Experimental Studies of Evolutionary Dynamics in Microbes

Epistasis facilitates functional evolution in an ancient transcription factor

A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

Quantifying stochastic establishment of mutants in microbial adaptation

Studies of microbial evolution, especially in applied contexts, have focused on the role of selection in shaping predictable, adaptive responses to the environment. However, chance events - the appearance of novel genetic variants and their establishment, i.e. outgrowth from a single cell to a sizeable population - also play critical initiating roles in adaptation. Stochasticity in establishment has received little attention in microbiology, potentially due to lack of awareness as well as practical challenges in quantification. However, methods for high-replicate culturing, mutant labelling and detection, and statistical inference now make it feasible to experimentally quantify the establishment probability of specific adaptive genotypes. I review methods that have emerged over the past decade, including experimental design and mathematical formulas to estimate establishment probability from data. Quantifying establishment in further biological settings and comparing empirical estimates to theoretical predictions represent exciting future directions. More broadly, recognition that adaptive genotypes may be stochastically lost while rare is significant both for interpreting common lab assays and for designing interventions to promote or inhibit microbial evolution.

gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution

Cellular lineage tracking provides a means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones' relative fitness. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the system's application and resolution by applying it to track tens of thousands of Saccharomyces cerevisiae lineages growing together under varying environmental conditions applied across multiple generations, revealing fitness differences and lineage-specific adaptations. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening of a huge number of randomly generated variants of the Hsp82 gene. We further show how our method allows isolation of variants, even if their frequency in the population is low, thus enabling unsupervised identification of modifications that lead to a behaviour of interest.

Barcoding Populations of Pseudomonas fluorescens SBW25

In recent years, evolutionary biologists have developed an increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms.

Phylogenetic Analysis of Escherichia coli Isolated from Australian Feedlot Cattle in Comparison to Pig Faecal and Poultry/Human Extraintestinal Isolates

The similarity of commensal Escherichia coli isolated from healthy cattle to antimicrobial-resistant bacteria causing extraintestinal infections in humans is not fully understood. In this study, we used a bioinformatics approach based on whole genome sequencing data to determine the genetic characteristics and phylogenetic relationships among faecal Escherichia coli isolates from beef cattle (n = 37) from a single feedlot in comparison to previously analysed pig faecal (n = 45), poultry extraintestinal (n = 19), and human extraintestinal E. coli isolates (n = 40) from three previous Australian studies. Most beef cattle and pig isolates belonged to E. coli phylogroups A and B1, whereas most avian and human isolates belonged to B2 and D, although a single human extraintestinal isolate belonged to phylogenetic group A and sequence type (ST) 10. The most common E. coli sequence types (STs) included ST10 for beef cattle, ST361 for pig, ST117 for poultry, and ST73 for human isolates. Extended-spectrum and AmpC β-lactamase genes were identified in seven out of thirty-seven (18.9%) beef cattle isolates. The most common plasmid replicons identified were IncFIB (AP001918), followed by IncFII, Col156, and IncX1. The results confirm that feedlot cattle isolates examined in this study represent a reduced risk to human and environmental health with regard to being a source of antimicrobial-resistant E. coli of clinical importance.

Gene amplification mutations originate prior to selective stress in Acinetobacter baylyi

The controversial theory of adaptive amplification states gene amplification mutations are induced by selective environments where they are enriched due to the stress caused by growth restriction on unadapted cells. We tested this theory with three independent assays using an Acinetobacter baylyi model system that exclusively selects for cat gene amplification mutants. Our results demonstrate all cat gene amplification mutant colonies arise through a multistep process. While the late steps occur during selection exposure, these mutants derive from low-level amplification mutant cells that form before growth-inhibiting selection is imposed. During selection, these partial mutants undergo multiple secondary steps generating higher amplification over several days to multiple weeks to eventually form visible high-copy amplification colonies. Based on these findings, amplification in this Acinetobacter system can be explained by a natural selection process that does not require a stress response. These findings have fundamental implications to understanding the role of growth-limiting selective environments on cancer development. We suggest duplication mutations encompassing growth factor genes may serve as new genomic biomarkers to facilitate early cancer detection and treatment, before high-copy amplification is attained.

Novelty Search Promotes Antigenic Diversity in Microbial Pathogens

Driven by host–pathogen coevolution, cell surface antigens are often the fastest evolving parts of a microbial pathogen. The persistent evolutionary impetus for novel antigen variants suggests the utility of novelty-seeking algorithms in predicting antigen diversification in microbial pathogens. In contrast to traditional genetic algorithms maximizing variant fitness, novelty-seeking algorithms optimize variant novelty. Here, we designed and implemented three evolutionary algorithms (fitness-seeking, novelty-seeking, and hybrid) and evaluated their performances in 10 simulated and 2 empirically derived antigen fitness landscapes. The hybrid walks combining fitness- and novelty-seeking strategies overcame the limitations of each algorithm alone, and consistently reached global fitness peaks. Thus, hybrid walks provide a model for microbial pathogens escaping host immunity without compromising variant fitness. Biological processes facilitating novelty-seeking evolution in natural pathogen populations include hypermutability, recombination, wide dispersal, and immune-compromised hosts. The high efficiency of the hybrid algorithm improves the evolutionary predictability of novel antigen variants. We propose the design of escape-proof vaccines based on high-fitness variants covering a majority of the basins of attraction on the fitness landscape representing all potential variants of a microbial antigen.

Microbial experimental evolution in a massively multiplexed and high-throughput era

Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.

Recombination resolves the cost of horizontal gene transfer in experimental populations of Helicobacter pylori

Horizontal gene transfer (HGT)—the transfer of DNA between lineages—is responsible for a large proportion of the genetic variation that contributes to evolution in microbial populations. While HGT can bring beneficial genetic innovation, the transfer of DNA from other species or strains can also have deleterious effects. In this study, we evolve populations of the bacteria Helicobacter pylori and use DNA sequencing to identify over 40,000 genetic variants transferred by HGT. We measure the cost of many of these and find that both strongly beneficial mutations and deleterious mutations are genetic variants transferred by natural transformation. Importantly, we also show how recombination that separates linked beneficial and deleterious mutations resolves the cost of HGT.

Horizontal gene transfer (HGT) is important for microbial evolution, yet we know little about the fitness effects and dynamics of horizontally transferred genetic variants. In this study, we evolve laboratory populations of Helicobacter pylori, which take up DNA from their environment by natural transformation, and measure the fitness effects of thousands of transferred genetic variants. We find that natural transformation increases the rate of adaptation but comes at the cost of significant genetic load. We show that this cost is circumvented by recombination, which increases the efficiency of selection by decoupling deleterious and beneficial genetic variants. Our results show that adaptation with HGT, pervasive in natural microbial populations, is shaped by a combination of selection, recombination, and genetic drift not accounted for in existing models of evolution.

Ecological Opportunity and Necessity: Biotic and Abiotic Drivers Interact During Diversification of Digital Host-Parasite Communities

Most of Earth’s diversity has been produced in rounds of adaptive radiation, but the ecological drivers of diversification, such as abiotic complexity (i.e., ecological opportunity) or predation and parasitism (i.e., ecological necessity), are hard to disentangle. However, most of these radiations occurred hundreds of thousands if not millions of years ago, and the mechanisms promoting contemporary coexistence are not necessarily the same mechanisms that drove diversification in the first place. Experimental evolution has been one fruitful approach used to understand how different ecological mechanisms promote diversification in simple microbial microcosms, but these microbial systems come with their own limitations. To test how ecological necessity and opportunity interact, we use an unusual system of self-replicating computer programs that diversify to fill niches in a virtual environment. These organisms are subject to ecological pressures just like their natural counterparts. They experience biotic interactions from digital parasites, which steal host resources to replicate their own code and spread in the population. With the control afforded by experimenting with computational ecologies, we begin to unweave the complex interplay between ecological drivers of diversification. In particular, we find that the complexity of the abiotic environment and the size of the phenotypic space in which organisms are able to interact play different roles depending on the ecological driver of diversification. We find that in some situations, both ecological opportunity and necessity drive similar levels of diversity. However, the phenotypes that hosts uncover while coevolving with parasites are dramatically more complex than hosts evolving alone.

Adaptation dynamics between copy-number and point mutations

Together, copy-number and point mutations form the basis for most evolutionary novelty, through the process of gene duplication and divergence. While a plethora of genomic data reveals the long-term fate of diverging coding sequences and their cis-regulatory elements, little is known about the early dynamics around the duplication event itself. In microorganisms, selection for increased gene expression often drives the expansion of gene copy-number mutations, which serves as a crude adaptation, prior to divergence through refining point mutations. Using a simple synthetic genetic reporter system that can distinguish between copy-number and point mutations, we study their early and transient adaptive dynamics in real time in Escherichia coli. We find two qualitatively different routes of adaptation, depending on the level of functional improvement needed. In conditions of high gene expression demand, the two mutation types occur as a combination. However, under low gene expression demand, copy-number and point mutations are mutually exclusive; here, owing to their higher frequency, adaptation is dominated by copy-number mutations, in a process we term amplification hindrance. Ultimately, due to high reversal rates and pleiotropic cost, copy-number mutations may not only serve as a crude and transient adaptation, but also constrain sequence divergence over evolutionary time scales.

High-throughput characterization of mutations in genes that drive clonal evolution using multiplex adaptome capture sequencing

Understanding how cells are likely to evolve can guide medical interventions and bioengineering efforts that must contend with unwanted mutations. The adaptome of a cell-the neighborhood of genetic changes that are most likely to drive adaptation in a given environment-can be mapped by tracking rare beneficial variants during the early stages of clonal evolution. We used multiplex adaptome capture sequencing (mAdCap-seq), a procedure that combines unique molecular identifiers and hybridization-based enrichment, to characterize mutations in eight Escherichia coli genes known to be under selection in a laboratory environment. We tracked 301 mutations at frequencies as low as 0.01% and inferred the fitness effects of 240 of these mutations. There were distinct molecular signatures of selection on protein structure and function for the three genes with the most beneficial mutations. Our results demonstrate how mAdCap-seq can be used to deeply profile a targeted portion of a cell's adaptome.

Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment

Gut microbial communities can respond to antibiotic perturbations by rapidly altering their taxonomic and functional composition. However, little is known about the strain-level processes that drive this collective response. Here, we characterize the gut microbiome of a single individual at high temporal and genetic resolution through a period of health, disease, antibiotic treatment, and recovery. We used deep, linked-read metagenomic sequencing to track the longitudinal trajectories of thousands of single nucleotide variants within 36 species, which allowed us to contrast these genetic dynamics with the ecological fluctuations at the species level. We found that antibiotics can drive rapid shifts in the genetic composition of individual species, often involving incomplete genome-wide sweeps of pre-existing variants. These genetic changes were frequently observed in species without obvious changes in species abundance, emphasizing the importance of monitoring diversity below the species level. We also found that many sweeping variants quickly reverted to their baseline levels once antibiotic treatment had concluded, demonstrating that the ecological resilience of the microbiota can sometimes extend all the way down to the genetic level. Our results provide new insights into the population genetic forces that shape individual microbiomes on therapeutically relevant timescales, with potential implications for personalized health and disease.

Effects of synonymous mutations beyond codon bias: The evidence for adaptive synonymous substitutions from microbial evolution experiments

Synonymous mutations are often assumed to be neutral with respect to fitness because they do not alter the encoded amino acid and so cannot be 'seen' by natural selection. Yet a growing body of evidence suggests that synonymous mutations can have fitness effects that drive adaptive evolution through their impacts on gene expression and protein folding. Here, we review what microbial experiments have taught us about the contribution of synonymous mutations to adaptation. A survey of site-directed mutagenesis experiments reveals the distributions of fitness effects for nonsynonymous and synonymous mutations are more similar, especially for beneficial mutations, than expected if all synonymous mutations were neutral, suggesting they should drive adaptive evolution more often than is typically observed. A review of experimental evolution studies where synonymous mutations have contributed to adaptation shows they can impact fitness through a range of mechanisms including the creation of illicit RNA polymerase binding sites impacting transcription and changes to mRNA folding stability that modulate translation. We suggest that clonal interference in evolving microbial populations may be the reason synonymous mutations play a smaller role in adaptive evolution than expected based on their observed fitness effects. We finish by discussing the impacts of falsely assuming synonymous mutations are neutral and discuss directions for future work exploring the role of synonymous mutations in adaptive evolution.

Evolutionary dynamics and structural consequences of de novo beneficial mutations and mutant lineages arising in a constant environment

BACKGROUND

Microbial evolution experiments can be used to study the tempo and dynamics of evolutionary change in asexual populations, founded from single clones and growing into large populations with multiple clonal lineages. High-throughput sequencing can be used to catalog de novo mutations as potential targets of selection, determine in which lineages they arise, and track the fates of those lineages. Here, we describe a long-term experimental evolution study to identify targets of selection and to determine when, where, and how often those targets are hit.

RESULTS

We experimentally evolved replicate Escherichia coli populations that originated from a mutator/nonsense suppressor ancestor under glucose limitation for between 300 and 500 generations. Whole-genome, whole-population sequencing enabled us to catalog 3346 de novo mutations that reached > 1% frequency. We sequenced the genomes of 96 clones from each population when allelic diversity was greatest in order to establish whether mutations were in the same or different lineages and to depict lineage dynamics. Operon-specific mutations that enhance glucose uptake were the first to rise to high frequency, followed by global regulatory mutations. Mutations related to energy conservation, membrane biogenesis, and mitigating the impact of nonsense mutations, both ancestral and derived, arose later. New alleles were confined to relatively few loci, with many instances of identical mutations arising independently in multiple lineages, among and within replicate populations. However, most never exceeded 10% in frequency and were at a lower frequency at the end of the experiment than at their maxima, indicating clonal interference. Many alleles mapped to key structures within the proteins that they mutated, providing insight into their functional consequences.

CONCLUSIONS

Overall, we find that when mutational input is increased by an ancestral defect in DNA repair, the spectrum of high-frequency beneficial mutations in a simple, constant resource-limited environment is narrow, resulting in extreme parallelism where many adaptive mutations arise but few ever go to fixation.

Genetic Diversity and Evolution of Viral Populations

Population genetic diversity plays a prominent role in viral evolution, pathogenesis, immune escape, and drug resistance. Different mechanisms are responsible for creating and maintaining genetic diversity in viruses, including error-prone replication, repair avoidance, and genome editing, among others. This diversity is subsequently modulated by natural selection and random genetic drift, whose action in turn depends on multiple factors including viral genetic architecture, viral demography, and ecology. Understanding these processes should contribute to the development of more efficient control and treatment strategies against viral pathogens.

Contingent evolution of alternative metabolic network topologies determines whether cross-feeding evolves

Metabolic exchange is widespread in natural microbial communities and an important driver of ecosystem structure and diversity, yet it remains unclear what determines whether microbes evolve division of labor or maintain metabolic autonomy. Here we use a mechanistic model to study how metabolic strategies evolve in a constant, one resource environment, when metabolic networks are allowed to freely evolve. We find that initially identical ancestral communities of digital organisms follow different evolutionary trajectories, as some communities become dominated by a single, autonomous lineage, while others are formed by stably coexisting lineages that cross-feed on essential building blocks. Our results show how without presupposed cellular trade-offs or external drivers such as temporal niches, diverse metabolic strategies spontaneously emerge from the interplay between ecology, spatial structure, and metabolic constraints that arise during the evolution of metabolic networks. Thus, in the long term, whether microbes remain autonomous or evolve metabolic division of labour is an evolutionary contingency.

Evolutionary dynamics drives role specialization in a community of players

The progression of game theory from classical to evolutionary and spatial games provided a powerful means to study cooperation, and enabled a better understanding of general cooperation-promoting mechanisms. However, current standard models assume that at any given point players must choose either cooperation or defection, meaning that regardless of the spatial structure in which they exist, they cannot differentiate between their neighbours and adjust their behaviour accordingly. This is at odds with interactions among organisms in nature who are well capable of behaving differently towards different members of their communities. We account for this natural fact by introducing a new type of player-dubbed link players-who can adjust their behaviour to each individual neighbour. This is in contrast to more common node players whose behaviour affects all neighbours in the same way. We proceed to study cooperation in pure and mixed populations, showing that cooperation peaks at moderately low densities of link players. In such conditions, players naturally specialize in different roles. Node players tend to be either cooperators or defectors, while link players form social insulation between cooperative and defecting clusters by acting both as cooperators and defectors. Such fairly complex processes emerging from a simple model reflect some of the complexities observed in experimental studies on social behaviour in microbes and pave a way for the development of richer game models.

High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast

In rapidly adapting asexual populations, including many microbial pathogens and viruses, numerous mutant lineages often compete for dominance within the population^1-5. These complex evolutionary dynamics determine the outcomes of adaptation, but have been difficult to observe directly. Previous studies have used whole-genome sequencing to follow molecular adaptation^6-10; however, these methods have limited resolution in microbial populations. Here we introduce a renewable barcoding system to observe evolutionary dynamics at high resolution in laboratory budding yeast. We find nested patterns of interference and hitchhiking even at low frequencies. These events are driven by the continuous appearance of new mutations that modify the fates of existing lineages before they reach substantial frequencies. We observe how the distribution of fitness within the population changes over time, and find a travelling wave of adaptation that has been predicted by theory^11-17. We show that clonal competition creates a dynamical 'rich-get-richer' effect: fitness advantages that are acquired early in evolution drive clonal expansions, which increase the chances of acquiring future mutations. However, less-fit lineages also routinely leapfrog over strains of higher fitness. Our results demonstrate that this combination of factors, which is not accounted for in existing models of evolutionary dynamics, is critical in determining the rate, predictability and molecular basis of adaptation.

High Fidelity Deep Sequencing Reveals No Effect of ATM, ATR, and DNA-PK Cellular DNA Damage Response Pathways on Adenovirus Mutation Rate

Most DNA viruses exhibit relatively low rates of spontaneous mutation. However, the molecular mechanisms underlying DNA virus genetic stability remain unclear. In principle, mutation rates should not depend solely on polymerase fidelity, but also on factors such as DNA damage and repair efficiency. Most eukaryotic DNA viruses interact with the cellular DNA damage response (DDR), but the role of DDR pathways in preventing mutations in the virus has not been tested empirically. To address this goal, we serially transferred human adenovirus type 5 in cells in which the telangiectasia-mutated PI3K-related protein kinase (ATM), the ATM/Rad3-related (ATR) kinase, and the DNA-dependent protein kinase (DNA-PK) were chemically inactivated, as well as in control cells displaying normal DDR pathway functioning. High-fidelity deep sequencing of these viral populations revealed mutation frequencies in the order of one-millionth, with no detectable effect of the inactivation of DDR mediators ATM, ATR, and DNA-PK on adenovirus sequence variability. This suggests that these DDR pathways do not play a major role in determining adenovirus genetic diversity.

The feedback between selection and demography shapes genomic diversity during coevolution

Species interactions and coevolution are integral to ecological communities, but we lack empirical information on when and how these interactions generate and purge genetic diversity. Using genomic time series data from host-virus experiments, we found that coevolution occurs through consecutive selective sweeps in both species, with temporal consistency across replicates. Sweeps were accompanied by phenotypic change (resistance or infectivity increases) and expansions in population size. In the host, population expansion enabled rapid generation of genetic diversity in accordance with neutral processes. Viral molecular evolution was, in contrast, confined to few genes, all putative targets of selection. This study demonstrates that molecular evolution during species interactions is shaped by both eco-evolutionary feedback dynamics and interspecific differences in how genetic diversity is generated and maintained.

Ecological and Evolutionary Processes Shaping Viral Genetic Diversity

The contemporary genomic diversity of viruses is a result of the continuous and dynamic interaction of past ecological and evolutionary processes. Thus, genome sequences of viruses can be a valuable source of information about these processes. In this review, we first describe the relevant processes shaping viral genomic variation, with a focus on the role of host–virus coevolution and its potential to give rise to eco-evolutionary feedback loops. We further give a brief overview of available methodology designed to extract information about these processes from genomic data. Short generation times and small genomes make viruses ideal model systems to study the joint effect of complex coevolutionary and eco-evolutionary interactions on genetic evolution. This complexity, together with the diverse array of lifetime and reproductive strategies in viruses ask for extensions of existing inference methods, for example by integrating multiple information sources. Such integration can broaden the applicability of genetic inference methods and thus further improve our understanding of the role viruses play in biological communities.

Transcriptional noise and exaptation as sources for bacterial sRNAs

Understanding how new genes originate and integrate into cellular networks is key to understanding evolution. Bacteria present unique opportunities for both the natural history and experimental study of gene origins, due to their large effective population sizes, rapid generation times, and ease of genetic manipulation. Bacterial small non-coding RNAs (sRNAs), in particular, many of which operate through a simple antisense regulatory logic, may serve as tractable models for exploring processes of gene origin and adaptation. Understanding how and on what timescales these regulatory molecules arise has important implications for understanding the evolution of bacterial regulatory networks, in particular, for the design of comparative studies of sRNA function. Here, we introduce relevant concepts from evolutionary biology and review recent work that has begun to shed light on the timescales and processes through which non-functional transcriptional noise is co-opted to provide regulatory functions. We explore possible scenarios for sRNA origin, focusing on the co-option, or exaptation, of existing genomic structures which may provide protected spaces for sRNA evolution.