Letter

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

• Nature Microbiology 2, Article number: 17083 (2017)
• doi:10.1038/nmicrobiol.2017.83
Accepted:
Published online:

## Abstract

Microbial ecologists are increasingly turning to small, synthesized ecosystems1,​2,​3,​4,​5 as a reductionist tool to probe the complexity of native microbiomes6,7. Concurrently, synthetic biologists have gone from single-cell gene circuits8,​9,​10,​11 to controlling whole populations using intercellular signalling12,​13,​14,​15,​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.

To engineer a stable co-culture of two competitive bacterial strains, we first characterized the dynamics of a small library of quorum-sensing (QS) components (Supplementary Fig. 1a–c). This was achieved by evaluating different components of natural QS systems to identify receptor–promoter pairs and signals (acyl homoserine lactone, AHL) that yield the desired characteristic upon combination (Supplementary Fig. 1d)23. From a range of possible configurations (Supplementary Fig. 2b), we identified that the Lux and Las systems were suitable for one-way orthogonal signalling, and the Lux and Rpa systems were suitable for two-way orthogonal signalling (Supplementary Fig. 1e–g). We used these components to design synchronized lysis circuits (SLCs)16 in two bacterial strains, where each strain is programmed to lyse upon reaching a critical population density.

To understand how an ecosystem harbouring an SLC can be altered, we established a range of possible self-limiting dynamics for the circuit (Fig. 1a,b). The circuit exhibits oscillations characterized by periodic lysis events, which are driven by activation of the Lux-controlled positive feedback loop upon reaching a quorum threshold of AHL, as seen in earlier work16. A lysis event reduces the population dramatically, and a few survivors resume the process, starting again below the quorum threshold. In microfluidic devices, the superfolding green fluorescent protein (sfGFP) reports the activation state of the circuit in this oscillatory state (Fig. 1c and Supplementary Video 1). We also discovered a constant lysis state characterized by a steady state in which growth and lysis are approximately balanced, and the stable ‘on’ state of the circuit is evidenced by the constant production of sfGFP (Fig. 1d and Supplementary Video 2). Tuning the degradation efficiency of the activator LuxI by changing its ssrA degradation tag, we demonstrated a bifurcation in the lysis dynamics of the population between these two states. In a deterministic model of the circuit (Fig. 1b), stronger enzymatic degradation of LuxI is represented by a lower basal signal production rate αq (see Methods for details). Consistently, the oscillatory lysis behaviour was observed for the highest level of activator degradation (Fig. 1e,f), dampened oscillations were observed at a lower level of degradation (Fig. 1g,h), and constant lysis behaviour was observed for the lowest levels of degradation (Fig. 1i,j). The SLC therefore exhibited two main modes of dynamic lysis with respect to changes in circuit parameters.

To build a synthetic ecosystem of two orthogonal SLC strains, we used the previously built circuit based on the Lux QS system and constructed a new circuit with the Rpa system. The Rpa system had RpaR in place of LuxR and an ssrA tagged RpaI in place of LuxI (Fig. 2a). These strains are called Lux-CFP and Rpa-GFP, respectively, for convenience. Both strains' gene expression is controlled by the PluxI promoter for consistency, considering p-coumaroyl-HSL-bound RpaR can activate PluxI at about 90% the efficiency of AHL-bound LuxR while still avoiding crosstalk (Supplementary Fig. 2b)23. Although these strains are in the same bacterial host, when started from equal densities in batch culture, Rpa-GFP shows a significant growth advantage over Lux-CFP (Fig. 2b). Because of this growth advantage, a 1:1 mixture of these strains in a batch culture (with or without the lysis gene) is primarily taken over by the faster-growing Rpa-GFP strain by the time the strains reach stationary phase (Fig. 2c). However, if the slower-growing Lux-CFP strain is enriched 100 times more than the Rpa-GFP strain, the population-stabilizing effects of the lysis circuit become evident. Without the lysis gene, the mixture is taken over by the Lux-CFP strain, but with the lysis gene, the population ratio over the initial 10 h keeps close to a 1:1 ratio. The ‘ortholysis’ strategy thus showed promise in batch co-culture.

We then grew the strains in microfluidic devices, with a seeding ratio of 1:10 (Rpa-GFP to Lux-CFP) optimized for the new system, to examine the long-term dynamics of the co-culture. The microfluidic trap (growth chamber) harbouring the two strains without the lysis gene quickly lost its co-culture and was taken over by the Rpa-GFP strain alone (Fig. 2d and Supplementary Video 3). This process was observed for 60 traps, and the time duration of the co-culture was measured over two days. All traps eventually lost their co-culture completely, with an average co-residence time of 6.5 h (Fig. 2h). However, when the two orthogonal lysis strains were grown together, most of the 60 traps maintained a co-culture for the duration of the two-day experiment (Fig. 2e and Supplementary Video 4); all traps that lost co-culture were completely taken over by the Rpa-GFP strain. Due to differences in the inherent parameters of the two QS systems, the Rpa-GFP circuit remains in the constant lysis regime and is therefore perpetually producing sfGFP. However, the Lux-CFP strain is in the oscillatory regime and remains dark until it reaches quorum threshold, and its lysis events are characterized by a punctuated burst of CFP production (Fig. 2g and Supplementary Fig. 3a–d). The bimodality of the co-residence time (either lost in the first couple hours or maintained to the end of the experiment) suggests that the small volume of these reactors and the non-deterministic loading conditions predispose some wells with very few Lux-CFP cells to stochastic loss of co-culture. Seemingly, depending on the environmental context, oscillatory strains are more susceptible to environmental perturbations than a strain in the constant lysis regime. However, the benefit of using a strain in the oscillatory lysis regime is that it leaves the possibility of engineering dynamic population profiles, which may be useful for certain applications, such as the timed delivery of two different payloads. Nevertheless, within our microfluidic device, the ‘ortholysis’ method is rather robust at co-culturing even competitive strains for long periods of time (Fig. 2i).

We used agent-based modelling to visually show how the ‘ortholysis’ strains might behave with different QS parameters. We first modelled a system where the QS parameters of the Rpa system were the same as the Lux system parameters used in previous studies16. However, we used the experimental difference in growth whereby the Rpa-GFP strain grows at 110% the rate of the Lux-CFP strain. With the Lux-CFP strain seeded in a 10:1 ratio with respect to the Rpa-GFP strain in the model simulation, the resulting dynamics show antiphase oscillations (Fig. 3a and Supplementary Video 5). Seemingly due to volume exclusion, as shown by their fluorescence time series, the populations enter an antiphase pattern where the strains switch off growing and lysing (Fig. 3c).

We then took into consideration the innate differences between the two QS systems23 by changing several of the Rpa-GFP strain's QS parameters in relation to the Lux parameters used. Furthermore, based on the observed phenotypic phenomenon, the probability of lysing was reduced tenfold, which allows more AHL to build up and a constant lysis dynamic to develop (Fig. 3b and Supplementary Video 6). The resulting dynamics were similar to the experimental observations, with a constantly lysing Rpa-GFP strain maintaining the majority of the population share and the Lux-CFP strain intermittently firing and lysing (Fig. 3d). To understand how these dynamics and the size of the growth container affect stability, the agent-based model was run many times under different conditions. For conditions where Lux-CFP is oscillating and Rpa-GFP is in constant lysis (lys/osc), or where both are oscillating (osc/osc), ten simulations were carried out in volumes of 20, 40 and 60 a.u. each. As the size of the space increases, so does the average residence time of the co-culture (Fig. 3e), suggesting that, as we expected, larger traps will have fewer issues with losing co-culture to stochastic events.

As evidenced by the agent-based model, our strains demonstrate only one particular dynamic of a wide-range of possibilities facilitated by QS-controlled self-lysing microorganisms with varying levels of orthogonality. We developed a reduced deterministic model to explore a wider space of possible dynamics achieved through differences in growth rates, QS systems and lysis circuit regimes. For each case, communication motifs are distinguished and suitable experimental candidate QS systems are chosen to achieve the displayed dynamics. For the two individual lysis circuits, we consider either non-lysing (no SLC), lysing (SLC) or weak lysing (less effective SLC). With two non-lysing strains, the faster-growing strain will eventually dominate the population (Fig. 4a). However, even a single strain equipped with the SLC can stabilize the co-culture, provided the non-lysing strain has the lower growth rate (Fig. 4b). In cases where both strains harbour an SLC, but there is one-way crosstalk, the strain that responds to both signals becomes entrained to the strain that only responds to its own (Fig. 4c,d). An example would be the Lux and Las systems; the Lux can respond to the Las signal, but Las is orthogonal to the Lux signal. The strength of the crosstalk determines the strength and delay of the entrainment, with strong crosstalk (Fig. 4c) exhibiting strong entrainment and weak crosstalk (Fig. 4d) showing time-delayed entrainment. In cases where each SLC operates independently, by using signal orthogonal QS systems, the most robust co-culturing is achieved where, for large ranges of growth rates, the time-averaged population ratio remains around 50/50 (Fig. 4e). If one of the strains exhibits weaker lysing dynamics, in that it has a lower probability of lysing given a quorum threshold, we obtain dynamics similar to those observed in our experimental system (Figs 2g and 4f). As seen in the experiment, the Rpa-GFP strain inhabits most of the space, with Lux-CFP periodically displacing it until it reaches quorum and self-limits its population. This dynamic, as with the dynamics of each set-up, offers a distinct advantage for certain purposes. For example, a system requiring a constant production of a particular chemical and periodic bursts of a second chemical could appropriate the set-up in Fig. 4f to its advantage.

Synthetic biologists have used lysis to control populations before12, but not until recently have populations been engineered to dynamically control their own population without exogenous input16. Because our system relies on DNA parts carried on plasmids, undesired mutations may arise that can hinder the function of the circuit. Bacteria may mutate toxic or burdensome genes and any possible mutants may gain a selective advantage over non-mutated members of the population. In this regard, strategies to enhance the stability of the circuit components inside the host cells would be necessary to ensure long-term robustness of the synthetic ecosystem24. Additionally, in the absence of antibiotics, bacteria would encounter a selective pressure to lose the circuit plasmids. This would be problematic when introducing the synthetic ecosystem to an environment without any selective agents. Possible ways this could be addressed are by either integrating circuit components within the genome or using plasmid-stabilizing elements in the circuit. Elements such as toxin/antitoxin systems and actin-like protein partitioning systems have previously been shown to stabilize plasmids in environments without antibiotics25. The emergence of escapees is a direct consequence of strong selection imposed by periodic lysis, and recent evidence also suggests that repeated pruning of a population suppresses beneficial mutations that confer growth advantages unrelated to the lysis circuit26. Therefore, the ortholysis strategy might be an attractive methodology to impose certain population dynamics or types of selection in evolution experiments.

The challenge in maintaining a population of metabolically competitive microbial organisms has long been recognized21. Strategies to maintain the long-term stability of engineered microbial ecosystems that have thus far been investigated mainly consist of mutualistic interactions, such as metabolic interdependencies or predator–prey type interactions27,28. Recent evidence suggests, however, that competition is probably the dominant interaction in microbial communities29. In this vein, the ‘ortholysis’ system can be viewed as a strategy to stabilize competitive strains without engineering positive and negative interactions between members of the population. Moreover, recent evidence has identified QS-controlled self-lysis as a naturally occurring phenomenon in Pseudomonas aeruginosa30, which is a relevant example of how the interests of synthetic biologists and microbial ecologists are merging in the field of engineered microbial ecosystems.

With the additional modelling of our circuit it becomes clear that the transition from monoculture synthetic biology to synthetic engineered ecosystems will be marked by an explosion of possibilities. A circuit designed for monocultures, such as the SLC, can have drastically broadened applications when expanded into the setting of a community. The ‘ortholysis’ system is immediately applicable for further expansion on the periodic in situ drug delivery system16. However, this phenomenon of stably co-culturing two metabolically competitive strains through orthogonal self-lysing offers the possibility of many unique applications beyond drug delivery where the use of synthetic microbial ecosystems is advantageous.

## Methods

### Plasmids and strains

Our circuit strains without the lysis plasmid were cultured in lysogeny broth (LB) medium with 50 µg ml−1 kanamycin, in a 37 °C incubator. Our circuit strains with the lysis plasmid were cultured in the same way but with 34 µg ml−1 of chloramphenicol as well as 0.2% glucose. For microscopy and plate reader experiments, 1 nM of 3-oxo-C6-HSL was added to all media. Plasmids were constructed using the circular polymerase extension cloning (CPEC) method of cloning or using standard restriction digest/ligation cloning31. The lux activator plasmid (Kan, ColE1) and lux-lysis plasmid (Chlor, p15A) were used in previous work from our group16,32. The RpaR and RpaI genes were obtained via PCR off the Rhodopseudomonas palustris genome (obtained through ATCC) to create the Rpa-activator and Rpa-lysis plasmids. The lux-sfGFP lysis circuit alone was characterized in Escherichia coli. Co-culturing was performed with non-motile Salmonella typhimurium, SL1344.

The SLC, in both the Lux and Rpa cases, is composed of an activator plasmid and a lysis plasmid. For the circuit characterization experiments there were three variations of the activator plasmid. The first was pTD103LuxI-sfGFP, which was used in previous work by our group32. This plasmid contains a LuxI with the ssrA-LAA degradation tag (amino-acid sequence, AANDENYALAA) and sfGFP, a superfolding green fluorescent protein variant33. pTD103LuxI (TS) sfGFP was constructed by adding the TS-linker (amino-acid sequence, TS) between the ssrA-LAA tag and LuxI. pTD103LuxI (-LAA) sfGFP was constructed by removing the ssrA-LAA tag from LuxI. For the dual lysis experiments, the Lux-CFP strain used the activator plasmid with the ssrA-LAA tagged LuxI with a CFP in place of the sfGFP. The Rpa-GFP strain's activator plasmid was created by replacing LuxR with RpaR and the LuxI with an ssrA-LAA tagged RpaI.

The lysis plasmids have a p15a origin of replication and a chloramphenicol resistance marker34 and have been previously described by our group16. The lysis gene E from the bacteriophage φX174, was provided by Lingchong You and was taken from the previously reported ePop plasmid via PCR35. The E gene was placed under the expression of the LuxR-AHL activatable LuxI promoter for both the Lux-CFP and Rpa-GFP strains. Most of the construction was done using the CPEC method of cloning31. Supplementary Fig. 4 and Supplementary Table 1 present maps of the plasmids used in this study.

### Microfluidics and microscopy

Our group has previously described in depth the microscopy and microfluidics techniques used in this study14. In short, micrometre-scale features were baked onto silicon wafers using crosslinked photoresist. The microfluidic device resin PDMS (polydimethylsiloxane) was then poured over the wafers and solidified by baking. The PDMS, containing numerous devices, was peeled off, and individual devices were cut out from the whole. Holes were then punched into the device at their input and output, where the fluid lines would eventually plug in. After puncturing, the devices were bonded onto glass coverslips by means of plasma activation. The devices were then put in a vacuum and the outlet loaded with cells and the inlet with medium. Vacuum pressure loaded the cells into traps, and media lines were plugged in before the cells could contaminate the upstream section of the device. The flow was then adjusted by changing the relative heights of the syringes (for all experiments the meniscus of the medium was set to one inch above the meniscus of the waste, resulting in a low, constant hydrostatic pressure-driven flow).

All microfluidic experiments were performed in a side-trap array device as previously described14 and in all cases 0.075% Tween20 was added to the medium to deter cells from sticking to the channels and the ports of the device. The bacteria growth chambers were 100 µm wide, 85 µm deep and 1.6 µm in height.

For lysis characterization (Fig. 1), cells were cultured until they reached an optical density (OD) of 0.1 (Plastibrand 1.5 ml cuvettes were used), at which point they were spun down and loaded via vacuum pressure onto the chip. The medium was LB with kanamycin and chloramphenicol.

For dual lysis and co-culturing experiments (Fig. 2), cells were cultured until they reached an optical density of 0.1 (Plastibrand 1.5 ml cuvettes were used), and 1.5 ml was spun down and resuspended in 50 µl of medium. This concentrate was used to vacuum-load the cells for single-strain experiments, or it was mixed at a 10:1 ratio (Lux-CFP:Rpa-GFP) in the co-culturing experiments before loading via vacuum pressure. The medium was LB with kanamycin (and chloramphenicol for lysis experiments) with 1 nM 3-oxo-C6-HSL added.

The microscope system used has also been described previously by our group32. In short, a Nikon Eclipse TI epifluorescent microscope with phase-contrast-based imaging was used. The camera was a Photometrics CoolSNAP HQ2 charge-coupled device.

The acquisition software was Nikon Elements. The microfluidic devices were housed in a plexiglass incubation chamber maintained at 37 °C by a heating unit.

For dual lysis and co-culturing experiments, phase-contrast images were taken at ×20 magnification with 50–200 ms exposure times. Fluorescent imaging at ×20 was performed at 300 ms for GFP, with a 30% setting on the Lumencor SOLA light source, and at 300 ms and 35% for CFP. Images were taken every 3 min for the course of the experiment (2 days). Co-culture was determined to be lost if the fluorescence of either CFP or GFP reduced below the background fluorescence, and was checked manually for the oscillatory lysing CFP strain, which can drop below threshold between lysis events.

For lysis characterization (Fig. 1) we counted cells using the following strategies. For experiments where the cell population was mostly aggregated together (non-sparse population), we first estimated the average area of an individual bacterial cell and the average void fraction (open space between bacteria in the trap). Taking into account the pixel density of the image, we measured the area of the trap taken up by cells using ImageJ, and divided by the average area of a bacterial cell. This value was then multiplied by (1 – void fraction) to yield the total estimated number of cells in the trap. Bacteria that were not close to the main group of cells were counted individually and added to the final number. For experiments where the growing population was sparse (due to the constant lysis regime), we used the Trainable Weka Segmentation plug-in for ImageJ to count cells. Plots were generated using MATLAB.

For co-culture experiments, co-culture was determined to be lost if the fluorescence of either CFP or GFP dropped below the background fluorescence, and images were then checked manually for the oscillatory lysing CFP strain, which can drop below threshold between lysis events.

### Plate reader fluorescence and population estimates

For the well-plate experiments, the strains were grown in a standard Falcon tissue culture 96-well flat bottom plate with appropriate antibiotics (kanamycin only for non-lysis, and kanamycin and chloramphenicol for lysis strains). For consistency with microfluidic experiments, 1 nM of 3-oxo-C6-HSL was added to all media. We grew the bacterial strains used in Fig. 2b in 4 ml cultures to an optical density of 0.15 before adding 10 µl of this culture to 10 ml of fresh LB with appropriate antibiotics and added HSL. For single-strain tests, 200 µl of the dilution was distributed into the well plate. For the 1:1 mixtures, 100 µl of each dilution was added to the same well. For the 1:100 mixtures, 200 µl of the Lux-CFP dilution was added with 2 µl of the Rpa-GFP dilution. For all cases there were four technical replicates.

These dilutions were then grown for 10 h (non-lysing) or 19 h (with lysis), and their optical density at 600 nm, GFP and CFP levels were measured every 10 min in a Tecan Infinite M200 Pro. GFP readings had an excitation of 485 nm and emission of 520 nm. CFP readings had a an excitation of 433 nm and emission of 475 nm. The resulting fluorescence curves were used to calculate the estimated populations of the co-cultures.

Population estimates in the co-culture mixtures were made in the following way. The GFP fluorescence time-series trace of Rpa-GFP alone was integrated and used as a standard for accumulated fluorescence of a culture with 100% of the Rpa-GFP strain. In the same way, the CFP fluorescence time-series trace of Lux-CFP alone was integrated and used as a standard for accumulated fluorescence of a culture with 100% of the Lux-CFP strain. The integrated GFP and CFP fluorescence curves of the mixtures were then divided by the standards to give a population estimate of Rpa-GFP and Lux-CFP, respectively. For all cases, the area of the background fluorescence was subtracted. Additionally, the GFP fluorescence required extra signal normalization because the Tecan's GFP sensor reads into the CFP emission profile (but not the other way around).

The equations used to calculate the population estimates, with appropriate filtering and normalization, are as follows: $PopulationLux=Area(CFPmix)−Area(BGCFP)Area(CFPLux)−Area(BGCFP)$$η=Area(GFPLux)−Area(BGGFP)Area(CFPLux)−Area(BGCFP)$$GFPCrosstalk=[Area(GFPmix)−Area(BGGFP)]$$GFPReal=GFPCrosstalk−[Area(CFPmix)−Area(BGCFP)]η$ $Population Rpa =GFPRealArea(GFPRpa)−Area(BGGFP)$ PopulationLux is the population estimate of the Lux-CFP strain in a co-culture. Area(CFPmix) is the area of the CFP fluorescence time-series curve of a given co-culture. Area(BGCFP) is the area of the background CFP fluorescence time-series line. Area(CFPLux) is the average area of the CFP fluorescence time-series curve in the wells with only the Lux-CFP strain. Area(GFPLux) is the average area of the GFP fluorescence time-series curve in the wells with only the Lux-CFP strain (for this strain, the GFP fluorescence should technically be at background, and further normalization is done because the Tecan's GFP sensor reads into the CFP emission profile). Area(BGGFP) is the area of the background GFP fluorescence time-series line. η is the calculated fluorescence emission crosstalk scalar and is only needed to scale GFP values, because the CFP sensor does not read any GFP. The normalized and filtered GFP value is thus given by GFPReal. Area(GFPmix) is the area of the GFP fluorescence time-series curve of a given co-culture. Area(GFPRpa) is the average area of the GFP fluorescence time-series curve in the wells with only the Rpa-GFP strain. Finally, PopulationRpa is the population estimate of the Lux-CFP strain in a co-culture.

### Agent-based modelling

For the agent-based model, to simulate bacterial motion, we adapted the mechanical agent-based model developed in our earlier work36,37. Each cell was modelled as a spherocylinder of unit diameter that grows linearly along its axis and divides equally after reaching a critical length ld = 4. It can also move along the plane due to forces and torques produced by interactions with other cells. The slightly inelastic cell–cell normal contact forces were computed using the standard spring-dashpot model and the tangential forces were computed as velocity-dependent friction.

To describe the intracellular dynamics of each cell, we adapted the ordinary differential equation model from ref. 16. Specifically, the intracellular dynamics are as follows:$Plux=α0+αH(Hi/H0)m1+(Hi/H0)m$$dHidt=bIiKI+Ii+Dm(He(xi,t)−Hi)$$dIidt=CIPlux−γIIi$$dLidt=CLPlux−γLLi$where Plux, Hi, Ii and Li are the activity of the LuxI promoter, intracellular AHL, LuxI and lysis protein of the ith cell. He(xi, t) is the extracellular concentration of AHL at the location of the ith cell. LuxI promoter is induced by AHL. b(Ii/(KI + Ii)) is the production term for AHL. Dm(He(xi, t) – Hi) describes the exchange of intra- and extracellular AHL across the cell membrane. CIPlux and γIIi are the production and degradation terms for LuxI. CLPlux and γLLi are the production and degradation terms for lysis protein.

The extracellular AHL concentration He(x,t) is governed by the following linear diffusion equation:$∂He(x,t)∂t=Dm∑Hiδ(x−xi)−He(x,t)−δHHe(x,t)+DH∇2He(x,t)$In the simulation, we use two-dimensional finite difference methods to describe the diffusion of AHL.

We implement the model in traps with different side lengths (20, 40 and 60 a.u.). To simulate the lysis of each cell, we assume that when the concentration of lysis protein Li is above a threshold Lth, the cell has a probability of Pr = pL(Li – Lth) per unit of time to lyse and, once a cell lyses, it is removed from the trap.

We chose model parameters to qualitatively fit the experimental results and the parameters H0, m, b, pL  were chosen to account for the differences of experimental measurements and dynamic behaviours between Lux-CFP and Rpa-GFP strains. The parameter values for the Lux-CFP strain are α0 = 0.1 (Lux promoter basal production), αH = 2 (Lux promoter AHL-induced production), H0 = 1 (AHL binding affinity to Lux promoter), m = 4 (Hill coefficient of AHL-induced production of Lux promoter), b = 1.5 (AHL production rate), KI = 1 (concentration of LuxI resulting in half maximum production of AHL), Dm = 10 (diffusion constant of AHL across the cell membrane), CI = 1 (LuxI copy number), γI = 1 (degradation rate of LuxI), CL = 1 (lysis gene copy number), γL = 0.5 (degradation rate of lysis protein), δH = 0.1 (dilution rate of extracellular AHL), DH = 65 (diffusion constant of extracellular AHL), pL = 0.3 (probability of lysing) and Lth = 1.6 (threshold of lysis protein for lysis).

To simulate the constant-lysis Rpa-GFP strain, these parameters have different values: H0 = 0.2, m = 1, b = 0.8, pL = 0.03. Also, the growth rate of the Rpa-GFP strain is 10% larger than that of the Lux-CFP strain.

### Deterministic modelling

#### Single lysis oscillator strain

We describe the population-level mechanisms that lead to oscillations in population size as observed with the synchronized lysis circuit. To gain an intuitive understanding, we used a reduced model that aims to reproduce the observed population level behaviour using only the fundamental ingredients of the circuit: autocatalytic production of the QS agent and QS agent-induced lysis of cells. The basic equations for a single strain equipped with the lysis circuit are as follows (see Supplementary Fig. 5 for model traces): $(1)dndt=αn−f(q)γn$ $(2)dqdt=[αq+αq∗f(q)]n−γqq$The cell density is denoted by n. Cells divide with rate α and die with maximal rate γ due to lysis. 0 ≤ f(q) ≤ 1 characterizes the promoter under which the QS and lysis proteins are expressed, so it determines the dependence of the death rate on q and the autocatalysed production of the QS agent q. αq is the basal production rate of the QS agent, which can be increased by the presence of q to a maximum production rate of $αq+αq∗$. q is diluted in the environment with a rate γq. We use a standard Hill function for f(q): $(3)f(q)=qmqcm+qm$where qc is the concentration of q that results in the half-maximum death rate (and auto-catalysed production of q) and m is the Hill coefficient.

A linear stability analysis shows that the system (1, 2) has a stable fixed point when $(4)m1−αγ<1+αqγαq∗α$The border of this stability region corresponds to the onset of oscillations. Basal parameters are, unless otherwise mentioned, α = 1, γ = 4, αq = 0.4, $αq∗=8$, γq = 1, qc = 1, m = 2. These parameters lead to oscillations according to equation (4). All simulations are carried out using the Runge–Kutta–Fehlberg (RKF45) method. An example trajectory is depicted in Supplementary Fig. 5.

Although we do not explicitly model individual proteins or enzymes, we can gain an understanding for the influence of LuxI degradation by ClpXP with model (1, 2) using the following logic. When there is very little LuxI (that is, the positive feedback loop has not been activated), fast degradation by ClpXP will have a strong influence on the steady-state level of LuxI. LuxI with a strong degradation tag will experience fast degradation by ClpXP, leading to a low basal production rate of QS agent (αq), whereas LuxI with a weak degradation tag will have a higher steady-state level and therefore a higher basal production rate αq. In contrast, once the positive feedback has been activated, the concentration of LuxI (and consequently parameter $αq∗$ of the model) has a much weaker dependence on its degradation tag because an abundance of LuxI produced from a fully activated promoter saturates the limited enzymatic processing capacity of ClpXP and therefore the level of LuxI will be determined mainly by dilution due to cell growth. As seen from equation (4), decreasing αq by a larger factor than $αq∗$ generally brings the system closer to oscillations, which is consistent with the requirement for a strong degradation tag for sustained oscillations demonstrated in Fig. 2. In summary, we model stronger (weaker) enzymatic degradation of LuxI by a lower (higher) value of αq.

#### Microfluidic traps and multiple strains

A microfluidic trap is clearly a finite environment, but because nutrients are constantly replenished by diffusion from fresh medium in the channel, logistic growth (as is often assumed in other scenarios with finite carrying capacities) would be an unrealistic description of the population dynamics. Instead, we assume that growth is unaffected as long as the population density is below the carrying capacity c of the trap. We then cap the cell density at c, corresponding to any extra cells being washed away by the flow in the main channel (‘spillover’). Numerically, we reset the cell density to c after every time step of the simulation if it exceeds c. In all our simulations c = 1. Supplementary Fig. 5 shows that the system with standard parameters lyses just before it reaches the carrying capacity of the trap, so it is truly self-limiting.

For simulations of multiple strains, we simulate two copies of system (1, 2) with variables {n1,q1} and {n2,q2}. Again, we let the system evolve freely as long as n1 + n2 < c. If n1 + n2 exceeds c after any time step, we set n1 and n2 according to $(5)n1=n1′n1′+n2′cn1=n2′n1′+n2′cifn1′+n2′>c$where n1′ and n2′ correspond to the population densities before the reset. More specifically, this way of limiting the total population density to the carrying capacity c corresponds to assuming a well-mixed environment, such that the relative population densities of the two strains remain unchanged upon spillover.

Consequently, two oscillating strains in one trap that use completely orthogonal QS systems only interact if the total population density hits the carrying capacity c. As shown in the main text, the strains will eventually lock into an antiphase pattern where they avoid reaching their peak density at the same time. To model crosstalk, we modify the equation of the ‘receiver’ strain (strain 2 in this case) to read $(6)dn2dt=α2n2−f(q2+ξq1)γ2n2$ $(7)dq2dt=[αq,2+αq,2∗f(q2+ξq1)]n2−γq,2q2$where ξ determines how much strain 2 responds to the QS agent of strain 1, that is, the strength of the crosstalk.

For the parameter scan of a single strain in Fig. 1, the model equations were simulated for 2,000 time units for different values of model parameter αq. The last 400 time units were used to determine the minimum, mean and maximum population densities. For all parameter scans of two strains, the model equations were simulated for 500 time units and the last 100 time units were analysed to determine the average cell densities $n¯1$ and $n¯2$ of the two strains. The ‘steady-state population ratio’ shown in Fig. 4 was then calculated as $(n¯1−n¯2)/(n¯1+n¯2)$, ranging from −1 (strain 2 dominates) to 1 (strain 1 dominates). For non-lysing strains, model parameter qc was set to infinity. Crosstalk parameters in Fig. 4c,d are ξ = 0.6 and ξ = 0.12, respectively. Weak lysis (strain 1, Fig. 4f) was achieved by reducing the lysis rate of the strain to γ = 0.5.

### Data availability

The data that support the findings of this study are available from the corresponding author upon request.

### Code availability

The modelling code for the agent-based as well as deterministic numerical simulations is available from the corresponding author upon request.

How to cite this article: Scott, S. R. et al. A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis. Nat. Microbiol. 2, 17083 (2017).

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## Acknowledgements

This material is based on work supported by NIH/NIGMS grant no. RO1-GM069811 and by the San Diego Center for Systems Biology under NIH/NIGMS grant no. P50-GM085764. S.R.S. was partially funded by the National Science Foundation Graduate Research Fellowship under grant no. DGE-1144086. P.B. acknowledges support from HFSP fellowship LT000840/2014-C. L.X. and L.S.T. were partially supported by ONR grant no. N00014-16-1-2093. Any opinion, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

## Affiliations

1. ### Department of Bioengineering, University of California, La Jolla, San Diego, California 92093, USA

• Spencer R. Scott
• , M. Omar Din
•  & Jeff Hasty
2. ### BioCircuits Institute, University of California, La Jolla, San Diego, California 92093, USA

• Philip Bittihn
• , Liyang Xiong
• , Lev S. Tsimring
•  & Jeff Hasty
3. ### The San Diego Center for Systems Biology, La Jolla, California 92093, USA

• Philip Bittihn
• , Liyang Xiong
• , Lev S. Tsimring
•  & Jeff Hasty
4. ### Department of Physics, University of California, La Jolla, San Diego, California 92093, USA

• Liyang Xiong

• Jeff Hasty

## Authors

### Contributions

All authors contributed extensively to the work presented in this paper.

### Competing interests

The authors declare no competing financial interests.

## Corresponding author

Correspondence to Jeff Hasty.

## PDF files

1. 1.

### Supplementary information

Supplementary Figures 1–5; Supplementary Table 1

## Videos

1. 1.

### Supplementary Video 1

This video shows timelapse fluorescence microscopy of the SLC in strain 2 (ssrA tag on LuxI) at 20x magnification. Images were taken every 2 min of a 100 × 100 μm chamber. Timestamp is in minutes.

2. 2.

### Supplementary Video 2

This video shows timelapse fluorescence microscopy of the SLC in strain 1 (no ssrA tag on LuxI) at 20x magnification. Images were taken every 2 min of a 100 × 100 μm chamber. Timestamp is in minutes.

3. 3.

### Supplementary Video 3

Video of non-lysis co-culture on a microfluidic device at 20x magnification. Strain 4 (non-lysis Lux-CFP) and strain 6 (non-lysis Rpa-GFP). Timelapse fluorescence microscopy images were taken every 3 min. Timestamp is in minutes.

4. 4.

### Supplementary Video 4

Video of dual lysis co-culture on a microfluidic device at 20x magnification. Strain 5 (lysis Lux-CFP) and Strain 7 (lysis Rpa-GFP). Timelapse fluorescence microscopy images were taken every 3 min. Timestamp is in minutes.

5. 5.

### Supplementary Video 5

Timelapse agent-based simulation of two lysis strains both in the oscillatory regime.

6. 6.

### Supplementary Video 6

Timelapse agent-based simulation of two lysis strains: one in the oscillatory regime and the other in the constant lysis regime.