Spatial interference scale as a determinant of microbial range expansion

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.

Modularized Design and Construction of Tunable Microbial Consortia with Flexible Topologies

Complex and fluid bacterial community compositions are critical to diversity, stability, and function. However, quantitative and mechanistic descriptions of the dynamics of such compositions are still lacking. Here, we develop a modularized design framework that allows for bottom-up construction and the study of synthetic bacterial consortia with different topologies. We showcase the microbial consortia design and building process by constructing amensalism and competition consortia using only genetic circuit modules to engineer different strains to form the community. Functions of modules and hosting strains are validated and quantified to calibrate dynamic parameters, which are then directly fed into a full mechanistic model to accurately predict consortia composition dynamics for both amensalism and competition without further fitting. More importantly, such quantitative understanding successfully identifies the experimental conditions to achieve coexistence composition dynamics. These results illustrate the process of both computationally and experimentally building up bacteria consortia complexity and hence achieve robust control of such fluid systems.

Genetic mixing and demixing on expanding spherical frontiers

Genetic fluctuation during range expansion is a key process driving evolution. When a bacterial population is expanding on a 2D surface, random fluctuations in the growth of the pioneers at the front line cause a strong demixing of genotypes. Even when there is no selective advantage, sectors of low genetic diversity are formed. Experimental studies of range expansions in surface-attached colonies of fluorescently labelled micro-organisms have contributed significantly to our understanding of fundamental evolutionary dynamics. However, experimental studies on genetic fluctuations in 3D range expansions have been sparse, despite their importance for tumour or biofilm development. We encapsulated populations of two fluorescent Escherichia coli strains in inoculation droplets (volumes [Formula: see text] nl). The confined ensemble of cells grew when embedded in a hydrogel-with nutrients-and developed 3D colonies with well-defined, sector-like regions. Using confocal laser scanning microscopy, we imaged the development of 3D colonies and the emergence of sectors. We characterized how cell concentration in the inoculation droplet controls sectors, growth rate, and the transition from branched colonies to quasi-spherical colonies. We further analysed how sectors on the surface change over time. We complement these experimental results with a modified 3D Eden growth model. The model in 3D spherical growth predicts a phase, where sectors are merging, followed by a steady increase (constant rate), and the experimentally analysed sectors were consistent with this prediction. Therefore, our results demonstrate qualitative differences between radial (2D) and spherical (3D) range expansions and their importance in gene fixation processes.

The evolution of short- and long-range weapons for bacterial competition

Bacteria possess a diverse range of mechanisms for inhibiting competitors, including bacteriocins, tailocins, type VI secretion systems and contact-dependent inhibition (CDI). Why bacteria have evolved such a wide array of weapon systems remains a mystery. Here we develop an agent-based model to compare short-range weapons that require cell-cell contact, with long-range weapons that rely on diffusion. Our model predicts that contact weapons are useful when an attacking strain is outnumbered, facilitating invasion and establishment. By contrast, ranged weapons tend to be effective only when attackers are abundant. We test our predictions with the opportunistic pathogen Pseudomonas aeruginosa, which naturally carries multiple weapons, including CDI and diffusing tailocins. As predicted, short-range CDI can function at low and high frequencies, while long-range tailocins require high frequency and cell density to function effectively. Head-to-head competition experiments with the two weapon types further support our predictions: a tailocin attacker defeats CDI only when it is numerically dominant, but then we find it can be devastating. Finally, we show that the two weapons work well together when one strain employs both. We conclude that short- and long-range weapons serve different functions and allow bacteria to fight both as individuals and as a group.

The Construction of the Self-Induced Sal System and Its Application in Salicylic Acid Production

The design and construction of more complex and delicate genetic control circuits suffer from poor orthogonality in quorum sensing (QS) systems. The Sal system, which relies on salicylic acid as a signaling molecule, is an artificially engineered regulatory system with a structure that differs significantly from that of natural QS signaling molecules. Salicylic acid is an important drug precursor, mainly used in the production of drugs such as aspirin and anti-HIV drugs. However, there have been no reports on the construction of a self-induced Sal system in single cells. In this study, a high-copy plasmid backbone was used to construct the regulatory proteins and a self-induced promoter of salicylic acid in E. coli by adjusting the precise regulation of key gene expression; the sensitivity and induction range of this system were improved. Subsequently, the exogenous gene pchBA was introduced in E. coli to extend the shikimate pathway and synthesize salicylic acid, resulting in the construction of the first complete self-induced Sal system. Finally, the self-induced Sal System was combined with artificial trans-encoded sRNAs (atsRNAs) to repress the growth-essential gene ppc and accumulate the precursor substance PEP, thereby increasing the titer of salicylic acid by 151%. This construction of a self-induced artificial system introduces a new tool for selecting communication tools and induction systems in synthetic biology and metabolic engineering, but also demonstrates a self-inducible pathway design strategy for salicylic acid biosynthesis.

Effectiveness of Pseudomonas aeruginosa type VI secretion system relies on toxin potency and type IV pili-dependent interaction

The type VI secretion system (T6SS) is an antibacterial weapon that is used by numerous Gram-negative bacteria to gain competitive advantage by injecting toxins into adjacent prey cells. Predicting the outcome of a T6SS-dependent competition is not only reliant on presence-absence of the system but instead involves a multiplicity of factors. Pseudomonas aeruginosa possesses 3 distinct T6SSs and a set of more than 20 toxic effectors with diverse functions including disruption of cell wall integrity, degradation of nucleic acids or metabolic impairment. We generated a comprehensive collection of mutants with various degrees of T6SS activity and/or sensitivity to each individual T6SS toxin. By imaging whole mixed bacterial macrocolonies, we then investigated how these P. aeruginosa strains gain a competitive edge in multiple attacker/prey combinations. We observed that the potency of single T6SS toxin varies significantly from one another as measured by monitoring the community structure, with some toxins acting better in synergy or requiring a higher payload. Remarkably the degree of intermixing between preys and attackers is also key to the competition outcome and is driven by the frequency of contact as well as the ability of the prey to move away from the attacker using type IV pili-dependent twitching motility. Finally, we implemented a computational model to better understand how changes in T6SS firing behaviours or cell-cell contacts lead to population level competitive advantages, thus providing conceptual insight applicable to all types of contact-based competition.

Agent-Based Modeling of Microbial Communities

Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.

Individual-Based Modeling of Spatial Dynamics of Chemotactic Microbial Populations

One important direction of synthetic biology is to establish desired spatial structures from microbial populations. Underlying this structural development process are different driving factors, among which bacterial motility and chemotaxis serve as a major force. Here, we present an individual-based, biophysical computational framework for mechanistic and multiscale simulation of the spatiotemporal dynamics of motile and chemotactic microbial populations. The framework integrates cellular movement with spatial population growth, mechanical and chemical cellular interactions, and intracellular molecular kinetics. It is validated by a statistical comparison of single-cell chemotaxis simulations with reported experiments. The framework successfully captures colony range expansion of growing isogenic populations and also reveals chemotaxis-modulated, spatial patterns of a two-species amensal community. Partial differential equation-based models subsequently validate these simulation findings. This study provides a versatile computational tool to uncover the fundamentals of microbial spatial ecology as well as to facilitate the design of synthetic consortia for desired spatial patterns.

Engineering microbial consortia with rationally designed cellular interactions

Synthetic microbial consortia represent a frontier of synthetic biology that promises versatile engineering of cellular functions. They are primarily developed through the design and construction of cellular interactions that coordinate individual dynamics and generate collective behaviors. Here we review recent advances in the engineering of synthetic communities through cellular-interaction programming. We first examine fundamental building blocks for intercellular communication and unidirectional positive and negative interactions. We then recap the assembly of the building blocks for creating bidirectional interactions in two-species ecosystems, which is followed by the discussion of engineering toward complex communities with increasing species numbers, under spatial contexts, and via model-guided design. We conclude by summarizing major challenges and future opportunities of engineered microbial ecosystems.

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.

Construction of Synthetic Microbial Ecosystems and the Regulation of Population Proportion

With the development of synthetic biology, the design and application of microbial consortia have received increasing attention. However, the construction of synthetic ecosystems is still hampered by our limited ability to rapidly develop microbial consortia with the required dynamics and functions. By using modular design, we constructed synthetic competitive and symbiotic ecosystems with Escherichia coli. Two ecological relationships were realized by reconfiguring the layout between the communication and effect modules. Furthermore, we designed inducible synthetic ecosystems to regulate subpopulation ratios. With the addition of different inducers, a wide range of strain ratios between subpopulations was achieved. These inducible synthetic ecosystems enabled a larger volume of population regulation and simplified culture conditions. The synthetic ecosystems we constructed combined both basic and applied functionalities and expanded the toolkit of synthetic biology research.

Antagonism between killer yeast strains as an experimental model for biological nucleation dynamics

Antagonistic interactions are widespread in the microbial world and affect microbial evolutionary dynamics. Natural microbial communities often display spatial structure, which affects biological interactions, but much of what we know about microbial antagonism comes from laboratory studies of well-mixed communities. To overcome this limitation, we manipulated two killer strains of the budding yeast Saccharomyces cerevisiae, expressing different toxins, to independently control the rate at which they released their toxins. We developed mathematical models that predict the experimental dynamics of competition between toxin-producing strains in both well-mixed and spatially structured populations. In both situations, we experimentally verified theory’s prediction that a stronger antagonist can invade a weaker one only if the initial invading population exceeds a critical frequency or size. Finally, we found that toxin-resistant cells and weaker killers arose in spatially structured competitions between toxin-producing strains, suggesting that adaptive evolution can affect the outcome of microbial antagonism in spatial settings.

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.

Exploiting interbacterial antagonism for microbiome engineering

Interbacterial antagonism can significantly impact microbiome assembly and stability and can potentially be exploited to modulate microbes and microbial communities in diverse environments, ranging from natural habitats to industrial bioreactors. Here we highlight key mechanisms of interspecies antagonism that rely on direct cell-to-cell contact or diffusion of secreted biomolecules, and discuss recent advances to provide altered function and specificities for microbiome engineering. We further outline the use of ecological design principles based on antagonistic interactions for bottom-up assembly of synthetic microbial communities. Manipulating microbial communities through these negative interactions will be critical for understanding complex microbiome processes and properties and developing new applications of microbiome engineering.

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.

Theoretical modelling of competitive microbial range expansion with heterogeneous mechanical interactions

Microbial range expansion experiments provide insight into the complex link between dynamic structure, pattern formation and evolutionary dynamics of growing populations. In this work, we develop a theoretical model in order to investigate the interplay of growth statistics and mechanical interactions which are implemented as division driven pushing and swapping of cells. For the case of the competitive growth of a strongly and a weakly interacting strain we investigate the influence of different mean division times, as well as different mechanical interactions on the development of the colony. Our results show that the susceptibility to cell division induced pushing has a much stronger influence on the structure of the colony than cell sorting towards the colony's perimeter. Motivated by microbial range expansion experiments of Neisseria gonorrhoeae bacteria, we also consider the influence of mutating cells on the structure of the colony. We show that the outgrowth of the three different strains is strongly influenced by the relative strengths of their mechanical interaction. The experimentally observed patterns are reproduced for mechanical interactions of the mutants, which range between those of the strongly and weakly interacting strain.

Higher-Order Interactions Dampen Pairwise Competition in the Zebrafish Gut Microbiome

The microbial communities resident in animal intestines are composed of multiple species that together play important roles in host development, health, and disease. Due to the complexity of these communities and the difficulty of characterizing them in situ, the determinants of microbial composition remain largely unknown. Further, it is unclear for many multispecies consortia whether their species-level makeup can be predicted based on an understanding of pairwise species interactions or whether higher-order interactions are needed to explain emergent compositions. To address this, we examine commensal intestinal microbes in larval zebrafish, initially raised germfree, to allow the introduction of controlled combinations of bacterial species. Using a dissection and plating assay, we demonstrate the construction of communities of one to five bacterial species and show that the outcomes from the two-species competitions fail to predict species abundances in more complex communities. With multiple species present, interbacterial interactions become weaker, suggesting that higher-order interactions in the vertebrate gut stabilize complex communities.IMPORTANCE Understanding the rules governing the composition of the diverse microbial communities that reside in the vertebrate gut environment will enhance our ability to manipulate such communities for therapeutic ends. Synthetic microbial communities, assembled from specific combinations of microbial species in germfree animals, allow investigation of the fundamental question of whether multispecies community composition can be predicted solely based on the combined effects of interactions between pairs of species. If so, such predictability would enable the construction of communities with desired species from the bottom up. If not, the apparent higher-order interactions imply that emergent community-level characteristics are crucial. Our findings using up to five coexisting native bacterial species in larval zebrafish, a model vertebrate, provide experimental evidence for higher-order interactions and, moreover, show that these interactions promote the coexistence of microbial species in the gut.

Information differences across spatial resolutions and scales for disease surveillance and analysis: The case of Visceral Leishmaniasis in Brazil

Nationwide disease surveillance at a high spatial resolution is desired for many infectious diseases, including Visceral Leishmaniasis. Statistical and mathematical models using data collected from surveillance activities often use a spatial resolution and scale either constrained by data availability or chosen arbitrarily. Sensitivity of model results to the choice of spatial resolution and scale is not, however, frequently evaluated. This study aims to determine if the choice of spatial resolution and scale are likely to impact statistical and mathematical analyses. Visceral Leishmaniasis in Brazil is used as a case study. Probabilistic characteristics of disease incidence, representing a likely outcome in a model, are compared across spatial resolutions and scales. Best fitting distributions were fit to annual incidence from 2004 to 2014 by municipality and by state. Best fits were defined as the distribution family and parameterization minimizing the sum of absolute error, evaluated through a simulated annealing algorithm. Gamma and Poisson distributions provided best fits for incidence, both among individual states and nationwide. Comparisons of distributions using Kullback-Leibler divergence shows that incidence by state and by municipality do not follow distributions that provide equivalent information. Few states with Gamma distributed incidence follow a distribution closely resembling that for national incidence. These results demonstrate empirically how choice of spatial resolution and scale can impact mathematical and statistical models.

Integrative Circuit-Host Modeling of a Genetic Switch in Varying Environments

Synthetic biology is advancing into a new phase where real-world applications are emphasized. There is hence an urgent need for mathematical modeling that can quantitatively describe the behaviors of genetic devices in natural, fluctuating environments. We utilize an integrative circuit-host modeling framework to examine the dynamics of a genetic switch and its host cell in varying environments. For both steady-state and transient cases, we find increasing nutrient reduces the bistability region of the phase space and eventually drives the switch from bistability to monostability. In response, cellular growth and proteome partitioning experience the same transition. Antibiotic perturbations cause the similar circuit and host responses as nutrient variations. However, one difference is the trend of growth rate, which augments with nutrient but declines with antibiotic levels. The framework provides a mechanistic scheme to account for both the dynamic and static characteristics of the circuit-host system upon environmental perturbations, underscoring the intimacy of gene circuits and their hosts and elucidating the complexity of circuit behaviors arising from environmental variations.

Formation of phage lysis patterns and implications on co-propagation of phages and motile host bacteria

Coexistence of bacteriophages, or phages, and their host bacteria plays an important role in maintaining the microbial communities. In natural environments with limited nutrients, motile bacteria can actively migrate towards locations of richer resources. Although phages are not motile themselves, they can infect motile bacterial hosts and spread in space via the hosts. Therefore, in a migrating microbial community coexistence of bacteria and phages implies their co-propagation in space. Here, we combine an experimental approach and mathematical modeling to explore how phages and their motile host bacteria coexist and co-propagate. When lytic phages encountered motile host bacteria in our experimental set up, a sector-shaped lysis zone formed. Our mathematical model indicates that local nutrient depletion and the resulting inhibition of proliferation and motility of bacteria and phages are the key to formation of the observed lysis pattern. The model further reveals the straight radial boundaries in the lysis pattern as a telltale sign for coexistence and co-propagation of bacteria and phages. Emergence of such a pattern, albeit insensitive to extrinsic factors, requires a balance between intrinsic biological properties of phages and bacteria, which likely results from coevolution of phages and bacteria.

Interaction variability shapes succession of synthetic microbial ecosystems

Cellular interactions are a major driver for the assembly and functioning of microbial communities. Their strengths are shown to be highly variable in nature; however, it is unclear how such variations regulate community behaviors. Here we construct synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction variability in ecosystem succession and to further determine if casting variability into modeling empowers bottom-up predictions. For a consortium of bacteriocin-mediated cooperation and competition, we find increasing the variations of cooperation, from either altered labor partition or random sampling, drives the community into distinct structures. When the cooperation and competition are additionally modulated by pH, ecosystem succession becomes jointly controlled by the variations of both interactions and yields more diversified dynamics. Mathematical models incorporating variability successfully capture all of these experimental observations. Our study demonstrates interaction variability as a key regulator of community dynamics, providing insights into bottom-up predictions of microbial ecosystems.

Cellular interactions are a major driver of microbial communities and shown highly variable in strength. Here the authors construct synthetic consortia and mathematical models to elucidate the role of interaction variability in driving ecosystem succession.

Spatial Organization of Expanding Bacterial Colonies Is Affected by Contact-Dependent Growth Inhibition

Identifying how microbes are able to manipulate, survive, and thrive in complex multispecies communities has expanded our understanding of how microbial ecosystems impact human health and the environment. The ability of bacteria to negatively affect neighbors, through explicit toxin delivery systems, provides them with an opportunity to manipulate the composition of growing microbial communities. Contact-dependent inhibition (CDI) systems (a Type Vb secretion system) are a distinct subset of competition systems whose contribution to shaping the development of spatially structured bacterial communities are yet to be fully understood. Here, we compare the impact of different CDI systems, at both the single-cell and population level, to determine the key drivers of CDI-mediated competition within spatially structured bacterial populations. Through an iterative approach using both an Escherichia coli experimental system and computational modeling, we show that CDI systems have subtle and system-specific effects at the single-cell level, generating single-cell-wide boundaries between CDI-expressing inhibitor cells and their neighboring targets. Despite the subtle effects of CDI at a single-cell level, CDI systems greatly diminished the ability of susceptible targets to expand their range during colony growth. The inoculum density of the population, together with the CDI system-specific variables of the speed of inhibition after contact and biological cost of CDI, strongly affects CDI-mediated competition. In contrast, the magnitude of the toxin-induced growth retardation of target cells only weakly impacts the composition of the population. Our work reveals how distinct CDI systems can differentially affect the composition and spatial arrangement of bacterial populations.

•

CDI causes subtle growth inhibition in a subset of contacted target cells

•

Model describes and predicts observed effects on spatial distribution of strains

•

CDI facilitates success of inhibitor strain increasing population patch size

•

A CDI system’s inhibition rate dominates toxicity in driving competition outcome

Synthetic, Context-Dependent Microbial Consortium of Predator and Prey

Synthetic microbial consortia are a rapidly growing area of synthetic biology. So far, most consortia are designed without considering their environments; however, in nature, microbial interactions are constantly modulated by cellular contexts, which, in principle, can dramatically alter community behaviors. Here we present the construction, validation, and characterization of an engineered bacterial predator-prey consortium that involves a chloramphenicol (CM)-mediated, context-dependent cellular interaction. We show that varying the CM level in the environment can induce success in the ecosystem with distinct patterns from predator dominance to prey-predator crossover to ecosystem collapse. A mathematical model successfully captures the essential dynamics of the experimentally observed patterns. We also illustrate that such a dependence enriches community dynamics under different initial conditions and further test the resistance of the consortium to invasion with engineered bacterial strains. This work exemplifies the role of the context dependence of microbial interactions in modulating ecosystem dynamics, underscoring the importance of including contexts into the design of engineered ecosystems for synthetic biology applications.