Need for Laboratory Ecosystems To Unravel the Structures and Functions of Soil Microbial Communities Mediated by Chemistry

SkinCom, a synthetic skin microbial community, enables reproducible investigations of the human skin microbiome

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes' demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

Linking methanotroph phenotypes to genotypes using a simple spatially resolved model ecosystem

Connecting genes to phenotypic traits in bacteria is often challenging because of a lack of environmental context in laboratory settings. Laboratory-based model ecosystems offer a means to better account for environmental conditions compared with standard planktonic cultures and can help link genotypes and phenotypes. Here, we present a simple, cost-effective, laboratory-based model ecosystem to study aerobic methane-oxidizing bacteria (methanotrophs) within the methane-oxygen counter gradient typically found in the natural environment of these organisms. Culturing the methanotroph Methylomonas sp. strain LW13 in this system resulted in the formation of a distinct horizontal band at the intersection of the counter gradient, which we discovered was not due to increased numbers of bacteria at this location but instead to an increased amount of polysaccharides. We also discovered that different methanotrophic taxa form polysaccharide bands with distinct locations and morphologies when grown in the methane-oxygen counter gradient. By comparing transcriptomic data from LW13 growing within and surrounding this band, we identified genes upregulated within the band and validated their involvement in growth and band formation within the model ecosystem using knockout strains. Notably, deletion of these genes did not negatively affect growth using standard planktonic culturing methods. This work highlights the use of a laboratory-based model ecosystem that more closely mimics the natural environment to uncover bacterial phenotypes missing from standard laboratory conditions, and to link these phenotypes with their genetic determinants.

Application of a quantitative framework to improve the accuracy of a bacterial infection model

Laboratory models are critical to basic and translational microbiology research. Models serve multiple purposes, from providing tractable systems to study cell biology to allowing the investigation of inaccessible clinical and environmental ecosystems. Although there is a recognized need for improved model systems, there is a gap in rational approaches to accomplish this goal. We recently developed a framework for assessing the accuracy of microbial models by quantifying how closely each gene is expressed in the natural environment and in various models. The accuracy of the model is defined as the percentage of genes that are similarly expressed in the natural environment and the model. Here, we leverage this framework to develop and validate two generalizable approaches for improving model accuracy, and as proof of concept, we apply these approaches to improve models of Pseudomonas aeruginosa infecting the cystic fibrosis (CF) lung. First, we identify two models, an in vitro synthetic CF sputum medium model (SCFM2) and an epithelial cell model, that accurately recapitulate different gene sets. By combining these models, we developed the epithelial cell-SCFM2 model which improves the accuracy of over 500 genes. Second, to improve the accuracy of specific genes, we mined publicly available transcriptome data, which identified zinc limitation as a cue present in the CF lung and absent in SCFM2. Induction of zinc limitation in SCFM2 resulted in accurate expression of 90% of P. aeruginosa genes. These approaches provide generalizable, quantitative frameworks for microbiological model improvement that can be applied to any system of interest.

Indirect Enrichment of Desirable, but Less Fit Phenotypes, from a Synthetic Microbial Community Using Microdroplet Confinement

Spatial structure within microbial communities can provide nearly limitless opportunities for social interactions and are an important driver for evolution. As metabolites are often molecular signals, metabolite diffusion within microbial communities can affect the composition and dynamics of the community in a manner that can be challenging to deconstruct. We used encapsulation of a synthetic microbial community within microdroplets to investigate the effects of spatial structure and metabolite diffusion on population dynamics and to examine the effects of cheating by one member of the community. The synthetic community was composed of three strains: a "Producer" that makes the diffusible quorum sensing molecule (N-(3-oxododecanoyl)-l-homoserine lactone, C12-oxo-HSL) or AHL; a "Receiver" that is killed by AHL; and a Non-Producer or "cheater" that benefits from the extinction of the Receivers, but without the costs associated with the AHL synthesis. We demonstrate that despite rapid diffusion of AHL between microdroplets, the spatial structure imposed by the microdroplets allows a more efficient but transient enrichment of more rare and slower-growing Producer subpopulations. Eventually, the Non-Producer population drove the Producers to extinction. By including fluorescence-activated microdroplet sorting and providing sustained competition by the Receiver strain, we demonstrate a strategy for indirect enrichment of a rare and unlabeled Producer. The ability to screen and enrich metabolite Producers from a much larger population under conditions of rapid diffusion provides an important framework for the development of applications in synthetic ecology and biotechnology.

Indirect enrichment of desirable, but less fit phenotypes, from a synthetic microbial community using microdroplet confinement

Spatial structure within microbial communities can provide nearly limitless opportunities for social interactions and are an important driver for evolution. As metabolites are often molecular signals, metabolite diffusion within microbial communities can affect the composition and dynamics of the community in a manner that can be challenging to deconstruct. We used encapsulation of a synthetic microbial community within microdroplets to investigate the effects of spatial structure and metabolite diffusion on population dynamics and to examine the effects of cheating by one member of the community. The synthetic community was comprised of three strains: a 'Producer' that makes the diffusible quorum sensing molecule ( N -(3-Oxododecanoyl)-L-homoserine lactone, C12-oxo-HSL) or AHL; a 'Receiver' that is killed by AHL and a Non-Producer or 'cheater' that benefits from the extinction of the Receivers, but without the costs associated with the AHL synthesis. We demonstrate that despite rapid diffusion of AHL between microdroplets, the spatial structure imposed by the microdroplets allow a more efficient but transient enrichment of more rare and slower growing 'Producer' subpopulations. Eventually, the Non-Producer population drove the Producers to extinction. By including fluorescence-activated microdroplet sorting and providing sustained competition by the Receiver strain, we demonstrate a strategy for indirect enrichment of a rare and unlabeled Producer. The ability to screen and enrich metabolite Producers from a much larger population under conditions of rapid diffusion provides an important framework for the development of applications in synthetic ecology and biotechnology.

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Artificial Soils Reveal Individual Factor Controls on Microbial Processes

Soil matrix properties influence microbial behaviors that underlie nutrient cycling, greenhouse gas production, and soil formation. However, the dynamic and heterogeneous nature of soils makes it challenging to untangle the effects of different matrix properties on microbial behaviors. To address this challenge, we developed a tunable artificial soil recipe and used these materials to study the abiotic mechanisms driving soil microbial growth and communication. When we used standardized matrices with varying textures to culture gas-reporting biosensors, we found that a Gram-negative bacterium (Escherichia coli) grew best in synthetic silt soils, remaining active over a wide range of soil matric potentials, while a Gram-positive bacterium (Bacillus subtilis) preferred sandy soils, sporulating at low water potentials. Soil texture, mineralogy, and alkalinity all attenuated the bioavailability of an acyl-homoserine lactone (AHL) signaling molecule that controls community-level microbial behaviors. Texture controlled the timing of AHL sensing, while AHL bioavailability was decreased ~10⁵-fold by mineralogy and ~10³-fold by alkalinity. Finally, we built artificial soils with a range of complexities that converge on the properties of one Mollisol. As artificial soil complexity increased to more closely resemble the Mollisol, microbial behaviors approached those occurring in the natural soil, with the notable exception of organic matter. IMPORTANCE Understanding environmental controls on soil microbes is difficult because many abiotic parameters vary simultaneously and uncontrollably when different natural soils are compared, preventing mechanistic determination of any individual soil parameter's effect on microbial behaviors. We describe how soil texture, mineralogy, pH, and organic matter content can be varied individually within artificial soils to study their effects on soil microbes. Using microbial biosensors that report by producing a rare indicator gas, we identify soil properties that control microbial growth and attenuate the bioavailability of a diffusible chemical used to control community-level behaviors. We find that artificial soils differentially affect signal bioavailability and the growth of Gram-negative (Escherichia coli) and Gram-positive (Bacillus subtilis) microbes. These artificial soils are useful for studying the mechanisms that underlie soil controls on microbial fitness, signaling, and gene transfer.

Construction of Environmental Synthetic Microbial Consortia: Based on Engineering and Ecological Principles

In synthetic biology, engineering principles are applied to system design. The development of synthetic microbial consortia represents the intersection of synthetic biology and microbiology. Synthetic community systems are constructed by co-cultivating two or more microorganisms under certain environmental conditions, with broad applications in many fields including ecological restoration and ecological theory. Synthetic microbial consortia tend to have high biological processing efficiencies, because the division of labor reduces the metabolic burden of individual members. In this review, we focus on the environmental applications of synthetic microbial consortia. Although there are many strategies for the construction of synthetic microbial consortia, we mainly introduce the most widely used construction principles based on cross-feeding. Additionally, we propose methods for constructing synthetic microbial consortia based on traits and spatial structure from the perspective of ecology to provide a basis for future work.

Microfabrication of a Chamber for High-Resolution, In Situ Imaging of the Whole Root for Plant-Microbe Interactions

Fabricated ecosystems (EcoFABs) offer an innovative approach to in situ examination of microbial establishment patterns around plant roots using nondestructive, high-resolution microscopy. Previously high-resolution imaging was challenging because the roots were not constrained to a fixed distance from the objective. Here, we describe a new 'Imaging EcoFAB' and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period. The device is capable of investigating root-microbe interactions of multimember communities. We examined nine strains of Pseudomonas simiae with different fluorescent constructs to B. distachyon and individual cells on root hairs were visible. Succession in the rhizosphere using two different strains of P. simiae was examined, where the second addition was shown to be able to establish in the root tissue. The device was suitable for imaging with different solid media at high magnification, allowing for the imaging of fungal establishment in the rhizosphere. Overall, the Imaging EcoFAB could improve our ability to investigate the spatiotemporal dynamics of the rhizosphere, including studies of fluorescently-tagged, multimember, synthetic communities.

MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes

Environmental and host-associated microbial communities are complex ecosystems, of which many members are still unknown. Hence, it is challenging to study community dynamics and important to create model systems of reduced complexity that mimic major community functions. Therefore, we developed MiMiC, a computational approach for data-driven design of simplified communities from shotgun metagenomes. We first built a comprehensive database of species-level bacterial and archaeal genomes (n = 22 627) consisting of binary (presence/absence) vectors of protein families (Pfam = 17 929). MiMiC predicts the composition of minimal consortia using an iterative scoring system based on maximal match-to-mismatch ratios between this database and the Pfam binary vector of any input metagenome. Pfam vectorization retained enough resolution to distinguish metagenomic profiles between six environmental and host-derived microbial communities (n = 937). The calculated number of species per minimal community ranged between 5 and 11, with MiMiC selected communities better recapitulating the functional repertoire of the original samples than randomly selected species. The inferred minimal communities retained habitat-specific features and were substantially different from communities consisting of most abundant members. The use of a mixture of known microbes revealed the ability to select 23 of 25 target species from the entire genome database. MiMiC is open source and available at https://github.com/ClavelLab/MiMiC.

Specialized Plant Growth Chamber Designs to Study Complex Rhizosphere Interactions

The rhizosphere is a dynamic ecosystem shaped by complex interactions between plant roots, soil, microbial communities and other micro- and macro-fauna. Although studied for decades, critical gaps exist in the study of plant roots, the rhizosphere microbiome and the soil system surrounding roots, partly due to the challenges associated with measuring and parsing these spatiotemporal interactions in complex heterogeneous systems such as soil. To overcome the challenges associated with in situ study of rhizosphere interactions, specialized plant growth chamber systems have been developed that mimic the natural growth environment. This review discusses the currently available lab-based systems ranging from widely known rhizotrons to other emerging devices designed to allow continuous monitoring and non-destructive sampling of the rhizosphere ecosystems in real-time throughout the developmental stages of a plant. We categorize them based on the major rhizosphere processes it addresses and identify their unique challenges as well as advantages. We find that while some design elements are shared among different systems (e.g., size exclusion membranes), most of the systems are bespoke and speaks to the intricacies and specialization involved in unraveling the details of rhizosphere processes. We also discuss what we describe as the next generation of growth chamber employing the latest technology as well as the current barriers they face. We conclude with a perspective on the current knowledge gaps in the rhizosphere which can be filled by innovative chamber designs.

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

Complex yeast-bacteria interactions affect the yield of industrial ethanol fermentation

Sugarcane ethanol fermentation represents a simple microbial community dominated by S. cerevisiae and co-occurring bacteria with a clearly defined functionality. In this study, we dissect the microbial interactions in sugarcane ethanol fermentation by combinatorically reconstituting every possible combination of species, comprising approximately 80% of the biodiversity in terms of relative abundance. Functional landscape analysis shows that higher-order interactions counterbalance the negative effect of pairwise interactions on ethanol yield. In addition, we find that Lactobacillus amylovorus improves the yeast growth rate and ethanol yield by cross-feeding acetaldehyde, as shown by flux balance analysis and laboratory experiments. Our results suggest that Lactobacillus amylovorus could be considered a beneficial bacterium with the potential to improve sugarcane ethanol fermentation yields by almost 3%. These data highlight the biotechnological importance of comprehensively studying microbial communities and could be extended to other microbial systems with relevance to human health and the environment.

The sum is greater than the parts: exploiting microbial communities to achieve complex functions

Multi-species microbial communities are ubiquitous in nature. The widespread prevalence of these communities is due to highly elaborated interactions among their members thereby accomplishing metabolic functions that are unattainable by individual members alone. Harnessing these communal capabilities is an emerging field in biotechnology. The rational intervention of microbial communities for the purpose of improved function has been facilitated in part by developments in multi-omics approaches, synthetic biology, and computational methods. Recent studies have demonstrated the benefits of rational interventions to human and animal health as well as agricultural productivity. Emergent technologies, such as in situ modification of complex microbial community and community metabolic modeling, represent an avenue to engineer sustainable microbial communities. In this opinion, we review relevant computational and experimental approaches to study and engineer microbial communities and discuss their potential for biotechnological applications.

Introducing the Mangrove Microbiome Initiative: Identifying Microbial Research Priorities and Approaches To Better Understand, Protect, and Rehabilitate Mangrove Ecosystems

Mangrove ecosystems provide important ecological benefits and ecosystem services, including carbon storage and coastline stabilization, but they also suffer great anthropogenic pressures. Microorganisms associated with mangrove sediments and the rhizosphere play key roles in this ecosystem and make essential contributions to its productivity and carbon budget. Understanding this nexus and moving from descriptive studies of microbial taxonomy to hypothesis-driven field and lab studies will facilitate a mechanistic understanding of mangrove ecosystem interaction webs and open opportunities for microorganism-mediated approaches to mangrove protection and rehabilitation.

Mangrove ecosystems provide important ecological benefits and ecosystem services, including carbon storage and coastline stabilization, but they also suffer great anthropogenic pressures. Microorganisms associated with mangrove sediments and the rhizosphere play key roles in this ecosystem and make essential contributions to its productivity and carbon budget. Understanding this nexus and moving from descriptive studies of microbial taxonomy to hypothesis-driven field and lab studies will facilitate a mechanistic understanding of mangrove ecosystem interaction webs and open opportunities for microorganism-mediated approaches to mangrove protection and rehabilitation. Such an effort calls for a multidisciplinary and collaborative approach, involving chemists, ecologists, evolutionary biologists, microbiologists, oceanographers, plant scientists, conservation biologists, and stakeholders, and it requires standardized methods to support reproducible experiments. Here, we outline the Mangrove Microbiome Initiative, which is focused around three urgent priorities and three approaches for advancing mangrove microbiome research.

La milpa como modelo para el estudio de la microbiodiversidad e interacciones planta-bacteria

La microbiología agrícola busca reemplazar a los agroquímicos por microorganismos o sus productos como agentes de control biológico, debido a que el uso de tecnologías de la revolución verde tiene efectos negativos sobre el ambiente, los productores y sus familias, los consumidores y la salud de los cultivos. Sin embargo, el conocimiento actual acerca de las interacciones benéficas planta-bacteria en ambientes complejos es limitado e insuficiente, para lograr el éxito esperado de los productos biológicos. Las milpas son agroecosistemas tradicionales donde se cultivan diversas variedades de maíz nativo con otras especies asociadas; no se utiliza riego, ni labranza y aunque su aplicación va en aumento, comúnmente no se utilizan agroquímicos; por esto, la milpa representa una fuente de conocimiento sobre prácticas sustentables. Recientemente, se han descrito cambios en las comunidades microbianas de los sistemas agrícolas a causa de la modernización y a la domesticación de las plantas. En la milpa, también se han identificado interacciones benéficas planta-bacteria que parecen haberse perdido en los cultivos modernos. En esta revisión, discutimos las estrategias clásicas y modernas de la microbiología agrícola que pueden ser aplicadas en el estudio de la milpa. El establecimiento de la milpa como modelo de estudio de las interacciones planta-bacteria puede resultar en la generación del conocimiento necesario para disminuir el uso de agroquímicos en los sistemas agrícolas modernos, así como evitar su creciente uso en las milpas.

Fitness and Productivity Increase with Ecotypic Diversity among Escherichia coli Strains That Coevolved in a Simple, Constant Environment

The productivity of a biological community often correlates with its diversity. In the microbial world this phenomenon can sometimes be explained by positive, density-dependent interactions such as cross-feeding and syntrophy. These metabolic interactions help account for the astonishing variety of microbial life and drive many of the biogeochemical cycles without which life as we know it could not exist. While it is difficult to recapitulate experimentally how these interactions evolved among multiple taxa, we can explore in the laboratory how they arise within one. These experiments provide insight into how different bacterial ecotypes evolve and from these, possibly new "species." We have previously shown that in a simple, constant environment a single clone of Escherichia coli can give rise to a consortium of genetically and phenotypically differentiated strains, in effect, a set of ecotypes, that coexist by cross-feeding. We marked these different ecotypes and their shared ancestor by integrating fluorescent protein into their genomes and then used flow cytometry to show that each evolved strain is more fit than the shared ancestor, that pairs of evolved strains are fitter still, and that the entire consortium is the fittest of all. We further demonstrate that the rank order of fitness values agrees with estimates of yield, indicating that an experimentally evolved consortium more efficiently converts primary and secondary resources to offspring than its ancestor or any member acting in isolation.IMPORTANCE Polymicrobial consortia occur in both environmental and clinical settings. In many cases, diversity and productivity correlate in these consortia, especially when sustained by positive, density-dependent interactions. However, the evolutionary history of such entities is typically obscure, making it difficult to establish the relative fitness of consortium partners and to use those data to illuminate the diversity-productivity relationship. Here, we dissect an Escherichia coli consortium that evolved under continuous glucose limitation in the laboratory from a single common ancestor. We show that a partnership consisting of cross-feeding ecotypes is better able to secure primary and secondary resources and to convert those resources to offspring than the ancestral clone. Such interactions may be a prelude to a special form of syntrophy and are likely determinants of microbial community structure in nature, including those having clinical significance such as chronic infections.

Common principles and best practices for engineering microbiomes

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.

Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities

Phototrophic communities of photosynthetic algae or cyanobacteria and heterotrophic bacteria or fungi are pervasive throughout the environment¹. How interactions between members contribute to the resilience and affect the fitness of phototrophic communities is not fully understood^2,3. Here, we integrated metatranscriptomics, metabolomics and phenotyping with computational modelling to reveal condition-dependent secretion and cross-feeding of metabolites in a synthetic community. We discovered that interactions between members are highly dynamic and are driven by the availability of organic and inorganic nutrients. Environmental factors, such as ammonia concentration, influenced community stability by shifting members from collaborating to competing. Furthermore, overall fitness was dependent on genotype and streamlined genomes improved growth of the entire community. Our mechanistic framework provides insights into the physiology and metabolic response to environmental and genetic perturbation of these ubiquitous microbial associations.

Modelling approaches for studying the microbiome

Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health.

EcoFABs: advancing microbiome science through standardized fabricated ecosystems

Microbiomes play critical roles in ecosystems and human health, yet in most cases scientists lack standardized and reproducible model microbial communities. The development of fabricated microbial ecosystems, which we term EcoFABs, will provide such model systems for microbiome studies.

Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior

The manipulation and engineering of microbiomes could lead to improved human health, environmental sustainability, and agricultural productivity. However, microbiomes have proven difficult to alter in predictable ways, and their emergent properties are poorly understood. The history of biology has demonstrated the power of model systems to understand complex problems such as gene expression or development. Therefore, a defined and genetically tractable model community would be useful to dissect microbiome assembly, maintenance, and processes. We have developed a tractable model rhizosphere microbiome, designated THOR, containing Pseudomonas koreensis, Flavobacterium johnsoniae, and Bacillus cereus, which represent three dominant phyla in the rhizosphere, as well as in soil and the mammalian gut. The model community demonstrates emergent properties, and the members are amenable to genetic dissection. We propose that THOR will be a useful model for investigations of community-level interactions.

The quest to manipulate microbiomes has intensified, but many microbial communities have proven to be recalcitrant to sustained change. Developing model communities amenable to genetic dissection will underpin successful strategies for shaping microbiomes by advancing an understanding of community interactions. We developed a model community with representatives from three dominant rhizosphere taxa, the Firmicutes, Proteobacteria, and Bacteroidetes. We chose Bacillus cereus as a model rhizosphere firmicute and characterized 20 other candidates, including “hitchhikers” that coisolated with B. cereus from the rhizosphere. Pairwise analysis produced a hierarchical interstrain-competition network. We chose two hitchhikers, Pseudomonas koreensis from the top tier of the competition network and Flavobacterium johnsoniae from the bottom of the network, to represent the Proteobacteria and Bacteroidetes, respectively. The model community has several emergent properties, induction of dendritic expansion of B. cereus colonies by either of the other members, and production of more robust biofilms by the three members together than individually. Moreover, P. koreensis produces a novel family of alkaloid antibiotics that inhibit growth of F. johnsoniae, and production is inhibited by B. cereus. We designate this community THOR, because the members are the hitchhikers of the rhizosphere. The genetic, genomic, and biochemical tools available for dissection of THOR provide the means to achieve a new level of understanding of microbial community behavior.

Metabolic niches in the rhizosphere microbiome: new tools and approaches to analyse metabolic mechanisms of plant-microbe nutrient exchange

Plants nourish rhizospheric microbes via provision of carbon substrates, and the composition of the microbiome is strongly influenced by metabolic phenomena such as niche differentiation, competitive exclusion, and cross-feeding. Despite intensive investigations of the taxonomic structure in root microbiomes, there is relatively little biochemical knowledge of the metabolic niches occupied by microbial strains in the rhizosphere. Here, we review new tools and approaches that are boosting our knowledge of the metabolic mechanisms that shape the composition of the root microbiome. New studies have elucidated biochemical pathways that mediate root colonisation and pathogen suppression, and synthetic communities are emerging as a powerful tool to understand microbe-microbe interactions. Knowledge of root exudate composition is being advanced by new metabolomics methodologies, which have highlighted that specific exudate components can inhibit pathogen growth, and that certain metabolites can recruit mutualistic strains according to substrate uptake preferences. Microbial genomics is rapidly advancing, with large collections of isolated rhizosphere strains and mutant libraries giving new insights into the metabolic mechanisms of root colonisation. Exometabolomics is emerging as a powerful methodology for directly observing microbial uptake of root metabolites, and also for profiling microbial cross-feeding. Integrative studies using these resources should enable rapid advances, particularly when applied to standardised experimental set-ups and model synthetic communities.

Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities

Microbiomes impact nearly every environment on Earth by modulating the molecular composition of the environment. Temporally changing environmental stimuli and spatial organization are major variables shaping the structure and function of microbiomes. The web of interactions among members of these communities and between the organisms and the environment dictates microbiome functions. Microbial interactions are major drivers of microbiomes and are modulated by spatiotemporal parameters. A mechanistic and quantitative understanding of ecological, molecular, and environmental forces shaping microbiomes could inform strategies to control microbiome dynamics and functions. Major challenges for harnessing the potential of microbiomes for diverse applications include the development of predictive modeling frameworks and tools for precise manipulation of microbiome behaviors.