Fitness landscapes for predicting evolution between environments

Prediction of evolution is an ambitious undertaking that would consolidate knowledge from all fields of biology for the benefit of global health and biodiversity. Although prediction has been a foundational goal of population genetics theory, this goal is obstructed by the common simplifying assumptions of absent or weak genetic interactions (G⇥G), gene-by-environment interac tions (G⇥E), and higher-order epistasis-by-environment inter actions (G⇥G⇥E). This thesis examines the challenges posed by genetic and environmental interactions to the goal of predict ing evolution. Fitness landscapes models are brought to bear on data from both wild populations and laboratory conditions in order to investigate the predictability of two pressing issues: species-level biodiversity and antibiotic resistance evolution

Challenges and pitfalls of inferring microbial growth rates from lab cultures

IntroductionAfter more than 100 years of generating monoculture batch culture growth curves, microbial ecologists and evolutionary biologists still lack a reference method for inferring growth rates. Our work highlights the challenges of estimating the growth rate from growth curve data. It shows that inaccurate estimates of growth rates significantly impact the estimated relative fitness, a principal quantity in evolution and ecology. Methods and resultsFirst, we conducted a literature review and found which methods are currently used to estimate growth rates. These methods differ in the meaning of the estimated growth rate parameter. Mechanistic models estimate the intrinsic growth rate µ, whereas phenomenological methods – both model-based and model-free – estimate the maximum per capita growth rate µmax. Using math and simulations, we show the conditions in which µmax is not a good estimator of µ. Then, we demonstrate that inaccurate absolute estimates of µ are not overcome by calculating relative values. Importantly, we find that poor approximations for µ sometimes lead to wrongly classifying a beneficial mutant as deleterious. Finally, we re-analyzed four published data sets, using most of the methods found in our literature review. We detected no single best-fitting model across all experiments within a data set and found that the Gompertz models, which were among the most commonly used, were often among the worst-fitting. DiscussionOur study suggests how experimenters can improve their growth rate and associated relative fitness estimates and highlights a neglected but fundamental problem for nearly everyone who studies microbial populations in the lab

Challenges and pitfalls of inferring microbial growth rates from lab cultures

Introduction: After more than 100 years of generating monoculture batch culture growth curves, microbial ecologists and evolutionary biologists still lack a reference method for inferring growth rates. Our work highlights the challenges of estimating the growth rate from growth curve data. It shows that inaccurate estimates of growth rates significantly impact the estimated relative fitness, a principal quantity in evolution and ecology. Methods and results: First, we conducted a literature review and found which methods are currently used to estimate growth rates. These methods differ in the meaning of the estimated growth rate parameter. Mechanistic models estimate the intrinsic growth rate μ, whereas phenomenological methods – both modelbased and model-free – estimate the maximum per capita growth rate μmax. Using math and simulations, we show the conditions in which μmax is not a good estimator of μ. Then, we demonstrate that inaccurate absolute estimates of μ are not overcome by calculating relative values. Importantly, we find that poor approximations for μ sometimes lead to wrongly classifying a beneficial mutant as deleterious. Finally, we re-analyzed four published data sets, using most of the methods found in our literature review. We detected no single best-fitting model across all experiments within a data set and found that the Gompertz models, which were among the most commonly used, were often among the worst-fitting. Discussion: Our study suggests how experimenters can improve their growth rate and associated relative fitness estimates and highlights a neglected but fundamental problem for nearly everyone who studies microbial populations in the lab

Epistasis decreases with increasing antibiotic pressure but not temperature

Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-byenvironment (G× E), gene-by-gene (G ×G or epistatic) or gene-by-geneby-environment (G ×G× E) interactions. We quantified G×G× E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G× E effects in our environments of interest. Then,we measured competitive fitness acrossacomplete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G×G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’

A Statistical Guide to the Design of Deep Mutational Scanning Experiments

The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deep-sequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately compared with increasing the sequencing depth or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increase experimental power and allow for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates

Doping As an Integral Part of Modern Sports

HSFY BEAST input file. Alignment, calibration nodes, nucleotide substitution model, and specification of priors for tree shown in Additional file 1: Figure S3. (XML 207 kb

Additional file 1 of Multicopy gene family evolution on primate Y chromosomes

Supplementary Information. Supplementary methods, supplementary figures, and supplementary tables. (PDF 9.8 Mb

Czechoslovak Social Photography in the Interwar Period

The topic of my bachelor's thesis is social photography in the interwar period, primarily I am discussing the situation in the Czechoslovak Republic. I try to present this photographic genre from the broader point of view and in the global context. The work focuses on the impact of foreign artists on our art scene as well as on illustrated magazines or exhibitions. Also, we cannot forget to involve the political background which is so important for this genre. A leading figure in my work is Lubomir Linhart, who in his book Social Photography, is discussing critical aspects of this photographic genre

Additional file 3 of Multicopy gene family evolution on primate Y chromosomes

CDY BEAST input file. Alignment, calibration nodes, nucleotide substitution model, and specification of priors for tree shown in Additional file 1: Figure S1. (XML 101 kb