Strong inter-population cooperation leads to partner intermixing in microbial communities

Integrative analysis of yeast colony growth

Yeast colonies are routinely grown on agar plates in everyday experimental settings to understand basic molecular processes, produce novel drugs, improve health, and so on. Standardized conditions ensure these colonies grow in a reproducible fashion, while in nature microbes are under a constantly changing environment. Here we combine the power of computational simulations and laboratory experiments to investigate the impact of non-standard environmental factors on colony growth. We present the developement and parameterization of a quantitative agent-based model for yeast colony growth to reproduce measurements on colony size and cell number in a colony at non-standard environmental conditions. Specifically, we establish experimental conditions that mimic the effects of humidity changes and nutrient gradients. Our results show how colony growth is affected by moisture changes, nutrient availability, and initial colony inoculation conditions. We show that initial colony spread, not initial cell number have higher impact on the final size and cell number of colonies. Parameters of the model were identified by fitting these experiments and the fitted model gives guidance to establish conditions which enable unlimited growth of yeast colonies.

Cooperation between autotrophic and heterotrophic denitrifiers under low C/N ratios revealed by individual-based modelling

Denitrifying biofilms, in which autotrophic denitrifiers (AD) and heterotrophic denitrifiers (HD) coexist, play a crucial role in removing nitrate from water or wastewater. However, it is difficult to elucidate the interactions between HD and AD through sequencing-based experimental methods. Here, we developed an individual-based model to describe the interspecies dynamics and priority effects between sulfur-based AD (Thiobacillus denitrificans) and HD (Thauera phenylcarboxya) under different C/N ratios. In test I (coexistence simulation), AD and HD were initially inoculated at a ratio of 1:1. The simulation results showed excellent denitrification performance and a coaggregation pattern of denitrifiers, indicating that cooperation was the predominant interaction at a C/N ratio of 0.25 to 1.5. In test II (invasion simulation), in which only one type of denitrifier was initially inoculated and the other was added at the invasion time, denitrifiers exhibited a stratification pattern in biofilms. When HD invaded AD, the final HD abundance decreased with increasing invasion time, indicating an enhanced priority effect. When AD invaded HD, insufficient organic carbon sources weakened the priority effect by limiting the growth of HD populations. This study reveals the interaction between autotrophic and heterotrophic denitrifiers, providing guidance for optimizing wastewater treatment process.

The effect of cooperator recognition on competition among clones in spatially structured microbial communities

In spatially structured microbial communities, clonal growth of stationary cells passively generates clusters of related individuals. This can lead to stable cooperation without the need for recognition mechanisms. However, recent research suggests that some biofilm-forming microbes may have mechanisms of kin recognition. To explore this unexpected observation, we studied the effects of different types of cooperation in a microbial colony using spatially explicit, agent-based simulations of two interacting strains. We found scenarios that favor a form of kin recognition in spatially structured microbial communities. In the presence of a "cheater" strain, a strain with greenbeard cooperation was able to increase in frequency more than a strain with obligate cooperation. This effect was most noticeable in high density colonies and when the cooperators were not as abundant as the cheaters. We also studied whether a polychromatic greenbeard, in which cells only cooperate with their own type, could provide a numerical benefit beyond a simple, binary greenbeard. We found the greatest benefit to a polychromatic greenbeard when cooperation is highly effective. These results suggest that in some ecological scenarios, recognition mechanisms may be beneficial even in spatially structured communities.

Cross-feeding promotes heterogeneity within yeast cell populations

Cellular heterogeneity in cell populations of isogenic origin is driven by intrinsic factors such as stochastic gene expression, as well as external factors like nutrient availability and interactions with neighbouring cells. Heterogeneity promotes population fitness and thus has important implications in antimicrobial and anticancer treatments, where stress tolerance plays a significant role. Here, we study plasmid retention dynamics within a population of plasmid-complemented ura3∆0 yeast cells, and show that the exchange of complementary metabolites between plasmid-carrying prototrophs and plasmid-free auxotrophs allows the latter to survive and proliferate in selective environments. This process also affects plasmid copy number in plasmid-carrying prototrophs, further promoting cellular functional heterogeneity. Finally, we show that targeted genetic engineering can be used to suppress cross-feeding and reduce the frequency of plasmid-free auxotrophs, or to exploit it for intentional population diversification and division of labour in co-culture systems.

Partner-assisted artificial selection of a secondary function for efficient bioremediation

Microbial enzymes can address diverse challenges such as degradation of toxins. However, if the function of interest does not confer a sufficient fitness effect on the producer, the enzymatic function cannot be improved in the host cells by a conventional selection scheme. To overcome this limitation, we propose an alternative scheme, termed "partner-assisted artificial selection" (PAAS), wherein the population of enzyme producers is assisted by function-dependent feedback from an accessory population. Simulations investigating the efficiency of toxin degradation reveal that this strategy supports selection of improved degradation performance, which is robust to stochasticity in the model parameters. We observe that conventional considerations still apply in PAAS: more restrictive bottlenecks lead to stronger selection but add uncertainty. Overall, we offer a guideline for successful implementation of PAAS and highlight its potentials and limitations.

Evaporation-induced hydrodynamics control plasmid transfer during surface-associated microbial growth

Droplet evaporation is a general process in unsaturated environments that results in micro-scale hydrodynamic flows which in turn determine the spatial distributions of microbial cells across surfaces. These spatial distributions can have significant effects on the development and functioning of surface-associated microbial communities, with consequences for important processes such as the spread of plasmids. Here, we experimentally quantified how evaporation-induced hydrodynamic processes modulate the initial deposition patterns of microbial cells (via the coffee ring effect and Marangoni convection) and how these patterns control the spread of an antibiotic resistance-encoding plasmid during surface-associated growth. We found that plasmid spread is a function of the initial density of cells deposited along the droplet periphery, which is a manifestation of the coffee ring effect. Using an individual-based model, we systematically linked how the different initial cell deposition patterns caused by the relative strengths of the coffee ring effect and Marangoni convection determine the extent of plasmid transfer during surface-associated growth. Our study demonstrates that evaporation-induced hydrodynamic processes that are common in nature can alter crucial ecological properties of surface-associated microbial communities and control the proliferation of plasmids, with consequences on the spread of antibiotic resistance and other plasmid-encoded traits.

P. aeruginosa interactions with other microbes in biofilms during co-infection

This review addresses the topic of biofilms, including their development and the interaction between different counterparts. There is evidence that various diseases, such as cystic fibrosis, otitis media, diabetic foot wound infections, and certain cancers, are promoted and aggravated by the presence of polymicrobial biofilms. Biofilms are composed by heterogeneous communities of microorganisms protected by a matrix of polysaccharides. The different types of interactions between microorganisms gives rise to an increased resistance to antimicrobials and to the host's defense mechanisms, with the consequent worsening of disease symptoms. Therefore, infections caused by polymicrobial biofilms affecting different human organs and systems will be discussed, as well as the role of the interactions between the gram-negative bacteria Pseudomonas aeruginosa, which is at the base of major polymicrobial infections, and other bacteria, fungi, and viruses in the establishment of human infections and diseases. Considering that polymicrobial biofilms are key to bacterial pathogenicity, it is fundamental to evaluate which microbes are involved in a certain disease to convey an appropriate and efficacious antimicrobial therapy.

Celebrating 50 years of microbial granulation technologies: From canonical wastewater management to bio-product recovery

Microbial granulation technologies (MGT) in wastewater management are widely practised for more than fifty years. MGT can be considered a fine example of human innovativeness-driven nature wherein the manmade forces applied during operational controls in the biological process of wastewater treatment drive the microbial communities to modify their biofilms into granules. Mankind, over the past half a century, has been refining the knowledge of triggering biofilm into granules with some definite success. This review captures the journey of MGT from inception to maturation providing meaningful insights into the process development of MGT-based wastewater management. The full-scale application of MGT-based wastewater management is discussed with an understanding of functional microbial interactions within the granule. The molecular mechanism of granulation through the secretion of extracellular polymeric substances (EPS) and signal molecules is also highlighted in detail. The recent research interest in the recovery of useful bioproducts from the granular EPS is also emphasized.

Formation and emergent dynamics of spatially organized microbial systems

Spatial organization is the norm rather than the exception in the microbial world. While the study of microbial physiology has been dominated by studies in well-mixed cultures, there is now increasing interest in understanding the role of spatial organization in microbial physiology, coexistence and evolution. Where studied, spatial organization has been shown to influence all three of these aspects. In this mini review and perspective article, we emphasize that the dynamics within spatially organized microbial systems (SOMS) are governed by feedbacks between local physico-chemical conditions, cell physiology and movement, and evolution. These feedbacks can give rise to emergent dynamics, which need to be studied through a combination of spatio-temporal measurements and mathematical models. We highlight the initial formation of SOMS and their emergent dynamics as two open areas of investigation for future studies. These studies will benefit from the development of model systems that can mimic natural ones in terms of species composition and spatial structure.

Impact of direct physical association and motility on fitness of a synthetic interkingdom microbial community

Mutualistic exchange of metabolites can play an important role in microbial communities. Under natural environmental conditions, such exchange may be compromised by the dispersal of metabolites and by the presence of non-cooperating microorganisms. Spatial proximity between members during sessile growth on solid surfaces has been shown to promote stabilization of cross-feeding communities against these challenges. Nonetheless, many natural cross-feeding communities are not sessile but rather pelagic and exist in turbulent aquatic environments, where partner proximity is often achieved via direct cell-cell adhesion, and cooperation occurs between physically associated cells. Partner association in aquatic environments could be further enhanced by motility of individual planktonic microorganisms. In this work, we establish a model bipartite cross-feeding community between bacteria and yeast auxotrophs to investigate the impact of direct adhesion between prokaryotic and eukaryotic partners and of bacterial motility in a stirred mutualistic co-culture. We demonstrate that adhesion can provide fitness benefit to the bacterial partner, likely by enabling local metabolite exchange within co-aggregates, and that it counteracts invasion of the community by a non-cooperating cheater strain. In a turbulent environment and at low cell densities, fitness of the bacterial partner and its competitiveness against a non-cooperating strain are further increased by motility that likely facilitates partner encounters and adhesion. These results suggest that, despite their potential fitness costs, direct adhesion between partners and its enhancement by motility may play key roles as stabilization factors for metabolic communities in turbulent aquatic environments.

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.

Spatial self-organization of metabolism in microbial systems: A matter of enzymes and chemicals

Most bacteria live in dense, spatially structured communities such as biofilms. The high density allows cells to alter the local microenvironment, whereas the limited mobility can cause species to become spatially organized. Together, these factors can spatially organize metabolic processes within microbial communities so that cells in different locations perform different metabolic reactions. The overall metabolic activity of a community depends both on how metabolic reactions are arranged in space and on how they are coupled, i.e., how cells in different regions exchange metabolites. Here, we review mechanisms that lead to the spatial organization of metabolic processes in microbial systems. We discuss factors that determine the length scales over which metabolic activities are arranged in space and highlight how the spatial organization of metabolic processes affects the ecology and evolution of microbial communities. Finally, we define key open questions that we believe should be the main focus of future research.

Assembly of gut-derived bacterial communities follows "early-bird" resource utilization dynamics

Diet can impact host health through changes to the gut microbiota, yet we lack mechanistic understanding linking nutrient availability and microbiota composition. Here, we use thousands of microbial communities cultured in vitro from human feces to uncover simple assembly rules and develop a predictive model of community composition upon addition of single nutrients from central carbon metabolism to a complex medium. Community membership was largely determined by the donor feces, whereas relative abundances were determined by the supplemental carbon source. The absolute abundance of most taxa was independent of the supplementing nutrient, due to the ability of fast-growing organisms to quickly exhaust their niche in the complex medium and then exploit and monopolize the supplemental carbon source. Relative abundances of dominant taxa could be predicted from the nutritional preferences and growth dynamics of species in isolation, and exceptions were consistent with strain-level variation in growth capabilities. Our study reveals that community assembly follows simple rules of nutrient utilization dynamics and provides a predictive framework for manipulating gut commensal communities through nutritional perturbations.

The contrasting roles of nitric oxide drive microbial community organization as a function of oxygen presence

Microbial assemblages are omnipresent in the biosphere, forming communities on the surfaces of roots and rocks and within living tissues. These communities can exhibit strikingly beautiful compositional structures, with certain members reproducibly occupying particular spatiotemporal microniches. Despite this reproducibility, we lack the ability to explain these spatial patterns. We hypothesize that certain spatial patterns in microbial communities may be explained by the exchange of redox-active metabolites whose biological function is sensitive to microenvironmental gradients. To test this, we developed a simple community consisting of synthetic Pseudomonas aeruginosa strains with a partitioned denitrification pathway: a strict consumer and strict producer of nitric oxide (NO), a key pathway intermediate. Because NO can be both toxic or beneficial depending on the amount of oxygen present, this system provided an opportunity to investigate whether dynamic oxygen gradients can tune metabolic cross-feeding and fitness outcomes in a predictable fashion. Using a combination of genetic analysis, controlled growth environments, and imaging, we show that oxygen availability dictates whether NO cross-feeding is deleterious or mutually beneficial and that this organizing principle maps to the microscale. More generally, this work underscores the importance of considering the double-edged and microenvironmentally tuned roles redox-active metabolites can play in shaping microbial communities.

Fungal hyphae regulate bacterial diversity and plasmid-mediated functional novelty during range expansion

The amount of bacterial diversity present on many surfaces is enormous; however, how these levels of diversity persist in the face of the purifying processes that occur as bacterial communities expand across space (referred to here as range expansion) remains enigmatic. We shed light on this apparent paradox by providing mechanistic evidence for a strong role of fungal hyphae-mediated dispersal on regulating bacterial diversity during range expansion. Using pairs of fluorescently labeled bacterial strains and a hyphae-forming fungal strain that expand together across a nutrient-amended surface, we show that a hyphal network increases the spatial intermixing and extent of range expansion of the bacterial strains. This is true regardless of the type of interaction (competition or resource cross-feeding) imposed between the bacterial strains. We further show that the underlying cause is that flagellar motility drives bacterial dispersal along the hyphal network, which counteracts the purifying effects of ecological drift at the expansion frontier. We finally demonstrate that hyphae-mediated spatial intermixing increases the conjugation-mediated spread of plasmid-encoded antibiotic resistance. In conclusion, fungal hyphae are important regulators of bacterial diversity and promote plasmid-mediated functional novelty during range expansion in an interaction-independent manner.

Environmental and ecological controls of the spatial distribution of microbial populations in aggregates

In microbial communities, the ecological interactions between species of different populations are responsible for the spatial distributions observed in aggregates (granules, biofilms or flocs). To explore the underlying mechanisms that control these processes, we have developed a mathematical modelling framework able to describe, label and quantify defined spatial structures that arise from microbial and environmental interactions in communities. An artificial system of three populations collaborating or competing in an aggregate is simulated using individual-based modelling under different environmental conditions. In this study, neutralism, competition, commensalism and concurrence of commensalism and competition have been considered. We were able to identify interspecific segregation of communities that appears in competitive environments (columned stratification), and a layered distribution of populations that emerges in commensal (layered stratification). When different ecological interactions were considered in the same aggregate, the resultant spatial distribution was identified as the one controlled by the most limiting substrate. A theoretical modulus was defined, with which we were able to quantify the effect of environmental conditions and ecological interactions to predict the most probable spatial distribution. The specific microbial patterns observed in our results allowed us to identify the optimal spatial organizations for bacteria to thrive when building a microbial community and how this permitted co-existence of populations at different growth rates. Our model reveals that although ecological relationships between different species dictate the distribution of bacteria, the environment controls the final spatial distribution of the community.

Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use

Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.

Emergence of sector and spiral patterns from a two-species mutualistic cross-feeding model

The ubiquitous existence of microbial communities marks the importance of understanding how species interact within the community to coexist and their spatial organization. We study a two-species mutualistic cross-feeding model through a stochastic cellular automaton on a square lattice using kinetic Monte Carlo simulation. Our model encapsulates the essential dynamic processes such as cell growth, and nutrient excretion, diffusion and uptake. Focusing on the interplay among nutrient diffusion and individual cell division, we discover three general classes of colony morphology: co-existing sectors, co-existing spirals, and engulfment. When the cross-feeding nutrient is widely available, either through high excretion or fast diffusion, a stable circular colony with alternating species sector emerges. When the consumer cells rely on being spatially close to the producers, we observe a stable spiral. We also see one species being engulfed by the other when species interfaces merge due to stochastic fluctuation. By tuning the diffusion rate and the growth rate, we are able to gain quantitative insights into the structures of the sectors and the spirals.

Calibrating spatiotemporal models of microbial communities to microscopy data: A review

Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.

Polymicrobial Biofilm Organization of Staphylococcus aureus and Pseudomonas aeruginosa in a Chronic Wound Environment

Biofilm on the skin surface of chronic wounds is an important step that involves difficulties in wound healing. The polymicrobial nature inside this pathogenic biofilm is key to understanding the chronicity of the lesion. Few in vitro models have been developed to study bacterial interactions inside this chronic wound. We evaluated the biofilm formation and the evolution of bacteria released from this biofilm on the two main bacteria isolated in this condition, Staphylococcus aureus and Pseudomonas aeruginosa, using a dynamic system (BioFlux™ 200) and a chronic wound-like medium (CWM) that mimics the chronic wound environment. We observed that all species constituted a faster biofilm in the CWM compared to a traditional culture medium (p < 0.01). The percentages of biofilm formation were significantly higher in the mixed biofilm compared to those determined for the bacterial species alone (p < 0.01). Biofilm organization was a non-random structure where S. aureus aggregates were located close to the wound surface, whereas P. aeruginosa was located deeper in the wound bed. Planktonic biofilm-detached bacteria showed decreased growth, overexpression of genes encoding biofilm formation, and an increase in the mature biofilm biomass formed. Our data confirmed the impact of the chronic wound environment on biofilm formation and on bacterial lifecycle inside the biofilm.

Growth instabilities shape morphology and genetic diversity of microbial colonies

Cellular populations assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of a population influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.

A systematic, complexity-reduction approach to dissect the kombucha tea microbiome

One defining goal of microbiome research is to uncover mechanistic causation that dictates the emergence of structural and functional traits of microbiomes. However, the extraordinary degree of ecosystem complexity has hampered the realization of the goal. Here, we developed a systematic, complexity-reducing strategy to mechanistically elucidate the compositional and metabolic characteristics of microbiome by using the kombucha tea microbiome as an example. The strategy centered around a two-species core that was abstracted from but recapitulated the native counterpart. The core was convergent in its composition, coordinated on temporal metabolic patterns, and capable for pellicle formation. Controlled fermentations uncovered the drivers of these characteristics, which were also demonstrated translatable to provide insights into the properties of communities with increased complexity and altered conditions. This work unravels the pattern and process underlying the kombucha tea microbiome, providing a potential conceptual framework for mechanistic investigation of microbiome behaviors.

ChemChaste: Simulating spatially inhomogeneous biochemical reaction-diffusion systems for modeling cell-environment feedbacks

Background

Spatial organization plays an important role in the function of many biological systems, from cell fate specification in animal development to multistep metabolic conversions in microbial communities. The study of such systems benefits from the use of spatially explicit computational models that combine a discrete description of cells with a continuum description of one or more chemicals diffusing within a surrounding bulk medium. These models allow the in silico testing and refinement of mechanistic hypotheses. However, most existing models of this type do not account for concurrent bulk and intracellular biochemical reactions and their possible coupling.

Conclusions

Here, we describe ChemChaste, an extension for the open-source C++ computational biology library Chaste. ChemChaste enables the spatial simulation of both multicellular and bulk biochemistry by expanding on Chaste’s existing capabilities. In particular, ChemChaste enables (i) simulation of an arbitrary number of spatially diffusing chemicals, (ii) spatially heterogeneous chemical diffusion coefficients, and (iii) inclusion of both bulk and intracellular biochemical reactions and their coupling. ChemChaste also introduces a file-based interface that allows users to define the parameters relating to these functional features without the need to interact directly with Chaste’s core C++ code. We describe ChemChaste and demonstrate its functionality using a selection of chemical and biochemical exemplars, with a focus on demonstrating increased ability in modeling bulk chemical reactions and their coupling with intracellular reactions.

Availability and implementation

ChemChaste version 1.0 is a free, open-source C++ library, available via GitHub at https://github.com/OSS-Lab/ChemChaste under the BSD license, on the Zenodo archive at zendodo doi, as well as on BioTools (biotools:chemchaste) and SciCrunch (RRID:SCR022208) databases.

Substrate availability and toxicity shape the structure of microbial communities engaged in metabolic division of labor

Metabolic division of labor (MDOL) represents a widespread natural phenomenon, whereby a complex metabolic pathway is shared between different strains within a community in a mutually beneficial manner. However, little is known about how the composition of such a microbial community is regulated. We hypothesized that when degradation of an organic compound is carried out via MDOL, the concentration and toxicity of the substrate modulate the benefit allocation between the two microbial populations, thus affecting the structure of this community. We tested this hypothesis by combining modeling with experiments using a synthetic consortium. Our modeling analysis suggests that the proportion of the population executing the first metabolic step can be simply estimated by Monod-like formulas governed by substrate concentration and toxicity. Our model and the proposed formula were able to quantitatively predict the structure of our synthetic consortium. Further analysis demonstrates that our rule is also applicable in estimating community structures in spatially structured environments. Together, our work clearly demonstrates that the structure of MDOL communities can be quantitatively predicted using available information on environmental factors, thus providing novel insights into how to manage artificial microbial systems for the wide application of the bioindustry.

Three-dimensional imaging for the quantification of spatial patterns in microbiota of the intestinal mucosa

Many human diseases are causally linked to the gut microbiota, yet the field still lacks mechanistic understanding of the underlying complex interactions, because existing tools cannot simultaneously quantify microbial communities and their native context. In this work, we provide an approach to tissue clearing and preservation that enables 3D visualization of the biogeography of the host–microbiota interface. We combine this tool with sequencing and multiplexed microbial labeling to provide the field with a platform on which to discover patterns in the spatial distribution of microbes. We validated this platform by quantifying bacterial distribution in cecal mucosa at different stages of antibiotic exposure. This approach may enable researchers to formulate and test new hypotheses about host–microbe and microbe–microbe interactions.

Improving our understanding of host–microbe relationships in the gut requires the ability to both visualize and quantify the spatial organization of microbial communities in their native orientation with the host tissue. We developed a systematic procedure to quantify the three-dimensional (3D) spatial structure of the native mucosal microbiota in any part of the intestines with taxonomic and high spatial resolution. We performed a 3D biogeographical analysis of the microbiota of mouse cecal crypts at different stages of antibiotic exposure. By tracking eubacteria and four dominant bacterial taxa, we found that the colonization of crypts by native bacteria is a dynamic and spatially organized process. Ciprofloxacin treatment drastically reduced bacterial loads and eliminated Muribaculaceae (or all Bacteroidetes entirely) even 10 d after recovery when overall bacterial loads returned to preantibiotic levels. Our 3D quantitative imaging approach revealed that the bacterial colonization of crypts is organized in a spatial pattern that consists of clusters of adjacent colonized crypts that are surrounded by unoccupied crypts, and that this spatial pattern is resistant to the elimination of Muribaculaceae or of all Bacteroidetes by ciprofloxacin. Our approach also revealed that the composition of cecal crypt communities is diverse and that Lactobacilli were found closer to the lumen than Bacteroidetes, Ruminococcaceae, and Lachnospiraceae, regardless of antibiotic exposure. Finally, we found that crypts communities with similar taxonomic composition were physically closer to each other than communities that were taxonomically different.

Rare and localized events stabilize microbial community composition and patterns of spatial self-organization in a fluctuating environment

Spatial self-organization is a hallmark of surface-associated microbial communities that is governed by local environmental conditions and further modified by interspecific interactions. Here, we hypothesize that spatial patterns of microbial cell-types can stabilize the composition of cross-feeding microbial communities under fluctuating environmental conditions. We tested this hypothesis by studying the growth and spatial self-organization of microbial co-cultures consisting of two metabolically interacting strains of the bacterium Pseudomonas stutzeri. We inoculated the co-cultures onto agar surfaces and allowed them to expand (i.e. range expansion) while fluctuating environmental conditions that alter the dependency between the two strains. We alternated between anoxic conditions that induce a mutualistic interaction and oxic conditions that induce a competitive interaction. We observed co-occurrence of both strains in rare and highly localized clusters (referred to as “spatial jackpot events”) that persist during environmental fluctuations. To resolve the underlying mechanisms for the emergence of spatial jackpot events, we used a mechanistic agent-based mathematical model that resolves growth and dispersal at the scale relevant to individual cells. While co-culture composition varied with the strength of the mutualistic interaction and across environmental fluctuations, the model provides insights into the formation of spatially resolved substrate landscapes with localized niches that support the co-occurrence of the two strains and secure co-culture function. This study highlights that in addition to spatial patterns that emerge in response to environmental fluctuations, localized spatial jackpot events ensure persistence of strains across dynamic conditions.

eGFP Gene Integration in HO: A Metabolomic Impact?

Integrating fluorescent genes including eGFP in the yeast genome is common practice for various applications, including cell visualization and population monitoring. The transformation of a commercial S. cerevisiae strain by integrating a cassette including a gene encoding an EGFP protein in the HO gene was carried out using CRISPR-Cas9 technology. Although this type of integration is often used and described as neutral at the phenotypic level of the cell, we have highlighted that under alcoholic fermentation (in a Chardonnay must), it has an impact on the exometabolome. We observed 41 and 82 unique biomarkers for the S3 and S3GFP strains, respectively, as well as 28 biomarkers whose concentrations varied significantly between the wild-type and the modified strains. These biomarkers were mainly found to correspond to peptides. Despite similar phenotypic growth and fermentation parameters, high-resolution mass spectrometry allowed us to demonstrate, for the first time, that the peptidome is modified when integrating this cassette in the HO gene.

Modular, robust and extendible multicellular circuit design in yeast

Division of labor between cells is ubiquitous in biology but the use of multi-cellular consortia for engineering applications is only beginning to be explored. A significant advantage of multi-cellular circuits is their potential to be modular with respect to composition but this claim has not yet been extensively tested using experiments and quantitative modeling. Here, we construct a library of 24 yeast strains capable of sending, receiving or responding to three molecular signals, characterize them experimentally and build quantitative models of their input-output relationships. We then compose these strains into two- and three-strain cascades as well as a four-strain bistable switch and show that experimentally measured consortia dynamics can be predicted from the models of the constituent parts. To further explore the achievable range of behaviors, we perform a fully automated computational search over all two-, three- and four-strain consortia to identify combinations that realize target behaviors including logic gates, band-pass filters and time pulses. Strain combinations that are predicted to map onto a target behavior are further computationally optimized and then experimentally tested. Experiments closely track computational predictions. The high reliability of these model descriptions further strengthens the feasibility and highlights the potential for distributed computing in synthetic biology.

Global dynamics of microbial communities emerge from local interaction rules

Most microbes live in spatially structured communities (e.g., biofilms) in which they interact with their neighbors through the local exchange of diffusible molecules. To understand the functioning of these communities, it is essential to uncover how these local interactions shape community-level properties, such as the community composition, spatial arrangement, and growth rate. Here, we present a mathematical framework to derive community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our framework consists of two parts: a biophysical model to derive the local interaction rules (i.e. interaction range and strength) from the molecular parameters underlying the cell-cell interactions and a graph based model to derive the equilibrium properties of the community (i.e. composition, spatial arrangement, and growth rate) from these local interaction rules. Our framework shows that key molecular parameters underlying the cell-cell interactions (e.g., the uptake and leakage rates of molecules) determine community-level properties. We apply our model to mutualistic cross-feeding communities and show that spatial structure can be detrimental for these communities. Moreover, our model can qualitatively recapitulate the properties of an experimental microbial community. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how community-level properties emerge from microscopic interactions between cells.

Microorganisms perform essential processes on our planet. Many of these processes result from interactions between different species growing in spatially structured communities. A central goal is to understand how community processes emerge from such interactions between cells. Here we develop a mathematical framework to derive community-level properties, such as the community composition, growth rate, and spatial organization, from the molecular mechanisms underlying these cell-cell interactions. We focus on mutualistic communities consisting of two cell types that need to interact with each other in order to grow. We derive equations that describe how changes in the molecular parameters of cellular interactions affect individuals’ and community properties. We find that spatial structure has a negative impact on these mutualistic communities: as cells become surrounded by their own type, they have less access to the other cell type with which they need to interact to grow well. We show that our framework can also be applied to other types of microbial communities and potentially to non-microbial systems such as tissues. More generally, this work advances our understanding of how scales are connected in biological systems, both in the microbial world and beyond.

Type IV Pilus Shapes a 'Bubble-Burst' Pattern Opposing Spatial Intermixing of Two Interacting Bacterial Populations

Microbes are social organisms that commonly live in sessile biofilms. Spatial patterns of populations within biofilms can be important determinants of community-level properties. Spatial intermixing emerging from microbial interaction is one of the best-studied characteristics of spatial patterns. The specific levels of spatial intermixing critically contribute to how the dynamics and functioning of such communities are governed. However, the precise factors that determine spatial patterns and intermixing remain unclear. Here, we investigated the spatial patterning and intermixing of an engineered synthetic consortium composed of two mutualistic Pseudomonas stutzeri strains that degrade salicylate via metabolic cross-feeding. We found that the consortium self-organizes across space to form a previously unreported spatial pattern (here referred to as a ‘bubble-burst’ pattern) that exhibits a low level of intermixing. Interestingly, when the genes encoding type IV pili were deleted from both strains, a highly intermixed spatial pattern developed and increased the productivity of the entire community. The intermixed pattern was maintained in a robust manner across a wide range of initial ratios between the two strains. Our findings show that the type IV pilus plays a role in mitigating spatial intermixing of different populations in surface-attached microbial communities, with consequences for governing community-level properties. These insights provide tangible clues for the engineering of synthetic microbial systems that perform highly in spatially structured environments.

IMPORTANCE When growing on surfaces, multispecies microbial communities form biofilms that exhibit intriguing spatial patterns. These patterns can significantly affect the overall properties of the community, enabling otherwise impermissible metabolic functions to occur as well as driving the evolutionary and ecological processes acting on communities. The development of these patterns is affected by several drivers, including cell-cell interactions, nutrient levels, density of founding cells, and surface properties. The type IV pilus is commonly found to mediate surface-associated behaviors of microorganisms, but its role on pattern formation within microbial communities is unclear. Here, we report that in a cross-feeding consortium, the type IV pilus affects the spatial intermixing of interacting populations involved in pattern formation and ultimately influences overall community productivity and robustness. This novel insight assists our understanding of the ecological processes of surface-attached microbial communities and suggests a potential strategy for engineering high-performance synthetic microbial communities.

McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems

Individual-based modeling is widely applied to investigate the ecological mechanisms driving microbial community dynamics. In such models, the population or community dynamics emerge from the behavior and interplay of individual entities, which are simulated according to a predefined set of rules. If the rules that govern the behavior of individuals are based on generic and mechanistically sound principles, the models are referred to as next-generation individual-based models. These models perform particularly well in recapitulating actual ecological dynamics. However, implementation of such models is time-consuming and requires proficiency in programming or in using specific software, which likely hinders a broader application of this powerful method. Here we present McComedy, a modeling tool designed to facilitate the development of next-generation individual-based models of microbial consumer-resource systems. This tool allows flexibly combining pre-implemented building blocks that represent physical and biological processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems is demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. With this article, we provide a versatile tool for developing next-generation individual-based models that can foster understanding of microbial ecology in both research and education.

Microorganisms such as bacteria and fungi can be found in virtually any natural environment. To better understand the ecology of these microorganisms–which is important for several research fields including medicine, biotechnology, and conservation biology–researchers often use computer models to simulate and predict the behavior of microbial communities. Commonly, a particular technique called individual-based modeling is used to generate structurally realistic models of these communities by explicitly simulating each individual microorganism. Here we developed a tool called McComedy that helps researchers applying individual-based modeling efficiently without having to program low-level processes, thus allowing them to focus on their actual research questions. To test whether McComedy is not only convenient to use but also generates meaningful models, we used it to reproduce previously reported findings of two other research groups. Given that our results could well recapitulate and furthermore extend the original findings, we are confident that McComedy is a powerful and versatile tool that can help to address fundamental questions in microbial ecology.

Slow expanders invade by forming dented fronts in microbial colonies

Living organisms never cease to evolve, so there is a significant interest in predicting and controlling evolution in all branches of life sciences. The most basic question is whether a trait should increase or decrease in a given environment. The answer seems to be trivial for traits such as the growth rate in a bioreactor or the expansion rate of a tumor. Yet, it has been suggested that such traits can decrease, rather than increase, during evolution. Here, we report a mutant that outcompeted the ancestor despite having a slower expansion velocity when in isolation. To explain this observation, we developed and validated a theory that describes spatial competition between organisms with different expansion rates and arbitrary competitive interactions.

Most organisms grow in space, whether they are viruses spreading within a host tissue or invasive species colonizing a new continent. Evolution typically selects for higher expansion rates during spatial growth, but it has been suggested that slower expanders can take over under certain conditions. Here, we report an experimental observation of such population dynamics. We demonstrate that mutants that grow slower in isolation nevertheless win in competition, not only when the two types are intermixed, but also when they are spatially segregated into sectors. The latter was thought to be impossible because previous studies focused exclusively on the global competitions mediated by expansion velocities, but overlooked the local competitions at sector boundaries. Local competition, however, can enhance the velocity of either type at the sector boundary and thus alter expansion dynamics. We developed a theory that accounts for both local and global competitions and describes all possible sector shapes. In particular, the theory predicted that a slower on its own, but more competitive, mutant forms a dented V-shaped sector as it takes over the expansion front. Such sectors were indeed observed experimentally, and their shapes matched quantitatively with the theory. In simulations, we further explored several mechanisms that could provide slow expanders with a local competitive advantage and showed that they are all well-described by our theory. Taken together, our results shed light on previously unexplored outcomes of spatial competition and establish a universal framework to understand evolutionary and ecological dynamics in expanding populations.

Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities

Microbial communities often perform important functions that depend on inter-species interactions. To improve community function via artificial selection, one can repeatedly grow many communities to allow mutations to arise, and "reproduce" the highest-functioning communities by partitioning each into multiple offspring communities for the next cycle. Since improvement is often unimpressive in experiments, we study how to design effective selection strategies in silico. Specifically, we simulate community selection to improve a function that requires two species. With a "community function landscape", we visualize how community function depends on species and genotype compositions. Due to ecological interactions that promote species coexistence, the evolutionary trajectory of communities is restricted to a path on the landscape. This restriction can generate counter-intuitive evolutionary dynamics, prevent the attainment of maximal function, and importantly, hinder selection by trapping communities in locations of low community function heritability. We devise experimentally-implementable manipulations to shift the path to higher heritability, which speeds up community function improvement even when landscapes are high dimensional or unknown. Video walkthroughs: https://go.nature.com/3GWwS6j ; https://online.kitp.ucsb.edu/online/ecoevo21/shou2/ .

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

Four species of bacteria deterministically assemble to form a stable biofilm in a millifluidic channel

Multispecies microbial adherent communities are widespread in nature and organisms, although the principles of their assembly and development remain unclear. Here, we test the possibility of establishing a simplified but relevant model of multispecies biofilm in a non-invasive laboratory setup for the real-time monitoring of community development. We demonstrate that the four chosen species (Bacillus thuringiensis, Pseudomonas fluorescens, Kocuria varians, and Rhodocyclus sp.) form a dynamic community that deterministically reaches its equilibrium after ~30 h of growth. We reveal the emergence of complexity in this simplified community as reported by an increase in spatial heterogeneity and non-monotonic developmental kinetics. Importantly, we find interspecies interactions consisting of competition for resources-particularly oxygen-and both direct and indirect physical interactions. The simplified experimental model opens new avenues to the study of adherent bacterial communities and their behavior in the context of rapid global change.

Causes and consequences of pattern diversification in a spatially self-organizing microbial community

Surface-attached microbial communities constitute a vast amount of life on our planet. They contribute to all major biogeochemical cycles, provide essential services to our society and environment, and have important effects on human health and disease. They typically consist of different interacting genotypes that arrange themselves non-randomly across space (referred to hereafter as spatial self-organization). While spatial self-organization is important for the functioning, ecology, and evolution of these communities, the underlying determinants of spatial self-organization remain unclear. Here, we performed a combination of experiments, statistical modeling, and mathematical simulations with a synthetic cross-feeding microbial community consisting of two isogenic strains. We found that two different patterns of spatial self-organization emerged at the same length and time scales, thus demonstrating pattern diversification. This pattern diversification was not caused by initial environmental heterogeneity or by genetic heterogeneity within populations. Instead, it was caused by nongenetic heterogeneity within populations, and we provide evidence that the source of this nongenetic heterogeneity is local differences in the initial spatial positionings of individuals. We further demonstrate that the different patterns exhibit different community-level properties; namely, they have different expansion speeds. Together, our results demonstrate that pattern diversification can emerge in the absence of initial environmental heterogeneity or genetic heterogeneity within populations and can affect community-level properties, thus providing novel insights into the causes and consequences of microbial spatial self-organization.

The spatial organization of microbial communities during range expansion

Microbes in nature often live in dense and diverse communities exhibiting a variety of spatial structures. Microbial range expansion is a universal ecological process that enables populations to form spatial patterns. It can be driven by both passive and active processes, for example, mechanical forces from cell growth and bacterial motility. In this review, we provide a taste of recent creative and sophisticated efforts being made to address basic questions in spatial ecology and pattern formation during range expansion. We especially highlight the role of motility to shape community structures, and discuss the research challenges and future directions.

Recurrent Urinary Tract Infections: Unraveling the Complicated Environment of Uncomplicated rUTIs

Urinary tract infections (UTIs) are frequent in humans, affecting the upper and lower urinary tract. Present diagnosis relies on the positive culture of uropathogenic bacteria from urine and clinical markers of inflammation of the urinary tract. The bladder is constantly challenged by adverse environmental stimuli which influence urinary tract physiology, contributing to a dysbiotic environment. Simultaneously, pathogens are primed by environmental stressors such as antibiotics, favoring recurrent UTIs (rUTIs), resulting in chronic illness. Due to different confounders for UTI onset, a greater understanding of the fundamental environmental mechanisms and microbial ecology of the human urinary tract is required. Such advancements could promote the tandem translation of bench and computational studies for precision treatments and clinical management of UTIs. Therefore, there is an urgent need to understand the ecological interactions of the human urogenital microbial communities which precede rUTIs. This review aims to outline the mechanistic aspects of rUTI ecology underlying dysbiosis between both the human microbiome and host physiology which predisposes humans to rUTIs. By assessing the applications of next generation and systems level methods, we also recommend novel approaches to elucidate the systemic consequences of rUTIs which requires an integrated approach for successful treatment. To this end, we will provide an outlook towards the so-called 'uncomplicated environment of UTIs', a holistic and systems view that applies ecological principles to define patient-specific UTIs. This perspective illustrates the need to withdraw from traditional reductionist perspectives in infection biology and instead, a move towards a systems-view revolving around patient-specific pathophysiology during UTIs.

The influence of the crowding assumptions in biofilm simulations

Microorganisms are frequently organized into crowded structures that affect the nutrients diffusion. This reduction in metabolite diffusion could modify the microbial dynamics, meaning that computational methods for studying microbial systems need accurate ways to model the crowding conditions. We previously developed a computational framework, termed CROMICS, that incorporates the effect of the (time-dependent) crowding conditions on the spatio-temporal modeling of microbial communities, and we used it to demonstrate the crowding influence on the community dynamics. To further identify scenarios where crowding should be considered in microbial modeling, we herein applied and extended CROMICS to simulate several environmental conditions that could potentially boost or dampen the crowding influence in biofilms. We explore whether the nutrient supply (rich- or low-nutrient media), the cell-packing configuration (square or hexagonal spherical cell arrangement), or the cell growing conditions (planktonic state or biofilm) modify the crowding influence on the growth of Escherichia coli. Our results indicate that the growth rate, the abundance and appearance time of different cell phenotypes as well as the amount of by-products secreted to the medium are sensitive to some extent to the local crowding conditions in all scenarios tested, except in rich-nutrient media. Crowding conditions enhance the formation of nutrient gradient in biofilms, but its effect is only appreciated when cell metabolism is controlled by the nutrient limitation. Thus, as soon as biomass (and/or any other extracellular macromolecule) accumulates in a region, and cells occupy more than 14% of the volume fraction, the crowding effect must not be underestimated, as the microbial dynamics start to deviate from the ideal/expected behaviour that assumes volumeless cells or when a homogeneous (reduced) diffusion is applied in the simulation. The modeling and simulation of the interplay between the species diversity (cell shape and metabolism) and the environmental conditions (nutrient quality, crowding conditions) can help to design effective strategies for the optimization and control of microbial systems.

The ecology of wine fermentation: a model for the study of complex microbial ecosystems

The general interest in microbial ecology has skyrocketed over the past decade, driven by technical advances and by the rapidly increasing appreciation of the fundamental services that these ecosystems provide. In biotechnology, ecosystems have many more functionalities than single species, and, if properly understood and harnessed, will be able to deliver better outcomes for almost all imaginable applications. However, the complexity of microbial ecosystems and of the interactions between species has limited their applicability. In research, next generation sequencing allows accurate mapping of the microbiomes that characterise ecosystems of biotechnological and/or medical relevance. But the gap between mapping and understanding, to be filled by "functional microbiomics", requires the collection and integration of many different layers of complex data sets, from molecular multi-omics to spatial imaging technologies to online ecosystem monitoring tools. Holistically, studying the complexity of most microbial ecosystems, consisting of hundreds of species in specific spatial arrangements, is beyond our current technical capabilities, and simpler model systems with fewer species and reduced spatial complexity are required to establish the fundamental rules of ecosystem functioning. One such ecosystem, the ecosystem responsible for natural alcoholic fermentation, can provide an excellent tool to study evolutionarily relevant interactions between multiple species within a relatively easily controlled environment. This review will critically evaluate the approaches that are currently implemented to dissect the cellular and molecular networks that govern this ecosystem. KEY POINTS: • Evolutionarily isolated fermentation ecosystem can be used as an ecological model. • Experimental toolbox is gearing towards mechanistic understanding of this ecosystem. • Integration of multidisciplinary datasets is key to predictive understanding.

Bacterial Behavior in Confined Spaces

In confined spaces, bacteria exhibit unexpected cellular behaviors that are related to the biogeochemical cycle and human health. Types of confined spaces include lipid vesicles, polymer vesicles, emulsion droplets, microfluidic chips, and various laboratory-made chambers. This mini-review summarizes the behaviors of living bacteria in these confined spaces, including (a) growth and proliferation, (b) cell communication, and (c) motion. Future trends and challenges are also discussed in this paper.

Droplet printing reveals the importance of micron-scale structure for bacterial ecology

Bacteria often live in diverse communities where the spatial arrangement of strains and species is considered critical for their ecology. However, a test of this hypothesis requires manipulation at the fine scales at which spatial structure naturally occurs. Here we develop a droplet-based printing method to arrange bacterial genotypes across a sub-millimetre array. We print strains of the gut bacterium Escherichia coli that naturally compete with one another using protein toxins. Our experiments reveal that toxin-producing strains largely eliminate susceptible non-producers when genotypes are well-mixed. However, printing strains side-by-side creates an ecological refuge where susceptible strains can persist in large numbers. Moving to competitions between toxin producers reveals that spatial structure can make the difference between one strain winning and mutual destruction. Finally, we print different potential barriers between competing strains to understand how ecological refuges form, which shows that cells closest to a toxin producer mop up the toxin and protect their clonemates. Our work provides a method to generate customised bacterial communities with defined spatial distributions, and reveals that micron-scale changes in these distributions can drive major shifts in ecology.

The spatial arrangement of bacterial strains and species within microbial communities is considered crucial for their ecology. Here, Krishna Kumar et al. use a droplet-based printing method to arrange different bacterial genotypes across a sub-millimetre array, and show that micron-scale changes in spatial distributions can drive major shifts in ecology.

Pleiotropic mutations can rapidly evolve to directly benefit self and cooperative partner despite unfavorable conditions

Cooperation, paying a cost to benefit others, is widespread. Cooperation can be promoted by pleiotropic ‘win-win’ mutations which directly benefit self (self-serving) and partner (partner-serving). Previously, we showed that partner-serving should be defined as increased benefit supply rate per intake benefit. Here, we report that win-win mutations can rapidly evolve even under conditions unfavorable for cooperation. Specifically, in a well-mixed environment we evolved engineered yeast cooperative communities where two strains exchanged costly metabolites, lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, ecm21 mutations repeatedly arose. ecm21 is self-serving, improving self’s growth rate in limiting lysine. ecm21 is also partner-serving, increasing hypoxanthine release rate per lysine consumption and the steady state growth rate of partner and of community. ecm21 also arose in monocultures evolving in lysine-limited chemostats. Thus, even without any history of cooperation or pressure to maintain cooperation, pleiotropic win-win mutations may readily evolve to promote cooperation.

The impact of interactions on invasion and colonization resistance in microbial communities

In human microbiota, the prevention or promotion of invasions can be crucial to human health. Invasion outcomes, in turn, are impacted by the composition of resident communities and interactions of resident members with the invader. Here we study how interactions influence invasion outcomes in microbial communities, when interactions are primarily mediated by chemicals that are released into or consumed from the environment. We use a previously developed dynamic model which explicitly includes species abundances and the concentrations of chemicals that mediate species interaction. Using this model, we assessed how species interactions impact invasion by simulating a new species being introduced into an existing resident community. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or decreased and whether the invader was maintained in the community or went extinct. We found that as the number of invaders introduced into the resident community increased, disruption rather than augmentation became more prevalent. With more facilitation of the invader by the resident community, resistance outcomes were replaced by displacement and augmentation. By contrast, with more facilitation among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, the majority of augmentation outcomes turned into displacement, while when inhibition of residents by invaders was eliminated, invasion outcomes were largely unaffected. Our results suggest that a better understanding of interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities.

Our resident microbiota can prevent diseases by making it harder for pathogens to grow and establish, a phenomenon called “colonization resistance.” Colonization resistance is one of the major benefits provided by human-associated microbiota and a viable alternative to the use of antibiotics for preventing or treating infections. Here we use a model of microbial interactions through production and consumption of metabolic compounds to assay invasion and colonization resistance. We systematically examine in simulations how interactions among resident members and those between residents and an invader impact colonization resistance and invasion outcomes. In our simulations, the common strategy of increasing the dosage of probiotics is often unsuccessful for augmenting a new species into a resident microbiota. Instead, we find that the net facilitation or inhibition between the resident members and the invader explains whether the community remains intact and whether the invader can establish. Our results suggest that a better understanding of microbial interactions can inform successful microbiota interventions.

The establishment of the CBE launched biofilms as a field of specialized research

The Center for Biofilm Engineering was the first center of excellence focused on biofilms and was originally funded through the Engineering Research Center Program from the U.S. National Science Foundation. After almost 30 years, biofilm continues to be a stand-alone scientific topic of inquiry that has broad implications for fundamental and applied science and engineering of bio-systems. However, much remains to be done, not only for research discovery but also education and outreach, to increase and grow the biofilm paradigm as well as our understanding of the microbial world.

Influence of interspecies interactions on the spatial organization of dual species bacterial communities

Interspecies interactions in bacterial biofilms have important impacts on the composition and function of communities in natural and engineered systems. To investigate these interactions, synthetic communities provide experimentally tractable systems. Biofilms grown on agar-surfaces have been used for investigating the eco-evolutionary and biophysical forces that determine community composition and spatial distribution of bacteria. Prior studies have used genetically identical bacterial strains and strains with specific mutations, that express different fluorescent proteins, to investigate intraspecies interactions. Here, we investigated interspecies interactions and, specifically, determined the community composition and spatial distribution in synthetic communities of Pseudomonas aeruginosa, Pseudomonas protegens and Klebsiella pneumoniae. Using quantitative microscopic imaging, we found that interspecies interactions in multispecies colonies were influenced by type IV pilus mediated motility, extracellular matrix secretion, environmental parameters, and these effects were also influenced by the specific partner in the dual species combinations. These results indicate that the patterns observable in mixed species colonies can be used to understand the mechanisms that drive interspecies interactions, which are dependent on the interplay between specific species’ physiology and environmental conditions.

•

Spatial patterns in bacterial colonies are species and interaction dependent.

•

Surface motility and extracellar matrix production affect interspecies interactions.

•

Agar surface colonies show how bacteria interact in biofilms.

Extracellular Metabolism Sets the Table for Microbial Cross-Feeding

The transfer of nutrients between cells, or cross-feeding, is a ubiquitous feature of microbial communities with emergent properties that influence our health and orchestrate global biogeochemical cycles. Cross-feeding inevitably involves the externalization of molecules. Some of these molecules directly serve as cross-fed nutrients, while others can facilitate cross-feeding. Altogether, externalized molecules that promote cross-feeding are diverse in structure, ranging from small molecules to macromolecules. The functions of these molecules are equally diverse, encompassing waste products, enzymes, toxins, signaling molecules, biofilm components, and nutrients of high value to most microbes, including the producer cell. As diverse as the externalized and transferred molecules are the cross-feeding relationships that can be derived from them. Many cross-feeding relationships can be summarized as cooperative but are also subject to exploitation. Even those relationships that appear to be cooperative exhibit some level of competition between partners. In this review, we summarize the major types of actively secreted, passively excreted, and directly transferred molecules that either form the basis of cross-feeding relationships or facilitate them. Drawing on examples from both natural and synthetic communities, we explore how the interplay between microbial physiology, environmental parameters, and the diverse functional attributes of extracellular molecules can influence cross-feeding dynamics. Though microbial cross-feeding interactions represent a burgeoning field of interest, we may have only begun to scratch the surface.

Spatial organization in microbial range expansion emerges from trophic dependencies and successful lineages

Evidence suggests that bacterial community spatial organization affects their ecological function, yet details of the mechanisms that promote spatial patterns remain difficult to resolve experimentally. In contrast to bacterial communities in liquid cultures, surface-attached range expansion fosters genetic segregation of the growing population with preferential access to nutrients and reduced mechanical restrictions for cells at the expanding periphery. Here we elucidate how localized conditions in cross-feeding bacterial communities shape community spatial organization. We combine experiments with an individual based mathematical model to resolve how trophic dependencies affect localized growth rates and nucleate successful cell lineages. The model tracks individual cell lineages and attributes these with trophic dependencies that promote counterintuitive reproductive advantages and result in lasting influences on the community structure, and potentially, on its functioning. We examine persistence of lucky lineages in structured habitats where expansion is interrupted by physical obstacles to gain insights into patterns in porous domains.

Drivers of Spatial Structure in Social Microbial Communities

Microbes are social organisms, interacting primarily through secreted biomolecules. Many traits have evolved based solely on their effects upon other community members, yet even individually beneficial traits often create social side effects that are mediated by spatial population structure. Predicting the evolution of many microbial traits thus requires a comprehensive understanding of their social consequences. In this review, we examine the critical role of population spatial structure in microbial social evolution. We briefly review key mechanisms structuring microbial communities, focusing primarily on the universal roles of cellular death and reproduction. Finally, we explain how spatial assortment can be efficiently calculated in two-dimensional, surface-attached populations.

Synthetic Symbiosis under Environmental Disturbances

The power of synthetic biology is immense. Will it, however, be able to withstand the environmental pressures once released in the wild. As new technologies aim to do precisely the same, we use a much simpler model to test mathematically the effect of a changing environment on a synthetic biological system. We assume that the system is successful if it maintains proportions close to what we observe in the laboratory. Extreme deviations from the expected equilibrium are possible as the environment changes. Our study provides the conditions and the designer specifications which may need to be incorporated in the synthetic systems if we want such “ecoblocs” to survive in the wild.

By virtue of complex ecologies, the behavior of mutualisms is challenging to study and nearly impossible to predict. However, laboratory engineered mutualistic systems facilitate a better understanding of their bare essentials. On the basis of an abstract theoretical model and a modifiable experimental yeast system, we explore the environmental limits of self-organized cooperation based on the production and use of specific metabolites. We develop and test the assumptions and stability of the theoretical model by leveraging the simplicity of an artificial yeast system as a simple model of mutualism. We examine how one-off, recurring, and permanent changes to an ecological niche affect a cooperative interaction and change the population composition of an engineered mutualistic system. Moreover, we explore how the cellular burden of cooperating influences the stability of mutualism and how environmental changes shape this stability. Our results highlight the fragility of mutualisms and suggest interventions, including those that rely on the use of synthetic biology.

IMPORTANCE The power of synthetic biology is immense. Will it, however, be able to withstand the environmental pressures once released in the wild. As new technologies aim to do precisely the same, we use a much simpler model to test mathematically the effect of a changing environment on a synthetic biological system. We assume that the system is successful if it maintains proportions close to what we observe in the laboratory. Extreme deviations from the expected equilibrium are possible as the environment changes. Our study provides the conditions and the designer specifications which may need to be incorporated in the synthetic systems if we want such “ecoblocs” to survive in the wild.