Design and application of genetically-encoded malonyl-CoA biosensors for metabolic engineering of microbial cell factories

Metabolic Engineering 44 (2017) 253–264

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Design and application of genetically-encoded malonyl-CoA biosensors for metabolic engineering of microbial cell factories

T

Abayomi Oluwanbe Johnsona, Miriam Gonzalez-Villanuevaa, Lynn Wongb, ⁎ ⁎ ⁎ Alexander Steinbüchelc,d, Kang Lan Teea, , Peng Xub, , Tuck Seng Wonga, a Department of Chemical & Biological Engineering and Advanced Biomanufacturing Centre, The University of Sheffield, Sir Robert Hadfield Building, Mappin Street, Sheffield S1 3JD, United Kingdom b Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, United States c Institut für Molekulare Mikrobiologie und Biotechnologie, Westfälische Wilhelms-Universität, Münster D-48149, Germany d Environmental Science Department, King Abdulaziz University, Jeddah, Saudi Arabia

A R T I C L E I N F O

A B S T R A C T

Keywords: Malonyl-CoA FapR Biosensors Fatty acid biosynthesis Synthetic biology Genetic circuits Dynamic regulation

Malonyl-CoA is the basic building block for synthesizing a range of important compounds including fatty acids, phenylpropanoids, flavonoids and non-ribosomal polyketides. Centering around malonyl-CoA, we summarized here the various metabolic engineering strategies employed recently to regulate and control malonyl-CoA metabolism and improve cellular productivity. Effective metabolic engineering of microorganisms requires the introduction of heterologous pathways and dynamically rerouting metabolic flux towards products of interest. Transcriptional factor-based biosensors translate an internal cellular signal to a transcriptional output and drive the expression of the designed genetic/biomolecular circuits to compensate the activity loss of the engineered biosystem. Recent development of genetically-encoded malonyl-CoA sensor has stood out as a classical example to dynamically reprogram cell metabolism for various biotechnological applications. Here, we reviewed the design principles of constructing a transcriptional factor-based malonyl-CoA sensor with superior detection limit, high sensitivity and broad dynamic range. We discussed various synthetic biology strategies to remove pathway bottleneck and how genetically-encoded metabolite sensor could be deployed to improve pathway efficiency. Particularly, we emphasized that integration of malonyl-CoA sensing capability with biocatalytic function would be critical to engineer efficient microbial cell factory. Biosensors have also advanced beyond its classical function of a sensor actuator for in situ monitoring of intracellular metabolite concentration. Applications of malonyl-CoA biosensors as a sensor-invertor for negative feedback regulation of metabolic flux, a metabolic switch for oscillatory balancing of malonyl-CoA sink pathway and source pathway and a screening tool for engineering more efficient biocatalyst are also presented in this review. We envision the genetically-encoded malonyl-CoA sensor will be an indispensable tool to optimize cell metabolism and cost-competitively manufacture malonyl-CoAderived compounds.

1. Introduction The field of metabolic engineering has witnessed rapid advancements, further consolidating it as an enabling technology for engineering biological cell factories for producing value-added chemicals and bio-products. The three pillars of metabolic pathway engineering are to achieve high titers, yield and productivity of desired product without detrimental effects on cell growth. Hence, it is critical to develop metabolite-based biosensors to execute feedback control and decouple metabolite production from cell growth. A recent crucial

⁎

contribution by synthetic biology to engineer more efficient microbial cell factory is the development of transcriptional factor (TF)-based biosensors. Whether coupled with a readable output to enable highthroughput screening or integrated into a genetic circuit to dynamically regulate key metabolic pathways, TF-based biosensors are an indispensable tool for redistributing carbon flux and adapt cell metabolism to the changing environment (Liao and Oh, 1999; Mainguet and Liao, 2010; van der Meer and Belkin, 2010). Such synthetic biosensors are derived from naturally evolved transcriptional factors that propagate changing environmental signals or cellular status into a transcriptional

Corresponding authors. E-mail addresses: k.tee@sheffield.ac.uk (K.L. Tee), [email protected] (P. Xu), t.wong@sheffield.ac.uk (T.S. Wong).

http://dx.doi.org/10.1016/j.ymben.2017.10.011 Received 24 July 2017; Received in revised form 17 September 2017; Accepted 27 October 2017 Available online 31 October 2017 1096-7176/ © 2017 The Authors. Published by Elsevier Inc. on behalf of International Metabolic Engineering Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

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Glucose metabolism, Amino acids metabolism, Degradation of fatty acids

S CoA O

Acetyl-CoA

HO

S CoA O

O

Malonyl-CoA

Fatty acids

Flavonoids e.g. naringenin

Bio-polymers e.g. poly(3-hydroxypropionic acid)

Biofuels e.g. fatty acid methyl ester

Polyketides e.g. 6-methylsalicylic acid

OH HO R

HO

OH

O

O

OH

O

O OH

O

O

R

O O n

Fig. 1. Compounds derived from malonyl-CoA. Malonyl-CoA, a direct product of acetyl-CoA, can be used as a precursor for the synthesis of fatty acids, flavonoids, bio-polymers, biofuels, and polyketides.

2013; Williams et al., 2016). These applications potentially help to accelerate design-build-test cycles in the engineering of metabolic pathways by facilitating genotype manipulation-phenotype evaluation processes (Rogers et al., 2015; Rogers and Church, 2016). Over all, many of these applications require that such sensors are orthogonal (without sensor cross-talk) and tunable, allowing for customized biosensor output relative to expected concentrations of metabolites for a range of physiological conditions. This constraint could be decomposed into three pillars: specificity, sensitivity and dynamic range, which are the primary design considerations for their proper function inside the cell. In this review, we explore the fundamental design principles and strategies in modifying and tuning the metaboliteresponsive transcription factor (MRTF), using malonyl-CoA biosensor as a prototype. We also explore how these genetically encoded circuits have been successfully applied as a tool in metabolic engineering to dynamically regulate intracellular malonyl-CoA metabolite pools and improve production of malonyl-CoA-derived chemicals in notable host microorganisms.

output or cellular phenotype that promotes either cell viability, survival or metabolic economics (Harrison and Dunlop, 2012; Liu et al., 2015). Structural feature of this biosensor is generally divided into two parts: a metabolite-responsive transcriptional regulator and a fluorescencecoupled or fitness-related read-out module (Harrison and Dunlop, 2012; Rogers et al., 2015). This enables metabolic engineers to efficiently quantify varying concentration of cellular metabolites in contrast to the laborious, time-consuming and low throughput analytical methods such as HPLC and LC-MS (Dietrich et al., 2013; Liu et al., 2015). Metabolitesensing genetic circuits have been reported for sensing various metabolites including macrolides (Mohrle et al., 2007), acetyl phosphate (Farmer and Liao, 2000), farnesyl pyrophosphate (Dahl et al., 2013), 3hydroxypropanoic acid (Rogers et al., 2015; Rogers and Church, 2016), 1-butanol (Dietrich et al., 2013), and more recently, malonyl-CoA (Ellis and Wolfgang, 2012). The use of biosensors for in vivo detection and/or quantification of metabolites essentially creates an input-output communication platform within biological cells. This platform has predominantly been exploited to monitor metabolite levels in real-time, especially for the detection of accumulated intermediates or metabolites present in relatively low abundance. Access to such crucial information allows metabolic engineers to gain a deep understanding of kinetics and regulatory mechanisms underlying the engineered metabolic network. In turn, this enables the design of more robust and effective metabolic intervention strategies to maximize production titers of metabolites of interest. That said, biosensors have also found front-end applications as dynamic metabolic pathway regulators and back-end applications as screening devices. When applied to dynamic metabolic pathway regulation, biosensors allow the cell to probe the exact metabolic state and actuate pathway expression that compensates for the metabolic activity of the engineered pathway, and improves the overall productivity and fitness of the engineered cell factory (Xu et al., 2014b). Biosensors can also be developed into a high-throughput screening platform by coupling with a readable output such as fluorescence. This approach is often used to select for high-producing genetic variants or to identify process conditions leading to high product titers (Dietrich et al., 2010,

2. Malonyl-CoA – a vital metabolite In practically every living system, a portion of the acetyl-CoA flux from the central metabolic pathway is diverted to malonyl-CoA for fatty acid and lipid membrane synthesis, with the aid of acetyl-CoA carboxylase (ACC). This suggests the vital roles that malonyl-CoA plays in cell metabolism and structure. Specifically, malonyl-CoA is a rate limiting substrate for fatty acid synthesis, which in turn, is pivotal for maintaining cell membrane integrity and energy conservation (Schujman et al., 2003, 2006, 2008). In mammals, malonyl-CoA has been identified as a crucial fatty acid oxidation regulator which inhibits the mitochondrial carnitine palmitoyltransferase (CPT) − an enzyme involved in fatty acid uptake in the heart and skeletal muscle (Folmes and Lopaschuk, 2007; Foster, 2012). This makes it an effective therapeutic target molecule for treating diseases caused by poor or excessive fatty acid uptake. This has also attracted medical interest in drug development to control malonyl-CoA metabolism at the enzymatic 254

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As an example, Xu et al. applied a genome-scale metabolic network model to achieve a balanced precursor distribution between biomass production and the synthesis of naringenin, a flavonoid compound, in E. coli (Xu et al., 2011). Guided by OptForce methodology (Ranganathan et al., 2010), the model predicted over-expression of ACC, phosphoglycerate kinase (PGK) and pyruvate dehydrogenase (PDH), in addition to double knockout of genes for the citric acid cycle enzymes: succinyl-CoA synthetase (sucC) and fumarase (fumC). The combination of these metabolic adjustments resulted in a 3.7-fold increase in malonyl-CoA amount and a 5.6-fold increase in naringenin production at 474 mg/L, compared to the wild-type. Similarly, Fowler et al. demonstrated the use of a computational model known as Cipher of Evolutionary Design (CiED) (Fowler et al., 2009). The tool predicted the knockout of several citric acid cycle genes (sdhCDAB and citE), an amino acid transporter (brnQ) and a pyruvate consumer (alcohol dehydrogenase adhE) for engineering an efficient flavonoid producing E. coli strain. This deletion strain was subsequently used to overexpress ACC, acetyl-CoA synthetase, biotin ligase and pantothenate kinase to achieve 660% and 420% increase in naringenin and eriotictyol production, respectively. These strategies have also led to the development of a high fatty acid-producing strain in later studies (Xu et al., 2013b). Complementing the strategy adopted by Xu et al. (2011) and Fowler et al. (2009) that relies on static control genetic modifications (i.e., overexpression and/or deletion), CRISPR/dCas9-mediated repression of endogenous targets predicted by OptForce was proven a highly promising strategy to increase flavonoid production (Cress et al., 2015). Besides the implementation of classical metabolic engineering strategies, chemicals that inhibit fatty acid synthesis, such as cerulenin and triclosan, have been shown to effectively boost intracellular malonyl-CoA levels (Davis et al., 2000). Cerulenin up-regulates intracellular malonyl-CoA amounts by inhibiting β-ketoacyl-acyl carrier protein synthase enzymes (FabB and FabF), which condense malonylACP with acyl-ACP to extend the fatty acid chain by two carbon atoms (Schujman et al., 2006, 2008). However, the inhibition in fatty acid synthesis under high cerulenin concentrations negatively impacts cell viability. The high cost of cerulenin prohibits its use for commercialscale fermentation production (Davis et al., 2000). Despite its limited commercial application, experimentation with cerulenin concentrations to mimic increasing malonyl-CoA level was crucial in establishing malonyl-CoA as the effector molecule for regulating fatty acid synthesis in Gram-positive bacterial species (Schujman et al., 2003, 2006).

level (Folmes and Lopaschuk, 2007). More relevant to this review, malonyl-CoA is the signaling molecule for regulating lipid metabolism in many Gram-positive bacteria, making it a desirable therapeutic target (Nunn et al., 1977; Dirusso et al., 1993; Magnuson et al., 1993; Marrakchi et al., 2001; James and Cronan, 2003; Schujman et al., 2003, 2006, 2008; Yao et al., 2012). In addition, malonyl-CoA is the building block for many commercially viable bio-products, such as biofuels, biopolymers, and plant natural products. Hence, it has garnered significant interest from the industrial biotechnology sector as a key metabolite target in their engineering of microbial cell factories, (James and Cronan, 2003; Schujman et al., 2003, 2006) (Fig. 1). These end chemicals are potentially useful as pharmaceutical intermediates, biofuels or other potentially useful chemical products vital to a sustainable bio-economy (Xu et al., 2014a, 2014b; Liu et al., 2015). However, a major challenge in malonyl-CoA pathway engineering is the perpetually low intracellular concentrations of malonyl-CoA in microbial hosts (4–90 μM or 0.01–0.23 nmol/mg dry weight in E. coli), thus necessitating the use of various metabolic engineering approaches to realize its commercial-scale applications (Takamura et al., 1985; Takamura and Nomura, 1988; Miyahisa et al., 2005). Early metabolic engineering strategies employed static manipulation of relevant pathway enzymes that are directly or indirectly involved in malonylCoA metabolism in conjunction with metabolic pathways to channel malonyl-CoA flux to the synthesis of desired products. Some of these approaches include overexpression of acetyl-CoA carboxylase (ACC) – the enzyme that converts acetyl-CoA to malonyl-CoA, increasing intracellular availability of acetyl-CoA – a malonyl-CoA precursor, and down-regulating malonyl-CoA sink pathways (Fig. 2 and Table 1). As such, early attempts at malonyl-CoA engineering relied on conventional analytical techniques such as HPLC and LC to quantify increased concentrations of malonyl-CoA and the desired end product in order to validate the effectiveness of various engineering strategies. In addition, an integrated computational and experimental approach has been employed to predict static genetic intervention targets to increase cellular malonyl-CoA concentration (Fowler et al., 2009; Xu et al., 2011). By studying the genome-scale metabolic network of a biological host, such models nominate target genes for knock out and/ or over-expression to improve malonyl-CoA amounts. Besides identifying gene targets directly related to malonyl-CoA metabolism, computational tools have been applied in highlighting potential impact of targeted genes on cell growth. Furthermore, they have been used to explore the potential roles of genes that are indirectly related to malonyl-CoA metabolism. The ability to identify distant pathways that are not directly involved in target metabolism demonstrates the effectiveness of genome-scale computational modeling. Overall, computational tools have been successfully applied in finding the most promising combination of approaches to achieve an improved malonyl-CoA yield.

3. Mechanism of malonyl-CoA sensing Malonyl-CoA biosensors are a synthetic mimicry of the native fatty acid biosynthesis transcriptional regulatory circuits, found naturally in many Gram-positive bacteria such as Bacillus subtilis, Staphylococcus aureus, Bacillus anthracis, Listeria monocytogenes and Streptococcus Fig. 2. A summary of metabolic engineering strategies to increase intracellular malonyl-CoA concentration through rational design. Increasing the pool of acetyl-CoA, decreasing the flux of acetylCoA towards non-malonyl-CoA producing acetyl-CoA sink pathways, increasing the expression and/or activity of acetyl-CoA carboxylase and decreasing the flux of malonyl-CoA towards fatty acid synthesis, are direct ways of improving intracellular malonylCoA accumulation.

255

256

Use predictive computational models to select a combination of metabolic engineering approaches for strain optimization

Expression of another malonyl-CoA source pathway

Decreased expression of malonyl-CoAconsuming enzymes

Increased acetyl-CoA flux towards malonylCoA accumulation

Overexpressing the malonate carrier protein (matC), which transports malonate into the cell and malonyl-CoA synthetase (matB), which converts malonate to malonyl-CoA. Overexpression of acetyl-CoA carboxylase (ACC), phosphoglycerate kinase (PGK) and pyruvate dehydrogenase (PDH), coupled with double knock-out of fumarase (fumC) and succinyl-CoA synthetase (sucC) genes. Overexpression of acetyl-CoA carboxylase (ACC), biotin ligase (BirA) and pantothenate kinase in a strain deficient in citric acid cycle genes (sdhCDAB and citE), an amino acid transporter gene (brnQ), and an alcohol dehydrogenase gene (adhE)

Overexpressing the four subunits of E. coli acetyl-CoA carboxylase (ACC) in a low copy number plasmid under the control of T7 promoter. Overexpression of the two subunits of Corynebacterium glutamicum acetyl-CoA carboxylase (ACC) in a high copy number plasmid under the control of T7 promoter. Changing the promoter of ACC1 to a strong constitutive promoter TEF1. Site-directed mutagenesis of S659 and A1157 to reduce SNF1mediated phosphorylation of Acc1; overexpression of Acc1 WT and mutants. T7 promoter-controlled overexpression of acetyl-CoA carboxylase (ACC), overexpression of acetyl-CoA synthetase, which recycles acetate to acetyl CoA; double knock-out of genes encoding phosphotransacetylase (Pta) and acetate kinase (AckA), which are genes responsible for acetate biosynthesis from acetyl-CoA; deletion of alcohol/aldehyde dehydrogenase (adhE). Overexpression of the four subunits of Photorhabdus luminescens acetyl-CoA carboxylase (ACC) in a low copy number plasmid and overexpression of Photorhabdus luminescens biotin ligase (BirA) in a high copy number episomal vector. Biotin ligase catalyzes the biotinylation of the biotin-dependent BCCP subunit of ACC. Use of anti-sense RNA to silence the expression of malonyl-CoA transacylase (FabD) – a fatty acid synthesis enzyme.

Increased expression of acetyl-CoA carboxylase (ACC)

Increased activity and expression of native acetyl-CoA carboxylase (ACC)

Mechanism

Strategy

Not quantified

Not quantified Not quantified

E. coli

S. cerevisiae S. cerevisiae

3.7-fold increase relative to WT

2.7-fold increase in relative to WT

E. coli

Not quantified

E. coli

E. coli

4.5-fold increase relative to WT

Not quantified

E. coli

E. coli

15-fold increase relative to WT

100-fold increase relative to WT

E. coli

E. coli

Malonyl-CoA

Microbial host

Table 1 Notable examples of conventional metabolic engineering strategies to increase intracellular malonyl-CoA amount and end chemicals.

660% increase in naringenin synthesis and 420% increase in eriodictyol synthesis

474 mg/L naringenin production

2.53-, 1.70-, 1.53-fold increase in the production of 4-hydroxycoumarin, resveratrol, and naringenin, respectively 100.64 mg/L of (2S)-naringenin

1166% increase in flavonone synthesis

65% increase in total fatty acid content, 3-fold increase in fatty acid ethyl esters (FAEE), and 3.5fold increase in 3-hydroxypropionic acid (3-HP) 4-fold increase in phloroglucinol production

Fowler et al. (2009)

Xu et al. (2011)

Wu et al. (2014)

Yang et al. (2015)

Leonard et al. (2007)

Zha et al. (2009)

Wattanachaisaereekul et al. (2008) Shi et al. (2014)

Miyahisa et al. (2005)

60 mg/L yield in flavanones

60% increase in 6-methyl acetyl–salicylic acid

Davis et al. (2000)

Ref.

6-fold increase in free fatty acid synthesis

End chemical

A.O. Johnson et al.

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biological host requires a number of design considerations. Firstly, the biological chassis tested (i.e., the host itself) potentially determines the ease of transformation/transfection of the genetic circuit as well as its replication and maintenance within the chassis. This factor must be built into the structural design of the circuit. In other words, the sensor must have a plasmid backbone with an origin of replication that is compatible with the replication machinery of the host (Li et al., 2015). Moreover, a dual plasmid circuit system with separate vectors for the repressor and reporter modules may be required for some organisms, where a large plasmid may prove difficult to be transformed or transfected. This latter case requires additional consideration of plasmid incompatibility and the choice of selection marker. A good example of how these considerations are crucial is demonstrated in the malonylCoA biosensors developed to investigate fatty acid metabolism in mammalian cells, where carefully selected genetic parts facilitated sensor functionality, with improved transfection, replication and expression efficiency, in the mammalian host (Ellis and Wolfgang, 2012). Secondly, a functionally effective sensor requires the proper balance in expression of both the repressor and reporter modules (Fig. 5). This is a pre-requisite for high detection limit and broad dynamic range of response. The upper detection limit of the sensor is determined by how much malonyl-CoA could saturate the FapR repressor (KD constrant), however, also physically constrained by the amount of FapR present inside the cell and how strong FapR interact with the DNA-binding site (fapO). This fact emphasizes the importance to control the amount of the repressor protein FapR. On the other hand, sensitivity is determined by the cooperativity of the FapR-faoO-malonyl-CoA interaction, that said the allosteric effect constant or the Hill coefficient. Increasing the number of FapR-binding site (fapO) might be useful to tune both the detection limit and sensitivity of the engineered malonyl-CoA sensor. Additionally, fine-tuning is necessary to achieve optimal sensitivity (i.e., responsive to small changes in intracellular malonyl-CoA concentration), responsivity (i.e., yielding a measurable read-out value), and a high signal-to-noise ratio in response to malonyl-CoA. This intricate balance requires intracellular FapR repressor concentration to be optimal: high enough to achieve high detection limit, but not too high to avoid poor sensitivity. Likewise, the expression of the reporter module should be high enough to achieve a detectable read-out response, but not too high to avoid detrimental effects on cell growth (Fig. 5). Despite the importance of a fine-tuned repressor (FapR) to operator (fapO) ratio, there is no algorithm to design a well-balanced malonylCoA sensor, at least not to our knowledge. In practice, most sensors are achieved by extensive experimentation and trial-and-error. Common strategies are summarized in Fig. 6; one of which concerns with the experimenting with plasmid copy number (low, medium, and high) of the genetic circuit to modulate the expression of both repressor and reporter modules. This parameter is more profound in cases where the repressor and reporter modules of the sensor circuit are expressed from

pneumonia (James and Cronan, 2003; Fujita et al., 2007; Xu et al., 2014a; Li et al., 2015; Liu et al., 2015). This naturally occurring regulon consists of an autogenously regulated FapR transcriptional repressor module that is typically located adjacent to a fatty acid synthesis operon, whose expression is driven by a hybrid promoter possessing a 17bp cis-regulatory fapO-operator (James and Cronan, 2003). FapR mediates the repression of fatty acid synthesis genes via DNA-protein interaction with the fapO-operator sequence using its N-terminal regulatory domain (KD = 0.12 μM) (Xu et al., 2014a). This interaction cascades into a FapR-fapO complex that sterically hinders RNA polymerase proceeding forward and blocks transcription of the downstream fatty acid synthesis genes. The C-terminal ligand-binding domain of FapR is typically a thioestease-like structure that specifically recognizes malonyl-CoA. Intracellular accumulation of malonyl-CoA gradually relieves this FapR-mediated repression through metabolite-protein interaction of malonyl-CoA with the C-terminal ligand-binding domain of FapR (KD = 2.4 μM) (Schujman et al., 2003, 2006; Ellis and Wolfgang, 2012; Xu et al., 2014a). Binding of malonyl-CoA with the C-terminus of FapR triggers a conformational change at the N-terminus of FapR, which destabilizes the FapR-fapO complex and relieves FapR from interacting with the fapO-operator, thereby permitting interaction of RNA polymerase with the promoter, and thus initiating transcription (Fig. 3). 4. The architecture of a malonyl-CoA biosensor The design of a typical malonyl-CoA-sensor (Fig. 4) requires the integration of the malonyl-CoA-responsive transcriptional factor FapR with a hybrid promoter containing the DNA binding site for FapR (Fig. 3). A repressor module is located on one end of the circuitry, comprising a fapR gene and a suitable promoter driving FapR expression. In the reporter module on the other end, there is a reporter gene typically encoding a fluorescence protein (e.g., egfp, rfp, mCherry and tdTomato); the expression of which is driven by a fapO-hybrid promoter with the cis-regulatory fapO-operator sequence located within or adjacent to the reporter promoter. Identical to the naturally occurring regulons, the reporter module translates the increase in intracellular malonyl-CoA concentration (i.e., input) into the expression level of the reporter protein (i.e., output), at a rate that is commensurate with the degree of de-repression of the reporter promoter. Hence, by perturbating the intracellular concentration of malonyl-CoA (e.g., through the use of cerulenin), the resulting increase in fluorescence signal generates a malonyl-CoA concentration-dependent calibration curve. This, in turn, serves as an input-output model for using such malonyl-CoA sensors to quantify intracellular malonyl-CoA concentrations derived from malonyl-CoA source pathways. 5. The design criteria of an effective malonyl-CoA biosensor Engineering an effective malonyl-CoA sensing genetic circuit for a

RNA pol Transcription repressed

FapR -35

fapO

-10

FASII gene

FapR Malonyl-CoA-FapR complex

RNA pol -35

fapO

Transcription induced -10

FASII gene

257

Fig. 3. Mechanism of malonyl-CoA sensing. Binding of malonyl-CoA to the C-terminus of FapR cascades into a conformation change at its N-terminus, which destabilizes FapR-fapO interaction. This enables the formation of an RNA polymerase-promoter complex, thus inducing transcription of the downstream fatty acid synthesis gene (FASII).

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Repressor module

Reporter module P2 T2 fapO

FapR

Fig. 4. Schematic diagram of the architecture of a malonyl-CoA sensor showing its modularity with a repressor module (P1 and FapR gene) and a reporter module (P2, fapO and RFP gene). P1 – repressor promoter, P2 – reporter promoter, fapO – fapO operator, T1, T2 – transcriptional terminators.

RFP

T1 P1

High detection limit Low sensitivity High signal fold-induction

High read-out response

An efficient malonyl-CoA sensor: High detection limit Optimal sensitivity Broad dynamic range of detectable response

Low detection limit High sensitivity Low signal fold-induction

Low read-out response

et al., 2013a), upstream activation sequence (UAS) (Aiyar et al., 1998) and mRNA stem-loop (Paulus et al., 2004) to attain a desired level of FapR repressor (Fig. 6). The copy number and location of the fapOoperator within the fapO-hybrid reporter promoter could significantly influence the degree of FapR-fapO interaction (Xu et al., 2014b), thus

separate plasmids. Alternatively, a wide range of promoters (constitutive or inducible) can be leveraged to tune the transcription for the right cellular dosage of FapR repressor inside the cell. The promoter strength can be utilized in conjunction with cis-regulatory biological parts such as ribosome binding site (RBS) (Peretti and Bailey, 1987; Xu

a. Promoter strength

Fig. 5. A schematic of the key design considerations for constructing an efficient malonyl-CoA biosensor.

Reporter module

Expression rate

Expression rate

Repressor module

b. cis-Regulatory parts

c. Plasmid copy number

d. Operator copy number and/or location

e. Dynamic range induction

2 x fapO Inducer conc

Low RBS

-35

-10

+1

1 x fapO

Medium mRNA stem loop

-35

-10

+1

High UAS

Inducible promoter

Fig. 6. Key biological parts for constructing effective malonyl-CoA sensors. (a) Choosing a promoter with the right transcriptional activity, (b) Tuning for the right promoter activity with cis-regulatory elements such as ribosome binding site (RBS), upstream activation sequence (UAS) or mRNA stem loop, (c) Varying the copy number of plasmid expressing the reporter and/or repressor module, (d) Varying the copy number and/or location of the fapO-operator sequence, and (e) Tuning the expression using varying inducer concentration, when an inducible promoter is utilized.

258

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259

Li et al. (2015) Malonyl-CoA amount equivalent to 1–12 mg/L cerulenin Binding of FapR to the fapO of GPM1, thus repressing the expression of tdTomato tdTomato Inserted before the TATA box of the GPM1 promoter

0.1–1.1 nmol/mgDW

TEF1 S. cerevisiae

FapR

Constitutive T7 promoter (lacO is removed in the reporter module) GPM1

pGAP FapR

Inducible T7 promoter (lacO is not removed in the repressor module) E. coli

PFR1 inducible PBAD L-arabinose

E. coli

FapR

egfp

rfp

fapO inserted into the flanking regions of −10 region of phage PA1 promoter resulting in PFR1 fapO inserted after the transcriptional start of pGAP. fapO inserted after the transcriptional start of T7 promoter

mCherry

Binding of FapR to the UAS of pGAP, thus activating the expression of egfp Binding of FapR to the fapO of T7, thus repressing the expression of mCherry

0.1–1.1 nmol/mgDW

Feher et al. (2015); Liu et al. (2015) Xu et al. (2014a) 0.14–24.4 μM

egfp fapO inserted after the transcriptional start of T7 promoter IPTG-inducible T7

Promoter Promoter

Microbial host

Table 2 A summary of reported malonyl-CoA sensors.

Repressor

Reporter module

The application of malonyl-CoA sensors for in vivo detection of malonyl-CoA relies heavily on the linearity between varying malonylCoA concentrations and fluorescence read-out responses as well as the orthogonality of the genetic circuit (i.e., elimination of regulatory protein and nonspecific DNA cross-communication). Hence, profiling how cellular fluorescence varies with intracellular malonyl-CoA level (and with time-dependent transcriptional dynamics) is a standard procedure to characterize the biosensor functionality. To illustrate this, Xu et al. (2014a) reported the construction of three promoter-regulator variants in E. coli. They exhibited different gene expression kinetic in response to increasing malonyl-CoA concentrations induced by increasing cerulenin concentrations. The first circuit (Fig. 7a) consisted of an IPTG-inducible T7 promoter driving the FapR expression, and an IPTG-inducible lacO-fapO-hybrid T7 promoter driving the expression of a downstream eGFP reporter gene. The second circuit (Fig. 7b) had the same circuitry as the former, except that the eGFP T7 promoter lacked the fapO-operator but had the lacO-operator. It was observed that the FapR repressor exhibited cross-communication with the lacO-operator, in both the first and second circuits. In other words, eGFP T7 promoter could be repressed via either FapR-fapO interaction or FapR-lacO interaction or both interactions simultaneously. IPTG induction of the T7 promoters in the above two circuits at low malonyl-CoA concentrations (< 0.63 nmol/mgDW, induced by addition of < 25 μM cerulenin) resulted in a biphasic parabolic kinetic response – exhibiting increasing eGFP fluorescence signal in the first phase (t < 300 min), and subsequently decreasing eGFP fluorescence in

Repressor module

6.1. Sensor-actuator

FapR

Operating site

Reporter

Other than the detection and quantification of intracellular malonylCoA accumulation, malonyl-CoA sensing capability has been integrated with complex genetic circuits to reprogram cell metabolism, primarily to dynamically regulate the level of malonyl-CoA or to facilitate the selection of high malonyl-CoA-producing strains. This of course requires more complex structural and system design considerations in developing such circuits with relevant modular functionalities specific to the intended application(s). In this section, we review the design of integrated genetic circuits and how they could be applied to tune cell metabolism with improved cellular production of malonyl-CoA-derived chemicals.

IPTG-inducible T7

6. Applications of malonyl-CoA biosensors

E. coli

Regulatory mechanism

Binding of FapR to the fapO/lacO/ fapO.lacO operator of the T7 egfp promoter and thus repressing the expression of egfp Binding of FapR to the fapO of PFR1 and thus repressing the expression of rfp

0.1–1.1 nmol/mgDW

Dynamic detection range

Ref.

potentially affecting response to increasing malonyl-CoA amounts. In some cases, the copy number and the precise location of the fapO-operator could also influence the response by altering the transcriptional activity of the fapO-hybrid reporter promoter. Lastly, unlike in naturally occurring regulons where the fapR promoter is autogenously regulated, the sensor promoters (both the fapR promoter and the reporter promoter excluding cis-regulatory fapO-operator sequence) in a malonyl-CoA biosensor must show minimal, ideally no, regulatory response to the FapR protein itself. This design criterion requires that there is no transcriptional cross-talk between the sensor promoter and the reporter promoter: expression of the metabolite-responsive transcriptional factor (MRTF) should be orthogonal to the expression of the reporter protein. In the case of malonyl-CoA sensor, to ensure sensor orthogonality, the activities of the chosen promoters must not be modulated by acyl-CoAs and/or any other intracellular metabolites. These are particularly crucial constraints to necessitate that the sensor's response to malonyl-CoA is exclusively an actuation of malonyl-CoA-FapR-fapO interactions. Following these design considerations will eliminate noise interference in sensor response and invariably validate the correlation between fluorescence read-out responses and intracellular malonyl-CoA amounts. Examples of how the aforementioned design considerations have been successfully applied are detailed in Table 2.

Xu et al. (2014b)

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Fig. 7. Promoter-regulatory circuits for sensing malonyl-CoA in E. coli. (a) A circuit with a fapO-lacO-hybrid T7 reporter promoter, (b) A circuit with a lacO-hybrid T7 promoter, and (c) A circuit with a fapO-hybrid T7 promoter.

the second phase (t > 300 min). The first phase kinetics is dominated by constitutive expression of lacI repressor: increasing the inducer IPTG abolishes the LacI-lacO interaction, thus leading to increased eGFP expression (a process called de-repression). Due to the fact that FapR is also controlled by LacI-lacO interaction, the amount of repressor FapR will positively correlate with the level of IPTG added to the system. With the amount of FapR keeps increasing, FapR will compete with LacI for the binding site lacO. Consequently, FapR-dominated gene repression will flip the gene expression pattern at the second phase (t > 300 min)., In a word, the second phase kinetics was characterized by IPTG-induced FapR expression and FapR-mediated repression of the eGFP T7 promoter (at both lacO and fapO sites in the first circuit and the lacO site in the second circuit). This biphasic parabolic gene expression pattern is a hallmark of transcriptional factor cross-talk. The biphasic response was however gradually abolished at high malonyl-CoA concentration (> 0.63 nmol/mgDW, induced by addition of > 25 μM cerulenin), as more FapR repressor molecules were antagonized by malonyl-CoA, thus actuating continued increase in fluorescence response beyond 300 min of cultivation. The inconsistent response profile at low and high malonyl-CoA concentration meant that the promoter-regulatory systems could not actuate fluorescence signals over time consistent with malonyl-CoA concentration, and as such was unusable for real-time monitoring of intracellular malonyl-CoA accumulation or consumption. To solve this problem, the lacO site in the eGFP T7 promoter in the first circuit was removed to yield a third circuit (Fig. 7c). This modification abolished the lacI-mediated repression of eGFP T7 promoter at the lacO-operator and cross-communication of FapR repressor at the lacO-operator, thus correcting the biphasic response to a linear response. The modification effectively improved the circuit's dynamic response range with malonyl-CoA concentrations up to 1 nmol/mgDW.

6.2. Sensor-inverter Liu et al. (2015) described the development of a sensor-invertor for negative feedback regulation of fatty acid synthesis in E. coli by modifying a malonyl-CoA-based sensor-actuator circuit. The original sensoractuator circuit (Fig. 8a) had a L-arabinose-inducible PBAD promoter driving the expression of FapR repressor, and this repressor module was grafted onto a low copy number plasmid. Inserting the fapO-operator sequence into two DNA regions flanking the − 10 region of a PA1 phage promoter resulted in the FapR-repressed fapO-hybrid reporter promoter PFR1, which drove the expression of an rfp reporter gene from a second plasmid. Varied intracellular malonyl-CoA concentrations were achieved via IPTG-inducible expression of the acetyl-CoA carboxylase gene (acc)-based malonyl-CoA source pathway, placed under the control of the LacI-repressed T7 promoter from a third plasmid. At an optimal expression of FapR repressor [with 0.01% (w/v) L-arabinose], a 4-fold increase in the fluorescence signal from the reporter module was achieved, when cellular malonyl-CoA amount was varied by induction with 0–1 mM IPTG. This increase in fluorescence signal correlated with increasing malonyl-CoA concentration, resulting in a fluorescence readout and malonyl-CoA concentration calibration curve for rapid fluorescence-based quantification of cellular malonyl-CoA concentrations (Liu et al., 2015). Subsequently, this sensor-actuator was modified into a sensor-invertor (Fig. 8b) to alleviate acc-overexpression-mediated cellular toxicity and for dynamic regulation of malonyl-CoA source pathways towards increased fatty acid production. By replacing the rfp reporter gene with a lacI gene, increasing malonyl-CoA concentrations (achieved with 0–40 μM IPTG) resulted in increasing expression of LacI repressor. This, in turn, would repress the expression of acc at unfavorably high malonyl-CoA concentrations, thus decreasing malonylCoA synthesis. Additionally, when a cytosolic thioesterase (tesA)-based fatty acid synthesis pathway under the control of an aTc-inducible PTet promoter was co-transformed with the sensor-invertor (Fig. 8b), 34% and 33% increases in fatty acid titer and productivity, respectively, 260

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a.

b.

PT7

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Malonyl-CoA

PBAD

tesA

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rfp

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Fig. 8. (a) Schematic representation of a malonylCoA sensor-actuator. The malonyl-CoA sensor-actuator comprises the PBAD-driven FapR repressor module, the PFR1-driven reporter module and the PT7driven malonyl-CoA source pathway (accABCD). (b) Schematic representation of a negative feedback regulatory circuit. The circuit comprises PTet-driven cytoplasmic thioesterase gene (tesA), the LacI-repressed, PT7-driven malonyl-CoA source pathway (accABCD), the FapR-repressed, PFR1-driven lacI gene and the PBAD-driven FapR. The biosensor turns on lacI expression at excessively high malonyl-CoA amount, thereby down-regulating acc expression and alleviating its cellular toxicity.

accABCD

the pGAP promoter, a series of finely tuned sensors with varying response range to malonyl-CoA were derived to balance the expression of the malonyl-CoA source and sink pathways. Varying the number of fapO-units in the metabolic switch was also leveraged to regulate the on-off flip-flop frequency of the metabolic switch. The authors then replaced the eGFP and mCherry reporter genes with acetyl-CoA carboxylase (accADBC), a malonyl-CoA-source pathway, and the fatty acid synthetase (fabADGI and tesA’), a malonylCoA-sink pathway, respectively (Fig. 9b). The pathways were chosen based on previous work on the modular optimization of multi-gene pathways for fatty acids production in E. coli (Xu et al., 2013b). The oscillatory pattern of the metabolic switch was used to regulate expression in both pathways to achieve 15.7- and 2.1-fold improvement in fatty acid titer compared to the wild type strain and the strain expressing both pathways without the metabolic switch, respectively. In this metabolic switch, when malonyl-CoA synthesized from the sourcepathway reached a threshold range, malonyl-CoA binds with FapR to remove the repression of the fatty acids pathway and thus convert the excess malonyl-CoA into fatty acid products. Conversely, when malonyl-CoA is depleted, FapR will bind with pGAP and activates gene expression for the malonyl-CoA source pathway; thus make the cell autonomously replenish the level of malonyl-CoA inside the cell (Fig. 9b and c). Interestingly, it was shown that variants with only one fapO-unit in the FapR-activated pGAP promoter showed a more favorable oscillatory pattern in regulating both the malonyl-CoA sink and source pathways, when compared to the switch variants with no fapO unit or with three fapO units. This optimal oscillatory pattern, characterized by a sink-source oscillation faster than the oscillation in the zero fapO-unit variant but slower than the oscillation in the three fapOunit variant, resulted in the highest titer of fatty acid (Xu et al., 2014b).

were achieved, relative to when it was co-transformed with just the sensor-actuator. It should be noted here that expression of the malonylCoA sink pathway (tesA) is controlled by the exogenously added aTC inducer, not by malonyl-CoA itself. In practice, malonyl-CoA inside the cell is not optimal and fluctuating along different cell growth stages. This creates the limitation that it is impossible to predict when and how much of the aTC inducer should be added to adjust the expression level of the fatty acids (tesA) pathway. 6.3. Metabolic switch for autonomous metabolism control Xu et al. (2014b) described a metabolic switch that reported the malonyl-CoA metabolic state of the cell by detecting both on and off fluorescence signals. In this sensor (Fig. 9a), an E. coli σ70-based pGAP promoter was used to drive the expression of eGFP reporter protein. It turned out that FapR expression resulted in a 7-fold increase in the pGAP-driven eGFP expression. Increasing the level of malonyl-CoA inside the cell gradually abolished this activation. This clearly suggested that FapR exhibited dual transcriptional activity and favored the formation of more stable RNAP-pGAP complex, rendering it a transcriptional activator instead of a repressor for pGAP promoter. Also, at malonyl-CoA concentrations above 1 nmol/mgDW, eGFP fluorescence signals were comparable to the control construct lacking the activator FapR, presumably because malonyl-CoA-FapR interaction annulled the activation and destabilized the RNAP-pGAP interaction. Further investigation with Surface Plasmon Resonance (SPR) analysis confirmed that the activating effect of FapR on the pGAP promoter was exerted on an upstream activating sequence (UAS), but not on the fapO-operator. A sensor variant without the fapO-operator reported higher fluorescence signal than the sensor with one fapO-operator placed after the transcriptional start of the pGAP promoter. Also, on creating a second sensor variant by replacing the pGAP promoter with a FapR-repressed T7-fapO-hybrid promoter and replacing eGFP reporter with mCherry reporter, and co-transforming this sensor variant with the FapR-activated pGAP-based malonyl-CoA sensor, they achieved a malonyl-CoA metabolic switch that responded to both high and low intracellular malonyl-CoA concentrations (Fig. 9a). At low malonyl-CoA concentrations, FapR binds with pGAP promoter and activates the expression of the reporter gene. Cell emits green fluorescence signal, while at the same time FapR shuts down the expression of mCherry from T7-fapObased promoter due to FapR repression. Conversely, at high malonylCoA concentration, the FapR-repressed T7-fapO hybrid promoter was turned on due to promoter de-repression, resulting in a red fluorescence signal while the FapR-activated pGAP-based malonyl-CoA sensor was turned off due to promoter de-activation. Thus, this metabolic switch reported the malonyl-CoA metabolic state of the cell by actuating distinct flip-flop states of green and red fluorescence signals. Additionally, increasing the copy number of fapO-units in the FapR-activated pGAPbased malonyl-CoA sensor gradually decreased the promoter activity of

6.4. Malonyl-CoA producer screening tool S. cerevisiae, a widely used industrial workhorse strain, is known to possess biological parts and expression systems different from other eukaryotic systems and yet are more complex than prokaryotic hosts, owing to its rather sophisticated transcriptional and regulatory networks (Li et al., 2015). This, in turn, has limited the development of genetically encoded circuits for metabolite sensing and related applications. However, by optimizing the functionality of carefully chosen biological parts to improve their compatibility with the host's regulatory requirements, Li et al. (2015) reported the development of the first ever malonyl-CoA biosensor for S. cerevisiae. In this dual plasmid sensor system, a strong constitutive TEF1 promoter was used to drive the transcription of a codon-optimized fapR gene, which had a strong SV40 nuclear localization sequence at its C-terminus to enhance nuclear import and an ADH1 terminator to terminate transcription. The tdTomato reporter protein was expressed from a separate plasmid under the control of an engineered GPM1 promoter, which had the fapO-operator 261

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Fig. 9. (a) Schematic representation of malonyl-CoA metabolic switch. The sensor comprises the FapR-activated, pGAP-driven eGFP gene, and the FapR-repressed, T7-driven mCherry gene. Both promoters possess a fapO operator site after their transcriptional start. The malonyl-CoA switch gives green fluorescence signal at low cellular malonyl-CoA amount due to activation of the pGAP promoter by FapR, and red fluorescence signal at high cellular malonyl-CoA amount due to de-repression of the T7 promoter. (b) Schematic representation of a malonyl-CoA metabolic switch. The metabolic switch is archetypal of the sensor with the eGFP and mCherry genes replaced with a malonyl-CoA source pathway (ACC) and a malonyl-CoA sink pathway (FAS), respectively. (c) The metabolic switch turns on the expression of the malonyl-CoA source pathway (ACC) and turns off the sink pathway (FAS) at low cellular malonyl-CoA amount. At high cellular malonyl-CoA amount, the switch turns on the sink pathway and turns off the source pathway.

transformant colonies with the highest fluorescence intensity from three rounds of FACS (Fluorescence-activated cell sorting) screening, genetic targets enhancing malonyl-CoA synthesis were identified from 11 different clones, which elicited 2-fold higher fluorescence signals. The genes PMP1 and TP1, encoding the plasma membrane proteolipid protein and triose-phosphate isomerase, respectively, were identified as genes which individually or collectively upregulates malonyl-CoA synthesis in yeast strain CEN.PK2 (Fig. 10) (Li et al., 2015). The genes were subsequently individually expressed in yeast and the improved malonyl-CoA phenotype was subsequently leveraged to achieve 120% increase in the titer of 3-hydroxypropanoic acid – a value-added compound and important basic chemical derived from malonyl-CoA. While the attachment of SV40 nuclear localization signal on C-terminal ligand-binding domain facilitated the import of FapR inside the nucleus, this, however, may negatively interfere with the malonyl-CoA-FapR interaction and change the transcriptional activity of the FapR protein.

inserted immediately upstream of the TATA box. To tune for a broad dynamic range of sensor responsiveness and optimal sensitivity, the authors varied the sensor FapR-fapO ratios. Single copy plasmid and multi-copy plasmids were also tested to adjust the FapR repressor expression, while the reporter expression was varied by using either a single fapO-unit or a double fapO-unit operator. By varying the intracellular concentration of malonyl-CoA through media supplementation with varying cerulenin concentrations ranging from 0 to 12 mg/L, malonyl-CoA-dependent response curves were derived for all sensor variants. Eventually, the sensor comprising multi-copy plasmid expressing FapR and a reporter module with a single fapO-unit resulted in the broadest dynamic range of response to increasing cellular malonylCoA induced by cerulenin concentrations ranging from 0 to 8 mg/L. Other sensor variants had detection limits of less than 8 mg/L owing either to poor FapR repressor expression (as in the single copy plasmid) or low FapR-fapO ratio (as in the sensor variant with a double fapOunit). The malonyl-CoA sensor with the broadest dynamic response range was subsequently used to screen for high malonyl-CoA producers from a mutant yeast library, co-transformed with the sensor and plasmid carrying genome-wide overexpression cassette. By identifying

7. Conclusion and future perspectives Malonyl-CoA genetic sensors allow real-time monitoring of the intracellular malonyl-CoA concentration, hence complementing 262

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Fig. 10. (a) Schematic representation of malonyl-CoA sensor in yeast. The sensor comprises the TEF1 promoter-driven fapR gene attached with a strong SV40 nuclear localization sequence at its C-terminus, an ADH1 terminator and the FapR-repressed GPM1 promoter-driven tdTomato reporter gene. The GPM1 promoter has an upstream activation to prevent native transcriptional regulation and a fapO-operator sequence inserted before the TATA sequence. (b) Schematic representation of the application of the sensor for screening high-malonyl-CoA mutant strains. Fluorescence actuation from the sensor was used to detect improved cellular synthesis of malonyl-CoA due to the plasma membrane proteolipid (PMP1) and the triose-phosphate isomerase gene (TPI1).

Moreover, we see the possibility of mathematically modeling existing experimental data to further polish the design and behavior of malonylCoA sensors and regulatory networks for more advanced applications (Liu et al., 2015).

analytical techniques like HPLC and LC-MS. Despite being able to conduct direct metabolite quantification, these analytical techniques are generally more time-consuming and of relatively lower throughput, due to laborious pre-treatment requirements. Analytical technique, however, remains a sine qua non for generating malonyl-CoA concentration-response calibration curve from which absolute malonylCoA level may be deduced. Yet, the diverse applications of malonyl-CoA biosensors, as discussed in this review, form a compelling rationale to justify their design and subsequent use in the metabolic engineering of microbial hosts for production of malony-CoA derived compounds. Some of these applications include regulating malonyl-CoA sink/source pathways, screening for high malonyl-CoA mutant strains and the compensation of metabolic activity towards autonomous control of malonyl-CoA level. Thus far, E. coli and S. cerevisiae are the two main microbial hosts that have been extensively engineered with synthetic malonyl-CoA sensor to control and optimize chemical production. This is largely owing to the availability of chassis-specific molecular biology and synthetic biology tools, ease of genetic manipulation, strain robustness and comprehensive understanding of the physiology of these two production hosts. As more biological hosts are engineered with malonylCoA-sensing capability, we envision that the current design of the malonyl-CoA biosensor will prove to be an invaluable prototype and can be easily adapted to other hosts. These may well include notable Gram-negative microbial hosts, such as Pseudomonas spp. and Ralstonia eutropha H16, and eukaryotic hosts such as Pichia pastoris and Yarrowia lipolytica (Xu et al., 2016, 2017; Qiao et al., 2017), which are potentially promising for industrial-scale biomanufacturing. The success of expanding the range of hosts in which malonyl-CoA biosensors are applicable would depend on whether the engineered malonyl-CoA sensing circuits are compatible with chassis-specific biological parts. This could be primarily ascribed to intrinsic biological differences in the replication, transcription, translation, post-translational modification and nuclear transport architectural network of different hosts. All these factors should be considered to design and engineer an effective malonyl-CoA sensor. Additionally, there is a need to ensure that the design of malonyl-CoA biosensors in such hosts is robust and efficient, with optimal sensitivity, broad dynamic range of response and high detection limit. This is potentially achievable by leveraging the successes in engineering effective malonyl-CoA biosensors in model microbial hosts as a vantage point for achieving similar design successes in other potential microbial hosts, as summarized in this review.

Acknowledgement We thank the Department of Chemical and Biological Engineering, ChELSI and EPSRC (EP/E036252/1) for financial support. AOJ and MGV are supported by the University of Sheffield and CONACYT (Mexico) scholarships, respectively. TSW and AS are grateful for the travel grant awarded by ERASynBio Twinning Program in Synthetic Biology. LW and PX would like to acknowledge the Department of Chemical, Biochemical and Environmental Engineering, College of Engineering and Information Technology, Office of the Vice President for Research at the University of Maryland Baltimore County for funding support. References Aiyar, S.E., Gourse, R.L., Ross, W., 1998. Upstream A-tracts increase bacterial promoter activity through interactions with the RNA polymerase alpha subunit. Proc. Natl. Acad. Sci. USA 95, 14652–14657. Cress, B.F., Toparlak, O.D., Guleria, S., Lebovich, M., Stieglitz, J.T., Englaender, J.A., Jones, J.A., Linhardt, R.J., Koffas, M.A., 2015. CRISPathBrick: modular combinatorial assembly of Type II-A CRISPR Arrays for dCas9-mediated multiplex transcriptional repression in E. coli. ACS Synth. Biol. 4, 987–1000. Dahl, R.H., Zhang, F., Alonso-Gutierrez, J., Baidoo, E., Batth, T.S., Redding-Johanson, A.M., Petzold, C.J., Mukhopadhyay, A., Lee, T.S., Adams, P.D., Keasling, J.D., 2013. Engineering dynamic pathway regulation using stress-response promoters. Nat. Biotechnol. 31, 1039–1046. Davis, M.S., Solbiati, J., Cronan, J.E., 2000. Overproduction of acetyl-CoA carboxylase activity increases the rate of fatty acid biosynthesis in Escherichia coli. J. Biol. Chem. 275, 28593–28598. Dietrich, J.A., McKee, A.E., Keasling, J.D., 2010. High-throughput metabolic engineering: advances in small-molecule screening and selection. Annu. Rev. Biochem. 79, 563–590. Dietrich, J.A., Shis, D.L., Alikhani, A., Keasling, J.D., 2013. Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis. ACS Synth. Biol. 2, 47–58. Dirusso, C.C., Metzger, A.K., Heimert, T.L., 1993. Regulation of transcription of genes required for fatty-acid transport and unsaturated fatty-acid biosynthesis in Escherichia coli by FadR. Mol. Microbiol. 7, 311–322. Ellis, J.M., Wolfgang, M.J., 2012. A genetically encoded metabolite sensor for malonylCoA. Chem. Biol. 19, 1333–1339. Farmer, W.R., Liao, J.C., 2000. Improving lycopene production in Escherichia coli by engineering metabolic control. Nat. Biotechnol. 18, 533–537. Feher, T., Libis, V., Carbonell, P., Faulon, J.-L., 2015. A sense of balance: Experimental investigation and modeling of a malonyl-CoA sensor in Escherichia coli. Front. Bioeng.

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