Recent advances in systems metabolic engineering tools and strategies

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ScienceDirect Recent advances in systems metabolic engineering tools and strategies Tong Un Chae1, So Young Choi1, Je Woong Kim, Yoo-Sung Ko and Sang Yup Lee Metabolic engineering has been playing increasingly important roles in developing microbial cell factories for the production of various chemicals and materials to achieve sustainable chemical industry. Nowadays, many tools and strategies are available for performing systems metabolic engineering that allows systems-level metabolic engineering in more sophisticated and diverse ways by adopting rapidly advancing methodologies and tools of systems biology, synthetic biology and evolutionary engineering. As an outcome, development of more efficient microbial cell factories has become possible. Here, we review recent advances in systems metabolic engineering tools and strategies together with accompanying application examples. In addition, we describe how these tools and strategies work together in simultaneous and synergistic ways to develop novel microbial cell factories. Address Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Institute for the BioCentury, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea Corresponding author: Lee, Sang Yup ([email protected]) Both authors contributed equally to this work.

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Current Opinion in Biotechnology 2017, 47:67–82 This review comes from a themed issue on Tissue, cell & pathway engineering Edited by Kobi Benenson and Matthias Lutolf

http://dx.doi.org/10.1016/j.copbio.2017.06.007 0958-1669/# 2017 Elsevier Ltd. All rights reserved.

occurring microorganisms are rather low. Metabolic engineering has made it possible to improve three major performance indices (titer, yield and productivity) in bio-based production of chemicals and materials of interest. Some of the early satisfactory results on performance improvement encouraged us to put more effort on advancing the strategies of metabolic engineering towards development of biorefinery processes capable of producing diverse chemicals and materials in cost effective manner. To meet the need to develop industrially competitive microorganisms, systems metabolic engineering that integrates metabolic engineering with tools and strategies of systems biology, synthetic biology and evolutionary engineering has been established [1–3]. Various systems metabolic engineering tools (Figure 1) have allowed us to perform advanced strain engineering tasks including construction of novel metabolic pathways, genome-wide identification of metabolic engineering targets, fine-tuning and control of gene expression, multiplex genome engineering, creation of synthetic circuits, and increasing tolerance to target chemicals or intermediates when needed. Such systems metabolic engineering tools and strategies have been employed not only for strain development but also for optimizing bioprocess variables such as medium composition, pH, aeration, cultivation mode, and nutrient feeding strategies. In this paper, we review recent advances in systems metabolic engineering tools and strategies together with accompanying application examples (Table 1). The tools and strategies are classified into three categories for convenience: systems biology, synthetic biology and evolutionary engineering. We also discuss how these tools and strategies have played synergistic roles in developing microbial cell factories.

Introduction Climate change is one of the most important global risks requiring attention with utmost priority. One of the major contributors for climate change is our heavy use of fossil oil and gas. Thus, there has been much interest in producing chemicals and materials by microbial cell factories from renewable non-food biomass, instead of relying on petrochemical routes. Microorganisms can theoretically produce all the metabolites present in their metabolic network, but the efficiencies of producing most, if not all, of these chemicals by employing naturally www.sciencedirect.com

Systems biology tools and strategies for metabolic engineering Systems biology aims at interpreting cellular phenomena at systems level by employing a wide range of data and tools, most notably omics data analysis and computational simulations. It plays important roles in system metabolic engineering by suggesting more systematic and highthroughput engineering approaches compared to conventional metabolic engineering [4]. To accomplish the desired phenotype such as cell growth and target chemical Current Opinion in Biotechnology 2017, 47:67–82

68 Tissue, cell & pathway engineering

Figure 1

(a)

(b)

Transcriptomics

(c) :to be deleted

Fluxomics

Proteomics

(d)

Multi-omics

Module 1

Module 2

Genome models

:up-regulated :down-regulated

MOMA ROOM OptKnock FSEOF FVSEOF with GR

Metabolomics

(e)

(f)

V1

GIMME GIMMEp GIM3E iMAT, E-Flux

Reduced flux space V2

V3

(g)

Ribosome

DNA

:Heterologous gene :Endogenous gene

BNICE GEMPath PathTracer

(h) Gene

sRNA DNA SDM SSM

Hfq mRNA Promoter

Structure prediction

Active site

(i)

RBS

Synthetic sRNA RNA interference Antisense RNA

Flux

Gene expression components

(j)

TIR

(k)

A

C

B Enz2 Enz1

DNA

Enz1 B Enz2 Enz1

Enz2

Coculture

(o)

Mg2+ Mn2+ ↑ dGTP dTTP↑ DNAP dCTP dATP↓ Ep-PCR

Promoter

System biology Synthetic biology

Output

Sensor

UV

Product

C

Microcompartment

Synthetic scaffold

(n)

Species2

B

C

3′ 5′

B

A

dCas9

(m)

A

Species1

Enz3 A

CRISPR, CRISPRi

STAR

D

PAM

RNAP

STAR Antisense

(l)

Cas9 sgRNA

RNAP

Recombineering

MNNG NTG

Evolutionary engineering

Input Promoter

DNA Shuffling

Sensor

Increasing diversity

Output

Biosensor

Oligo pool

MAGE YOGE RAGE Current Opinion in Biotechnology

Tools and strategies for systems metabolic engineering. Systems biology tools and strategies (represented in purple box) include (a) omics, (b) in silico-based gene target identification, (c) omics integrated genome-scale metabolic model, (d) pathway prediction and (e) enzyme design. Synthetic biology tools and strategies (represented by blue box) include (f) gene expression component based pathway optimization, (g) RNA based gene downregulation, (h) RNA based gene overexpression, (i) CRISPR based gene over/downregulation, (j) synthetic scaffold, (k) microcompartment and (l) co-culture. Evolutionary engineering tools and strategies include (m) increasing genetic diversity, (n) biosensor and (o) oligo-mediated evolution.

production, various tools and strategies in system metabolic engineering have been developed and used in constructing high performance production strains.

Omics Analyses of omics including genomics, transcriptomics, proteomics, metabolomics, and fluxomics can provide Current Opinion in Biotechnology 2017, 47:67–82

invaluable system-wide information on cellular and metabolic characteristics under various genotypic and environmental conditions. By analyzing omics data, target genes to be manipulated can be identified for enhancing chemical production capability (Figure 1a). Recently interest in fluxomics is keep increasing since ultimate purpose of metabolic engineering is to optimize flux www.sciencedirect.com

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Table 1 Recent applications of systems metabolic engineering tools and strategies Category Systems biology

Purpose Understanding cellular metabolic status, bottleneck or potential

Genome-wide knockout target identification

Pathway prediction

Current Opinion in Biotechnology 2017, 47:67–82

Enzyme design

Synthetic biology

Pathway optimization

Strain

Product

S. cerevisiae

Ethanol

M. succiniciproducens

Succinic acid

C. tyrobutyricum

N/A a

Genomics/ Phenomics/ Transcriptomics/ Modeling approach MOMA

E. coli

40 chemicals

S. cerevisiae

Amorphadiene

OptKnock

E. coli

1,4-Butanediol

Flux response analysis FSEOF

E. coli

Fumaric acid

E. coli

Lycopene

FVSEOF with GR

E. coli

Putrescine

tSOT

S. coelicolor

Actinorhodin

System based on semisupervised Gaussian process GEM-Path

N/A

Pinocembrin

E. coli

20 Chemicals

MapMaker/ PathTracer

E. coli

1777 Chemicals

Homology modeling

E. coli

3-HP

de novo computational design Promoters

N/A

N/A

E. coli

Violacein

RBS library calculator

E. coli

Cartenoid

Effect of tool/strategy utilization

References

Identify reason for inefficient ethanol production from xylose to be low glycolysis flux Reveal underlying mechanism allowing simultaneously usage sucrose and glycerol by fruA deleted mutant Whole genome sequence and unique metabolic characteristics were elucidated allowing start of metabolic engineering Potential of seven E. coli strains as host for 40 chemicals were evaluated in both aerobic and anaerobic condition

[7]

Identification of novel 10 gene knockout target and increase production yield by 8 to 10-fold Production enhanced by knocking out suggested knock out targets (ldhA, pflB, adhE, and mdhA) with a modified lpdA in microaerobic condition Identification of ppc as overexpression target and result in enhancement of production titer by 2.8-fold Prediction of idi and mdh as overexpression target and result in enhanced production by 2.7-fold Successful prediction of 5 overexpression targets (glk, acnA, acnB, ackA and ppc) which resulted in increased production by 20.5% on average Overexpression of ribulose 5-phosphate 3-epimerase and NADP dependent malic enzyme increased production by 2 and 1.8-fold, respectively Successfully predicted most efficient pathway for production

[17]

[8] [9]

[10]

[18]

[22] [23] [24]

[26]

[32]

Identified 245 unique heterologous pathways for 20 commodity compounds Demonstrated total 1777 non-natural products can be potentially synthesized by E. coli by adding known heterologous reactions Homology modeling based engineering of GabD4 enzyme resulted in increased activity to 1.4-fold and also produced 71.9 g/L of 3-HP Developed new energy-efficient formate assimilation pathway

[33]

Balancing each biosynthesis gene (vioABEDC) expressions using 5 mutants T4 promoters resulted in 63fold improvement in titer Generated RBS sets to efficient explore expression landscape of crtEBI gene helping to find optimal expression levels for optimal production

[52]

[35]

[39]

[40]

[56]

Recent systems metabolic engineering tools and strategies Chae et al. 69

Gene-wide overexpression target identification

Tool/Strategy Metabolomics/ Fluxomics Transcriptomics/ Proteomics Genomics/ Proteomics

Category

Purpose

Tool/Strategy

Strain

Product

sRNA

E. coli

1,3-DAP

RNAi

S. cerevisiae

Itaconic acid

asRNA

E. coli

Malonyl-CoA derivatives

CRISPR

S. cerevisiae

Mevalonate

E. coli

b-Carotene

C. glutamicum

L-Lysine/L-

CRISPRi

Glutamate

Synthetic scaffold

Microcompartment

Coculture

E. coli

PHAs

E. coli

Mevalonate

B. subtilis

GlcNAs

S. cerevisiae

Isobutanol

E. coli

Ethanol

T. reesei/E. coli

Isobutanol

E. coli/S. cerevisiae

UV and NTG

C. glutamicum

Oxygenated taxanes cis,cis-Muconic acid L-arginine

NTG/genome shuffling

C. beijerinckii

Isopropanol

RAGE

S. cerevisiae

Acetic acid

Ep-PCR

S. cerevisiae

Lycopene

Biosensor RBS libraries Biosensor MAGE

C. glutamicum E. coli C. glutamicum E. coli

L-Lysine PHAs L-Valine Naringenin/ Glucaric acid

E. coli/E. coli Evolutionary engineering

Improving tolerance

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Improving efficiency of pathway

Global optimization of pathway a

N/A, not applicable.

Effect of tool/strategy utilization Enable fast screening of knockout target ( pfkA) required and led to 169.5% increase in titer Identified ade3 as engineering target for enhanced production Malonyl-CoA pool was increased by repression of fatty acid biosynthesis genes ( fabD, fabH, fabB and fabF) resulting in enhanced production of its derivatives Five gene related to mevalonate biosynthesis were deleted in combinatorial way and found best combination More than 100 genetic variants related of MEP pathway were generated and best mutant harboring 15 mutations successfully produced 2.0 g/L of b-carotene Enhanced production in C. glutamicum was achieved through separate downregulation of pyk, pck and pgi gene Multiple knockdown of sad, sucD, sucC, sdhA and sdhB genes increased 4-hydroxybutyrate fraction in PHAs Intermediate toxicity problem was solved by using protein scaffold resulting in 77-fold increase in production titer GlcNAc production was optimized by spatial organization of GlmS and GNA1 in 1:2 ratio resulting in 20.58 g/L production Isobutanol production pathway was constructed in mitochondria resulting in 260% increase in production titer Ethanol production pathway was constructed in BMC and production has enhanced Demonstrated direct fermentation of lignocellulosic biomass into isobutanol Demonstrated direct fermentative production oxygenated taxanes from xylose Succeeded high yield production from mixed xylose and glucose Increased tolerance against L-arginine analogues and production of L-arginine by 2-fold Increased tolerance to 35 g/L by NTG and further enhancement to 50 g/L by genome shuffling resulting in increased IBE production Three synergistic knockdown targets which confers improved tolerance were identified Create CrtYB with solely phytoene synthase function and increase activity of CrtE resulting in 2-fold increased production Increased production by 89% Increased PHB accumulation contents up to 92% Increased production by 100% Increased production of naringenin and glucaric acid by 36-fold and 22-fold

References [47] [63] [65,66]

[73] [74]

[76]

[77] [78] [79]

[80]

[81] [83] [84] [85] [86] [88]

[97] [87]

[92] [93] [91] [95]

70 Tissue, cell & pathway engineering

Current Opinion in Biotechnology 2017, 47:67–82

Table 1 (Continued )

Recent systems metabolic engineering tools and strategies Chae et al. 71

distribution towards product formation. To quantitate the flux distribution, 13C metabolic flux analysis (13C-MFA) is usually applied using isotope labeling patterns of metabolites as direct experimental parameters [5]. The flux calculation using 13C-MFA is becoming more accurate as various methodologies are in development to increase the precision. As one example, study on selecting optimal tracer for parallel labeling experiment has been reported [6]. Nowadays, multi-omics approach, which analyzes various omics data together, rather than just a single type of omics, is usually applied. The multi-omics approach can offer comprehensive understanding of the host strain by compensating each omics’ drawbacks. The multiomics approach can be used to elucidate various phenomena in a metabolically engineered strain and to identify further engineering targets. For example, by analyzing metabolomic and fluxomic data, low glycolysis flux was revealed to be one of the potential reasons for inefficient ethanol production by Saccharomyces cerevisiae when using xylose as carbon source [7]. More recently, transcriptomics and proteomics were applied simultaneously to reveal underlying mechanism for sucrose and glycerol dual carbon source utilization of homo-succinic acid producing Mannheimia succiniciproducens PALFK strain. The analysis showed that deletion of fruA gene encoding fructose phosphotransferase system (PTS) system allowed simultaneous uptake of sucrose and glycerol by increasing cAMP level, which activates glycerol utilization pathway [8]. Using the PALFK strain, succinic acid could be produced with titer, yield and productivity of 78.4 g/L, 1.64 mol/mol glucose equivalent, and 6.02 g/L/ h, respectively. When a membrane cell recycle bioreactor system was used, this engineered strain was able to produce succinic acid with an impressively high productivity of 38.6 g/L/h [8]. Multi-omics approaches are not only applied to genetically/metabolically engineered strains, but also to wildtype host strains to select potentially the best host strain to work with for further metabolic engineering. Clostridium tyrobutyricum is a native butyric acid producer and has a potential for further enhanced production of butyric acid and its derivative products through metabolic engineering. However, metabolic engineering studies have not been extensively performed on C. tyrobutyricum due to the limited information on its genetic and metabolic characteristics. To address this problem, genomic and proteomic studies were performed, which revealed interesting metabolic characteristics very different from other clostridial species; butyric acid formation does not rely on the well-known phosphotransbutyrylase and butyrate kinase pathway, but rather uses CoA transferase system [9]. In another study, strain specific differences of seven widely used Escherichia coli strain (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110 and W) were quantified using www.sciencedirect.com

multi-omics (genomics, phenomics and transcriptomics) together with in silico approaches. Through integrated modeling, the potentials and capabilities of these seven strains for producing 40 selected chemicals were quantitatively evaluated in both aerobic and anaerobic conditions [10].

Genome-scale metabolic model (GEM) As the first whole genome sequence was reported in the mid-1990s, researches on the GEM opened new era in metabolic engineering. Nowadays, GEM and various simulation tools have become a fundamental framework for the system-wide analysis and prediction of cellular metabolisms and functions, and also for providing system metabolic engineering strategies. Accordingly, various GEMs have been developed for various metabolic engineering platform strains such as E. coli, Corynebacterium glutamicum, Ralstonia eutropha, C. acetobutyricum, M. succiniciproducens, S. cerevisiae, microalgae, and many others [11]. To utilize GEMs for genome-scale metabolic analyses, various algorithms based on constraint-based flux analysis have been developed to predict genetic alteration targets such as gene knockout or amplification targets (Figure 1b). As detailed algorithms applied to each tools have already been extensively reviewed in several papers [11,12,13], here we will focus more on applications examples. Several computational tools for identifying gene knockout targets for increased production of target compound have been developed [11]. MOMA is one of the early algorithms developed and widely used for identifying gene knockout targets to enhance production of various chemicals such as lycopene [14], L-valine [15], poly(lactic acid) [16] and terpenoid [17]. As a recent example, ten novel knockout targets for terpenoid production in S. cerevisiae were identified and once deleted from the chromosome, most resulted in enhanced terpenoid yield (8 to 10-fold) compared to the wild-type [17]. OptKnock is another popular algorithm for identifying gene knockout targets and has been used in metabolic engineering for the enhanced production of various chemicals. One representative example is 1,4-butanediol production [18]. In detail, OptKnock simulation suggested ldhA, pflB, adhE and mdhA as knockout targets in anaerobic condition. Three target genes (ldhA, pflB and adhE) were deleted to implement the simulation result but the mutant resulted in no cell growth. The problem was solved by introducing anaerobically functioning pyruvate dehydrogenase E3 subunit (lpdA gene from Klebsiella pneumonia) and changing culture condition to micro-aerobic. In the modified condition, deletion of all suggested knockout targets (ldhA, pflB, adhE and mdhA) finally led to increased production of 1,4-butandiol [18]. RobustKnock is an algorithm derived from OptKnock to overcome the potential optima problem caused by competing pathways [19]. OptSwap is another algorithm based on RobustKnock with additional function Current Opinion in Biotechnology 2017, 47:67–82

72 Tissue, cell & pathway engineering

of optimizing cofactor specificities of oxidoreductases. OptSwap has been successfully applied to an E. coli model to theoretically design efficient producers of L-alanine, succinate, acetate and lactate [20]. Cofactor modification analysis (CMA) allows finding targets for cofactor specificity by monitoring flux distribution changes [21]. Several algorithms have also been used for identifying gene amplification targets. Several notable ones include flux response analysis, flux scanning based on enforced objective flux (FSEOF), and flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) [11]. By using flux response analysis, ppc gene was selected as an overexpression target to enhance fumaric acid production in E. coli. Experimental result was consistent with the simulation prediction, resulting in 2.8-fold higher production of fumaric acid [22]. Similarly, FSEOF was used to identify gene amplification targets for enhanced lycopene production. Indeed, overexpression of idi and mdh genes identified by FSEOF in E. coli increased lycopene production by 2.7fold [23]. FVSEOF with GR constraints was developed to overcome limitations of FSEOF such as alternative optima and lack of thorough consideration of physiological state. It was applied to metabolically engineered E. coli strain producing putrescine, and successfully predicted glk, acnA, acnB, ackA, and ppc genes as amplification targets. All of these target genes enhanced putrescine production by 20.5% upon their amplification on average [24]. Although various algorithms have been developed to reduce the solution space of GEMs, it is still not enough for accurate prediction of cellular metabolic phenotypes. To address this problem, GEMs integrated with omics data have been developed to build more accurate condition-specific models (Figure 1c). Various tools for integrating transcriptomic data with GEMs have been extensively developed such as gene inactivity moderated by metabolism and expression (GIMME), integrative metabolic analysis tool (iMAT), E-Flux, E-Flux2, probabilistic regulation of metabolism (PROM) and transcriptomics-based strain optimization tool (tSOT) [11,25,26]. Furthermore, proteomic and metabolomic data have also been integrated with GEMs by gene inactivity moderated by metabolism and expression by proteome (GIMMEp) and gene inactivation moderated by metabolism, metabolomics and expression (GIM3E), respectively [11]. Recently, tSOT was applied for secondary metabolite overproduction in Streptomyces (actinorhodin) coelicolor. Overexpression of tSOT targets, ribulose 5phosphate 3-epimerase and NADP-dependent malic enzyme, resulted in 2 and 1.8-fold increased production, respectively [26]. Also, GEMs have expanded the capabilities to integrate protein structure as in GEM-PRO [27], metabolism and protein expression as in metabolism with gene expression (ME) model [28], and many Current Opinion in Biotechnology 2017, 47:67–82

cellular processes by modular simulations at separate time points to give a whole-cell model [29], the details of which described in two recent minireviews [30,31].

Pathway prediction Computer algorithms have also been developed to predict the synthetic pathways for biosynthesis of various natural and non-natural chemicals (Figure 1d); these algorithms include biochemical network integrated computational explorer (BNICE), RetroPath, GEM-Path, OptStrain and DESHARKY [11]. Recently, a systematic approach to select enzyme for designed biosynthetic pathway was developed based on semisupervised Gaussian process. The system has successfully predicted a promiscuous enzyme responsible for generating N-acetyl-L-leucine and the most efficient metabolic pathway for producing pinocembrin. Furthermore, quantitative prediction of Michaelis constant (KM) value for given enzyme-substrate pair was possible [32]. GEM-Path is another great tool that has successfully been used to identify 245 unique heterologous pathways for 20 commodity chemicals based on reaction promiscuity [33]. Computational metabolic pathway search with analysis tools including MapMaker and PathTracer [34] were used to suggest the great potential of E. coli to produce 1777 non-native chemicals, of which 279 have commercial usage [35]. As synthetic pathway design is no longer constrained for any target molecule, identification of useful and industrially important chemicals to produce is becoming increasingly important. One of the promising and less explored fields is traditional oriental medicine (TOM). Recently, systematic analysis of TOM-derived compounds with natural human metabolites revealed potentially active components. Although some more time will be needed to fully characterize active compounds and their mechanisms of action, these molecules will be promising targets to produce by metabolic engineering [36].

Enzyme design As more and more advanced metabolic engineering strategies are being developed for producing increasing number of chemicals, the need for developing or creating enzymes with new characteristics such as novel activity, enhanced activity or enhanced stability are keep increasing. Such new or improved enzymes can be created with computational protein design tools (extensively reviewed in [37,38]), which can identify core parts of protein structure, provide target sites for engineering, and even allow de novo protein design from the scratch (Figure 1e). These protein design tools are now widely used in metabolic engineering fields. Recently, a novel aldehyde dehydrogenase (GabD4 from R. eutropha) which can efficiently convert 3-hydroxypropionaldehyde (3-HPA) to 3-hydroxypropionic acid (3-HP) was identified. To further enhance the activity, homology modeling was used to predict crystal structure and select engineering target site. When the target site was engineered by www.sciencedirect.com

Recent systems metabolic engineering tools and strategies Chae et al. 73

site-directed mutagenesis (SDM) and site-saturation mutagenesis (SSM), a mutant GabD4 having 1.4-fold higher activity compared to the wild-type enzyme was obtained. The use of this mutant GabD4 resulted in production of 71.9 g/L of 3-HP [39]. In another example, Siegel and coworkers created artificial and highly efficient one-carbon assimilation pathway by de novo computational design of a new enzyme, named as formolase [40].

Synthetic biology tools and strategies for metabolic engineering Synthetic biology that covers DNA synthesis, synthetic protein scaffolds design, construction and modulation of synthetic expression components, design and construction of novel metabolic pathways, complex cellular network and even multicellular systems helps us to develop designable and reprogrammable organism with the objectives [1,41]. Synthetic biology can complement traditional metabolic engineering by providing various strategies including those described above for producing non-native and non-natural chemicals and materials and optimizing cellular fluxes. Some of the key contributions of synthetic biology in metabolic engineering are described below.

Pathway construction Enzymes found from nature can be used for establishing novel metabolic pathways, and often the enzyme promiscuity further enlarges the space of biological reactions that can be realized. For example, succinic semialdehyde dehydrogenase (encoded by the yneI gene) in E. coli was recently found to have promiscuous activity on malonic semialdehyde. The finding has expanded E. coli metabolism and allowed microbial production of malonic acid for the first time [42]. Furthermore, new metabolic pathway can be designed rationally or through the use of in silico computational tools, followed by experimental development of corresponding enzymes and metabolic pathways. Development of synthetic pathways to produce increasing number of currently petroleum-based chemicals showcases great potential of microbial cell factories towards moving into bio-based chemical industry. Various chemicals have been produced by engineered microorganisms harboring diverse synthetic pathways. Recent representative examples include fuels (short chain alkane [43], branched alcohols [44]), polymers (poly(lactate-co-glycolate) [45]), nylon precursors (adipic acid [46], 1,3-diaminopropane [47], 6-aminocaproic acid [48]), and natural products (opoids [49], protoilludene [50], artemisinic acid [51]). In one example, the nondecarboxylative Claisen condensation (mediated by thiolase) and subsequent b-reduction reaction were used to synthesize 18 chemicals of 10 classes including v-hydroxy, v-1-oxo, 2-methyl acids and v-1-methyl alcohols [46]. The selected starting chemicals termed as primers (phenylacetyl, succinyl, glutaryl, isobutyryl, acetyl, propionyl and glycolyl-CoAs) and extenders (acetyl, propionyl and www.sciencedirect.com

glycolyl-CoAs) were combinatorially fed into E. coli expressing thiolase and reductase, which yielded various important chemicals such as adipic acid [46]. In addition to bulk chemicals, many natural products that can be used as flavors, fragrances, nutraceuticals, and pharmaceuticals normally derived from plants are receiving much attention. This is because the chemical synthesis of such natural products is rather difficult due to the complex molecular structures with regio-selectivities and enantioselectivities, while current plant extraction-based process typically results in extremely low yield. One of the representative examples is production of artemisinic acid which can be chemically converted to potent antimalarial drug, artemisinin. Systematic metabolic engineering of S. cerevisiae allowed production of artemisinic acid to more than 25 g/L [51], and the upgraded process of which has been commercialized.

Pathway optimization Once desired synthetic pathway is constructed, the next step is optimizing the metabolic pathway fluxes to improve product titer, productivity and yield in order to achieve an economically viable bioprocess. Traditional metabolic engineering approaches allowed pathway flux optimization to good extents, but more precise and predictable tools for metabolic flux control are required to develop higher performance strains with less time and effort. An intuitive way to fine-tune metabolic flux is to control gene expression level by modulating gene expression components such as promoter, ribosomal binding site (RBS), terminator, 30 or 50 untranslated region (UTR) and transcription factor (Figure 1f). These components can be designed to accomplish desired expression levels of target genes. Optimizing promoter sets are one of the most widely used strategy and recently applied successfully to violacein production in E. coli [52]. Violacein is synthesized from L-tryptophan through five enzymatic reactions catalyzed by VioA, B, E, D and C followed by a non-enzymatic reaction. Five mutant T7 promoters having different strengths were designed and used to express the five violacein biosynthetic genes in various combinations; 3125 variants were constructed. The best violacein producer showing 63-fold improvement over the control strain could be obtained [52]. The 50 UTR, especially RBS, are also widely used to regulate gene expression level. Various computational tools to design RBS were developed such as RBS calculator [53], RBS designer [54] and UTR designer [55]. Recently, a tool called RBS library calculator [56] was developed for designing RBS’s to control expression of multiple genes. This tool can generate a set of RBS’s to cover broad expression spaces of multiple genes (proteins) across 10,000-fold range. Using this RBS library calculator, 73 RBS variants for three genes (crtEBI) of Current Opinion in Biotechnology 2017, 47:67–82

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carotenoid biosynthesis were designed and tested. As a result, optimal expression levels of crtEBI genes for carotenoid production could be found. As an alternative approach different from computational tools, the empirical model and oligos for protein expression changes (EMOPEC), the free tool for designing RBS based on empirical data was developed [57]. This tool allowed modulating expression level of any gene in E. coli strain within 2-fold of desired target expression level by changing a few base pairs. The design tools described above enable better forward metabolic engineering through more easily designable and controllable expression of genes. Another powerful and well-studied tool for controlling gene expression employs regulatory RNAs. Regulatory RNAs can control gene expression by interacting with DNA, RNA, protein and metabolite at different stages such as mRNA degradation, translation and transcription elongation [58]. Among various regulatory RNAs, transacting RNAs, which work based on recognition of target DNA or RNA sequences by the base-pairing rule, are modular, tunable and designable. Due to such fascinating characteristics, trans-acting RNAs have been employed extensively for genome-wide engineering and fine control of gene expression in various forms such as synthetic small regulatory RNA (sRNA), RNA interference (RNAi), antisense RNA (asRNA), small transcription activating RNAs (STARs) and CRISPRi. The sRNA system (Figure 1g) was developed for efficient attenuation of target gene expression in genome-wide scale, and has been demonstrated for efficient metabolic engineering of E. coli [59,60]. The system consists of a MicC scaffold recruiting Hfq protein and a target-specific sequence binding to the translation initiation region of the target gene. Once Hfq protein is bound to MicC scaffold, Hfq protein facilitates binding of sRNA with target mRNA as well as degradation of mRNA. The target binding sequence of sRNA can be forward designed to achieve desirable level of knockdown. Using the sRNA system, gene knockdown can be achieved in more rapid, easy, modular, portable, multiplex, fine-tunable and undoable manner, offering unique advantages over conventional genome engineering. The sRNA knockdown system was successfully applied to metabolic engineering of E. coli for production of various chemicals such as cadaverine, L-tyrosine [59], phenol [61] and 1,3-diaminopropane (1,3-DAP) [47]. In the case of 1,3-DAP production, 128 synthetic sRNAs targeting the genes encoding glycolytic enzymes, TCA cycle enzymes, transcription factors, transporters and cell division proteins were applied and 11 positive targets leading to enhanced 1,3-DAP production were identified [47]. Among these 11 knockdown targets screened, the three most effective and nonessential targets ( pykF, ptsI and pfkA) were knocked out; knockout of the pfkA gene led to 165.9% increase in Current Opinion in Biotechnology 2017, 47:67–82

1,3-DAP titer. The sRNA system was also successfully applied to C. acetobutylicum [62]. Since C. acetobutyricum is one of the most difficult bacteria to genetically engineer, the sRNA technology has a great potential as a new easy-to-use and rapid knockdown tool in metabolic engineering of clostridial species. As demonstrated for Clostridia, the sRNA tool can be further expanded to facilitate systems metabolic engineering of various microorganisms. RNAi, a gene silencing system in eukaryotes, can also be used to control and optimize metabolic fluxes (Figure 1g). The key component of RNAi system is an RNA-induced silencing complex (RISC) protein. The RISC protein is guided to the target mRNA by a small interfering RNA (siRNA), which is generated by degradation of foreign double stranded RNA by a protein called dicer. Then, the target mRNA is cleaved by argonaute, which is an essential catalytic component of RISC protein. As a metabolic engineering tool, RNAi system has been introduced to S. cerevisiae by expressing hairpin structure RNA (degraded into siRNA by dicer), dicer and argonaute using plasmids [63]. The system was applied to itaconic acid production, and suggested the potential of ade3 gene as an engineering target and Sigma 10560-4A as a promising base strain [63]. Another example is asRNA, a single-stranded RNA complementary to the target mRNA (Figure 1g). Once asRNA binds to the target mRNA, ribosome cannot bind to the mRNA thereby blocking translation. The asRNA system has been successfully applied to knocking down gene expression in C. acetobutylicum [64], a strain that is extremely difficult to genetically manipulate. As mentioned earlier, the sRNA system is now added for gene expression knockdown control in C. acetobutyricum [62]. Recently, the asRNA system has also been applied to E. coli. Using the loop-stem structure synthetic asRNA having complementary sequences to the target at the loop position, the fatty acid biosynthesis genes ( fabD, fabH, fabB and fabF) were effectively repressed to enhance intracellular malonyl-CoA concentration. This resulted in enhanced production of resveratrol, naringenin and 4-hydroxycoumarin [65,66]. While above mentioned RNA systems have been used to down-regulate gene expression, recently developed STARs (Figure 1h) demonstrate that the small RNAs can also be used to up-regulate gene expression [67]. The STARs are designed starting from the natural pT181 attenuator to disrupt the formation of a target transcriptional terminator hairpin placed upstream of a gene. This plasmid-based system was highly orthogonal and showed up to 94-fold activation. However, application of the STARs system is limited for metabolic engineering due to the inability to increase the expression level of chromosomal genes. It can be used as a new tool in constructing logic gates in synthetic circuits [67]. www.sciencedirect.com

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CRISPR-Cas system is a prokaryotic adaptive immune system that has been receiving much interest for genome engineering of various hosts, especially eukaryotes, since it enables rapid, simple and robust engineering compared with the conventional tools. Among various types of CRISPR-Cas system, the type II system is mainly used for genome engineering application due to its simplicity. In the type II system, trans-activating CRISPR RNA (tracrRNA) together with CRISPR RNA (crRNA) are used to direct Cas9 to the target DNA sequence. Cas9, an endonuclease, introduces double strand break. The tracrRNA and crRNA can be combined into a single guide RNA (sgRNA), which makes use of the type II system more convenient. Differently from the RNA-based systems described above, CRISPR-Cas system can be used to directly engineer DNA, thereby enabling gene knockout as well as insertion [Figure 1i; [68]]. CRISPR-Cas based system has been used to engineer various host strains including Bacillus subtilis, Clostridium sp., E. coli, Lactobacillus reuteri, S. cerevisiae, Streptococcus sp. and Streptomyces sp. [68]. One of the attractive advantages of the CRISPR-Cas system for metabolic engineering is its multiplex capability. Multiplex genome engineering has been demonstrated in E. coli [69], B. subtilis [70], Streptomyces sp. [71] and S. cerevisiae [72]. In the case of S. cerevisiae, all possible combinatorial knockout of 5 genes related to mevalonate production were rapidly constructed to find the best combination [73]. In another study, CRISPR-Cas system was used in multiplex mode to generate various types of genomic modifications in E. coli to produce b-carotene [74]. The system could integrate the b-carotene synthetic pathways into E. coli genome to combinatorially optimize methylerythritolphosphate (MEP) pathway together with central metabolic pathway for improved b-carotene production. Among more than 100 genetic variants comprising 33 genomic modifications, the strain (XF237T) having 15 mutations showed the highest b-carotene production (2.0 g/L titer) by fed-batch fermentation [74]. The CRISPR-Cas system can be also used for gene downregulation by using a deactivated Cas9 (dCas9) protein. The dCas9 protein has the target DNA binding capability without the endonuclease activity, and thus can block transcriptional initiation or elongation [Figure 1i; [75]]. This phenomenon called CRISPR interference (CRISPRi) has been applied in several metabolic engineering hosts including B. subtilis, Clostridium sp., C. glutamicum, E. coli and Streptomyces sp. [68]. For example, the pyk, pck and pgi genes in C. glutamicum were separately downregulated using the CRISPRi system, which resulted in the increase of L-lysine and L-glutamate titers comparable to those obtained by corresponding gene deletions [76]. This CRISPRi based system was also applied for increasing the 4-hydroxybutyrate monomer fraction in poly(3-hydroxybutyrate-co-4-hydroxybutyrate) www.sciencedirect.com

in E. coli through knocking down multiple genes (sad, sucD, sucC, sdhA and sdhB) simultaneously [77]. Another way to control the metabolic fluxes is spatial modulation of enzymes using synthetic protein or DNA scaffold (Figure 1j). When the enzymes comprising the metabolic pathway are located closely, the product of the first enzyme more efficiently reaches the next enzyme with low probability of side reactions by other enzymes. Thus, metabolite channeling through this approach is particularly advantageous when the biosynthetic steps include unstable or toxic intermediates. This strategy was first adopted using protein scaffold for the production of mevalonate, resulting in 77-fold increase of titer by resolving intermediate toxicity problem [78]. In another recent example, B. subtilis producing N-acetylglucosamine (GlcNAc) was developed by spatial organization of the two key biosynthetic enzymes (GlmS and GNA1) in various ratios using a DNA scaffold system; the GlmS and GNA1 scaffolds at 1:2 ratio showed the highest GlcNAc production up to 20.58 g/L by fed-batch fermentation [79]. Oppositely from the protein scaffold concept, spatial separation of enzymes from other enzymes or cell components can also be beneficial for metabolic flux optimization in some cases (Figure 1k). This can be done by using natural organelles in eukaryotic cells. Although prokaryotic cells are mostly lacking in specific organelles, spatial separation of enzymes can also be achieved by using bacterial microcompartment (BMC). Encapsulation of enzymes within natural organelles or BMCs can increase the metabolic intermediate concentration, reduce the effect of toxic intermediates to general cell metabolism, and offer beneficial environment that is different from the cytosol. As an example, this strategy was applied to isobutanol production in S. cerevisiae [80]. When an isobutanol biosynthetic pathway was constructed using an a-ketoacid decarboxylase and alcohol dehydrogenase in mitochondria, isobutanol production increased up to 260% mainly due to greater local enzyme concentration. Also, BMCs were successfully used to enhance ethanol production in E. coli by encapsulating two enzymes, pyruvate decarboxylase and alcohol dehydrogenase. In addition, it was demonstrated that the purified BMCs containing two enzymes could produce ethanol from pyruvate [81]. Another approach to optimize metabolic fluxes is employing a co-culture system that involves a consortia of multiple microorganisms (Figure 1l). Coculture system can provide several advantages such as efficient utilization of various carbon sources and no need to construct a long and complex metabolic pathway in one host strain, thereby avoiding potential metabolic burden [82]. In one example, Trichoderma reesei, which hydrolyzes lignocellulosic biomass into simple sugars, and engineered E. coli, Current Opinion in Biotechnology 2017, 47:67–82

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which utilizes the produced sugars to biosynthesize isobutanol, were cocultured to directly produce isobutanol from cellulosic biomass [83]. Also, an engineered E. coli strain capable of producing taxadiene and S. cerevisiae capable of converting taxadiene to oxygenated taxanes were cocultured to directly produce oxygenated taxanes from xylose [84]. The E. coli–E. coli consortia was also employed for the efficient production of various chemicals such as cis,cis-muconic acid, 4-hydroxybenzoic acid, 3aminobenzoic acid, butanol, and flavan-3-ols [82,85]. Although coculture system is an attractive strategy for producing chemicals and materials through synergistically using the advantages of different host strains, there are also various challenges to be solved as systems gets more complex compared to monoculture system. Maintaining the stability and population ratio of cocultured species as originally designed is a key challenge. This might become even more difficult in industrial scale.

Evolutionary engineering tools and strategies for metabolic engineering In developing an industrial microbial strain, several interlinked complex factors should be considered, such as product titer, yield and productivity, byproducts formation, product toxicity and tolerance, and cell growth rate in the context of fermentation (culture medium, culture condition, and feeding mode) and downstream processes. In some cases, it is difficult, if not impossible, to rationally optimize the cellular performance considering such complex factors, especially when links between genotype and phenotype are unknown due to the lack of sufficient prior knowledge. Evolutionary engineering is a powerful method to address this problem. Evolutionary engineering, which mimics natural evolution process but performed in much more rapid, rational, and efficient ways, allows improvement of cellular phenotype through applying smart evolution pressure. For convenience, evolutionary engineering strategies are divided into two types, directed and semi-rational evolution, in this paper.

Directed evolution In directed evolution, increasing genetic diversity is performed in random way by using numerous mutagenesis methods (Figure 1m). Traditional random mutagenesis methods using error prone-PCR (ep-PCR) and/or applying the chemical and physical mutagens are still widely used. As an example, a C. glutamicum mutant strain having increased tolerance against L-arginine analogues could be obtained by N-methyl-N-nitro-N0 -nitroguanidine (NTG) and ultraviolet treatment. In addition to tolerance enhancement, this mutant strain could produce 34.2 g/L of L-arginine, which was 2-fold higher than that obtained with the parent strain. Further systems metabolic engineering of this mutant strain allowed development of an engineered C. glutamicum strain capable of producing 92.5 and 81.2 g/L of L-arginine by laboratory and industrial scale fermentation, respectively [86]. EpCurrent Opinion in Biotechnology 2017, 47:67–82

PCR was recently used to optimize production of lycopene in S. cerevisiae by creating CrtYB only possessing phytoene synthase function and by enhancing the catalytic activity of CrtE. Additional metabolic engineering of this strain produced 1.61 g/L of lycopene [87]. Increasing genetic diversity has been further accelerated through various techniques such as genome shuffling, staggered extension process (StEP), incremental truncation for the creation of hybrid enzymes (ITCHY) and nonhomologous random recombination (NPR). Genome shuffling was applied to C. beijerinckii DSM6423 strain to enhance its tolerance to isopropanol. As a first step, the wild-type strain was chemically mutated with NTG to obtain a mutant strain which can tolerate more than 35 g/L of isobutanol. Then, the selected mutant was subjected to genome shuffling. As a result, mutant tolerating up to 50 g/L of isopropanol was obtained. The resulting strain also showed enhanced isopropanol/butanol/ethanol (IBE) titer [88]. When a large library of evolved strains is used, a proper screening method is required to select mutants with desired characteristics. If the desired characteristic can be linked with distinguishable features such as color and fast growth, the desired mutant can be easily screened using instruments such as fluorescence-activated cell sorting (FACS), calorimetric assays, spectrophotometer or microfluidic devices. One of the easy characteristics to be evolved is tolerance towards the target chemical since it can be directly accessed by measuring growth rate in presence of the target chemical or its derivatives. Also, enhanced production of a target chemical or reduced production of a byproduct can be easily screened if the product or byproduct has its own color. As an example, carotenoid hyper-producing S. cerevisiae strain could be screened using oxidative stress as a selection pressure. The resulting mutant strain accumulated carotenoid up to 18 mg/g dry cell weight, which was 3-fold higher than that obtained with the parent strain [89]. When there are no easy intuitive ways of screening the desired phenotype, biosensors can be developed and used to link production capability with distinguishable characteristics such as color (e.g., by triggering expression of fluorescence protein) or antibiotic resistance (e.g., by triggering expression of antibiotic resistance gene) (Figure 1n). Numerous biosensors for screening metabolites such as butanol, fatty acid, isopentenyl pyrophosphate, L-lysine, L-tryptophan, L-valine, malonyl-CoA, NADPH, naringenin and succinic acid have been developed [90]. In addition, transcriptional regulators are also widely used as biosensor. For example, an L-valine responsive sensor based on Lrp transcriptional regulator of C. glutamicum was developed and used for adaptive laboratory evolution for enhanced production of L-valine in C. glutamicum by 100% [91]. Riboswitches are also used to construct biosensors. Riboswitches regulate gene expreswww.sciencedirect.com

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sion at the translational and transcriptional level by changing the conformations of secondary RNA structures when target metabolites are bound. For example, an Llysine riboswitch was constructed for the dynamic control of L-lysine exporter lysE, which successfully enhanced Llysine production in C. glutamicum [92].

Semi-rational evolution Generating genetic diversity can proceed at single genelevel, pathway-level and even at whole genome-levels. Since the random mutation space becomes explosively larger when the system to be evolved goes from single gene-level to the whole genome-level, it become extremely difficult to screen the right mutant/variant if the library is generated in a completely random way; it becomes nearly impossible to cover all random mutants when it becomes genome-level. To address this issue, various methods which can introduce mutations in semirational manner have been developed. By using these methods, focused libraries which contain fewer mutants but are richer in beneficial mutations can be generated, thereby making the desired screening easier. A semi-rational evolution approach of using rationally designed RBS libraries was taken to optimize metabolic fluxes for poly(3-hydroxybutyrate) (PHB) production in E. coli [93]. Expression of phaC, phaA and phaB genes involved in PHB biosynthesis was regulated by the RBS library, which was constructed by oligo-linker mediated assembly (OLMA) method. Then, the library was screened by visual selection and high-throughput screening (FACS), which allowed identification of strain variants accumulating PHB to the contents of 0–92%. This kind of semi-rational approach, which combines directed evolution and rational design, is likely to be more widely applied to developing strains capable of producing other products. In order to make focused libraries at genomic level, oligo mediated targeted mutation generation methodologies can be used (Figure 1o). One of the popular methods is multiplex automated genome engineering (MAGE), which can increase the diversity of the targeted sites by using recursive rounds of single-stranded DNA (ssDNA) recombination-based engineering in E. coli strain [94]. Recently, MAGE was applied to enrich rare high-producing strains and enhance production of naringenin and glucaric acid [95]. More recently, a modified method of MAGE called yeast oligo-mediated genome engineering (YOGE) was developed for genome engineering of S. cerevisiae [96]. As another approach for genome-scale evolution, RNAi-assisted genome evolution (RAGE) was developed and used in engineering S. cerevisiae. Application of RAGE has identified three synergistic knockdown targets conferring improved tolerance against acetic acid [97]. www.sciencedirect.com

Synergistic applications of the tools and strategies of systems biology, synthetic biology and evolutionary engineering: systems metabolic engineering All the tools and strategies of systems biology, synthetic biology and evolutionary engineering describe above can be simultaneously used in synergistic manner in order to construct high performance microbial cell factories. Two representative examples are shown below to demonstrate how various tools and strategies can be synergistically employed to perform systems metabolic engineering (Figure 2). Poly(lactate-co-glycolate) (PLGA) is US Food and Drug Administration (FDA) approved commercial polyester widely used for medical applications due to its biodegradability and biocompatibility. Recently, a metabolically engineered E. coli strain capable of producing PLGA was developed [Figure 2a; [45]]. Microbial production of this non-natural polyester PLGA could not be achieved until recently due to the lack of known natural metabolic pathway. Thus, a synthetic metabolic pathway for the biosynthesis of PLGA was designed by mimicking natural polyhydroxyalkanoates (PHAs) biosynthesis. The pathway is composed of two steps: activation of lactate and glycolate into lactyl-CoA and glycolyl-CoA, respectively, and copolymerization of lactyl-CoA and glycolyl-CoA. Since there is no natural enzyme catalyzing the polymerization of glycolyl-CoA, evolutionary engineering approach was taken to develop a PHA synthase variant [98]. However, introduction of the synthetic pathway to the wildtype E. coli XL1-Blue strain did not result in PLGA production due to the limited intracellular lactate and glycolate pools. This problem was solved by designing a new pathway through the application of systems biology tools. More specifically, the heterologous Dahms pathway from Caulobacter crescentus was introduced into E. coli to increase glycolate pool using xylose as a sole carbon source. However, the growth of the engineered strain was significantly retarded. To diagnose the cause for growth retardation, in silico genome-scale metabolic simulation was performed, which identified reduced ATP availability and poor glycolysis flux as the reasons. This problem was solved by simultaneous utilization of glucose through ptsG gene deletion for removal of catabolite repression. Furthermore, fermentative byproducts were removed (deletion of adhE, pflB, frdB and poxB genes) and lactate pool was enhanced (overexpression of ldhA gene and deletion of dld gene) through system-wide metabolic analyses. Finally, PLGA could be successfully produced from glucose and xylose. This example demonstrates the efficiency of systems metabolic engineering that integrates systems biology, synthetic biology and evolutionary engineering tools and strategies in strain development. Another great example of systems metabolic engineering is recent demonstration of opioids (specifically, thebaine Current Opinion in Biotechnology 2017, 47:67–82

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Figure 2

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Current Opinion in Biotechnology

Two representative examples showing synergistic applications of system biology, synthetic biology and evolutionary engineering tools and strategies. (a) Production of poly(lactate-co-glycolate) from glucose and xylose by metabolically engineered E. coli. (b) Production of opoids (thebaine and hydrocodone) from glucose by metabolically engineering S. cerevisiae. Application of systems biology, synthetic biology, evolutionary engineering tools and strategies are represented by purple, blue and gray box, respectively. Genes and proteins are (in alphabetical order): aceB, malate synthase A; adhE, aldehyde-alcohol dehydrogenase; Aro4p, 3-deoxy-D-arabino-2-heptulosonic acid 7-phosphate synthase; Aro7p, chorismate mutase; CNMT, coclaurine N-methyltransferase; DRS-DRR, 1,2-dehydroreticuline synthase-1,2-dehydroreticuline reductase; dld, lacate dehydrogenase; DODC, 3,4-dihydroxyphenylalanine decarboxylase; frdD, fumarate reductase; glcB, malate synthase G; glcDEFG, glycolate oxidase; ldhA, lactate dehydrogenase; morB, morphinone reductase; NCS, (S)-norcoclaurine synthase; NMCH, N-methylcoclaurine hydroxylase; 40 OMT, 30 -hydroxy-N-methylcoclurine 40 -O-methyltransferase; 6OMT, norcoclaurine 6-O-methyltransferase; pct, propionyl-CoA transferase; phaC, polyhydroxyalkanoate synthase; pflB, pyruvate formate lyase; poxB, pyruvate dehydrogenase; SalAT, salutaridinol 7-Oacetyltransferase; SalR, salutaridine reductase; SalSyn, salutaridine synthase; T6ODM, thebaine 6-O-demethylase; TyrH, L-tyrosine hydroxylase; xylB, xylose dehydrogenase; xylC, xylonolactonase. Metabolites are (in alphabetical order): Ac-CoA, acetyl-CoA; 7-ASTDO, 7-O-acetylsalutaridinol; DAHP, 3-deoxy-D-arabino-2-heptulosonic acid 7-phosphate; DOP, dopamine; L-DOPA, 3,4-dihydroxyphenylalanine; E4P, erythrose 4-phophate; G6P, glucose 6-phosphate; GA, glycolate; GA-CoA, glycolyl-CoA; 4-HPAA, 4-hydroxyphenylacetaldehyde; 4-HPP, 4-hydroxyphenylpyruvate; LA, lactate; LA-CoA, lactyl-CoA; NCCL, (S)-norcoclaurine; PEP, phosphoenolpyruvate; L-TYR, L-tyrosine.

and hydrocodone) production in an engineered yeast strain [49] (Figure 2b). The synthetic biosynthetic pathways for thebaine and hydrocodone comprising 21 and 23 genes, respectively, were constructed by carefully selecting genes from plants, mammals, bacteria and yeast. As expected, it was not trivial to construct such complex whole pathway and make it functional in one strain. The biosynthesis of opioids requires isomerization of (S)-reticuline to (R)-reticuline. In Papaver species, (S)reticuline is converted to (R)-reticuline via its oxidation to 1,2-dehydroeticuline followed by stereospecific reduction. However, the genes encoding the required enzymes, 1,2-dehydroreticuline synthase (DRS) and 1,2-dehydroreticuline reductase (DRR), were unknown. Based on the previous finding that gene-silencing of codeinon reductase (COR) resulted in (S)-reticuline Current Opinion in Biotechnology 2017, 47:67–82

accumulation, bioinformatic searches were performed to find 38 COR-like enzymes. Interestingly, four of the identified enzymes possessed both COR-like domain and cytochrome P450 oxidase 82Y1-like domain in one open reading frame. Since epimerization of (S)-reticuline required both oxidation and reduction, it was considered that this natural fusion protein could catalyze the isomerization reaction. One of the fusion protein from P. bracteatum (Pbr.89405) was confirmed to have (S)-reticuline isomerization activity. Loss of the protein activity due to improper protein folding or processing is one of the most frequent challenges often encountered when genes from heterologous sources are used to construct a synthetic metabolic pathway. The same problem happened with SalSyn, which converts (R)-reticuline to salutaridine. The properly processed SalSyn anchors its N-terminus www.sciencedirect.com

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at the outer endoplasmic reticulum (ER) domain and the catalytic domain in the cytosol. However, SalSyn was incorrectly processed when expressed in yeast, resulting in catalytic domain at the rumen of ER and abnormal glycosylation. Simple modification of glycosylation site to change glycosylation pattern was unsuccessful. As an alternative, a chimeric protein was designed by fusion of SalSyn from P. somniferum and P. bracteatum with Cheilanthifoline synthase (CFS) from plant, which is 61–68% identical to SalSyn but properly processed without glycosylation. Several fusion proteins were found to be properly processed and showed efficient conversion of (R)-reticuline to salutaridine. This great example also demonstrates the importance of taking systems approach in efficient strain development through the integrated use of synthetic biology and evolutionary engineering.

Conflict of interest Authors declare that they do not have any conflict of interest.

Acknowledgements We would like to thank Won Jun Kim for helpful discussion. This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (NRF2012M1A2A2026556 and NRF-2012M1A2A2026557) and also by the Intelligent Synthetic Biology Center through the Global Frontier Project (2011-0031963) from the Ministry of Science, ICT and Future Planning through the National Research Foundation of Korea.

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Conclusions and future perspectives Systems metabolic engineering has become a core technology for constructing efficient microbial cell factories. Recently, the power of systems metabolic engineering has been further enhanced by the development of advanced tools and strategies as discussed throughout the paper. Successful examples on developing microbial strains by systems metabolic engineering for the enhanced production of desired chemicals and materials are increasingly available. However, there are still more rooms for new tools and strategies that can further facilitate microbial cell factory development. Although genome-scale metabolic models have been successfully employed in flux optimization and strain design, more precise genome-scale metabolic model needs to be developed by incorporating missing metabolic information and integrating regulatory and signaling networks in simulation-competent mode. More accurate enzyme and pathway design tools for the production of nonnatural chemicals are also needed. In particular, a current bottleneck in systems metabolic engineering is to engineer or even create an enzyme for a desired reaction. Protein structure prediction tools are still not perfect, and forward design of proteins having altered reaction characteristics based on the structure is even more difficult, if not impossible. Additionally, determination of the structures of proteins and their mutant/engineered variants requires much time and effort. In this sense, a recent paper [99] reporting successful generation of structural models for hundreds of protein families of previously unknown structures is of great interest. Furthermore, more efficient, convenient, multiplex, robust, and large-scale genome manipulation tools still need to be developed. As such tools and strategies of systems metabolic engineering are being continuously developed and used in synergistic way, more efficient and cost-effective development of microbial cell factories suitable for the bio-based industrial production of chemicals and materials will become a reality. www.sciencedirect.com

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anaerobic culture and showed that metabolic physiologies and expression level of encoding genes were widely different among seven strains. Their comprehensive data types from genomics, phenomics, transcriptomics, and genome-scale modeling were highly reliable, resulting in the validation of the link between phenotypes and molecular features. 11. Kim BJ, Kim WJ, Kim DI, Lee SY: Applications of genome-scale metabolic network model in metabolic engineering. J Ind Microbiol Biotechnol 2015, 42:339-348. 12. O’Brien EJ, Monk JM, Palsson BO: Using genome-scale models  to predict biological capabilities. Cell 2015, 161:971-987. This paper described the entire processes how genome-scale models have been developed and utilized for understanding the cellular metabolism and predicting the phenotype under certain perturbations. Not only just its effect on system biology was introduced, but also related tools and methods to interpret the cellular behavior and the direction that has been made for the development were very nicely described. The six grand challenges from cell to systems biology were pointed out by using GEMs and this Primer paper summarized those related contents very nicely. 13. Mala P, Rocha M, Rocha I: In silico constraint-based strain optimization methods: the quest for optimal cell factories. Microbiol Mol Biol Rev 2016, 80:45-67. 14. Alper H, Jin Y, Moxley JF, Stephanopoulos G: Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab Eng 2005, 7:155-164. 15. Park JH, Lee KH, Kim TY, Lee SY: 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 2007, 104:7782-7797. 16. Jung YK, Kim TY, Park SJ, Lee SY: Metabolic engineering of Escherichia coli for the production of polylactic acid and its copolymers. Biotechnol Bioeng 2010, 105:161-171. 17. Sun Z, Meng H, Li J, Wang J, Li Q, Wang Y, Zhang Y: Identification of novel knockout targets for improving terpenoids  biosynthesis in Saccharomyces cerecisiae. PLoS ONE 2014 http://dx.doi.org/10.1371/journal.pone.0112615. The production of terpenoid, specifically amorphadiene, was enhanced from S. cerevisiae single knockout mutants. To improve the precursor isopentenyl diphosphate, several target genes were predicted by MOMA as deletion targets and those were predicted as targets that would not inhibit the yeast growth. Among them, hxk2 (encoding hexokinase) deleted mutant produced the highest titer of amorphadiene (54.55 mg/ L) which is 12-fold increase compared to wild type.

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