Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

The distribution of beneficial mutational effects between two sister yeast species poorly explains natural outcomes of vineyard adaptation

Domesticated strains of Saccharomyces cerevisiae have adapted to resist copper and sulfite, two chemical stressors commonly used in winemaking. S. paradoxus , has not adapted to these chemicals despite being consistently present in sympatry with S. cerevisiae in vineyards. This contrast represents a case of apparent evolutionary constraints favoring greater adaptive capacity in S. cerevisiae . In this study, we used a comparative mutagenesis approach to test whether S. paradoxus is mutationally constrained with respect to acquiring greater copper and sulfite resistance. For both species, we assayed the rate, effect size, and pleiotropic costs of resistance mutations and sequenced a subset of 150 mutants isolated from our screen. We found that the distributions of mutational effects displayed by the two species were very similar and poorly explained the natural pattern. We also found that chromosome VIII aneuploidy and loss of function mutations in PMA1 confer copper resistance in both species, whereas loss of function mutations in REG1 were only a viable route to copper resistance in S. cerevisiae . We also observed a single de novo duplication of the CUP1 gene in S. paradoxus but none in S. cerevisiae . For sulfite, loss of function mutations in RTS1 and KSP1 confer resistance in both species, but mutations in RTS1 have larger average effects in S. paradoxus . Our results show that even when the distributions of mutational effects are largely similar, species can differ in the adaptive paths available to them. They also demonstrate that assays of the distribution of mutational effects may lack predictive insight concerning adaptive outcomes.

Identification of the mutual gliding locus as a factor for gut colonization in non-native bee hosts using the ARTP mutagenesis

BACKGROUND

The gut microbiota and their hosts profoundly affect each other's physiology and evolution. Identifying host-selected traits is crucial to understanding the processes that govern the evolving interactions between animals and symbiotic microbes. Current experimental approaches mainly focus on the model bacteria, like hypermutating Escherichia coli or the evolutionary changes of wild stains by host transmissions. A method called atmospheric and room temperature plasma (ARTP) may overcome the bottleneck of low spontaneous mutation rates while maintaining mild conditions for the gut bacteria.

RESULTS

We established an experimental symbiotic system with gnotobiotic bee models to unravel the molecular mechanisms promoting host colonization. By in vivo serial passage, we tracked the genetic changes of ARTP-treated Snodgrassella strains from Bombus terrestris in the non-native honeybee host. We observed that passaged isolates showing genetic changes in the mutual gliding locus have a competitive advantage in the non-native host. Specifically, alleles in the orphan mglB, the GTPase activating protein, promoted colonization potentially by altering the type IV pili-dependent motility of the cells. Finally, competition assays confirmed that the mutations out-competed the ancestral strain in the non-native honeybee gut but not in the native host.

CONCLUSIONS

Using the ARTP mutagenesis to generate a mutation library of gut symbionts, we explored the potential genetic mechanisms for improved gut colonization in non-native hosts. Our findings demonstrate the implication of the cell mutual-gliding motility in host association and provide an experimental system for future study on host-microbe interactions. Video Abstract.

Evolutionary Adaptation of an RNA Bacteriophage to Repeated Freezing and Thawing Cycles

Bacteriophage fitness is determined by factors influencing both their replication within bacteria and their ability to maintain infectivity between infections. The latter becomes particularly crucial under adverse environmental conditions or when host density is low. In such scenarios, the damage experienced by viral particles could lead to the loss of infectivity, which might be mitigated if the virus undergoes evolutionary optimization through replication. In this study, we conducted an evolution experiment involving bacteriophage Qβ, wherein it underwent 30 serial transfers, each involving a cycle of freezing and thawing followed by replication of the surviving viruses. Our findings show that Qβ was capable of enhancing its resistance to this selective pressure through various adaptive pathways that did not impair the virus replicative capacity. Notably, these adaptations predominantly involved mutations located within genes encoding capsid proteins. The adapted populations exhibited higher resistance levels than individual viruses isolated from them, and the latter surpassed those observed in single mutants generated via site-directed mutagenesis. This suggests potential interactions among mutants and mutations. In conclusion, our study highlights the significant role of extracellular selective pressures in driving the evolution of phages, influencing both the genetic composition of their populations and their phenotypic properties.

Neutral diversity in experimental metapopulations

New automated and high-throughput methods allow the manipulation and selection of numerous bacterial populations. In this manuscript we are interested in the neutral diversity patterns that emerge from such a setup in which many bacterial populations are grown in parallel serial transfers, in some cases with population-wide extinction and splitting events. We model bacterial growth by a birth-death process and use the theory of coalescent point processes. We show that there is a dilution factor that optimises the expected amount of neutral diversity for a given number of cycles, and study the power law behaviour of the mutation frequency spectrum for different experimental regimes. We also explore how neutral variation diverges between two recently split populations by establishing a new formula for the expected number of shared and private mutations. Finally, we show the interest of such a setup to select a phenotype of interest that requires multiple mutations.

Microfluidics for adaptation of microorganisms to stress: design and application

Microfluidic systems have fundamentally transformed the realm of adaptive laboratory evolution (ALE) for microorganisms by offering unparalleled control over environmental conditions, thereby optimizing mutant generation and desired trait selection. This review summarizes the substantial influence of microfluidic technologies and their design paradigms on microbial adaptation, with a primary focus on leveraging spatial stressor concentration gradients to enhance microbial growth in challenging environments. Specifically, microfluidic platforms tailored for scaled-down ALE processes not only enable highly autonomous and precise setups but also incorporate novel functionalities. These capabilities encompass fostering the growth of biofilms alongside planktonic cells, refining selection gradient profiles, and simulating adaptation dynamics akin to natural habitats. The integration of these aspects enables shaping phenotypes under pressure, presenting an unprecedented avenue for developing robust, stress-resistant strains, a feat not easily attainable using conventional ALE setups. The versatility of these microfluidic systems is not limited to fundamental research but also offers promising applications in various areas of stress resistance. As microfluidic technologies continue to evolve and merge with cutting-edge methodologies, they possess the potential not only to redefine the landscape of microbial adaptation studies but also to expedite advancements in various biotechnological areas. KEY POINTS: • Microfluidics enable precise microbial adaptation in controlled gradients. • Microfluidic ALE offers insights into stress resistance and distinguishes between resistance and persistence. • Integration of adaptation-influencing factors in microfluidic setups facilitates efficient generation of stress-resistant strains.

Parallel evolution of the G protein-coupled receptor GrlG and the loss of fruiting body formation in the social amoeba Dictyostelium discoideum evolved under low relatedness

Aggregative multicellularity relies on cooperation among formerly independent cells to form a multicellular body. Previous work with Dictyostelium discoideum showed that experimental evolution under low relatedness profoundly decreased cooperation, as indicated by the loss of fruiting body formation in many clones and an increase of cheaters that contribute proportionally more to spores than to the dead stalk. Using whole-genome sequencing and variant analysis of these lines, we identified 38 single nucleotide polymorphisms in 29 genes. Each gene had 1 variant except for grlG (encoding a G protein-coupled receptor), which had 10 unique SNPs and 5 structural variants. Variants in the 5' half of grlG-the region encoding the signal peptide and the extracellular binding domain-were significantly associated with the loss of fruiting body formation; the association was not significant in the 3' half of the gene. These results suggest that the loss of grlG was adaptive under low relatedness and that at least the 5' half of the gene is important for cooperation and multicellular development. This is surprising given some previous evidence that grlG encodes a folate receptor involved in predation, which occurs only during the single-celled stage. However, non-fruiting mutants showed little increase in a parallel evolution experiment where the multicellular stage was prevented from happening. This shows that non-fruiting mutants are not generally selected by any predation advantage but rather by something-likely cheating-during the multicellular stage.

The stressostat: A novel approach in adaptive laboratory evolution to improve end-product resistance

End-product inhibition in pH-controlled batch cultures, is the major limiting factor for bacterial biomass formation in the starter culture industry as well as in many other biotechnological processes. Adaptive laboratory evolution (ALE) has emerged over the past decades as a powerful tool for phenotype optimization, but none of the existing ALE methods could select for improved end-product resistance. Therefore, we developed the stressostat (STress Resistance Evolution in Substrate Surplus) as a novel continuous ALE method. Stressostat cultivation applies end-product concentrations as constant evolutionary pressure on microorganisms in the presence of substrate surplus. In this study, we improved the lactate resistance of Lactococcus lactis FM03P in 35 days of stressostat cultivations. The lactate concentrations increased over time from 530 to 675 mM, indicating the successful selection for variants with improved lactate resistance. Thirty-four variants were isolated and grouped into four clusters based on their growth rates at high lactate concentrations. In the high-throughput screening without pH control, most isolated variants could grow at high lactate concentrations (870-928 mM), while the wild type was completely inhibited. The variants grew slower than wild type at low lactate media indicating possible evolutionary trade-off. However, in pH-controlled batch cultivations, most variants produced more biomass than the wild type. In conclusion, stressostat cultivation is a valuable method to obtain L. lactis variants with improved end-product resistance and further characterization is needed to elucidate underlying resistance mechanisms and potential industrial applications.

Experimental evolution for cell biology

Evolutionary cell biology explores the origins, principles, and core functions of cellular features and regulatory networks through the lens of evolution. This emerging field relies heavily on comparative experiments and genomic analyses that focus exclusively on extant diversity and historical events, providing limited opportunities for experimental validation. In this opinion article, we explore the potential for experimental laboratory evolution to augment the evolutionary cell biology toolbox, drawing inspiration from recent studies that combine laboratory evolution with cell biological assays. Primarily focusing on approaches for single cells, we provide a generalizable template for adapting experimental evolution protocols to provide fresh insight into long-standing questions in cell biology.

Species and condition shape the mutational spectrum in experimentally evolved biofilms

IMPORTANCE

Biofilm formation is a vital factor for the survival and adaptation of bacteria in diverse environmental niches. Experimental evolution combined with the advancement of whole-population genome sequencing provides us a powerful tool to understand the genomic dynamic of evolutionary adaptation to different environments, such as during biofilm development. Previous studies described the genetic and phenotypic changes of selected clones from experimentally evolved Bacillus thuringiensis and Bacillus subtilis that were adapted under abiotic and biotic biofilm conditions. However, the full understanding of the dynamic evolutionary landscapes was lacking. Furthermore, the differences and similarities of adaptive mechanisms in B. thuringiensis and B. subtilis were not identified. To overcome these limitations, we performed longitudinal whole-population genome sequencing to study the underlying genetic dynamics at high resolution. Our study provides the first comprehensive mutational landscape of two bacterial species' biofilms that is adapted to an abiotic and biotic surface.

Haplotype based testing for a better understanding of the selective architecture

BACKGROUND

The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies.

RESULTS

Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits.

CONCLUSIONS

Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning.

Selective Pressure by Rifampicin Modulates Mutation Rates and Evolutionary Trajectories of Mycobacterial Genomes

Resistance to the frontline antibiotic rifampicin constitutes a challenge to the treatment and control of tuberculosis. Here, we analyzed the mutational landscape of Mycobacterium smegmatis during long-term evolution with increasing concentrations of rifampicin, using a mutation accumulation assay combined with whole-genome sequencing. Antibiotic treatment enhanced the acquisition of mutations, doubling the genome-wide mutation rate of the wild-type cells. While antibiotic exposure led to extinction of almost all wild-type lines, the hypermutable phenotype of the ΔnucS mutant strain (noncanonical mismatch repair deficient) provided an efficient response to the antibiotic, leading to high rates of survival. This adaptative advantage resulted in the emergence of higher levels of rifampicin resistance, an accelerated acquisition of drug resistance mutations in rpoB (β RNA polymerase), and a wider diversity of evolutionary pathways that led to drug resistance. Finally, this approach revealed a subset of adaptive genes under positive selection with rifampicin that could be associated with the development of antibiotic resistance. IMPORTANCE Rifampicin is the most important first-line antibiotic against mycobacterial infections, including tuberculosis, one of the top causes of death worldwide. Acquisition of rifampicin resistance constitutes a major global public health problem that makes the control of the disease challenging. Here, we performed an experimental evolution assay under antibiotic selection to analyze the response and adaptation of mycobacteria, leading to the acquisition of rifampicin resistance. This approach explored the total number of mutations that arose in the mycobacterial genomes under long-term rifampicin exposure, using whole-genome sequencing. Our results revealed the effect of rifampicin at a genomic level, identifying different mechanisms and multiple pathways leading to rifampicin resistance in mycobacteria. Moreover, this study detected that an increase in the rate of mutations led to enhanced levels of drug resistance and survival. In summary, all of these results could be useful to understand and prevent the emergence of drug-resistant isolates in mycobacterial infections.

ShinyBioHEAT: an interactive shiny app to identify phenotype driver genes in E.coli and B.subtilis

SUMMARY

In any population under selective pressure, a central challenge is to distinguish the genes that drive adaptation from others which, subject to population variation, harbor many neutral mutations de novo. We recently showed that such genes could be identified by supplementing information on mutational frequency with an evolutionary analysis of the likely functional impact of coding variants. This approach improved the discovery of driver genes in both lab-evolved and environmental Escherichia coli strains. To facilitate general adoption, we now developed ShinyBioHEAT, an R Shiny web-based application that enables identification of phenotype driving gene in two commonly used model bacteria, E.coli and Bacillus subtilis, with no specific computational skill requirements. ShinyBioHEAT not only supports transparent and interactive analysis of lab evolution data in E.coli and B.subtilis, but it also creates dynamic visualizations of mutational impact on protein structures, which add orthogonal checks on predicted drivers.

AVAILABILITY AND IMPLEMENTATION

Code for ShinyBioHEAT is available at https://github.com/LichtargeLab/ShinyBioHEAT. The Shiny application is additionally hosted at http://bioheat.lichtargelab.org/.

Parallel genetic adaptation of Bacillus subtilis to different plant species

Plant growth-promoting rhizobacteria benefit plants by stimulating their growth or protecting them against phytopathogens. Rhizobacteria must colonize and persist on plant roots to exert their benefits. However, little is known regarding the processes by which rhizobacteria adapt to different plant species, or behave under alternating host plant regimes. Here, we used experimental evolution and whole-population whole-genome sequencing to analyse how Bacillus subtilis evolves on Arabidopsis thaliana and tomato seedlings, and under an alternating host plant regime, in a static hydroponic setup. We observed parallel evolution across multiple levels of biological organization in all conditions, which was greatest for the two heterogeneous, multi-resource, spatially structured environments at the genetic level. Species-specific adaptation at the genetic level was also observed, possibly caused by the selection stress imposed by different host plants. Furthermore, a trade-off between motility and biofilm development was supported by mutational changes in motility- and biofilm-related genes. Finally, we identified several condition-specific and common targeted genes in different environments by comparing three different B. subtilis biofilm adaptation settings. The results demonstrate a common evolutionary pattern when B. subtilis is adapting to the plant rhizosphere in similar conditions, and reveal differences in genetic mechanisms between different host plants. These findings will likely support strain improvements for sustainable agriculture.

PyEcoLib: a python library for simulating stochastic cell size dynamics

Recently, there has been an increasing need for tools to simulate cell size regulation due to important applications in cell proliferation and gene expression. However, implementing the simulation usually presents some difficulties, as the division has a cycle-dependent occurrence rate. In this article, we gather a recent theoretical framework inPyEcoLib, a python-based library to simulate the stochastic dynamics of the size of bacterial cells. This library can simulate cell size trajectories with an arbitrarily small sampling period. In addition, this simulator can include stochastic variables, such as the cell size at the beginning of the experiment, the cycle duration timing, the growth rate, and the splitting position. Furthermore, from a population perspective, the user can choose between tracking a single lineage or all cells in a colony. They can also simulate the most common division strategies (adder, timer, and sizer) using the division rate formalism and numerical methods. As an example of PyecoLib applications, we explain how to couple size dynamics with gene expression predicting, from simulations, how the noise in protein levels increases by increasing the noise in division timing, the noise in growth rate and the noise in cell splitting position. The simplicity of this library and its transparency about the underlying theoretical framework yield the inclusion of cell size stochasticity in complex models of gene expression.

Rapid evolutionary repair by secondary perturbation of a primary disrupted transcriptional network

The discrete steps of transcriptional rewiring have been proposed to occur neutrally to ensure steady gene expression under stabilizing selection. A conflict-free switch of a regulon between regulators may require an immediate compensatory evolution to minimize deleterious effects. Here, we perform an evolutionary repair experiment on the Lachancea kluyveri yeast sef1Δ mutant using a suppressor development strategy. Complete loss of SEF1 forces cells to initiate a compensatory process for the pleiotropic defects arising from misexpression of TCA cycle genes. Using different selective conditions, we identify two adaptive loss-of-function mutations of IRA1 and AZF1. Subsequent analyses show that Azf1 is a weak transcriptional activator regulated by the Ras1-PKA pathway. Azf1 loss-of-function triggers extensive gene expression changes responsible for compensatory, beneficial, and trade-off phenotypes. The trade-offs can be alleviated by higher cell density. Our results not only indicate that secondary transcriptional perturbation provides rapid and adaptive mechanisms potentially stabilizing the initial stage of transcriptional rewiring but also suggest how genetic polymorphisms of pleiotropic mutations could be maintained in the population.

Identifying Targets of Selection in Laboratory Evolution Experiments

Adaptive evolution navigates a balance between chance and determinism. The stochastic processes of mutation and drift generate phenotypic variation; however, once mutations reach an appreciable frequency in the population, their fate is governed by the deterministic action of selection, enriching for favorable genotypes and purging the less-favorable ones. The net result is that replicate populations will traverse similar-but not identical-pathways to higher fitness. This parallelism in evolutionary outcomes can be leveraged to identify the genes and pathways under selection. However, distinguishing between beneficial and neutral mutations is challenging because many beneficial mutations will be lost due to drift and clonal interference, and many neutral (and even deleterious) mutations will fix by hitchhiking. Here, we review the best practices that our laboratory uses to identify genetic targets of selection from next-generation sequencing data of evolved yeast populations. The general principles for identifying the mutations driving adaptation will apply more broadly.

Experimental approaches to study evolutionary cell biology using yeasts

The past century has witnessed tremendous advances in understanding how cells function. Nevertheless, how cellular processes have evolved is still poorly understood. Many studies have highlighted surprising molecular diversity in how cells from diverse species execute the same processes, and advances in comparative genomics are likely to reveal much more molecular diversity than was believed possible until recently. Extant cells remain therefore the product of an evolutionary history that we vastly ignore. Evolutionary cell biology has emerged as a discipline aiming to address this knowledge gap by combining evolutionary, molecular, and cellular biology thinking. Recent studies have shown how even essential molecular processes, such as DNA replication, can undergo fast adaptive evolution under certain laboratory conditions. These developments open new lines of research where the evolution of cellular processes can be investigated experimentally. Yeasts naturally find themselves at the forefront of this research line. Not only do they allow the observation of fast evolutionary adaptation, but they also provide numerous genomic, synthetic, and cellular biology tools already developed by a large community. Here we propose that yeasts can serve as an "evolutionary cell lab" to test hypotheses, principles, and ideas in evolutionary cell biology. We discuss various experimental approaches available for this purpose, and how biology at large can benefit from them.

Antifungal alternation can be beneficial for durability but at the cost of generalist resistance

The evolution of resistance to pesticides is a major burden in agriculture. Resistance management involves maximizing selection pressure heterogeneity, particularly by combining active ingredients with different modes of action. We tested the hypothesis that alternation may delay the build-up of resistance not only by spreading selection pressure over longer periods, but also by decreasing the rate of evolution of resistance to alternated fungicides, by applying an experimental evolution approach to the economically important crop pathogen Zymoseptoria tritici. Our results show that alternation is either neutral or slows the overall resistance evolution rate, relative to continuous fungicide use, but results in higher levels of generalism in evolved lines. We demonstrate that the nature of the fungicides, and therefore their relative intrinsic risk of resistance may underly this trade-off, more so than the number of fungicides and the rhythm of alternation. This trade-off is also dynamic over the course of resistance evolution. These findings open up new possibilities for tailoring resistance management effectively while optimizing interplay between alternation components.

DNA uptake from a laboratory environment drives unexpected adaptation of a thermophile to a minor medium component

DNA uptake is widespread among microorganisms and considered a strategy for rapid adaptation to new conditions. While both DNA uptake and adaptation are referred to in the context of natural environments, they are often studied in laboratories under defined conditions. For example, a strain of the thermophile Thermoanaerobacter kivui had been adapted to growth on high concentrations of carbon monoxide (CO). Unusual phenotypes of the CO-adapted strain prompted us to examine it more closely, revealing a horizontal gene transfer (HGT) event from another thermophile, Thermoanaerobacter sp. strain X514, being cultured in the same laboratory. The transferred genes conferred on T. kivui the ability to utilize trehalose, a trace component of the yeast-extract added to the media during CO-adaptation. This same HGT event simultaneously deleted a native operon for thiamine biosynthesis, which likely explains why the CO-adapted strain grows poorly without added vitamins. Attempts to replicate this HGT by providing T. kivui with genomic DNA from Thermoanaerobacter sp. strain X514 revealed that it is easily reproducible in the lab. This subtle form of "genome contamination" is difficult to detect, since the genome remains predominantly T. kivui, and no living cells from the original contamination remain. Unexpected HGT between two microorganisms as well as simultaneous adaptation to several conditions may occur often and unrecognized in laboratory environments, requiring caution and careful monitoring of phenotype and genotype of microorganisms that are naturally-competent for DNA uptake.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.

A road map for in vivo evolution experiments with blood-borne parasitic microbes

Laboratory experiments in which blood-borne parasitic microbes evolve in their animal hosts offer an opportunity to study parasite evolution and adaptation in real time and under natural settings. The main challenge of these experiments is to establish a protocol that is both practical over multiple passages and accurately reflects natural transmission scenarios and mechanisms. We provide a guide to the steps that should be considered when designing such a protocol, and we demonstrate its use via a case study. We highlight the importance of choosing suitable ancestral genotypes, treatments, number of replicates per treatment, types of negative controls, dependent variables, covariates, and the timing of checkpoints for the experimental design. We also recommend specific preliminary experiments to determine effective methods for parasite quantification, transmission, and preservation. Although these methodological considerations are technical, they also often have conceptual implications. To this end, we encourage other researchers to design and conduct in vivo evolution experiments with blood-borne parasitic microbes, despite the challenges that the work entails.

Increased genetic diversity loss and genetic differentiation in a model marine diatom adapted to ocean warming compared to high CO2

Although high CO2 and warming could act interactively on marine phytoplankton, little is known about the molecular basis for this interaction on an evolutionary scale. Here we explored the adaptation to high CO2 in combination with warming in a model marine diatom Phaeodactylum tricornutum. Whole-genome re-sequencing identifies, in comparison to populations grown under control conditions, a larger genetic diversity loss and a higher genetic differentiation in the populations adapted for 2 years to warming than in those adapted to high CO2. However, this diversity loss was less under high CO2 combined with warming, suggesting that the evolution driven by warming was constrained by high CO2. By integrating genomics, transcriptomics, and physiological data, we found that the underlying molecular basis for this constraint is associated with the expression of genes involved in some key metabolic pathways or biological processes, such as the glyoxylate pathway, amino acid and fatty acid metabolism, and diel variability. Our results shed new light on the evolutionary responses of marine phytoplankton to multiple environmental changes in the context of global change and provide new insights into the molecular basis underpinning interactions among those multiple drivers.

Viruses in astrobiology

Viruses are the most abundant biological entities on Earth, and yet, they have not received enough consideration in astrobiology. Viruses are also extraordinarily diverse, which is evident in the types of relationships they establish with their host, their strategies to store and replicate their genetic information and the enormous diversity of genes they contain. A viral population, especially if it corresponds to a virus with an RNA genome, can contain an array of sequence variants that greatly exceeds what is present in most cell populations. The fact that viruses always need cellular resources to multiply means that they establish very close interactions with cells. Although in the short term these relationships may appear to be negative for life, it is evident that they can be beneficial in the long term. Viruses are one of the most powerful selective pressures that exist, accelerating the evolution of defense mechanisms in the cellular world. They can also exchange genetic material with the host during the infection process, providing organisms with capacities that favor the colonization of new ecological niches or confer an advantage over competitors, just to cite a few examples. In addition, viruses have a relevant participation in the biogeochemical cycles of our planet, contributing to the recycling of the matter necessary for the maintenance of life. Therefore, although viruses have traditionally been excluded from the tree of life, the structure of this tree is largely the result of the interactions that have been established throughout the intertwined history of the cellular and the viral worlds. We do not know how other possible biospheres outside our planet could be, but it is clear that viruses play an essential role in the terrestrial one. Therefore, they must be taken into account both to improve our understanding of life that we know, and to understand other possible lives that might exist in the cosmos.

Biofilm antimicrobial susceptibility through an experimental evolutionary lens

Experimental evolution experiments in which bacterial populations are repeatedly exposed to an antimicrobial treatment, and examination of the genotype and phenotype of the resulting evolved bacteria, can help shed light on mechanisms behind reduced susceptibility. In this review we present an overview of why it is important to include biofilms in experimental evolution, which approaches are available to study experimental evolution in biofilms and what experimental evolution has taught us about tolerance and resistance in biofilms. Finally, we present an emerging consensus view on biofilm antimicrobial susceptibility supported by data obtained during experimental evolution studies.

Experimental test of the contributions of initial variation and new mutations to adaptive evolution in a novel environment

Experimental evolution is an approach that allows researchers to study organisms as they evolve in controlled environments. Despite the growing popularity of this approach, there are conceptual gaps among projects that use different experimental designs. One such gap concerns the contributions to adaptation of genetic variation present at the start of an experiment and that of new mutations that arise during an experiment. The primary source of genetic variation has historically depended largely on the study organisms. In the long-term evolution experiment (LTEE) using Escherichia coli, for example, each population started from a single haploid cell, and therefore, adaptation depended entirely on new mutations. Most other microbial evolution experiments have followed the same strategy. By contrast, evolution experiments using multicellular, sexually reproducing organisms typically start with preexisting variation that fuels the response to selection. New mutations may also come into play in later generations of these experiments, but it is generally difficult to quantify their contribution in these studies. Here, we performed an experiment using E. coli to compare the contributions of initial genetic variation and new mutations to adaptation in a new environment. Our experiment had four treatments that varied in their starting diversity, with 18 populations in each treatment. One treatment depended entirely on new mutations, while the other three began with mixtures of clones, whole-population samples, or mixtures of whole-population samples from the LTEE. We tracked a genetic marker associated with different founders in two treatments. These data revealed significant variation in fitness among the founders, and that variation impacted evolution in the early generations of our experiment. However, there were no differences in fitness among the treatments after 500 or 2,000 generations in the new environment, despite the variation in fitness among the founders. These results indicate that new mutations quickly dominated, and eventually they contributed more to adaptation than did the initial variation. Our study thus shows that preexisting genetic variation can have a strong impact on early evolution in a new environment, but new beneficial mutations may contribute more to later evolution and can even drive some initially beneficial variants to extinction.

Selection pressure on the rhizosphere microbiome can alter nitrogen use efficiency and seed yield in Brassica rapa

Microbial experimental systems provide a platform to observe how networks of groups emerge to impact plant development. We applied selection pressure for microbiome enhancement of Brassica rapa biomass to examine adaptive bacterial group dynamics under soil nitrogen limitation. In the 9th and final generation of the experiment, selection pressure enhanced B. rapa seed yield and nitrogen use efficiency compared to our control treatment, with no effect between the random selection and control treatments. Aboveground biomass increased for both the high biomass selection and random selection plants. Soil bacterial diversity declined under high B. rapa biomass selection, suggesting a possible ecological filtering mechanism to remove bacterial taxa. Distinct sub-groups of interactions emerged among bacterial phyla such as Proteobacteria and Bacteroidetes in response to selection. Extended Local Similarity Analysis and NetShift indicated greater connectivity of the bacterial community, with more edges, shorter path lengths, and altered modularity through the course of selection for enhanced plant biomass. In contrast, bacterial communities under random selection and no selection showed less complex interaction profiles of bacterial taxa. These results suggest that group-level bacterial interactions could be modified to collectively shift microbiome functions impacting the growth of the host plant under soil nitrogen limitation.

Community diversity determines the evolution of synthetic bacterial communities under artificial selection

Artificial selection can be conducted at the community level in the laboratory through a differential propagation of the communities according to their level of expression of a targeted function. Working with communities instead of individuals as selection units brings in additional sources of variation in the considered function that can influence the outcome of the artificial selection. In this study, we wanted to assess the effect of manipulating the initial community richness on artificial selection efficiency, defined as the change in the targeted function over time. We applied artificial selection for a high productivity on synthetic bacterial communities varying for their richness (from one to 16 strains). Overall, the selected communities were 16% more productive than the control communities, but a convergence of community composition might have limited the effect of diversity on artificial selection efficiency. Community richness positively influenced community productivity and metabolic capacities and was a strong determinant of the dynamics of community evolution. We propose that applying artificial selection on communities varying for their diversity could be a way to find communities differing for their level of expression of a function but also for their responsiveness to artificial selection, provided that their initial composition is different enough.

Mesostats—A multiplexed, low-cost, do-it-yourself continuous culturing system for experimental evolution of mesocosms

Microbial experimental evolution allows studying evolutionary dynamics in action and testing theory predictions in the lab. Experimental evolution in chemostats (i.e. continuous flow through cultures) has recently gained increased interest as it allows tighter control of selective pressures compared to static batch cultures, with a growing number of efforts to develop systems that are easier and cheaper to construct. This protocol describes the design and construction of a multiplexed chemostat array (dubbed “mesostats”) designed for cultivation of algae in 16 concurrent populations, specifically intended for studying adaptation to herbicides. We also present control data from several experiments run on the system to show replicability, data illustrating the effects of common issues like leaks, contamination and clumps, and outline possible modifications and adaptations of the system for future research.

Experimental evolution of Bacillus subtilis on Arabidopsis thaliana roots reveals fast adaptation and improved root colonization

Bacillus subtilis is known to promote plant growth and protect plants against disease. B. subtilis rapidly adapts to Arabidopsis thaliana root colonization, as evidenced by improved root colonizers already after 12 consecutive transfers between seedlings in a hydroponic setup. Re-sequencing of single evolved isolates and endpoint populations revealed mutations in genes related to different bacterial traits, in accordance with evolved isolates displaying increased root colonization associated with robust biofilm formation in response to the plant polysaccharide xylan and impaired motility. Interestingly, evolved isolates suffered a fitness disadvantage in a non-selective environment, demonstrating an evolutionary cost of adaptation to the plant root. Finally, increased root colonization by an evolved isolate was also demonstrated in the presence of resident soil microbes. Our findings highlight how a plant growth-promoting rhizobacterium rapidly adapts to an ecologically relevant environment and reveal evolutionary consequences that are fundamental to consider when evolving strains for biocontrol purposes.

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Bacillus subtilis shows fast adaptation to Arabidopsis thaliana roots in a hydroponic setup

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Evolved isolates exhibit robust biofilms in response to xylan and impaired motility

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Adaptation to A. thaliana roots is accompanied by an evolutionary cost

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An evolved isolate shows higher root colonization in the presence of soil bacteria

Adaptation and phenotypic diversification of Bacillus thuringiensis biofilm are accompanied by fuzzy spreader morphotypes

Bacillus cereus group (Bacillus cereus sensu lato) has a diverse ecology, including various species that produce biofilms on abiotic and biotic surfaces. While genetic and morphological diversification enables the adaptation of multicellular communities, this area remains largely unknown in the Bacillus cereus group. In this work, we dissected the experimental evolution of Bacillus thuringiensis 407 Cry- during continuous recolonization of plastic beads. We observed the evolution of a distinct colony morphotype that we named fuzzy spreader (FS) variant. Most multicellular traits of the FS variant displayed higher competitive ability versus the ancestral strain, suggesting an important role for diversification in the adaptation of B. thuringiensis to the biofilm lifestyle. Further genetic characterization of FS variant revealed the disruption of a guanylyltransferase gene by an insertion sequence (IS) element, which could be similarly observed in the genome of a natural isolate. The evolved FS and the deletion mutant in the guanylyltransferase gene (Bt407ΔrfbM) displayed similarly altered aggregation and hydrophobicity compared to the ancestor strain, suggesting that the adaptation process highly depends on the physical adhesive forces.

Evolving perspective on the origin and diversification of cellular life and the virosphere

The tree of life (TOL) is a powerful framework to depict the evolutionary history of cellular organisms through time, from our microbial origins to the diversification of multicellular eukaryotes that shape the visible biosphere today. During the past decades, our perception of the TOL has fundamentally changed, in part, due to profound methodological advances, which allowed a more objective approach to studying organismal and viral diversity and led to the discovery of major new branches in the TOL as well as viral lineages. Phylogenetic and comparative genomics analyses of these data have, among others, revolutionized our understanding of the deep roots and diversity of microbial life, the origin of the eukaryotic cell, eukaryotic diversity, as well as the origin, and diversification of viruses. In this review, we provide an overview of some of the recent discoveries on the evolutionary history of cellular organisms and their viruses and discuss a variety of complementary techniques that we consider crucial for making further progress in our understanding of the TOL and its interconnection with the virosphere.

The challenges of monitoring and manipulating anaerobic microbial communities

Mixed anaerobic microbial communities are a key component in valorization of waste biomass via anaerobic digestion. Similar microbial communities are important as soil and animal microbiomes and have played a critical role in shaping the planet as it is today. Understanding how individual species within communities interact with others and their environment is important for improving performance and potential applications of an inherently green technology. Here, the challenges associated with making measurements critical to assessing the status of anaerobic microbial communities are considered. How these measurements could be incorporated into control philosophies and augment the potential of anaerobic microbial communities to produce different and higher value products from waste materials are discussed. The benefits and pitfalls of current genetic and molecular approaches to measuring and manipulating anaerobic microbial communities and the challenges which should be addressed to realise the potential of this exciting technology are explored.

Effects of the Expression of Random Sequence Clones on Growth and Transcriptome Regulation in Escherichia coli

Comparative genomic analyses have provided evidence that new genetic functions can emerge out of random nucleotide sequences. Here, we apply a direct experimental approach to study the effects of plasmids harboring random sequence inserts under the control of an inducible promoter. Based on data from previously described experiments dealing with the growth of clones within whole libraries, we extracted specific clones that had shown either negative, neutral or positive effects on relative cell growth. We analyzed these individually with respect to growth characteristics and the impact on the transcriptome. We find that candidate clones for negative peptides lead to growth arrest by eliciting a general stress response. Overexpression of positive clones, on the other hand, does not change the exponential growth rates of hosts, and they show a growth advantage over a neutral clone when tested in direct competition experiments. Transcriptomic changes in positive clones are relatively moderate and specific to each clone. We conclude from our experiments that random sequence peptides are indeed a suitable source for the de novo evolution of genetic functions.

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

Evolutionary Dynamics of Asexual Hypermutators Adapting to a Novel Environment

How microbes adapt to a novel environment is a central question in evolutionary biology. Although adaptive evolution must be fueled by beneficial mutations, whether higher mutation rates facilitate the rate of adaptive evolution remains unclear. To address this question, we cultured Escherichia coli hypermutating populations, in which a defective methyl-directed mismatch repair pathway causes a 140-fold increase in single-nucleotide mutation rates. In parallel with wild-type E. coli, populations were cultured in tubes containing Luria-Bertani broth, a complex medium known to promote the evolution of subpopulation structure. After 900 days of evolution, in three transfer schemes with different population-size bottlenecks, hypermutators always exhibited similar levels of improved fitness as controls. Fluctuation tests revealed that the mutation rates of hypermutator lines converged evolutionarily on those of wild-type populations, which may have contributed to the absence of fitness differences. Further genome-sequence analysis revealed that, although hypermutator populations have higher rates of genomic evolution, this largely reflects strong genetic linkage. Despite these linkage effects, the evolved population exhibits parallelism in fixed mutations, including those potentially related to biofilm formation, transcription regulation, and mutation-rate evolution. Together, these results are generally inconsistent with a hypothesized positive relationship between the mutation rate and the adaptive speed of evolution, and provide insight into how clonal adaptation occurs in novel environments.

Advantages of laboratory natural selection in the applied sciences

In the past three decades, laboratory natural selection has become a widely used technique in biological research. Most studies which have utilized this technique are in the realm of basic science, often testing hypotheses related to mechanisms of evolutionary change or ecological dynamics. While laboratory natural selection is currently utilized heavily in this setting, there is a significant gap with its usage in applied studies, especially when compared to the other selection experiment methodologies like artificial selection and directed evolution. This is despite avenues of research in the applied sciences which seem well suited to laboratory natural selection. In this review, we place laboratory natural selection in context with other selection experiments, identify the characteristics which make it well suited for particular kinds of applied research and briefly cover key examples of the usefulness of selection experiments within applied science. Finally, we identify three promising areas of inquiry for laboratory natural selection in the applied sciences: bioremediation technology, identifying mechanisms of drug resistance and optimizing biofuel production. Although laboratory natural selection is currently less utilized in applied science when compared to basic research, the method has immense promise in the field moving forward.

Are Efficient-Dose Mixtures a Solution to Reduce Fungicide Load and Delay Evolution of Resistance? An Experimental Evolutionary Approach

Pesticide resistance poses a critical threat to agriculture, human health and biodiversity. Mixtures of fungicides are recommended and widely used in resistance management strategies. However, the components of the efficiency of such mixtures remain unclear. We performed an experimental evolutionary study on the fungal pathogen Z. tritici to determine how mixtures managed resistance. We compared the effect of the continuous use of single active ingredients to that of mixtures, at the minimal dose providing full control of the disease, which we refer to as the "efficient" dose. We found that the performance of efficient-dose mixtures against an initially susceptible population depended strongly on the components of the mixture. Such mixtures were either as durable as the best mixture component used alone, or worse than all components used alone. Moreover, efficient dose mixture regimes probably select for generalist resistance profiles as a result of the combination of selection pressures exerted by the various components and their lower doses. Our results indicate that mixtures should not be considered a universal strategy. Experimental evaluations of specificities for the pathogens targeted, their interactions with fungicides and the interactions between fungicides are crucial for the design of sustainable resistance management strategies.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Eco-Evolutionary Dynamics in Microbial Communities from Spontaneous Fermented Foods

Eco-evolutionary forces are the key drivers of ecosystem biodiversity dynamics. This resulted in a large body of theory, which has partially been experimentally tested by mimicking evolutionary processes in the laboratory. In the first part of this perspective, we outline what model systems are used for experimental testing of eco-evolutionary processes, ranging from simple microbial combinations and, more recently, to complex natural communities. Microbial communities of spontaneous fermented foods are a promising model system to study eco-evolutionary dynamics. They combine the complexity of a natural community with extensive knowledge about community members and the ease of manipulating the system in a laboratory setup. Due to rapidly developing sequencing techniques and meta-omics approaches incorporating data in building ecosystem models, the diversity in these communities can be analysed with relative ease while hypotheses developed in simple systems can be tested. Here, we highlight several eco-evolutionary questions that are addressed using microbial communities from fermented foods. These questions relate to analysing species frequencies in space and time, the diversity-stability relationship, niche space and community coalescence. We provide several hypotheses of the influence of these factors on community evolution specifying the experimental setup of studies where microbial communities of spontaneous fermented food are used.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Inference of population genetic parameters from an irregular time series of seasonal influenza virus sequences

Basic summary statistics that quantify the population genetic structure of influenza virus are important for understanding and inferring the evolutionary and epidemiological processes. However, the sampling dates of global virus sequences in the last several decades are scattered nonuniformly throughout the calendar. Such temporal structure of samples and the small effective size of viral population hampers the use of conventional methods to calculate summary statistics. Here, we define statistics that overcome this problem by correcting for the sampling-time difference in quantifying a pairwise sequence difference. A simple linear regression method jointly estimates the mutation rate and the level of sequence polymorphism, thus providing an estimate of the effective population size. It also leads to the definition of Wright's FST for arbitrary time-series data. Furthermore, as an alternative to Tajima's D statistic or the site-frequency spectrum, a mismatch distribution corrected for sampling-time differences can be obtained and compared between actual and simulated data. Application of these methods to seasonal influenza A/H3N2 viruses sampled between 1980 and 2017 and sequences simulated under the model of recurrent positive selection with metapopulation dynamics allowed us to estimate the synonymous mutation rate and find parameter values for selection and demographic structure that fit the observation. We found that the mutation rates of HA and PB1 segments before 2007 were particularly high and that including recurrent positive selection in our model was essential for the genealogical structure of the HA segment. Methods developed here can be generally applied to population genetic inferences using serially sampled genetic data.

Adaptive Phenotypic Plasticity Stabilizes Evolution in Fluctuating Environments

Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypic plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution.

Synergistic epistasis enhances the co-operativity of mutualistic interspecies interactions

Early evolution of mutualism is characterized by big and predictable adaptive changes, including the specialization of interacting partners, such as through deleterious mutations in genes not required for metabolic cross-feeding. We sought to investigate whether these early mutations improve cooperativity by manifesting in synergistic epistasis between genomes of the mutually interacting species. Specifically, we have characterized evolutionary trajectories of syntrophic interactions of Desulfovibrio vulgaris (Dv) with Methanococcus maripaludis (Mm) by longitudinally monitoring mutations accumulated over 1000 generations of nine independently evolved communities with analysis of the genotypic structure of one community down to the single-cell level. We discovered extensive parallelism across communities despite considerable variance in their evolutionary trajectories and the perseverance within many evolution lines of a rare lineage of Dv that retained sulfate-respiration (SR+) capability, which is not required for metabolic cross-feeding. An in-depth investigation revealed that synergistic epistasis across pairings of Dv and Mm genotypes had enhanced cooperativity within SR- and SR+ assemblages, enabling their coexistence within the same community. Thus, our findings demonstrate that cooperativity of a mutualism can improve through synergistic epistasis between genomes of the interacting species, enabling the coexistence of mutualistic assemblages of generalists and their specialized variants.

Genetic Diversity for Accelerating Microbial Adaptive Laboratory Evolution

Adaptive laboratory evolution (ALE) is a widely used and highly effective tool for improving microbial phenotypes and investigating the evolutionary roots of biological phenomena. Serving as the raw materials of evolution, mutations have been extensively utilized to increase the chances of engineering molecules or microbes with tailor-made functions. The generation of genetic diversity is therefore a core technology for accelerating ALE, and a high-quality mutant library is crucial to its success. Because of its importance, technologies for generating genetic diversity have undergone rapid development in recent years. Here, we review the existing techniques for the construction of mutant libraries, briefly introduce their mechanisms and applications, discuss ongoing and emerging efforts to apply engineering technologies in the construction of mutant libraries, and suggest future perspectives for library construction.

Cryopreservation of clonal and polyclonal populations of Chlamydomonas reinhardtii

Long-term preservation of laboratory strains of Chlamydomonas reinhardtii has historically involved either liquid nitrogen cryopreservation, which is expensive and labor intensive, or storage on agar plates, which requires frequent transfer to new plates, and which may leave samples susceptible to contamination as well as genetic drift and/or selection. The emergence of C. reinhardtii as a model organism for genetic analysis and experimental evolution has produced an increasing demand for an efficient method to cryopreserve C. reinhardtii populations. The GeneArt™ Cryopreservation Kit for Algae provides the first method for algal storage at −80°C; however, little is known about how this method affects recovery of different clones, much less polyclonal populations. Here, we compare postfreeze viability of clonal and genetically mixed samples frozen at −80°C using GeneArt™ or cryopreserved using liquid nitrogen. We find that the GeneArt™ protocol yields similar percent recoveries for some but not all clonal cultures, when compared to archiving via liquid N2. We also find that relative frequency of different strains recovered from genetically mixed populations can be significantly altered by cryopreservation. Thus, while cryopreservation using GeneArt™ is an effective means for archiving certain clonal populations, it is not universally so. Strain-specific differences in freeze–thaw tolerance complicate the storage of different clones, and may also bias the recovery of different genotypes from polyclonal populations.

Adaptive laboratory evolution principles and applications in industrial biotechnology

Adaptive laboratory evolution (ALE) is an innovative approach for the generation of evolved microbial strains with desired characteristics, by implementing the rules of natural selection as presented in the Darwinian Theory, on the laboratory bench. New as it might be, it has already been used by several researchers for the amelioration of a variety of characteristics of widely used microorganisms in biotechnology. ALE is used as a tool for the deeper understanding of the genetic and/or metabolic pathways of evolution. Another important field targeted by ALE is the manufacturing of products of (high) added value, such as ethanol, butanol and lipids. In the current review, we discuss the basic principles and techniques of ALE, and then we focus on studies where it has been applied to bacteria, fungi and microalgae, aiming to improve their performance to biotechnological procedures and/or inspect the genetic background of evolution. We conclude that ALE is a promising and efficacious method that has already led to the acquisition of useful new microbiological strains in biotechnology and could possibly offer even more interesting results in the future.