Engineering microbial systems to explore ecological and evolutionary dynamics

Construction of stable microbial consortia for effective biochemical synthesis

Microbial consortia can complete otherwise arduous tasks through the cooperation of multiple microbial species. This concept has been applied to produce commodity chemicals, natural products, and biofuels. However, metabolite incompatibility and growth competition can make the microbial composition unstable, and fluctuating microbial populations reduce the efficiency of chemical production. Thus, controlling the populations and regulating the complex interactions between different strains are challenges in constructing stable microbial consortia. This Review discusses advances in synthetic biology and metabolic engineering to control social interactions within microbial cocultures, including substrate separation, byproduct elimination, crossfeeding, and quorum-sensing circuit design. Additionally, this Review addresses interdisciplinary strategies to improve the stability of microbial consortia and provides design principles for microbial consortia to enhance chemical production.

Reproducible Propagation of Species-Rich Soil Bacterial Communities Suggests Robust Underlying Deterministic Principles of Community Formation

Microbiomes are typically characterized by high species diversity but it is poorly understood how such system-level complexity can be generated and propagated. Here, we used soil microcosms as a model to study development of bacterial communities as a function of their starting complexity and environmental boundary conditions. Despite inherent stochastic variation in manipulating species-rich communities, both laboratory-mixed medium complexity (21 soil bacterial isolates in equal proportions) and high-diversity natural top-soil communities followed highly reproducible succession paths, maintaining 16S rRNA gene amplicon signatures prominent for known soil communities in general. Development trajectories and compositional states were different for communities propagated in soil microcosms than in liquid suspension. Compositional states were maintained over multiple renewed growth cycles but could be diverged by short-term pollutant exposure. The different but robust trajectories demonstrated that deterministic taxa-inherent characteristics underlie reproducible development and self-organized complexity of soil microbiomes within their environmental boundary conditions. Our findings also have direct implications for potential strategies to achieve controlled restoration of desertified land.

IMPORTANCE There is now a great awareness of the high diversity of most environmental (“free-living”) and host-associated microbiomes, but exactly how diverse microbial communities form and maintain is still highly debated. A variety of theories have been put forward, but testing them has been problematic because most studies have been based on synthetic communities that fail to accurately mimic the natural composition (i.e., the species used are typically not found together in the same environment), the diversity (usually too low to be representative), or the environmental system itself (using designs with single carbon sources or solely mixed liquid cultures). In this study, we show how species-diverse soil bacterial communities can reproducibly be generated, propagated, and maintained, either from individual isolates (21 soil bacterial strains) or from natural microbial mixtures washed from top-soil. The high replicate consistency we achieve both in terms of species compositions and developmental trajectories demonstrates the strong inherent deterministic factors driving community formation from their species composition. Generating complex soil microbiomes may provide ways for restoration of damaged soils that are prevalent on our planet.

Understanding Interaction Patterns within Deep-Sea Microbial Communities and Their Potential Applications

Environmental microbes living in communities engage in complex interspecies interactions that are challenging to decipher. Nevertheless, the interactions provide the basis for shaping community structure and functioning, which is crucial for ecosystem service. In addition, microbial interactions facilitate specific adaptation and ecological evolution processes particularly essential for microbial communities dwelling in resource-limiting habitats, such as the deep oceans. Recent technological and knowledge advancements provide an opportunity for the study of interactions within complex microbial communities, such as those inhabiting deep-sea waters and sediments. The microbial interaction studies provide insights into developing new strategies for biotechnical applications. For example, cooperative microbial interactions drive the degradation of complex organic matter such as chitins and celluloses. Such microbiologically-driven biogeochemical processes stimulate creative designs in many applied sciences. Understanding the interaction processes and mechanisms provides the basis for the development of synthetic communities and consequently the achievement of specific community functions. Microbial community engineering has many application potentials, including the production of novel antibiotics, biofuels, and other valuable chemicals and biomaterials. It can also be developed into biotechniques for waste processing and environmental contaminant bioremediation. This review summarizes our current understanding of the microbial interaction mechanisms and emerging techniques for inferring interactions in deep-sea microbial communities, aiding in future biotechnological and therapeutic applications.

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

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

The microbiome and human cancer

Microbial roles in cancer formation, diagnosis, prognosis, and treatment have been disputed for centuries. Recent studies have provocatively claimed that bacteria, viruses, and/or fungi are pervasive among cancers, key actors in cancer immunotherapy, and engineerable to treat metastases. Despite these findings, the number of microbes known to directly cause carcinogenesis remains small. Critically evaluating and building frameworks for such evidence in light of modern cancer biology is an important task. In this Review, we delineate between causal and complicit roles of microbes in cancer and trace common themes of their influence through the host's immune system, herein defined as the immuno-oncology-microbiome axis. We further review evidence for intratumoral microbes and approaches that manipulate the host's gut or tumor microbiome while projecting the next phase of experimental discovery.

Growth interferences between bacterial strains from raw cow's milk and their impact on growth of Listeria monocytogenes and Staphylococcus aureus

AIMS

The purpose of this study was to detect growth enhancing or inhibiting activity between bacterial populations from raw milk under different conditions (temperature, medium).

METHODS AND RESULTS

The interference of 24 raw milk isolates on growth of each other and on Listeria monocytogenes, Staphylococcus aureus, Bacillus subtilis and Micrococcus luteus was screened by drop assay and for selected pairs in co-cultivation experiments. By drop assay, antibacterial activity was observed for 40% of the strains. About 30% of the strains showed growth-enhancing activity on other strains. Most of the isolates were well adapted to cold temperatures and showed consistent or even increased inhibiting or enhancing effects on growth of other strains at 10°C. The growth of L. monocytogenes DSM 20600^T and S. aureus DSM 1104^T was significantly (P < 0·05) reduced in co-cultivation with Pseudomonas protegens JZ R-192.

CONCLUSIONS

Growth interferences between bacterial populations have an impact on the structure of raw milk microbiota, especially when it develops under cold storage, and it may have an effect on the prevalence of certain foodborne pathogens.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study demonstrates growth-inhibiting and also growth-enhancing interactions between raw milk bacteria, which must be considered when predicting bacterial growth and spoilage in food. A Ps. protegens strain isolated from raw milk showed an antagonistic effect on growth of L. monocytogenes in refrigerated raw milk.

The engineering of spatially linked microbial consortia - potential and perspectives

Traditional biotechnological applications of microorganisms employ mono-cultivation or co-cultivation in well-mixed vessels disregarding the potential of spatially organized cultures. Metabolic specialization and guided species interactions facilitated through spatial isolation would enable consortia of microbes to accomplish more complex functions than currently possible, for bioproduction as well as biodegradation processes. Here, we review concepts of spatially linked microbial consortia in which spatial arrangement is optimized to increase control and facilitate new species combinations. We highlight that genome-scale metabolic network models can inform the design and tuning of synthetic microbial consortia and suggest that a standardized assembly of such systems allows the combination of 'incompatibles', potentially leading to countless novel applications.

A potential tradeoff between feeding rate and aversive learning determines intoxication in a Caenorhabditis elegans host-pathogen system

Despite being the first line of defense against infection, little is known about how host-pathogen interactions determine avoidance. Caenorhabditis elegans can become infected by chemoattractant-producing bacteria through ingestion. The worms can learn to associate these chemoattractants with harm through aversive learning. As a result, the worms will avoid the pathogen. Evolutionary constraints have likely shaped the attraction, intoxication and learning dynamics between bacteria and C. elegans, but these have not been explored. Using bacteria engineered to express an acylhomoserine lactone chemoattractant and a nematicidal protein, we explored how manipulating the amount of attractant produced by the bacteria affects learning and intoxication in mixed stage populations of C. elegans. We found that increasing the production rate of the chemoattractant increased the feeding rate in C. elegans, but decreased the time required for C. elegans to learn to avoid the chemoattractant. Learning generally coincided with a decreased feeding rate. We also observed that the percentage of intoxicated worms was maximized at intermediate production rates of the attractant. We propose that interactions between attractant driven feeding rate and aversive learning are likely responsible for this trend. Our results increase our understanding of behavioral avoidance in C. elegans and have implications in understanding host-pathogen dynamics that shape avoidance.

Timing between successive introduction events determines establishment success in bacteria with an Allee effect

Propagule pressure is a leading determinant of population establishment. Yet, an experimental understanding of how propagule size and number (two principal parts of propagule pressure) determine establishment success remains incomplete. Theoretical studies suggest that the timing between introduction events, a component of propagule number, can influence establishment success. However, this dynamic has rarely been explored experimentally. Using Escherichia coli engineered with an Allee effect, we investigated how the timing of two introduction events influences establishment. For populations introduced below the Allee threshold, establishment occurred if the time between two introduction events was sufficiently short, with the length of time between events further reduced by reducing growth rate. Interestingly, we observed that as the density of bacteria introduced in one introduction event increased, the time between introduction events that allowed for establishment increased. Using a mathematical model, we provide support that the mechanism behind these trends is the ability of the first population to modify the environment, which can pave the way for establishment of the second population. Our results provide experimental evidence that the temporal distribution of introduction events regulates establishment, furthering our understanding of propagule pressure and may have implications in invasion biology and infectious disease.

Exosome Research and Co-culture Study

In biological systems, extracellular vesicles including exosomes have recently been revealed to play a significant role in the communication between various cells, and the number of papers on this subject has dramatically increased. In current conventional exosome studies, the standard research method is to use liquid biopsies to analyze extracts of various disease exosomes. However, exosomes are only one of many key players in natural cellular interactions. Reproducing the phenomena occurring in vivo and investigating the interactions are required in order to examine their role fully. For exosome research, an alternative to the liquid biopsy method for observing natural interactions is the co-culturing technique. It does not require an exosome extraction procedure, and while the technique has been used in many studies thus far, its application to exosome research has been limited. However, the use of co-culturing technologies is necessary to examine the essential interactions of exosomes. An overview of exosome research methodologies and co-culturing systems is thus provided here.

Exploiting rhizosphere microbial cooperation for developing sustainable agriculture strategies

The rhizosphere hosts a considerable microbial community. Among that community, bacteria called plant growth-promoting rhizobacteria (PGPR) can promote plant growth and defense against diseases using diverse distinct plant-beneficial functions. Crop inoculation with PGPR could allow to reduce the use of pesticides and fertilizers in agrosystems. However, microbial crop protection and growth stimulation would be more efficient if cooperation between rhizosphere bacterial populations was taken into account when developing biocontrol agents and biostimulants. Rhizospheric bacteria live in multi-species biofilms formed all along the root surface or sometimes inside the plants (i.e., endophyte). PGPR cooperate with their host plants and also with other microbial populations inside biofilms. These interactions are mediated by a large diversity of microbial metabolites and physical signals that trigger cell-cell communication and appropriate responses. A better understanding of bacterial behavior and microbial cooperation in the rhizosphere could allow for a more successful use of bacteria in sustainable agriculture. This review presents an ecological view of microbial cooperation in agrosystems and lays the emphasis on the main microbial metabolites involved in microbial cooperation, plant health protection, and plant growth stimulation. Eco-friendly inoculant consortia that will foster microbe-microbe and microbe-plant cooperation can be developed to promote crop growth and restore biodiversity and functions lost in agrosystems.

Initial evenness determines diversity and cell density dynamics in synthetic microbial ecosystems

The effect of initial evenness on the temporal trajectory of synthetic communities in comprehensive, low-volume microcosm studies remains unknown. We used flow cytometric fingerprinting and 16S rRNA gene amplicon sequencing to assess the impact of time on community structure in one hundred synthetic ecosystems of fixed richness but varying initial evenness. Both methodologies uncovered a similar reduction in diversity within synthetic communities of medium and high initial evenness classes. However, the results of amplicon sequencing showed that there were no significant differences between and within the communities in all evenness groups at the end of the experiment. Nevertheless, initial evenness significantly impacted the cell density of the community after five medium transfers. Highly even communities retained the highest cell densities at the end of the experiment. The relative abundances of individual species could be associated to particular evenness groups, suggesting that their presence was dependent on the initial evenness of the synthetic community. Our results reveal that using synthetic communities for testing ecological hypotheses requires prior assessment of initial evenness, as it impacts temporal dynamics.

Modeling Microbial Communities: A Call for Collaboration between Experimentalists and Theorists

With our growing understanding of the impact of microbial communities, understanding how such communities function has become a priority. The influence of microbial communities is widespread. Human-associated microbiota impacts health, environmental microbes determine ecosystem sustainability, and microbe-driven industrial processes are expanding. This broad range of applications has led to a wide range of approaches to analyze and describe microbial communities. In particular, theoretical work based on mathematical modeling has been a steady source of inspiration for explaining and predicting microbial community processes. Here, we survey some of the modeling approaches used in different contexts. We promote classifying different approaches using a unified platform, and encourage cataloging the findings in a database. We believe that the synergy emerging from a coherent collection facilitates a better understanding of important processes that determine microbial community functions. We emphasize the importance of close collaboration between theoreticians and experimentalists in formulating, classifying, and improving models of microbial communities.

A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis

Microbial ecologists are increasingly turning to small, synthesized ecosystems1-5 as a reductionist tool to probe the complexity of native microbiomes6,7. Concurrently, synthetic biologists have gone from single-cell gene circuits8-11 to controlling whole populations using intercellular signalling12-16. The intersection of these fields is giving rise to new approaches in waste recycling17, industrial fermentation18, bioremediation19 and human health16,20. These applications share a common challenge7 well-known in classical ecology21,22-stability of an ecosystem cannot arise without mechanisms that prohibit the faster-growing species from eliminating the slower. Here, we combine orthogonal quorum-sensing systems and a population control circuit with diverse self-limiting growth dynamics to engineer two 'ortholysis' circuits capable of maintaining a stable co-culture of metabolically competitive Salmonella typhimurium strains in microfluidic devices. Although no successful co-cultures are observed in a two-strain ecology without synthetic population control, the 'ortholysis' design dramatically increases the co-culture rate from 0% to approximately 80%. Agent-based and deterministic modelling reveal that our system can be adjusted to yield different dynamics, including phase-shifted, antiphase or synchronized oscillations, as well as stable steady-state population densities. The 'ortholysis' approach establishes a paradigm for constructing synthetic ecologies by developing stable communities of competitive microorganisms without the need for engineered co-dependency.

Cooperation and competition shape ecological resistance during periodic spatial disturbance of engineered bacteria

Cooperation is fundamental to the survival of many bacterial species. Previous studies have shown that spatial structure can both promote and suppress cooperation. Most environments where bacteria are found are periodically disturbed, which can affect the spatial structure of the population. Despite the important role that spatial disturbances play in maintaining ecological relationships, it remains unclear as to how periodic spatial disturbances affect bacteria dependent on cooperation for survival. Here, we use bacteria engineered with a strong Allee effect to investigate how the frequency of periodic spatial disturbances affects cooperation. We show that at intermediate frequencies of spatial disturbance, the ability of the bacterial population to cooperate is perturbed. A mathematical model demonstrates that periodic spatial disturbance leads to a tradeoff between accessing an autoinducer and accessing nutrients, which determines the ability of the bacteria to cooperate. Based on this relationship, we alter the ability of the bacteria to access an autoinducer. We show that increased access to an autoinducer can enhance cooperation, but can also reduce ecological resistance, defined as the ability of a population to resist changes due to disturbance. Our results may have implications in maintaining stability of microbial communities and in the treatment of infectious diseases.

Collective Space-Sensing Coordinates Pattern Scaling in Engineered Bacteria

Scale invariance refers to the maintenance of a constant ratio of developing organ size to body size. Although common, its underlying mechanisms remain poorly understood. Here, we examined scaling in engineered Escherichia coli that can form self-organized core-ring patterns in colonies. We found that the ring width exhibits perfect scale invariance to the colony size. Our analysis revealed a collective space-sensing mechanism, which entails sequential actions of an integral feedback loop and an incoherent feedforward loop. The integral feedback is implemented by the accumulation of a diffusive chemical produced by a colony. This accumulation, combined with nutrient consumption, sets the timing for ring initiation. The incoherent feedforward is implemented by the opposing effects of the domain size on the rate and duration of ring maturation. This mechanism emphasizes a role of timing control in achieving robust pattern scaling and provides a new perspective in examining the phenomenon in natural systems.

Exometabolomics Assisted Design and Validation of Synthetic Obligate Mutualism

Synthetic microbial ecology has the potential to enhance the productivity and resiliency of biotechnology processes compared to approaches using single isolates. Engineering microbial consortia is challenging; however, one approach that has attracted significant attention is the creation of synthetic obligate mutualism using auxotrophic mutants that depend on each other for exchange or cross-feeding of metabolites. Here, we describe the integration of mutant library fitness profiling with mass spectrometry based exometabolomics as a method for constructing synthetic mutualism based on cross-feeding. Two industrially important species lacking known ecological interactions, Zymomonas mobilis and Escherichia coli, were selected as the test species. Amino acid exometabolites identified in the spent medium of Z. mobilis were used to select three corresponding E. coli auxotrophs (proA, pheA and IlvA), as potential E. coli counterparts for the coculture. A pooled mutant fitness assay with a Z. mobilis transposon mutant library was used to identify mutants with improved growth in the presence of E. coli. An auxotroph mutant in a gene (ZMO0748) with sequence similarity to cysteine synthase A (cysK), was selected as the Z. mobilis counterpart for the coculture. Exometabolomic analysis of spent E. coli medium identified glutathione related metabolites as potentially available for rescue of the Z. mobilis cysteine synthase mutant. Three sets of cocultures between the Z. mobilis auxotroph and each of the three E. coli auxotrophs were monitored by optical density for growth and analyzed by flow cytometry to confirm high cell counts for each species. Taken together, our methods provide a technological framework for creating synthetic mutualisms combining existing screening based methods and exometabolomics for both the selection of obligate mutualism partners and elucidation of metabolites involved in auxotroph rescue.

On the Origins and Control of Community Types in the Human Microbiome

Microbiome-based stratification of healthy individuals into compositional categories, referred to as "enterotypes" or "community types", holds promise for drastically improving personalized medicine. Despite this potential, the existence of community types and the degree of their distinctness have been highly debated. Here we adopted a dynamic systems approach and found that heterogeneity in the interspecific interactions or the presence of strongly interacting species is sufficient to explain community types, independent of the topology of the underlying ecological network. By controlling the presence or absence of these strongly interacting species we can steer the microbial ecosystem to any desired community type. This open-loop control strategy still holds even when the community types are not distinct but appear as dense regions within a continuous gradient. This finding can be used to develop viable therapeutic strategies for shifting the microbial composition to a healthy configuration.

Population-reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population-reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population-reaction model. We also show that population-reaction models can be applied to various ecological concepts, such as predator-prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms.

Bacterial social interactions drive the emergence of differential spatial colony structures

Background

Social interactions have been increasingly recognized as one of the major factors that contribute to the dynamics and function of bacterial communities. To understand their functional roles and enable the design of robust synthetic consortia, one fundamental step is to determine the relationship between the social interactions of individuals and the spatiotemporal structures of communities.

Results

We present a systematic computational survey on this relationship for two-species communities by developing and utilizing a hybrid computational framework that combines discrete element techniques with reaction-diffusion equations. We found that deleterious interactions cause an increased variance in relative abundance, a drastic decrease in surviving lineages, and a rough expanding front. In contrast, beneficial interactions contribute to a reduced variance in relative abundance, an enhancement in lineage number, and a smooth expanding front. We also found that mutualism promotes spatial homogeneity and population robustness while competition increases spatial segregation and population fluctuations. To examine the generality of these findings, a large set of initial conditions with varying density and species abundance was tested and analyzed. In addition, a simplified mathematical model was developed to provide an analytical interpretation of the findings.

Conclusions

This work advances our fundamental understanding of bacterial social interactions and population structures and, simultaneously, benefits synthetic biology for facilitated engineering of artificial microbial consortia.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0188-5) contains supplementary material, which is available to authorized users.

Stress-response balance drives the evolution of a network module and its host genome

Stress response genes and their regulators form networks that underlie drug resistance. These networks often have an inherent tradeoff: their expression is costly in the absence of stress, but beneficial in stress. They can quickly emerge in the genomes of infectious microbes and cancer cells, protecting them from treatment. Yet, the evolution of stress resistance networks is not well understood. Here, we use a two-component synthetic gene circuit integrated into the budding yeast genome to model experimentally the adaptation of a stress response module and its host genome in three different scenarios. In agreement with computational predictions, we find that: (i) intra-module mutations target and eliminate the module if it confers only cost without any benefit to the cell; (ii) intra- and extra-module mutations jointly activate the module if it is potentially beneficial and confers no cost; and (iii) a few specific mutations repeatedly fine-tune the module's noisy response if it has excessive costs and/or insufficient benefits. Overall, these findings reveal how the timing and mechanisms of stress response network evolution depend on the environment.

An Economic Framework of Microbial Trade

A large fraction of microbial life on earth exists in complex communities where metabolic exchange is vital. Microbes trade essential resources to promote their own growth in an analogous way to countries that exchange goods in modern economic markets. Inspired by these similarities, we developed a framework based on general equilibrium theory (GET) from economics to predict the population dynamics of trading microbial communities. Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities. We find that the economic concept of comparative advantage is a necessary condition for mutualistic trade. Our model suggests that microbial communities can grow faster when species are unable to produce essential resources that are obtained through trade, thereby promoting metabolic specialization and increased intercellular exchange. Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum. We experimentally tested this tradeoff using a synthetic consortium of Escherichia coli cells and found the results match the predictions of the model. This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

Collective antibiotic tolerance: mechanisms, dynamics and intervention

Bacteria have developed resistance against every antibiotic at a rate that is alarming considering the timescale at which new antibiotics are developed. Thus, there is a critical need to use antibiotics more effectively, extend the shelf life of existing antibiotics and minimize their side effects. This requires understanding the mechanisms underlying bacterial drug responses. Past studies have focused on survival in the presence of antibiotics by individual cells, as genetic mutants or persisters. Also important, however, is the fact that a population of bacterial cells can collectively survive antibiotic treatments lethal to individual cells. This tolerance can arise by diverse mechanisms, including resistance-conferring enzyme production, titration-mediated bistable growth inhibition, swarming and interpopulation interactions. These strategies can enable rapid population recovery after antibiotic treatment and provide a time window during which otherwise susceptible bacteria can acquire inheritable genetic resistance. Here, we emphasize the potential for targeting collective antibiotic tolerance behaviors as an antibacterial treatment strategy.

Mobile genetic elements are involved in bacterial sociality

Mobile genetic elements in bacteria are enriched in genes participating in social behaviors, suggesting an evolutionary link between gene mobility and social evolution. Cooperative behaviors, like the production of secreted public good molecules, are susceptible to the invasion of non-cooperative individuals, and their evolutionary maintenance requires mechanisms ensuring that benefits are directed preferentially to cooperators. In order to investigate the reasons for the mobility of public good genes, we designed a synthetic bacterial system where we control and quantify the transfer of public good production genes. In our recent study, we have experimentally shown that horizontal transfer helps maintain public good production in the face of both non-producer organisms and non-producer plasmids. Transfer spreads genes to neighboring cells, thus increasing relatedness and directing a higher proportion of public good benefits to producers. The effect is the strongest when public good genes undergo epidemics dynamics, making horizontal transfer especially relevant for pathogenic bacteria that repeatedly infect new hosts and base their virulence on costly public goods. The promotion of cooperation may be a general consequence of horizontal gene transfer in prokaryotes. Our work has an intriguing parallel, cultural transmission, where horizontal transfer, such as teaching, may preferentially promote cooperative behaviors.

Co-culture systems and technologies: taking synthetic biology to the next level

Co-culture techniques find myriad applications in biology for studying natural or synthetic interactions between cell populations. Such techniques are of great importance in synthetic biology, as multi-species cell consortia and other natural or synthetic ecology systems are widely seen to hold enormous potential for foundational research as well as novel industrial, medical and environmental applications with many proof-of-principle studies in recent years. What is needed for co-cultures to fulfil their potential? Cell-cell interactions in co-cultures are strongly influenced by the extracellular environment, which is determined by the experimental set-up, which therefore needs to be given careful consideration. An overview of existing experimental and theoretical co-culture set-ups in synthetic biology and adjacent fields is given here, and challenges and opportunities involved in such experiments are discussed. Greater focus on foundational technology developments for co-cultures is needed for many synthetic biology systems to realize their potential in both applications and answering biological questions.

Modulating the frequency and bias of stochastic switching to control phenotypic variation

Mechanisms that control cell-to-cell variation in gene expression ('phenotypic variation') can determine a population's growth rate, robustness, adaptability and capacity for complex behaviours. Here we describe a general strategy (termed FABMOS) for tuning the phenotypic variation and mean expression of cell populations by modulating the frequency and bias of stochastic transitions between 'OFF' and 'ON' expression states of a genetic switch. We validated the strategy experimentally using a synthetic fim switch in Escherichia coli. Modulating the frequency of switching can generate a bimodal (low frequency) or a unimodal (high frequency) population distribution with the same mean expression. Modulating the bias as well as the frequency of switching can generate a spectrum of bimodal and unimodal distributions with the same mean expression. This remarkable control over phenotypic variation, which cannot be easily achieved with standard gene regulatory mechanisms, has many potential applications for synthetic biology, engineered microbial ecosystems and experimental evolution.

Synthetic microbial communities

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Microbial interactions and system function are two ways to study communities.

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Natural microbial communities are difficult to define and to study.

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Synthetic microbial communities are comprehensible systems of reduced complexity.

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Synthetic communities keep key features of natural ones and are amenable to modelling.

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Synthetic microbial communities are gaining importance in biotechnology.

While natural microbial communities are composed of a mix of microbes with often unknown functions, the construction of synthetic microbial communities allows for the generation of defined systems with reduced complexity. Used in a top-down approach, synthetic communities serve as model systems to ask questions about the performance and stability of microbial communities. In a second, bottom-up approach, synthetic microbial communities are used to study which conditions are necessary to generate interaction patterns like symbiosis or competition, and how higher order community structure can emerge from these. Besides their obvious value as model systems to understand the structure, function and evolution of microbial communities as complex dynamical systems, synthetic communities can also open up new avenues for biotechnological applications.

Programmed Allee effect in bacteria causes a tradeoff between population spread and survival

Dispersal is necessary for spread into new habitats, but it has also been shown to inhibit spread. Theoretical studies have suggested that the presence of a strong Allee effect may account for these counterintuitive observations. Experimental demonstration of this notion is lacking due to the difficulty in quantitative analysis of such phenomena in a natural setting. We engineered Escherichia coli to exhibit a strong Allee effect and examined how the Allee effect would affect the spread of the engineered bacteria. We showed that the Allee effect led to a biphasic dependence of bacterial spread on the dispersal rate: spread is promoted for intermediate dispersal rates but inhibited at low or high dispersal rates. The shape of this dependence is contingent upon the initial density of the source population. Moreover, the Allee effect led to a tradeoff between effectiveness of population spread and survival: increasing the number of target patches during dispersal allows more effective spread, but it simultaneously increases the risk of failing to invade or of going extinct. We also observed that total population growth is transiently maximized at an intermediate number of target patches. Finally, we demonstrate that fluctuations in cell growth may contribute to the paradoxical relationship between dispersal and spread. Our results provide direct experimental evidence that the Allee effect can explain the apparently paradoxical effects of dispersal on spread and have implications for guiding the spread of cooperative organisms.

Transcriptome analysis of a microbial coculture in which the cell populations are separated by a membrane

The microbial coculture of multiple cell populations is used to study community evolution and for bioengineering applications. The cells in coculture undergo dynamic changes because of cell-cell and cell-environment interactions. Transcriptome analysis allows us to study the molecular basis of these changes in cell physiology. For transcriptome analysis, it is essential that the cell populations in the coculture are harvested separately. Here, we describe a method for transcriptome analysis of a microbial coculture in which two different cell populations are separated by a porous membrane.

Synthetic microbial ecosystems: an exciting tool to understand and apply microbial communities

Many microbial ecologists have described the composition of microbial communities in a plenitude of environments, which has greatly improved our basic understanding of microorganisms and ecosystems. However, the factors and processes that influence the behaviour and functionality of an ecosystem largely remain black boxes when using conventional approaches. Therefore, synthetic microbial ecology has gained a lot of interest in the last few years. Because of their reduced complexity and increased controllability, synthetic communities are often preferred over complex communities to examine ecological theories. They limit the factors that influence the microbial community to a minimum, allowing their management and identifying specific community responses. However, besides their use for basic research, synthetic ecosystems also found their way towards different applications, like industrial fermentation and bioremediation. Here, we review why and how synthetic microbial communities are applied for research purposes and for which applications they have been and could be successfully used.

Genetic engineering of multispecies microbial cell factories as an alternative for bioenergy production

There is currently much interest in developing technology to use microlgae or cyanobacteria for the production of bioenergy and biomaterials. Here, we summarize some remarkable achievements in strains improvement by traditional genetic engineering and discuss common drawbacks for further progress. We present general knowledge on natural microalgal-bacterial mutualistic interactions and discuss the potential of recent developments in genetic engineering of multispecies microbial cell factories. This synthetic biology approach would rely on the assembly of complex metabolic networks from optimized metabolic modules such as photosynthetic or nitrogen-fixing parts.

Construction of bacteria-eukaryote synthetic mutualism

Mutualism is ubiquitous in nature but is known to be intrinsically vulnerable with regard to both population dynamics and evolution. Synthetic ecology has indicated that it is feasible for organisms to establish novel mutualism merely through encountering each other by showing that it is feasible to construct synthetic mutualism between organisms. However, bacteria-eukaryote mutualism, which is ecologically important, has not yet been constructed. In this study, we synthetically constructed mutualism between a bacterium and a eukaryote by using two model organisms. We mixed a bacterium, Escherichia coli (a genetically engineered glutamine auxotroph), and an amoeba, Dictyostelium discoideum, in 14 sets of conditions in which each species could not grow in monoculture but potentially could grow in coculture. Under a single condition in which the bacterium and amoeba mutually compensated for the lack of required nutrients (lipoic acid and glutamine, respectively), both species grew continuously through several subcultures, essentially establishing mutualism. Our results shed light on the establishment of bacteria-eukaryote mutualism and indicate that a bacterium and eukaryote pair in nature also has a non-negligible possibility of establishing novel mutualism if the organisms are potentially mutualistic.

A synthetic biology approach to understanding cellular information processing

The survival of cells and organisms requires proper responses to environmental signals. These responses are governed by cellular networks, which serve to process diverse environmental cues. Biological networks often contain recurring network topologies called "motifs". It has been recognized that the study of such motifs allows one to predict the response of a biological network and thus cellular behavior. However, studying a single motif in complete isolation of all other network motifs in a natural setting is difficult. Synthetic biology has emerged as a powerful approach to understanding the dynamic properties of network motifs. In addition to testing existing theoretical predictions, construction and analysis of synthetic gene circuits has led to the discovery of novel motif dynamics, such as how the combination of simple motifs can lead to autonomous dynamics or how noise in transcription and translation can affect the dynamics of a motif. Here, we review developments in synthetic biology as they pertain to increasing our understanding of cellular information processing. We highlight several types of dynamic behaviors that diverse motifs can generate, including the control of input/output responses, the generation of autonomous spatial and temporal dynamics, as well as the influence of noise in motif dynamics and cellular behavior.

Stochastic simulations of a synthetic bacteria-yeast ecosystem

BACKGROUND

The field of synthetic biology has greatly evolved and numerous functions can now be implemented by artificially engineered cells carrying the appropriate genetic information. However, in order for the cells to robustly perform complex or multiple tasks, co-operation between them may be necessary. Therefore, various synthetic biological systems whose functionality requires cell-cell communication are being designed. These systems, microbial consortia, are composed of engineered cells and exhibit a wide range of behaviors. These include yeast cells whose growth is dependent on one another, or bacteria that kill or rescue each other, synchronize, behave as predator-prey ecosystems or invade cancer cells.

RESULTS

In this paper, we study a synthetic ecosystem comprising of bacteria and yeast that communicate with and benefit from each other using small diffusible molecules. We explore the behavior of this heterogeneous microbial consortium, composed of Saccharomyces cerevisiae and Escherichia coli cells, using stochastic modeling. The stochastic model captures the relevant intra-cellular and inter-cellular interactions taking place in and between the eukaryotic and prokaryotic cells. Integration of well-characterized molecular regulatory elements into these two microbes allows for communication through quorum sensing. A gene controlling growth in yeast is induced by bacteria via chemical signals and vice versa. Interesting dynamics that are common in natural ecosystems, such as obligatory and facultative mutualism, extinction, commensalism and predator-prey like dynamics are observed. We investigate and report on the conditions under which the two species can successfully communicate and rescue each other.

CONCLUSIONS

This study explores the various behaviors exhibited by the cohabitation of engineered yeast and bacterial cells. The way that the model is built allows for studying the dynamics of any system consisting of two species communicating with one another via chemical signals. Therefore, key information acquired by our model may potentially drive the experimental design of various synthetic heterogeneous ecosystems.