taxonomic groups; and finally, we show that CpG hypermutability is an important evolutionary factor in rendering relative synonymous codon usage. All source code is available as a GitHub repository (https://github.com/Simonll/LikelihoodFreePhylogenetics.git).

Heterogeneity in efflux pump expression predisposes antibiotic-resistant cells to mutation

Antibiotic resistance is often the result of mutations that block drug activity; however, bacteria also evade antibiotics by transiently expressing genes such as multidrug efflux pumps. A crucial question is whether transient resistance can promote permanent genetic changes. Previous studies have established that antibiotic treatment can select tolerant cells that then mutate to achieve permanent resistance. Whether these mutations result from antibiotic stress or preexist within the population is unclear. To address this question, we focused on the multidrug pump AcrAB-TolC. Using time-lapse microscopy, we found that cells with higher acrAB expression have lower expression of the DNA mismatch repair gene mutS, lower growth rates, and higher mutation frequencies. Thus, transient antibiotic resistance from elevated acrAB expression can promote spontaneous mutations within single cells.

Yeast Spontaneous Mutation Rate and Spectrum Vary with Environment

Mutation is the ultimate genetic source of evolution and biodiversity, but to what extent the environment impacts mutation rate and spectrum is poorly understood. Past studies discovered mutagenesis induced by antibiotic treatment or starvation, but its relevance and importance to long-term evolution is unclear because these severe stressors typically halt cell growth and/or cause substantial cell deaths. Here, we quantify the mutation rate and spectrum in Saccharomyces cerevisiae by whole-genome sequencing following mutation accumulation in each of seven environments with relatively rapid cell growths and minimal cell deaths. We find the point mutation rate per generation to differ by 3.6-fold among the seven environments, generally increasing in environments with slower cell growths. This trend renders the mutation rate per year more constant than that per generation across environments, which has implications for neutral evolution and the molecular clock. Additionally, we find substantial among-environment variations in mutation spectrum, such as the transition to transversion ratio and AT mutational bias. Other main mutation types, including small insertion or deletion, segmental duplication or deletion, and chromosome gain or loss also tend to occur more frequently in environments where yeast grows more slowly. In contrast to these findings from the nuclear genome, the yeast mitochondrial mutation rate rises with the growth rate, consistent with the metabolic rate hypothesis. Together, these observations indicate that environmental changes, which are ubiquitous in nature, influence not only natural selection, but also the amount and type of mutations available to selection, and suggest that ignoring the latter impact, as is currently practiced, may mislead evolutionary inferences.

Predicting mutational routes to new adaptive phenotypes

Predicting evolutionary change poses numerous challenges. Here we take advantage of the model bacterium Pseudomonas fluorescens in which the genotype-to-phenotype map determining evolution of the adaptive 'wrinkly spreader' (WS) type is known. We present mathematical descriptions of three necessary regulatory pathways and use these to predict both the rate at which each mutational route is used and the expected mutational targets. To test predictions, mutation rates and targets were determined for each pathway. Unanticipated mutational hotspots caused experimental observations to depart from predictions but additional data led to refined models. A mismatch was observed between the spectra of WS-causing mutations obtained with and without selection due to low fitness of previously undetected WS-causing mutations. Our findings contribute toward the development of mechanistic models for forecasting evolution, highlight current limitations, and draw attention to challenges in predicting locus-specific mutational biases and fitness effects.

Opposing effects of final population density and stress on Escherichia coli mutation rate

Evolution depends on mutations. For an individual genotype, the rate at which mutations arise is known to increase with various stressors (stress-induced mutagenesis-SIM) and decrease at high final population density (density-associated mutation-rate plasticity-DAMP). We hypothesised that these two forms of mutation-rate plasticity would have opposing effects across a nutrient gradient. Here we test this hypothesis, culturing Escherichia coli in increasingly rich media. We distinguish an increase in mutation rate with added nutrients through SIM (dependent on error-prone polymerases Pol IV and Pol V) and an opposing effect of DAMP (dependent on MutT, which removes oxidised G nucleotides). The combination of DAMP and SIM results in a mutation rate minimum at intermediate nutrient levels (which can support 7 × 108 cells ml-1). These findings demonstrate a strikingly close and nuanced relationship of ecological factors-stress and population density-with mutation, the fuel of all evolution.

Detecting Rare Mutations and DNA Damage with Sequencing-Based Methods

There is a great need in biomedical and genetic research to detect DNA damage and de novo mutations, but doing so is inherently challenging because of the rarity of these events. The enormous capacity of current DNA sequencing technologies has opened the door for quantifying sequence variants present at low frequencies in vivo, such as within cancerous tissues. However, these sequencing technologies are error prone, resulting in high noise thresholds. Most DNA sequencing methods are also generally incapable of identifying chemically modified bases arising from DNA damage. In recent years, numerous specialized modifications to sequencing methods have been developed to address these shortcomings. Here, we review this landscape of emerging techniques, highlighting their respective strengths, weaknesses, and target applications.

Effective polyploidy causes phenotypic delay and influences bacterial evolvability

Whether mutations in bacteria exhibit a noticeable delay before expressing their corresponding mutant phenotype was discussed intensively in the 1940s to 1950s, but the discussion eventually waned for lack of supportive evidence and perceived incompatibility with observed mutant distributions in fluctuation tests. Phenotypic delay in bacteria is widely assumed to be negligible, despite the lack of direct evidence. Here, we revisited the question using recombineering to introduce antibiotic resistance mutations into E. coli at defined time points and then tracking expression of the corresponding mutant phenotype over time. Contrary to previous assumptions, we found a substantial median phenotypic delay of three to four generations. We provided evidence that the primary source of this delay is multifork replication causing cells to be effectively polyploid, whereby wild-type gene copies transiently mask the phenotype of recessive mutant gene copies in the same cell. Using modeling and simulation methods, we explored the consequences of effective polyploidy for mutation rate estimation by fluctuation tests and sequencing-based methods. For recessive mutations, despite the substantial phenotypic delay, the per-copy or per-genome mutation rate is accurately estimated. However, the per-cell rate cannot be estimated by existing methods. Finally, with a mathematical model, we showed that effective polyploidy increases the frequency of costly recessive mutations in the standing genetic variation (SGV), and thus their potential contribution to evolutionary adaptation, while drastically reducing the chance that de novo recessive mutations can rescue populations facing a harsh environmental change such as antibiotic treatment. Overall, we have identified phenotypic delay and effective polyploidy as previously overlooked but essential components in bacterial evolvability, including antibiotic resistance evolution.

What is the time delay between the occurrence of a genetic mutation in a bacterial cell and manifestation of its phenotypic effect? We show that antibiotic resistance mutations in Escherichia coli show a remarkably long phenotypic delay of three to four bacterial generations. The primary underlying mechanism of this delay is effective polyploidy. If a mutation arises on one of the multiple chromosomes in a polyploid cell, the presence of nonmutated, wild-type gene copies on other chromosomes may mask the phenotype of the mutation. We show here that mutation rate estimation needs to consider polyploidy, which influences the potential for bacterial adaptation. The fact that a new mutation may become useful only in the “great-great-grandchildren” suggests that preexisting mutations are more important for surviving sudden environmental catastrophes.

Errors in mutagenesis and the benefit of cell-to-cell signalling in the evolution of stress-induced mutagenesis

Stress-induced mutagenesis is a widely observed phenomenon. Theoretical models have shown that stress-induced mutagenesis can be favoured by natural selection due to the beneficial mutations it generates. These models, however, assumed an error-free regulation of mutation rate in response to stress. Here, we explore the effects of errors in the regulation of mutagenesis on the evolution of stress-induced mutagenesis, and consider the role of cell-to-cell signalling. Using theoretical models, we show (i) that stress-induced mutagenesis can be disadvantageous if errors are common; and (ii) that cell-to-cell signalling can allow stress-induced mutagenesis to be favoured by selection even when error rates are high. We conclude that cell-to-cell signalling can facilitate the evolution of stress-induced mutagenesis in microbes through second-order selection.