Selection for Intermediate Genotypes Enables a Key Innovation in Phage Lambda
Alita Burmeister , Richard Lenski , Justin Meyer
The evolution of qualitatively new functions is fundamental for shaping the diversity of life. Such innovations are rare because they require multiple coordinated changes. We sought to understand the evolutionary processes involved in a particular key innovation, whereby phage λ evolved the ability to exploit a novel receptor, OmpF, on the surface of Escherichia coli cells. Previous work has shown that this transition repeatedly evolves in the laboratory, despite requiring four mutations in specific regions of a single gene. Here we examine how this innovation evolved by studying six intermediate genotypes that arose during independent transitions to use OmpF. In particular, we tested whether these genotypes were favored by selection, and how a coevolved change in the hosts influenced the fitness of the phage genotypes. To do so, we measured the fitness of the intermediate types relative to the ancestral λ when competing for either ancestral or coevolved host cells. All six intermediates had improved fitness on at least one host, and four had higher fitness on the coevolved host than on the ancestral host. These results show that the evolution of the phage’s new ability to use OmpF was repeatable because the intermediate genotypes were adaptive and, in many cases, because coevolution of the host favored their emergence.
Adaptation, Clonal Interference, and Frequency-Dependent Interactions in a Long-Term Evolution Experiment with Escherichia coli
Rohan Maddamsetti , Richard E. Lenski , Jeffrey E. Barrick
Twelve replicate populations of Escherichia coli have been evolving in the laboratory for more than 25 years and 60,000 generations. We analyzed bacteria from whole-population samples frozen every 500 generations through 20,000 generations for one well-studied population, called Ara???1. By tracking 42 known mutations in these samples, we reconstructed the history of this population???s genotypic evolution over this period. The evolutionary dynamics of Ara???1 show strong evidence of selective sweeps as well as clonal interference between competing lineages bearing different beneficial mutations. In some cases, sets of several mutations approached fixation simultaneously, often conveying no information about their order of origination; we present several possible explanations for the existence of these mutational cohorts. Against a backdrop of rapid selective sweeps both earlier and later, we found that two clades coexisted for over 6000 generations before one drove the other extinct. In that time, at least nine mutations arose in the clade that prevailed. We found evidence that the clades evolved a frequency-dependent interaction, which prevented the competitive exclusion of either clade, but which eventually collapsed as beneficial mutations accumulated in the clade that prevailed. Clonal interference and frequency dependence can occur even in the simplest microbial populations. Furthermore, frequency dependence may generate dynamics that extend the period of coexistence that would otherwise be sustained by clonal interference alone.
Breaking through evolutionary constraint by environmental fluctuations
Marjon GJ de Vos , Alexandre Dawid , Vanda Sunderlikova , Sander J Tans
Epistatic interactions can frustrate and shape evolutionary change. Indeed, phenotypes may fail to evolve because essential mutations can only be selected positively if fixed simultaneously. How environmental variability affects such constraints is poorly understood. Here we studied genetic constraints in fixed and fluctuating environments, using the Escherichia coli lac operon as a model system for genotype-environment interactions. The data indicated an apparent paradox: in different fixed environments, mutational trajectories became trapped at sub-optima where no further improvements were possible, while repeated switching between these same environments allowed unconstrained adaptation by continuous improvements. Pervasive cross-environmental trade-offs transformed peaks into valleys upon environmental change, thus enabling escape from entrapment. This study shows that environmental variability can lift genetic constraint, and that trade-offs not only impede but can also facilitate adaptive evolution.
Empirical determinants of adaptive mutations in yeast experimental evolution
Celia Payen, Anna B Sunshine, Giang T Ong, Jamie L Pogachar, Wei Zhao, Maitreya J Dunham
High-throughput sequencing technologies have enabled expansion of the scope of genetic screens to identify mutations that underlie quantitative phenotypes, such as fitness improvements that occur during the course of experimental evolution. This new capability has allowed us to describe the relationship between fitness and genotype at a level never possible before, and ask deeper questions, such as how genome structure, available mutation spectrum, and other factors drive evolution. Here we combined functional genomics and experimental evolution to first map on a genome scale the distribution of potential beneficial mutations available as a first step to an evolving population and then compare these to the mutations actually observed in order to define the constraints acting upon evolution. We first constructed a single-step fitness landscape for the yeast genome by using barcoded gene deletion and overexpression collections, competitive growth in continuous culture, and barcode sequencing. By quantifying the relative fitness effects of thousands of single-gene amplifications or deletions simultaneously we revealed the presence of hundreds of accessible evolutionary paths. To determine the actual mutation spectrum used in evolution, we built a catalog of >1000 mutations selected during experimental evolution. By combining both datasets, we were able to ask how and why evolution is constrained. We identified adaptive mutations in laboratory evolved populations, derived mutational signatures in a variety of conditions and ploidy states, and determined that half of the mutations accumulated positively affect cellular fitness. We also uncovered hundreds of potential beneficial mutations never observed in the mutational spectrum derived from the experimental evolution catalog and found that those adaptive mutations become accessible in the absence of the dominant adaptive solution. This comprehensive functional screen explored the set of potential adaptive mutations on one genetic background, and allows us for the first time at this scale to compare the mutational path with the actual, spontaneously derived spectrum of mutations.
Modeling and quantifying frequency-dependent fitness in microbial populations with cross-feeding interactions
Noah Ribeck, Richard E. Lenski
Coexistence of multiple populations by frequency-dependent selection is common in nature, and it often arises even in well-mixed experiments with microbes. If ecology is to be incorporated into models of population genetics, then it is important to represent accurately the functional form of frequency-dependent interactions. However, measuring this functional form is problematic for traditional fitness assays, which assume a constant fitness difference between competitors over the course of an assay. Here, we present a theoretical framework for measuring the functional form of frequency-dependent fitness by accounting for changes in abundance and relative fitness during a competition assay. Using two examples of ecological coexistence that arose in a long-term evolution experiment with Escherichia coli, we illustrate accurate quantification of the functional form of frequency-dependent relative fitness. Using a Monod-type model of growth dynamics, we show that two ecotypes in a typical cross-feeding interaction—such as when one bacterial population uses a byproduct generated by another—yields relative fitness that is linear with relative frequency.
Gaussian process test for high-throughput sequencing time series: application to experimental evolution
Hande Topa, Ágnes Jónás, Robert Kofler, Carolin Kosiol, Antti Honkela
Comments: 26 pages, 13 figures
Subjects: Populations and Evolution (q-bio.PE); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM); Applications (stat.AP)
Motivation: Recent advances in high-throughput sequencing (HTS) have made it possible to monitor genomes in great detail. New experiments not only use HTS to measure genomic features at one time point but to monitor them changing over time with the aim of identifying significant changes in their abundance. In population genetics, for example, allele frequencies are monitored over time to detect significant frequency changes that indicate selection pressures. Previous attempts at analysing data from HTS experiments have been limited as they could not simultaneously include data at intermediate time points, replicate experiments and sources of uncertainty specific to HTS such as sequencing depth.
Results: We present the beta-binomial Gaussian process (BBGP) model for ranking features with significant non-random variation in abundance over time. The features are assumed to represent proportions, such as proportion of an alternative allele in a population. We use the beta-binomial model to capture the uncertainty arising from finite sequencing depth and combine with a Gaussian process model over the time series. In simulations that mimic the features of experimental evolution data, the proposed method clearly outperforms classical testing in average precision of finding selected alleles. We also present results on real data from Drosophila experimental evolution experiment in temperature adaptation.
Availability: R software implementing the test is available at https://github.com/handetopa/BBGP.
An experimentally determined evolutionary model dramatically improves phylogenetic fit
Jesse D Bloom
All modern approaches to molecular phylogenetics require a quantitative model for how genes evolve. Unfortunately, existing evolutionary models do not realistically represent the site-heterogeneous selection that governs actual sequence change. Attempts to remedy this problem have involved augmenting these models with a burgeoning number of free parameters. Here I demonstrate an alternative: experimental determination of a parameter-free evolutionary model via mutagenesis, functional selection, and deep sequencing. Using this strategy, I create an evolutionary model for influenza nucleoprotein that describes the gene phylogeny far better than existing models with dozens or even hundreds of free parameters. High-throughput experimental strategies such as the one employed here provide fundamentally new information that has the potential to transform the sensitivity of phylogenetic analyses.