Letter pubs.acs.org/synthbio
Enhancing Intercellular Coordination: Rewiring Quorum Sensing Networks for Increased Protein Expression through Autonomous Induction Amin Zargar,†,‡ David N. Quan,†,‡ and William E. Bentley*,†,‡ †
Institute for Bioscience and Biotechnology Research (IBBR), University of Maryland, College Park, Maryland 20742, United States Fischell Department of Bioengineering, University of Maryland, College Park, Maryland 20742, United States
‡
S Supporting Information *
ABSTRACT: While inducing agents are often used to redirect resources from growth and proliferation toward product outputs, they can be prohibitively expensive on the industrial scale. Previously, we developed an autonomously guided protein production system based on the rewiring of E. coli’s native quorum sensing (QS) signal transduction cascade. Selfsecreted autoinducer, AI-2, accumulated over time and actuated recombinant gene expressionits design, co-opting the collective nature of QS-mediated behavior. We recently demonstrated that desynchronization of autoinduced intercellular feedback leads to bimodality in QS activation. In this work, we developed a new QS-enabled system with enhanced feedback to reduce cell heterogeneity. This narrows the population distribution of protein expression, leading to significant per cell and overall increases in productivity. We believe directed engineering of cell populations and/or cell consortia will offer many such opportunities in future bioprocessing applications. KEYWORDS: autoinducer 2, quorum sensing, autonomous, pathway engineering, protein production, feedback
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causing autoinduction of the lsr operon. The lsr operon regulates AI-2 uptake: AI-2 is imported into the cell through the ABC-type transporter LsrACDB, phosphorylated to AI-2P by the kinase LsrK, and degraded by enzymes LsrG and LsrF.12 AI-2P triggers a genetic cascade from QS activation that increases native transcription of the lsr operon, including its transporter and kinase, promoting clearance of AI-2 from the extracellular space. In the production system, because the native lsr promoter is relatively weak, we amplify the native feedback loop by incorporating on the pCT6 plasmid, the AI2-P induced expression of T7RNA polymerase, which, in turn, amplifies the superfolding green fluorescent protein (sfGFP) or other encoded gene products from the commercially available pET plasmid. In our previous work,10 this amplification scheme reached ∼85% of that produced by the IPTG-inducible tac system. We recently demonstrated that actuation of the Lsr system results in subpopulation heterogeneity as noted by the emergence of “winners” and “losers” among genetically identical cells.13,14 QS “winners” activate their QS pathways at earlier times; they more readily take up AI-2, thereby robbing the “losers” of AI-2 and of their transition to the AI-2-mediated phenotype.14 This QS-based heterogeneity among genetically identical cells is an outcome of intrapopulation variation in the rate of AI-2 induced uptake, which is different from other QS
central challenge in metabolic engineering is balancing the resources needed for host metabolism while maximizing product synthesis. Referred to as a “zero-sum game”,1 redirecting metabolites toward pathways for product outputs leads to reduced flux in endogenous processes, potentially resulting in undesirable physiological outcomes.2,3 Often, metabolic engineering has sought to produce more of a target product by increasing precursor flux through rate-limiting steps or by deleting competing pathways.4,5 While these approaches have netted significant gains in cell productivity and product yield, gene expression platforms that employ dynamic metabolic control have the added benefit of autonomous redirection of flux through the desired routes.6−8 As a consequence, reliability is improved against environmental perturbation, and the need for expensive inducing agents, which may be cost prohibitive at industrial scales, is eliminated.9 In our prior work, we leveraged quorum sensing (QS), the process through which bacteria coordinate gene expression in response to cell density, to create a dynamic gene expression system under the control of the QS signaling molecule autoinducer-2 (AI-2).10 Recognizing that accumulated extracellular AI-2 reflects the “metabolic burden” associated with heterologous protein expression in E. coli,11 we had sought to create a minimally altered signal transduction system that employed E. coli’s natural QS pathways to autonomously induce protein expression in a metabolically benign manner (Scheme 1A).10 Illustrated in Scheme 1A, LuxS produces AI-2 that is pooled extracellularly until a critical threshold is reached, © XXXX American Chemical Society
Received: December 1, 2015
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DOI: 10.1021/acssynbio.5b00261 ACS Synth. Biol. XXXX, XXX, XXX−XXX
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ACS Synthetic Biology Scheme 1a
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(A) In the top schematic, the process diagram of the native feedback system is illustrated where an input (grey) is fed to a process (blue) and results in QS activation (red) and the production of an output (green). The activation of QS initiates the native feedback loop (thin red lines). Below the process diagram, the autonomous protein production system previously developed is illustrated, where LuxS (grey) generates the input AI-2 that is processed by importer LsrACDB (blue) and kinase LsrK (blue) into intracellular AI-2P. AI-2P causes the expression of the lsr operon, thereby initiating the native feedback loop (thin red lines) to produce more LsrACDB and LsrK. AI-2P also activates the transcription of the genetic cascade of plasmids pCT6 and pET (blue) to generate the output sfGFP (green). (B) In the top schematic, the process diagram of the enhanced feedback system is illustrated where the process diagram includes a synthetic feedback loop (purple). Below the process diagram, the autonomous protein production system with an enhanced feedback loop that is developed in this work is also illustrated, where the pET plasmid produces not only the output sfGFP, but also more LsrACDB and LsrK than the native lsr operon, through the synthetic feedback loop (thick purple lines).
systems (e.g., AHL-driven expression systems) where autoinduction variability is minimized by positive intercellular feedback.15 While heterogeneity in gene expression can be beneficial for driving cellular diversity and evolution,16 from a control perspective, we view the heterogeneous QS response as a pressure point for impacting bioprocess performance. Such heterogeneity in plasmid-based gene function and protein expression is, in fact, not new17,18 but has to our knowledge not been exploited for a processing advantage. After developing “controller cells” to guide QS subpopulations in a microbial consortium,19 we now seek to engineer cell populations to respond tightly and quickly to changing environmental conditions, narrowing heterogeneity and thereby organizing entire populations in a directed manner. To increase protein expression in the autonomous system, we inserted an enhanced synthetic feedback loop (Scheme 1B) to amplify system response, coalesce group behavior, and minimize heterogeneity. Overexpression of the kinase, LsrK, or the transporter, LsrACDB, results in increased cellular AI-2 uptake19 by increasing metabolic flux toward AI-2P through direct phosphorylation of AI-2 (LsrK) or increased transport of extracellular AI-2 into the cell (LsrACDB). We sought to apply these kinetics to reengineer the host cell, not just the expression system. As shown in Scheme 1B, the pET plasmid was altered to concurrently transcribe either LsrK or LsrACDB downstream of the sfGFP gene. The result is an enhanced feedback system where transcription of the signal product (sfGFP) and elements necessary for accelerating its production occur simultaneously. The design principle is that by concurrently modifying both the product transcription and the signal uptake process that actuates product transcription, we can engineer the entire population, minimize heterogeneity and maximize performance on the population scale. We hypothesized that the root causes of incomplete QS activation in the native QS system are 2-fold: (i) relatively slow
kinetics of AI-2 mediated gene expression, and (ii) high cell− cell variability of AI-2 uptake. First, AI-2 is rapidly imported into the cells near the stationary growth phase in batch cultures20 so that natural culture progression toward growth limitation moderates the development of QS-mediated phenotypes, and second, high cell−cell variability in AI-2 uptake kinetics results in an incomplete and more heterogeneous QS-activated population.14,19 The additional feedback system conceptually speeds the induction of gene expression by increasing the rate of derepression and, at the same time, reducing its variation. That is, more rapid AI-2 uptake kinetics, introduced through a new feedback loop, should increase the sensitivity of the protein expression system because the initiation of QS-mediated gene expression can occur at earlier time points and in more synchrony. Second, as low expression levels are a key factor in driving fluctuations in endogenous protein levels,21 enhanced feedback would not only increase the rate of AI-2 uptake, but should also narrow the cellular noise in AI-2 processing kinetics. We characterized the system by growing each strain to midexponential phase (OD ∼ 0.4), and monitored growth rate, extracellular AI-2 levels and protein production (see Methods). As expected, this additional loop did not affect growth rate compared to the control system (Figure 1), as AI-2 mediated protein expression results in a minimal metabolic burden,10 and AI-2 does not sufficiently accumulate in early growth phases to trigger protein expression (Supplementary Figure S1). Thus, the growth and the production phases remain decoupled. The enhanced feedback systems W3110 pET-sfGFP-LsrK (denoted Enhanced-LsrK) and W3110 pET-sfGFP-LsrACDB (denoted Enhanced-LsrACDB) resulted in a faster clearance of AI-2 from the extracellular space compared to the original W3110 pETsfGFP (denoted control) system (Figure 2). For the previous expression system (control) and the Enhanced-LsrK system, AI-2 initially increased for the first few hours. For the B
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feature, as we have previously illustrated that transcription of the T7-pET system compared to the genome is many fold higher.13 We hypothesized an improved process would result from narrowed heterogeneity and that our rewired cells would build on QS circuitry as a means for control. Specifically, we had previously simulated QS activation, signal diffusion and cell migration using 2D cell systems, predicting that a more uniform QS-activated population for the Lsr QS system could result by reducing initial variability and heterogeneity.14 That is, by increasing the uptake kinetics through overexpression, reduced variability was predicted: upon QS activation, all cells would reach a higher rate of AI-2 uptake and do so more uniformly. The net effect was a rapid depletion of AI-2 (Figure 2). Population distribution and protein expression were analyzed using flow cytometry. In this way, the specific per cell productivity was evaluated along with the distribution of producing cells from among the total. The enhanced systems resulted in a focusing of the population distribution (Figure 3A). At the maximum level of protein expression (4 h for the enhanced system and 8 h for the control system), the coefficient of variation was much lower for the enhanced feedback systems compared to the control feedback system (Figure 3A). Our results indeed demonstrated that a narrower distribution was maintained at all time points for the enhanced feedback systems compared to the control (Figure 3B). By narrowing the population distribution, we concurrently increased autoinduced protein expression in the cell population. The enhanced feedback systems produced protein at earlier times (∼2 h compared to ∼4 h, Figure 3C). sfGFP was first detected near the midexponential growth phase (OD ∼ 1.2, red arrows in Figure 1) for the enhanced systems compared to later in the growth phase for the control feedback system (OD ∼ 2.5, black arrows in Figure 1). Importantly, both enhanced feedback systems, Enhanced-LsrK and Enhanced-LsrACDB, were observed to enhance gene expression in nearly the entire population (reaching a fluorescent population of 89.9% and 94.1%, respectively in ∼4 h). In the control case (native Lsr signal transduction), only 71.1% of cells were fluorescent, reaching this level after ∼8 h. Further, the enhanced feedback systems also had a greater per cell fluorescence with pETsfGFP-LsrK and pET-sfGFP-LsrACDB having intensities 1.7 and 1.6-fold higher than the control feedback system (Figure 3D). These conclusions are supported by phase contrast and fluorescence micrographs in Supplementary Figure S3. Thus, the enhanced feedback system resulted in QS-driven protein activation earlier in the midexponential growth phase, reaching a higher percentage of cells, and at an increased rate per cell compared to the control system. While autonomous redirection of biological pathways through metabolic state and even quorum sensing controllers represents a significant development in metabolic engineering,22,23 there is significant added complexity in regards to time course behavior, appropriate sensors, and tuning of target expression.24 Here, we have rewired native genetic circuits to exploit natural time-dependent behaviors and at the same time increase the robustness of the sensor, leading to increased protein expression and reduction in the heterogeneity among members of a cell population. By more tightly actuating heterologous protein synthesis in response to self-secreted metabolites (e.g., no operator input required), we envision applications beyond industrial bioprocessing as noted here, but in microfabricated devices such as multifunctional “plug and
Figure 1. Growth rates of W3110 pCT6 pET-sfGFP (Control), W3110 pCT6 pET-sfGFP-LsrK (Enhanced LsrK), W3110 pCT6 pETsfGFP-LsrACDB (Enhanced LsrACDB). Each culture was grown from a single colony to OD ∼ 0.4 as the first time point (t = 0 h) and cell density was measured every 2 h at OD600 (Methods). Black arrow signifies first time point with detectable levels of protein in the native feedback system and red arrow signifies first time point with detectable levels of protein in the enhanced feedback systems. All conditions were tested in biological triplicate and bars indicate standard deviation.
Figure 2. Extracellular AI-2 levels of W3110 pCT6 pET-sfGFP (Control), W3110 pCT6 pET-sfGFP-LsrK (Enhanced LsrK), W3110 pCT6 pET-sfGFP-LsrACDB (Enhanced LsrACDB). Each culture was grown from a single colony to OD ∼ 0.4 as the first time point (t = 0 h), and AI-2 levels were monitored every 2 h with AI-2 activity assays (Methods). All conditions were tested in biological triplicate and a representative sample is illustrated. Error bars indicate standard deviation.
Enhanced-LsrACDB, the AI-2 level remained high and actually decreased relatively early. We found complete removal of AI-2 for the enhanced feedback systems (OD ∼ 2.5) by 4 h, compared to complete removal after ∼8 h for the control feedback system (OD ∼ 3.2). We also measured the mRNA levels of LsrK and LsrA using qPCR at 2 and 6 h (Supplementary Figure S2). The Enhanced-LsrACDB and Enhanced-LsrK systems had from 20 to 90-fold more transcript levels than the empty vector control at the 2 h time point and 10 to 1.5-fold more transcript levels at the 6 h time point, near the stationary phase. These data are consistent with the AI-2 uptake data. We note that in the control feedback system, autoinduction relies solely upon genomic transcription of the kinase and transporter, where one copy of the lsr operon is activated for AI-2 uptake. Faster AI-2 uptake kinetics in the plasmid-based enhanced system was an intentional design C
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Figure 3. Protein expression and population distribution of W3110 pCT6 pET-sfGFP (Control), W3110 pCT6 pET-sfGFP-LsrK (Enhanced LsrK), W3110 pCT6 pET-sfGFP-LsrACDB (Enhanced LsrACDB). Each strain was inoculated from a single colony to an OD ∼ 0.4 as the first time point (t = 0 h) and subsequently, samples were taken every 2 h thereafter for flow cytometry measurements with a minimum of 20 000 events (Methods). (A) At the peak level of fluorescence for each strain, the histogram of the fluorescent population is displayed with the red bars indicating the standard of deviation and CV denoting coefficient of variation. A representative sample is illustrated (B) The coefficient of variation in cell populations at all time points is illustrated. In (C) and (D), the percentage of fluorescent population and intensity of fluorescence is shown. All conditions were tested in biological duplicate and error bars indicate standard deviation.
play” chips25,26 where on-chip unit operations equate with device complexity. We believe this work also represents a significant step in population engineering for “reprograming” cell performance wherein the intended modifications enable cells to stay “on task” in the midst of changing environments. In a recent work where we direct and assemble “quantized quorums”, we ask if autonomous expression systems can be engineered so that 100% of the cell population produces high levels of protein, despite the removal of the metabolite when the QS switch is
triggered. By minimizing the desynchronization of the QS system, we have taken a step to attain this goal.
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METHODS Plasmid Construction. The bacterial strains and plasmids used in this study are listed in Table S1, and were constructed according to standard procedures.27 Briefly, the Champion pET200 Directional TOPO Expression Kit was used to clone pET-LsrK, pET-LsrACDB in the pET200/D/TOPO (Invitrogen) plasmid. NheI was then used to linearize pET-LsrK, pET-LsrACDB and pET200, and sfGFP was cloned upstream D
DOI: 10.1021/acssynbio.5b00261 ACS Synth. Biol. XXXX, XXX, XXX−XXX
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ACS Synthetic Biology using Gibson assembly28 to construct plasmids pET-sfGFPLsrK, pET-sfGFP-LsrACDB, and pET-sfGFP. The sequences for lsrK, lsrACDB were amplified by PCR using Q5 polymerase (New England Biolabs) from E. coli K-12 strain W3110. The sequence sfGFP was amplified from pAS014A.29 W3110 pCT6 was then transformed with each plasmid.10 Oligonucleotide primers were obtained from Integrated DNA Technologies (Coralville, IA) and are listed in Table S2. AI-2 Assay. Cultured media was tested for the presence of AI-2 by inducing luminescence in the Vibrio harveyi reporter strain BB170.30 Briefly, BB170 was grown for 16 h with shaking at 30 °C in AB (AI-2 Bioassay) media. AB media is made by adjusting 400 mL of distilled (DI) water to pH 7.5, and adding 7 g of NaCl, 2.4 g of MgSO4, 0.8 g casamino acid, and 8 mL of glycerol. AB media is supplemented with 400 μL of potassium phosphate buffer (K2HPO4 10.71 and 5.24 g KH2PO4 in 100 mL of DI water), 400 μL of 0.1 M L-arginine (0.17 g in 10 mL of DI water), 40 μL of riboflavin (10 μg/mL), 40 μL of thiamine (1 mg/mL) and 40 μL kanamycin (50 mg/mL). Overnight cultures were diluted 1:5,000 in fresh AB media with kanamycin, and aliquoted into sterile 12 × 75 mm tubes (Fisher Scientific). Test samples were added to BB170 cultures at a final concentration of 10% (v/v). Luminescence was measured by quantifying light production with a luminometer (EG&G Berthold LB 9509 Jr) and assays were adjusted, if needed, so that values were in the linear range. Data are presented as “fold change” compared to negative controls. All conditions were tested in triplicate. Protein Expression Assays. Each strain was inoculated from colonies growing on kanamycin (50 μg/mL) and ampicillin (50 μg/mL) Luria Broth (LB) agar plates. Each culture was grown in a 15 mL culture tube and grown to an optical density (OD) ∼ 0.4−0.6 at 37 °C with 250 rpm shaking. That is, cells were grown in fresh medium from single colonies and the first time point (t = 0 h) was taken as OD ∼ 0.4−0.6 for each subsequent experiment. Then, every 2 h, optical density was measured and samples were collected for flow cytometry and AI-2 assays. RNA Isolation and Analysis. 1−0.75 mL of bacterial cultures ranging from 0.5−4.0 OD600 were centrifuged at 16 000g for 1 min, and resuspended in 0.5 mL RNALater (Ambion). Once all samples were collected, cells were again centrifuged at 16 000g for 1 min and resuspended in 160 μL of 10 mg/mL lysozyme (Sigma) in 10 mM Tris pH 8.0 (Quality Biologicals) with 2 mM EDTA (Life Technologies). Samples were incubated for 10 min at room temperature. Twenty μL of 10% SDS (Sigma) was added, and samples were incubated at 64 °C for 2 min. Twenty μL of 1 M sodium acetate (Sigma) was added and the samples mixed well with a pipet. One mL of Trizol reagent (Ambion) was added and the samples were incubated at 64 °C for 6 min with inversion every min. Samples were then chilled on ice and 0.5 mL of ice cold chloroform was added. After 30 min shaking and 5 min of incubation at room temperature, the top aqueous layer was removed and subjected to isopropanol precipitation at −20 °C for 30 min. After decanting of the supernatant, RNA pellets were washed with 70% ethanol. Prior to evaluation of relative mRNA concentrations by qPCR with a Sensifast Sybr Hi-Rox One-Step kit (Bioline), samples were subjected to treatment with amplification grade DNase I (Sigma) to minimize any potential DNA contamination. qPCR was performed using an Applied Biosystems HT7900. cysG was used as a reference gene and tested genes were lsrK and lsrA. At each time point harvested,
mRNA levels of the enhanced systems were normalized to the empty vector controls, and analyzed using ExpressionSuite software (Agilent)
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssynbio.5b00261. Figures S1−S3. (PDF) Tables S1−S2. (PDF)
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Funding provided by the Defense Threat Reduction Agency (DTRA, HDTRA1-13-1-00037), the Office of Naval Research (N000141010446), the National Science Foundation (CBET 1160005, CBET 1264509), and the R. W. Deutsch Foundation.
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