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Synthetic microbial consortium with specific roles designated by genetic circuits for cooperative chemical production
Metabolic Engineering 55 (2019) 268–275
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Synthetic microbial consortium with specific roles designated by genetic circuits for cooperative chemical production
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Hiroshi Honjoa,1, Kenshiro Iwasakia,1, Yuki Somaa,b, Keigo Tsurunoa, Hiroyuki Hamadaa, Taizo Hanaia,* a
Laboratory for Bioinformatics, Graduate School of Systems Lifesciences, Kyushu University, 729 West Building 5, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 8128582, Japan
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A B S T R A C T
Synthetic microbial consortia consisting of microorganisms with different synthetic genetic circuits or divided synthetic metabolic pathway components can exert functions that are beyond the capacities of single microorganisms. However, few consortia of microorganisms with different synthetic genetic circuits have been developed. We designed and constructed a synthetic microbial consortium composed of an enzyme-producing strain and a target chemical-producing strain using Escherichia coli for chemical production with efficient saccharification. The enzyme-producing strain harbored a synthetic genetic circuit to produce beta-glucosidase, which converts cellobiose to glucose, destroys itself via the lytic genes, and release the enzyme when the desired cell density is reached. The target chemicalproducing strain was programmed by a synthetic genetic circuit to express enzymes in the synthetic metabolic pathway for isopropanol production when the enzymeproducing strain grows until release of the enzyme. Our results demonstrate the benefits of synthetic microbial consortia with distributed tasks for effective chemical production from biomass.
1. Introduction A microbial consortium is a community of various microorganisms; it can exhibit functions not possessed by a single species via complex interspecific interactions. In a microbial consortia, metabolites are mutually used by different microorganisms, several information are shared among different microorganisms via common functional biomolecules. These metabolites and the information affect the regulation of specific genetic networks in individual cells (Xavier, 2011). In synthetic biology, various biological systems, such as toggle switches (Gardner et al., 2000) and oscillators (Stricker et al., 2008), have been reconstructed using synthetic genetic circuits combining biomolecules with known functions. For example, a synthetic microbial consortium that imitates a predator–prey relationship including two types of microorganisms harboring different synthetic genetic circuits utilizing common functional biomolecules has recently been constructed (Balagaddé et al., 2008). Additionally, dividing a large synthetic metabolic pathway into modules expressed in specific microorganisms enables the construction of a synthetic microbial consortium that effectively produces high-value natural metabolites (Zhou et al., 2015). Increasingly complex systems have been developed and the use of multiple microorganisms with different synthetic genetic circuits
and/or divided synthetic metabolic pathways will be one of major research goal in synthetic biology (Brenner et al., 2008). The microbial production of chemicals and energy from biomass is a promising strategy for realizing a sustainable society. Recombinant microorganisms with synthetic metabolic pathways have been constructed for the fermentation of decomposed monosaccharides to target products (Nielsen and Keasling, 2011). As the first step for chemical and energy production from biomass, a saccharification process is necessary because the most of microorganism cannot utilize polysaccharide as carbon source without extracellular digestion into absorbable sugars (e.g. glucose, xylose, sucrose). For fermentation process downstream of the saccharification, various strategies have been developed to improve the production of targets from monosaccharides, including the construction of synthetic metabolic pathways (Lee et al., 2012; Prather and Martin, 2008), gene deletion and/or upregulation (Park et al., 2007), the dynamic regulation of metabolic flux (Solomon et al., 2012; Soma et al., 2014; Zhang et al., 2012), and optimization of culture conditions. However, the major limitations in chemical and energy production from biomass have come from the effort and the cost required for saccharification using purified enzymes like bacterial cellulolytic enzymes. To reduce those limitations and to simplify the saccharification process, several studies have focused on the extracellular heterologous
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Corresponding author. E-mail address: [email protected] (T. Hanai). 1 Co-first authors. https://doi.org/10.1016/j.ymben.2019.08.007 Received 25 June 2019; Received in revised form 7 August 2019; Accepted 7 August 2019 Available online 08 August 2019 1096-7176/ © 2019 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.
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Chatsworth, CA, USA). DNA purification was performed using the DNA Clean & Concentrator™ Kit (Zymo Research, Irvine, CA, USA). Difco™ Luria-Bertani (LB)-Miller medium (Becton, Dickinson and Company, Sparks, MD, USA) and was used as the base medium for all E. coli cultivation.
expression of cellulolytic enzymes by microorganisms itself such as Escherichia coli or yeast. For example, cellobiose is a disaccharide consists of two β-glucose molecules linked by a β-1,4-glycosidic bond which cannot be incorporated into E. coli cell without extracellular digestion into glucose by beta-glucosidase (BGL), a cellulolytic enzyme. Soma et al. developed an E. coli strain anchoring BGL to its cell membrane for isopropanol fermentation from cellobios (Soma et al., 2012). Desai et al. developed an E. coli strain that excretes BGL for isobutanol fermentation from cellobiose. However, difficulties in anchoring and excreting enzymes result in insufficient productivity (Desai et al., 2014). Other novel strategies are required to achieve more efficient utilization of biomass as carbon source for chemical and energy production. By cooperation and task distribution, a synthetic microbial consortium can exert functions that are beyond the capacity of a single microorganism, potentially contributing to chemical and energy production. Despite some studies of chemical and energy production using synthetic microbial consortia with divided synthetic metabolic pathways, few reports have evaluated consortia with different synthetic genetic circuits. In this study, we developed a synthetic microbial consortium with different synthetic genetic circuits to produce a desired product from oligosaccharides as a carbon source from biomass. In particular, we designed and constructed a synthetic microbial consortium composed of an enzyme-producing strain and a target chemical-producing strain using E. coli (Fig. 1). The enzyme-producing strain contained a synthetic genetic circuit to produce beta-glucosidase, which converts the disaccharide cellobiose to glucose; the strain destroys itself by the expression of the lytic genes holin and endolysin derived from T4 phage (Marguet et al., 2010; Morita et al., 2001) and releases the enzyme when a desired cell density is reached. The target chemical-producing strain was programmed by a synthetic genetic circuit to express enzymes in the synthetic metabolic pathway for the production of isopropanol (Hanai et al., 2007) when the enzyme-producing strain grows until release of beta-glucosidase. We used a synthetic quorum sensing (QS) system in the genetic circuits to form the cascade of the tasks execution through the cell-cell communication among the consortia.
2.2. Strains and plasmids Strains, plasmids, and primers used in this study are listed in Tables S1–S3. Standard DNA cloning was performed using chemically competent cells of XL1-Blue (Agilent Technologies, Santa Clara, CA, USA) and DH5alphaZ1 (Expressys, Ruelzheim, Germany). The major plasmids used in this study are roughly divided into the QS-CL circuit (pTA1241), the BGL production unit (pTA1538), the IPA production units (pTA395 and pTA1191), the N-acyl homoserine lactones (AHL) sensor unit (pTA1420), and the GFP reporter unit (pTA1545). QS-CL strains harbor the QS-CL circuit and/or the BGL production unit. IPA producing strains harbor the IPA production unit, the GFP reporter unit, and the AHL sensor unit. Detailed construction procedures for each strain and plasmid were described in the supplementary file. All purified plasmids were introduced to electrocompetent cells of appropriate E. coli strains by electroporation and were screened for antibiotic resistance. 2.3. Culture conditions Fresh colonies of E. coli strains were inoculated to LB medium and cultured at 37 °C with agitation at 250 rpm in the Innova® 44 incubated shaker (Eppendorf, Hamburg, Germany) with a working volume of 3 mL in 15 mL test tubes. Cultures were collected when the cell density reached OD600 = 1 and were used to make original freeze stocks of E. coli strains, which were stored in 15% (v/v) glycerol at −80 °C. Original freeze stocks were inoculated to fresh LB medium and were cultured as described above. Cultures were harvested to make frozen seed stocks when the culture reached an OD600 of 0.6, which were stored in 15% (v/v) glycerol at −80 °C and were used for test-tube scale preculture followed by flask-scale fermentation. The frozen seed stocks were melted on ice and 100 μL of each melted solution was inoculated to 3 mL of fresh LB medium (corresponded to 3.33% (v/v)) in a 15 mL test tube. The test-tube scale pre-cultivation was continued until the cell density reached OD600 = 0.6. Pre-cultures were passaged to fresh LB medium containing 10 or 20 g/L cellobiose (29.2 or 58.4 mM) and appropriate antibiotics to set the initial cell density to OD600 = 0.05. The main flask-scale cultivation was performed at 37 °C with agitation at 150 rpm in a shaking water bath MM-10 (TAITEC co., Koshigaya, Japan) with a working volume of 25 mL in a 300-mL baffled flask. The biomass concentration and specific growth rates were determined by periodic measurements of OD600 of 200 μL of culture in 96-well plates using an Infinite® 2000 plate reader (Tecan, Männedorf, Zürich, Switzerland). The LB medium contained 10 or 20 g/L cellobiose or
2. Materials and methods 2.1. Chemicals and reagents All chemicals were purchased from Wako Pure Chemical Industry, Ltd. (Osaka, Japan), unless otherwise specified. Restriction enzymes and phosphatase were obtained from New England Biolabs (Ipswich, MA, USA), ligase (Rapid DNA Ligation Kit) was obtained from Roche (Manheim, Germany), and KOD Plus Neo DNA polymerase was obtained from TOYOBO Co., Ltd. (Osaka, Japan). Oligonucleotides were synthesized by Life Technologies Japan Ltd. (Tokyo, Japan). Plasmids were harvested using the QIAprep Spin Miniprep Kit (Qiagen,
Fig. 1. Synthetic microbial consortium with specific role designation by genetic circuits for cooperative chemical production. The enzyme producing strain is programed to release the saccharification enzyme into the medium when the strain reaches at desired cell density according to QS signal. The target chemical producing strain is programed to produce target chemical following the saccharification.
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sensor-actuator luxR were placed under the control of PluxlacO and a QS-independent promoter, PLtetO1, respectively (Haseltine and Arnold, 2008). The antiholin gene was also placed under the control of PLtetO1 as a cell lysis inhibitor (Calendar, 1988; Pasotti et al., 2011). It prevents the undesired cell lysis and the destabilization of the genetic circuit caused by stochastic leakage of lytic genes. We confirmed that this circuit worked as expected by flask-scale batch-cultivation, which means AND-gate like response to the inducers IPTG and anhydrotetracycline (aTc) (Fig. 2b). Only when 0.1 mM IPTG and 50 ng/mL aTc were added to the culture broth at 0 h, bacterial cell growth was interrupted at 3 h (OD600 = 1.3 ± 0.0) and the cell density decreased by 88% within 25 min. In other conditions, the final cell density of TA4291 was equivalent to that of the base strain TA1021.
10 g/L glucose. 2.4. Metabolite analysis To quantify extracellular metabolites, 50 μL of bacterial culture broth was diluted 10 fold with MilliQ water and centrifuged for 1 min at 13,000×g in a micro-centrifuge and the supernatant was filtered through a 0.2 μm syringe filter for HPLC. The extracellular glucose and cellobiose concentration was quantified using a high-pressure liquid chromatographer (HPLC, LC-20AD, SIL-20ACHT, CTO-20AC, RID-10 A; Shimadzu, Kyoto, Japan) equipped with a ligand exchange chromatography column (Shodex SP0810, SHOWA DENKO K.K., Tokyo, Japan). The mobile phase was MilliQ water, the flow rate was 1.0 mM/min, the column was kept at 80 °C, and 1 μL was injected per sample. Alcohol compounds were quantified using a GC-2010 Plus gas chromatograph (Shimadzu) equipped with a flame ionization detector and an AOC-20 automatic injector and sampler (Shimadzu). The separation of alcohol compounds was performed using a DB-WAX capillary column (30 m; 0.32 mm inner diameter; 0.50 μm film thickness; Agilent Technologies). The gas chromatograph oven temperature was initially held at 40 °C for 5 min, increased at 15 °C/min to 100 °C, and then increased at 100 °C/ min to 230 °C, where it was maintained for 1.4 min. Helium was used as the carrier gas with a column flow rate of 48 mL/min. The injector and detector temperatures were maintained at 225 °C and 235 °C, respectively. For each measurement, 0.5 μL of the culture supernatant was injected in the split-injection mode (1:12 split ratio). 1-Propanol was used as the internal standard. The fermentation by-products (α-ketoglutarate, pyruvate, citrate, lactate, acetate, formate, fumarate, malate, and succinate) were quantified by high pressure liquid chromatography (HPLC, LC-30AD, SIL-30AC, CTO-20AC, CDD-10AVP; Shimadzu) equipped with an ion-exclusion chromatography column (Showdex RSpak KC-811; SHOWA DENKO K.K., Tokyo, Japan). The separation of samples was attained using 5.0 mM p-nitrophenylphosphate as the mobile phase. The column temperature was maintained at 40 °C.
3.2. Evaluation of the QS-CL system for BGL release To demonstrate target protein production and release by the QSdependent cell lysis, a protein production unit was added to the QS-CL circuit. It consisted of the PLtetO1 promoter and the beta-glucosidase (BGL) gene bglC from Thermobifida fusca (ATCC®BAA-629D-5™), shown in Fig. 3a (QS-CL-bglC strain, TA4064). BGL hydrolyzes cellobiose into two glucose molecules, which is the final step in the enzymatic hydrolysis of cellulose. Since E. coli cannot utilize cellobiose unless it is hydrolyzed by BGL into glucose in the extracellular environment, exposure to BGL is essential for the glucose supply from lignocellulosic polysaccharides (or biomass). For efficient glucose supply from cellobiose, it is necessary to optimize BGL production and release via the dynamic characteristics of the QS-CL system. For this purpose, the IPTG concentration in the culture medium was changed from 0 to 1 mM, which controls the QS signal via changes in the transcriptional efficiency of the PluxlacO promoter. During 5 h of flask-scale cultivation with 0.025 mM or 0 mM IPTG and 50 ng/mL aTc, the growth of the QSCL-bglC strain (TA4064) was equivalent to that of the base strain, TA1021 (Fig. 3b). About 7 g/L (18.8 mM) cellobiose was gradually hydrolyzed into glucose in the above conditions. These results suggested that GBL release to the environment was accompanied by natural cell destruction, apart from autonomous cell lysis. Similar degradation of cellobiose (17.3 mM) was observed when the strain harboring the BGL production unit but lacking the lytic genes was induced (Fig. S1). This result supports the hypothesis that autonomous cell lysis was not induced by less than or equal to 0.025 mM IPTG. As the concentration of IPTG increased, QS-dependent cell lysis increased and the degradation of cellobiose was initiated simultaneously with cell lysis (Fig. 3c). Furthermore, 20 g/L (58.4 mM) cellobiose was converted to glucose within 1 h after cell lysis when the IPTG concentration was in the range of 0.05–1.0 mM. Since the maximum apparent glucose concentration in the culture broth was less than the theoretical value based on the initial feed of cellobiose, glucose might be consumed by residual cells of the QS-CL-bglC strain even after the autonomous lysis phase.
2.5. Flow cytometry analysis A total of 50 μL of culture broth was collected in a 1.5-mL tube and was centrifuged at 1500×g for 3 min. Cells were resuspended in 0.8% NaCl solution and immediately assayed using a CytoFLEX S (Beckman Coulter, Brea, CA, USA). CytExpert ver. 2.0 (Beckman Coulter) was used for data analysis and visualization. Fluorescence was measured with 488-nm argon laser excitation and a 525–540 nm emission filter. To ensure consistency between samples, 10,0000 cells were collected for each sample and consistently gated by forward scatter (FSC) and side scatter (SSC). A consistent fluorescence threshold was applied to determine the percentage of cells that were GFP+ (TA4179 or TA4180) or GFP− (TA4222). 3. Results
3.3. Design and evaluation of a synthetic microbial consortium for cooperative isopropanol production from cellobiose
3.1. Design of a synthetic genetic circuit for QS-dependent cell lysis (QS-CL) We designed a synthetic microbial consortium for cooperative isopropanol (IPA) production from cellobiose using two different recombinant E. coli strains, an enzyme-producing (QS-CL-bglC) strain and a target chemical-producing strain. For the rational construction of the synthetic microbial consortium, it is necessary to prevent the QS-CLbglC strain from consuming glucose prior to the target chemical-producing strain. For this purpose, the hexose transporter genes ptsG and manZ and the glucokinase gene glk were deleted from E. coli and the QSCL-bglC circuit was introduced to obtain the QS-CL-bglC strain TA4222 (Fig. S2). As a target chemical-producing strain, TA4179 was constructed, which detects AHL produced by TA4222 to induce the IPA production pathway (Fig. 4a). A GFP expression unit based on the PLtetO1 promoter was also introduced to TA4179 to distinguish the
E. coli strain harboring a synthetic genetic circuit, shown in Fig. 2a (TA4291), was designed for QS-dependent cell lysis (QS-CL) and was evaluated by flask-scale cultivation. For the induction of cell lysis in response to increased QS signaling, environmental AHL concentration, the cell lytic genes holin and endolysin were placed under the control of the synthetic QS promoter PluxlacO (Soma and Hanai, 2015). PluxlacO is a dual-input type synthetic promoter that is activated by the AHL-LuxR complex and repressed by the LacI repressor. In other words, PluxlacO is activated in the co-presence of AHL-LuxR complex and isopropyl β-D-1thiogalactopyranoside (IPTG) in appropriate amount. To achieve QSspecific dynamics, with dramatic switching of genetic expression profiles at the threshold cell density, the AHL synthetase luxI and the AHL 270
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Fig. 2. Design and in vivo evaluation of the QS dependent self cell lysis system (QS-CL). (a) Diagram of genetic network for QS-CL. (b) Bacterial cell growth of the QS-CL strain (TA4291) under different inducer condition. All inducers added to the culture at 0 h. The final concentration of IPTG and aTc were 0.1 mM and 50 ng/mL, respectively. Error bars represent the standard deviation (n = 3).
Fig. 3. GBL release to the culture broth via the autonomous cell lysis by the QS-CL-bglC strain. (a) Diagram of genetic network for the QS-CL-bglC stain, TA4064. (b) Invivo profiles of TA4064 cultivation under different inducer conditions ([aTc] = 50 ng/mL, [IPTG] = 0–1.0 mM). All inducers added to the culture at 0 h. Each Scatter plot shows the time course of microbial cell density (left), cellobiose concentration (center), glucose concentration (right). (c) Correlation between the IPTG concentration and the maximum cell density (OD) or cultivation time (LT) for the autonomous cell lysis. Error bars represent the standard deviation (n = 3).
strain from TA4222 in flask-scale co-cultivation by flow cytometry. Using TA4222 and TA4179, we carried out flask-scale co-cultivation with 25 mL of LB medium containing 0.1 mM IPTG, 50 ng/mL aTc, and 10 g/L (29.2 mM) cellobiose as the sole carbon source. The density of TA4222 cells decreased by 85% within 30 min due to autonomous cell lysis at 2 h, from OD600 = 0.36 ± 0.03 (at 2 h) to 0.05 ± 0.04 (at 2.5 h) (Fig. 4b). The cell density of TA4179 reached the stationary phase by 10 h after the start of cultivation. The decrease in the cell density of TA4222 corresponded to the rapid degradation of cellobiose in the culture broth, and it was within 3 h for complete degradation of
cellobiose after the cell lysis (Fig. 4d). As a result, glucose accumulated in the culture broth, reaching 37.9 ± 1.6 mM at 6 h. From the time point when glucose was detected in the culture broth, IPA production was also observed, reaching 12.8 ± 3.2 mM at 24 h. These results proved that TA4222 saccharified cellobiose through the QS-CL dependent cell lysis releasing BGL, and it simultaneously induced the IPA production of TA4179 via AHL signaling. In order to investigate the performance of the QS dependent induction of IPA production, we compared the IPA production efficiencies between the QS dependent system and a conventional induction system 271
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Fig. 4. Synthetic microbial consortium for simultaneous saccharification and fermentation by co-cultivation of the IPA production strain detecting QS signal and the QS-CL-bglC strain. (a) A diagram of the flask-scale co-cultivation. (b) Fluorescence histograms of collected co-culture broth at various time points. (c) Time course of cell density of TA4222 and TA4179 during the flask-scale co-cultivation. Each red triangle, blue square and black circle indicates the cell density in the flask of TA4222, TA4179 and the sum of them (current total cell density), respectively. The former two values were calculated using the actual current total cell density and the result of flow cytometry (See Supplementary File). (d) Time course of the cellobiose, glucose, and IPA concentration in co-culture broth. Each circle, square, triangle indicates the IPA, glucose and cellobiose, respectively. 0.025 mM IPTG and 50 ng/mL aTc were added to culture at 0 h. Error bars represent the standard deviation (n = 3). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
by QS signal from the other strain in our synthetic microbial consortium. At last, we compared the IPA production efficiencies from cellobiose between the synthetic microbial consortium and the conventional single strain system. E. coli cannot utilize cellobiose as carbon source directly, thus the IPA production by E. coli is dependent on availability of glucose and other nutrients as shown in Fig. S5. For the direct IPA production from cellobiose using E. coli strain, we previously developed E. coli expressing BGL-Blc fusion protein, which display the BGL on its cell surface via Blc anchor domain (Soma et al., 2012). According to this strain, we constructed TA4593 for the IPA production from cellobiose by single strain cultivation (Fig. S6c). TA4593 express enzymes for the
using Tet promoter. When the promoter regulating the IPA production pathway in TA4179 was changed from PluxlacO to PLtetO1, which requires induction by exogenous aTc (TA4180, Fig. S3), the final IPA production titer (16.0 ± 2.2 mM at 24 h) was almost similar to that of TA4179. This result proved that the QS-dependent induction of IPA production was not disadvantageous to the conventional induction system, at least in a synthetic microbial consortium for cooperative IPA production from cellobiose. Additionally, the IPA production yield by TA4179 and TA4180 for single cultivation in LB medium containing 10 g/L (55.5 mM) glucose were not significantly different (Fig. S4). From both above validations for the IPA production, we proved that activity of target chemical production strain can be induced sufficiently 272
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As describe above, the tunability of our QS-CL circuit by changing concentration of exogenous inducers (IPTG and aTc) realized the parameter scanning (e.g. LuxR expression level, promoter strength for LuxI expression) to achieve its desired dynamics characteristic which resulted in the designed collective behavior of microbes. Recently, Gupta et al. have reported a pathway-independent quorum-sensing circuit using a constitutive promoters from the BioFAB library aiming to achieve the optimal dynamic characteristics of synthetic QS circuit for versatile chemical production (Gupta et al., 2017). Once we obtain the optimal parameters with tunable our QS-CL circuits, we also can replace the tunable promoters (PLlacO1, PLtetO1, and PluxlacO) with the constitutive ones that have almost same properties with the optimal condition. With this replacement, our QS-CL circuit also would be redesigned as not to need expensive exogenous inducers. It is also useful to consider cultivation conditions to achieve the desired dynamic characteristics of the circuit (e.g., temperature, pH, aeration, and medium composition). The QS-CL circuit was relatively robust to changes in the initial cell density (Fig. S7), but it was highly sensitive to changes in the culture temperature (Fig. S8). The induction of cell lysis was delayed with an increase in temperature, indicating the delay of QS signaling at higher temperatures. The optimal temperature for LuxI is 30 °C and its activity decreases at higher temperatures (Schaefer et al., 1996). This suggests that the delay in cell lysis was caused by a decrease in the AHL synthesis rate at a higher temperature. Natural biological systems based on the coexistence of microorganisms with distinct roles have formed by evolution in response to environmental changes including human activities and natural selection. The multiple parallel fermentation of sake is a semi-artificial ecosystem based on a microbial consortium consisting of yeast and fungi with different roles. In this study, a synthetic microbial consortium of two different recombinant E. coli strains was reconstructed; the strains have different roles in the saccharification of cellobiose (QSCL-bglC, TA4222) and target chemical production (IPA producer receiving the QS signal, TA4179). The synthetic QS system resulted in collective behavior in an environment where two different cells coexist. Both strains, TA4222 and TA 4179, showed logarithmic growth due to the nutrients derived from LB medium before the initiation of the glucose supply from cellobiose saccharification and the lysis of TA4222 cells. Fewer residual cells of TA4222 after lysis remained as compared with its single cultivation. Additionally, the regrowth of residual cells of TA4222 was not observed using the synthetic microbial consortium. Although the chemical-producing strain TA4179 could utilize glucose for both cell growth and IPA production even after the depletion of nutrients in the LB medium, the enzyme-producing strain TA4222 could not utilize glucose due to gene deletions. These results suggested that the enzyme-producing strain was culled by the chemicalproducing strain in the synthetic microbial consortium, even it could survive the cell lysis system. Because of the resource limitation within the system, it is beneficial that the strain completes its function and is expelled from the system for reducing competition for substrates and space. However, there was still competition for carbon allocated to endogenous metabolism and IPA production in the producer strain TA4179. Additionally, TA4179 expressed GFP for experimental convenience, which should be a burden on the production of other proteins. Therefore, IPA productivity was not so high, although the target chemical-producing strain (TA4179) nearly monopolized the space and substrates during fermentation. To increase productivity, further engineering of the strain for target chemical production is necessary. It would be effective to engineer production strains using the current synthetic biological tools, especially for dynamic metabolic regulation, eliminating such metabolic imbalance (Brockman and Prather, 2015; Holtz and Keasling, 2010; Venayak et al., 2015). To incorporate such a strategy, a more sophisticated design that combines a synthetic metabolic pathway and genetic circuit is required. For industrial application of this system, there are still many challenging issues to be solved, including of application to fed-batch
IPA production and BGL-Blc fusion protein from PLlacO1 promoter in the presence of IPTG. TA4593 was cultured in same LB medium containing cellobiose as the synthetic microbial consortium for IPA production from cellobiose above (Fig. S6). 0.1 mM IPTG and 50 ng/mL of aTc were added to culture medium at 0 h. TA4593 saccharified 23.1 ± 3.1 mM of cellobiose and consumed 88.5% of all glucose that was supplied from cellobiose saccharification. The final IPA production titer of TA4593 was 5.8 ± 0.5 mM, which was much lower than the case using the synthetic microbial consortium (16–19 mM). TA4593 spent 24 h on cellobiose saccharification, which was over 4-fold longer than the case using the synthetic microbial consortium. 4. Discussion In synthetic biology, numerous artificial biomolecular parts have been developed (Cameron et al., 2014; Smolke, 2009; Vilanova and Porcar, 2014). Synthetic promoters controlled by multiple regulatory factors can be used as logic gates, with a specific response to multiple inputs, enabling the strict control of gene expression (Brödel et al., 2016; Cox et al., 2007; Tamsir et al., 2011). Using the synthetic promoter PluxlacO, cell lytic genes and AHL synthase were only expressed when the AHL-LuxR complex and IPTG were present in sufficient amounts. Using the circuit design shown in Fig. 2 (TA4291), the existence of LuxR in the cell was also dependent on the presence of exogenous aTc. Thus, QS-dependent cell lysis was regulated by two external signals (IPTG and aTc) and an endogenous signal (AHL). This strategy enabled the strict control of the expression of genes responsible for cell suicide. A critical leak of cell lytic genes affecting cell growth was not detected when the required inputs were not satisfied according to the cell growth assay. Although the induction of cell lysis was strictly regulated, residual cells remained (around OD600 = 0.3) even after QS-dependent cell lysis occurred using the QS-CL strain (TA4291) and QS-CL-bglC strain (TA4064) (Figs. 2b and 3b). These results suggest that a certain number of lytic gene-resistant strains or low lytic gene-expressing strains appeared. Even when the concentration of IPTG, which enhances holin and endolysin expression, was increased using the QS-CL-bglC strain (TA4064), the residual cell density did not decrease (Fig. 3b). Moreover, the regrowth of residual cells was observed during long-term cultivation (Fig. S2a). Similar results for cell lysis and the regrowth of residual cells have been reported in previous studies of the reconstruction of synthetic cell lysis systems (Hsu et al., 2014; Lo et al., 2013; Morita et al., 2001; Pasotti et al., 2011). The residual cells of the QS-CL-bglC strain (TA4064) consumed glucose after the saccharification of cellobiose by BGL in the culture medium (Fig. S2c). To prevent competition for glucose consumption between the enzyme-producing strain and the chemical-producing strain, we constructed another QS-CL-bglC strain (TA4222) by the deletion of genes responsible for glucose consumption (manZ, ptsG, and glk). TA4222 also showed regrowth on the LB medium after cell lysis, but quite low glucose consumption was observed (Fig. S2c). The tunability of the QS-CL circuit for the determination of the threshold cell density and the timing of cell lysis was investigated by altering the IPTG concentration in flask cultivation. QS-CL was able to function for 60 min (at 2–3 h), corresponding to 40% of the logarithmic growth phase (from at 0–3.5 h). The dynamic range of the cell density at the time of cell lysis was OD600 = 0.63–1.52 when the initial cell density was set to around OD600 = 0.06 (Fig. 3b). To further expand the dynamic characteristics of the QS-CL circuit, it is necessary to modify the parameters related to the expression level of each gene and/or to change the structure of the circuit itself. The QS-CL circuit was controlled by the synthesis of LacI, LuxI (AHL), and LuxR, so these were important for the dynamic characteristics of the QS-CL circuit. These parameters can be optimized using well-established biomolecular parts (e.g., promoters (Hammer et al., 2006) and RBS (Levin-Karp et al., 2013)) and prediction tools (Bonde et al., 2016; Salis, 2011). 273
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Basic Science Research Project from the Sumitomo Foundation.
culture, long-term stability, and price of medium. However, the synthetic microbial consortium consisting of strains harboring genetic circuits in this study worked as a good example of task distribution for chemical production from model biomass. The saccharification of polysaccharides into monosaccharides is an essential process for chemical production from biomass. The efficiency of cellobiose saccharification and IPA production by the synthetic microbial consortium were much higher than those in the case with the single strain displaying BGL on cell surface. The task distribution by designed microbial consortium succeeded to reduce the burden of the protein expression and other physiological activities in each strain leading to improvement of fermentation process. The saccharification of cellobiose to glucose was executed by the collective cell behavior of the enzyme-producing strain harboring a genetic circuit regulating QS-dependent cell lysis for BGL release to the fermenter. Target chemical production was assigned to the chemical-producing strain using a genetic circuit for detecting the QS signal from the enzyme-producing strain. We presented potentials of genetic circuits for assignation specific tasks to individual strains and to design interactions among strains. Genetic circuits for QS-dependent cell lysis and specific protein release has potential for various bioengineering applications, such as useful protein production, agriculture, and bioremediation. The approach has also been employed for in vivo drug delivery (Din et al., 2016). In the co-culture for the IPA fermentation from cellobiose, the QS signal generated by the enzyme-producing strain (TA4222) was also used for the induction of the IPA production from glucose assigned to the chemical-producing strain (TA4179). Although the IPA productivity of TA4179 triggered by QS signal wasn't so much advantageous over the case of TA4180 triggered by external addition inducer aTc in coculture, the cell density after the logarithmic growth phase of TA4179 was ~40% higher than that of TA4180. Those results indicated that the toxicity or the physiological burden of the enzymes expression for the chemical production pathway on the cell growth was reduced with QS dependent induction system compared with conventional induction by external addition of aTc (at 0 h). The automation and optimization of induction for the chemical production pathway by QS would show higher superiority in the case with target chemical production showing high toxicity or metabolic and/or other physiological burden. We showed that the synthetic reconstruction of the QS system is a promising strategy to control signal transduction among both autologous and heterologous strains.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ymben.2019.08.007. References Balagaddé, F.K., Song, H., Ozaki, J., Collins, C.H., Barnet, M., Arnold, F.H., Quake, S.R., You, L., 2008. A synthetic Escherichia coli predator-prey ecosystem. Mol. Syst. Biol. 4, 1–8. https://doi.org/10.1038/msb.2008.24. Bonde, M.T., Pedersen, M., Klausen, M.S., Jensen, S.I., Wulff, T., Harrison, S., Nielsen, A.T., Herrgård, M.J., Sommer, M.O.A., 2016. Predictable tuning of protein expression in bacteria. Nat. Methods 13, 233–236. https://doi.org/10.1038/nmeth.3727. Brenner, K., You, L., Arnold, F.H., 2008. Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 26, 483–489. https://doi.org/10.1016/j. tibtech.2008.05.004. Brockman, I.M., Prather, K.L.J., 2015. Dynamic metabolic engineering: new strategies for developing responsive cell factories. Biotechnol. J. 10, 1360–1369. https://doi.org/ 10.1002/biot.201400422. Brödel, A.K., Jaramillo, A., Isalan, M., 2016. Engineering orthogonal dual transcription factors for multi-input synthetic promoters. Nat. Commun. 7, 13858. https://doi.org/ 10.1038/ncomms13858. Calendar, R., 1988. The Bacteriophages, vol. 1 Oxford University Press. https://doi.org/ 10.1006/rwgn.2001.0106. Cameron, D.E., Bashor, C.J., Collins, J.J., 2014. A brief history of synthetic biology. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro3239. Cox, R.S., Surette, M.G., Elowitz, M.B., 2007. Programming gene expression with combinatorial promoters. Mol. Syst. Biol. 3, 145. https://doi.org/10.1038/msb4100187. Desai, S.H., Rabinovitch-Deere, C.A., Tashiro, Y., Atsumi, S., 2014. Isobutanol production from cellobiose in Escherichia coli. Appl. Microbiol. Biotechnol. 98, 3727–3736. https://doi.org/10.1007/s00253-013-5504-7. Din, M.O., Danino, T., Prindle, A., Skalak, M., Selimkhanov, J., Allen, K., Julio, E., Atolia, E., Tsimring, L.S., Bhatia, S.N., Hasty, J., 2016. Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536, 81–85. https://doi.org/10.1038/nature18930. Gardner, T.S., Cantor, C.R., Collins, J.J., 2000. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342. https://doi.org/10.1038/35002131. Gupta, A., Reizman, I.M.B., Reisch, C.R., Prather, K.L.J., 2017. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit. Nat. Biotechnol. 35, 273–279. https://doi.org/10.1038/nbt.3796. Hammer, K., Mijakovic, I., Jensen, P.R., 2006. Synthetic promoter libraries – tuning of gene expression. Trends Biotechnol. 24, 53–55. https://doi.org/10.1016/J.TIBTECH. 2005.12.003. Hanai, T., Atsumi, S., Liao, J.C., 2007. Engineered synthetic pathway for isopropanol production in Escherichia coli. Appl. Environ. Microbiol. 73, 7814–7818. https://doi. org/10.1128/AEM.01140-07. Haseltine, E.L., Arnold, F.H., 2008. Implications of rewiring bacterial quorum sensing. Appl. Environ. Microbiol. 74, 437–445. https://doi.org/10.1128/AEM.01688-07. Holtz, W.J., Keasling, J.D., 2010. Engineering static and dynamic control of synthetic pathways. Cell 140, 19–23. https://doi.org/10.1016/j.cell.2009.12.029. Hsu, C.-Y., Yu, T.-C., Lin, L.-J., Hu, R.-H., Chen, B.-S., 2014. Systematic approach to Escherichia coli cell population control using a genetic lysis circuit. BMC Syst. Biol. 8, S7. https://doi.org/10.1186/1752-0509-8-S5-S7. Kong, W., Meldgin, D.R., Collins, J.J., Lu, T., 2018. Designing microbial consortia with defined social interactions. Nat. Chem. Biol. 14, 821–829. https://doi.org/10.1038/ s41589-018-0091-7. Lee, J.W., Na, D., Park, J.M., Lee, J., Choi, S., Lee, S.Y., 2012. Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat. Chem. Biol. 8, 536–546. https://doi.org/10.1038/nchembio.970. Levin-Karp, A., Barenholz, U., Bareia, T., Dayagi, M., Zelcbuch, L., Antonovsky, N., Noor, E., Milo, R., 2013. Quantifying translational coupling in E. coli synthetic operons using RBS modulation and fluorescent reporters. ACS Synth. Biol. 2, 327–336. https://doi.org/10.1021/sb400002n. Lo, T.M., Tan, M.H., Hwang, I.Y., Chang, M.W., 2013. Designing a synthetic genetic circuit that enables cell density-dependent auto-regulatory lysis for macromolecule release. Chem. Eng. Sci. 103, 29–35. https://doi.org/10.1016/j.ces.2013.03.021. Marguet, P., Tanouchi, Y., Spitz, E., Smith, C., You, L., 2010. Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology. PLoS One 5, e11909. https://doi.org/10.1371/journal.pone.0011909. Morita, M., Asami, K., Tanji, Y., Unno, H., 2001. Programmed Escherichia coli cell lysis by expression of cloned T4 phage lysis genes. Biotechnol. Prog. 17, 573–576. https:// doi.org/10.1021/bp010018t. Nielsen, J., Keasling, J.D., 2011. Synergies between synthetic biology and metabolic engineering. Nat. Biotechnol. 29, 693–695. https://doi.org/10.1038/nbt.1937. Park, J.H., Lee, K.H., Kim, T.Y., Lee, S.Y., 2007. Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. U.S.A. 104, 7797–7802. https://doi.org/ 10.1073/pnas.0702609104. Pasotti, L., Zucca, S., Lupotto, M., Gabriella, M., Angelis, C. De, Magni, P., 2011. Characterization of a synthetic bacterial self- destruction device for programmed cell death and for recombinant proteins release. J. Biol. Eng. 5, 8. https://doi.org/10. 1186/1754-1611-5-8.
5. Conclusion Designing the microbial consortia are an emerging frontier in synthetic biology that enable versatile microbiome engineering. Recently, extensibility of microbial consortia based on design theory has been proven (Kong et al., 2018), and thus the potential of the development of novel synthetic ecosystems has been payed attention for diverse purposes. Here, we designed a novel synthetic microbial consortia using E. coli strains harboring synthetic genetic circuits in order to implement the task distribution in bioprocess. Each genetic circuit was installed to E. coli strain to assign the specific task in bioprocess, which means the saccharification of cellulosic carbon sources and the production of target chemical. Synthetic QS system was implemented in the genetic circuits to form the cascade of the tasks execution through the cell-cell communication among the consortia. The synthetic microbial consortia presented much higher efficiencies in the IPA fermentation requiring cellobiose saccharification compared with conventional fermentation using single engineered microbe. Acknowledgement This work was supported by JSPS KAKENHI Grant Numbers JP23119002, JP18K14065, Chemical Innovation Encouragement Prize from Japan Association for Chemical Innovation, and a Grant for a 274
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from a central metabolic pathway toward a synthetic pathway using a metabolic toggle switch. Metab. Eng. 23, 175–184. https://doi.org/10.1016/j.ymben.2014.02. 008. Stricker, J., Cookson, S., Bennett, M.R., Mather, W.H., Tsimring, L.S., Hasty, J., 2008. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519. https://doi. org/10.1038/nature07389. Tamsir, A., Tabor, J.J., Voigt, C.A., 2011. Robust multicellular computing using genetically encoded NOR gates and chemical “wiresg.”. Nature 469, 212–215. https://doi. org/10.1038/nature09565. Venayak, N., Anesiadis, N., Cluett, W.R., Mahadevan, R., 2015. Engineering metabolism through dynamic control. Curr. Opin. Biotechnol. 34, 142–152. https://doi.org/10. 1016/j.copbio.2014.12.022. Vilanova, C., Porcar, M., 2014. iGEM 2.0—refoundations for engineering biology. Nat. Biotechnol. 325 2014. Xavier, J.B., 2011. Social interaction in synthetic and natural microbial communities. Mol. Syst. Biol. 7, 483. https://doi.org/10.1038/msb.2011.16. Zhang, F., Carothers, J.M., Keasling, J.D., 2012. Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat. Biotechnol. https://doi.org/10.1038/nbt.2149. Zhou, K., Qiao, K., Edgar, S., Stephanopoulos, G., 2015. Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33, 377–383. https://doi.org/10.1038/nbt.3095.
Prather, K.L.J., Martin, C.H., 2008. De novo biosynthetic pathways: rational design of microbial chemical factories. Curr. Opin. Biotechnol. 19, 468–474. https://doi.org/ 10.1016/j.copbio.2008.07.009. Salis, H.M., 2011. The ribosome binding site calculator. Methods Enzymol. 498, 19–42. https://doi.org/10.1016/B978-0-12-385120-8.00002-4. Schaefer, A.L., Val, D.L., Hanzelka, B.L., Cronan, J.E., Greenberg, E.P., 1996. Generation of cell-to-cell signals in quorum sensing: acyl homoserine lactone synthase activity of a purified Vibrio fischeri LuxI protein. Proc. Natl. Acad. Sci. U.S.A. 93, 9505–9509. https://doi.org/10.1073/pnas.93.18.9505. Smolke, C.D., 2009. Building outside of the box: iGEM and the BioBricks foundation. Nat. Biotechnol. 2712 2009. Solomon, K.V., Sanders, T.M., Prather, K.L.J., 2012. A dynamic metabolite valve for the control of central carbon metabolism. Metab. Eng. 14, 661–671. https://doi.org/10. 1016/j.ymben.2012.08.006. Soma, Y., Hanai, T., 2015. Self-induced metabolic state switching by a tunable cell density sensor for microbial isopropanol production. Metab. Eng. 30, 7–15. https://doi.org/ 10.1016/j.ymben.2015.04.005. Soma, Y., Inokuma, K., Tanaka, T., Ogino, C., Kondo, A., Okamoto, M., Hanai, T., 2012. Direct isopropanol production from cellobiose by engineered Escherichia coli using a synthetic pathway and a cell surface display system. J. Biosci. Bioeng. 114, 80–85. https://doi.org/10.1016/j.jbiosc.2012.02.019. Soma, Y., Tsuruno, K., Wada, M., Yokota, A., Hanai, T., 2014. Metabolic flux redirection
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Metabolic Engineering journal homepage: www.elsevier.com/locate/meteng
Synthetic microbial consortium with specific roles designated by genetic circuits for cooperative chemical production
T
Hiroshi Honjoa,1, Kenshiro Iwasakia,1, Yuki Somaa,b, Keigo Tsurunoa, Hiroyuki Hamadaa, Taizo Hanaia,* a
Laboratory for Bioinformatics, Graduate School of Systems Lifesciences, Kyushu University, 729 West Building 5, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 8128582, Japan
b
A B S T R A C T
Synthetic microbial consortia consisting of microorganisms with different synthetic genetic circuits or divided synthetic metabolic pathway components can exert functions that are beyond the capacities of single microorganisms. However, few consortia of microorganisms with different synthetic genetic circuits have been developed. We designed and constructed a synthetic microbial consortium composed of an enzyme-producing strain and a target chemical-producing strain using Escherichia coli for chemical production with efficient saccharification. The enzyme-producing strain harbored a synthetic genetic circuit to produce beta-glucosidase, which converts cellobiose to glucose, destroys itself via the lytic genes, and release the enzyme when the desired cell density is reached. The target chemicalproducing strain was programmed by a synthetic genetic circuit to express enzymes in the synthetic metabolic pathway for isopropanol production when the enzymeproducing strain grows until release of the enzyme. Our results demonstrate the benefits of synthetic microbial consortia with distributed tasks for effective chemical production from biomass.
1. Introduction A microbial consortium is a community of various microorganisms; it can exhibit functions not possessed by a single species via complex interspecific interactions. In a microbial consortia, metabolites are mutually used by different microorganisms, several information are shared among different microorganisms via common functional biomolecules. These metabolites and the information affect the regulation of specific genetic networks in individual cells (Xavier, 2011). In synthetic biology, various biological systems, such as toggle switches (Gardner et al., 2000) and oscillators (Stricker et al., 2008), have been reconstructed using synthetic genetic circuits combining biomolecules with known functions. For example, a synthetic microbial consortium that imitates a predator–prey relationship including two types of microorganisms harboring different synthetic genetic circuits utilizing common functional biomolecules has recently been constructed (Balagaddé et al., 2008). Additionally, dividing a large synthetic metabolic pathway into modules expressed in specific microorganisms enables the construction of a synthetic microbial consortium that effectively produces high-value natural metabolites (Zhou et al., 2015). Increasingly complex systems have been developed and the use of multiple microorganisms with different synthetic genetic circuits
and/or divided synthetic metabolic pathways will be one of major research goal in synthetic biology (Brenner et al., 2008). The microbial production of chemicals and energy from biomass is a promising strategy for realizing a sustainable society. Recombinant microorganisms with synthetic metabolic pathways have been constructed for the fermentation of decomposed monosaccharides to target products (Nielsen and Keasling, 2011). As the first step for chemical and energy production from biomass, a saccharification process is necessary because the most of microorganism cannot utilize polysaccharide as carbon source without extracellular digestion into absorbable sugars (e.g. glucose, xylose, sucrose). For fermentation process downstream of the saccharification, various strategies have been developed to improve the production of targets from monosaccharides, including the construction of synthetic metabolic pathways (Lee et al., 2012; Prather and Martin, 2008), gene deletion and/or upregulation (Park et al., 2007), the dynamic regulation of metabolic flux (Solomon et al., 2012; Soma et al., 2014; Zhang et al., 2012), and optimization of culture conditions. However, the major limitations in chemical and energy production from biomass have come from the effort and the cost required for saccharification using purified enzymes like bacterial cellulolytic enzymes. To reduce those limitations and to simplify the saccharification process, several studies have focused on the extracellular heterologous
*
Corresponding author. E-mail address: [email protected] (T. Hanai). 1 Co-first authors. https://doi.org/10.1016/j.ymben.2019.08.007 Received 25 June 2019; Received in revised form 7 August 2019; Accepted 7 August 2019 Available online 08 August 2019 1096-7176/ © 2019 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.
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Chatsworth, CA, USA). DNA purification was performed using the DNA Clean & Concentrator™ Kit (Zymo Research, Irvine, CA, USA). Difco™ Luria-Bertani (LB)-Miller medium (Becton, Dickinson and Company, Sparks, MD, USA) and was used as the base medium for all E. coli cultivation.
expression of cellulolytic enzymes by microorganisms itself such as Escherichia coli or yeast. For example, cellobiose is a disaccharide consists of two β-glucose molecules linked by a β-1,4-glycosidic bond which cannot be incorporated into E. coli cell without extracellular digestion into glucose by beta-glucosidase (BGL), a cellulolytic enzyme. Soma et al. developed an E. coli strain anchoring BGL to its cell membrane for isopropanol fermentation from cellobios (Soma et al., 2012). Desai et al. developed an E. coli strain that excretes BGL for isobutanol fermentation from cellobiose. However, difficulties in anchoring and excreting enzymes result in insufficient productivity (Desai et al., 2014). Other novel strategies are required to achieve more efficient utilization of biomass as carbon source for chemical and energy production. By cooperation and task distribution, a synthetic microbial consortium can exert functions that are beyond the capacity of a single microorganism, potentially contributing to chemical and energy production. Despite some studies of chemical and energy production using synthetic microbial consortia with divided synthetic metabolic pathways, few reports have evaluated consortia with different synthetic genetic circuits. In this study, we developed a synthetic microbial consortium with different synthetic genetic circuits to produce a desired product from oligosaccharides as a carbon source from biomass. In particular, we designed and constructed a synthetic microbial consortium composed of an enzyme-producing strain and a target chemical-producing strain using E. coli (Fig. 1). The enzyme-producing strain contained a synthetic genetic circuit to produce beta-glucosidase, which converts the disaccharide cellobiose to glucose; the strain destroys itself by the expression of the lytic genes holin and endolysin derived from T4 phage (Marguet et al., 2010; Morita et al., 2001) and releases the enzyme when a desired cell density is reached. The target chemical-producing strain was programmed by a synthetic genetic circuit to express enzymes in the synthetic metabolic pathway for the production of isopropanol (Hanai et al., 2007) when the enzyme-producing strain grows until release of beta-glucosidase. We used a synthetic quorum sensing (QS) system in the genetic circuits to form the cascade of the tasks execution through the cell-cell communication among the consortia.
2.2. Strains and plasmids Strains, plasmids, and primers used in this study are listed in Tables S1–S3. Standard DNA cloning was performed using chemically competent cells of XL1-Blue (Agilent Technologies, Santa Clara, CA, USA) and DH5alphaZ1 (Expressys, Ruelzheim, Germany). The major plasmids used in this study are roughly divided into the QS-CL circuit (pTA1241), the BGL production unit (pTA1538), the IPA production units (pTA395 and pTA1191), the N-acyl homoserine lactones (AHL) sensor unit (pTA1420), and the GFP reporter unit (pTA1545). QS-CL strains harbor the QS-CL circuit and/or the BGL production unit. IPA producing strains harbor the IPA production unit, the GFP reporter unit, and the AHL sensor unit. Detailed construction procedures for each strain and plasmid were described in the supplementary file. All purified plasmids were introduced to electrocompetent cells of appropriate E. coli strains by electroporation and were screened for antibiotic resistance. 2.3. Culture conditions Fresh colonies of E. coli strains were inoculated to LB medium and cultured at 37 °C with agitation at 250 rpm in the Innova® 44 incubated shaker (Eppendorf, Hamburg, Germany) with a working volume of 3 mL in 15 mL test tubes. Cultures were collected when the cell density reached OD600 = 1 and were used to make original freeze stocks of E. coli strains, which were stored in 15% (v/v) glycerol at −80 °C. Original freeze stocks were inoculated to fresh LB medium and were cultured as described above. Cultures were harvested to make frozen seed stocks when the culture reached an OD600 of 0.6, which were stored in 15% (v/v) glycerol at −80 °C and were used for test-tube scale preculture followed by flask-scale fermentation. The frozen seed stocks were melted on ice and 100 μL of each melted solution was inoculated to 3 mL of fresh LB medium (corresponded to 3.33% (v/v)) in a 15 mL test tube. The test-tube scale pre-cultivation was continued until the cell density reached OD600 = 0.6. Pre-cultures were passaged to fresh LB medium containing 10 or 20 g/L cellobiose (29.2 or 58.4 mM) and appropriate antibiotics to set the initial cell density to OD600 = 0.05. The main flask-scale cultivation was performed at 37 °C with agitation at 150 rpm in a shaking water bath MM-10 (TAITEC co., Koshigaya, Japan) with a working volume of 25 mL in a 300-mL baffled flask. The biomass concentration and specific growth rates were determined by periodic measurements of OD600 of 200 μL of culture in 96-well plates using an Infinite® 2000 plate reader (Tecan, Männedorf, Zürich, Switzerland). The LB medium contained 10 or 20 g/L cellobiose or
2. Materials and methods 2.1. Chemicals and reagents All chemicals were purchased from Wako Pure Chemical Industry, Ltd. (Osaka, Japan), unless otherwise specified. Restriction enzymes and phosphatase were obtained from New England Biolabs (Ipswich, MA, USA), ligase (Rapid DNA Ligation Kit) was obtained from Roche (Manheim, Germany), and KOD Plus Neo DNA polymerase was obtained from TOYOBO Co., Ltd. (Osaka, Japan). Oligonucleotides were synthesized by Life Technologies Japan Ltd. (Tokyo, Japan). Plasmids were harvested using the QIAprep Spin Miniprep Kit (Qiagen,
Fig. 1. Synthetic microbial consortium with specific role designation by genetic circuits for cooperative chemical production. The enzyme producing strain is programed to release the saccharification enzyme into the medium when the strain reaches at desired cell density according to QS signal. The target chemical producing strain is programed to produce target chemical following the saccharification.
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sensor-actuator luxR were placed under the control of PluxlacO and a QS-independent promoter, PLtetO1, respectively (Haseltine and Arnold, 2008). The antiholin gene was also placed under the control of PLtetO1 as a cell lysis inhibitor (Calendar, 1988; Pasotti et al., 2011). It prevents the undesired cell lysis and the destabilization of the genetic circuit caused by stochastic leakage of lytic genes. We confirmed that this circuit worked as expected by flask-scale batch-cultivation, which means AND-gate like response to the inducers IPTG and anhydrotetracycline (aTc) (Fig. 2b). Only when 0.1 mM IPTG and 50 ng/mL aTc were added to the culture broth at 0 h, bacterial cell growth was interrupted at 3 h (OD600 = 1.3 ± 0.0) and the cell density decreased by 88% within 25 min. In other conditions, the final cell density of TA4291 was equivalent to that of the base strain TA1021.
10 g/L glucose. 2.4. Metabolite analysis To quantify extracellular metabolites, 50 μL of bacterial culture broth was diluted 10 fold with MilliQ water and centrifuged for 1 min at 13,000×g in a micro-centrifuge and the supernatant was filtered through a 0.2 μm syringe filter for HPLC. The extracellular glucose and cellobiose concentration was quantified using a high-pressure liquid chromatographer (HPLC, LC-20AD, SIL-20ACHT, CTO-20AC, RID-10 A; Shimadzu, Kyoto, Japan) equipped with a ligand exchange chromatography column (Shodex SP0810, SHOWA DENKO K.K., Tokyo, Japan). The mobile phase was MilliQ water, the flow rate was 1.0 mM/min, the column was kept at 80 °C, and 1 μL was injected per sample. Alcohol compounds were quantified using a GC-2010 Plus gas chromatograph (Shimadzu) equipped with a flame ionization detector and an AOC-20 automatic injector and sampler (Shimadzu). The separation of alcohol compounds was performed using a DB-WAX capillary column (30 m; 0.32 mm inner diameter; 0.50 μm film thickness; Agilent Technologies). The gas chromatograph oven temperature was initially held at 40 °C for 5 min, increased at 15 °C/min to 100 °C, and then increased at 100 °C/ min to 230 °C, where it was maintained for 1.4 min. Helium was used as the carrier gas with a column flow rate of 48 mL/min. The injector and detector temperatures were maintained at 225 °C and 235 °C, respectively. For each measurement, 0.5 μL of the culture supernatant was injected in the split-injection mode (1:12 split ratio). 1-Propanol was used as the internal standard. The fermentation by-products (α-ketoglutarate, pyruvate, citrate, lactate, acetate, formate, fumarate, malate, and succinate) were quantified by high pressure liquid chromatography (HPLC, LC-30AD, SIL-30AC, CTO-20AC, CDD-10AVP; Shimadzu) equipped with an ion-exclusion chromatography column (Showdex RSpak KC-811; SHOWA DENKO K.K., Tokyo, Japan). The separation of samples was attained using 5.0 mM p-nitrophenylphosphate as the mobile phase. The column temperature was maintained at 40 °C.
3.2. Evaluation of the QS-CL system for BGL release To demonstrate target protein production and release by the QSdependent cell lysis, a protein production unit was added to the QS-CL circuit. It consisted of the PLtetO1 promoter and the beta-glucosidase (BGL) gene bglC from Thermobifida fusca (ATCC®BAA-629D-5™), shown in Fig. 3a (QS-CL-bglC strain, TA4064). BGL hydrolyzes cellobiose into two glucose molecules, which is the final step in the enzymatic hydrolysis of cellulose. Since E. coli cannot utilize cellobiose unless it is hydrolyzed by BGL into glucose in the extracellular environment, exposure to BGL is essential for the glucose supply from lignocellulosic polysaccharides (or biomass). For efficient glucose supply from cellobiose, it is necessary to optimize BGL production and release via the dynamic characteristics of the QS-CL system. For this purpose, the IPTG concentration in the culture medium was changed from 0 to 1 mM, which controls the QS signal via changes in the transcriptional efficiency of the PluxlacO promoter. During 5 h of flask-scale cultivation with 0.025 mM or 0 mM IPTG and 50 ng/mL aTc, the growth of the QSCL-bglC strain (TA4064) was equivalent to that of the base strain, TA1021 (Fig. 3b). About 7 g/L (18.8 mM) cellobiose was gradually hydrolyzed into glucose in the above conditions. These results suggested that GBL release to the environment was accompanied by natural cell destruction, apart from autonomous cell lysis. Similar degradation of cellobiose (17.3 mM) was observed when the strain harboring the BGL production unit but lacking the lytic genes was induced (Fig. S1). This result supports the hypothesis that autonomous cell lysis was not induced by less than or equal to 0.025 mM IPTG. As the concentration of IPTG increased, QS-dependent cell lysis increased and the degradation of cellobiose was initiated simultaneously with cell lysis (Fig. 3c). Furthermore, 20 g/L (58.4 mM) cellobiose was converted to glucose within 1 h after cell lysis when the IPTG concentration was in the range of 0.05–1.0 mM. Since the maximum apparent glucose concentration in the culture broth was less than the theoretical value based on the initial feed of cellobiose, glucose might be consumed by residual cells of the QS-CL-bglC strain even after the autonomous lysis phase.
2.5. Flow cytometry analysis A total of 50 μL of culture broth was collected in a 1.5-mL tube and was centrifuged at 1500×g for 3 min. Cells were resuspended in 0.8% NaCl solution and immediately assayed using a CytoFLEX S (Beckman Coulter, Brea, CA, USA). CytExpert ver. 2.0 (Beckman Coulter) was used for data analysis and visualization. Fluorescence was measured with 488-nm argon laser excitation and a 525–540 nm emission filter. To ensure consistency between samples, 10,0000 cells were collected for each sample and consistently gated by forward scatter (FSC) and side scatter (SSC). A consistent fluorescence threshold was applied to determine the percentage of cells that were GFP+ (TA4179 or TA4180) or GFP− (TA4222). 3. Results
3.3. Design and evaluation of a synthetic microbial consortium for cooperative isopropanol production from cellobiose
3.1. Design of a synthetic genetic circuit for QS-dependent cell lysis (QS-CL) We designed a synthetic microbial consortium for cooperative isopropanol (IPA) production from cellobiose using two different recombinant E. coli strains, an enzyme-producing (QS-CL-bglC) strain and a target chemical-producing strain. For the rational construction of the synthetic microbial consortium, it is necessary to prevent the QS-CLbglC strain from consuming glucose prior to the target chemical-producing strain. For this purpose, the hexose transporter genes ptsG and manZ and the glucokinase gene glk were deleted from E. coli and the QSCL-bglC circuit was introduced to obtain the QS-CL-bglC strain TA4222 (Fig. S2). As a target chemical-producing strain, TA4179 was constructed, which detects AHL produced by TA4222 to induce the IPA production pathway (Fig. 4a). A GFP expression unit based on the PLtetO1 promoter was also introduced to TA4179 to distinguish the
E. coli strain harboring a synthetic genetic circuit, shown in Fig. 2a (TA4291), was designed for QS-dependent cell lysis (QS-CL) and was evaluated by flask-scale cultivation. For the induction of cell lysis in response to increased QS signaling, environmental AHL concentration, the cell lytic genes holin and endolysin were placed under the control of the synthetic QS promoter PluxlacO (Soma and Hanai, 2015). PluxlacO is a dual-input type synthetic promoter that is activated by the AHL-LuxR complex and repressed by the LacI repressor. In other words, PluxlacO is activated in the co-presence of AHL-LuxR complex and isopropyl β-D-1thiogalactopyranoside (IPTG) in appropriate amount. To achieve QSspecific dynamics, with dramatic switching of genetic expression profiles at the threshold cell density, the AHL synthetase luxI and the AHL 270
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Fig. 2. Design and in vivo evaluation of the QS dependent self cell lysis system (QS-CL). (a) Diagram of genetic network for QS-CL. (b) Bacterial cell growth of the QS-CL strain (TA4291) under different inducer condition. All inducers added to the culture at 0 h. The final concentration of IPTG and aTc were 0.1 mM and 50 ng/mL, respectively. Error bars represent the standard deviation (n = 3).
Fig. 3. GBL release to the culture broth via the autonomous cell lysis by the QS-CL-bglC strain. (a) Diagram of genetic network for the QS-CL-bglC stain, TA4064. (b) Invivo profiles of TA4064 cultivation under different inducer conditions ([aTc] = 50 ng/mL, [IPTG] = 0–1.0 mM). All inducers added to the culture at 0 h. Each Scatter plot shows the time course of microbial cell density (left), cellobiose concentration (center), glucose concentration (right). (c) Correlation between the IPTG concentration and the maximum cell density (OD) or cultivation time (LT) for the autonomous cell lysis. Error bars represent the standard deviation (n = 3).
strain from TA4222 in flask-scale co-cultivation by flow cytometry. Using TA4222 and TA4179, we carried out flask-scale co-cultivation with 25 mL of LB medium containing 0.1 mM IPTG, 50 ng/mL aTc, and 10 g/L (29.2 mM) cellobiose as the sole carbon source. The density of TA4222 cells decreased by 85% within 30 min due to autonomous cell lysis at 2 h, from OD600 = 0.36 ± 0.03 (at 2 h) to 0.05 ± 0.04 (at 2.5 h) (Fig. 4b). The cell density of TA4179 reached the stationary phase by 10 h after the start of cultivation. The decrease in the cell density of TA4222 corresponded to the rapid degradation of cellobiose in the culture broth, and it was within 3 h for complete degradation of
cellobiose after the cell lysis (Fig. 4d). As a result, glucose accumulated in the culture broth, reaching 37.9 ± 1.6 mM at 6 h. From the time point when glucose was detected in the culture broth, IPA production was also observed, reaching 12.8 ± 3.2 mM at 24 h. These results proved that TA4222 saccharified cellobiose through the QS-CL dependent cell lysis releasing BGL, and it simultaneously induced the IPA production of TA4179 via AHL signaling. In order to investigate the performance of the QS dependent induction of IPA production, we compared the IPA production efficiencies between the QS dependent system and a conventional induction system 271
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Fig. 4. Synthetic microbial consortium for simultaneous saccharification and fermentation by co-cultivation of the IPA production strain detecting QS signal and the QS-CL-bglC strain. (a) A diagram of the flask-scale co-cultivation. (b) Fluorescence histograms of collected co-culture broth at various time points. (c) Time course of cell density of TA4222 and TA4179 during the flask-scale co-cultivation. Each red triangle, blue square and black circle indicates the cell density in the flask of TA4222, TA4179 and the sum of them (current total cell density), respectively. The former two values were calculated using the actual current total cell density and the result of flow cytometry (See Supplementary File). (d) Time course of the cellobiose, glucose, and IPA concentration in co-culture broth. Each circle, square, triangle indicates the IPA, glucose and cellobiose, respectively. 0.025 mM IPTG and 50 ng/mL aTc were added to culture at 0 h. Error bars represent the standard deviation (n = 3). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
by QS signal from the other strain in our synthetic microbial consortium. At last, we compared the IPA production efficiencies from cellobiose between the synthetic microbial consortium and the conventional single strain system. E. coli cannot utilize cellobiose as carbon source directly, thus the IPA production by E. coli is dependent on availability of glucose and other nutrients as shown in Fig. S5. For the direct IPA production from cellobiose using E. coli strain, we previously developed E. coli expressing BGL-Blc fusion protein, which display the BGL on its cell surface via Blc anchor domain (Soma et al., 2012). According to this strain, we constructed TA4593 for the IPA production from cellobiose by single strain cultivation (Fig. S6c). TA4593 express enzymes for the
using Tet promoter. When the promoter regulating the IPA production pathway in TA4179 was changed from PluxlacO to PLtetO1, which requires induction by exogenous aTc (TA4180, Fig. S3), the final IPA production titer (16.0 ± 2.2 mM at 24 h) was almost similar to that of TA4179. This result proved that the QS-dependent induction of IPA production was not disadvantageous to the conventional induction system, at least in a synthetic microbial consortium for cooperative IPA production from cellobiose. Additionally, the IPA production yield by TA4179 and TA4180 for single cultivation in LB medium containing 10 g/L (55.5 mM) glucose were not significantly different (Fig. S4). From both above validations for the IPA production, we proved that activity of target chemical production strain can be induced sufficiently 272
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As describe above, the tunability of our QS-CL circuit by changing concentration of exogenous inducers (IPTG and aTc) realized the parameter scanning (e.g. LuxR expression level, promoter strength for LuxI expression) to achieve its desired dynamics characteristic which resulted in the designed collective behavior of microbes. Recently, Gupta et al. have reported a pathway-independent quorum-sensing circuit using a constitutive promoters from the BioFAB library aiming to achieve the optimal dynamic characteristics of synthetic QS circuit for versatile chemical production (Gupta et al., 2017). Once we obtain the optimal parameters with tunable our QS-CL circuits, we also can replace the tunable promoters (PLlacO1, PLtetO1, and PluxlacO) with the constitutive ones that have almost same properties with the optimal condition. With this replacement, our QS-CL circuit also would be redesigned as not to need expensive exogenous inducers. It is also useful to consider cultivation conditions to achieve the desired dynamic characteristics of the circuit (e.g., temperature, pH, aeration, and medium composition). The QS-CL circuit was relatively robust to changes in the initial cell density (Fig. S7), but it was highly sensitive to changes in the culture temperature (Fig. S8). The induction of cell lysis was delayed with an increase in temperature, indicating the delay of QS signaling at higher temperatures. The optimal temperature for LuxI is 30 °C and its activity decreases at higher temperatures (Schaefer et al., 1996). This suggests that the delay in cell lysis was caused by a decrease in the AHL synthesis rate at a higher temperature. Natural biological systems based on the coexistence of microorganisms with distinct roles have formed by evolution in response to environmental changes including human activities and natural selection. The multiple parallel fermentation of sake is a semi-artificial ecosystem based on a microbial consortium consisting of yeast and fungi with different roles. In this study, a synthetic microbial consortium of two different recombinant E. coli strains was reconstructed; the strains have different roles in the saccharification of cellobiose (QSCL-bglC, TA4222) and target chemical production (IPA producer receiving the QS signal, TA4179). The synthetic QS system resulted in collective behavior in an environment where two different cells coexist. Both strains, TA4222 and TA 4179, showed logarithmic growth due to the nutrients derived from LB medium before the initiation of the glucose supply from cellobiose saccharification and the lysis of TA4222 cells. Fewer residual cells of TA4222 after lysis remained as compared with its single cultivation. Additionally, the regrowth of residual cells of TA4222 was not observed using the synthetic microbial consortium. Although the chemical-producing strain TA4179 could utilize glucose for both cell growth and IPA production even after the depletion of nutrients in the LB medium, the enzyme-producing strain TA4222 could not utilize glucose due to gene deletions. These results suggested that the enzyme-producing strain was culled by the chemicalproducing strain in the synthetic microbial consortium, even it could survive the cell lysis system. Because of the resource limitation within the system, it is beneficial that the strain completes its function and is expelled from the system for reducing competition for substrates and space. However, there was still competition for carbon allocated to endogenous metabolism and IPA production in the producer strain TA4179. Additionally, TA4179 expressed GFP for experimental convenience, which should be a burden on the production of other proteins. Therefore, IPA productivity was not so high, although the target chemical-producing strain (TA4179) nearly monopolized the space and substrates during fermentation. To increase productivity, further engineering of the strain for target chemical production is necessary. It would be effective to engineer production strains using the current synthetic biological tools, especially for dynamic metabolic regulation, eliminating such metabolic imbalance (Brockman and Prather, 2015; Holtz and Keasling, 2010; Venayak et al., 2015). To incorporate such a strategy, a more sophisticated design that combines a synthetic metabolic pathway and genetic circuit is required. For industrial application of this system, there are still many challenging issues to be solved, including of application to fed-batch
IPA production and BGL-Blc fusion protein from PLlacO1 promoter in the presence of IPTG. TA4593 was cultured in same LB medium containing cellobiose as the synthetic microbial consortium for IPA production from cellobiose above (Fig. S6). 0.1 mM IPTG and 50 ng/mL of aTc were added to culture medium at 0 h. TA4593 saccharified 23.1 ± 3.1 mM of cellobiose and consumed 88.5% of all glucose that was supplied from cellobiose saccharification. The final IPA production titer of TA4593 was 5.8 ± 0.5 mM, which was much lower than the case using the synthetic microbial consortium (16–19 mM). TA4593 spent 24 h on cellobiose saccharification, which was over 4-fold longer than the case using the synthetic microbial consortium. 4. Discussion In synthetic biology, numerous artificial biomolecular parts have been developed (Cameron et al., 2014; Smolke, 2009; Vilanova and Porcar, 2014). Synthetic promoters controlled by multiple regulatory factors can be used as logic gates, with a specific response to multiple inputs, enabling the strict control of gene expression (Brödel et al., 2016; Cox et al., 2007; Tamsir et al., 2011). Using the synthetic promoter PluxlacO, cell lytic genes and AHL synthase were only expressed when the AHL-LuxR complex and IPTG were present in sufficient amounts. Using the circuit design shown in Fig. 2 (TA4291), the existence of LuxR in the cell was also dependent on the presence of exogenous aTc. Thus, QS-dependent cell lysis was regulated by two external signals (IPTG and aTc) and an endogenous signal (AHL). This strategy enabled the strict control of the expression of genes responsible for cell suicide. A critical leak of cell lytic genes affecting cell growth was not detected when the required inputs were not satisfied according to the cell growth assay. Although the induction of cell lysis was strictly regulated, residual cells remained (around OD600 = 0.3) even after QS-dependent cell lysis occurred using the QS-CL strain (TA4291) and QS-CL-bglC strain (TA4064) (Figs. 2b and 3b). These results suggest that a certain number of lytic gene-resistant strains or low lytic gene-expressing strains appeared. Even when the concentration of IPTG, which enhances holin and endolysin expression, was increased using the QS-CL-bglC strain (TA4064), the residual cell density did not decrease (Fig. 3b). Moreover, the regrowth of residual cells was observed during long-term cultivation (Fig. S2a). Similar results for cell lysis and the regrowth of residual cells have been reported in previous studies of the reconstruction of synthetic cell lysis systems (Hsu et al., 2014; Lo et al., 2013; Morita et al., 2001; Pasotti et al., 2011). The residual cells of the QS-CL-bglC strain (TA4064) consumed glucose after the saccharification of cellobiose by BGL in the culture medium (Fig. S2c). To prevent competition for glucose consumption between the enzyme-producing strain and the chemical-producing strain, we constructed another QS-CL-bglC strain (TA4222) by the deletion of genes responsible for glucose consumption (manZ, ptsG, and glk). TA4222 also showed regrowth on the LB medium after cell lysis, but quite low glucose consumption was observed (Fig. S2c). The tunability of the QS-CL circuit for the determination of the threshold cell density and the timing of cell lysis was investigated by altering the IPTG concentration in flask cultivation. QS-CL was able to function for 60 min (at 2–3 h), corresponding to 40% of the logarithmic growth phase (from at 0–3.5 h). The dynamic range of the cell density at the time of cell lysis was OD600 = 0.63–1.52 when the initial cell density was set to around OD600 = 0.06 (Fig. 3b). To further expand the dynamic characteristics of the QS-CL circuit, it is necessary to modify the parameters related to the expression level of each gene and/or to change the structure of the circuit itself. The QS-CL circuit was controlled by the synthesis of LacI, LuxI (AHL), and LuxR, so these were important for the dynamic characteristics of the QS-CL circuit. These parameters can be optimized using well-established biomolecular parts (e.g., promoters (Hammer et al., 2006) and RBS (Levin-Karp et al., 2013)) and prediction tools (Bonde et al., 2016; Salis, 2011). 273
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Basic Science Research Project from the Sumitomo Foundation.
culture, long-term stability, and price of medium. However, the synthetic microbial consortium consisting of strains harboring genetic circuits in this study worked as a good example of task distribution for chemical production from model biomass. The saccharification of polysaccharides into monosaccharides is an essential process for chemical production from biomass. The efficiency of cellobiose saccharification and IPA production by the synthetic microbial consortium were much higher than those in the case with the single strain displaying BGL on cell surface. The task distribution by designed microbial consortium succeeded to reduce the burden of the protein expression and other physiological activities in each strain leading to improvement of fermentation process. The saccharification of cellobiose to glucose was executed by the collective cell behavior of the enzyme-producing strain harboring a genetic circuit regulating QS-dependent cell lysis for BGL release to the fermenter. Target chemical production was assigned to the chemical-producing strain using a genetic circuit for detecting the QS signal from the enzyme-producing strain. We presented potentials of genetic circuits for assignation specific tasks to individual strains and to design interactions among strains. Genetic circuits for QS-dependent cell lysis and specific protein release has potential for various bioengineering applications, such as useful protein production, agriculture, and bioremediation. The approach has also been employed for in vivo drug delivery (Din et al., 2016). In the co-culture for the IPA fermentation from cellobiose, the QS signal generated by the enzyme-producing strain (TA4222) was also used for the induction of the IPA production from glucose assigned to the chemical-producing strain (TA4179). Although the IPA productivity of TA4179 triggered by QS signal wasn't so much advantageous over the case of TA4180 triggered by external addition inducer aTc in coculture, the cell density after the logarithmic growth phase of TA4179 was ~40% higher than that of TA4180. Those results indicated that the toxicity or the physiological burden of the enzymes expression for the chemical production pathway on the cell growth was reduced with QS dependent induction system compared with conventional induction by external addition of aTc (at 0 h). The automation and optimization of induction for the chemical production pathway by QS would show higher superiority in the case with target chemical production showing high toxicity or metabolic and/or other physiological burden. We showed that the synthetic reconstruction of the QS system is a promising strategy to control signal transduction among both autologous and heterologous strains.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ymben.2019.08.007. References Balagaddé, F.K., Song, H., Ozaki, J., Collins, C.H., Barnet, M., Arnold, F.H., Quake, S.R., You, L., 2008. A synthetic Escherichia coli predator-prey ecosystem. Mol. Syst. Biol. 4, 1–8. https://doi.org/10.1038/msb.2008.24. Bonde, M.T., Pedersen, M., Klausen, M.S., Jensen, S.I., Wulff, T., Harrison, S., Nielsen, A.T., Herrgård, M.J., Sommer, M.O.A., 2016. Predictable tuning of protein expression in bacteria. Nat. Methods 13, 233–236. https://doi.org/10.1038/nmeth.3727. Brenner, K., You, L., Arnold, F.H., 2008. Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 26, 483–489. https://doi.org/10.1016/j. tibtech.2008.05.004. Brockman, I.M., Prather, K.L.J., 2015. Dynamic metabolic engineering: new strategies for developing responsive cell factories. Biotechnol. J. 10, 1360–1369. https://doi.org/ 10.1002/biot.201400422. Brödel, A.K., Jaramillo, A., Isalan, M., 2016. Engineering orthogonal dual transcription factors for multi-input synthetic promoters. Nat. Commun. 7, 13858. https://doi.org/ 10.1038/ncomms13858. Calendar, R., 1988. The Bacteriophages, vol. 1 Oxford University Press. https://doi.org/ 10.1006/rwgn.2001.0106. Cameron, D.E., Bashor, C.J., Collins, J.J., 2014. A brief history of synthetic biology. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro3239. Cox, R.S., Surette, M.G., Elowitz, M.B., 2007. Programming gene expression with combinatorial promoters. Mol. Syst. Biol. 3, 145. https://doi.org/10.1038/msb4100187. Desai, S.H., Rabinovitch-Deere, C.A., Tashiro, Y., Atsumi, S., 2014. Isobutanol production from cellobiose in Escherichia coli. Appl. Microbiol. Biotechnol. 98, 3727–3736. https://doi.org/10.1007/s00253-013-5504-7. Din, M.O., Danino, T., Prindle, A., Skalak, M., Selimkhanov, J., Allen, K., Julio, E., Atolia, E., Tsimring, L.S., Bhatia, S.N., Hasty, J., 2016. Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536, 81–85. https://doi.org/10.1038/nature18930. Gardner, T.S., Cantor, C.R., Collins, J.J., 2000. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342. https://doi.org/10.1038/35002131. Gupta, A., Reizman, I.M.B., Reisch, C.R., Prather, K.L.J., 2017. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit. Nat. Biotechnol. 35, 273–279. https://doi.org/10.1038/nbt.3796. Hammer, K., Mijakovic, I., Jensen, P.R., 2006. Synthetic promoter libraries – tuning of gene expression. Trends Biotechnol. 24, 53–55. https://doi.org/10.1016/J.TIBTECH. 2005.12.003. Hanai, T., Atsumi, S., Liao, J.C., 2007. Engineered synthetic pathway for isopropanol production in Escherichia coli. Appl. Environ. Microbiol. 73, 7814–7818. https://doi. org/10.1128/AEM.01140-07. Haseltine, E.L., Arnold, F.H., 2008. Implications of rewiring bacterial quorum sensing. Appl. Environ. Microbiol. 74, 437–445. https://doi.org/10.1128/AEM.01688-07. Holtz, W.J., Keasling, J.D., 2010. Engineering static and dynamic control of synthetic pathways. Cell 140, 19–23. https://doi.org/10.1016/j.cell.2009.12.029. Hsu, C.-Y., Yu, T.-C., Lin, L.-J., Hu, R.-H., Chen, B.-S., 2014. Systematic approach to Escherichia coli cell population control using a genetic lysis circuit. BMC Syst. Biol. 8, S7. https://doi.org/10.1186/1752-0509-8-S5-S7. Kong, W., Meldgin, D.R., Collins, J.J., Lu, T., 2018. Designing microbial consortia with defined social interactions. Nat. Chem. Biol. 14, 821–829. https://doi.org/10.1038/ s41589-018-0091-7. Lee, J.W., Na, D., Park, J.M., Lee, J., Choi, S., Lee, S.Y., 2012. Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat. Chem. Biol. 8, 536–546. https://doi.org/10.1038/nchembio.970. Levin-Karp, A., Barenholz, U., Bareia, T., Dayagi, M., Zelcbuch, L., Antonovsky, N., Noor, E., Milo, R., 2013. Quantifying translational coupling in E. coli synthetic operons using RBS modulation and fluorescent reporters. ACS Synth. Biol. 2, 327–336. https://doi.org/10.1021/sb400002n. Lo, T.M., Tan, M.H., Hwang, I.Y., Chang, M.W., 2013. Designing a synthetic genetic circuit that enables cell density-dependent auto-regulatory lysis for macromolecule release. Chem. Eng. Sci. 103, 29–35. https://doi.org/10.1016/j.ces.2013.03.021. Marguet, P., Tanouchi, Y., Spitz, E., Smith, C., You, L., 2010. Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology. PLoS One 5, e11909. https://doi.org/10.1371/journal.pone.0011909. Morita, M., Asami, K., Tanji, Y., Unno, H., 2001. Programmed Escherichia coli cell lysis by expression of cloned T4 phage lysis genes. Biotechnol. Prog. 17, 573–576. https:// doi.org/10.1021/bp010018t. Nielsen, J., Keasling, J.D., 2011. Synergies between synthetic biology and metabolic engineering. Nat. Biotechnol. 29, 693–695. https://doi.org/10.1038/nbt.1937. Park, J.H., Lee, K.H., Kim, T.Y., Lee, S.Y., 2007. Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. U.S.A. 104, 7797–7802. https://doi.org/ 10.1073/pnas.0702609104. Pasotti, L., Zucca, S., Lupotto, M., Gabriella, M., Angelis, C. De, Magni, P., 2011. Characterization of a synthetic bacterial self- destruction device for programmed cell death and for recombinant proteins release. J. Biol. Eng. 5, 8. https://doi.org/10. 1186/1754-1611-5-8.
5. Conclusion Designing the microbial consortia are an emerging frontier in synthetic biology that enable versatile microbiome engineering. Recently, extensibility of microbial consortia based on design theory has been proven (Kong et al., 2018), and thus the potential of the development of novel synthetic ecosystems has been payed attention for diverse purposes. Here, we designed a novel synthetic microbial consortia using E. coli strains harboring synthetic genetic circuits in order to implement the task distribution in bioprocess. Each genetic circuit was installed to E. coli strain to assign the specific task in bioprocess, which means the saccharification of cellulosic carbon sources and the production of target chemical. Synthetic QS system was implemented in the genetic circuits to form the cascade of the tasks execution through the cell-cell communication among the consortia. The synthetic microbial consortia presented much higher efficiencies in the IPA fermentation requiring cellobiose saccharification compared with conventional fermentation using single engineered microbe. Acknowledgement This work was supported by JSPS KAKENHI Grant Numbers JP23119002, JP18K14065, Chemical Innovation Encouragement Prize from Japan Association for Chemical Innovation, and a Grant for a 274
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from a central metabolic pathway toward a synthetic pathway using a metabolic toggle switch. Metab. Eng. 23, 175–184. https://doi.org/10.1016/j.ymben.2014.02. 008. Stricker, J., Cookson, S., Bennett, M.R., Mather, W.H., Tsimring, L.S., Hasty, J., 2008. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519. https://doi. org/10.1038/nature07389. Tamsir, A., Tabor, J.J., Voigt, C.A., 2011. Robust multicellular computing using genetically encoded NOR gates and chemical “wiresg.”. Nature 469, 212–215. https://doi. org/10.1038/nature09565. Venayak, N., Anesiadis, N., Cluett, W.R., Mahadevan, R., 2015. Engineering metabolism through dynamic control. Curr. Opin. Biotechnol. 34, 142–152. https://doi.org/10. 1016/j.copbio.2014.12.022. Vilanova, C., Porcar, M., 2014. iGEM 2.0—refoundations for engineering biology. Nat. Biotechnol. 325 2014. Xavier, J.B., 2011. Social interaction in synthetic and natural microbial communities. Mol. Syst. Biol. 7, 483. https://doi.org/10.1038/msb.2011.16. Zhang, F., Carothers, J.M., Keasling, J.D., 2012. Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat. Biotechnol. https://doi.org/10.1038/nbt.2149. Zhou, K., Qiao, K., Edgar, S., Stephanopoulos, G., 2015. Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33, 377–383. https://doi.org/10.1038/nbt.3095.
Prather, K.L.J., Martin, C.H., 2008. De novo biosynthetic pathways: rational design of microbial chemical factories. Curr. Opin. Biotechnol. 19, 468–474. https://doi.org/ 10.1016/j.copbio.2008.07.009. Salis, H.M., 2011. The ribosome binding site calculator. Methods Enzymol. 498, 19–42. https://doi.org/10.1016/B978-0-12-385120-8.00002-4. Schaefer, A.L., Val, D.L., Hanzelka, B.L., Cronan, J.E., Greenberg, E.P., 1996. Generation of cell-to-cell signals in quorum sensing: acyl homoserine lactone synthase activity of a purified Vibrio fischeri LuxI protein. Proc. Natl. Acad. Sci. U.S.A. 93, 9505–9509. https://doi.org/10.1073/pnas.93.18.9505. Smolke, C.D., 2009. Building outside of the box: iGEM and the BioBricks foundation. Nat. Biotechnol. 2712 2009. Solomon, K.V., Sanders, T.M., Prather, K.L.J., 2012. A dynamic metabolite valve for the control of central carbon metabolism. Metab. Eng. 14, 661–671. https://doi.org/10. 1016/j.ymben.2012.08.006. Soma, Y., Hanai, T., 2015. Self-induced metabolic state switching by a tunable cell density sensor for microbial isopropanol production. Metab. Eng. 30, 7–15. https://doi.org/ 10.1016/j.ymben.2015.04.005. Soma, Y., Inokuma, K., Tanaka, T., Ogino, C., Kondo, A., Okamoto, M., Hanai, T., 2012. Direct isopropanol production from cellobiose by engineered Escherichia coli using a synthetic pathway and a cell surface display system. J. Biosci. Bioeng. 114, 80–85. https://doi.org/10.1016/j.jbiosc.2012.02.019. Soma, Y., Tsuruno, K., Wada, M., Yokota, A., Hanai, T., 2014. Metabolic flux redirection
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