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Genetically engineered control of phenotypic structure in microbial colonies

Nature Microbiology volume 5, pages 697–705 (2020)Cite this article

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Abstract

Rapid advances in cellular engineering1,2 have positioned synthetic biology to address therapeutic3,4 and industrial5 problems, but a substantial obstacle is the myriad of unanticipated cellular responses in heterogeneous real-world environments such as the gut6,7, solid tumours8,9, bioreactors10 or soil11. Complex interactions between the environment and cells often arise through non-uniform nutrient availability, which generates bidirectional coupling as cells both adjust to and modify their local environment through phenotypic differentiation12,13. Although synthetic spatial gene expression patterns14,15,16,17 have been explored under homogeneous conditions, the mutual interaction of gene circuits, growth phenotype and the environment remains a challenge. Here, we design gene circuits that sense and control phenotypic structure in microcolonies containing both growing and dormant bacteria. We implement structure modulation by coupling different downstream modules to a tunable sensor that leverages Escherichia coli’s stress response and is activated on growth arrest. One is an actuator module that slows growth and thereby alters nutrient gradients. Environmental feedback in this circuit generates robust cycling between growth and dormancy in the interior of the colony, as predicted by a spatiotemporal computational model. We also use the sensor to drive an inducible gating module for selective gene expression in non-dividing cells, which allows us to radically alter population structure by eliminating the dormant phenotype with a ‘stress-gated lysis circuit‘. Our results establish a strategy to leverage and control microbial colony structure for synthetic biology applications in complex environments.

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Fig. 1: Microfluidic platform for observing E. coli growth patterns in spatially heterogeneous microcolonies.
Fig. 2: Actuator growth modulation and spatiotemporal feedback.
Fig. 3: Synthetic stress gate primes cells for phenotype-specific expression.
Fig. 4: A colony-level phenotype filter.

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Data availability

The data that support the findings of this study are either attached as source data or available from the corresponding authors upon request. The blueprints of the microfluidic device necessary to recreate the specific microenvironments used in this study are also available from the corresponding authors upon request.

Code availability

The modelling code for the numerical simulations and the scripts used to analyse experimental data are available from the corresponding authors upon request.

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Acknowledgements

The microfluidic mixer is derived from a design developed by M. Dueck. We also thank G. Graham, R. Cooper and R. Golestanian for stimulating discussions and Q. Tran for technical help. This material is based on work supported by the National Institutes of Health/National Institute of General Medical Sciences (grant no. RO1-GM069811) and the National Science Foundation (grant no. DMS-1463657). P.B. was supported by the Human Frontiers Science Program fellowship LT000840/2014-C. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the funding agencies.

Author information

Author notes

  1. Philip Bittihn

    Present address: Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany

  2. Andriy Didovyk

    Present address: Vertex Pharmaceuticals, San Diego, CA, USA

Authors and Affiliations

  1. BioCircuits Institute, University of California, San Diego, La Jolla, CA, USA

    Philip Bittihn, Andriy Didovyk, Lev S. Tsimring & Jeff Hasty

  2. The San Diego Center for Systems Biology, La Jolla, CA, USA

    Philip Bittihn, Lev S. Tsimring & Jeff Hasty

  3. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Jeff Hasty

  4. Molecular Biology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA

    Jeff Hasty

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Contributions

P.B. and A.D. created the stress promoter construct and discovered the growth modulation feedback in microfluidic experiments. P.B., A.D., L.S.T. and J.H. designed the study. P.B. designed the microfluidic chip, generated the remaining constructs and strains, performed the plate reader and microscopy experiments, and analysed the data. P.B. and L.S.T. carried out the numerical simulations. P.B., A.D., L.S.T. and J.H. interpreted the results and wrote the manuscript.

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Correspondence to Lev S. Tsimring or Jeff Hasty.

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Extended data

Extended Data Fig. 1 Establishment of phenotypic heterogeneity.

a, High-resolution image of full microfluidic trap after establishment of phenotypic heterogeneity. Blue color indicates IIDs (see Methods). Scale bar, 20μm. b, Initial growth in the microfluidic trap. Time-lapse images as in Fig. 1b with zoomed-in region around the eventual growth boundary (see also Supplementary Video 1). Blue color indicates IIDs (see Methods). Scale bars in top left and bottom left image, 15μm.

Extended Data Fig. 2 Growth patterns in different media compositions.

a, Kymographs indicating inter-image differences (IIDs, see Methods) as in Fig. 1c during the establishment of a steady state growth pattern in 170μm deep traps for growth media containing different amounts of glucose (the standard concentration is 0.2% w/v). For lower concentrations, growth extends further into the back of the trap with a smoother transition between regions of growth and no growth. Kymographs on the right of the black line show consistent behavior in the smaller (140μm deep) traps (the dashed line indicates the end of the trap). b, Distance from the mouth of the microfluidic trap at which the inter-image difference (IID) shown in a drops below detectable levels. For this purpose, the IID profile was averaged over 4 h of steady-state growth. The threshold for growth detection was chosen as the lowest possible level that safely detected the no-growth region in standard growth medium.

Extended Data Fig. 3 Transition to dormancy and protein expression in dormant cells.

a, Stress sensor activation at different background osmolarities. Kymographs showing growth (IIDs, blue) and GFP fluorescence (green) for lower NaCl concentrations. Compared to Fig. 1f, the fluorescence signal has been rescaled to visualize even weak activation of the pOsmY stress sensor. While the sensor is transiently activated upon growth arrest even in the absence of basal osmotic stress (0 mM NaCl), higher NaCl also elicits a measurable response in dividing cells close to the growth boundary. b, Position of the growth interface during sensor activation, analyzed as in Extended Data Fig. 2b, showing no detectable growth modulation by NaCl itself or the activation of the sensor module. See Extended Data Fig. 6c for additional controls. c, Expression of untagged RFP from the pLuxI promoter in growth-arrested cells. As in Fig. 1g, h, the plot shows average fluorescence measured in the growth-arrested back of the microfluidic trap. The blue shaded area marks the window of induction with AHL.

Extended Data Fig. 4 Actuator circuit in batch culture and microfluidics.

a, IPTG was added when the culture of E. coli cells equipped with the actuator circuit reached an OD600 of 0.13. Growth is halted through expression of GRP-ssrA from pLlacO-1 at a higher OD compared to Fig. 2a, consistent with the later addition of IPTG. b, Time series of growth (calculated as inter-image difference, IID, see Methods) and fluorescence in our microfluidic environment, averaged over the area in the trap indicated in c. Blue shaded regions mark the induction windows for all 8 IPTG concentrations tested. Cumulative effects can be observed when pulses of IPTG are given in short succession. c, Time-lapse images showing growth (IIDs, blue) and fluorescence (green) for the experiment shown in panel b during the last pulse of 300 μM IPTG (see Supplementary Video 3). The pulse begins at t = 17.6 h. d, Kymographs of growth (IIDs, blue) and fluorescence (green) for cell populations carrying the actuator circuit, which is induced continuously starting around t = 4 h. With increasing GRP-ssrA expression, the area of active growth extends further and further into the trap. e, Same data as in d, omitting fluorescence for better visualization of the growth pattern.

Extended Data Fig. 5 Numerical simulations of the sensor-actuator circuit (see Supplementary Discussion 2 for modeling details).

a, Activation of the stress sensor upon growth arrest in numerical simulations of a well-mixed batch culture of cells. The promoter strength of the sensor is modeled to increase with the NaCl concentration in the media (cf. Figure 1e). b, Actuator and population dynamics in batch culture upon IPTG induction. The arrow marks the start of induction. The production rate of the actuator protein is proportional to the IPTG induction level, halting growth earlier for higher IPTG (cf. Figure 2a). c, Spatiotemporal simulations of actuator protein induction and population dynamics in a microfluidic trap. 1 h and 5 h pulses of IPTG (amplitudes 0.5) lead to reversible growth resumption in the growth arrested region of the simulated microcolony (cf. Figure 2b and Extended Data Fig. 4). d, Numerical simulations of the full spatiotemporal model of the sensor-actuator circuit coupled with population and nutrient dynamics. Oscillations near the growth interface are observed for sufficiently high induction (NaCl) levels of the sensor promoter. e, In the oscillatory layer of the microcolony (compare d), induction of the actuator protein is followed by growth resumption, which is followed by growth arrest and induction of the actuator protein from the stress sensor, restarting the cycle (cf. Figure 2g).

Extended Data Fig. 6 Induction of oscillations in the sensor-actuator circuit.

a, Kymographs of growth (IIDs, blue) and fluorescence (green) for populations carrying the sensor-actuator circuit (Fig. 2c). Basal osmotic stress (NaCl) activates the sensor close to the interface (Fig. 1f) and initiates negative feedback leading to oscillations. b, Period and amplitude of fluorescence oscillations. Period is only shown for NaCl concentrations yielding finite oscillation amplitudes. Data points for each concentration correspond to two traps imaged at high resolution (see Methods). c, NaCl-dependent growth delay (measured as the time to reach OD 0.1 relative to 0 mM NaCl) in the sensor-actuator circuit (pOsmY-GRP-LAA) and multiple control strains: MG1655Z1 (no construct), pOsmY-GFP-LAA (the sensor component), pOsmY(LAA)-GFP-LAA (the sensor component with an additional ssrA-LAA degradation tag on the N-terminal OsmY part of pOsmY) and pOsmY-GRP (the sensor-actuator circuit without the ssrA-LAA degradation tag on GRP). The presence of growth delays exclusively in the GRP-containing constructs (exacerbated when the ssrA degradation tag is not present and GRP levels are increased) shows that the N-terminal portion of the OsmY protein (no matter whether expressed at high or low levels) has no growth-modulating effect for the media, NaCl concentrations and resulting expression levels relevant for this study (cf. Extended Data Fig. 3b). Note that these additional control constructs are not included in Supplementary Figure 4.

Source data

Extended Data Fig. 7 Unsuccessful elimination of growth-arrested cells with the ungated lysis circuit.

a, Time-lapse images of unsuccessful phenotype elimination with the ungated lysis circuit (LuxR-pLuxR-pLuxI-E-ssrA) for induction with 490 nM AHL (compare b,c). Lysis starts in the growing part of the population, causing growth-arrested cells to receive fresh nutrients and resume growth, while simultaneously also lysing. Cells regrowing despite circuit activation form disorganized colonies and display no visible phenotype pruning. Time stamps denote time after beginning of induction. Blue color indicates inter-image differences (IIDs, see Methods). See also Supplementary Video 5. Scale bar, 50μm. b, Growth and lysis dynamics (calculated via inter-image differences, IIDs, see Methods) in the region of the trap usually occupied by growth-arrested cells for different AHL induction levels of the ungated lysis circuit. After the initial lysis, no sharp lysis events are observed. Instead, uncoordinated phases of spatially inhomogeneous lysis, regrowth and growth arrest without lysis lead to fluctuating IID that settles at a low level (cf. end states in c). c, Images taken after 18 h of AHL induction with the indicated concentration, when traps have settled into a steady state. Blue color indicates inter-image differences (IIDs, see Methods). While cells in all traps initially lyse (b), no distinct phenotype elimination is observed and colony structure is disorganized (see Supplementary Discussion 1).

Extended Data Fig. 8 Stress-gated expression of RFP.

a, Example traces of OD and RFP fluorescence in plate reader experiments with the gating circuit (Fig. 3a) with RFP-ssrA-AAV as the target gene (left), compared to ungated expression from a standard bidirectional LuxR-pLuxR-pLuxI cassette with constitutive LuxR (right). Plotted induction levels were chosen to show similar levels of expression. b, Peak fluorescence in plate reader experiments of the same RFP-ssrA-AAV gating circuit (Fig. 3a). The stress-gated circuit exhibits increased sensitivity at low concentrations, which enabled us to determine gating fidelity (Fig. 3c) for much lower concentrations for this circuit compared to constitutive LuxR. Plot shows mean ± s.d., n = 4 wells. c, We tested the same circuits in microfluidics and measured the fluorescence in the growth-arrested region in the back of the trap in response to a 10 h induction with different AHL concentrations. t20% is the time until 20% of peak fluorescence (as shown in Fig. 3d) are reached. Plot shows mean ± s.d., n = 7 microfluidic traps. d, Gating fidelity for stress-gated and ungated RFP-ssrA-AAV from plate reader experiments as in Fig. 3c, but measured in LB. e, Gating fidelity for stress-gated and ungated RFP-ssrA-AAV from plate reader experiments as in Fig. 3c, but measured in LB supplemented with 0.2% glucose. Symbols in d, e represent means of 2 wells each.

Source data

Extended Data Fig. 9 Numerical simulations of the gating circuit (see Supplementary Discussion 2 for modeling details).

a, Time traces for OD, nutrients and RFP as the target protein in the ungated circuit with constitutive production of LuxR. The arrow marks the beginning of AHL induction. b, Same time traces for the gated circuit with LuxR expressed from pOsmY, showing reduced expression during exponential phase. c, Gating fidelity (defined the same way as for Fig. 3c and Extended Data Fig. 8d,e) for the gated and ungated circuits, showing a ca. 10-fold stronger preference for stationary-phase expression in the gating circuit compared the ungated circuit. d, Time traces of OD and cellular lysis protein concentration in the ungated circuit for different AHL levels. Exponential growth rate is impacted at the same time as stationary-phase lysis becomes effective. e, Same time traces for the gated circuit for different AHL levels. The reduction of LuxR during exponential phase and its increase in stationary phase leads to efficient lysis without impacting exponential growth.

Extended Data Fig. 10 Behavior of the SGLC at low AHL concentrations near onset.

a, Images taken after 24 h of AHL induction with the indicated concentration, when traps have settled into a steady state. Blue color indicates inter-image differences (IIDs, see Methods). Lower concentrations than those shown here do not settle into steady states (see c) or show imperfect pruning. b, Growth and lysis dynamics (calculated via inter-image differences, IIDs, see Methods) in the region of the trap usually occupied by growth-arrested cells. No lysis occurred below AHL concentration of 2 nM. For higher concentrations and lysis onset times across parameters, see Fig. 4d–f. c, Time-lapse images showing oscillatory behavior of the SGLC close to onset of lysis (3 nM AHL, cf. panel b and Fig. 4d). Long delay between LuxR priming by the gating circuit and accumulation of sufficient lysis protein (Fig. 4e) causes sequential fill-up and lysis (see Supplementary Video 7). Time stamps denote time after beginning of induction.

Supplementary information

Supplementary Information

Supplementary Discussions 1 and 2, Table 1 and Figs. 1–4.

Reporting Summary

Supplementary Video 1

Establishment of phenotypic heterogeneity in 170-μm-deep traps. The data in this video correspond to Fig. 1b and Extended Data Fig. 1b. Blue colour indicates IIDs (see Methods).

Supplementary Video 2

Activation of the pOsmY sensor during pattern formation and close to the no-growth boundary. The data in this video correspond to the time-lapse images in Fig. 1f. Blue colour indicates IIDs (see Methods).

Supplementary Video 3

Actuator induction modulates the growth pattern. The data in this video correspond to the time-lapse images in Extended Data Fig. 4c. Blue colour indicates IIDs (see Methods).

Supplementary Video 4

A diffusion-mediated spatiotemporal feedback loop leads to oscillations in growth and fluorescence. The data in this video correspond to the time-lapse images in Fig. 2f. Blue colour indicates IIDs (see Methods).

Supplementary Video 5

Unsuccessful elimination of dormant cells by the ungated lysis circuit. The data in this video correspond to the time-lapse images in Extended Data Fig. 7a. Blue colour indicates IIDs (see Methods).

Supplementary Video 6

The SGLC as a phenotype filter. The data in this video correspond to the time-lapse images in Fig. 4a. Blue colour indicates IIDs (see Methods).

Supplementary Video 7

Oscillatory lysis with the SGLC at low AHL concentrations. The data in this video correspond to the time-lapse images in Extended Data Fig. 10c. Blue colour indicates IIDs (see Methods).

Supplementary Video 8

Demonstration of efficient gating for the SGLC at high induction levels. The data in this video correspond to selected AHL concentrations for the final states in Fig. 4f. Blue colour indicates IIDs (see Methods).

Supplementary Data

Annotated nucleotide sequences of all plasmids shown in Supplementary Fig. 4 (best viewed with the plasmid editor ApE to reproduce the colour coding from Supplementary Fig. 4).

Source data

Source Data Fig. 3

Statistical Source Data

Source Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 6

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Source Data Extended Data Fig. 8

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Bittihn, P., Didovyk, A., Tsimring, L.S. et al. Genetically engineered control of phenotypic structure in microbial colonies. Nat Microbiol 5, 697–705 (2020). https://doi.org/10.1038/s41564-020-0686-0

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