AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape

We present Annealed Mutational approximated Landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiments sequencing data. Such experiments typically start from a single wild-type sequence, which undergoes Darwinian in vitro evolution via multiple rounds of mutation and selection for a target phenotype. In the last years, Directed Evolution is emerging as a powerful instrument to probe fitness landscapes under controlled experimental conditions and as a relevant testing ground to develop accurate statistical models and inference algorithms (thanks to high-throughput screening and sequencing). Fitness landscape modeling either uses the enrichment of variants abundances as input, thus requiring the observation of the same variants at different rounds or assuming the last sequenced round as being sampled from an equilibrium distribution. AMaLa aims at effectively leveraging the information encoded in the whole time evolution. To do so, while assuming statistical sampling independence between sequenced rounds, the possible trajectories in sequence space are gauged with a time-dependent statistical weight consisting of two contributions: (i) an energy term accounting for the selection process and (ii) a generalized Jukes–Cantor model for the purely mutational step. This simple scheme enables accurately describing the Directed Evolution dynamics and inferring a fitness landscape that correctly reproduces the measures of the phenotype under selection (e.g., antibiotic drug resistance), notably outperforming widely used inference strategies. In addition, we assess the reliability of AMaLa by showing how the inferred statistical model could be used to predict relevant structural properties of the wild-type sequence.

Changes in the distribution of fitness effects and adaptive mutational spectra following a single first step towards adaptation

Historical contingency and diminishing returns epistasis have been typically studied for relatively divergent genotypes and/or over long evolutionary timescales. Here, we use Saccharomyces cerevisiae to study the extent of diminishing returns and the changes in the adaptive mutational spectra following a single first adaptive mutational step. We further evolve three clones that arose under identical conditions from a common ancestor. We follow their evolutionary dynamics by lineage tracking and determine adaptive outcomes using fitness assays and whole genome sequencing. We find that diminishing returns manifests as smaller fitness gains during the 2nd step of adaptation compared to the 1st step, mainly due to a compressed distribution of fitness effects. We also find that the beneficial mutational spectra for the 2nd adaptive step are contingent on the 1st step, as we see both shared and diverging adaptive strategies. Finally, we find that adaptive loss-of-function mutations, such as nonsense and frameshift mutations, are less common in the second step of adaptation than in the first step.

AMaLa: analysis of Directed Evolution experiments via Annealed Mutational approximated Landscape

We present Annealed Mutational approximated landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiment sequencing data. Directed Evolution experiments typically start from a single wild-type sequence, which undergoes Darwinian in vitro evolution acted via multiple rounds of mutation and selection with respect to a target phenotype. In the last years, Directed Evolution is emerging as a powerful instrument to probe fitness landscapes under controlled experimental condition and, thanks to the use of high-throughput sequencing of the different rounds, as a relevant testing ground to develop accurate statistical models and inference algorithms. Fitness landscape modeling strategies, either use as input data the enrichment of variants abundances and hence require observing the same variants at different rounds, or they simply assume that the variants at the last sequenced round are the results of a sampling process at equilibrium. AMaLa aims at leveraging effectively the information encoded in the time evolution of all sequenced rounds. To do so, on the one hand we assume statistical sampling independence between sequenced rounds, and on the other we gauge all possible trajectories in sequence space with a time-dependent statistical weight consisting of two contributions: (i) a statistical energy term accounting for the selection process, (ii) a simple generalized Jukes-Cantor model to describe the purely mutational step. This simple scheme allows us to accurately describe the Directed Evolution dynamics in a concrete experimental setup and to infer a fitness landscape that reproduces correctly the measures of the phenotype under selection (e.g. antibiotic drug resistance), notably outperforming widely used inference strategies. We assess the reliability of AMaLa by showing how the inferred statistical model could be used to predict relevant structural properties of the wild-type sequence, and to reproduce the mutational effects of large scale functional screening not used to train the model.

Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics and treatment regimens

The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: i) a single mutation, which provides a large resistance benefit, or ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.

Rampant loss of social traits during domestication of a Bacillus subtilis natural isolate

Most model bacteria have been domesticated in laboratory conditions. Yet, the tempo with which a natural isolate diverges from its ancestral phenotype under domestication to a novel laboratory environment is poorly understood. Such knowledge, however is essential to understanding the rate of evolution, the time scale over which a natural isolate can be propagated without loss of its natural adaptive traits, and the reliability of experimental results across labs. Using experimental evolution, phenotypic assays, and whole-genome sequencing, we show that within a week of propagation in a common laboratory environment, a natural isolate of Bacillus subtilis acquires mutations that cause changes in a multitude of traits. A single adaptive mutational step in the gene coding for the transcriptional regulator DegU impairs a DegU-dependent positive autoregulatory loop and leads to loss of robust biofilm architecture, impaired swarming motility, reduced secretion of exoproteases, and to changes in the dynamics of sporulation across environments. Importantly, domestication also resulted in improved survival when the bacteria face pressure from cells of the innate immune system. These results show that degU is a target for mutations during domestication and underscores the importance of performing careful and extremely short-term propagations of natural isolates to conserve the traits encoded in their original genomes.

Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics and treatment regimens

The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: i) a single mutation, which provides a large MIC increase, or ii) multiple mutations, each conferring a small increase, which combine to yield high-level resistance. Using stochastic modeling we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the most efficacious drug type depends on the pharmacokinetic profile. Further, we demonstrate that, for resistance evolution in multiple steps, adaptive treatment, which only suppresses the bacterial population, is favored over aggressive treatment, which aims at eradication.

Changes in the distribution of fitness effects and adaptive mutational spectra following a single first step towards adaptation

The fitness effects of random mutations are contingent upon the genetic and environmental contexts in which they occur, and this contributes to the unpredictability of evolutionary outcomes at the molecular level. Despite this unpredictability, the rate of adaptation in homogeneous environments tends to decrease over evolutionary time, due to diminishing returns epistasis, causing relative fitness gains to be predictable over the long term. Here, we studied the extent of diminishing returns epistasis and the changes in the adaptive mutational spectra after yeast populations have already taken their first adaptive mutational step. We used three distinct adaptive clones that arose under identical conditions from a common ancestor, from which they diverge by a single point mutation, to found populations that we further evolved. We followed the evolutionary dynamics of these populations by lineage tracking and determined adaptive outcomes using fitness assays and whole genome sequencing. We found compelling evidence for diminishing returns: fitness gains during the 2nd step of adaptation are smaller than those of the 1st step, due to a compressed distribution of fitness effects in the 2nd step. We also found strong evidence for historical contingency at the genic level: the beneficial mutational spectra of the 2nd-step adapted genotypes differ with respect to their ancestor and to each other, despite the fact that the three founders’ 1st-step mutations provided their fitness gains due to similar phenotypic improvements. While some targets of selection in the second step are shared with those seen in the common ancestor, other targets appear to be contingent on the specific first step mutation, with more phenotypically similar founding clones having more similar adaptive mutational spectra. Finally, we found that disruptive mutations, such as nonsense and frameshift, were much more common in the first step of adaptation, contributing an additional way that both diminishing returns and historical contingency are evident during 2nd step adaptation.

Evolutionary branching in distorted trait spaces

Biological communities are thought to have been evolving in trait spaces that are not only multi-dimensional, but also distorted in a sense that mutational covariance matrices among traits depend on the parental phenotypes of mutants. Such a distortion may affect diversifying evolution as well as directional evolution. In adaptive dynamics theory, diversifying evolution through ecological interaction is called evolutionary branching. This study analytically develops conditions for evolutionary branching in distorted trait spaces of arbitrary dimensions, by a local nonlinear coordinate transformation so that the mutational covariance matrix becomes locally constant in the neighborhood of a focal point. The developed evolutionary branching conditions can be affected by the distortion when mutational step sizes have significant magnitude difference among directions, i.e., the eigenvalues of the mutational covariance matrix have significant magnitude difference.

Rampant loss of social traits during domestication of a Bacillus subtilis natural isolate

Most well-studied bacteria have been domesticated to some extent. How fast can a natural isolate diverge from its ancestral phenotypes under domestication to a novel laboratory environment is poorly known. Yet such information is key to understand rates of evolution, the time scale at which a natural isolate can be propagated without loss of its natural adaptive traits and the reliability of experimental results across labs. Using experimental evolution, phenotypic assays and whole-genome sequencing, we show that within a week of propagation in a common laboratory environment, a natural isolate of Bacillus subtilis acquires mutations that cause changes in a multitude of traits. A single adaptive mutational step, in the gene coding for the transcriptional regulator DegU, impairs a DegU-dependent positive autoregulatory loop and leads to loss of robust biofilm architecture, impaired swarming motility, reduced secretion of exoproteases and changes in the dynamics of sporulation across environments. Importantly, domestication also resulted in improved survival when the bacteria face pressure from cells of the innate immune system. These results show that degU is a key target for mutations during domestication and also underscore the importance of performing careful and extremely short-term propagations of natural isolates to conserve the traits encoded in their original genomes.SummaryDomestication is the process by which organisms are selected to live in specific conditions and an important phenomenon that shapes the evolution and variation in many animals and plants. In microbes, domestication is also a key driver of adaptation. It can be beneficial, when improving microbes abilities that are important for biotechnology, but also problematic, especially when studying microbe-host interactions and the microbe’s natural behavior. Using a natural isolate of Bacillus subtilis, we determined the speed and genetic basis of microbial domestication using experimental evolution. Within one week of growth in the common laboratory media, mutations in the pleiotropic transcriptional regulator, DegU, emerge and spread in the populations. These lead to loss of social traits, increased resistance to bacteriophages and increased survival in the presence of macrophages. The data highlights the extreme caution that is needed when culturing natural microbial isolates and may help explain why some key microbial social traits and behaviors may differ between different laboratories, even when studying the same strains.

Fluctuating environments select for short-term phenotypic variation leading to long-term exploration

Genetic spaces are often described in terms of fitness landscapes or genotype-to-phenotype maps, where each genetic sequence is associated with phenotypic properties and linked to other genotypes that are a single mutational step away. The positions close to a genotype make up its “mutational landscape” and, in aggregate, determine the short-term evolutionary potential of a population. Populations with wider ranges of phenotypes in their mutational neighborhood are known to be more evolvable. Likewise, those with fewer phenotypic changes available in their local neighborhoods are more mutationally robust. Here, we examine whether forces that change the distribution of phenotypes available by mutation profoundly alter subsequent evolutionary dynamics.We compare evolved populations of digital organisms that were subject to either static or cyclically-changing environments. For each of these, we examine diversity of the phenotypes that are produced through mutations in order to characterize the local genotype-phenotype map. We demonstrate that environmental change can push populations toward more evolvable mutational landscapes where many alternate phenotypes are available, though purely deleterious mutations remain suppressed. Further, we show that populations in environments with harsh changes switch phenotypes more readily than those in environments with more benign changes. We trace this effect to repeated population bottlenecks in the harsh environments, which result in shorter coalescence times and keep populations in regions of the mutational landscape where the phenotypic shifts in question are more likely to occur. Typically, static environments select solely for immediate optimization, at the expensive of long-term evolvability. In contrast, we show that with changing environments, short-term pressures to deal with immediate challenges can align with long-term pressures to explore a more productive portion of the mutational landscape.