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The rate of directional environmental change may have profound consequences for evolutionary dynamics and outcomes. Yet, most evolution experiments impose a sudden large change in the environment, after which the environment is kept constant. We previously cultured replicate Saccharomyces cerevisiae populations for 500 generations in the presence of either gradually increasing or constant high concentrations of the heavy metals cadmium, nickel, and zinc. Here, we investigate how each of these treatments affected genomic evolution. Whole-genome sequencing of evolved clones revealed that adaptation occurred via a combination of SNPs, small indels, and whole-genome duplications and other large-scale structural changes. In contrast to some theoretical predictions, gradual and abrupt environmental change caused similar numbers of genomic changes. For cadmium, which is toxic already at comparatively low concentrations, mutations in the same genes were used for adaptation to both gradual and abrupt increase in concentration. Conversely, for nickel and zinc, which are toxic at high concentrations only, mutations in different genes were used for adaptation depending on the rate of change. Moreover, evolution was more repeatable following a sudden change in the environment, particularly for nickel and zinc. Our results show that the rate of environmental change and the nature of the selection pressure are important drivers of evolutionary dynamics and outcomes, which has implications for a better understanding of societal problems such as climate change and pollution.

Environmental conditions are an important factor driving pathogens’ evolution. Here, we explore the effects of drought stress in plant virus evolution. We evolved turnip mosaic potyvirus in wellwatered and drought conditions in Arabidopsis thaliana accessions that differ in their response to virus infection. Virus adaptation occurred in all accessions independently of watering status. Droughtevolved viruses conferred a significantly higher drought tolerance to infected plants. By contrast, nonsignificant increases in tolerance were observed in plants infected with viruses evolved under standardwatering. The magnitude of this effect was dependent on the plant accessions. Differences in tolerance were correlated to alterations in the expression of host genes, some involved in regulation of the circadian clock, as well as in deep changes in the balance of phytohormones regulating defense and growth signaling pathways. Our results show that viruses can promote host survival in situations of abiotic stress, with the magnitude of such benefit being a selectable trait.

Microbial experimental evolution uses a few well-characterized model systems to answer fundamental questions about how evolution works. This special section highlights novel model systems for experimental evolution, with a focus on marine model systems that can be used to understand evolutionary responses to global change in the oceans.

Microbial communities are complex multi-species assemblages that are characterized by a multitude of interspecies interactions, which can range from mutualism to competition. The overall sign and strength of interspecies interactions have important consequences for emergent community-level properties such as productivity and stability. It is not well understood how interspecies interactions change over evolutionary timescales. Here, we review the empirical evidence that evolution is an important driver of microbial community properties and dynamics on timescales that have traditionally been regarded as purely ecological. Next, we briefly discuss different modelling approaches to study evolution of communities, emphasizing the similarities and differences between evolutionary and ecological perspectives. We then propose a simple conceptual model for the evolution of interspecies interactions in communities. Specifically, we propose that to understand the evolution of interspecies interactions, it is important to distinguish between direct and indirect fitness effects of a mutation. We predict that in well-mixed environments, traits will be selected exclusively for their direct fitness effects, while in spatially structured environments, traits may also be selected for their indirect fitness effects. Selection of indirectly beneficial traits should result in an increase in interaction strength over time, while selection of directly beneficial traits should not have such a systematic effect. We tested our intuitions using a simple quantitative model and found support for our hypotheses. The next step will be to test these hypotheses experimentally and provide input for a more refined version of the model in turn, thus closing the scientific cycle of models and experiments. This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’.

Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current “gold standard” for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.

Rapid phenotypic adaptation is often observed in natural populations and selection experiments. However, detecting the genomewide impact of this selection is difficult since adaptation often proceeds from standing variation and selection on polygenic traits, both of which may leave faint genomic signals indistinguishable from a noisy background of genetic drift. One promising signal comes from the genome-wide covariance between allele frequency changes observable from temporal genomic data (e.g., evolve-and-resequence studies). These temporal covariances reflect how heritable fitness variation in the population leads changes in allele frequencies at one time point to be predictive of the changes at later time points, as alleles are indirectly selected due to remaining associations with selected alleles. Since genetic drift does not lead to temporal covariance, we can use these covariances to estimate what fraction of the variation in allele frequency change through time is driven by linked selection. Here, we reanalyze three selection experiments to quantify the effects of linked selection over short timescales using covariance among time points and across replicates. We estimate that at least 17 to 37% of allele frequency change is driven by selection in these experiments. Against this background of positive genome-wide temporal covariances, we also identify signals of negative temporal covariance corresponding to reversals in the direction of selection for a reasonable proportion of loci over the time course of a selection experiment. Overall, we find that in the three studies we analyzed, linked selection has a large impact on short-term allele frequency dynamics that is readily distinguishable from genetic drift.

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. A renewable barcoding system reveals the evolutionary dynamics of laboratory budding yeast, showing that fitness changes over time in a travelling wave of adaptation that can fluctuate owing to leapfrogging events.

There is increasing interest in the plant microbiome as it relates to both plant health and agricultural sustainability. One key unanswered question is whether we can select for a plant microbiome that is robust after colonization of target hosts. We used a successive passaging experiment to address this question by selecting upon the tomato phyllosphere microbiome. Beginning with a diverse microbial community generated from field-grown tomato plants, we inoculated replicate plants across 5 plant genotypes for 4 45-d passages, sequencing the microbial community at each passage. We observed consistent shifts in both the bacterial (16S amplicon sequencing) and fungal (internal transcribed spacer region amplicon sequencing) communities across replicate lines over time, as well as a general loss of diversity over the course of the experiment, suggesting that much of the naturally observed microbial community in the phyllosphere is likely transient or poorly adapted within the experimental setting. We found that both host genotype and environment shape microbial composition, but the relative importance of genotype declines through time. Furthermore, using a community coalescence experiment, we found that the bacterial community from the end of the experiment was robust to invasion by the starting bacterial community. These results highlight that selecting for a stable microbiome that is well adapted to a particular host environment is indeed possible, emphasizing the great potential of this approach in agriculture and beyond. In light of the consistent response of the microbiome to selection in the absence of reciprocal host evolution (coevolution) described here, future studies should address how such adaptation influences host health.

Candida auris is a recently discovered human fungal pathogen and has shown an alarming potential for developing multi- and pan-resistance toward all classes of antifungals most commonly used in the clinic. Currently, C. auris has been globally recognized as a nosocomial pathogen of high concern due to this evolutionary potential. Candida auris is globally recognized as an opportunistic fungal pathogen of high concern, due to its extensive multidrug resistance (MDR). Still, molecular mechanisms of MDR are largely unexplored. This is the first account of genome-wide evolution of MDR in C. auris obtained through serial in vitro exposure to azoles, polyenes, and echinocandins. We show the stepwise accumulation of copy number variations and novel mutations in genes both known and unknown in antifungal drug resistance. Echinocandin resistance was accompanied by a codon deletion in FKS1 hot spot 1 and a substitution in FKS1 “novel” hot spot 3. Mutations in ERG3 and CIS2 further increased the echinocandin MIC. Decreased azole susceptibility was linked to a mutation in transcription factor TAC1b and overexpression of the drug efflux pump Cdr1, a segmental duplication of chromosome 1 containing ERG11 , and a whole chromosome 5 duplication, which contains TAC1b . The latter was associated with increased expression of ERG11 , TAC1b , and CDR2 but not CDR1 . The simultaneous emergence of nonsense mutations in ERG3 and ERG11 was shown to decrease amphotericin B susceptibility, accompanied with fluconazole cross-resistance. A mutation in MEC3 , a gene mainly known for its role in DNA damage homeostasis, further increased the polyene MIC. Overall, this study shows the alarming potential for and diversity of MDR development in C. auris , even in a clade until now not associated with MDR (clade II), stressing its clinical importance and the urge for future research. IMPORTANCE Candida auris is a recently discovered human fungal pathogen and has shown an alarming potential for developing multi- and pan-resistance toward all classes of antifungals most commonly used in the clinic. Currently, C. auris has been globally recognized as a nosocomial pathogen of high concern due to this evolutionary potential. So far, this is the first study in which the stepwise progression of multidrug resistance (MDR) in C. auris is monitored in vitro . Multiple novel mutations in known resistance genes and genes previously not or vaguely associated with drug resistance reveal rapid MDR evolution in a C. auris clade II isolate. Additionally, this study shows that in vitro experimental evolution can be a powerful tool to discover new drug resistance mechanisms, although it has its limitations.

Isolated populations derived from a common ancestor are expected to diverge genetically and phenotypically as they adapt to different local environments. To examine this process, 30 populations of Escherichia coli were evolved for 2,000 generations, with six in each of five different thermal regimes: constant 20 °C, 32 °C, 37 °C, 42 °C, and daily alternations between 32 °C and 42 °C. Here, we sequenced the genomes of one endpoint clone from each population to test whether the history of adaptation in different thermal regimes was evident at the genomic level. The evolved strains had accumulated ∼5.3 mutations, on average, and exhibited distinct signatures of adaptation to the different environments. On average, two strains that evolved under the same regime exhibited ∼17% overlap in which genes were mutated, whereas pairs that evolved under different conditions shared only ∼4%. For example, all six strains evolved at 32 °C had mutations in nadR, whereas none of the other 24 strains did. However, a population evolved at 37 °C for an additional 18,000 generations eventually accumulated mutations in the signature genes strongly associated with adaptation to the other temperature regimes. Two mutations that arose in one temperature treatment tended to be beneficial when tested in the others, although less so than in the regime in which they evolved. These findings demonstrate that genomic signatures of adaptation can be highly specific, even with respect to subtle environmental differences, but that this imprintmay become obscured over longer timescales as populations continue to change and adapt to the shared features of their environments.