Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment

Effects of periodic bottlenecks on the dynamics of adaptive evolution in microbial populations

Population bottlenecks can impact the rate of adaptation in evolving populations. On the one hand, each bottleneck reduces the genetic variation that fuels adaptation. On the other hand, each founder that survives a bottleneck can undergo more generations and leave more descendants in a resource-limited environment, which allows surviving beneficial mutations to spread more quickly. A theoretical model predicted that the rate of fitness gains should be maximized using ~8-fold dilutions. Here we investigate the impact of repeated bottlenecks on the dynamics of adaptation using numerical simulations and experimental populations of Escherichia coli. Our simulations confirm the model's prediction when populations evolve in a regime where beneficial mutations are rare and waiting times between successful mutations are long. However, more extreme dilutions maximize fitness gains in simulations when beneficial mutations are common and clonal interference prevents most of them from fixing. To examine these predictions, we propagated 48 E. coli populations with 2-, 8-, 100-, and 1000-fold dilutions for 150 days. Adaptation began earlier and fitness gains were greater with 100- and 1000-fold dilutions than with 8-fold dilutions, consistent with the simulations when beneficial mutations are common. However, the selection pressures in the 2-fold treatment were qualitatively different from the other treatments, violating a critical assumption of the model and simulations. Thus, varying the dilution factor during periodic bottlenecks can have multiple effects on the dynamics of adaptation caused by differential losses of diversity, different numbers of generations, and altered selection.

Asynchronous abundance fluctuations can drive giant genotype frequency fluctuations

Large stochastic population abundance fluctuations are ubiquitous across the tree of life1-7, impacting the predictability of population dynamics and influencing eco-evolutionary outcomes. It has generally been thought that these large abundance fluctuations do not strongly impact evolution, as the relative frequencies of alleles in the population will be unaffected if the abundance of all alleles fluctuate in unison. However, we argue that large abundance fluctuations can lead to significant genotype frequency fluctuations if different genotypes within a population experience these fluctuations asynchronously. By serially diluting mixtures of two closely related E. coli strains, we show that such asynchrony can occur, leading to giant frequency fluctuations that far exceed expectations from models of genetic drift. We develop a flexible, effective model that explains the abundance fluctuations as arising from correlated offspring numbers between individuals, and the large frequency fluctuations result from even slight decoupling in offspring numbers between genotypes. This model accurately describes the observed abundance and frequency fluctuation scaling behaviors. Our findings suggest chaotic dynamics underpin these giant fluctuations, causing initially similar trajectories to diverge exponentially; subtle environmental changes can be magnified, leading to batch correlations in identical growth conditions. Furthermore, we present evidence that such decoupling noise is also present in mixed-genotype S. cerevisiae populations. We demonstrate that such decoupling noise can strongly influence evolutionary outcomes, in a manner distinct from genetic drift. Given the generic nature of asynchronous fluctuations, we anticipate that they are widespread in biological populations, significantly affecting evolutionary and ecological dynamics.

Development of a genome-scale metabolic model for the lager hybrid yeast S. pastorianus to understand the evolution of metabolic pathways in industrial settings

In silico tools such as genome-scale metabolic models have shown to be powerful for metabolic engineering of microorganisms. Saccharomyces pastorianus is a complex aneuploid hybrid between the mesophilic Saccharomyces cerevisiae and the cold-tolerant Saccharomyces eubayanus. This species is of biotechnological importance because it is the primary yeast used in lager beer fermentation and is also a key model for studying the evolution of hybrid genomes, including expression pattern of ortholog genes, composition of protein complexes, and phenotypic plasticity. Here, we created the iSP_1513 GSMM for S. pastorianus CBS1513 to allow top-down computational approaches to predict the evolution of metabolic pathways and to aid strain optimization in production processes. The iSP_1513 comprises 4,062 reactions, 1,808 alleles, and 2,747 metabolites, and takes into account the functional redundancy in the gene-protein-reaction rule caused by the presence of orthologous genes. Moreover, a universal algorithm to constrain GSMM reactions using transcriptome data was developed as a python library and enabled the integration of temperature as parameter. Essentiality data sets, growth data on various carbohydrates and volatile metabolites secretion were used to validate the model and showed the potential of media engineering to improve specific flavor compounds. The iSP_1513 also highlighted the different contributions of the parental sub-genomes to the oxidative and non-oxidative parts of the pentose phosphate pathway. Overall, the iSP_1513 GSMM represent an important step toward understanding the metabolic capabilities, evolutionary trajectories, and adaptation potential of S. pastorianus in different industrial settings.

IMPORTANCE

Genome-scale metabolic models (GSMM) have been successfully applied to predict cellular behavior and design cell factories in several model organisms, but no models to date are currently available for hybrid species due to their more complex genetics and general lack of molecular data. In this study, we generated a bespoke GSMM, iSP_1513, for this industrial aneuploid hybrid Saccharomyces pastorianus, which takes into account the aneuploidy and functional redundancy from orthologous parental alleles. This model will (i) help understand the metabolic capabilities and adaptive potential of S. pastorianus (domestication processes), (ii) aid top-down predictions for strain development (industrial biotechnology), and (iii) allow predictions of evolutionary trajectories of metabolic pathways in aneuploid hybrids (evolutionary genetics).

Proteome allocation and the evolution of metabolic cross-feeding

In a common instance of metabolic cross-feeding (MCF), an organism incompletely metabolizes nutrients and releases metabolites that are used by another to produce energy or building blocks. Why would the former waste edible food, and why does this preferentially occur at specific locations in a metabolic pathway have challenged evolutionary theory for decades. To address these questions, we combine adaptive dynamics with an explicit model of cell metabolism, including enzyme-driven catalysis of metabolic reactions and the cellular constraints acting on the proteome that may incur a cost to expressing all enzymes along a pathway. After pointing out that cells should in principle prioritize upstream reactions when metabolites are restrained inside the cell, we show that the occurrence of permeability-driven MCF is rare and requires that an intermediate metabolite be extremely diffusive. Indeed, only at very high levels of membrane permeability (consistent with those of acetate and glycerol, for instance) and under distinctive sets of parameters should the population diversify and MCF evolve. These results help understand the origins of simple microbial communities, such as those that readily evolve in short-term evolutionary experiments, and may later be extended to investigate how evolution has progressively built up today's extremely diverse ecosystems.

Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform

The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.

Eco-evolutionary dynamics: The repeatability of diversification in an experimental microbial community

Understanding the evolution and subsequent maintenance of ecological diversity is a daunting task. Using a historical microbial evolution experiment, a new paper demonstrates the extent to which diversity can re-emerge in reduced communities and the traits through which rediversification occurs.

Rediversification following ecotype isolation reveals hidden adaptive potential

Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about whether communities can regenerate ecological diversity following ecotype removal or extinction and how the rediversified communities would compare to the original ones. Here, we show that simple two-ecotype communities from the E. coli long-term evolution experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different from the original community in ways relevant to the mechanism of ecotype coexistence-for example, in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, although the rediversified community showed unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species where there are even more potential niches, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.

Evolution of a cross-feeding interaction following a key innovation in a long-term evolution experiment with Escherichia coli

The evolution of a novel trait can profoundly change an organism's effects on its environment, which can in turn affect the further evolution of that organism and any coexisting organisms. We examine these effects and feedbacks following the evolution of a novel function in the Long-Term Evolution Experiment (LTEE) with Escherichia coli. A characteristic feature of E. coli is its inability to grow aerobically on citrate (Cit-). Nonetheless, a Cit+ variant with this capacity evolved in one LTEE population after 31 000 generations. The Cit+ clade then coexisted stably with another clade that retained the ancestral Cit- phenotype. This coexistence was shaped by the evolution of a cross-feeding relationship based on C4-dicarboxylic acids, particularly succinate, fumarate, and malate, that the Cit+ variants release into the medium. Both the Cit- and Cit+ cells evolved to grow on these excreted resources. The evolution of aerobic growth on citrate thus led to a transition from an ecosystem based on a single limiting resource, glucose, to one with at least five resources that were either shared or partitioned between the two coexisting clades. Our findings show that evolutionary novelties can change environmental conditions in ways that facilitate diversity by altering ecosystem structure and the evolutionary trajectories of coexisting lineages.

Rediversification Following Ecotype Isolation Reveals Hidden Adaptive Potential

Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about if communities can regenerate ecological diversity following species removal or extinction, and how the rediversified communities would compare to the original ones. Here we show that simple two-ecotype communities from the E. coli Long Term Evolution Experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different compared to the original community in ways relevant to the mechanism of ecotype coexistence, for example in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, but with unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.

Dynamics of co-substrate pools can constrain and regulate metabolic fluxes

Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of these two parameters can allow regulation of flux dynamics in branched and coupled pathways. To support these theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by the dynamics of co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate dynamics is crucial for understanding and engineering metabolic fluxes in cells.

Quantifying the local adaptive landscape of a nascent bacterial community

The fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes ("L" and "S") shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community.

Population regulation and adaptive dynamics of cross-feeding

The particular importance of evolutionary studies in microbial experimental systems is that starting from the level of the metabolism of individual cells, the adaptive dynamics can be followed step by step by biochemical, genetic, and population dynamical tools. Moreover, the coincidence of evolutionary and ecological time scales helps to clarify the mutual role of ecological and evolutionary principles in predicting adaptive dynamics in general. Ecological principles define the ecological conditions under which adaptive branching can occur. This paper overviews and interprets the results of empirical and modeling studies of the evolution of metabolic cross-feeding in glucose-limited E.coli chemostats and batch cultures in the context of theories of robust coexistence and adaptive dynamics. Empirical results consistently demonstrate that the interactions between cells are mediated by the changing metabolite concentrations in the cultures and modeling confirms that these changes may control the adaptive dynamics of the clones. In consequence, the potential results of evolution can be predicted at the functional level by evolutionary flux balance analysis (evoFBA), while the genetic changes are more contingent. evoFBA follows the scheme of adaptive dynamics theory by calculating the feedback environment that changes during the evolutionary process and provides a promising tool to further investigate adaptive divergence in small microbial communities. Three general conclusions close the paper.

Extracellular Vesicles from 50,000 Generation Clones of the Escherichia coli Long-Term Evolution Experiment

Extracellular vesicles (EVs) are critical elements of cell-cell communication. Here, we characterized the outer membrane vesicles (OMVs) released by specific clones of Escherichia coli isolated from the Long-Term Evolution Experiment after 50,000 generations (50K) of adaptation to glucose minimal medium. Compared with their ancestor, the evolved clones produce small OMVs but also larger ones which display variable amounts of both OmpA and LPS. Tracking ancestral, fluorescently labelled OMVs revealed that they fuse with both ancestral- and 50K-evolved cells, albeit in different proportions. We quantified that less than 2% of the cells from one 50K-evolved clone acquired the fluorescence delivered by OMVs from the ancestral strain but that one cell concomitantly fuses with several OMVs. Globally, our results showed that OMV production in E. coli is a phenotype that varies along bacterial evolution and question the contribution of OMVs-mediated interactions in bacterial adaptation.

The Evolution of Microbial Facilitation: Sociogenesis, Symbiogenesis, and Transition in Individuality

Metabolic cooperation is widespread, and it seems to be a ubiquitous and easily evolvable interaction in the microbial domain. Mutual metabolic cooperation, like syntrophy, is thought to have a crucial role in stabilizing interactions and communities, for example biofilms. Furthermore, cooperation is expected to feed back positively to the community under higher-level selection. In certain cases, cooperation can lead to a transition in individuality, when freely reproducing, unrelated entities (genes, microbes, etc.) irreversibly integrate to form a new evolutionary unit. The textbook example is endosymbiosis, prevalent among eukaryotes but virtually lacking among prokaryotes. Concerning the ubiquity of syntrophic microbial communities, it is intriguing why evolution has not lead to more transitions in individuality in the microbial domain. We set out to distinguish syntrophy-specific aspects of major transitions, to investigate why a transition in individuality within a syntrophic pair or community is so rare. We review the field of metabolic communities to identify potential evolutionary trajectories that may lead to a transition. Community properties, like joint metabolic capacity, functional profile, guild composition, assembly and interaction patterns are important concepts that may not only persist stably but according to thought-provoking theories, may provide the heritable information at a higher level of selection. We explore these ideas, relating to concepts of multilevel selection and of informational replication, to assess their relevance in the debate whether microbial communities may inherit community-level information or not.

Evolution of Bacterial Persistence to Antibiotics during a 50,000-Generation Experiment in an Antibiotic-Free Environment

Failure of antibiotic therapies causes > 700,000 deaths yearly and involves both bacterial resistance and persistence. Persistence results in the relapse of infections by producing a tiny fraction of pathogen survivors that stay dormant during antibiotic exposure. From an evolutionary perspective, persistence is either a ‘bet-hedging strategy’ that helps to cope with stochastically changing environments or an unavoidable minimal rate of ‘cellular errors’ that lock the cells in a low activity state. Here, we analyzed the evolution of persistence over 50,000 bacterial generations in a stable environment by improving a published method that estimates the number of persister cells based on the growth of the reviving population. Our results challenged our understanding of the factors underlying persistence evolution. In one case, we observed a substantial decrease in persistence proportion, suggesting that the naturally observed persistence level is not an unavoidable minimal rate of ‘cellular errors’. However, although there was no obvious environmental stochasticity, in 11 of the 12 investigated populations, the persistence level was maintained during 50,000 bacterial generations.

Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, Cantón R, Coque TM. Evolutionary Pathways and Trajectories in Antibiotic Resistance

Evolution is the hallmark of life. Descriptions of the evolution of microorganisms have provided a wealth of information, but knowledge regarding "what happened" has precluded a deeper understanding of "how" evolution has proceeded, as in the case of antimicrobial resistance. The difficulty in answering the "how" question lies in the multihierarchical dimensions of evolutionary processes, nested in complex networks, encompassing all units of selection, from genes to communities and ecosystems. At the simplest ontological level (as resistance genes), evolution proceeds by random (mutation and drift) and directional (natural selection) processes; however, sequential pathways of adaptive variation can occasionally be observed, and under fixed circumstances (particular fitness landscapes), evolution is predictable. At the highest level (such as that of plasmids, clones, species, microbiotas), the systems' degrees of freedom increase dramatically, related to the variable dispersal, fragmentation, relatedness, or coalescence of bacterial populations, depending on heterogeneous and changing niches and selective gradients in complex environments. Evolutionary trajectories of antibiotic resistance find their way in these changing landscapes subjected to random variations, becoming highly entropic and therefore unpredictable. However, experimental, phylogenetic, and ecogenetic analyses reveal preferential frequented paths (highways) where antibiotic resistance flows and propagates, allowing some understanding of evolutionary dynamics, modeling and designing interventions. Studies on antibiotic resistance have an applied aspect in improving individual health, One Health, and Global Health, as well as an academic value for understanding evolution. Most importantly, they have a heuristic significance as a model to reduce the negative influence of anthropogenic effects on the environment.

Microbial adaptive evolution

Bacterial species can adapt to significant changes in their environment by mutation followed by selection, a phenomenon known as “adaptive evolution.” With the development of bioinformatics and genetic engineering, research on adaptive evolution has progressed rapidly, as have applications of the process. In this review, we summarize various mechanisms of bacterial adaptive evolution, the technologies used for studying it, and successful applications of the method in research and industry. We particularly highlight the contributions of Dr. L. O. Ingram. Microbial adaptive evolution has significant impact on our society not only from its industrial applications, but also in the evolution, emergence, and control of various pathogens.

Quantifying rapid bacterial evolution and transmission within the mouse intestine

Due to limitations on high-resolution strain tracking, selection dynamics during gut microbiota colonization and transmission between hosts remain mostly mysterious. Here, we introduced hundreds of barcoded Escherichia coli strains into germ-free mice and quantified strain-level dynamics and metagenomic changes. Mutations in genes involved in motility and metabolite utilization are reproducibly selected within days. Even with rapid selection, coprophagy enforced similar barcode distributions across co-housed mice. Whole-genome sequencing of hundreds of isolates revealed linked alleles that demonstrate between-host transmission. A population-genetics model predicts substantial fitness advantages for certain mutants and that migration accounted for ∼10% of the resident microbiota each day. Treatment with ciprofloxacin suggests interplay between selection and transmission. While initial colonization was mostly uniform, in two mice a bottleneck reduced diversity and selected for ciprofloxacin resistance in the absence of drug. These findings highlight the interplay between environmental transmission and rapid, deterministic selection during evolution of the intestinal microbiota.

Adaptation of metabolite leakiness leads to symbiotic chemical exchange and to a resilient microbial ecosystem

Microbial communities display remarkable diversity, facilitated by the secretion of chemicals that can create new niches. However, it is unclear why cells often secrete even essential metabolites after evolution. Based on theoretical results indicating that cells can enhance their own growth rate by leaking even essential metabolites, we show that such "leaker" cells can establish an asymmetric form of mutualism with "consumer" cells that consume the leaked chemicals: the consumer cells benefit from the uptake of the secreted metabolites, while the leaker cells also benefit from such consumption, as it reduces the metabolite accumulation in the environment and thereby enables further secretion, resulting in frequency-dependent coexistence of multiple microbial species. As supported by extensive simulations, such symbiotic relationships generally evolve when each species has a complex reaction network and adapts its leakiness to optimize its own growth rate under crowded conditions and nutrient limitations. Accordingly, symbiotic ecosystems with diverse cell species that leak and exchange many metabolites with each other are shaped by cell-level adaptation of leakiness of metabolites. Moreover, the resultant ecosystems with entangled metabolite exchange are resilient against structural and environmental perturbations. Thus, we present a theory for the origin of resilient ecosystems with diverse microbes mediated by secretion and exchange of essential chemicals.

Maintenance of Metabolic Plasticity despite Relaxed Selection in a Long-Term Evolution Experiment with Escherichia coli

AbstractTraits that are unused in a given environment are subject to processes that tend to erode them, leading to reduced fitness in other environments. Although this general tendency is clear, we know much less about why some traits are lost while others are retained and about the roles of mutation and selection in generating different responses. We addressed these issues by examining populations of a facultative anaerobe, Escherichia coli, that have evolved for >30 years in the presence of oxygen, with relaxed selection for anaerobic growth and the associated metabolic plasticity. We asked whether evolution led to the loss, improvement, or maintenance of anaerobic growth, and we analyzed gene expression and mutational data sets to understand the outcomes. We identified genomic signatures of both positive and purifying selection on aerobic-specific genes, while anaerobic-specific genes showed clear evidence of relaxed selection. We also found parallel evolution at two interacting loci that regulate anaerobic growth. We competed the ancestor and evolved clones from each population in an anoxic environment, and we found that anaerobic fitness had not decayed, despite relaxed selection. In summary, relaxed selection does not necessarily reduce an organism's fitness in other environments. Instead, the genetic architecture of the traits under relaxed selection and their correlations with traits under positive and purifying selection may sometimes determine evolutionary outcomes.

Changes in Cell Size and Shape during 50,000 Generations of Experimental Evolution with Escherichia coli

Bacteria adopt a wide variety of sizes and shapes, with many species exhibiting stereotypical morphologies. How morphology changes, and over what timescales, is less clear. Previous work examining cell morphology in an experiment with Escherichia coli showed that populations evolved larger cells and, in some cases, cells that were less rod-like. That experiment has now run for over two more decades. Meanwhile, genome sequence data are available for these populations, and new computational methods enable high-throughput microscopic analyses. In this study, we measured stationary-phase cell volumes for the ancestor and 12 populations at 2,000, 10,000, and 50,000 generations, including measurements during exponential growth at the last time point. We measured the distribution of cell volumes for each sample using a Coulter counter and microscopy, the latter of which also provided data on cell shape. Our data confirm the trend toward larger cells while also revealing substantial variation in size and shape across replicate populations. Most populations first evolved wider cells but later reverted to the ancestral length-to-width ratio. All but one population evolved mutations in rod shape maintenance genes. We also observed many ghost-like cells in the only population that evolved the novel ability to grow on citrate, supporting the hypothesis that this lineage struggles with maintaining balanced growth. Lastly, we show that cell size and fitness remain correlated across 50,000 generations. Our results suggest that larger cells are beneficial in the experimental environment, while the reversion toward ancestral length-to-width ratios suggests partial compensation for the less favorable surface area-to-volume ratios of the evolved cells.IMPORTANCE Bacteria exhibit great morphological diversity, yet we have only a limited understanding of how their cell sizes and shapes evolve and of how these features affect organismal fitness. This knowledge gap reflects, in part, the paucity of the fossil record for bacteria. In this study, we revived and analyzed samples extending over 50,000 generations from 12 populations of experimentally evolving Escherichia coli to investigate the relation between cell size, shape, and fitness. Using this "frozen fossil record," we show that all 12 populations evolved larger cells concomitant with increased fitness, with substantial heterogeneity in cell size and shape across the replicate lines. Our work demonstrates that cell morphology can readily evolve and diversify, even among populations living in identical environments.

Metabolic modelling approaches for describing and engineering microbial communities

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.

Investigating the Evolution and Development of Biological Systems from the Perspective of Thermo-Kinetics and Systems Theory

Life itself is grander than the sum of its constituent molecules. Any living organism may be regarded as a part of a dissipative process that connects irreversible energy consumption with growth, reproduction, and evolution. Under energy-fuelled, far-from-equilibrium conditions, chemical systems capable of exponential growth can manifest a specific form of stability- dynamic kinetic stability (DKS) - indicating the persistence of self-reproducible entities. This kinetic behavior is associated with thermodynamic conditions far from equilibrium leading to an evolutionary view of the origin of life in which increasing entities have to be associated with the dissipation of free energy. This review aims to reformulate Darwinian theory in physicochemical terms so that it can handle both animate and inanimate systems, thus helping to overcome this theoretical divide. The expanded formulation is based on the principle of dynamic kinetic stability and evidence from the emerging field of systems chemistry. Although the classic Darwinian theory is useful for understanding the origins and evolution of species, it is not meant to primarily build an explicit framework for predicting potential evolution routes. Throughout the last century, the inherently systemic and dynamic nature of the biological systems has been brought to the attention of researchers. During the last decades, "systems" approaches to biology and genome evolution are gaining ever greater significance providing the possibility of a deeper interpretation of the basic concepts of life. Further progress of this approach depends on crossing disciplinary boundaries and complex simulations of biological systems. Evolutionary systems biology (ESB) through the integration of methods from evolutionary biology and systems biology aims to the understanding of the fundamental principles of life as well as the prediction of biological systems evolution.

Environmental and Physiological Factors Affecting High-Throughput Measurements of Bacterial Growth

Bacterial growth under nutrient-rich and starvation conditions is intrinsically tied to the environmental history and physiological state of the population. While high-throughput technologies have enabled rapid analyses of mutant libraries, technical and biological challenges complicate data collection and interpretation. Here, we present a framework for the execution and analysis of growth measurements with improved accuracy over that of standard approaches. Using this framework, we demonstrate key biological insights that emerge from consideration of culturing conditions and history. We determined that quantification of the background absorbance in each well of a multiwell plate is critical for accurate measurements of maximal growth rate. Using mathematical modeling, we demonstrated that maximal growth rate is dependent on initial cell density, which distorts comparisons across strains with variable lag properties. We established a multiple-passage protocol that alleviates the substantial effects of glycerol on growth in carbon-poor media, and we tracked growth rate-mediated fitness increases observed during a long-term evolution of Escherichia coli in low glucose concentrations. Finally, we showed that growth of Bacillus subtilis in the presence of glycerol induces a long lag in the next passage due to inhibition of a large fraction of the population. Transposon mutagenesis linked this phenotype to the incorporation of glycerol into lipoteichoic acids, revealing a new role for these envelope components in resuming growth after starvation. Together, our investigations underscore the complex physiology of bacteria during bulk passaging and the importance of robust strategies to understand and quantify growth.IMPORTANCE How starved bacteria adapt and multiply under replete nutrient conditions is intimately linked to their history of previous growth, their physiological state, and the surrounding environment. While automated equipment has enabled high-throughput growth measurements, data interpretation and knowledge gaps regarding the determinants of growth kinetics complicate comparisons between strains. Here, we present a framework for growth measurements that improves accuracy and attenuates the effects of growth history. We determined that background absorbance quantification and multiple passaging cycles allow for accurate growth rate measurements even in carbon-poor media, which we used to reveal growth-rate increases during long-term laboratory evolution of Escherichia coli Using mathematical modeling, we showed that maximum growth rate depends on initial cell density. Finally, we demonstrated that growth of Bacillus subtilis with glycerol inhibits the future growth of most of the population, due to lipoteichoic acid synthesis. These studies highlight the challenges of accurate quantification of bacterial growth behaviors.

Contingent evolution of alternative metabolic network topologies determines whether cross-feeding evolves

Metabolic exchange is widespread in natural microbial communities and an important driver of ecosystem structure and diversity, yet it remains unclear what determines whether microbes evolve division of labor or maintain metabolic autonomy. Here we use a mechanistic model to study how metabolic strategies evolve in a constant, one resource environment, when metabolic networks are allowed to freely evolve. We find that initially identical ancestral communities of digital organisms follow different evolutionary trajectories, as some communities become dominated by a single, autonomous lineage, while others are formed by stably coexisting lineages that cross-feed on essential building blocks. Our results show how without presupposed cellular trade-offs or external drivers such as temporal niches, diverse metabolic strategies spontaneously emerge from the interplay between ecology, spatial structure, and metabolic constraints that arise during the evolution of metabolic networks. Thus, in the long term, whether microbes remain autonomous or evolve metabolic division of labour is an evolutionary contingency.

A model for the interplay between plastic tradeoffs and evolution in changing environments

Performance tradeoffs are ubiquitous in both ecological and evolutionary modeling, yet they are usually postulated and built into fitness and ecological landscapes. However, tradeoffs depend on genetic background and evolutionary history and can themselves evolve. We present a simple model capable of capturing the key feedback loop: evolutionary history shapes tradeoff strength, which, in turn, shapes evolutionary future. One consequence of this feedback is that genomes with identical fitness can have different evolutionary properties shaped by prior environmental exposure. Another is that, generically, the best adaptations to one environment may evolve in another. Our simple framework bridges the gap between the phenotypic Fisher's Geometric Model and the genotypic properties, such as modularity and evolvability, and can serve as a rich playground for investigating evolution in multiple or changing environments.

In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

BACKGROUND

The fungus Aspergillus niger is an important industrial organism for citric acid fermentation; one of the most efficient biotechnological processes. Previously we introduced a dynamic model that captures this process in the industrially relevant batch fermentation setting, providing a more accurate predictive platform to guide targeted engineering. In this article we exploit this dynamic modelling framework, coupled with a robust genetic algorithm for the in silico evolution of A. niger organic acid production, to provide solutions to complex evolutionary goals involving a multiplicity of targets and beyond the reach of simple Boolean gene deletions. We base this work on the latest metabolic models of the parent citric acid producing strain ATCC1015 dedicated to organic acid production with the required exhaustive genomic coverage needed to perform exploratory in silico evolution.

RESULTS

With the use of our informed evolutionary framework, we demonstrate targeted changes that induce a complete switch of acid output from citric to numerous different commercially valuable target organic acids including succinic acid. We highlight the key changes in flux patterns that occur in each case, suggesting potentially valuable targets for engineering. We also show that optimum acid productivity is achieved through a balance of organic acid and biomass production, requiring finely tuned flux constraints that give a growth rate optimal for productivity.

CONCLUSIONS

This study shows how a genome-scale metabolic model can be integrated with dynamic modelling and metaheuristic algorithms to provide solutions to complex metabolic engineering goals of industrial importance. This framework for in silico guided engineering, based on the dynamic batch growth relevant to industrial processes, offers considerable potential for future endeavours focused on the engineering of organisms to produce valuable products.

Advantage of Leakage of Essential Metabolites for Cells

Microbial cells generally leak various metabolites including those necessary to grow. Why cells secrete even essential chemicals so often is, however, still unclear. Based on analytical and numerical calculations, we show that if the intracellular metabolism includes multibody (e.g., catalytic) reactions, leakage of essential metabolites can promote the leaking cell's growth. This advantage is typical for most metabolic networks via "flux control" and "growth-dilution" mechanisms, as a general consequence of the balance between synthesis and growth-induced dilution with autocatalytic reactions. We further argue that this advantage may lead to a novel form of symbiosis among diverse cells.

Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism

It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals before genetically encoded enzymes. A major challenge in unravelling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms with physico-chemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fuelled by a thioester- and redox-driven variant of the reductive tricarboxylic acid cycle that is capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors are not necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a protocell, and find that different combinations of carbon sources and electron donors can support the continuous production of a minimal ancient 'biomass' composed of putative early biopolymers and fatty acids.

Trusting the hand that feeds: microbes evolve to anticipate a serial transfer protocol as individuals or collectives

BACKGROUND

Experimental evolution of microbes often involves a serial transfer protocol, where microbes are repeatedly diluted by transfer to a fresh medium, starting a new growth cycle. This has revealed that evolution can be remarkably reproducible, where microbes show parallel adaptations both on the level of the phenotype as well as the genotype. However, these studies also reveal a strong potential for divergent evolution, leading to diversity both between and within replicate populations. We here study how in silico evolved Virtual Microbe "wild types" (WTs) adapt to a serial transfer protocol to investigate generic evolutionary adaptations, and how these adaptations can be manifested by a variety of different mechanisms.

RESULTS

We show that all WTs evolve to anticipate the regularity of the serial transfer protocol by adopting a fine-tuned balance of growth and survival. This anticipation is done by evolving either a high yield mode, or a high growth rate mode. We find that both modes of anticipation can be achieved by individual lineages and by collectives of microbes. Moreover, these different outcomes can be achieved with or without regulation, although the individual-based anticipation without regulation is less well adapted in the high growth rate mode.

CONCLUSIONS

All our in silico WTs evolve to trust the hand that feeds by evolving to anticipate the periodicity of a serial transfer protocol, but can do so by evolving two distinct growth strategies. Furthermore, both these growth strategies can be accomplished by gene regulation, a variety of different polymorphisms, and combinations thereof. Our work reveals that, even under controlled conditions like those in the lab, it may not be possible to predict individual evolutionary trajectories, but repeated experiments may well result in only a limited number of possible outcomes.

FLYCOP: metabolic modeling-based analysis and engineering microbial communities

Motivation

Synthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.

Results

In contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.

Availability and implementation

Code reproducing the study cases described in this manuscript are available on-line: https://github.com/beatrizgj/FLYCOP.

Supplementary information

Supplementary data are available at Bioinformatics online.

Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria

Understanding plant⁻microbe interactions is crucial for improving plants' productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.

Interrogating metabolism as an electron flow system

Metabolism is generally considered as a neatly organised system of modular pathways, shaped by evolution under selection for optimal cellular growth. This view falls short of explaining and predicting a number of key observations about the structure and dynamics of metabolism. We highlight these limitations of a pathway-centric view on metabolism and summarise studies suggesting how these could be overcome by viewing metabolism as a thermodynamically and kinetically constrained, dynamical flow system. Such a systems-level, first-principles based view of metabolism can open up new avenues of metabolic engineering and cures for metabolic diseases and allow better insights to a myriad of physiological processes that are ultimately linked to metabolism. Towards further developing this view, we call for a closer interaction among physical and biological disciplines and an increased use of electrochemical and biophysical approaches to interrogate cellular metabolism together with the microenvironment in which it exists.

Effective models and the search for quantitative principles in microbial evolution

Microbes evolve rapidly. Yet they do so in idiosyncratic ways, which depend on the specific mutations that are beneficial or deleterious in a given situation. At the same time, some population-level patterns of adaptation are strikingly similar across different microbial systems, suggesting that there may also be simple, quantitative principles that unite these diverse scenarios. We review the search for simple principles in microbial evolution, ranging from the biophysical level to emergent evolutionary dynamics. A key theme has been the use of effective models, which coarse-grain over molecular and cellular details to obtain a simpler description in terms of a few effective parameters. Collectively, these theoretical approaches provide a set of quantitative principles that facilitate understanding, prediction, and potentially control of evolutionary phenomena, though formidable challenges remain due to the ecological complexity of natural populations.

Co-adaption of tRNA gene copy number and amino acid usage influences translation rates in three life domains

Although more and more entangled participants of translation process were realized, how they cooperate and co-determine the final translation efficiency still lacks details. Here, we reasoned that the basic translation components, tRNAs and amino acids should be consistent to maximize the efficiency and minimize the cost. We firstly revealed that 310 out of 410 investigated genomes of three domains had significant co-adaptions between the tRNA gene copy numbers and amino acid compositions, indicating that maximum efficiency constitutes ubiquitous selection pressure on protein translation. Furthermore, fast-growing and larger bacteria are found to have significantly better co-adaption and confirmed the effect of this pressure. Within organism, highly expressed proteins and those connected to acute responses have higher co-adaption intensity. Thus, the better co-adaption probably speeds up the growing of cells through accelerating the translation of special proteins. Experimentally, manipulating the tRNA gene copy number to optimize co-adaption between enhanced green fluorescent protein (EGFP) and tRNA gene set of Escherichia coli indeed lifted the translation rate (speed). Finally, as a newly confirmed translation rate regulating mechanism, the co-adaption reflecting translation rate not only deepens our understanding on translation process but also provides an easy and practicable method to improve protein translation rates and productivity.

An enormous potential for niche construction through bacterial cross-feeding in a homogeneous environment

Microorganisms modify their environment by excreting by-products of metabolism, which can create new ecological niches that can help microbial populations diversify. A striking example comes from experimental evolution of genetically identical Escherichia coli populations that are grown in a homogeneous environment with the single carbon source glucose. In such experiments, stable communities of genetically diverse cross-feeding E. coli cells readily emerge. Some cells that consume the primary carbon source glucose excrete a secondary carbon source, such as acetate, that sustains other community members. Few such cross-feeding polymorphisms are known experimentally, because they are difficult to screen for. We studied the potential of bacterial metabolism to create new ecological niches based on cross-feeding. To do so, we used genome scale models of the metabolism of E. coli and metabolisms of similar complexity, to identify unique pairs of primary and secondary carbon sources in these metabolisms. We then combined dynamic flux balance analysis with analytical calculations to identify which pair of carbon sources can sustain a polymorphic cross-feeding community. We identified almost 10,000 such pairs of carbon sources, each of them corresponding to a unique ecological niche. Bacterial metabolism shows an immense potential for the construction of new ecological niches through cross feeding.

Biodiversity can emerge in a completely homogeneous environment from populations with initially genetically identical individuals. This striking observation comes from experimental evolution of bacteria, which create new ecological niches when they excrete nutrient-rich waste products that can sustain the life of other bacteria. It is difficult to estimate the potential of any one organism for such metabolic niche construction experimentally, because it is challenging to screen for novel metabolic abilities on a large scale. We therefore used experimentally validated models of bacterial metabolism to predict how many novel niches organisms like Escherichia coli can construct, if a novel niche must be able to sustain a stable community of microbes that differ in the nutrients they consume. We identify thousands of such niches. They differ in their primary carbon source and a secondary carbon source that is excreted by some microbes and used by others. Because we restricted ourselves to chemically simple environments, we may even have underestimated the enormous potential of microbes for niche construction.

Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss

Background

Metabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear.

Results

To address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic ‘missed opportunities’, wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction.

Conclusion

Our findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0588-4) contains supplementary material, which is available to authorized users.

Impact of spatial organization on a novel auxotrophic interaction among soil microbes

A key prerequisite to achieve a deeper understanding of microbial communities and to engineer synthetic ones is to identify the individual metabolic interactions among key species and how these interactions are affected by different environmental factors. Deciphering the physiological basis of species-species and species-environment interactions in spatially organized environments requires reductionist approaches using ecologically and functionally relevant species. To this end, we focus here on a defined system to study the metabolic interactions in a spatial context among the plant-beneficial endophytic fungus Serendipita indica, and the soil-dwelling model bacterium Bacillus subtilis. Focusing on the growth dynamics of S. indica under defined conditions, we identified an auxotrophy in this organism for thiamine, which is a key co-factor for essential reactions in the central carbon metabolism. We found that S. indica growth is restored in thiamine-free media, when co-cultured with B. subtilis. The success of this auxotrophic interaction, however, was dependent on the spatial and temporal organization of the system; the beneficial impact of B. subtilis was only visible when its inoculation was separated from that of S. indica either in time or space. These findings describe a key auxotrophic interaction in the soil among organisms that are shown to be important for plant ecosystem functioning, and point to the potential importance of spatial and temporal organization for the success of auxotrophic interactions. These points can be particularly important for engineering of minimal functional synthetic communities as plant seed treatments and for vertical farming under defined conditions.

Feedback between environment and traits under selection in a seasonal environment: consequences for experimental evolution

Batch cultures are frequently used in experimental evolution to study the dynamics of adaptation. Although they are generally considered to simply drive a growth rate increase, other fitness components can also be selected for. Indeed, recurrent batches form a seasonal environment where different phases repeat periodically and different traits can be under selection in the different seasons. Moreover, the system being closed, organisms may have a strong impact on the environment. Thus, the study of adaptation should take into account the environment and eco-evolutionary feedbacks. Using data from an experimental evolution on yeast Saccharomyces cerevisiae, we developed a mathematical model to understand which traits are under selection, and what is the impact of the environment for selection in a batch culture. We showed that two kinds of traits are under selection in seasonal environments: life-history traits, related to growth and mortality, but also transition traits, related to the ability to react to environmental changes. The impact of environmental conditions can be summarized by the length of the different seasons which weight selection on each trait: the longer a season is, the higher the selection on associated traits. Since phenotypes drive season length, eco-evolutionary feedbacks emerge. Our results show how evolution in successive batches can affect season lengths and strength of selection on different traits.

Engineering microbial communities using thermodynamic principles and electrical interfaces

Microbial communities present the next research frontier. We argue here that understanding and engineering microbial communities requires a holistic view that considers not only species-species, but also species-environment interactions, and feedbacks between ecological and evolutionary dynamics (eco-evo feedbacks). Due this multi-level nature of interactions, we predict that approaches aimed soley at altering specific species populations in a community (through strain enrichment or inhibition), would only have a transient impact, and species-environment and eco-evo feedbacks would eventually drive the microbial community to its original state. We propose a higher-level engineering approach that is based on thermodynamics of microbial growth, and that considers specifically microbial redox biochemistry. Within this approach, the emphasis is on enforcing specific environmental conditions onto the community. These are expected to generate higher-level thermodynamic bounds onto the system, which the community structure and function can then adapt to. We believe that the resulting end-state can be ecologically and evolutionarily stable, mimicking the natural states of complex communities. Toward designing the exact nature of the environmental enforcement, thermodynamics and redox biochemistry can act as coarse-grained principles, while the use of electrodes-as electron providing or accepting redox agents-can provide implementation with spatiotemporal control.

Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.

The rise of metabolic interdependencies among microbes is still poorly understood. Here, taking the underlying biochemical networks into consideration, Zomorrodi and Segrè integrate genome-scale metabolic models with evolutionary game theory to study the rise of cross-feeding in microbial communities.

Cooperation in microbial communities and their biotechnological applications

Microbial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross-feeding, which can be understood in the general framework of ecological and evolutionary (eco-evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of 'cheating' cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications.

Genetic Basis of Exploiting Ecological Opportunity During the Long-Term Diversification of a Bacterial Population

Adaptive diversification is an essential evolutionary process, one that produces phenotypic innovations including the colonization of available ecological niches. Bacteria can diverge in sympatry when ecological opportunities allow, but the underlying genetic mechanisms are often unknown. Perhaps, the longest-lasting adaptive diversification seen in the laboratory occurred during the long-term evolution experiment, in which 12 populations of Escherichia coli have been evolving independently for more than 65,000 generations from a common ancestor. In one population, two lineages, S and L, emerged at ~6500 generations and have dynamically coexisted ever since by negative frequency-dependent interactions mediated, in part, by acetate secretion by L. Mutations in spoT, arcA, and gntR promoted the emergence of the S lineage, although they reproduced only partially its phenotypic traits. Here, we characterize the evolved mechanism of acetate consumption by the S lineage that enabled invasion and coexistence with the L lineage. We identified an additional mutation in acs that, together with the arcA mutation, drove an early restructuring of the transcriptional control of central metabolism in S, leading to improved acetate consumption. Pervasive epistatic interactions within the S genome contributed to the exploitation of this new ecological opportunity. The emergence and maintenance of this long-term polymorphism is a complex multi-step process.

Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations

Evolution is an on-going process, and it can be studied experimentally in organisms with rapid generations. My team has maintained 12 populations of Escherichia coli in a simple laboratory environment for >25 years and 60 000 generations. We have quantified the dynamics of adaptation by natural selection, seen some of the populations diverge into stably coexisting ecotypes, described changes in the bacteria's mutation rate, observed the new ability to exploit a previously untapped carbon source, characterized the dynamics of genome evolution and used parallel evolution to identify the genetic targets of selection. I discuss what the future might hold for this particular experiment, briefly highlight some other microbial evolution experiments and suggest how the fields of experimental evolution and microbial ecology might intersect going forward.

Advances on plant-pathogen interactions from molecular toward systems biology perspectives

In the past 2 decades, progress in molecular analyses of the plant immune system has revealed key elements of a complex response network. Current paradigms depict the interaction of pathogen-secreted molecules with host target molecules leading to the activation of multiple plant response pathways. Further research will be required to fully understand how these responses are integrated in space and time, and exploit this knowledge in agriculture. In this review, we highlight systems biology as a promising approach to reveal properties of molecular plant-pathogen interactions and predict the outcome of such interactions. We first illustrate a few key concepts in plant immunity with a network and systems biology perspective. Next, we present some basic principles of systems biology and show how they allow integrating multiomics data and predict cell phenotypes. We identify challenges for systems biology of plant-pathogen interactions, including the reconstruction of multiscale mechanistic models and the connection of host and pathogen models. Finally, we outline studies on resistance durability through the robustness of immune system networks, the identification of trade-offs between immunity and growth and in silico plant-pathogen co-evolution as exciting perspectives in the field. We conclude that the development of sophisticated models of plant diseases incorporating plant, pathogen and climate properties represent a major challenge for agriculture in the future.

What is adaptation by natural selection? Perspectives of an experimental microbiologist

Ever since Darwin, the role of natural selection in shaping the morphological, physiological, and behavioral adaptations of animals and plants across generations has been central to understanding life and its diversity. New discoveries have shown with increasing precision how genetic, molecular, and biochemical processes produce and express those organismal features during an individual's lifetime. When it comes to microorganisms, however, understanding the role of natural selection in producing adaptive solutions has historically been, and sometimes continues to be, contentious. This tension is curious because microbes enable one to observe the power of adaptation by natural selection with exceptional rigor and clarity, as exemplified by the burgeoning field of experimental microbial evolution. I trace the development of this field, describe an experiment with Escherichia coli that has been running for almost 30 years, and highlight other experiments in which natural selection has led to interesting dynamics and adaptive changes in microbial populations.

Beware batch culture: Seasonality and niche construction predicted to favor bacterial adaptive diversification

Metabolic cross-feeding interactions between microbial strains are common in nature, and emerge during evolution experiments in the laboratory, even in homogeneous environments providing a single carbon source. In sympatry, when the environment is well-mixed, the reasons why emerging cross-feeding interactions may sometimes become stable and lead to monophyletic genotypic clusters occupying specific niches, named ecotypes, remain unclear. As an alternative to evolution experiments in the laboratory, we developed Evo²Sim, a multi-scale model of in silico experimental evolution, equipped with the whole tool case of experimental setups, competition assays, phylogenetic analysis, and, most importantly, allowing for evolvable ecological interactions. Digital organisms with an evolvable genome structure encoding an evolvable metabolic network evolved for tens of thousands of generations in environments mimicking the dynamics of real controlled environments, including chemostat or batch culture providing a single limiting resource. We show here that the evolution of stable cross-feeding interactions requires seasonal batch conditions. In this case, adaptive diversification events result in two stably co-existing ecotypes, with one feeding on the primary resource and the other on by-products. We show that the regularity of serial transfers is essential for the maintenance of the polymorphism, as it allows for at least two stable seasons and thus two temporal niches. A first season is externally generated by the transfer into fresh medium, while a second one is internally generated by niche construction as the provided nutrient is replaced by secreted by-products derived from bacterial growth. In chemostat conditions, even if cross-feeding interactions emerge, they are not stable on the long-term because fitter mutants eventually invade the whole population. We also show that the long-term evolution of the two stable ecotypes leads to character displacement, at the level of the metabolic network but also of the genome structure. This difference of genome structure between both ecotypes impacts the stability of the cross-feeding interaction, when the population is propagated in chemostat conditions. This study shows the crucial role played by seasonality in temporal niche partitioning and in promoting cross-feeding subgroups into stable ecotypes, a premise to sympatric speciation.

Stable bacterial cross-feeding interactions, where one strain feeds on the waste of the other, are important to understand, as they can be a first step toward bacterial speciation. Their emergence is commonly observed in laboratory experiments using Escherichia coli as a model organism. Yet it is not clear how cross-feeding interactions can resist the invasion of a fitter mutant when the environment contains a single resource since there seems to be a single ecological niche. Here, we used another kind of “model organism”, a digital one, allowing for detailed and fast investigations, and providing a way to disentangle generic evolutionary mechanisms from specificities associated with E. coli. Digital organisms with evolvable genomes and metabolic networks compete for resources in conditions mimicking laboratory evolution experiments. In chemostat simulations, although cross-feeding interactions regularly emerged, selective sweeps regularly purged the population of its diversity. By contrast, batch culture allowed for much more stable cross-feeding interactions, because it includes seasons and thus distinct temporal niches, thereby favoring the adaptive diversification of proto-species.