Synthetic, Context-Dependent Microbial Consortium of Predator and

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A Synthetic, Context-Dependent Microbial Consortium of Predator and Prey Feng Liu, Junwen Mao, Ting Lu, and Qiang Hua ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.9b00110 • Publication Date (Web): 06 Aug 2019 Downloaded from pubs.acs.org on August 6, 2019

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ACS Synthetic Biology

A Synthetic, Context-Dependent Microbial Consortium of Predator and Prey

Feng Liu1,2, Junwen Mao3, Ting Lu2,4-7,*, and Qiang Hua1,*

1State

Key Laboratory of Bioreactor Engineering, East China University of Science and

Technology, Shanghai 200237, China 2Department

of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801,

USA 3Department

of Physics, Huzhou University, Huzhou 313000, China

4Department

of Physics, 5Center for Biophysics and Quantitative Biology, 6Carl R. Woese Institute

for Genomic Biology, and 7National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

*Corresponding authors: Ting Lu (Tel: +1-217-333-4627, E-mail: [email protected]); and Qiang Hua (Tel: +86-10-64250972, E-mail: [email protected])

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ACS Synthetic Biology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Keywords: synthetic biology, microbial consortia, predator-prey, context dependence, microbial interactions, ecosystem dynamics

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Abstract 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 alter community behaviors dramatically. 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 the ecosystem to success with distinct patterns from predator dominance to prey-predator crossover and 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 to include contexts into the design of engineered ecosystems for synthetic biology applications.

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Synthetic microbial consortia are a rapidly growing area of synthetic biology which focuses on the development of artificial gene networks in the ecosystems of interacting microorganisms.1–3 Compared to single populations, synthetic communities possess a set of compelling advantages: They enable diversification of task, compartmentalization of function, spatiotemporal control, modular systems optimization, and reduced metabolic burden.4–6 As a result, such engineered systems promise to enhance the robustness and performance for desired phenotypes and, simultaneously, expand the feasibility of creating functions that are difficult or impossible for single populations.1,3,4 A set of proof-of-concept ecosystems have thus been created, primarily by rational programming of microbial interactions because cellular interactions dictate ecological dynamics and manipulating these interactions allows effective control of community behaviors.711 Successful

examples include ecosystems with various pairwise interactions like commensalism

and amensalism,12,13 with defined dynamics such as cross-feeding cooperation,14–17 predator-prey dynamics18 and emergent spatial oscillations,19 as well as with specific industrial purposes including enhanced production of chemicals.20,21 So far, most synthetic consortia are presumed to fulfill desired functions regardless of their environments. However, microbial interactions are often highly context-dependent: Their strength is subject to physical and chemical parameters of environment such as nutrient and pH.22–24 They are also dependent on the populations of interaction-generating cells and other microbial

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species.10,25-30 One class of such examples is the production of bacteriocins, a ubiquitous mechanism that microbes utilize to inhibit others. Bacteriocins are shown to be modulated by environmental pH (e.g., the pediocin production by Pediococcusacidilactici27) and ion concentration (e.g., the 2-al-kyl-4(1H)-quinolones production of Pseudomonas aeruginosa28); They are also regulated by the density of bacteria generating them such as nisin production by Lactococcus lactis29 and subtilin production by Bacillus subtilis.30 As cellular interactions dictate the assembly and development of microbial communities,7–11 such an environmental dependence of interactions suggest that the dynamics of microbial communities shall be regulated by their environmental contexts. Meanwhile, microbes constantly take up substrates from and release metabolites into extracellular milieu, thereby constantly remodeling the environments they inhabit and, consequently, further modulating ecosystem behaviors. Such a microbe-environment coupling naturally complicates the behaviors of ecosystems.31–34 Thus, an explicit characterization of context dependence is required to describe and design synthetic microbial consortia. In this study, we plan to determine how the context dependence of microbial interactions regulate ecosystem dynamics by using a synthetic predator-prey consortium as an example. We chose the predation as our model interaction because it occurs ubiquitously in various ecological systems from viruses to microbes and to animals and generates interesting modes of dynamics.35,36 Specifically, we first constructed and validated the predatory consortium composed

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of two Lactococcus lactis strains whose beneficial interaction from the prey to the predator is mediated by the detoxification of the antibiotic chloramphenicol (CM) in the environment. We then explored the context-dependence of ecosystem dynamics by varying CM levels, and proceeded to evaluate the complexity of system behaviors under different initial conditions. Furthermore, we constructed a mathematical model to quantitatively understand the key dynamics of the contextdependent consortium.16 We finally tested the resistance of the consortium to invasion with engineered bacterial strains. Together, our study exemplifies the importance of context dependence of microbial interactions and the complexity of ecosystem dynamics it causes, underscoring the importance of including context into the design of engineered microbial ecosystems.

RESULTS AND DISCUSSION Construction of an antibiotic-mediated predator-prey ecosystem. Our synthetic consortium involves two engineered strains of L. lactis, a prey strain Py and a predator strain Pd (Figure 1A). As detailed in Figure 1B, the prey Py was derived from L. lactis MG1363 by introducing a constitutive promoter Pcat to drive the chloramphenicol acetyltransferase gene, cat, that detoxifies CM. The predator, Pd (Figure 1C), was created by transplanting into L. lactis MG1363 a complete biosynthetic pathway of lactococcin A (lcnA), a bacteriocin that effectively inhibits L. lactis strains

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unless immunized. Two constitutive promoters were used to drive the four genes of the pathway, including the precursor gene lcnA, ABC transporter gene lceA, accessory protein gene lcmA and immunity gene lciA, for constant lcnA production. In the presence of CM, Py confers a protection to Pd by detoxifying CM; in turn, Pd inhibits Py by producing lcnA. Together, Py and Pd effectively form a predator-prey relationship. Notably, Pd does not directly feed on Py; instead, Pd kills Py while receiving a sheltering benefit from Py. Thus, although different from the classical definition of predation, functionally speaking, this synthetic community resembles a microbial model for predator-prey ecosystems that involve both positive and negative interactions. In the absence of CM, Py does not confer a benefit to Pd while continuing to be suppressed by Pd, forming an amensal relationship from Pd to Py. Thus, the interaction between Py and Pd is not constant but, instead, varies with the CM level of the environment. Another words, the Py-Pd interaction relation is context-dependent. Additionally, to enable direct observation and quantification of the ecosystem dynamics, Py and Pd were loaded with constitutively expressed red and green fluorescent protein genes, mCherry and yemGFP.

Validation of the synthetic consortium. To validate the antibiotic-mediated interaction, we conducted a set of Py-Pd co-culture experiments. Overnight Py and Pd monocultures were

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inoculated with 1:1 ratio and a total optical density at 600 nm (OD) of 0.1 into fresh GM17 broth supplemented with a final concentration of 5 μg/ml CM. Then, the co-culture was grown in 30°C for 10 hours (hr), during which the total OD and the relative subpopulation abundances were measured using spectrophotometer and plate-counting respectively (Materials and Methods). Our result (Figure 1D) showed that, in the first 4 hr, Py increased rapidly while Pd decreased, leading to the increase of the Py relative abundance from 50.0% to 95.2% (Figure S1A) owing to its selective advantage over Pd under the lethal dose of CM (5 μg/ml). The detailed reason is following: For the same level of CM inside Py and Pd cells, CAT in Py covalently attaches with a certain rate an acetyl group to CM to abolish its ability bind to ribosomes; as a result, only the portion of intact CM inside Py has the ability to suppress Py’s translation and hence its growth. In contrast, inside Pd, all of its intracellular CM is intact and capable of suppressing translation, thus having a more significant reduction of cell growth. However, Pd did not go extinct because Py’s detoxification of CM, a sheltering effect provided to Pd, reduces the overall effective CM concentration in the culture. With the continuation of CM detoxification, Pd grew better; meanwhile, it further released the bacteriocin lcnA to counterattack Py, which gradually favored Pd over Py and eventually caused the turnover of the population abundance at 4.5 hr. Eventually, Pd triumphed in the competition in the Py-Pd co-culture. By contrast, in the absence of CM, Py decreased monotonically but Pd increased (Figure 1E), leading to the continued dominance of

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Pd throughout the course of experiment (Figure S1B). These results confirmed that the dynamics of the consortium was modulated qualitatively by CM in the environment. For comparison, we also co-cultured Py and Pd in GM17 broth supplemented with tetracycline, an antibiotic that both Py and Pd are sensitive to. The result showed that the coculture population collapsed and neither of the strains was able to grow (Figure 1F and Figure S1C). Additionally, to rule out the possibility that the observed population dynamics is caused by the native growth rate difference between Py and Pd, we measured their individual growth profiles in fresh GM17 medium without CM supplementation and found them highly comparable (Figure 1G). Together, we concluded that the engineered Py-Pd consortium fulfilled the ecological interaction of predator and prey in a CM-dependent manner, allowing to be utilized as a welldefined platform to examine the impact of environmental modulation of cellular interaction on ecosystem behaviors.

Predatory dynamics in an antibiotic dose-dependent manner. To systematically examine the context-dependence of the cellular interaction and its consequences in ecosystem behaviors, we performed a set of co-culture assays for the consortium in GM17 media supplemented with various levels of CM, and further measured the time courses of the ecosystem populations

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(Materials and Methods). Specifically, we varied the CM from 0.25 to 100 μg/ml based on the growths of Py and Pd monocultures (Materials and methods, Figure S2). Our results showed that, at a low level of CM (0.25 μg/ml), Pd grew but Py declined over time (Figure 2A), resulting in the monotonic increase of the Pd relative abundance and the dominance of the ecosystem population by Pd (Figure S3A). In this case, effectively the consortium regressed to a community of amensalism with a one-way inhibition from Pd to Py as the Py-to-Pd benefit abolished at a minimal CM concentration. In the intermediate range of CM (2.5, 12.5, 25 and 50 μg/ml), Py first grew but later declined; in contrast, Pd decreased initially but later increased to be dominant (Figure 2B-E), resulting in a crossover of their relative abundances (Figure S3B-E). This intertwined, phase-opposite population dynamics of Py and Pd represents the canonical behavioral pattern of predator-prey ecosystems:35 The initial drop of the Pd population arose from the inhibition of CM in the culture and the rise later was due to the reduction of CM by Py’s detoxification; Py grew initially because it was resistant to CM and lcnA from Pd was very low and, later, Py shrank owing to the inhibition by lcnA that Pd constantly produced. Remarkably, across these intermediate CM levels, the time for population crossover, at which the Pd becomes more abundant than Py, prolonged with the increase of CM level. The underlying reason is that a higher CM concentration in co-culture requires a longer time for Py to detoxify CM and reduce its concentration to enable Pd’s proliferation. At an extremely high initial level of

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CM (e.g., 100 μg/ml), neither Py nor Pd was able to survive and, in that case, the whole population collapsed (Figure 2F and Figure S3F). Collectively, these results demonstrated that the dynamics of the synthetic ecosystem is highly sensitive to the environmental context: Altering environmental parameters—CM level to be specific—modulated the emergence and strength of the predator-prey interaction and, thereby, control the dynamics of the ecosystem qualitatively.

Context-dependence enriches community dynamics under varied initial conditions. In our synthetic consortium, the environmental dependence of its predation was cast via the CM degradation of the Py population. As single Py cells have a constant detoxification capacity, we deduced that cellular populations matter to the overall rate of CM degradation in culture and, for a given CM level, different times are required to break down CM when the Py population varies. To test the deduction, we performed two sets of co-culture experiments with the consortium. In the first set, we cultured the ecosystem in fresh GM17 broth supplemented a standard level of CM (5 μg/ml) with a fixed 1:1 initial ratio but varied initial total ODs (10-1, 10-2, 10-3 and 10-4). When starting from the OD of 10-1, the ecosystem experienced the characteristic crossover of the cellular populations around 4.5 hr (Figure 3A, Figure S4A). When the co-culture grew from an OD of 10-2, the abundance crossover was still present but the time for the system to reverse

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the abundance extended to 8 hr (Figure 3B, Figure S4B). When the initial OD was down to 10-3, Py managed to grow slowly over time while Pd decreased and stayed in a barely detectable density (Figure 3C) and, as a result, no population abundance crossover was observed (Figure S4C). In this case, due to the low Py density at beginning, the overall degradation rate of CM was so slow that it took 14 hr for Py to grow from an OD of 0.5X10-3 to 10-2. When the initial OD was further reduced to 10-4, neither Py nor Pd was able to grow and the whole ecosystem collapsed (Figure 3D) although the Py has a relatively higher density (Figure S4D). These results showed that, originating from the context-dependence of cellular interactions, reducing inoculum size can shift or even impair the emergence of predator-prey dynamics. In the second set, we grew the ecosystem in fresh GM17 medium supplemented with 5 μg/ml of CM from a fixed total OD (10-1) but altered initial Py:Pd ratios (100:1, 10:1, 5:1, 2:1, 1:1, 1:2, 1:5, 1:10, 1:100). Our results illustrated that the ecosystem possesses three modes of dynamics: Py dominance, population crossover, and population collapse. In details, when the initial Py:Pd ratio was 100:1, only Py was detected throughout the experiment (Figure 4A and Figure S5A). While in the ranges from 10:1, to 5:1, 2:1, and 1:1, the ecosystem persistently displayed the characteristic population crossover (Figure 4B-E and Figure S5B-E). When the initial ratios were in 1:2, 1:5 and 1:10, there were two population crossovers with Py becoming major in the first and Pd becoming dominant in the second (Figure 4F-H and Figure S5F-H).

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Notably, in these two regimes, the time for Pd to be dominant was non-monotonic, varying from 7 hr to 4.5 hr and back to 5.5 hr. Furthermore, when the initial ratio became 1:100, both Py and Pd failed to proliferate and the whole ecosystem collapsed (Figure 4I and Figure S5I), owing to the extremely low CM-detoxification capacity of the consortium. Collectively, these results demonstrated that the context dependence of microbial interactions enriched ecosystem dynamics under varied initial conditions.

Mathematical modeling the synthetic consortium. To quantitatively synthesize the behaviors of the predator-prey consortium, we constructed a mathematical model using ordinary differential questions (Eqs. S1). As detailed in Supplementary Section 1, the model consists of five key variables including the nutrient in the culture, cell density of the predator, density of the prey, concentration of the bacteriocin lcnA released by predator, and the concentration of antibiotic CM in the culture. We then used the model to systematically explore the population dynamics of the ecosystem. Figure 5A shows a phase diagram that elucidates three distinct modes of ecosystem dynamics with altered CM concentration and initial total OD. Here, the three regimes from left to right correspond to predator dominance (I), prey-predator crossover (II) and ecosystem collapse (III) accordingly.

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To directly illustrate different modes of population dynamics, we simulated the temporal behaviors of the predatory community which starts from varied initial CM concentrations (0.25, 5, 25, and 100 μg/ml) but a fixed initial total OD of 0.1 and a fixed 1:1 Py:Pd ratio. The results (Figure 5B-E) confirmed that varying the CM level indeed drove the ecosystem dynamics from predator dominance to prey-predator crossover and to ecosystem collapse, as our experimental findings (Figure 2). To elucidate the context dependence of the ecosystem behaviors, we also computed the CM concentration of the co-culture throughout the simulations. We found that the ecosystem successfully detoxified CM for the initial CM of 0.25, 5 and 25 μg/ml as shown in the insets of Figure 5B-D, in agreement with the predator dominance and prey-predator crossover in experiment. However, for the case of initial 100 μg/ml (Figure 5E, inset), CM was only able to be reduced minimally, consistent with the collapse of the community. To further showcase the value of our model, we simulated the population dynamics of the predator-prey consortium at various initial Py:Pd ratios for a given initial total OD (0.1) and a given initial CM (5 μg/ml). Figure 5F-I shows that the ecosystem dynamics changes from Py dominance, to population crossover (one and two crossovers), and to population collapse when the Py:Pd ratio is altered from 100:1, to 10:1, 1:10, and to 1:100, which successfully reproduces our experimental findings in Figure 4.

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Invasion test over the predatory ecosystem. One important measure of an ecosystem is its resilience to invasion by other species. To characterize our context-dependent consortium, we built an engineered invader strain I1. As shown in Figure 6A, I1 harbors the constitutively expressed genes, lagA, lagB, lagC, lagD and lagE, which together constitute the complete biosynthesis pathway for the bacteriocin lactococcin G (lcnG) which inhibits lactococcus species including L. lactis MG1363 unless immunized. I1 was also loaded with the gene gusA3 to enable colorimetric quantification. When I1 is introduced, the predation consortium is changed to a threestrain community involving Py, Pd and I1. The interaction topology of the three-strain system (Figure 6B) suggested that I1 is functionally similar to Pd because it relies on Py for CM protection and simultaneously produces toxins to inhibit Py’s growth as Pd. The network also suggested that there is an additional interference competition between Pd and I1 due to the respective bacteriocin (lcnG and lcnA) production. Experimentally, we co-cultured Py, Pd and I1 in GM17 medium supplemented with CM (5 μg/ml) at the 1:1:1 ratio and an initial OD of 0.1. Figure 6C and Figure S6A show that Py and Pd (filled red and green circles) continued to generate a characteristic population crossover as in the original two strain community (open red and green circles). Meanwhile, the invader I1 largely followed the dynamics of Pd for the first few 5 hr but, afterwards, deviated from Pd and declined to null. These results suggested that, in this case, the Py-Pd consortium rejected I1 invasion and

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maintained the predation behavior. As Pd and I1 are functionally identical to Py but only Pd survived in the succession, the results also implied that Pd outperformed I1, which was confirmed subsequently using a direct Pd-I1 competition assay (Figure S7). We further tested the resilience of the consortium by creating and utilizing another invader strain, I2, which is derived from I1 by adding the CM-resistant gene cat (Figure 6D). Compared to I1, I2 is resistant to and detoxify CM while still producing lcnG to inhibit both Py and Pd. As a result, the network of inter-strain interaction is changed as illustrated in Figure 6E. With the same procedure as for the Py-Pd-I1 co-culture experiment, we found that the dynamics of Py and Pd was shifted dramatically: The both species declined monotonically without showing the crossover pattern (Figure 6F and Figure S6B, filled red and green circles), which is qualitatively distinct from the dynamics of the Py-Pd co-culture (Figure 6F and Figure S6B, open red and green circles) and of the I1 invasion case (Figure 6E).

CONCLUSIONS In this paper, we present the construction, validation and characterization of an engineered bacterial predator-prey consortium that involves a CM-mediated, context-dependent cellular interaction. Such a dependence is commonly observed in naturally occurring ecologies, such as the degradation of the tetracycline that causes the rise of antibiotic resistance decay.26 Our work

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demonstrated such a dependence of microbial interactions on environmental contexts can enrich significantly the dynamics of microbial communities, and give rise to distinct community succession under varying initial conditions. The work thus exemplifies the dynamic and complex nature of ecosystem behaviors arising from context dependence, underscoring the need to include context into the design of synthetic microbial communities. As the field of synthetic biology moves toward real-world applications, such efforts are highly valuable to advance our fundamental understanding about the interplay between engineered ecosystems and their environments as well as the creation of synthetic microbial communities for various applications.

MATERIALS AND METHODS

Strains and growth conditions. Lactococcus lactis MG1363 was used as the host for strains used in the study. Lactococcal strains were cultured in M17 medium with 0.5% glucose (GM17) at 30 °C. Tween 80 was added at a final concentration of 0.1% (v/v) when necessary. All plasmids were first constructed and sequenced in E. coli JM109, and then transformed into L. lactis by electroporation. Antibiotics were added as required: tetracycline (3 µg/ml), chloramphenicol (10 µg/ml), and erythromycin (250 µg/ml) for E. coli; chloramphenicol (5 µg/ml), erythromycin (5 µg/ml) and tetracycline (10 µg/ml) for L. lactis. Strains and plasmids used in this study are described in Table 1.

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Plasmid construction. Oligos for plasmid construction are listed in Supplementary Table S1. All plasmids were developed from an L. lactis-E. coli shuttle vector, pLeiss-Nuc,37 which contains a pSH71 origin, a chloramphenicol resistance gene, a PnisA promoter from a nisin gene cluster, and a Nuc reporter. Gibson assembly was used to construct all plasmids. To generate the plasmid pleiss with chloramphenicol resistance, the PnisA promoter and Nuc gene were deleted by reverse PCR and Gibson assembly using two pairs of primers: P1-F/P1-R and F1-F/F1-R. To construct lcnA expression plasmid pleiss-lcnA, the lcnA gene cluster from the plasmids pFI2396 and pFI2348 was assembled into pleiss in the original order.38 To delete the cat gene (chloramphenicol resistant gene) in the plasmid pleiss-lcnA and simplify the selective procedure, ermE gene (erythromycin resistant gene) was amplified from pCCAMβ139,40 using the primers erm-F and erm-R, and substituted the cat gene to obtain plasmid pleiss-lcnA (ermR). To enable screening and counting of cells with different plasmids, a green or red fluorescence reporter gene, yemGFP or mCherry was introduced using primers Py-F/Py-R and rfp-F/rfp-R (Pd-F/Pd-R and gfpF/gfp-R) to generate the plasmids pleiss-RFP and pleiss-lcnA-GFP (ermR). These plasmids were subsequently transformed into L. lactis MG1363 to create the prey and predator strains Py and Pd. The lactococcin G (lcnG) producing plasmid pleiss-lcnG was constructed by assembling the lcnG gene cluster from Lactococcus lactis LMG2081 genome41 and pleiss vector as previously described. To obtain the erythromycin resistant version of lcnG producing plasmid, pleiss-lcnG (ermR), ermE gene was amplified to replace cat gene in plasmid pleiss-lcnG with the primers P2F/P2-R and erm-F/erm-R. To enable the detection of the lcnG producing strains, a GusA3 reporter gene gusA342 was amplified from the plasmid pTRK892 and inserted into plasmids pleiss-lcnG and pleiss-lcnG (ermR) using primers Pi-F/Pi-R and gusA-F/gusA-R. The resulting plasmids, pleiss-lcnG-gusA3 (ermR) and pleiss-lcnG-gusA3, were transformed into L. lactis MG1363 to generate the invaders I1 and I2.

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Growth and relative abundance measurements for the predator-prey consortium. To validate the antibiotic-mediated predator-prey dynamics in the co-culture experiment, Py and Pd monocultures were inoculated in GM17 medium at 30°C overnight. Then, they were equally mixed with a start total optical density at 600 nm (OD) of 0.1 in fresh GM17 broth supplemented with 5 μg/ml CM. During incubation, samples were taken for measuring their total ODs; the relative fractions of Py (red) and Pd (green) cells were determined by counting colonies on the GM17 agar plates. Notably, in the invasion test, Py, Pd and I1 (I2) monocultures were inoculated into fresh GM17/CM media at 1:1:1 ratio based on OD with a total initial OD of 0.1. For the invaders I1 and I2, GusA3 protein was used as a colorimetric reporter. Colony forming units (CFUs) were counted to calculate the relative abundances of I1 and I2in the populations by adding a final concentration of 2 mM 5-bromo-4-chloro-3-indolyl β-d-glucuronide sodium salt (X-Gluc) into the GM17 agar plates. Based on their specific ODs, the collected samples were diluted with 106, 107 or 108 folds to ensure appropriate number of colonies grow on the plates.

Determination of CM concentration for the survival of the prey and predator strains. To determine the effect of CM concentration on the cell growth in fresh medium, monocultures of Py and Pd were inoculated in GM17 broth supplemented with a range of CM concentration: 0.25, 2.5, 12.5, 25, 50 and 100 μg/ml while the initial total OD was set at 0.1. Samples were taken to measure their ODs with spectrophotometer. Each culture condition has three trials.

Antibiotic-varying experiments. To compare the effect of different antibiotics on predatory dynamics, Py and Pd strains were mixed at 1:1 ratio and a start total OD of 0.1 in GM17 medium supplemented with 5 µg/ml chloramphenicol or 10 µg/ml tetracycline. To examine the impact of

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different levels of antibiotic CM on population behaviors, a range of CM concentration, from 0.25, 2.5, 12.5, 25, 50 to 100 µg/ml, was added into the Py-Pd co-cultures. During incubation, the samples were taken to measure the total OD by spectrophotometer and relative abundances with colony counting. Specifically, at high initial concentrations of CM (e.g., 50 µg/ml), the co-cultures were incubated at 30°C and propagated for 20 hours in order to for the cells grow up to the stationary phase.

Predator-prey ratio-varying experiment. The initial total OD of two strains Py and Pd was set at 0.1. The start CM concentration was added at 5 μg/ml in co-cultures but the Py:Pd ratio was varied from 100:1, to 10:1, 5:1, 2:1, 1:1, 1:2, 1:5, 1:10 and 1:100. Samples were taken for measuring their ODs and relative abundances.

Mathematical modeling. A mathematical model consisting of five ordinary differential equations was developed with details provided in Supporting Information. Parameters were chosen based on previous literature and our own experimental data in this work. Custom-tailored code was developed to simulate the model in MATLAB.

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ASSOCIATED CONTENT

Supporting Information The Supporting Information is available free of charge on the ACS Publications website.

Figures S1-S7 and Tables S1-S3.

Author Information Corresponding Author *E-mail: [email protected] (T.L.) and [email protected] (Q.H.)

Notes The authors declare no competing financial interest.

Acknowledgements F.L. was supported by the Chinese Scholarship Council. J.M. was supported by the National Natural Science Foundation of China (11575059) and Key Laboratory of Vector Biology and Pathogen Control of Zhejiang Province. T.L. was supported by the National Science Foundation (1553649), Department of Energy (DE-SC0019185) and Office of Naval Research (N000141612525). Q.H. was supported by the National Natural Science Foundation of China (21776081).

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REFERENCES (1) Brenner, K., You, L. C., and Arnold, F. H. (2008) Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 26, 483–489. (2) Großkopf, T., and Soyer, O. S. (2014) Synthetic microbial communities. Curr. Opin. Microbiol. 18, 72–77. (3) Bittihn, P., Din, M. O., Tsimring, L. S., and Hasty, J. (2018) Rational engineering of synthetic microbial systems: from single cells to consortia. Curr. Opin. Microbiol. 45, 92–99. (4) Johns, N. I., Blazejewski, T., Gomes, A. L., and Wang, H. H. (2016) Principles for designing synthetic microbial communities. Curr. Opin. Microbiol. 31, 146–153. (5) Lindemann, S. R., Bernstein, H. C., Song, H. S., Fredrickson, J. K., Fields, M. W., Shou, W. Y., Johnson, D. R., and Beliaev, A. S. (2016) Engineering microbial consortia for controllable outputs. ISME J. 10, 2077–2084. (6) Hays, S. G., Patrick, W. G., Ziesack, M., Oxman, N., and Silver, P. A. (2015) Better together: engineering and application of microbial symbioses. Curr. Opin. Biotechnol. 36, 40–49. (7) Faust, K., and Raes, J. (2012) Microbial interactions: from networks to models. Nat. Rev. Microbiol.10, 538-550. (8) Gotelli, N. J. (1999) How do communities come together? Science 286, 1684–1685. (9) Cordero, O. X., and Datta, M. S. (2016) Microbial interactions and community assembly at microscales. Curr. Opin. Microbiol. 31, 227–234. (10) Blanchard, A. E., Liao, C., and Lu, T. (2016) An ecological understanding of quorum sensingcontrolled bacteriocin production, Cell Mol. Bioeng.9, 443-454. (11) Ozgen, V. C., Kong, W. T., Blanchard, A. E., Liu, F., and Lu, T. (2018) Spatial interference scale as a determinant of microbial range expansion. Sci. Adv. 4, eaau0695.

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(22) Rivett, D. W., Scheuerl, T., Culbert, C. T., Mombrikotb, S. B., Johnstone, E., Barraclough, T. G., and Bell, T. (2016) Resource-dependent attenuation of species interactions during bacterial succession. ISME J. 10, 2259–2268. (23) LaSarre, B., McCully, A. L., Lennon, J. T., and McKinlay, J. B. (2017) Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients. ISME J. 11, 337–348. (24) Bachmann, H., Molenaar, D., Kleerebezem, M., and Vlieg, J. E. T. V. (2011) High local substrate availability stabilizes a cooperative trait. ISME J. 5, 929–932. (25) Coburn, P. S., Pillar, C. M., Jett, B. D., Haas, W., and Gilmore, M. S. (2004). Enterococcus faecalis senses target cells and in response expresses cytolysin. Science 306, 2270–2272. (26) Palmer, A. C., Angelino, E., and Kishony, R. (2010) Chemical decay of an antibiotic inverts selection for resistance. Nat. Chem. Biol. 6, 105–107. (27) Biswas, S. R., Ray, P., Johnson, M. C., and Ray, B. (1991) Influence of growth conditions on the production of a bacteriocin, Pediocin AcH, by Pediococcus acidilactici H. Appl. Environ. Microbiol. 57, 1265–1267. (28) Nguyen, A. T., Jones, J. W., Ruge, M. A., Kane, M. A., and Oglesby-Sherrouse, A. G. (2015) Iron depletion enhances production of antimicrobials by Pseudomonas aeruginosa. J Bacteriol. 197, 2265–2275. (29) Kuipers, O. P., Beerthuyzen, M. M., De Ruyter, P. G. G. A., Luesink, E. J., and De Vos, W. M. (1995) Autoregulation of nisin biosynthesis in Lactococcus lactis by signal transduction. J Bacteriol. 270, 27299–27304. (30) Spiess, T., Korn, S. M., Kotter, P., and Entian, K. D. (2015) Autoinduction specificities of the lantibiotics subtilin and nisin. Appl. Environ. Microb. 81, 7914–7923.

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(31) Hart, S. F. M., Mi, H. B., Green, R., Xie, L., Pineda, J. M. B., Momeni, B., and Shou, W. Y. (2019) Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells. PLoS Biol. 17, e3000135. (32) Ratzke, C., and Gore, J. (2018) Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248. (33) Bissett, A., Brown, M. V., Siciliano, S. D., and Thrall, P. H. (2013) Microbial community responses to anthropogenically induced environmental change: towards a systems approach. Ecol. Lett. 16, 128–139. (34) Klitgord, N., and Segre, D. (2010) Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 6, e1001002. (35) May, R.M. (1973) Stability and complexity in model ecosystems. Princeton University Press, Princeton, New Jersey. (36) Murray, J. D. (2002) Mathematical biology I: An Introduction. 3rd Ed, Springer, New York. (37) Le Loir, Y., Gruss, A., Ehrlich, S. D., and Langella, P. (1998) A nine-residue synthetic propeptide enhances secretion efficiency of heterologous proteins in Lactococcus lactis. J. Bacteriol. 180, 1895–1903. (38) Fernández, A., Horn, N., Gasson, M. J., Dodd, H. M., and Rodríguez, J. M. (2004) High-level coproduction of the bacteriocins nisin A and lactococcin A by Lactococcus lactis. J. Dairy Res. 71, 216–221. (39) Kong, W. T., Kapuganti, V. S., and Lu, T. (2016) A gene network engineering platform for lactic acid bacteria. Nucleic Acids Res. 44, e37. (40) Kong, W. T., and Lu, T. (2014) Cloning and optimization of a nisin biosynthesis pathway for bacteriocin harvest. ACS Synth. Biol. 3, 439–445.

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(41) Mirkovic, N., Polovic, N., Vukotic, G., Jovcic, B., Miljkovic, M., Radulovic, Z., Diep, D. B., and Kojic, M. (2016) Lactococcus lactis LMG2081 produces two bacteriocins, a non lantibiotic and a novel lantibiotic. Appl. Environ. Micobiol. 82, 2555–2562. (42) Callanan, M. J., Russell, W. M., and Klaenhammer, T. R. (2007) Modification of Lactobacillus β-glucuronidase activity by random mutagenesis. Gene 389, 122–127.

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Table 1. Strains and plasmids used in this work Name

Description

Source

Strains E. coli JM109

General cloning host

L. lactis MG1363

Host strain for bacteriocin expression in this work

(37)

L. lactis LMG 2081

Wild-type lactococcin G (lcnG) producing strain

(41)

Py

Chloramphenicol degradation strain with a

This work

mCherry reporter; Contains plasmid pleiss-RFP Pd

Lactococcin A (lcnA) producing strain with a

This work

yemGFP reporter and a ermE selective gene; Contains plasmid pleiss-lcnA-GFP (ermR) I1

LcnG producing strain with a gusA3 reporter and a

This work

ermE selective gene; Contains plasmid pleisslcnG-gusA3 (ermR) I2

LcnG producing strain with a gusA3 reporter and a

This work

cat resistant gene; Contains plasmid pleiss-lcnGgusA3 Plasmids pleiss-Nuc

Plasmid used for cloning in this work;

(37)

chloramphenicol resistant pleiss-RFP

Plasmid for constitutive RFP expression; Contains

This work

mCherry gene and a cat resistant gene pleiss-lcnA

Plasmid for lcnA production; Contains the whole

(38)

lcnA genetic cluster and a cat resistant gene pleiss-lcnA (ermR)

Plasmid for lcnA production; Contains the whole

This work

lcnA genetic cluster and a ermE selective gene pleiss-lcnA-GFP

Plasmid for lcnA production and constitutive GFP

(ermR)

expression; Contains the whole lcnA genetic

This work

cluster and a ermE selective gene and a yemGFP gene pleiss-lcnG (ermR)

Plasmid for lcnG production; Contains the whole lcnG genetic cluster and a ermE selective gene

27


This work


pleiss-lcnG-gusA3

Plasmid for lcnG production and constitutive

Page 28 of 41

This work

GusA3 expression; Contains the whole lcnG genetic cluster and a gusA3 gene and a cat resistant gene pleiss-lcnG-gusA3

Plasmid for lcnG production and constitutive

(ermR)

GusA3 expression; Contains the whole lcnG genetic cluster and a ermE selective gene and a gusA3 gene

28


This work

Page 29 of 41 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


FIGURES

Figure 1. A synthetic, antibiotic-mediated predator-prey consortium. (A) Conceptual design. The consortium consists of a prey strain, Py, and a predator strain Pd. In the presence of the antibiotic chloramphenicol (CM), Py confers a protection to Pd by degrading CM; in turn, Pd inhibits Py by producing lactococcin A. Together, Py and Pd form a predator-prey ecosystem whose interaction depends on the level of CM in the environment. (B) Detailed design of Py. Py carries a constitutively expressed gene cat, allowing to deactivate CM in the environment and, hence, survives in the presence of CM. Py also contains the fluorescence reporter gene mCherry. (C) Detailed design of Pd. Pd harbors four constitutively expressed genes lcnA, lceA, lciA and lcmA, enabling the secretion of lactococcin A. Pd also contains the fluorescence reporter gene yemGFP. (D, E) Population dynamics of the Py-Pd co-culture in the presence of 5 μg/ml CM (D) and absence of CM (E). The co-cultures were inoculated at 1:1 ratio with a total initial OD of 0.1 in GM17 broth supplemented with 5 μg/ml CM or without CM. (F) Growth profiles of Py and Pd in the presence of tetracycline (10 μg/ml). (G) Growth profiles of Py and Pd in monocultures without CM. Each experiment has three replicates.

29



Figure 2. Predatory dynamics of the consortium in the presence of various CM levels. The consortium was inoculated at 1:1 ratio and a total OD of 0.1 in GM17 broth.CM was supplemented at different initial concentrations from 0.25 (A), to 2.5 (B), 12.5 (C), 25 (D), 50 (E) and 100 (F) μg/ml. Green and red circles are the ODs of Pd and Py respectively.

30


Page 30 of 41

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Figure 3. Temporal dynamics of the consortium starting from different initial ODs. The consortium was inoculated at 1:1 ratio in GM17 broth supplemented with 5 μg/ml CM. The initial total OD was varied from 10-1 (A), to 10-2 (B), 10-3 (C), and 10-4 (D). Green and red circles are the ODs of Pd and Py respectively.

31



Figure 4. Temporal dynamics of the consortium starting with different initial Py:Pd ratios. Green and red circles are the ODs of Pd and Py respectively. The consortium was inoculated at a total OD of 0.1 in GM17 broth supplemented with 5 μg/ml CM. The initial ratio was varied from 100:1 (A), to 10:1 (B), 5:1 (C), 2:1 (D), 1:1 (E), 1:2 (F), 1:5 (G), 1:10 (H), and 1:100 (I).

32


Page 32 of 41

Page 33 of 41 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 5. Simulated population dynamics of the predator-prey consortium. (A) Phase diagram that illustrates three distinct modes of population dynamics from predator dominance (regime I) to prey-predator crossover (regime II) and to ecosystem collapse (regime III) with varying CM concentrations and initial conditions. (B-E) Simulated temporal behaviors of the predator (green lines)and the prey (red lines) in four different initial settings (a CM concentration of 0.25 (B), 5 (C), 25 (D) and 100 (E) μg/ml; a 1:1 Py:Pd ratio and a total initial OD of 0.1). The four settings correspond to the green dots in (A). The blue lines in the insets are the corresponding CM concentration. as a function of time. (F-I) Simulated temporal dynamics of the consortium starting with different initial Py:Pd ratios but a fixed CM level (5 μg/ml) and a fixed initial total OD (0.1). The Py:Pd ratio was varied from 100:1 (F), to 10:1 (G), 1:10 (H), and 1:100 (I).

33



Figure 6. Introduction of invaders into the predatory consortium. (A) Design of the invader I1. In I1, the entire lcnG biosynthesis pathway, including the genes lagA, lagB, lagC, lagD and lagE, is placed under constitutive promoters Pg1 and Pg2 to enable constant lcnG secretion. The gene gusA3 is also introduced for colorimetric quantification. (B) The Py-Pd consortium when introduced with I1. Besides the existing predator-prey interaction between Py and Pd (Figure 1A), the ecosystem has new interactions associated with I1: It receives protection from Py by its CM detoxification and is inhibited by lcnA from Pd; meanwhile, I1 opposes both Py and Pd by producing lcnG. (C) Temporal dynamics of the Py-Pd-I1 co-culture. The co-cultures were inoculated at 1:1:1 ratio and a total OD of 0.1 in GM17 broth supplemented with 5 μg/ml CM. The filled red, green and blue circles are the ODs of Py, Pd, and I1 in the Py-Pd-I1 co-culture; open red and green circles are the ODs of Py and Pd in the absence of I1for comparison. (D) Design of the invader I2. I2 is derived from I1 by introducing the CM-resistant gene cat under a constitutive promoter of Pcat. (E) The Py-Pd consortium when introduced with I2. Different from I1, I2 participates in CM detoxification and is resistant to CM. (F) Temporal dynamics of the Py-Pd-I2 co-culture. The incubation conditions were as same as described as above. The filled red, green and blue circles are the ODs of Py, Pd, and I2 in the Py-Pd-I2 co-culture; open red and green circles are the ODs of Py and Pd in the absence of I2 for comparison.

34


Page 34 of 41

OD600

1 2 Environment CM lcnA 3 4 Pd 5 Predator 6 7 8 9 E co-culture (no CM) 10 11 Pd Py 12 101 13 100 14 -1 Pd 15 10 Py 16 10-2 17 -3 18 10 19 10-4 0 2 4 6 8 10 20 Time (hr) 21

Py

cat

Pcat

Pa1

Pd

lcnA lcmA

Pa2

F

lceA lciA

Pg1

10-2

0

G

Py

2

4

6

Time (hr)

8

10

monoculture (no CM) Pd

101

Py

100

10-1

Pd Py

10-2 10-3

ACS-4Paragon Plus Environment 10

Pd Py

10-1

10-4

Tet

Pd

Py

10-3

gfp

co-culture (10 µg/ml Tet) 100

CM

Pd

100

C Pa1

co-culture (5 µg/ml CM) 101

rfp

OD600

Prey Py

D


0

2

4

6

Time (hr)

8

10

OD600

B

OD600

A Page 35 of 41

Pd Py

10-1 10-2 10-3 10-4

0

2

4

6

Time (hr)

8

10

100

OD600

10-1 10-2 10-3 10-4

C

CM=2.5 μg/mL ACS Synthetic Biology Pd Py

10-1 10-2

2

4

6

Time (hr)

8

E

CM=25 μg/mL

101

10-4

10

Pd Py

100

101 100

10-1 10-2

10-1 10-2 10-3

10-3 0

0

2

Pd Py

4

6

Time (hr)

8

10-4

10

CM=50 μg/mL

F

10-1 10-2

ACS -4 Paragon Plus Environment 10 0 5 10 15 20

10-4

Time (hr)

8

10

Time (hr)

6

8

10

CM=100 μg/mL

10-2

10-4

6

4

Time (hr)

10-1

10-3

4

2

Pd Py

100

10-3 2

0

101

10-3 0

CM=12.5Page μg/mL 36 of 41 Pd Py

101 100

OD600

100

101

OD600

OD600

OD600

1 2 3 4 5 6 7 8 9 10 D 11 12 13 14 15 16 17 18 19 20 21 22

B

CM=0.25 μg/mL Pd Py

101

OD600

A

0

5

10

Time (hr)

15

20

C Initial OD=10-2 Initial OD=10-3 ACS Synthetic10 Biology 1 Pd Pd 100 Py Py

101 100 10-1 10-2 10

-3

10

-4

10-1 10

2

4

8 10 12

10-3

Time (hr)

10

100 10-2 10-3

ACS Paragon Plus Environment -5 6

Initial OD=10-4 Pd Py

101 10-1

-2

10-4

0

D

OD600

OD600

1 10-1 2 -2 3 10 4 10-3 5 -4 6 10 0 2 4 6 8 10 12 7 Time (hr)

B

OD600

Initial OD=10-1 Page 37 of 41 101 Pd 0 Py 10

OD600

A

0

5

10-4 10 15 20 25

Time (hr)

10-5

0

5

10 15 20 25

Time (hr)

B

Py:Pd=100 :1

C

101 P ACS Synthetic y:P d=10:1Biology

101 100

10-1

10-1

10-1

10-2

101

10-2 10-3

10-3 10-4

OD600

100

OD600

100

0

2

4

6

Time (hr)

8

10-4

10

E

Py:Pd=2:1

101

0

2

4

6

Time (hr)

8

10-4

10

F

Py:Pd=1:1

101

10-1

10-1

10-1

10-4

101 10

OD600

100

OD600

100

10-3

10-2 10-3

0

2

4

6

Time (hr)

8

10-4

10

H

Py:Pd=1:5

101 10

0

10-1 10-2

0

2

4

6

Time (hr)

8

10-4

10

Py:Pd=1:10

I

101 10

0

10-1 10-2

10-4

8

10

8

10

6

8

10

6

8

10

Py:Pd=1:2

Time (hr)

0

2

4

Time (hr)

Py:Pd=1:100

10-2

ACS -4 Paragon Plus Environment 10 0 2 4 6 8 10

6

6

10-1

10-4

4

4

Time (hr)

0

10-3

Time (hr)

2

10-3

10-3 2

0

10-2

10-3 0

Page 38 of 41

10-2

100

10-2

Py:Pd=5:1

10-3

OD600

OD600

OD600

OD600

1 2 3 4 5 6 7 8 9 10 D 11 12 13 14 15 16 17 18 19 20 21 22 G 23 24 25 26 27 28 29 30 31 32 33 34

101

OD600

A

0

2

4

Time (hr)

A

B

Py:Pd=1:1

OD600

-3 0.1

C4

0.01 20

40

60

80

100

D

10

-1

20

-1

10

-3

-1

10

0

4

Time (hr)

8

H

1

10

0

4

Time (hr)

0

E

8

4

8

Time (hr)

CM=100µg/ml

1

10

C4

-1

100

CM

60

10

20 0

20

10

8

4

8

12

0

Time (hr)

Py:Pd=1:10

-1

10

4 Paragon 8 Plus Environment0 ACS

4

-3

I

4

8

12

16

Time (hr)

Py:Pd=1:100

1

10

-1

10

-3

10

10 0

0

2

10

-3

-3

10

CM

0

Py:Pd=10:1

1

10

Time (hr)

CM

0 0

8

C3

-3 10

OD600

G OD600

OD600

Py:Pd=100:1

1

10

4

10

10

4

8

CM=25µg/ml

1

CM concentration F

4

0

C2

-1

10 0

C2

10

-3

10

C3

0

CM

OD600

C1

III

0.3

CM=5µg/ml

1

10

C1

-1

10

C

OD600

0.1

II

OD600

Initial OD

I

CM=0.25µg/ml

1

10

1

CM=5µg/ml

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42


OD600

Page 39 of 41

10 4

8

Time (hr)

0

4

8

Time (hr)

B

C

Strain I1 gusA 1 lagA lagB Pa1 2 Pg1 3 lagC lagD lagE Pg2 4 5 6 7 D8 9 Strain I2 10 11 lagA lagB gusA cat Pcat Pa1 12Pg1 13 lagC lagD lagE 14 Pg2 15 16 17

CM

Py

Pd

lcnA

CM lcnG

OD600

ACS Synthetic Biology Environment

lcnG

I1

101 10-1 10-2

F

Environment CM

Py

lcnA lcnG

lcnG

Py-Pd-I1 Pd Py I1

10-3 10-5

E

y-P d Page 40Pof 41 Pd Py

100

10-4

lcnA

0

2

4

6

Time (hr)

8

10

101

Py-Pd Pd Py

100

Pd

OD600

A

10-1 10-2

Py-Pd-I2 Pd Py I2

10-3

I2 lcnA 10-4 ACS Paragon Plus Environment 10-5

0

2

4

6

Time (hr)

8

10

1 Environment 2 3 4 5

101

Prey ACS Synthetic Biology 100 Py CM lcnA

OD600

Page 41 of 41

Pd Py

10-1 10-2

10-3 ACS Paragon Plus Environment Pd 10-4 Predator 0 2

4

6

Time (hr)

8

10

Synthetic, Context-Dependent Microbial Consortium of Predator and

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