Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.1 .
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. Expected final online publication date for the Annual Review of Biophysics, Volume 50 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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.
To develop a quantitative theory that can predict how microbiomes assemble, and how they respond to perturbations, we must identify which descriptive features of microbial communities are reproducible and predictable, which are unpredictable, and why. The emergent metagenomic structure of communities is often quantitatively convergent in similar habitats, with highly similar fractions of the metagenome being devoted to the same metabolic pathways. By contrast, the species-level taxonomic composition is often highly variable even in replicate environments. The mechanisms behind these patterns are not yet understood. By studying the self-assembly of hundreds of communities in replicate, synthetic habitats, we show that the reproducibility of microbial community assembly reflects an emergent metabolic structure, which is quantitatively predictable from first-principles, genome-scale metabolic models. Taxonomic variability within functional groups arises through multistability in population dynamics, and the species-level community composition is predictably governed by the mutual competitive exclusion of two sub-dominant strains. Our findings provide a mechanistic bridge between microbial community structure at different levels of organization, and show that the evolutionary conservation of metabolic traits, both in terms of growth responses and niches constructed, can be leveraged to quantitatively predict the taxonomic and metabolic structure of microbial communities.
Dispersal is central to the ecology and evolution of spatially structured communities. While flower microbial communities are spatially structured among floral organs, how dispersal vectors distribute microbes among floral organs is unknown. Pollinators are recognized as key microbial vectors, but effects of their different foraging behaviors on transfer dynamics among flowers or different floral organs are not known. We asked how foraging behaviors of a model pollinator (Bombus impatiens) affect acquisition and dispersal of microbes among flower organs. We used monkeyflowers (Mimulus guttatus) to examine dispersal within a natural context and artificial flowers to test how common bee foraging behaviors (nectaring, buzzing, or scrabbling) shaped dispersal of a green fluorescent protein‐labeled bacteria, Pseudomonas fluorescens. Bees acquired 1% of a flower's microbes and dispersed 31% of acquired microbes to the next flower. All bees acquired microbes, and 85% and 76% of bees dispersed microbes to live and artificial flowers, respectively. Microbes acquired from the corolla were mainly deposited on the corolla, followed by the stamens, and least on the nectary/pistil. Bee foraging behavior affected acquisition, with scrabbling for pollen resulting in 23% more microbes acquired than nectaring, and with buzzing for pollen resulting in a 79% slower rate of microbial acquisition relative to scrabbling. Bee foraging behavior also affected deposition but depended on the floral organ: Scrabbling and buzzing for pollen led to greater deposition than nectaring for corolla and stamen but not nectary. Our results have implications for transmission of beneficial and pathogenic microbes among plants and pollinators, and thus the ecology and evolution of floral microbial communities.
Summary The flower is the hallmark of angiosperms and its evolution is key to their diversification. As knowledge of ecological interactions between flowers and their microbial communities (the anthosphere) expands, it becomes increasingly important to consider the evolutionary impacts of these associations and their potential eco‐evolutionary dynamics. In this Viewpoint we synthesize current knowledge of the anthosphere within a multilevel selection framework and illustrate the potential for the extended floral phenotype (the phenotype expressed from the genes of the plant and its associated flower microbes) to evolve. We argue that flower microbes are an important, but understudied, axis of variation that shape floral trait evolution and angiosperm reproductive ecology. We highlight knowledge gaps and discuss approaches that are critical for gaining a deeper understanding of the role microbes play in mediating plant reproduction, ecology, and evolution.
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