Evolution is often deemed irreversible. The evolution of complex traits that require many mutations makes their reversal unlikely. Even in simpler traits, reversals might become less likely as neutral or beneficial mutations, with deleterious effects in the ancestral context, become fixed in the novel background. This is especially true in changes that involve large reorganizations of the organism and its interactions with the environment. The evolution of multicellularity involves the reorganization of previously autonomous cells into a more complex organism; despite the complexity of this change, single cells have repeatedly evolved from multicellular ancestors. These repeated reversals to unicellularity undermine the generality of Dollo's law. In this article, we evaluated the dynamics of reversals to unicellularity from recently evolved multicellular phenotypes of the brewers yeast Saccharomyces cerevisae. Even though multicellularity in this system evolved recently, it involves the evolution of new levels of selection. Strong selective pressures against multicellularity lead to rapid reversibility to single cells in all of our replicate lines, whereas counterselection favoring multicellularity led to minimal reductions to the rates of reversal. History and chance played an important role in the tempo and mode of reversibility, highlighting the interplay of deterministic and stochastic events in evolutionary reversals.

Recent advances in robotics and affordable genomic sequencing technologies have made it possible to establish and quantitatively track the assembly of enrichment communities in high-throughput. By conducting community assembly experiments in up to thousands of synthetic habitats, where the extrinsic sources of variation among replicates can be controlled, we can now study the reproducibility and predictability of microbial community assembly at different levels of organization, and its relationship with nutrient composition and other ecological drivers. Through a dialog with mathematical models, high-throughput enrichment communities are bringing us closer to the goal of developing a quantitative predictive theory of microbial community assembly. In this short review, we present an overview of recent research on this growing field, highlighting the connection between theory and experiments and suggesting directions for future work.

The metabolic capabilities of microbes are the basis for many major biotechnological advances, exploiting microbial diversity by selection or engineering of single strains. However, there are limits to the advances that can be achieved with single strains, and attention has turned toward the metabolic potential of consortia and the field of synthetic ecology. The main challenge for the synthetic ecology is that consortia are frequently unstable, largely because evolution by constituent members affects their interactions, which are the basis of collective metabolic functionality. Current practices in modeling consortia largely consider interactions as fixed circuits of chemical reactions, which greatly increases their tractability. This simplification comes at the cost of essential biological realism, stripping out the ecological context in which the metabolic actions occur and the potential for evolutionary change. In other words, evolutionary stability is not engineered into the system. This realization highlights the necessity to better identify the key components that influence the stable coexistence of microorganisms. Inclusion of ecological and evolutionary principles, in addition to biophysical variables and stoichiometric modeling of metabolism, is critical for microbial consortia design. This review aims to bring ecological and evolutionary concepts to the discussion on the stability of microbial consortia. In particular, we focus on the combined effect of spatial structure (connectivity of molecules and cells within the system) and ecological interactions (reciprocal and non-reciprocal) on the persistence of microbial consortia. We discuss exemplary cases to illustrate these ideas from published studies in evolutionary biology and biotechnology. We conclude by making clear the relevance of incorporating evolutionary and ecological principles to the design of microbial consortia, as a way of achieving evolutionarily stable and sustainable systems.

Microorganisms display a stunning metabolic diversity. Understanding the origin of this diversity requires understanding how macroevolutionary processes such as innovation and diversification play out in the microbial world. Metabolic networks, which govern microbial resource use, can evolve through different mechanisms, e.g., horizontal gene transfer or de novo evolution of enzymes and pathways. This process is governed by a combination of environmental factors, selective pressures, and the constraints imposed by the genetic architecture of metabolic networks. In addition, many independent results hint that the process of niche construction, by which organisms actively modify their own and each other's niches and selective pressures, could play a major role in microbial innovation and diversification. Yet, the general principles by which niche construction shapes microbial macroevolutionary patterns remain largely unexplored. Here, we discuss several new hypotheses and directions, and suggest metabolic modeling methods that could allow us to explore large-scale empirical genotype-phenotype-(G-P)-environment spaces in order to study the macroevolutionary effects of niche construction. We hope that this short piece will further stimulate a systematic and quantitative characterization of macroevolutionary patterns and processes in microbial metabolism.

Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype-phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure-function landscape and propose practical guidelines for navigating these ecological landscapes.

Polyploidy is a key driver of ecological and evolutionary processes in plants, yet little is known about its effects on biotic interactions. This gap in knowledge is especially profound for nutrient acquisition mutualisms, despite the fact that they regulate global nutrient cycles and structure ecosystems. Generalism in mutualistic interactions depends on the range of potential partners (niche breadth), the benefits obtained and ability to maintain benefits across a variety of partners (fitness plasticity). Here, we determine how each of these is influenced by polyploidy in the legume-rhizobium mutualism. We inoculated a broad geographic sample of natural diploid and autotetraploid alfalfa (Medicago sativa) lineages with a diverse panel of Sinorhizobium bacterial symbionts. To analyze the extent and mechanism of generalism, we measured host growth benefits and functional traits. Autotetraploid plants obtained greater fitness enhancement from mutualistic interactions and were better able to maintain this across diverse rhizobial partners (i.e. low plasticity in fitness) relative to diploids. These benefits were not attributed to increases in niche breadth, but instead reflect increased rewards from investment in the mutualism. Polyploid plants displayed greater generalization in bacterial mutualisms relative to diploids, illustrating another axis of advantage for polyploids over diploids.

The timing of life history events has important fitness consequences. Since the 1950s, researchers have combined first principles and data to predict the optimal timing of life history transitions. Recently, a striking mystery has emerged. Such transitions can be shaped by a completely different branch of the tree of life: species in the microbiome. Probing these interactions using testable predictions from evolutionary theory could illuminate whether and how host-microbiome integrated life histories can evolve and be maintained. Beyond advancing fundamental science, this research program could yield important applications. In an age of microbiome engineering, understanding the contexts that lead to microbiota signaling shaping ontogeny could offer novel mechanisms for manipulations to increase yield in agriculture by manipulating plant responses to stressful environments, or to reduce pathogen transmission by affecting vector efficiency. We combine theory and evidence to illuminate the essential questions underlying the existence of microbiome-dependent ontogenetic timing (MiDOT) to fuel research on this emerging topic.

The microbiome of flowers (anthosphere) is an understudied compartment of the plant microbiome. Within the flower, petals represent a heterogeneous environment for microbes in terms of resources and environmental stress. Yet, little is known of drivers of structure and function of the epiphytic microbial community at the within-petal scale. We characterized the petal microbiome in two co-flowering plants that differ in the pattern of ultraviolet (UV) absorption along their petals. Bacterial communities were similar between plant hosts, with only rare phylogenetically distant species contributing to differences. The epiphyte community was highly culturable (75% of families) lending confidence in the spatially explicit isolation and characterization of bacteria. In one host, petals were heterogeneous in UV absorption along their length, and in these, there was a negative relationship between growth rate and position on the petal, as well as lower UV tolerance in strains isolated from the UV-absorbing base than from UV reflecting tip. A similar pattern was not seen in microbes isolated from a second host whose petals had uniform patterning along their length. Across strains, the variation in carbon usage and chemical tolerance followed common phylogenetic patterns. This work highlights the value of petals for spatially explicit explorations of bacteria of the anthosphere.

The timing of life history events has important fitness consequences. Since the 1950s, researchers have combined first principles and data to predict the optimal timing of life history transitions. Recently, a striking mystery has emerged. Such transitions can be shaped by a completely different branch of the tree of life: species in the microbiome. Probing these interactions using testable predictions from evolutionary theory could illuminate whether and how host-microbiome integrated life histories can evolve and be maintained. Beyond advancing fundamental science, this research program could yield important applications. In an age of microbiome engineering, understanding the contexts that lead to microbiota signaling shaping ontogeny could offer novel mechanisms for manipulations to increase yield in agriculture by manipulating plant responses to stressful environments, or to reduce pathogen transmission by affecting vector efficiency. We combine theory and evidence to illuminate the essential questions underlying the existence of microbiome-dependent ontogenetic timing (MiDOT) to fuel research on this emerging topic.

For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly.