DCEO Biotechnology - ACS Publications - American Chemical Society

Review pubs.acs.org/CR

DCEO Biotechnology: Tools To Design, Construct, Evaluate, and Optimize the Metabolic Pathway for Biosynthesis of Chemicals Xiulai Chen,†,§ Cong Gao,†,§ Liang Guo,†,§ Guipeng Hu,†,§ Qiuling Luo,†,§ Jia Liu,†,§ Jens Nielsen,‡,∥ Jian Chen,†,§ and Liming Liu*,†,‡,§ †

State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden § Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China ∥ Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Lyngby, Denmark ‡

ABSTRACT: Chemical synthesis is a well established route for producing many chemicals on a large scale, but some drawbacks still exist in this process, such as unstable intermediates, multistep reactions, complex process control, etc. Biobased production provides an attractive alternative to these challenges, but how to make cells into efficient factories is challenging. As a key enabling technology to develop efficient cell factories, design-construction-evaluation-optimization (DCEO) biotechnology, which incorporates the concepts and techniques of pathway design, pathway construction, pathway evaluation, and pathway optimization at the systems level, offers a conceptual and technological framework to exploit potential pathways, modify existing pathways and create new pathways for the optimal production of desired chemicals. Here, we summarize recent progress of DCEO biotechnology and examples of its application, and provide insights as to when, what and how different strategies should be taken. In addition, we highlight future perspectives of DCEO biotechnology for the successful establishment of biorefineries. 4.2.1. β-Amyrin 4.3. X-omic Technology 4.3.1. Spider Dragline Silk 4.4. Targeted Engineering 4.4.1. Farnesene 4.5. Biosensor Engineering 4.5.1. Triacetic Acid Lactone 5. Pathway Optimization 5.1. Promoter Engineering 5.1.1. Promoter Library 5.1.2. Promoter Replacement 5.1.3. Synthetic RBS Regulation 5.1.4. Intergenic Regions 5.2. Transcription Factor Engineering 5.2.1. Zinc-Finger Protein Transcription Factors 5.2.2. MYB and bHLH Transcription Factors 5.2.3. ORCA Proteins 5.3. Synthetic RNA Switch 5.3.1. Ribozyme Switch 5.3.2. Riboswitch 5.3.3. Antisense Switch 5.4. Protein Engineering 5.4.1. Improving Enzyme Activity 5.4.2. Altering Substrate/Product Specificity 5.4.3. Modifying Regulatory Elements

CONTENTS 1. Introduction 2. Pathway Design 2.1. Literature Mining via Text-Mining Tools 2.1.1. Malate 2.1.2. Fumarate 2.2. Model Prediction via Network Models 2.2.1. L-Threonine 2.2.2. Lycopene 2.3. Omics Analysis via Omics Data 2.3.1. Dimyo-inositol-phosphate 2.3.2. L-Valine 2.4. Artificial Pathway via Synthetic Biology 2.4.1. Artemisinic Acid 2.4.2. Amorphadiene 3. Pathway Construction 3.1. Restriction Enzymes-Based Methods 3.1.1. (S)-Reticuline 3.2. Sequence Homology-Based Methods 3.2.1. β-Ionone 3.2.2. 1,4-Butanediol 3.3. Homology Recombination-Based Methods 3.3.1. L-Ornithine 3.4. Bridging Oligo-Based Methods 3.4.1. Lipids 4. Pathway Evaluation 4.1. Model Technology 4.1.1. Putrescine 4.2. Reverse Metabolic Engineering © 2017 American Chemical Society

5 5 6 11 12 14 14 14 15 15 15 16 16 17 17 17 18 18 18 18 19 19 20 20 22 22 22 23

23 23 23 24 24 25 25 26 26 26 27 28 28 28 30 30 32 32 33 33 34 35 35 36 37

Special Issue: Biocatalysis in Industry Received: November 30, 2016 Published: April 26, 2017 4

DOI: 10.1021/acs.chemrev.6b00804 Chem. Rev. 2018, 118, 4−72

Chemical Reviews 5.5. Cofactor Engineering 5.5.1. Cofactor Specificity System 5.5.2. Cofactor Regeneration System 5.6. Structural Biotechnology 5.6.1. DNA Scaffold 5.6.2. RNA Scaffold 5.6.3. Protein Scaffold 5.7. Compartmentalization Engineering 5.7.1. Mitochondria Engineering 5.7.2. Peroxisome Engineering 5.7.3. Carboxysome Engineering 5.8. Modular Pathway Engineering 5.8.1. Biochemistry-Based Modular 5.8.2. Metabolic Branch-Based Modular 5.8.3. Enzyme Turnover Rate-Based Modular 5.9. Genome-Scale Engineering 5.9.1. Global Transcriptional Machinery Engineering 5.9.2. Multiplex Automated Genome Engineering 5.9.3. Trackable Multiplex Recombineering 5.10. Multiplex Genome Editing 5.10.1. ZFNs Editing 5.10.2. TALENs Editing 5.10.3. CRISPR/Cas9 Editing 5.11. Transporter Engineering 5.11.1. ABC Transporters 5.11.2. Secondary Efflux Pumps 5.12. Morphology Engineering 5.12.1. Fungal Morphology 5.12.2. Bacterial Morphology 5.13. Consortia Engineering 5.13.1. Synthetic Consortia 5.13.2. Synthetic Ecosystems 6. Concluding Remarks Author Information Corresponding Author ORCID Notes Biographies Acknowledgments References

Review

To solve these problems, natural cell factories, especially bacteria and fungi, come into notice, due to their superior metabolic capabilities. Traditional microbe breeding for industrial applications involves application of random mutagenesis and screening processes to reform productive strains, get rid of byproduct formation and add beneficial characters.3 However, this breeding technique also leads to uncertain changes in genotype and phenotype. This might pose potential problems for the random mutant cells, when the optimal fermentation conditions are determined and further metabolic engineering strategies have to be applied.4,5 Consequently, rational metabolic engineering is becoming an essential platform technology for developing novel cell factories. However, metabolic engineering traditionally concentrates on deleting genes in competing pathways to increase the availability of precursors/intermediates and overexpressing genes in desired pathways to achieve maximum production rates.6 This approach has generated significant improvements in product yields, e.g. in the production of amorphadiene,7 but it also encounters many metabolic bottlenecks in improving cellular performances such as toxic intermediates accumulation, cofactor imbalance, and inefficient enzyme activities.8,9 These reasons are probably due to the fact that cells have evolved extensive regulation and complex interactions between metabolic pathways, but the scope of engineering cells is often local rather than systems-wide. DCEO (design-construction-evaluation-optimization) biotechnology systematically offers a conceptual and technological framework to exploit potential pathways, modify existing pathways, and create new pathways for the optimal production of desired products (Figure 1). DCEO biotechnology mainly includes four technical links: Design biotechnology offers approaches to discover and combine new biochemical pathways that leads toward the desired chemicals; construction biotechnology is adopted to efficiently and rapidly assemble a metabolic pathway in the host; evaluation biotechnology is used to precisely determine metabolic bottlenecks by identifying the difference of ideal and reality; and optimization biotechnology can modify the metabolic channel for optimal production of desired products. On one hand, with the development of DCEO biotechnology, the yield of target products could be increased noticeably in a heterologous host. For example, Van Dien and coworkers successfully redesigned multiple pathways for the biosynthesis of 1,4-butanediol in Escherichia coli, which produced 1,4-butanediol with much higher productivity, titer, and yield.10 On the other hand, with the versatility of DCEO biotechnology, various microbial cell factories are being developed to efficiently manufacture chemicals (Table 1), including biofuels (alcohols, fatty acids, alkanes, etc.),11 bulk chemicals (diols, organic acids, etc.),2,12 pharmaceuticals, and nutraceuticals (amino acids, hydroxycinnamic acids, flavonoids, stilbenoids, coumarins, isoprenoids, etc.).13 Here, we review recent progress in DCEO biotechnology for biosynthesis of valuable chemicals. We use recent successful examples to illustrate this concept and offer guidance as to when the methods should be adopted, what the limitations should be considered, and how the strategies should be designed. Finally, we suggest future perspectives on the challenge and potential of DCEO biotechnology to efficiently build biosynthesis platforms.

37 38 38 38 39 40 41 42 42 43 44 44 45 45 45 46 46 47 47 48 48 49 49 50 50 50 51 51 52 52 52 53 53 54 54 54 54 54 54 54

1. INTRODUCTION Chemical synthesis is a well established route for designing new synthetic schemes or inventing novel reactions to maximize the desired products and minimize byproducts,1 and it has been used to produce many chemicals on a large scale, such as nutraceuticals, pharmaceuticals, bulk chemicals, etc. Thus, chemical synthesis lays the foundations for our daily life that involves life necessities such as food, ensure good health such as medicines, and provide energy resources such as fuels.1 However, some drawbacks also exist in the process of chemical synthesis,2 such as unstable intermediates, multistep reactions, complex process control, etc., which results in an significant decrease in the desired chemical productivity and the atom economy yield. These drawbacks are motivating the development of green and sustainable processes for simplifying operations in chemicals production. Biobased production provides an attractive alternative to these challenges, but how to make cells into efficient factories is challenging.

2. PATHWAY DESIGN Microorganisms have been used to produce diverse chemicals from renewable resources, but some chemicals can not be synthesized via native metabolic pathways.14 Thus, a series of 5


Chemical Reviews

Review

Figure 1. Cell factory for chemicals production by DECO biotechnology. With biomass materials as substrates, cell factory is engineered through pathway design, pathway construction, pathway evaluation, and pathway optimization to improve chemicals production, such as organic acids (RCOOH), alkenes (RR), alcohols (R−OH), ammonia materials (R-NH2), etc. Pathway design to explore synthetic pathways for the efficient production of chemicals; pathway construction to assemble a metabolic pathway in a high-throughput manner; pathway evaluation to identify the difference between ideal and reality; and pathway optimization achieve the balance of cellular network and chemicals biosynthesis.

Figure 2. Overview of innovative methods for pathway design. (A) Literature mining via text-mining tools is used not only to identify, extract, integrate and analyze literature data, but also to discover new, hidden, or unsuspected information. (B) Model prediction via network models is utilized to bridge the gap between genotype and phenotype. (C) Omics analysis via omics data is adopted to comprehensively describe nearly all components and interactions within the cell. (D) Artificial pathway via synthetic biology is taken to design and construct new biological systems that do not exist in nature.

innovative strategies and tools, such as literature mining via text-

designed and created to perform the given functions, such as the capacity of producing non-natural chemicals.15

mining tools, model prediction via network models, omics analysis via omics data, artificial pathway via synthetic biology,

2.1. Literature Mining via Text-Mining Tools

are required to explore and design synthetic pathways for

The forefront of biology research and accumulation of data source in pathway design are still recorded in journal articles, which provide an overwhelming amount of biological information. Literature mining has developed from simple

efficient production of desired chemicals (Figure 2). Based on this, new metabolic pathways and cellular regulatory circuits are 6


acid

E. coli ALS974 E. coli 090B3 E. coli DSM03

L-lactate

7

A. terreus E. coli SH501

itaconic acid 3-hydroxypropionic acid glycolic acid

S. cerevisiae M1cM2qM3a

C. glutamicum AR6

L-threonine

L-ornithine

L-arginine

E. coli YP1617 (pACYA177-aroF-pheA) E. coli P2 rpoA14R

L-phenylalanine

L-tyrosine

L-homoalanine

E. coli ATCC 98082 ΔrhtA (pZElac_ilvABS_GDH) E. coli W14 (pR15BABKG)

L-valine

E. coli VAMF (pKBRilvBNCED, pTrc184ygaZHlrp) E. coli WLA (pKBRilvBNmutCED, pTrc184ygaZHlrp) E. coli TH28C (pBRThrABCR3)

E. coli EYX-2

E. coli DH10B (pBad33MevTA-D)

mevalonate

adipic acid

3-dehydroshikimic acid

E. coli JT9 (pJD727, pJD765)

E.coli AA7 (pTrc-ter-paaJ, pZS*27ptbbuk1-paaH1-ech) E. coli NR7

D-glucaric

E. coli BW lacI

q

E. coli BuT-8L-ato

E. coli B0013−070B

D-lactate

butyrate

E. coli EF02 C. glabrata T. G-KS(H)-S(M)−A-2S

S. cerevisiae RWB525

E. coli XZ658

strain

fumarate

malate

product

note

E. coli WL3110 (W3110 ΔlacI) thrAC1034T lysCC1055T Pthr::Ptac ΔlysA ΔmetA ilvAC290T Δtdh ΔiclR Pppc::Ptrc ΔtdcC Pacs::CmR-Ptrc; vector-based overexpression of thrAC1034T, thrBC, rhtABC [Key point: Model Prediction via Network Models] S. cerevisiae CEN.PK 113-11C; vector-based overexpression of CAR1, PDA1, CIT1, ACO2, IDP1, PYC2, AOX1, NDI1, MTH1-ΔT, ARG5,6, ARG7, ARG8, ARG2, ORT1, AGC1, GDH1, GDH3, GLT1, GLN1, ODC1, argA, argB, argC, argD, argE; downregulation of CAR2; deletion of ARG3 and KGD2 [Key points: Homology Recombination-based methods; Modular Pathway Engineering] C. glutamicum ATCC 21831: Δpgi ΔargR ΔfarR; replacing the native promoter of opcA, pgl, tal, tkt, zwf and carAB with sod promoter; replacing the native promoter of argGH with strong elongation factor Tu (EF-Tu) promoter [Key points: Promoter Engineering; Cofactor Engineering] E. coli ATCC 98082 (VNIIgenetika 472T23; ilvA442 supE spot thrC1010 Sac+ thrR) ΔrhtA; vector-based overexpression of ilvA (B. subtilis) and gdhAK92 V/T195S [Key point: Protein Engineering] E. coli WSH-Z06 (CCTCCM 2010009) Δcrr ΔtyrA; vector-based overexpression of pheA, aroG15, ydiB, aroK, tyrB and yddG [Key point: Protein Engineering] E. coli YP1617 (L-tyrosine auxotroph, CCTCC M 2013320); vector-based overexpression of aroF and pheA E. coli P2 contain PL-aroGD146N-CM/PDHM53I/A354 V operon with two copies, plasmid-encoded rpoA and rpoD mutants from gTME libraries [Key point: Genome-scale Engineering]

E. coli BW25113 ΔaceB, ΔglcB::WAK cassette (harboring aceA, ycdW, aceK); adaptive evolution Amino Acids and Derivatives E. coli W3110 ΔlacI ilvGatt::Ptac ilvBatt::Ptac ilvHG41A,C50T ΔilvA ΔpanB ΔleuA ΔaceF Δmdh ΔpfkA::KmR; vector-based overexpression of ilvB, ilvN, ilvC, ilvE, ilvD, ygaZ, ygaH, and lrp [Key point: Omics Analysis via Omics Data] E. coli ΔlacI ΔilvA; vector-based overexpression of ilvB, ilvN, f br, ilvC, ilvE, ilvD, ygaZ, ygaH, and lrp

Organic Acid and Derivatives KJ060:(ATCC 8739 ΔldhA ΔackA ΔadhE Δpf lB) ΔmgsA ΔpoxB Δf rdBC ΔsfcA ΔmaeB Δf umB Δf umAC [Key point: Literature Mining via Textmining Tools] S. cerevisiae TAM; vector-based overexpression of PYC2, MDH3ΔSKL, and SpMAE1 (S. pombe) [Key points: Literature Mining, Transporter Engineering] Evolved strain E. coli E2: ΔfumB, Δf umAC, ppc:trc, ΔaspA, harboring pSCppc [Key point: Literature Mining via Text-mining Tools] C. glabrata CCTCC M202019 Δura3Δarg8; vector-based overexpression of KGD2-SUCLG2, SDH1, SpMAE1 (S. pombe), SFC1, and ASL [Key points: Compartmentalization Engineering, Transporter Engineering] E. coli B0013-070(ΔackA, Δpta, Δpps, ΔpflB, ΔldhA, ΔpoxB, ΔadhE Δf rdA); vector-based overexpression of LDH with λPRPL promoter [Key point: Promoter Engineering] E. coli YYC202 (DSM 14335, Hf r zbi::Tn10 poxB1 Δ(aceEF) rpsL pps-4 pf l-1) f rdABCD E. coli B0013−070, ΔldhA::dif lldD::Pldh-ldhBcoa [Key point: Promoter Engineering] E. coli MG1655(DE3)4f rdA::FRT4pta::FRT4ldhA::FRT4adhE::FRT, Plac::atoB (E. coli)-tesB (E. coli), Plac::hbdGBDL-crtSH3L-terPDZL; pJD758 vector-based arrangement of protein scaffold [Key point: Structural Biotechnology] E. coli BL21 ΔptsG ΔpoxB HK022::PRPL-glf (Z. mobiliz) ΔldhA Δf rdA φ80attB::PL-ter (T. denticola) λattB::PL-crt (C. acetobutylicum) ΔadhE:: φ80attB::PL-phaA (C. necator)-hbd (C. acetobutylicum) PL-atoDABD Δpta E. coli BW25113 F′; vector-based overexpression of phaA (R. eutropha), phaB (R. eutropha), phaJ (A. caviae), ter (T. denticola), and endogenous thioesterase E. coli BL21 Star (DE3) (F− ompT hsdSB (rB−mB−) gal dcm rne131 (DE3)); vector-based overexpression of ino1 (S. cerevisiae)-GBD, MIOX (mouse)SH3, udh (P. syringae)-PDZ; vector-based arrangement of protein scaffold [Key point: Structural Biotechnology] E. coli QZ1111 (MG1655 ΔptsG ΔpoxB Δpta ΔsdhA ΔiclR); vector-based overexpression of paaJ (E. coli), paaH1 (R. eutropha H16), ech (R. eutropha H16), ter (E. gracilis), ptb (C. acetobutylicum), and buk1 (C. acetobutylicum) Strain pNR8.294: aroE353 serA::aroB aroF::CmR aroH::KanR aroG::TcR; vector-based overexpression of Ptrc-evolved KDPGal aldolase mutant (DgoAF33I/D58N/Q72H/A75 V/V85A/V154F/Y180F, NR8.276−2)-serA-aroB [Key point: Protein Engineering] E. coli DH10B: vector-based overexpression of atoB (S. cerevisiae), HMGS (S. cerevisiae) and tHMGR (S. cerevisiae). Tunable intergenic regions (TIGRs) were used between atoB/HMGS and HMGS/tHMGR [Key point: Promoter Engineering] Clump diameter was controlled at 0.45 mm [Key point: Morphology Engineering] E. coli W3110 Δpta-ackA, ΔyqhD; vector-based overexpression of dhaB (K. pneumonia) and gdrAB (K. pneumonia), mutant gabD4 (C. necator)

Table 1. DCEO Biotechnology for Biosynthesis of Chemicals

56.2 13.8

47

5.4

92.5

5.1

699 578

433

419

305

207

90

698

60.7 82.4

127

697

695 696

309

424

694

500

693

692

690 691 494

457

31 30

22

23

ref

7.61

56.44

0.596 mM/OD 40.2 71.9

0.639 mg/L 19

2.5

12.3

10

138 142.2 7.2

122.8

41.5 15.76

59

34

titer (g/L)

Chemical Reviews Review


E. coli FMP’ 1.5gapA ΔmgsA(pSYCO106)

E. coli BL21(DE3) (p18COR) E. coli MG1655 (pTHKLcfgldA mgsAyqhD (pTHKLcfgldAmgsAyqhD) E. coli W3110 (pBAD-PD-ZF-Enz)

E. coli JCL260 (pSA65, pSA69) S. cerevisiae (pJLA) E. coli KO12 S. cerevisiae BY4741 E. coli L19S

1,3-propanediol

2,3-butanediol 1,2-propanediol

isobutanol

8

E. coli JM101(pACCAR16ΔcrtX, pHP11) E. coli ZF237T

E. coli K12

β-carotene

lycopene

farnesene

amorphadiene

artemisinic acid

E. coli M1−12-PactiD+TE10

trans unsaturated fatty acids

E. coli ispALaFS-NA (pTispALaFS and pSNA)

E. coli B86 (pAM52, pMBIS,pADS)

S. cerevisiae CEN.PK2 (pAM426)

E. coli DH1 (pAM92,pCWoriA13AMOaaCPRct)

S. cerevisiae EPY224

E. coli LYC010

R. toruloides RT880-AD E. coli SIMD 70

P. tricornutum

triacylglycerol

lipid biofuels

E. coli RB03 (pTHfadBA.fadM-) E. coli (rbs29-mGLY-lACA-hFAS)

free fatty acids

myo-inositol

ethanol

E. coli ECKh-422 (pZS*13-sucCDsucD4hbd/sucA, E.coli pZE23S-025B-34)

strain

1,4-butanediol

product

Table 1. continued note

R. toruloides IFO0880; pGI2-PGAPDH(880)-ACC1(880)-TACC1(880)-PACL(880)-DGA1(880)- TDGA1(880) [Key point: Bridging Oligo-based Methods] Eight target genes including deoA, prpR, serS, tqsA, tap, ddpA, yahF, and clcB were chosen to screen acetate tolerance by TRMR [Key point: Genomescale Engineering] E. coli MG1655, ldhA: FRT-Cat-FRT, PM1−12(promoter)-Pacti (P. aeruginosa), ΔfadD, pTrc-TE10 (A. tetradius) [Key points: Promoter Engineering, Compartmentalization Engineering] Secondary Metabolites E. coli JM101; vector-based overexpression of crtE (E. uredovora), crtB (E. uredovora), crtI (E. uredovora), crtY (E. uredovora), and ipp (H. pluvialis) E. coli MG1655; ΔbioA::λ-Red-kan, CRISPR/Cas9 system for genomic modification; overexpression of gps-crtE-ispA-gps-idi-ispA-ispH-dxs-ispE-GPtpiA [Key point: Multiplex Genome Editing] E. coli K12: ΔgdhAΔaceEΔfdhF; integrated expression of dxs, idi, and ispFD; pACLYC vector-based overexpression of crtEBI operon [Key point: Model Prediction] E. coli CAR001 (ATCC 8739 ldhA::M1−93::crtEXYIB (P. agglomerans)::ldhA M1−37::dxs M1−37::idi) ΔcrtXY M1−46::sucAB M1−46::talB M1− 46::sdhABCD RBSL9::crtE RBSL12::dxs RBSL7::idi S. cerevisiae EPY strain; vector-based overexpression of ADS (A. annua), CYP71AV1 (A. annua) and CPR (A. annua) [Key point: Artificial Pathway via Synthetic Biology] E. coli DH1; vector-based overexpression of A13AMO (opt, engineering N-terminal transmembrane domain of CYP71AV1, A. annua), aaCPRct (opt, A. annua), atoB (E. coli), ERG13 (S. cerevisiae), tHMGR (truncated, S. cerevisiae) ERG12 (S. cerevisiae), ERG8 (S. cerevisiae), MVD1 (S. cerevisiae), idi (E. coli), ispA (E. coli), and ADS (A. annua L) [Key point: Artificial Pathway via Synthetic Biology] S. cerevisia CEN.PK2; erg9Δ::kanr_PMET3-ERG9leu2-3; 112::HIS_PGAL1-MVD1_PGAL10-ERG8 his3Δ1::HIS_PGAL1-ERG12_PGAL10-ERG10 ade1Δ:: PGAL1-tHMG1_PGAL10-IDI1_ADE1ura3-52::PGAL1-tHMG1_PGAL10−ERG13_URA3 trp1-289::PGAL1-tHMG1_PGAL10-ERG20_TRP1 [Key point: Artificial Pathway via Synthetic Biology] E. coli DH1; vector-based overexpression of atoB (E. coli), mvaS (S. aureus), mvaA (S. aureus), ERG12 (S. cerevisiae), ERG8 (S. cerevisiae), MVD1 (S. cerevisiae), idi (E. coli), ispA (E. coli), and ADS (A. annua L) [Key point: Artificial Pathway via Synthetic Biology] E. coli DH5α; vector-based overexpression of FS (opt, Malus x domestica)-(GGGGS)2-ispA (E. coli), mvaE (E. faecalis), mvaS (E. faecalis), mvaK1 (S. pneumoniae), mvaK2 (S. pneumoniae), mvaD (S. pneumoniae), and idi (E. coli) [Key point: Artificial Pathway via Synthetic Biology]

Alcohols E. coli MG1655 lacIq ΔadhE ΔldhA ΔpflB ΔlpdA::lpdD354 K (K. pneumonia) Δmdh ΔarcA gltAR163L SmR; vector-based overexpression of sucC (E. coli), sucD (E. coli), sucD (P. gingivalis W83), 4hbd (P. gingivalis W83), sucA (M. bovis), cat2 (P. gingivalis W83), and 025B (aldehyde dehydrogenase, C. beijerinckii) [Key point: Sequence Homology-based Methods] E. coli FM5 glpK gldA ndh pstHIcrr galP-Ptrc glk-Ptrc* arcA edd gapA-P1.5 mgsA; vector-based overexpression of DAR1 (S. cerevisiae), GPP2 (S. cerevisiae), dhaB1 (K. pneumoniae), dhaB2 (K. pneumoniae), dhaB3 (K. pneumoniae), dhaX (K. pneumoniae) E. coli BL21(DE3); vector-based overexpression of budA (co, K. pneumonia) and budC (co, K. pneumoniae) [Key point: Promoter Engineering] E. coli MG1655ΔackA-pta ΔldhA ΔdhaK, overexpressing MgsA (E. coli), GldA (E. coli), yqhD (E. coli) and replacing DHAK (E. coli) with ATPdependent DHAK (C. f reundii) E. coli W3110; vector-based overexpression of MgsA (E. coli)-ZFa, DkgA (E. coli)-ZFb, and GldA (E. coli)-ZFc; vector-based arrangement of DNA scaffold [Key point: Structural Biotechnology] E. coli JCL260; vector-based overexpression of kivd (L. lactis), adhA (L. lactis), ilvC, ilvD, and alsS (B. subtilis) S. cerevisiae JAy1, locating ILV2, ILV3, ILV5, α-KDC and ADH in mitochondria [Key point: Compartmentalization Engineering] E. coli W pf l+ pf l::(pdc (Z. mobiliz)-adhB (Z. mobiliz)-CmR) f rd recA Two gTME mutant libraries were created by SPT15 or TAF25; best mutation: SPT15F177S/Y195H/K218R [Key point: Genome-scale Engineering] E. coli MG1655 ΔendA Δzwf Δpf kB pf kA::P esaS-pf kA(LAA) HK022::104-esaRI170 V 186(O)::apFAB296-apFAB700-esaI [Key point: Consortia Engineering] Fatty Acids and Derivatives E. coli MG1655 fadR atoCc ΔarcA Δcrp::crp* ΔadhE Δpta Δf rdA Δf ucO ΔyqhD ΔfadD; vector-based overexpression of fadB, fadA, and fadM E. coli BL21ΔfadD; pCDM4-[RBS29]-pgk-gapA-aceE-aceF-lpdA, pACM4-fabD-accA-accB-accC-accD and pETM6-CnfatB2-fabA-fabH-fabG-fabI [Key points: Promoter Engineering, Modular Pathway Engineering] UDP-glucose pyrophosphorylase disruption via TALE nucleases [Key point: Multiplex Genome Editing]

0.380

27.4

40

0.1

0.115

18 mg/g DCW 3.53

∼0.01 2.0

0.93

1.5 mg/ 109 cells 16.4

7 8.6

>1.7

268

157

158

707

152

706

100

705 624

704

220 598

615

703 343

702 502 597 567 673

460

0.59 50.8 0.635 54

701 483

700

10

ref

1.04 5.6

135.3

18

titer (g/L)



E. coli S16 (pCDF-Trc-CHS-Trc-CHI, pET-CHS-CHI,pACYC-matC-matB) E. coli BL21* Δf umBCΔsucC w/ACC, PGK, GAPD,PDH E. coli MG1655 (CRBS,ERBS)

S. cerevisiae 1031.βA S. cerevisiae SGibS

Black Mexican Sweet cells S. cerevisiae ABC

(2S)-naringenin

β-amyrin

anthocyanins (S)-reticuline

9

E.coli 1pC (pJBEI-6409)

E. coli BL21 (DE3) (pMVAidi,pTAC:LS: AGPPS2) S. cerevisiae JLS07

E. coli EcHW47

C. roseus

avermectin taxol

limonene

β-ionone

indigo

terpenoid indole alkaloids cobalamin vitamin C (2-keto-Lgulonic acid) levopimaradiene

polyhydroxyalkanoate P(3HB-co-4HB)

E. coli Rosetta (DE3) pLysS (pET-ResZF-Enz) S. avermitilis 3-115 (pJTU3470) E. coli MG1655 Strain 26

E. coli XL1-Red (pPS2-M1) E. coli JM109SG

E. coli MG1655 (pTrc-GGPPSS239C/G295DLPSM593I/Y700F)

B. megaterium DSM 319 (pSJM129) Erwinia cells

E. coliBW27784(pUCo-Vvsts-At4cl1)

resveratrol

astaxanthin

E. coli strain 4

S. cerevisiae (pTEF1-VioB/pREV1-VioE)

E. coli Vio-4 (pBvioABCE-Km)

E. coli F4

strain

(2S)-pinocembrin

violacein

product

Table 1. continued note

0.7

86 wt %

Monomers and Polymers E. coli XL1-Red; pBBR1MCS-2 vector-based overexpression of phaCF518I (A. punctata) [Key point: Protein Engineering] E. coli JM109: Δsad ΔgabD ΔmreB::f rt; pTK-mreB-PBAD:sulA/pBHR68 [Key point: Morphology Engineering]

>8.6 mg/g DCW 8.11 mM/ g DCW 0.22 mg/L

5 mg/L

2.7

0.435

4.8 1.02

11.5 mg/L

2.3

80.6 μg/L

416 657

409

398 451

369

568

193

643

638

626 539

460

710

365 175

253 254

307

709

0.474 5.8 mg/g DCW 3.93 mg/L 0.139

561

708

503

521

271

ref

0.1

0.04

0.71

1.1

titer (g/L)

B. megaterium DSM 319; vector-based overexpression of Sau3AI (cbi operon, B. megaterium DSM509) [Key point: Synthetic RNA Switch] Erwinia; vector-based overexpression of 2,5-DKG reductase mutant (2,5-DKGF22Y/K232G/R238H/A272G) [Key points: Cofactor Engineering, Protein Engineering] E. coli MG1655ΔendAΔrecA; vector-based overexpression of GGPPSS239C/G295D and LPSM593I/Y700F [Key point: Protein Engineering]

Secondary Metabolites E. coli BL21 (DE3); pFZ81 vector-based overexpression of ERG12, ERG8, MVD1 and N-terminal His-tagged Idi); pFZ71 vector-based overexpression of AFS, N-terminal His-tagged IspA and Idi) [Key point: Targeted Engineering] E. coli MG1655 ΔtrpR::FRT ΔtnaA::FRT ΔsdaA::FRT Δlac::Ptac-aroFBL-FRT ΔtrpL trpEfbr Δgal::Ptac-tktA-FRT Δxyl::Ptac-serAfbr-FRT Δf uc::PtacvioD-FRT; vector-based overexpression of vioA (C. violaceum), vioB (C. violaceum), vioC (C. violaceum) and vioE (C. violaceum) S. cerevisiae BY4741MATa his3D1 leu2D0 met15D0 ura3D0 Δpex5D; vector-based overexpression of VioA in cytosol, VioE and VioB in peroxisome [Key point: Compartmentalization Engineering] E. coli BL21 Star (DE3); vector-based overexpression of aroF-pheAfbr, PAL (R. glutinis)-4CL (P. crispum), CHS (P. hybrida)-CHI (M. sativa), matB (R. trifolii)-matC (R. trifolii). [Key point: Modular Pathway Engineering] E. coli BL21 (DE3); vector-based overexpression of TAL (opt, R. glutinis), 4CL (P. crispum), CHS (P. hybrida), CHI (M. sativa), matB (R. trifolii), matC (R. trifolii) [Key point: Modular Pathway Engineering] E. coli BL21 Star (DE3) Δf umBC ΔsucC; vector-based overexpression of ACC, PGK, GAPD and PDH [Key point: Model Prediction via Network Models] E. coli K12 MG1655; Six RBS expression modulators were constructed to regulate expression of idi, crtE, crtB, crtI, lcyB, crtW and crtZ [Key point: Promoter Engineering] S. cerevisiae CEN.PK 113-7D; vector-based overexpression of Erg8, Erg9, HFA1 and PSY (P. sativum) [Key point: Reverse Metabolic Engineering] S. cerevisiae INVSc1 (MATa/MAT α his3Δ1 leu2 trp1−289ura3−52); vector-based overexpression of bAS (opt, G. glabra) under PFBA1 promoter, IDI (E. coli) under PADH1 promoter, ERG1 (C. albicans) under PTYS1 promoter, ERG20 (S. cerevisiae) under PALA1 promoter, ERG9 (S. cerevisiae) under PGPM1 promoters [Key point: Promoter Engineering] Expression of transcription factor bHLH and MYB [Key point: Transcription Factor Engineering] S. cerevisiae BY4741; vector-based overexpression of module A (CYP76AD1W13L F309L and DODC), module C (6OMT, CNMT and 4′OMT (P. somniferum), and NMCH (E. californica)); integrated expression of module B (NCS, P. somniferum); enzyme-coupled biosensor for tyrosine hydroxylase screening [Key points: Restriction Enzymes-based Methods, Biosensor Engineering] E. coli BW27784 (F− Δ(araD-araB)567 ΔlacZ4787(::rrnB-3) λ− Δ(araH-araF)570(::FRT) ΔaraEp-532::FRT φPcp18-araE553 Δ(rhaD-rhaB)568 hsdR514; vector-based overexpression of sts (V. vinifera) and 4cl-1 (A. thaliana) E. coli Rosetta (DE3) pLysS; vector-based overexpression of 4CL (A. thaliana)-Zif268 (opt, zinc finger region) and STS (V. vinifera)-PBSII (opt, zinc finger region); vector-based arrangement of DNA scaffold [Key point: Structural Biotechnology] S. avermitilis 3-115; vector-based overexpression of avtAB pump [Key point: Transporter Engineering] E. coli MG1655 EDE3Ch1TrcMEPp5T7TG; ΔrecAΔendA; PTrc-dxs-idi-ispDF in chromosome with one copy; pSC101 vector-based overexpression of TS-GPPS [Key point: Modular Pathway Engineering] E.coli DH1; vector-based overexpression of atoB (E. coli), HMGS (opt, S. aureus), HMGR (opt, S. aureus), MK (S. cerevisiae), PMK (S. cerevisiae), PMD (S. cerevisiae), idi (E. coli), trGPPS (opt, truncated, A. grandis), and LS (opt, M. spicata) E. coli BL21 (DE3); vector-based overexpression of LS (opt, M. spicata)-GPPS2 (opt, A. grandis) under Ptac promoter and atoB, erg13, tHMGR1, erg12, erg8, erg19, idi1 under Plac promoter [Key point: Transporter Engineering] S. cerevisiae SCIGS22: MATa MAL2-8c SUC2 ura3−52 lpp1Δ:: loxP dpp1Δ:: loxP PERG9Δ:: loxP-PHXT1 gdh1Δ:: loxP PTEF1-ERG20 PPGK1-GDH2 PTEF1-tHMG1 (X. dendrorhous); integrated expression of BTS1 (X. dendrorhous); vector-based overexpression of crtYB (X. dendrorhous) and PhCCD1 (P. hybrida) [Key point: Sequence Homology-based Methods] E. coli H33: Strain E2N-derivative; inactivated trpR; aroFP148L, trpEM293T and aroGD146N, pJ401 vector-based overexpression of bfmo (Methylophaga sp.); T7p insertions in aroC and trpE [Key point: Genome-scale Engineering] C. roseus; vector-based overexpression of Orca3 to activate CPR, TDC, STR, SGD and D4H [Key point: Transcription Factor Engineering]



10

S. cerevisiae W303 MATa his3−11,15 trp1−1 leu2−3 ura3−1 ade2−1; pRzS vector-based overexpression of yEGFP; xanthine-responsive switch of L2bulge9 and L2bulgeOff3 [Key point: Synthetic RNA Switch] The inhibited expression of transcription factors Ntlim1 [Key point: Transcription Factor Engineering] L. lactis MG1363: ΔaldB: pB6-noxE (L. lactis MG1363) [Key point: Promoter Engineering] C. glabrata CCTCC M202019 ΔAHAIR ΔDR; vector-based overexpression of ALS (B. subtilis) E. coli BL21(DE3); pQL1 vector-based overexpression of SgcE10 and SgcE [Key point: Promoter Engineering] E. coli K12 strain W3110 Thy DE3, manXYZ8, Δnag::TcRΔlacZ::T7lac-glmS-CmR, mutant glmSE14K/D386 V/S449P/E524G [Key point: Protein Engineering] E. coli BL21-Star (DE3); vector-based overexpression of hydrogenase (HydAEFG) and pyruvate ferredoxin oxidoreductase (PFOR); vector-based arrangement of RNA scaffold [Key point: Structural Biotechnology] S. elongatus PCC7942; integrated expression of Ptrc-α-KDC (L. lactis), PLlacO1-ILV2 (B. subtilis)-ILV5 (E. coli)-ILV3 (E. coli) and Ptac-rbcLS (S. elongatus PCC6301) [Key point: Compartmentalization Engineering] E. coli ΔaraFGH, ΔaraBAD, ΔlacZ, ΔaraC; integrated expression of PBAD-lacZ reporter; vector-based overexpression of TAL-responsive AraC variant AraCP8 V/T24I/H80G/Y82L/H93R and 2-PS variant 2-PSL202G/M259L/L261N [Key point: Biosensor Engineering] C. ammoniagenes DN510; vector-based expression of galK (E. coli W3110), galT (E. coli W3110), galU (E. coli W3110) and ppa (E. coli W3110) in E. coli NM522/pNT25/pNT32, vector-based expression of LgtC (N. gonorrheae) in E. coli NM522/pGT5 [Key point: Consortia Engineering] C. glabrata CCTCC M202019 Δura3 Δarg8; vector-based expression of ALDC with signal peptide CoxIV (opt, B. amyloliquefacien), ALS (opt, B. sutiliz) with signal peptide CoxIV, MPC1 (S. cerevisiae) and MPC2 (S. cerevisiae) [Key point: Compartmentalization Engineering]

S. cerevisiae W303

S. elongatus SA665

E. coli HF22 ()

C.ammoniagenes DN510, E. coli NM522/ pNT25/pNT32, E. coli NM522/pGT5 C. glabrata CmA5

hydrogen

isobutyraldehyde

triacetic acid lactone

globotriose

acetoin

E. coli BL21- Star(DE3) (D0FH)

pentadecane glucosamine

A. pernix

Transgenic tobacco L. lactis (DA/pB6nox) C. glabrata DA-3 E. coli BL21 (DE3) (pQL1) E. coli 2123-72

lignin diacetyl

dimyo-inositol-phosphate xanthine

Fut8−/− cells [Key point: Multiplex Genome Editing] E. coli BL21(DE3); vector-based overexpression of glyV, glyX, glyY, glyA [Key point: X-omic Technology] E. coli BL21(DE3); pET25b(+) vector-based overexpression of SELP-59-A Miscellaneous Two genes dipA and dipB were identified by comparative genomics strategy [Key point: Omics Analysis via Omics Data]

CHO-K1

nonfucosylated antibody spider dragline silk silk elastin-like proteins

note Monomers and Polymers E. coli XL1-Blue ΔackA PldhA::Ptrc Δppc ΔadhE Pacs::Ptrc; vector-based expression of phaC1E130D/S325T/S477R/Q481M (Pseudomonas sp. MBEL6−19), and pctA243T (C. propionicum) [Key point: Protein Engineering] E. coli XL1-Blue ΔackA PldhA::Ptrc Δppc ΔadhE Pacs::Ptrc; vector-based expression of phaAB (C. neactor), phaC1E130D/S477F/Q481 K (Pseudomonas sp. MBEL 6-19), and pctV193A (four silent mutations: T78C, T669C, A1125G, and T1158C; C. propionicum) [Key point: Protein Engineering] E. coli K12 (DE3); F‑ λ‑ ilvG- rf b-50 rph- (DE3) ΔrecA ΔendA ΔrecA ΔendA ΔglcD; vector-based expression of pct, phaC, phaA, phaB, ycdW, aceA, and aceK E. coli W3110 ΔspeE ΔspeG ΔargI ΔpuuPA ΔrpoS PargECBH::Ptrc PspeF-potE::Ptrc PargD::Ptrc PspeC::Ptrc; vector-based overexpression of speC [Key point: Model Technology] E. coli W3110 ΔspeE ΔspeG ΔargI ΔpuuPA ΔrpoS PargECBH::Ptrc PspeF-potE::Ptrc PargD::Ptrc PspeC::Ptrc; vector-based overexpression of speC-glk [Key point: Model Technology] E. coli WL3110-ΔspeE ΔspeG ΔygjG ΔpuuPA PdapA::Ptrc; vector-based overexpression of cadA E. coli WL3110-ΔspeE ΔspeG ΔygjG ΔpuuPA PdapA::Ptrc; vector-based overexpression of cadA; expressing anti-murE sRNA variant [Key point: Synthetic RNA Switch] C. glutamicum ATCC 13032Δact ΔlysE; vector-based expression of xylA and xylB under Pgro promoter; Psod-tkt-Peftu-f bp, icdGTG Proteins A. niger; PglaA-glaA-GFP; the addition of titanate microparticles [Key point: Morphology Engineering]

E. coli (pSH96, pTetgly2-glyAn) E. coli (pET25b(+)-SELP-59-A)

A. niger ANip7-MCS-gfp2

C. glutamicum DAP-Xyl2

E. coli XQ56 (p15CadA) E. coli XQ56 (p15CadA, anti-murE sRNA)

glucoamylase

cadaverine

E. coli XQ52 (p15SpeC-glk)

E. coli XQ52 (p15SpeC)

E. coli JLX10 (pMCS103CnAB, pPs619C1310-CpPCT540) E. coli K12 (DE3) strain PGA1

P(3HB-co-lactate)

poly(glycolate-co-lactate-co-3HB) putrescine

E. coli JLX10 (pPs619C1400-CpPCT532)

strain

polylactic acid

product

Table 1. continued

3.26

188

2.06

1.1

0.36 4.7 0.14 17

0.7 4.3

1080 U/ mL 2

103

9.61 12.6

2.23

24.2

55.9 wt %

46 wt %

11 wt %

titer (g/L)

715

674

292

536

461

358 313 312 316 431

277

122

260 714

608

646

713

405 374

244

243

712

711

711

ref



Chemical Reviews

Review

Figure 3. Schematic of malate (A) and fumarate (B) biosynthetic pathway. (A) PEP: phosphoenolpyruvate; ldhA: lactate dehydrogenase; mgsA: methylglyoxal synthase; ackA: acetate kinase; poxB: pyruvate oxidase; adhE: alcohol dehydrogenase; focA: formate transporter; pflB: pyruvate-formate lyase; pyk: pyruvate kinase; pckA: phosphoenolpyruvate carboxykinase; sfcA/maeB: malic enzyme; pyc: pyruvate carboxylase; mdh: malate dehydrogenase; fumABC: fumarase; frdBC: fumarate reductase. (B) ARGSUC: argininosuccinate; ARG: arginine; ADESUC: adenylosuccinate; AMP: adenosine monophosphate; PYK: pyruvate kinase; PYC: pyruvate carboxylase; MDH: malate dehydrogenase; FUM1: fumarase; SDH: succinate dehydrogenase; KGD: α-ketoglutarate dehydrogenase complex; SUCLG: succinyl-CoA synthetase; ASL: argininosuccinate lyase; ADSL: adenylosuccinate lyase.

2.1.1. Malate. Malate, a four-carbon dicarboxylic acid, is mainly used as an acidulant and taste enhancer in the beverage and food industry.22 There are three metabolic pathways for producing malate (Figure 3A):22 (i) oxidation of citrate via the TCA cycle (blue arrow); (ii) formation of succinic acid from acetyl-CoA and oxaloacetate via the noncyclic glyoxylate route (green arrow); (iii) carboxylation of pyruvate to oxaloacetate, followed by reduction of oxaloacetate to malate (red arrow). Because the third pathway results in a maximum theoretical malate yield of 2 mol/mol glucose, it has been carried out to produce malate in the engineered microorganisms, such as Escherichia coli23 and Saccharomyces cerevisiae.24 Derivatives of E. coli have been constructed using metabolic engineering strategies, such as combinations of gene deletions (ldhA, adhE, ackA, focA, pf lB, mgsA, and poxB) and metabolic evolution, to redesign the metabolic network for enhancing the production of phosphoenolpyruvate (PEP) as the precursor of malate. The final engineered strain E. coli WGS-10 produced 9.25 g/L malate, which was obtained by overexpression of phosphoenolpyruvate carboxykinase (pckA) from Mannheimia succiniciproducens to channel PEP to malate.25 Further, by inactivation of fumarate reductase (fumABC) to prevent conversion from malate to fumarate and deletion of malic enzyme (sfcA/maeB) and fumarate reductase (frdBC) to block further conversion to pyruvate and succinate in the succinate-producing strains E. coli KJ060 and KJ073, the final engineered strains could produce 22 and 34 g/L malate, respectively.23 Malate has also been produced

recognition of terms to comprehensive analysis of interaction relationships, and has broadened from single cognition of protein interactions to systematic regulation of pathway cooperations. On the one hand, platforms for efficient handling of vast literatures have been built, mainly including Pubmed, MEDLINE and CiteXplore. With PubMed as example, it contains more than 26 million citations for biomedical literature from MEDLINE, life science journals and online books (Last updated: 13 September 2016), covering portions of the life sciences, behavioral sciences, chemical sciences and bioengineering. On the other hand, a series of text-mining tools, such as Textpresso,16 PubFinder,17 PubMatrix,18 LitMiner and WikiGene,19 and MineBlast,20 have been developed not only to identify, extract, integrate and analyze literature data, but also to discover new, hidden or unsuspected information.21 Based on the above-mentioned progress, literature mining can offer new insights on (i) metabolic pathways, such as metabolic intermediates, key enzymes and its inhibitor/activator, cofactors; (ii) metabolic regulation network, such as metabolic and protein interaction networks, transcriptional regulation networks, membrane transporter systems; (iii) phenotypic traits, such as metabolic substrate, environmental adaptability, physiological parameters. Thus, literature mining is appropriate for efficiently searching which metabolic pathways is valid to improve metabolites production and predicting which genes will be engineered to circumvent metabolic bottlenecks. 11


Chemical Reviews

Review

Figure 4. Schematic of threonine, valine, cadaverine, and L-homoalanine biosynthetic pathways. pyk: pyruvate kinase; aspC: aspartate aminotransferase; thrA: aspartokinase I/homoserine dehydrogenase; metL: aspartokinase II/homoserine dehydrogenase II; asd: aspartate semialdhyde dehydrogenase; lysC: aspartokinase III; thrB: homoserine kinase; thrC: L-threonine synthase; ilvA: L-threonine dehydratase; ilvBN: acetolactate synthase; ilvC: ketolacid reductoisomerase; ilvD: dihydroxyacid dehydratase; ilvE: branched-chain amino-acid aminotransferase; dapA: dihydrodipicolinate synthase; cadA: L-lysine decarboxylase; GDH*: glutamate dehydrogenase mutation.

by the engineered strain S. cerevisiae.22 The final production of malate was increased to 59 g/L by overexpression of pyruvate carboxylase (pyc) and cytosolic malate dehydrogenase (mdh) to redirect pyruvate to malate in the pyruvate-producing strain S. cerevisiae TAM. 2.1.2. Fumarate. Fumarate, a four-carbon dicarboxylic acid, has received considerable attention, due to the fact that it can be converted into therapeutic drugs and used as a starting material for polymerization and esterification.26 Four metabolic routes have been engineered for fumarate production,27 mainly relating to three microorganisms, Candida glabrata, S. cerevisiae, and E. coli (Figure 3B): (i) the reductive reactions of the TCA cycle (red arrow); (ii) the urea cycle and the purine nucleotide cycle (yellow arrow); (iii) the oxidation of citrate via the TCA cycle (blue arrow); (iv) the noncyclic glyoxylate cycle (green arrow). In route I, the final engineered S. cerevisiae strain could produce up to 5.64 g/L fumarate by deleting thiamine biosynthesis

regulatory factor (THI2) and fumarase (FUM1), and overexpressing the exogenous pyruvate carboxylase (PYC), malate dehydrogenase (MDH), and FUM1.28 In route II, the final concentration of 8.83 g/L fumarate was obtained with C. glabrata strain T.G-ASL(H)-ADSL(L)-SpMAE1 by regulating the strength of argininosuccinate lyase (ASL) at a high level and adenylosuccinate lyase (ADSL) at a low level and overexpressing of SpMAE1.29 In route III, the highest fumarate titer was increased to 15.76 g/L in the engineered C. glabrata strain T.GKS(H)-S(M)-A-2S through mitochondrial engineering of the TCA cycle beginning with α-ketoglutarate catalyzed by α-ketoglutarate dehydrogenase complex (KGD), succinyl-CoA synthetase (SUCLG), and succinate dehydrogenase (SDH).30 In route IV, the evolved mutant E. coli E2 can accumulate 41.5 g/L fumarate through deletion of three fumarases, overexpression of phosphoenolpyruvate carboxylase and the glyoxylate shunt operon.31 12


Chemical Reviews

Review

Figure 5. Schematic of lycopene, artemisinic acid, amorphadiene, β-ionone, β-amyrin, farnesene, astaxanthin, mevalonate, levopimaradiene, taxol, βcarotene, and limonene biosynthetic pathways. DXP: 1-deoxy-D-xylulose-5-phosphate; MEP: 2-C-methyl-D-erythritol4-phosphate; CDP-ME: 4diphosphocytidyl-2-C-methylerythritol; CDP-MEP: DP-ME 2-phosphate; MEC: 2-C-methyl-D-erythritol-2,4-cyclo-diphosphate; HMBPP: (E)-4hydroxy-3-methylbut-2-enyl-diphosphate; HMG-CoA: 3-hydroxy-3-methylglutaryl-CoA; MEV: mevalonate; MEV-5-P: phosphomevalonate; MEVPP: diphosphomevalonate; IPP: isopentenyl diphosphate; DMAPP: dimethylallyl diphosphate; GPP: geranyl diphosphate; FPP: farnesyl diphosphate; GGPP: geranylgeranyl diphosphate. IspC: 1-deoxy-D-xylulose-5-phosphate reductoisomerase; IspD: 4-diphosphocytidyl-2C-methyl-D-erythritol synthase; IspE: 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase; IspF: 2C-methyl-D-erythritol-2,4-cyclodiphosphate synthase; IspG: 1-hydroxy-2methyl-2-(E)-butenyl-4-diphosphate synthase; IspH: 1-hydroxy-3-methyl-2-(E)-butenyl-4-diphosphate reductase; AtoB: acetyl-CoA acetyltransferase; 13


Chemical Reviews

Review

Figure 5. continued ERG13/HMGS: HMG-CoA synthase; tHMGR: HMG-CoA reductase; Erg12: mevalonate kinase; ERG8: phosphomevalonate kinase; MVDI: mevalonate diphosphate decarboxylase; Idi: isopentenyl-diphosphate isomerase; ERG20/IspA: FPP synthase; CrtE/BTS1/GGPPS: GGPP synthase; CrtB: phytoene synthase; CrtI: phytoene desaturase; ADS: amorphadiene synthase; CYP71AV1: cytochrome P450 monooxygenase; CrtYB/lcpB: phytoene synthase/lycopene cyclase; CCD1: carotenoid cleavage dioxigenase; ERG9: squalene synthase; ERG1: squalene epoxidase; BAS: β-amyrin synthase; AFS: farnesene synthase; CrtW: β-carotene ketolase; CrtZ: β-carotenoid hydroxylase; CrtS: astaxanthin synthase; CrtR: cytochrome P450 reductase; LPS: levopimaradiene synthase; TS: taxadiene synthase; T5α−OH: taxoid-5α-hydroxylase; LS: limonene synthase.

2.2. Model Prediction via Network Models

(thrC). Various L-threonine-producing strains have been successfully developed through traditional approaches, including:87,88 (i) the elimination of feedback inhibitions by mutation S345F in thrA and T342I in lysC, respectively;86,89,90 (ii) the deregulation of enzymes by replacing the native promoter of the thrABC operon with the tac promoter; (iii) the elimination of competitive pathways by deleting the genes lysA (diaminopimelate decarboxylase), metA (homoserine succinyltransferase), and tdh (threonine dehydrogenase);86 (iv) the amplification of key genes by overexpressing the L-threonine exporter (rhtABC). Based on these strategies, the final engineered E. coli strain MT201 produced up to 52.0 g/L L-threonine. To further improve L-threonine production, in silico analysis was performed for identifying target genes to be engineered using E. coli MBEL979 GEM.90 According to linear programming-based optimization, the steady-state flux values for in silico mutants were calculated as follows: (i) to construct in silico mutant strains, knockout of genes was reflected in model by setting the corresponding flux to zero; (ii) with maximization of L-threonine and acetate production rate as an objective function, the PPC or ICL flux was perturbed from the minimum to maximum value; (iii) by plotting and comparing L-threonine and acetate production rate, the flux profile graph was generated. With this analysis, it was not considered to delete genes pta-ack or poxB in the acetate pathway, because it can retard growth or increase pyruvate excretion. As a consequence, overexpressing acetylCoA synthase (acs) was selected to decrease acetate production. Then, threonine transporter gene (tdcC) was knocked out and rhtABC gene was amplified for enhancing the transport of Lthreonine. The final strain E. coli TH28C could produce 82.4 g/L L-threonine in fed-batch culture. 2.2.2. Lycopene. Lycopene, a carotenoid, is widely used as an antioxidant and has great potential in reduction of prostate cancer risk.91,92 It can prevent various diseases caused by senility and descent of immunity effectively. At present, the biosynthetic pathway of lycopene in heterologous hosts can be reconstructed in heterologous hosts by partly combining the upstream pathway of isoprenoid biosynthesis with the downstream pathway of carotenoid biosynthesis (Figure 5). In previous studies, carotenoid genes from carotenogenic bacterias, such as CrtE (geranylgeranyl diphosphate synthase), CrtB (phytoene synthase), and CrtI (phytoene desaturase), have been used to reconstruct carotenoid biosynthetic pathways in E. coli,92−95 thus achieving high-level lycopene production.96−98 To further increase the yield of lycopene, a method for the rational design of strains with a global stoichiometric analysis was applied to identify single and multiple gene knockout targets in recombinant E. coli strains.99 Based on this foundation, stoichiometric modeling was used to investigate genome-wide gene knockout targets, and seven single and multiple gene deletions, ΔgdhA, ΔaceE, ΔytjC, ΔfdhF, ΔgdhAΔaceE, ΔgdhAΔytjC, ΔgdhAΔaceEΔfdhF, were validated to improve lycopene production by increasing the supply of precursors and cofactors. Then, a global transposon library was undertaken to

With the development of genomics and relevant technologies, the genomes of a large number of microbes have been published. These genome sequences have been used for generating highquality genome-scale metabolic reconstruction that can be used for generation of comprehensive metabolic models. Thus, there are now 134 metabolic models completed in the BIGG database (http://bigg.ucsd.edu), covering 78 microbial species.32 For example, there are 7 models for S. cerevisiae, iFF708, iND750, iLL672, iIN800, iMH805/775, iMM904, and iTO977, and 5 models for E. coli, iJE660, iJR904, iAF1260, iCA1273, and iJO1366. Applications of these genome-scale metabolic models (GEMs) ranging from theoretical to pragmatic studies are to bridge the gap between genotype and phenotype, including seven major ends:33,34 (i) guiding metabolic engineering, (ii) directing biological discovery, (iii) assessing phenotypic behavior, (iv) analyzing biological network, (v) studying bacterial evolution, (vi) contextualizing omic data, and (vii) interrogating multispecies relationships. To use genome-scale models to explore the metabolic potential for cell factories and to identify the target genes for metabolic engineering, the metabolic genotype-phenotype relationship is mechanistically described by constraint-based reconstruction and analysis methods in four aspects,35,36 including: (i) Flux balance analysis: E-flux,37 FBAwMC,38 FBAME,39 Genomic-context analysis,40 pFBA,41 MD-FBA,42 DMMM,43 Dynamic FBA,44 SIM,45 SEM,45 SMM,46 PhPP,47 FBA, Geometric FBA,48 AOS,49 FVA,50 Bayesian FBA,51 FCF,52 FFCA;53 (ii) Strain design analysis: FSEOF,54 GDLS,55 CiED,56 OptGene,57 SA,58 SEAs,58 OptORF,59 OptStrain,60 OptReg,61 EMILiO,62 OptForce,63 RobustKnock-proxy,64 RobustKnock,65 Objective tilting,66 OptKnock;67 (iii) Thermodynamic constraints analysis: EBA,68 ll-COBRA,69 NET analysis,70 TMFA,71 Thermodynamic realizability,72 Flux minimization;73 (iv) Incorporating regulation analysis: MBA,74 Shlomi-NBT08,75 tFBA,76 MADE,77 GIMME,78 PROM,79 idFBA,80 iFBA,81 GeneForce,82 SR-FBA,83 rFBA.84 Especially, several common methods, such as OptKnock, OptForce, OptORF, OptGene, GDLS, OptStrain, FBA, FVA, etc., offer a broader perspective for a newcomer to develop the metabolic potential of cell factories. These methods lay the foundation of cognizing and regulating the physiological function in microbes. In addition, the construction, simulation and analysis of genome-scale gene regulatory network, signal transduction network and protein− protein interaction network will impel metabolic engineering to systematical metabolic engineering which is more rational. 2.2.1. L-Threonine. L-Threonine, an essential amino acid, has been supplemented to improve the nutritional value of animal feeds and human foods.85,86 In addition, L-threonine is used in pharmaceutical and chemical reagents. L-Threonine belongs to the aspartate family of amino acids and its biosynthetic pathway consists of five enzymatic steps from L-aspartate (Figure 4), including aspartokinase I, II, III (thrA, metL, lysC), aspartate semialdehyde dehydrogenase (asd), homoserine dehydrogenase I, II (thrA, metL), homoserine kinase (thrB), threonine synthase 14


Chemical Reviews

Review

Figure 6. Schematic of DIP biosynthetic pathway. IPS: myo-inositol-1-phosphate synthase; IPCT (dipA): inositol-1-phosphate cytidylyltransferase; DIPPS (dipB): DIPP synthase; IMP: DIPP phosphatase.

most widespread components of the solute pools in organisms adapted to grow at high temperatures.117 DIP biosynthesis from glucose-6-phosphate contains four steps (Figure 6):118 (i) glucose-6-phosphate is converted to myo-inositol-1-phosphate by myo-inositol-1-phosphate synthase (IPS);119 (ii) myoinositol-1-phosphate is activated into CDP-inositol by inositol1-phosphate cytidylyltransferase (IPCT); (iii) CDP-inositol is condensed with another molecule of myo-inositol-1-phosphate into dimyo-inositol-phosphate phosphate (DIPP) by DIPP synthase (DIPPS); (iv) DIPP is dephosphorylated into DIP by a yet unknown DIPP phosphatase (IMP).120,121 However, genes encoding only two of the four required enzymes, IPS and IMP, have been identified. To find the other two genes of this pathway (dipA and dipB), comparative genomics strategy for the entire genomes of the DIP-producing microorganisms in SEED database was applied to predict candidate genes.122 An observed chromosomal clustering of two uncharacterized genes with an IPS gene in a number of Thermophilic archaea and bacteria provided the first strong evidence for their possible roles in the DIP biosynthesis pathway. Based on the long-range homology analysis, the role of the missing IPCT and DIPPS were assigned to the dipA and dipB gene products, respectively, and thus conjectured the function of dipA and dipB.122 Finally, the DIP biosynthesis pathway was successfully reconstructed in E. coli through the expression of both candidate genes on a plasmid. 2.3.2. L-Valine. L-Valine, an essential amino acid, is used as a component of cosmetics and pharmaceuticals as well as nutritional supplement and feed additives.123 The L-valine biosynthesis pathway from pyruvate is composed of four reactions catalyzed by acetohydroxy acid synthase (ilvBN), acetohydroxy acid isomeroreductase (ilvC), dihydroxy acid dehydratase (ilvD), and transaminase B (ilvE) (Figure 4).124,125 A previous study described the production of L-valine in C. glutamicum by removing feedback inhibition in ilvN, deleting the panB (pantothenate synthase), ilvA (L-threonine dehydratase) and leuA (2-isopropylmalate synthase), overexpressing the ilvBNC and ilvGMEDA (acetohydroxy acid synthase II isoenzyme) operon.126,127 Based on this foundation, to increase the yield of L-valine in E. coli, an efficient pathway was constructed by rational metabolic engineering based on transcriptome analysis.127 Comparative transcriptome profiling was performed during batch fermentation of the engineered strain Val (pKKilvBN) and the control strain, and the results showed that (i) while the expression levels of the ilvCED genes were increased, the increase extents were much less than those of the ilvBN genes; (ii) as an activator of the ilvIH operon, the

identify additional knockout targets that were unaccounted in stoichiometric models, and three gene targets that correlated with lycopene overproduction were identified, rssB, yjf P, and yjiD. Finally, several combinations which would be beneficial for lycopene production were identified:100 ΔgdhAΔaceEΔfdhF, ΔgdhAΔaceEΔpyjiD, ΔgdhAΔaceEΔfdhFΔrssBΔyjf P, ΔgdhAΔaceEΔfdhFΔyjf P, and ΔgdhAΔaceEΔfdhFΔyjf PΔpyjiD. One of these strains with ΔgdhAΔaceEΔfdhF was capable of producing upward of 18000 ppm lycopene in optimized culturing conditions, which was a 8.5-fold increase over the reference strain only having lycopene pathway reconstruct. 2.3. Omics Analysis via Omics Data

With the fast development of high-throughput analytical methods, the accumulation of omics data has enabled the comprehensive descriptions of nearly all components and interactions within cells.101 Omics data can either be generated specifically for the particular study, but large amounts of data can also be collected and obtained through publicly accessible databases. These databases can be classified into three categories:102 (i) components database, including genomics, transcriptomics, proteomics, metabolomics, glycomics, lipidomics, localizomics, etc; (ii) interactions database, including protein−DNA interaction, protein−protein interaction, etc; (iii) functional-states database, including fluxomics, phenomics, growthrate, etc. The availability of omics data in common databases such as EBI,103 BioGRID,104 CeCaFDB,105 etc., provides global cellular information to generate predictive computational models of biological systems, in particular when integrating with metabolic, signaling and regulatory networks. The process of omics data integration mainly contains three steps:102 (i) identifying the network scaffold by algorithms, such as REDUCE,106 MODEM,106 GRAM,107 etc; (ii) decomposing the network scaffold by methods, such as SAMBA,108 SANDY,109 mfinder/mDraw,110 Cytoscape,111 etc; (iii) developing cellular modeling and analysis by tools, such as COBRA method,112,113 BioTapestry tool,114 etc. Omics data and their integration provide much new metabolic characters of host cells, predict the metabolic proficiency of cell factories, and discover new functional genes or new pathways of synthetic biology, thus improving our understanding in multilevel regulation of different pathways and laying a good foundation for efficient pathway design. 2.3.1. Dimyo-inositol-phosphate. Dimyo-inositol-phosphate (DIP) is one of the major compatible solutes in a number of hyperthermophilic archaea and bacteria,115,116 which are the 15


Chemical Reviews

Review

Figure 7. Overview of assembly methods for pathway construction. (A) Restriction enzymes-based methods is used to assemble DNA parts by restriction endonuclease and DNA ligase. (B) Sequence homology-based methods is utilized to assemble DNA parts in vitro by overlap regions between parts. (C) Homology recombination-based methods is adopted to assemble DNA parts in vivo by homologous arms between neighboring parts. (D) Bridging oligo-based methods is taken to assemble DNA parts by single-stranded bridging oligos, which are designed to be complementary to two ends of neighboring DNA parts.

computer-aided design software, such as RBS Calculator,141 Gene Designer,142 GeneDesign,143 DNAWorks,144 TinkerCell,145 GenoCAD,146 SynBioSS,147 etc; (vi) product synthesis to bring microbial systems to industrial standards by optimal process engineering, such as high cell density cultivation, utilization of various carbon sources, etc. Combined with systems biology through various kinds of common tools such as BNICE, RetroPath, COBRA toolbox, RBS Calculator, GeneDesign, etc., synthetic biology plays a crucial role in exploring the synthetic capabilities of biological systems to broaden the range of biosynthetic pathways and create the platform of cell factories for chemicals production, which is necessary to go beyond natural pathways.148 2.4.1. Artemisinic Acid. Artemisinin, a sesquiterpene lactone, is isolated from the aerial parts of Artemisia annua L. plants,149 and is mainly used to treat malaria. Its more potent derivatives contain artesunate, artemether, and dihydroartemisinin.150 The precursors of artemisinin, isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), can be synthesized via the mevalonate (MEV) pathway or deoxyxylulose 5-phosphate (DXP) pathway (Figure 5). After IPP and DMAPP are condensed into farnesyldiphosphate (FPP) by FPP synthase (EGR20, IspA), FPP is transformed into the direct precursor of artemisinin, amorphadiene, by amorphadiene synthase (ADS). Finally, a novel cytochrome P450 monooxygenase (CYP71AV1) from A. annua was used to oxidize amorphadiene to artemisinic acid. Because traditional methods for the total synthesis of artemisinin is difficult and costly,151 current biotechnological efforts have been focused on devising novel biological processes to manufacture artemisinin using the engineered microbial platforms by introducing the non-natural biosynthesis pathway. Keasling and co-workers constructed a new pathway for artemisinic acid production in S. cerevisiae based on synthetic biology, breaking through the traditional restriction of artemisinin production.152 This strategy contained three steps: (i) engineering FPP biosynthetic pathway in S. cerevisiae to

expression of the lrp (leucine responsive protein) was downregulated; (iii) as a L-valine exporter, the gene ygaZ (predicted transporter) and ygaH (predicted inner membrane protein) expression levels were decreased, respectively. These results suggested that the genes ilvCED, lrp, and ygaZH should be amplified to enahnce L-valine peoduction. Then, the corresponding genetic modification was used to engineer E. coli Val (pKKilvBN), and the final strain E. coli Val (pKBRilvBNCED/ pTrc184ygaZHlrp) produced 7.61 g/L L-valine, which was 113% higher than that of the control strain.127 2.4. Artificial Pathway via Synthetic Biology

Synthetic biology is a method to design and construct new biological systems that do not exist in nature by the assembly of well-characterized, standardized and reusable components, such as genetic control systems, metabolic pathways, chromosomes, and cells.128 Synthetic biology emphasizes two aspects, “design” and “redesign”, suggesting that synthetic biology is not only just experiment but also a new tool to use existing knowledge of biology, conduct “design” and “redesign” based on the actual need, and use mathematical models to guide experiment. The generalized workflow for de novo designing artificial pathway via synthetic biology, from initial idea to final product, consists of six steps:129,130 (i) pathway predictions to search possible pathways for generating a certain compound by computational tools, such as BNICE,131 DESHARKY,132 FMM,133 RetroPath,134 etc; (ii) pathway prioritization to provide interesting alternative designs for industrial implementation by software tools, such as DESHARKY,132 RetroPath,134 etc; (iii) metabolic modeling to predicte the behavior of each ranked pathways in candidate host organisms by computational tools, such as COBRA toolbox,135 SurreyFBA,136 CycSim,137 BioMet toolbox,138 iPATH2,139 GLAMM,140 etc; (iv) pathway selection to gain the pathway that will work efficiently in a candidate host for generating a certain compound by comparing in silico maximized fluxes to the target compound; (v) pathway refactoring and integration to systematically construct and optimize biosynthetic pathways by 16


Chemical Reviews

Review

Figure 8. Schematic of (S)-reticuline, 3-dehydroshikimate, phenylalanine, L-tyrosine, and indigo biosynthetic pathways. TKT: transketolase; PEPS: phosphoenolpyruvate synthase; DAHPS/aroFGH: 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase; aroB: 3-dehydroquinate synthase; aroD: DHQ dehydratase; aroE/ydiB: dehydroshikimate reductase; aroK/L: shikimate kinase; aroA: 5-enolpyruvylshikimate-3-phosphate synthetase; aroC: chorismate synthase; CM/PDH: chorismate mutase/prephenate dehydrogenase; tyrB: tyrosine aminotransferase; TYR: tyrosinase; DODC: L-dopa specific decarboxylase; MAO: monoamine oxidase; NCS: norcoclaurine synthase; 4′OMT: 4′-Omethyltransferas; NMCH: N-methylcoclaurine hydroxylase; CNMT: coclaurine N-methyltransferase; 6OMT: 6-O-methyltransferase; CM/PDT: chorismate mutase/prephenate dehydratase; trpE: component I of anthranilate synthase; trpCD: indole-3-glycerolphosphate synthetase; trpA: tryptophan synthase; FMO: flavin-containing monooxygenase.

such as ERG12 (mevalonate kinase), ERG8 (phosphomevalonate kinase), MVD1 (mevalonate pyrophosphate decarboxylase), and Idi (IPP isomerase).158 Based on these, development of fermentation processes for the reengineered S. cerevisiae strain resulted in amorphadiene production up to 40 g/L.

increase FPP production and decrease its use for sterols; (ii) introducing ADS from A. annua into the high FPP producer to convert FPP to amorphadiene; (iii) cloning and expressing CYP71AV1 from A. annua in the amorphadiene producer to oxidize amorphadiene to artemisinic acid. Finally, artemisinic acid was produced up to 115 mg/L in the engineered S. cerevisiae strain. 2.4.2. Amorphadiene. Amorphadiene, the precursor of artemisinin first isolated from sweet wormwood or A. annua, is a valuable and powerful antimalarial natural product,153 and it is also used for commercial flavors, fragrances, and medicines.151 Because amorphadiene is naturally produced in small quantities and chemical synthesis is not economically feasible,151 amorphadiene production via microbial fermentation may be an effective choice. Heterologous production of amorphadiene in E. coli was first achieved by expressing the mevalonate pathway from S. cerevisiae and amorphadiene synthase (ADS) from A. annua (Figure 5),151 and its production was increased to 0.5 g/L in a two-phase partitioning bioreactor.154 To further improve amorphadiene production, transcriptional responses and pathway metabolites were analyzed, and the results showed that HMG-CoA reductase was the bottleneck due to its perturbation to fatty acid biosynthesis through generalizing membrane stress.155,156 Subsequently, two strategies were used: (i) replacement of the S. cerevisiae HMG-CoA synthase (HMGS) and HMG-CoA reductase (tHMGR) with equivalent enzymes from S. aureus;157 (ii) overexpression of every enzyme of the mevalonate pathway to ERG20 (FPP synthase) in S. cerevisiae,

3. PATHWAY CONSTRUCTION Biosynthesis of chemicals often requires multiple-step pathways containing various genes and its control elements, and these bioparts need to be assembled into an operational pathway in microbes. However, the classical restriction-ligation-based cloning is extremely inefficient, which is a limiting factor for rapidly obtaining the prototyping of many devices. To eliminate this limiting factor, an ability to reliably assemble and test DNA components in high-throughput manner is essential to the advancement of synthetic biology. Thus, various DNA assembly methods have been developed to improve flexibility speed and clone precision, and these methods can be divided into four groups based on the different assembly mechanisms (Figure 7), including restriction enzymes-based methods, sequence homology-based methods, homology recombination-based methods, and bridging oligo-based methods. 3.1. Restriction Enzymes-Based Methods

Gene cloning via type II restriction enzymes and DNA ligases has been used successfully in molecular biology, which can only join two DNA parts at a time using the complemented sticky end in the enzymatically digested fragments between target DNA and target vector. It mainly contains BioBrick standard (EcoRI, XbaI, 17


Chemical Reviews

Review

SpeI, and PstI),159 BglBrick standard (EcoRI, BglII, BamHI, and XhoI),160 ePathBrick standard (SpeI, XbaI, NheI, and AvrII),161 Standard European Vector Architecture (SEVA),162 HomeRun Vector Assembly System (HVAS),163 etc.164,165 Although all type II restriction endonuclease methods can be used to assemble DNA parts into desired constructs, it is also limited by the forbidden digestion sites within DNA parts and/or large scar sequences in final construct.165 To improve unction of restriction enzymes-based methods efficiently in synthetic biology, type IIs restriction endonuclease assembly emerges largely at the right moment, which can provide freedom to choose overhang sequences owing to the fact that the cleavage site of type IIs restriction endonuclease is a few bases away from its recognition site.166 It mainly contains Golden Gate method,166 GoldenBraid 2.0 standard,167 Modular cloning system (MoClo),168 Methylation-Assisted Tailorable Ends Rational (MASTER), 169 etc.164,165 These methods add many advantages to DNA assembly and enable scar-free seamless assembly, which are suitable for performing parallel assembly of multiple parts without PCR and can be applied to express multiple genes in one destination vector together.170 Taking MoClo as an example, MoClo offers a novel approach to assemble multiple fragments in successive cloning steps, which is a distinct tool for multigene constructs in the second tier of Golden Gate assembly.168 MoClo assembly can be achieved in 5−6 h by one step reaction via type IIs restriction endonuclease and ligase, and the cloning efficiency for up to 10 fragments is about 90%.171,172 3.1.1. (S)-Reticuline. (S)-Reticuline is a non-narcotic building block for novel antimalarial and anticancer drugs, and also acts as a main branch-point intermediate for biosynthesis of benzylisoquinoline alkaloids (BIAs).173 A major breakthrough in (S)-reticuline production was achieved in E. coli through three pathways (Figure 8):174 (i) an L-tyrosine overproducing pathway by disrupting tyrR gene, overexpressing phosphoenolpyruvate synthetase (PEPS), transketolase (TKT), 3-deoxy-D-arabinoheptulosonate-7-phosphate synthase (DAHPS), and chorismate mutase/prephenate dehydrogenase (CM/PDH); (ii) a dopamine producing pathway from L-tyrosine by overexpressing tyrosinase (TYR) and L-dopa specific decarboxylase (DODC); (iii) an (S)-reticuline producing pathway from dopamine by overexpressing monoamine oxidase (MAO), norcoclaurine synthetase (NCS), 4′-O-methyltransferase (4′OMT), cytochrome P450 N-methylcoclaurine hydroxylase (NMCH), coclaurine N-methyltransferase (CNMT), and 6-O-methyltransferase (6OMT). Given the early successes in E. coli, (S)-reticuline production from central metabolites in S. cerevisiae has proven unexpectedly difficult. In previous study, the upstream steps required for synthesis of (S)-reticuline from L-tyrosine were first constructed by overexpressing multiple genes, DODC, NCS, 6OMT, CNMT, 4′OMT, and NMCH, but the functional integration expression of so many enzymes for (S)-reticuline production in S. cerevisiae still remained a challenge.175 Thus, MoClo was used to build yeast expression vectors carrying multiple genes, DODC, NCS, 6OMT, CNMT, 4′OMT, and NMCH, and these vector sequences were derived from the pRS series of plasmids.175 All biobricks, including promoter, gene and terminator, were amplified by PCR primers with the IIs restriction endonuclease site, and then this integration expression vector was successfully assembled. The final engineered S. cerevisiae strain produced maximum titers of (S)reticuline from glucose up to 80.6 μg/L.

3.2. Sequence Homology-Based Methods

Sequence homology-based methods are in vitro techniques, which usually utilize long arbitrary overlap regions between parts to join DNA fragments that share these homologous sequences at their ends. These methods mainly contain Overlap Extension Polymerase Chain Reaction (OE-PCR),176 Circular Polymerase Extension Cloning (CPEC),177 Sequence and Ligation-Independent Cloning (SLIC),178 Nicking Endonucleases for Ligation-Independent Cloning (NE-LIC),179 Gibson assembly method,180 Seamless Ligation Cloning Extract (SLiCE),181 Uracil-Specific Excision Reagent cloning (USER),182 Serine Integrase Recombinational Assembly (SIRA),183 Isothermal Assembly, In-Fusion kit, Gateway kit, etc.164,165 The structural features of sequence homology-based methods are designed to share homology that makes it highly sequence-independent, and thus the same issues as restriction enzymes-based methods are prevented. In addition, this sequence homology ensures high efficiency and specificity of DNA assembly, suggesting that homology-based approaches can easily assemble five or more DNA parts together in one step.184 As a good example for DNA assembly, USER is used to assemble multiple DNA fragments via the short overhangs,185 which can be inserted at one deoxyuridine (dU) nucleotide by PCR reaction with dUcontaining primers and a high-fidelity DNA polymerase.186 USER assembly is convenient for assembly of 2−7 DNA fragments with high efficiency up to 90%.187 Another example, Gibson assembly method can utilize fusion DNA polymerase and Taq DNA ligase to covalently join together the single-stranded complementary overhangs digested by T5 exonuclease.180 Thus, as a useful molecular engineering tool, these methods can be used to seamlessly assemble synthetic and natural genes, genetic pathways, and entire genomes. 3.2.1. β-Ionone. β-Ionone is a fragrance ingredient used in many fragrance compounds, such as decorative cosmetics, fine fragrances, shampoos, toilet soaps and other toiletries. It is also a key intermediate for the synthesis of vitamins A, E and K.188 βIonone biosynthesis initiates from FPP, and then FPP is transformed into geranylgeranyl diphosphate (GGPP) by geranylgeranyl diphosphate synthase (CrtE, BTS1). GGPP is catalyzed to form β-ionone by phytoene synthase/lycopene cyclase (CrtYB), phytoene desaturase (CrtI) and carotenoid cleavage dioxygenase (CCD1) (Figure 5).189 A multistep metabolic engineering strategy was undertaken by combining four different approaches to increase β-ionone production,190 including: (i) modulation and optimization of the FPP branch point; (ii) modulation of the synthetic pathway to increase the precursor pool for β-carotene synthesis; (iii) coexpression of CrtI, CrtE, CrtYB, and CCD1 to enhance β-ionone production. However, the low translational efficiency of this system limited the efficient production of β-ionone (0.22 mg/L).191,192 To overcome this bottleneck, an alternative S. cerevisiae platform was constructed to synthesize β-ionone by combining two genetic engineering approaches:193 USER cloning-compatible integrative vectors and high copy number episomal expressions systems. Carotenoid production was increased by integrating an extra copy of a truncated version of tHMG1 and the endogenous BTS1 via USER, together with the CrtYB and the CrtI genes in an FPP overproducing strain S. cerevisiae SCGIS22. Then, expression of CrtYB and CCD1 in a high copy number plasmid via USER assembly led to a final 8.5-fold increase in β-ionone concentration up to 0.63 mg/g DCW. 3.2.2. 1,4-Butanediol. 1,4-Butanediol (1,4-BD) is an important chemical intermediate that is widely used for 18


Chemical Reviews

Review

Figure 9. Schematic of 1,4-butanediol biosynthetic pathway. SucCD: succinyl-coA synthetase; SucA: 2-oxoglutarate decarboxylase; SucD: succinate semialdehyde dehydrogenase; 4HBd: 4-hydroxybutyrate dehydrogenase; Cat2:4-hydroxybutyryl-CoA transferase; Hcar: 4-hydroxybutyryl-CoA reductase; Adh: alcohol dehydrogenase.

production of plastics, elastic fibers, polyurethanes, and pharmaceuticals. In order to overcome the limitation in chemical synthesis, a successful 1,4-BD producing pathway was found in microorganisms by pairing computer aided pathway design with genome-scale metabolic modeling, including two parts (Figure 9):10 (i) upstream pathway for biosynthesis of 4-hydroxybutyrate (4HB) from glucose, including 2-oxoglutarate decarboxylase (SucA), succinyl-CoA synthetase (SucCD), CoA-dependent succinate semialdehyde dehydrogenase (SucD), 4-hydroxybutyrate dehydrogenase (4HBd); (ii) downstream pathway for conversion of 4HB to 1,4-BD, including 4-hydroxybutyryl-CoA transferase (Cat2), 4-hydroxybutyryl-CoA reductase (Hcar), alcohol dehydrogenase (Adh). When the upstream and downstream pathways were assembled together using three vectors, the final E. coli strain was capable of producing 1,4-BD up to 18 g/L. Simultaneously, metabolic burdens on microbial workhorses also emerged, and resulted in undesirable physiological changes, placing hidden constraints on host productivity.194 Thus, Gibson assembly method was used to assemble metabolic system for autonomous 1,4-BD biosynthesis, and this system could be partitioned into two parts according to desired functionality:195 (i) enzymatic reactor, responsible for converting substrates and intermediates to the desired product; (ii) genetic controller, responsible for controlling the enzymatic reactor in a dynamic and programmable fashion. When metabolic system for autonomous 1,4-BD production was constructed by integrating genetic controllers with enzymatic reactor, the resulting strain E. coli EWCB3 was capable of autonomously producing 1,4-BD.

pathways by assembling multiple fragments.197 In this method, all DNA parts are designed with homologous arms between neighboring parts in the pathway, and these DNA parts can be directly transformed into S. cerevisiae. Then, circular plasmids are successfully constructed by its native recombinase. However, everything has two sides and is not an exception, homology recombination-based methods also has disadvantages. Homology recombination-based methods leave repeated scar sequences between all of the assembled parts due to the nature of integrase sites, which may be problematic for maintaining DNA integrity or mRNA folding.165 In addition, these approaches mainly depend on native recombinases, thus limiting application range of species. 3.3.1. L-Ornithine. L-Ornithine is a nonprotein amino acid and has been used in many fields, including health care, drug manufacturing, and chemical industry.205 Several attempts have been made to engineer the arginine biosynthetic pathway for Lornithine production through overexpression of genes argCEBD, inactivation of gluconate kinase (gntK), and arginine repressor (argR).206 To further increase L-ornithine production, modular pathway engineering was used to rewire the biosynthetic pathway for L-ornithine (Figure 10),207 including: (i) rewiring of the urea cycle through overexpression of CAR1 (arginase), downregulation of CAR2 (L-ornithine transaminase), and deletion of ARG3 (L-ornithine carbamoyltransferase); (ii) subcellular trafficking engineering and pathway relocalization by overexpression of ARG5,6, ARG7, ARG8, ARG2, ORT1 (ornithine transporter), AGC1 (glutamate uniporter), GDH1, GDH3, GLT1, GLN1, ODC1 (α-ketodicarboxylate or α-ketoglutarate transporter), argA, argB, argC, argD, argE; (iii) improving precursor supply by overexpression of PDA1, CIT1, ACO2, IDP1, PYC2, AOX1 (NADH alternative oxidase), NDI1 (NADH:ubiquinone oxidoreductase), MTH1-ΔT (truncated glucose sensing regulator), and deletion of KGD2 (dihydrolipoyl transsuccinylase). However, it was difficult to simultaneously overexpress so many genes, and thus Yeast DNA assembler was adopted to assemble this multigene pathway, including promoter replacement, chromosome integration and plasmid construction. The modules for gene expression were composed of a promoter, a structural gene, a terminator and promoter of the next module for homologous recombination. In this way, more than 37 different genetic modifications were achieved, and more than 64 engineered yeast strains were constructed and evaluated. The best-performing strain showed that rewiring of arginine metabolism led to L-ornithine production with a titer of 5.1 g/ L in fed-batch fermentations.

3.3. Homology Recombination-Based Methods

Homology recombination-based methods are in vivo techniques, which can omit any need for restriction endonucleases and instead use native cell homologous recombination machinery to assemble multiple fragments nearly at the same time. These methods include Yeast DNA assembler,196,197 B. subtilis DNA assembly,198,199 E. coli RecET system,200 E. coli Redαβ system,201 etc.164,165 Homology recombination-based methods are simple, efficient and reliable, and are exploited with wide application scope from simple cloning of small inserts to the assembly of an entire genome, including four levels:202,203 large metabolic pathways, circular plasmids, eukaryotic chromosome and bacterial genome. In addition, these methods are also used for generation of clone libraries and expression analysis of eukaryotic and prokaryotic systems.204 Taking Yeast DNA assembler as an example, this method naturally occurs in S. cerevisiae with high efficiency and fidelity, which is developed to reconstruct large 19


Chemical Reviews

Review

Figure 10. Schematic of L-ornithine, putrescine and L-arginine biosynthetic pathways. CAR1: arginase; ARG2: glutamate N-acetyltransferase; ARG5,6: acetylglutamate kinase and N-acetyl-gamma-glutamyl-phosphate reductase; ARG7: mitochondrial ornithine acetyltransferase; ARG8: acetylornithine aminotransferase; GDH1,3: NADP+-dependent glutamate dehydrogenase; GDH2: NAD+-dependent glutamate dehydrogenase; GLT1: NAD+dependent glutamate synthase; GLN1: glutamine synthetase; CIT1: citrate synthase; PYC2: pyruvate carboxylase isoform; ACO2: putative mitochondrial aconitase isozyme; IDP1: mitochondrial NADP+-specific isocitrate dehydrogenase; PDA1: E1 alpha subunit of the pyruvate dehydrogenase complex (PDH); glk: glucokinase; gdhA: glutamate dehydrogenase; argA: glutamate N-acetyltransferase; argB: acetylglutamate kinase; argC: N-acetyl-gamma-glutamyl-phosphate reductase; argD: acetylornithine aminotransferase; argE: ornithine acetyltransferase; argH: argininosuccinate lyase; argG: argininosuccinate synthase; argF: ornithine carbamoyltransferase; speC: biosynthetic ornithine decarboxylase; speF: degradative ornithine decarboxylase; potE: putrescine/ornithine antiporter.

3.4. Bridging Oligo-Based Methods

with the help of several linkers at one time. In LCR method, DNA parts that will be joined are mixed with bridge oligos and thermostable DNA ligase. With support of a thermostable ligase, DNA parts can be brought together by bridging oligos after repeated cycles of denaturation, annealing and ligation. Further, multiple cycles of denaturation-annealing-ligation will allow the assembly of multiple DNA parts. Thus, LCR is considered as being faster, cheaper and more convenient than homology-based methods.196 Under the optimized conditions, LCR is used for the rapid assembly of >20 kb multiple DNA parts with 60−100% precision, exceeding the efficiency of CPEC or Gibson assembly.202 3.4.1. Lipids. Microbial lipids is considered as a potential feedstock for the biodiesel industry and there is therefore much interest to produce these chemicals through industrial

Different from sequence homology-based methods, bridging oligo-based methods provide a new method to scarlessly assemble multiple fragments by single-stranded bridging oligos, such as Ligase Cycling Reaction (LCR),172,196 PaperClip method,208 Single Strand Assembly method (SSA),209 Multigene Pathway Engineering with Regulatory Linkers method (MPERL),210 etc. Bridging oligo-based methods are novel, reliable and rapid DNA assembly strategies for multigene pathway engineering to construct and tune multigene pathways at all control levels for the biotechnological production of complex metabolites. PaperClip method reduces cost and preparation time for assembly through recycling bridging oligonucleotides, SSA method is easily automatable with double stranded DNA, and M-PERL method can generate libraries of a whole pathway 20


Chemical Reviews

Review

Figure 11. Schematic of lipid, pentadecane, free fatty acid, triacylglycerol, avermectin, and triacetic acid lactone biosynthetic pathways. PD: pyruvate dehydrogenase; ACC1: acetyl-CoA carboxylase; FAS: fatty acid synthase; DGA1: diacylglycerol acyltransferase; SgcE (PKS): Type I polyketide synthase; SgcE10 (TE): thioesterase; KA: β-keto-synthase; AT: acyltransferase; ACP: acyl carrier protein; KR: β-keto-reductase; DH: dehydratase; PTT: phosphopantetheinyl transferase; FADD: fatty-acyl-CoA synthetase; AvtAB: ABC transporter AvtAB; aveR: positive regulator; aveF: C5ketoreductase; aveD: C5−O-methyltransferase; aveA1/aveA2/aveA3/aveA4: Type I polyketide synthase; aveE: cytochrome P450 hydroxylase; 2-PS: typeIII polyketide synthase 2-pyrone synthase.

Figure 12. Overview of analysis methods for pathway evaluation. (A) Model technology is used for simulation analysis of predictive data and experimental data. (B) Reverse metabolic engineering is utilized for comparative analysis of the reference strain and the engineered strain. (C) X-omic technology is adopted for in vivo analysis of the difference between different strains or the same strain under different conditions. (D) Targeted engineering is used for in vitro analysis of the metabolic bottleneck of biosynthesis pathway and the component of substrate and enzyme. (E) Biosensor engineering is applied for constructing closed loop control systems, minimizing cellular stress, monitoring and optimizing native and synthetic pathways.

21


Chemical Reviews

Review

biotechnology.211−213 Lipid biosynthesis begins with acetylCoA, and needs three steps (Figure 11): (i) acetyl-CoA is converted into malonyl-CoA by acetyl-CoA carboxylase (ACC1), and then followed into acyl-CoA by fatty acid synthase (FAS); (ii) acyl-CoA and glycerol-3-phosphate are acylated to form lysophosphatidic acid, and then is further acylated into phosphatidic acid (PA); (iii) PA is dephosphorylated to form diacylglycerol, and then is acylated into lipid by diacylglycerol acyltransferase (DGA1). While oleaginous yeasts have the potential to produce lipids, cell growth is limited due to the fact that these yeasts store excess carbon as neutral lipids within intracellular bodies.214,215 Thus, metabolic engineering has been performed to improve lipid production in oleaginous yeast, such as overexpressing glycerol-3-phosphate dehydrogenase (GPD1) and δ-9 stearoyl-CoA desaturase (SCD), and deleting a second isoform of glycerol-3-phosphate dehydrogenase (GUT2).216−218 Based on this, Tai and co-workers employed a “push-pull” strategy by overexpressing the native ACC1 and DGA1, thus resulting in a 5-fold increase in lipid production.219 To further increase lipid production, promoter strength needed to be enhanced, but there was no enough sites for restriction digest. Thus, expression cassettes were reassembled using LCR with the bridge primers, i.e., LCR was used to clone the GAPDH and ACL promoter fused to the ACC1 and DGA1 genes, forming PGAPDHACC1 and PACL-DGA1, respectively.220 The best strain R. toruloides RT880-AD was able to produce 16.4 g/L lipid from 70 g/L glucose in shake-flask experiments.

Expression (CARRIE),228 Multiple EM for Motif Elicitation (MEME),229 TRANSFAC,230 JASPAR,231 etc.; (iii) genomescale protein−protein interaction network model232,233 to explore enzyme structure and function,234 drug targets235 and functional hubs.236 Several web sites or software packages are available for searching the desired data, such as Search Tool for the Retrieval of Interacting Genes (STRING),237 Database of Interacting Proteins (DIP),238 Database of Three-Dimensional Interacting Domains (3did),239 Biomolecular Interaction Network Database (BIND),240 etc.; (iv) whole-cell model241,242 to simulate whole cell metabolism and predict phenotypic outcomes in various environmental and genetic conditions by integratively modeling multilayer data, such as metabolic, environmental perturbations, transcriptional regulation, signal transduction, biochemical testing data, etc. Combining these tools with metabolic databases, high through-put technology and computational algorithms, model technology enables systematic evaluation of cell behaviors at different conditions for different genotypes. However, it is still difficult to precisely predict the near-holistic metabolic profiles of cells. To solve this challenge, it is essential to integrate all the available networks into one cellular network by establishing the appropriate interfaces between different subnetworks, and this is still a new field in development. 4.1.1. Putrescine. Putrescine has many industrial applications, such as polymers, pharmaceuticals, agrochemicals, surfactants, and other additives.243 The putrescine biosynthetic pathway is started from the TCA metabolite α-ketoglutarate and combined with multistep reactions catalyzed by glutamate dehydrogenase (gdhA), N-acetylglutamate synthase (argA), acetylglutamate kinase (argB), N-acetylglutamylphosphate reductase (argC), acetylornithine transaminase (argD), acetylornithine deacetylase (argE), and biosynthetic/degradative ornithine decarboxylase (speC/speF) (Figure 10). Recently, a successful engineered E. coli was constructed with two approaches: (i) enhancement of the synthetic pathway by replacing promotors of argininosuccinate lyase (argH), argECB, speF, putrescine/ ornithine antiporter (potE), argD, speC; (ii) deletion of a sink pathway including spermidine synthase (speE), spermidine acetyltransferase (speG), ornithine carbamoyltransferase (argI), glutamate-putrescine ligase (puuPA), and stress responsive RNA polymerase sigma factor (rpoS). Based on these genetic manipulations, the engineered E. coli could produce 1.68 g/L putrescine.243 However, it is difficult to further enhance production of putrescine, and thus selection of gene targets became the crucial factor for enhancing putrescine production. As a consequence, a new method, that is, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, was employed for identifying target genes to improve putrescine production. Based on this analysis 20 two genes involved in glycolysis (eno, pgm, gapA, f baAB, tpiA, pgk, pykAF, and glk), TCA cycle (icd, acnA, acnB, and gltA), putrescine biosynthesis (gdhA, argA, argB, argC, argD, argE, speC, and speF), and other pathways (ackA and ppc) were identified as potential targets for improving putrecine production and were subsequently evaluated. Six predicted genes including argB, argC, argD, argE, speC, and speF were consistent with previous experimental results. Among the other 16 genes identified from modeling as amplification targets, five were experimentally verified as amplification targets. Finally, expressing glk gene together with the above predicted amplification targets successfully increased production of putrescine from 1.68 to 2.23 g/L in E. coli XQ52 (p15SpeC), with the yield enhancement of 32.7%.244 The strategy reported has great potential for

4. PATHWAY EVALUATION Metabolic engineering has been applied to span the entire spectrum of biotechnology and encompass creation of new pathways as well as improvement of existing pathways. Once a synthetic metabolic pathway has been introduced into a host strain, the next step is to evaluate the practical possibility of the reconstructed pathway in industry. However, the result is almost predictable, that is to say, the genetic manipulation usually fails to achieve the desired change in phenotype. This raises questions, how to precisely evaluate the reconstructed metabolic pathways and how to accurately identify the difference between ideal and reality. Thus, four innovative strategies have been employed to solve these questions (Figure 12): (i) simulation analysis: model technology; (ii) comparison analysis: reverse metabolic engineering; (iii) in vivo analysis: X-omic technology; (iv) in vitro analysis: targeted engineering; (v) selection analysis: biosensor engineering. 4.1. Model Technology

A large amount of genomic data, literature and databases have laid a good foundation for comprehensively understanding the physiological function of cells. Especially, recent advances in simulation techniques have made further progress in this notion: (i) genome-scale metabolic network model221,222 to predict target of genes to be deleted and amplified, target of enzymes to be up- and down-regulated, and target of pathways to be engineered and reconstructed using computational algorithms, such as FBA, 44 minimization of metabolic adjustment (MOMA),223 OptKnock,67 OptGene,224 regulatory on/off minimization algorithm (ROOM),225 etc.; (ii) genome-scale transcriptional regulatory network model226,227 to analyze the critical transcriptional regulators in metabolic pathways and its interaction between enzymes expression and other transcriptional regulators by computational tools, such as Computational Ascertainment of Regulatory Relationships Inferred from 22


Chemical Reviews

Review

Next, β-amyrin pathway was optimized by promoters reconstruction and directed transcriptional regulation, and the final βamyrin titer was increased to 138.80 mg/L in fed-batch fermentation.254 These results indicate that linking genotype and phenotype of S. cerevisiae strains reveals metabolic engineering targets and leads to triterpene hyper-producers.

developing industrial strains that can be used for commercial production of putrecine. 4.2. Reverse Metabolic Engineering

In the early emergence of metabolic engineering, the classical problem is how to identify a flux-limiting step in a specified metabolic pathway. Generally, the feasibility of introducing any heterologous genes or making any change in host genome is almost infinite, but the effective strategies of engineering metabolic pathway still remains indefinite. To elucidate such effective strategies, reverse metabolic engineering can be used and it involves four steps:245 (i) constructing a mutant library using nontargeted approaches,246 such as random mutagenesis, gene overexpression libraries, gTME, MAGE, TRMR, ribosome engineering and genome shuffling, etc.;247 (ii) identifying desired phenotype by high-throughput screening technology using detectable parameters as filters, such as pH, growth rate, fluorescence, etc.;248 (iii) analyzing the genetic information for this phenotype by comparative analysis of the difference in phenotypes between desired mutant type and wild type using Xomic data, such as genomics, transcriptomics, proteomics, etc.; (iv) endowing this phenotype on another organism by reintroducing the genetic information responsible for desired phenotype into a candidate host by directed modification, such as site-specific mutagenesis technology, homologous recombination, CRISPR/Cas9 editing, etc. Biological systems are complex and the relationship between genes and phenotypes are usually nonlinear,249 suggesting that it is difficult to obtain desired phenotype by traditional metabolic engineering. However, reverse metabolic engineering is designed by comparative analysis of wild type and its mutant type, which can significantly enhance the possibility of obtaining desired phenotype. In addition, with the help of parallel strain improvement experiments246 and diverse simulation algorithms,250 the corresponding relationship between genetic information and phenotypic information can be accurately established, which will further accelerate the procedure of getting desired phenotype. 4.2.1. β-Amyrin. β-Amyrin serves as an olefin precursor for a wide range of downstream products, such as various triterpene saponins,251 which can be applied in dietary supplements, anticancer agents and vaccine adjuvants. β-Amyrin biosynthesis begins with FPP generated from the MEP pathway, and then FPP is transformed into β-amyrin by three enzymes in the sterol pathway (Figure 5), i.e., squalene synthase (ERG9), squalene epoxidase (ERG1), and β-amyrin synthase (BAS). When the whole genome Illumina-Solexa sequencing of S. cerevisiae CEN.PK113−7D and S288C was used to identify single nucleotide polymorphisms (SNPs), three genes were detected with a significant number of SNPs, i.e., ERG9 in the sterol pathway, ERG8 in the MEP pathway and HFA1 (acetyl-CoA carboxylase) in the initial step of the fatty acid pathway.252 These differences were confirmed by the corresponding physiological characterization that ergosterol content in CEN.PK113−7D was significantly higher than that of S288C. These results indicated that ERG9, ERG8, and HFA1 might be the important points as metabolic engineering targets. To test this hypothesis, these three corresponding genes were overexpressed, and the results showed that the final concentration of β-amyrin was increased by almost 500% up to 3.93 mg/L compared with that of the control strain.253 To further enhance β-amyrin production, the sterol pathway was engineered by integrated expression of ERG1, inducible expression of BAS and down-regulation of lanosterol synthase (ERG7), and the titer of β-amyrin was up to 6 mg/L.251

4.3. X-omic Technology

Comparative analysis using X-omics has been employed to accelerate the process of strain improvement, in which the potential genes or enzymes responsible for specific pathway can be obtained by comparing X-omics data from different strains or the same strain under different conditions.14 In system metabolic engineering, X-omics mainly consists of five layers:249 (i) genomics to discover new genes using comparative analysis or uncover the cowork mechanism of genes sets using functional analysis,255 which can be sequenced by next generation sequencing technology;256 (ii) transcriptomics to identify target genes for metabolic engineering through detecting the amounts of mRNA in a given time or environment, which can be obtained by high-throughput microarray,257 or RNA deep sequencing technology;256 (iii) proteomics to analyze differences of protein expression levels as well as their interaction relationships when modifying target enzymes in specific routes, which can be measured by two-dimensional electrophoresis, mass spectrometry (MS), matrix-assisted laser desorption ionization-time-offlight MS, and liquid chromatography-tandem MS technology; (iv) metabolomics to balance transfer of intermediates or supply of cofators by quantitatively measuring the concentration of metabolites,9 which can be performed by nuclear magnetic resonance, high-performance liquid chromatography, and MS; (v) fluxomics to quantify fluxes in metabolic networks by elucidating the distribution of carbon fluxes into desired pathway and other pathways in cells that are competing for the precursor metabolites or cofactors, which can be traced by isotopic-based flux analysis technology. However, it is difficult to reconstruct a framework in which X-omics data can be systematically reintegrated to discover the underlying interactions in different layers when searching for targets for system metabolic engineering. Thus, a framework termed “Trans-Omics” has been proposed, in which an X-omics network is reconstructed by connecting multiple omics layers with five technologies:258 metabolic regulation, transcriptional regulation, kinase-substrate relationship, protein−protein interaction, and allosteric regulation. With a comprehensive analysis of biochemical interactions in trans-omics networks, it is helpful to understand the static and dynamic signal flow of intracellular metabolism on cell systems and determine the factors that truly affect desired products or phenotypes. 4.3.1. Spider Dragline Silk. Spider dragline silk (SDLS), originally produced by spiders, is 5-fold stronger than steel and 3fold tougher than the man-made fiber Kevlar.259 Due to the unique mechanical properties, numerous applications can be realized in military and pharmacy, including the manufacture of body armor, parachute cords, protective clothing, aircraft materials and artificial joint.260 SDLS consists of two proteins, the major ampullate spidroins 1 and 2, each of which is approximately 100 amino acids with glycine and alanine rich repeats.260 Various recombinant SDLS have been expressed with the size range from 25 to 140 kDa, but these SDLS showed many defective features compared to the native SDLS.261,262 These results suggest that the complete structure of recombinant SDLS is the key factor in controlling its mechanical properties.260,263 23


Chemical Reviews

Review

Figure 13. Schematic of spider dragline silk biosynthetic pathway. GlyA: serine hydroxymethyltransferase; glyQ: glycine tRNA synthetase, α subunit; glyS: glycine tRNA synthetase β subunit; glyVXY: tRNA-glycine; cycA: glycine transporter.

However, it is difficult to express the native-sized recombinant SDLS owing to its huge molecular weight (250−320 kDa) and high GC content (70%).260 To identify the related gene targets for SDLS expression, comparative proteomic analysis was taken, and the results displayed that enzymes in glycine biosynthetic pathway were upregulated such as serine hydroxymethyltransferase (GlyA) and glycyl-tRNA synthetase β subunit (glyS), suggesting that the L-glycine and glycyl-tRNA pools were insufficient for SDLS production. Thus, GlyA and glyVXY were overexpressed to increase the supply of L-glycine and glycyltRNA (Figure 13), which might be beneficial for production of high molecular weight SDLS. Finally, the engineered E. coli strain BL21(DE3)/pSH96+pTetgly2-glyAn allowed a 10−35 fold increase in high molecular weight (193, 239, and 285 kDa) recombinant SDLS production.260 This successful SDLS production indicates that X-omics technology can provide new insights in understanding the relationship between the metabolic status of cells and those producing phenotype.

as well as after the traditional genetic manipulation. Transforming reaction process from intracellular to a cell-free system, not only the metabolic bottleneck of biosynthesis pathway can be easily found by adjusting the component of substrate and enzyme systematically, but also the substrate channel of pathway enzymes can be balanced without reengineering organisms laboriously. Despite these powerful advantages, several limitations still need to be overcome: (i) As an isolated part of intracellular metabolic networks, the specific biosynthesis pathway can not take all influences of cell metabolism into account; (ii) although cofactors can be supplied at a proper concentration freely during reactions, it is difficult to implement intracellularly; (iii) the cascade reactions of spatially defined enzymes intracellular is difficult to simulate in a tube containing enzymes mixture. Therefore, developing detection methods with multiple synthesis fluorescent may enable investigators to obtain multienzyme kinetics in both open system and confined environment in the future.267 4.4.1. Farnesene. Farnesene, one of the simplest acyclic sesquiterpenes, is used as a precursor for biofuel production due to its low hygroscopicity and high energy density.268 Farnesene biosynthesis starts with isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), generated in the MEP pathway (Figure 5),268 which is mainly catalyzed by acetyl-CoA acetyltransferase (AtoB), HMG-CoA synthase (ERG13), HMGCoA reductase (tHMGR), mevalonate kinase (ERG12), phosphomevalonate kinase (ERG8), mevalonate pyrophosphate decarboxylase (MVD1), IPP isomerase (Idi). IPP and DMAPP are condensed into farnesyldiphosphate (FPP) by FPP synthase (IspA), and then FPP is converted into farnesene by farnesene synthase (AFS).268 Based on this, farnesene production was performed in E. coli, S. cerevisiae, and Y. lipolytica, and farnesene titers were up to 380.0, 170, and 260 mg/L, respectively.268−270 These low productivities indicated that there was no available principles to guide metabolic engineering for farnesene biosynthesis in microbial hosts, that is, the key factors in channelling metabolic flux to farnesene accumulation were still unknown, in particular steady-state kinetics information. Thus, the farnesene biosynthetic pathway was reconstructed in vitro by expressing and purifying nine recombinant proteins, i.e., AtoB, ERG13, tHMG1, ERG12, ERG8, MVD1, Idi, IspA, and AFS.271 These proteins were used to analyze the contribution of each enzyme to farnesene accumulation, thus obtaining its optimal molar ratio and predicting the distributed metabolic bottlenecks. Based on the results from in vitro system, farnesene production in E. coli was rationally optimized by quantitatively overexpressing each component. Especially, an extra Idi overexpression resulted in a 5.5-fold increase in farnesene production over the control strain.

4.4. Targeted Engineering

Traditional metabolic engineering has made great advances in optimization and innovation of the industrial fermentation. However, there still remains two challenges:264 (i) many key gene targets are not precisely identified to improve specific cellular functions; (ii) many engineering works have not achieved the expected results. To overcome these challenges, targeted engineering is proposed and used to build high-efficiency synthetic pathways,265,266 mainly including four steps:264 (i) in vitro pathway reconstitution and its steady-state kinetic analysis to analyze the optimal proportion of enzymes in metabolic pathways and predict the distributed bottlenecks in specific pathways, which will be critical in guiding the following biosynthesis optimization; (ii) in vivo rational design and directed pathway modification to achieve the optimal ratio of enzymes in cascade reactions, which can be redesigned precisely using genetic methods, such as gene overexpression, deactivation and down-regulation; (iii) metabolic status recognition and targeted proteomics analysis to detect not only the concentration of intermediates but also the expression level of each enzymes in the targeted pathway, which can provide valuable reference informations to the previous manipulation in vivo; (iv) microbiol cell factories reconstruction to integrate other metabolic strategies for further facilitating product biosynthesis, such as cofactor engineering, modular pathway engineering and promotor engineering, etc. As an excellent supplement for metabolic engineering, targeted engineering emphasizes two aspects, “in vitro design” and “in vivo evaluation”, indicating that it can provide precise information with high data density before 24


Chemical Reviews

Review

Figure 14. Overview of genetic hierarchy control for pathway optimization. Metabolic pathways can be systematically optimized to achieve the balance of cellular network and chemicals biosynthesis by powerful synthetic biology tools, such as promoter engineering, transcription factor engineering, synthetic RNA switch, protein engineering, cofactor engineering, structural biotechnology, compartmentalization engineering, modular pathway engineering, genome-scale engineering, multiplex genome editing, transporter engineering, morphology engineering, and consortia engineering.

production of valuable metabolites by constructing closed loop control systems with complexity equivalent to that in native metabolic pathways. 4.5.1. Triacetic Acid Lactone. Triacetic acid lactone (TAL) can be used as a precursor in biosynthesis of polyketides290 (e.g., lovastatin and 6-methylsalicylic acid) and chemical synthesis of materials291 (e.g., phloroglucinol, TATB, and resorcinol).292 The g2 ps1-encoded typeIII polyketide synthase (PKS) 2-pyrone synthase (2-PS) from Gerbera hybrida uses acetyl-CoA as a starter substrate and catalyzes two condensation reactions with malonyl-CoA, resulting in TAL formation (Figure 11).293 In addition, three other enzymes also can be used for the microbial synthesis of TAL, chalconesynthase (CHS) from Medicago sativa,294 fatty acid synthase B (FAS-B) from Brevibacterium ammoniagenes, and 6-methylsalycilic acid synthase (6-MSAS) from Penicillium patulum. 295 However, CHS mutation CHST197L/G256L/S338I results in altered specificity for acetyl-CoA and reduce delongations, and FAS-B and 6-MSAS require a phosphopantetheinyl transferase for activation.296 Thus, the current study focuses on 2-PS to enhance TAL production, but the highest TAL titers in E. coli were only 0.47 g/L by expressing wild-type 2-PS.295 Part of the reason is that the modification of genetic components for a target metabolite is often limited by lacking sensitive and rapid screening methods to identify desirable candidates from large gene libraries. Cirino and coworkers engineered the E. coli regulatory protein AraC to recognize TAL as an effector.292 An endogenous TAL reporter system, which was built by expressing β-galactosidase (LacZ) under AraC’s cognate promoter PBAD, was applied for screening 2-PS variants to elevate TAL production in E. coli. First, a TALresponsive AraC variant AraCP8 V/T24I/H80G/Y82L/H93R (AraCTAL) was isolated; then, the g2 ps1 gene was randomly mutated using error-prone PCR to build the library of 2-PS mutagenesis; next, AraC-TAL and the PBAD-lacZ reporter were used to screen this 2-PS library. Finally, the catalytic efficiency (kcat/Km) of the variant 2-PSL202G/M259L/L261N toward the substrate malonyl-CoA was improved 19-fold, and its corresponding TAL production was resulted in 20-fold increase up to 2.06 g/L.292 These results display the power of biosensor-based strategy in screening and engineering polyketide biosynthetic pathways. In addition, this

Then, targeted proteomics and mass spectrometry (MS)-based intermediate analysis were used to precisely determine the metabolic status of each mutant. The final concentration of farnesene was increased by 2000-fold, up to 1.1 g/L. These results indicate that targeted engineering can rationally control and precisely evaluate each stage in the optimal reconstruction of the biosynthetic pathway. 4.5. Biosensor Engineering

Metabolic engineering plays an important role in guiding the engineering of biological systems for biosynthesis of chemicals and materials. However, an enormous investment in time and resources are required for modifying each metabolic pathway, thus limiting the number of compounds to which these strategies can be applied. Currently, synthetic biology is a rapidly growing field that focuses on the development of new tools to design, construct, and optimize biological systems.272 As a new engineering discipline in synthetic biology, biosensor is emerging for the construction and control of biosynthetic pathways.273 Biosensors consist of two functional components:274 (i) the input component to detect the small molecule and undergo a conformational change, which can modulate the activity of the output component; (ii) the output component to translate its activity into measurable genetic outputs, which can mediate regulatory processes through diverse mechanisms. Biosensors mainly contain two types:274 (i) RNA sensors, including natural RNA sensing-regulatory elements,275 engineered RNA sensingregulatory devices,276−278 generation of new RNA sensing functions,279 etc.; (ii) Protein sensors, including transcriptional activators as sensors (e.g., transcription factor sensors,280−282 yeast three-hybrid sensors,283 chemical complementation sensors,284 etc.), protein activity-based sensors (e.g., combined domain sensors,285,286 intein-based sensors,287 etc.), fluorescent sensors,288 cell sensors,289 etc. Genetically encoded biosensors are valuable tools in the field of metabolic engineering:274 (i) Sensors can be used to monitor and optimize native and synthetic pathways by sensing and responding to the change of small molecules within a host cell; (ii) sensors can be applied to minimize cellular stress by balancing flux in the engineered pathways and regulating just-in-time synthesis at individual pathway steps; (iii) sensors can be adopted to enhance the 25


Chemical Reviews

Review

Figure 15. Strategies are used for promoter engineering. (A) Promoter library can be constructed by synthetic promoters with a broad range of transcription efficiency. (B) Promoter replacement can be built by selecting natural promoter with various strengths. (C) Synthetic RBS regulation can be established by RBS calculator v1.1 with a wide range of ribosome-binding strength. (D) Intergenic regions can be structured by various mRNA secondary structures with RNase cleavage sites.

problem, many attempts at promoter engineering have been made both in expanding the transcriptional capacity of the whole cell at transcriptional levels such as promoter library and promoter replacement, and in enabling tunable levels of gene expression at post-transcriptional levels such as synthetic RBS regulation and tunable intergenic regions (Figure 15). Many advances have been made to efficiently improve production of valuable chemicals: (i) promoter library to finely tune gene expression with a broad range of transcription efficiency using synthetic promoter, which can be designed by online analysis tools,300 such as iGEM,301−303 PlantCARE,304 etc.; (ii) promoter replacement to improve the expression of the rate-limiting enzyme through selecting natural promoter with various strengths via software tools,305 such as iGEM,301−303 CellML,306 etc.; (iii) synthetic RBS regulation to precisely control the translation initiation rate with a wide range of ribosome-binding strength,307 which can be predicted by RBS Calculator,141 RBS Designer,308 etc.; (iv) tunable intergenic regions to optimize in a combinatorial fashion the multigene pathways by generating libraries of various mRNA secondary structures,309 in which RNase cleavage sites can be predicted and changed by online tools, such as GeneSplicer,310 SplicePort,311 etc. Therefore, promoter engineering is essential for rational design to balance the expression of multiple enzymes that constitute a metabolic pathway or genetic program, and is a prerequisite for synthetic biology to achieve the optimal performance of biosynthetic pathway in a host strain. 5.1.1. Promoter Library. Synthetic control of gene expression is critical for metabolic engineering, especially precise control of key pathway enzymes. Promoter engineering is an effective strategy to generate the dynamic range, which is necessary to finely control gene expression for metabolic engineering applications.300 All kinds of strategies have been used to construct promoter libraries with various strengths for fine-tuning gene expression in metabolic pathway. Diacetyl is widely used in artificial butter flavoring, margarines, or similar oil-based products.312 Under aerobic conditions, pyruvate is converted to α-acetolactate by acetolactate synthase (ALS), and then α-acetolactate is transformed into diacetyl

strategy has been successfully used for production of mevalonate.280

5. PATHWAY OPTIMIZATION Natural metabolic pathways are regulated to produce the desired amount of different chemicals required for cell growth. However, synthetic metabolic pathways are not under such regulatory control, due to the fact that they are usually reconstructed using heterologous enzymes.297 Thus, introducing such pathways into cells often results in growth retardation and metabolic imbalance, owing to the accumulation of intermediates.14 To overcome these bottlenecks, powerful synthetic biology tools have opened new avenues for the systematic optimization of metabolic pathways in engineered microbial hosts to achieve the balance of cellular network and chemicals biosynthesis.298 These tools can be broadly classified into six categories on the basis of genetic hierarchy control (Figure 14): (i) Engineering at DNA level, including promoter engineering; (ii) engineering at RNA level, including transcription factor engineering, synthetic RNA switch; (iii) engineering at protein level, including protein engineering, cofactor engineering; (iv) engineering at metabolite level, including structural biotechnology, compartmentalization engineering, modular pathway engineering; (v) engineering at genome level, including genome-scale engineering, multiplex genome editing; (vi) engineering at cell level, including transporter engineering, morphology engineering, and consortia engineering. 5.1. Promoter Engineering

Synthetic biology is widely used for constructing genetic circuits to strengthen production of native metabolites or to endow cells with the productive ability of new chemicals. In this process, the precise control of gene expression is a critical step for metabolic engineering, which can influence the quantity of key pathway enzymes for maximizing production of desired chemicals. Transcriptional control are initially used to drive gene expression by promoter elements, but endogenous promoters are limited by the fact that they can not timely control and continuously maximize the transcription levels in cells.299 To solve this 26


Chemical Reviews

Review

Figure 16. Schematic of diacetyl, 2,3-butanediol, L-lactate, and hydrogen biosynthetic pathways. PK: pyruvate kinase; LDH: lactate dehydrogenase; PFL: pyruvate formate lyase; ALS: α-acetolactate synthase; ALDC: acetolactate decarboxylase; DR: diacetyl reductase; BDH: 2,3-butanediol dehydrogenase; PDH: pyruvate dehydrogenase; NoxE: H2O-forming NADH oxidase; PFOR: pyruvate ferredoxin oxidoreductase; HydAEFG: hydrogenases; NOD: nonenzymatic decarboxylation.

strain could produce the highest level of PD (140 mg/L). It is obvious that constructing promoters with diverse strengths will be used more commonly in metabolic engineering to improve the performance of engineered strains. 5.1.2. Promoter Replacement. Some native metabolic pathways are weak and slow in organism as rate-limiting enzymes occurring, which limit the yield and production of target product. For solving this problem, the strategies for promoter replacement are used to improve the expression of rate-limiting enzymes. L-arginine is an industrially important, semiessential amino acid, which has numerous applications in food and dietary supplement, pharmaceutical and cosmetics industries.317,318 The L-arginine biosynthetic pathway is comprised of eight enzymatic steps from glutamate (Figure 10), including acetyltransferase (argA), acetylglutamate kinase (argB), acetylglutamate semialdehyde dehydrogenase (argC), acetylornithine transaminase (argD), ornithine acetyltransferase (argE), ornithine transcarbamylase (argF), argininosuccinate synthase (argG), and arginosuccinase (argH).319,320 Many attempts have been made to design an L-arginine overproducer,321 such as removing feedback inhibition and repressors, overexpressing the arg operon, increasing carbamoyl phosphate pool, deleting exporter for glutamate. Based on this observation, the bottlenecks in Larginine production are analyzed, and the results show that the NADPH level may be a key point due to the fact that the biosynthesis of 1 mol L-arginine requires 3 mol NADPH. To increase NADPH generation in the pentose phosphate pathway, the native promoter of genes located in one operon including opcA, pgl, tal, tkt, and zwf, were replaced with the strong sod promoter in C. glutamicum AR3 strain.305 Next, to improve the availability of carbamoyl phosphate for ArgF, the native promoter of carbamoyl phosphate synthase (carAB) was replaced with the

through nonenzymatic decarboxylation (NOD) (Figure 16). Many metabolic engineering strategies were designed to improve diacetyl production, such as overexpressing ALS and NADH oxidase (NoxE), blocking acetolactate decarboxylase (ALDC), lactate dehydrogenase (LDH), and diacetyl reductase (DR). However, the final diacetyl titers were low, indicating that gene expression should be optimized to channel more carbon flux to diacetyl. Thus, a constitutive promoter library was constructed by randomizing promoter sequence, in which a wide range of expression activities was covered by 30 promoters. Eleven typical promoters in this library were selected for the constitutive expression of NoxE in L. lactis, and the results indicated that the variation of intracellular NADH/NAD+ ratios change the distribution of glycolytic flux at the pyruvate branch from lactate to diacetyl, and the corresponding diacetyl production was increased from 1.07 to 4.16 mM.313 Pentadecane (PD) is valued as primary components of diesel fuels. PD biosynthesis begins with malonyl-CoA, which is derived from acetyl-CoA by acetyl-CoA carboxylase (ACC1) and then is converted into pentadecaheptaene(PDH) by the iterative Type I polyketide synthase (SgcE) and the cognate thioesterase (SgcE10) (Figure 11).314 Finally, PDH is hydrogenated to PD. E. coli has been engineered to convert fatty acyl-ACP into longchain alkanes and alkenes by expressing the cyanobacteria alkane pathway, but the final titer of PD was only 35 mg/L.315 One of the possible reasons is that PD production in E. coli is strongly dependent on the ratio of SgcE10 with SgcE.316 Thus, the promoter library of lacO1 was constructed through random mutagenesis of the conserved regions of lacO1 promoter, and seven functional mutants of lacO1 promoter were applied to finely tune the expression of SgcE10 under various strengths, while SgcE expression was controlled by T7 promoter. When the SgcE10:SgcE ratio was optimized to 9:1, the final engineered 27


Chemical Reviews

Review

reduction, dehydration and elongation;339 (iii) FFAs are produced by introducing ACP thioesterase (TE).337,340−342 Four distinct genotypic alterations to overproduce FFAs in E. coli were systematically introduced:337 (i) knocking out the endogenous acyl-CoA synthetase (FADD) to block fatty acid degradation; (ii) heterologous expression of TE to increase short chain fatty acids; (iii) overexpressing ACC1 to improve malonylCoA supply; (iv) endogenous expression of TE to release feedback inhibition. Based on so many strategies, only 2.5 g/L FFAs was obtained in the engineered E. coli strain, and the reasons were mainly attributed to the effect of the genetic recalcitrance of T7 promoter on translation initiation rate. Thus, the 5′-UTR region of the native T7 promoter was replaced by four RBSs from the MIT Registry of Biological Standard Parts to form the engineered RBSs with differential translational activities. The engineered RBSs with the desirable dynamic ranges were used to regulate the GLY and FAS modules for further achieving the balance between the supply of malonylCoA and consumption of malonyl-ACP in expression system.343 The optimized E. coli strain (rbs29-mGLY-lACA-hFAS), in which the GLY and FAS modules were expressed with moderate and high strength RBSs, respectively, resulted in a final FFAs production up to 8.6 g/L under the optimal culture conditions. 5.1.4. Intergenic Regions. Many applications of synthetic biology need to balance the expression of multiple genes. Although operons actively promote the conversion of the possibility that multiple genes coordinately express into reality in prokaryotes and eukaryotes, the coordination in post-transcriptional process by a priori design that can control the gene expression levels in operons still remains a challenge.309 Thus, a new method is proposed to finely tune the multiple genes expression, in which tunable intergenic regions (TIGRs) are designed and inserted into operons to rebuild post-transcriptional control elements. Mevalonate (MEV) plays an important role in the mevalonate pathway, and acts as a ubiquitous biosynthetic intermediate for terpenoids and cholesterol.344,345 The mevalonate biosynthetic pathway involves three key enzymes from acetyl-CoA, acetylCoA acetyltransferase (AtoB), HMG-CoA synthase (HMGS), and HMG-CoA reductase (tHMGR) (Figure 5). The biosynthetic gene cluster of the mevalonate pathway from Streptomyces sp. strain CL190 has been cloned346 and introduced into Streptomyces lividans TK23 for heterologous mevalonate production,347 but the final concentration of MEV was only 0.50 g/L. Thus, the biosyntheic pathway of MEV was reconstructed in E. coli with AtoB, HMGS, and tHMGR clustered into one operon,309 but this overexpression inhibited both cell growth and MEV accumulation, possibly due to the toxicity of imbalanced gene expression. To overcome these difficulties, the TIGR libraries were designed to contain three regions,348 two variable hairpin sequences incorporating various RNase E sites. This design was useful for optimizing the expression of multiple genes in synthetic operons. Then, TIGRs were put between AtoB/ HMGS or HMGS/tHMGR through a megaprimer PCR approach to produce a wide range of phenotypes, and subsequently the phenotypes with functional operons were screened to improve MEV production. Finally, the strain E. coli DH10B harboring the improved pBad33MevT resulted in a 7fold increase in MEV titers.309

strong sod promoter. Then, to enhance the efficiency for converting L-citrulline to L-arginine, the native promoter of argGH was replaced with the strong elongation factor Tu (EFTu) promoter. The final C. glutamicum AR6 strain was able to produce 92.5 g/L L-arginine with the yield of 0.40 g/g. 2,3-Butanediol (2,3-BD) is considered as a platform green chemical, and has been used for printing inks, cosmetics, fumigants, explosives, plasticizers, and pharmaceuticals.322,323 The 2,3-BD biosynthetic pathway is involved in three key enzymes from pyruvate (Figure 16), i.e., α-acetolactate synthase (ALS), α-acetolactate decarboxylase (ALDC), and 2,3-BD dehydrogenase (BDH).324−326 The 2,3-BD gene clusters from B. subtilis 168, Bacillus licheniformis, Klebsiella pneumoniae, Serratia marcescens, and Enterobacter cloacae have been cloned and utilized for constructing an efficient 2,3-BD biosynthesis pathway,327,328 and the cluster from E. cloacae displayed the best ability to produce 2,3-BD.329,330 To reduce the metabolic burden and improve 2,3-BD production, the E. cloacae cluster was expressed under different strength of promoters respectively, including the IPTG-inducible promoter PT7 and Ptac, the constitutive promoter Pc and the native promoter Pabc. The activities of ALDC, ALS, and BDH in strain E. coli BL21/pETRABC with Pabc as superior promoter were higher than the other strains, and the final concentration of 2,3-BD was increased to 73.8 g/L under fed-batch fermentation.330 5.1.3. Synthetic RBS Regulation. To achieve the connection between genetic circuits and flux control in metabolic pathway, microbial engineering often requires to finely regulate protein expression. A rapid and modular method is designed to span the expression space of several proteins in parallel by pairing genes in combinatorial fashion with ribosome binding sites. Astaxanthin has been widely used as a feed supplement in poultry and aquaculture industries for color enhancement. Its applications also emerge in pharmaceutical and personal care industries. For the astaxanthin production pathway, the critical steps are the sequential reactions catalyzed by phytoene synthase (CrtYB), phytoene desaturase (CrtI), lycopene cyclase (CrtYB), β-carotenoid hydroxylase (CrtZ), and β-carotene ketolase (CrtW) (Figure 5).331 In addition, β-carotene can also be converted to astaxanthin by astaxanthin synthase (CrtS) and cytochrome P450 reductase (CrtR). As an alternative, astaxanthin biosynthesis in E. coli,332,333 S. cerevisiae,331,334 and Phaf f ia rhodozyma,335 has been achieved by overexpression and optimization of CrtZ/CrtW or CrtS/CrtR to stimulate the entire carotenogenesis pathway, and the final concentration of astaxanthin was up to 4.7 mg/g DCW. This low production is possibly due to the imbalance between enzymes production and intermediate metabolites consumption. To overcome this imbalance, six RBS expression modulators were selected to quantify the effect of RBS sequences on protein expression levels, and these RBS sequences were used to assemble a combinatorial library of operons for spaning a high-dimensional expression space. Based on this library, the expression levels of multiple genes in astaxanthin biosynthesis pathway were modulated in parallel to obtain an ideally balanced pathway. Finally, the valuable compound astaxanthin was accumulated up to 5.8 mg/g DCW in E. coli.307 Free fatty acids (FFAs) are usually used as feedstocks for chemicals and ex vivo production of biodiesel.336 FFAs biosynthesis pathways mainly contain three steps (Figure 11): (i) carboxylation of acetyl-CoA to malonyl-CoA by acetyl-CoA carboxylase (ACC1);337,338 (ii) conversion of acetyl-CoA and malonyl-ACP to acetoacetyl-ACP, which joins into the cycle of

5.2. Transcription Factor Engineering

Biosynthetic pathways should be regulated on the level of the whole cell, rather than the level of a single gene, because the 28


Chemical Reviews

Review

Figure 17. Strategies are used for transcription factor engineering. (A) Zinc-finger protein transcription factors consist of a zinc-finger protein (ZFP), a transcriptional regulatory domain (TRD) and a nuclear localization signal (NLS), which can function as the modularity of structure and function. (B) MYB and bHLH transcription factors can act as MYB TF family, bHLH TF family or MYB/bHLH complex, which is conducive to achieving more efficient functions. (C) ORCA proteins can be activated by post-translational modification or protein interaction, which can be used to regulate the terpenoid indole alkaloids biosynthetic pathways.

phenotypic variation often results from coordinated gene expression and protein−protein interaction.349 Transcription factors are sequence-specific proteins that usually consist of a DNA-binding domain, a transcription regulation domain and a nuclear localization signal, which can regulate the transcriptional rate by interacting with the promoter regions of target genes.350 Recently, transcription factors have caused widespread interests, due to the fact that they can be used to improve the production of desired products through controlling the abundance or activity of multiple enzymes in metabolic pathways.349 This approach has been described as transcription factor engineering, and it is a novel technology for up or down-regulating metabolic pathways to overproduce target metabolites.351 Many transcription factors have been demonstrated to be efficient for improving the production of valuable chemicals (Figure 17): (i) Zinc-finger

protein transcription factors (ZFP TFs) have one major advantage in the modularity of structure and function,350 such as TFIIIA, Cys2-His2, Cys4, Cys6, Cys4-His-Cys3, etc. This modularity makes ZFP TFs convenient for simultaneously expressing several TF genes to control the transcription of multiple genes, thus resulting in the fine-tuning of cascade catalysis in target pathways. (ii) MYB and bHLH transcription factors (MbH TFs) can act as MYB TF family such as MYB30, MYB114 and PAP1, or bHLH TF family such as MYC2, MYC3, and MYC4. Usually, both MYB and bHLH TF family can interact with each other and result in MYB/bHLH complex to achieve more efficient functions, which can enable the organisms to keep up with the increased metabolic complexity.352 (iii) Octadecanoid-Responsive Catharanthus AP2/ERF-domain (ORCA) proteins such as ORCA, ORCA2, and ORCA3 can be used to 29


Chemical Reviews

Review

Figure 18. Schematic of lignin biosynthetic pathway. PAL: phenylalanine ammonia lyase; TAL: tyrosine ammonia lyase; C4H: cinnamate 4hydroxylase; C3H: p-coumarate 3-hydroxylase; COMT: caffeate/5-hydroxyferulate-O-methyltransferase; F5H: ferulate-5-hydroxylase; 4CL: hydroxycinnamate CoA ligase; CCR: hydroxycinnamoyl-CoA: NADPH oxidoreductase; CAD: cinnamyl alcohol dehydrogenase. CCoA3H: pcoumarate Coenzyme A 3-hydroxylase; CCoAOMT: caffeoyl-CoA O-methyltransferase.

regulate the terpenoid indole alkaloids biosynthetic pathways,353 which make it feasible to improve the production of secondary metabolites. Transcription factor engineering is a useful tool for improving the production of target metabolites, but the challenge is how to discover and engineer more suitable transcription factors to activate or inactivate specific metabolic pathways to produce target chemicals. 5.2.1. Zinc-Finger Protein Transcription Factors. Zincfinger protein transcription factors (ZFP TFs) share a similar structure, which is composed of a highly conserved carboxyterminal region containing from four to six zinc fingers, and a much more divergent amino-terminal region. This zinc finger domain is conducive to interact with or bind DNA, RNA, or other proteins.351 Based on this understanding to the structure and function of ZFP TFs, several design strategies have been proposed to create artificial ZFP TFs to bind unique DNA sequences and discriminate effectively against nonspecific DNA,354 thus achieving gene regulation and gene therapy.355−357 Lignin is used to strengthen the walls of certain cells,358 which plays an important role in mechanical support, water transport and pathogen resistance. Lignin biosynthetic pathway mainly contains three steps (Figure 18):358 (i) the deamination steps of phenylalanine or tyrosine catalyzed by phenylalanine ammonia-

lyase (PAL), tyrosine ammonia-lyase (TAL) and cinnamate-4hydroxylase (C4H); (ii) the methylation step of monolignols catalyzed by p-coumarate 3-hydroxylase (C3H), caffeate/5hydroxyferulate-O-methyltransferase (COMT), and ferulate-5hydroxylase (F5H); (iii) the last biosynthesis steps of lignin catalyzed by hydroxycinnamate CoA ligase (4CL), hydroxycinnamoyl-CoA: NADPH oxidoreductase (CCR) and cinnamyl alcohol dehydrogenase (CAD). To better understand the mechanism of lignin biosynthesis, transcription factors have attracted attention. TFHP1 and Ntlim1 have been revealed as a positive cis-acting element Pal-box, which is a highly conserved sequence involved to the gene expression in lignin biosynthesis, such as PAL, 4CL, chalcone synthase (CHS) and CAD.358,359 When the endogenous Ntlim1 expression was completely inhibited, a 27% reduction of lignin content was observed in the transgenic tobacco, indirectly predicting that lignin production might be enhanced by overexpressing Ntlim1. These results indirectly indicate that transcription factors engineering may be an efficient way to enhance the biosynthesis of chemicals. 5.2.2. MYB and bHLH Transcription Factors. Most MYB transcription factors are formed by two related helix-turn-helix motifs, and the R2 and R3 repeats are responsible for binding 30


Chemical Reviews

Review

Figure 19. Schematic of anthocyanin biosynthetic pathway. PAL: phenylalanine ammonia lyase; C4H: cinnamate 4-hydroxylase; 4CL: 4coumarate:CoA ligase; CHS: chalcone synthase; CHI: chalcone-flavanone isomerase; F3H: flavanone 3-hydroxylase; DFR: dihydroflavonol 4reductase; ANS: anthocyanidin synthase; LDOX: leucoanthocyanidin dioxygenase; UFGT: UDP glucose-flavonol glucosyl transferase.

Figure 20. Schematic of vinblastine and violacein biosynthetic pathway. IPA: indole-3-pyruvic acid; AS: anthranilate synthase; VioA: tryptophan 2monooxygenase; VioB: polyketide synthase; VioC: monooxygenase; VioD: hydroxylase; VioE: violacein synthetase; TDC: tryptophan decarboxylase; DXS: D-1-deoxyxylulose 5-phosphate synthase; CPR: cytochrome P450-reductase; G10H: geraniol 10-hydroxylase; SLS: secologanin synthease; STR: strictosidine synthase; SGD: strictosidine β-D-glucosidase; T16H: tabersonine 16-hydroxylase; D4H: desacetoxyvindoline 4-hydroxylase; DAT: acetylCoA:4-O-deacetylvindoline 4-O-acetyltransferase.

Anthocyanins are the main pigments in flowers and fruits, which can act as insect and animal attractants.362 Anthocyanins biosynthesis is mainly dependent on the phenylpropanoid pathway,363 and two classes of genes are required (Figure 19):363 (i) the structural genes participating in anthocyanins formation, such as PAL, C4H, 4CL, chalcone synthase (CHS), chalcone-flavanone isomerase (CHI), flavanone 3-hydroxylase (F3H), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (LDOX), UDP glucose-flavonol glucosyl trans-

target DNA sequences to regulate gene expression. Coordinate control of multigene in biosynthetic pathway emerges as a potential way to guide the production of secondary metabolites,360 which can be achieved by specific transcription factors, such as MYB and bHLH transcription factors (MbH TFs). For example, flavonoid pathway can be regulated by combining specific MYB transcription factors with specific bHLH protein partners.361 31


Chemical Reviews

Review

Figure 21. Strategies are used for synthetic RNA switch. (A) Ribozyme switch consist of a sensor domain and an actuator domain, which function as cellular sensors to regulate target genes expression. (B) Riboswitch is one kind of cis-encoded regulatory RNAs, which act their function through inducing transcription termination or inhibiting translation initiation of target genes. (C) Antisense RNA switch is composed of a target-binding sequence and a scaffold sequence, which can be used to regulate many genes in biosynthetic pathways simultaneously.

directly from tryptophan and geraniol is catalyzed by geraniol 10hydroxylase (G10H), cytochrome P450 reductase (CPR), secologanin synthase (SLS), tryptophan decarboxylase (TDC), strictosidine synthase (STR); (ii) the biosynthesis routes from strictosidine are diverged toward the various TIAs. For example, dimeric alkaloids (Vinblastine) are formed from strictosidine via a peroxidase-catalyzed condensation of vindoline and catharanthine, which is mainly catalyzed by strictosidine β-Dglucosidase (SGD), tabersonine 16-hydroxylase (T16H), desacetoxyvindoline 4-hydroxylase (D4H), acetyl-CoA:4-Odeacetylvindoline 4-O-acetyltransferase (DAT). TIAs biosynthesis pathway highly depends on external signals, and is tightly induced by internal signals, such as jasmonate.367,368 Since ORCA2 and ORCA3 were jasmonate responsive transcription factors, overexpression of ORCA2 and ORCA3 induced the upregulation of multiple genes in TIAs biosynthesis pathway, such as CPR, TDC, STR, SGD, D4H. This upregulation selectively activated TIAs biosynthesis, and resulted in a large increase in TIAs formation.351,360,369 These results indicate that ORCAs have become powerful tools to increase the production of valuable secondary metabolites.360

ferase (UFGT); (ii) the regulatory genes controlling structural genes transcription, such as MYB and bHLH transcription factors. When five flavonoid pathway genes, PAL, CHS, F3H, DFR, and anthocyanidin synthase (ANS) were simultaneously expressed during the later stages of ripening, the content of anthocyanins increased significantly. However, the enzyme activities are unstable, possibly owing to the fact that the overall activity of flavonoid biosynthesis is tightly regulated by transcription factors. When bHLH and MYB proteins were ectopically expressed, a global expression of the structural genes was upregulated in response to these transcription factors.361 Thus, a significant enhancement of flavonoid pathway was caused, and the biosynthesis and accumulation of anthocyanins were observed.364,365 5.2.3. ORCA Proteins. Transcription factors can regulate gene transcription depending on internal signals, as well as external signals.360 External signals may function through internal signals, thus internal signals is decisive. As an example, the elicitor-dependent accumulation of secondary metabolites is mediated by jasmonate.353 Gene expression and metabolism are induced by jasmonate via octadecanoid-responsive catharanthus AP2-domain (ORCA) proteins, which are members of the AP2/ ERF-domain family of plant transcription factors.353 Terpenoid indole alkaloids (TIAs) have been widely used in modern medicine, such as antineoplastic agents (vinblastine), antihypertensive drugs (reserpine), antiarrhythmic drug (ajmaline), etc. TIAs biosynthesis is mainly involved to two steps (Figure 20):366 (i) the formation of intermediate strictosidine

5.3. Synthetic RNA Switch

Synthetic biology is widely used in native and non-native production of chemicals, mainly due to its potential to create new biological components in biosynthetic pathways. RNA molecules, with the ability to form diverse secondary structure and function, has become a malleable and attractive platform to 32


Chemical Reviews

Review

Figure 22. Schematic of xanthine biosynthetic pathway. AMP: adenosine monophosphate; IMP: inosine monophosphate; GMP: guanosine monophosphate; XMP: santhosine-5-monophosphate. ADA: AMP deaminase, NO: nucleotidase; NP: nucleoside phosphorylase; XDH: xanthine dehydrogenase; IDH: IMP dehydrogenase.

derive sophisticated tools from synthetic biology.370 Synthetic RNA design should fully reflect the advantages of RNA as a programmable design substrate, and make RNA molecules exhibit diverse activities, including sensing, regulatory, information processing, and scaffolding activities.371 These activities are beneficial to achieve accurate cell functions and can be utilized as key control elements for regulating accumulation of target metabolites. Recently, several synthetic RNA switches have been demonstrated to be efficient for improving production of valuable chemicals (Figure 21), including: (i) Ribozyme switches consist of a sensor domain and an actuator domain, which function as cellular sensors through an aptamer sequence and monitor temporal and spatial fluctuations through a ribozyme sequence.372 Ribozyme switches can be used as rational tuning tools for regulating target genes expression and improving metabolites accumulation. (ii) Riboswitches are one kind of cisencoded regulatory RNAs such as AdoCbl, FMN, Sadenosylmethionine and glycine riboswitches, which act their functions through inducing transcription termination or inhibiting translation initiation of target genes.373 Riboswitches can be used to regulate biosynthetic gene expression in a broad spectrum as they can bind to diverse sets of ligands such as metabolites, coenzymes and ions. (iii) Antisense switches are usually composed of two parts: a target-binding sequence for recognizing target mRNAs and a scaffold sequence for recruiting auxiliary protein.374 Antisense switches can be used to simultaneously regulate multiple genes in biosynthetic pathways with the ability to optimize gene expression rather than modify chromosomal sequences. As shown above, synthetic RNA switches are widely used for fine-tuning metabolic flux in production of chemicals, mainly owing to their versatile structure and convenient rebuilding properties. These advantages are also well-suited to meet the emerging challenges in the fields of circuit regulatory, metabolic pathways and microbial fuel cells.370 5.3.1. Ribozyme Switch. Ribozymes show similar mechanism with biocatalyst enzymes in biochemical reactions370 and have many important physiological functions, such as nucleotide splicing, phosphodiester bond cleavage and formation.375 During protein synthesis, ribozymes mainly mediate the formation of peptide bonds in the ribosomes.376−379 To achieve this target,

ribozymes usually operate in a sequence-specific way and use metal, theophylline or tetracycline as effectors.380,381 Building on these mechanisms, the engineered ribozyme elements may be developed as synthetic ligand-controlled gene-regulatory systems. Xanthine is generally used as a mild stimulant and bronchodilator for treatment of asthma symptoms. Xanthine is a product of purine degradation pathway, with the biosynthesis routes as follow (Figure 22):382 (i) AMP → IMP → inosine → hypoxanthine → xanthine catalyzed by AMP deaminase (ADA), nucleotidase (NO), nucleoside phosphorylase (NP), and xanthine dehydrogenase (XDH); (ii) AMP → IMP → XMP → xanthosine → xanthine catalyzed by ADA, IMP dehydrogenase (IDH), NO, and NP. Because the second pathway involves xanthosine as an intermediate,383 exogenous xanthosine can be directly converted to xanthine by NP.384,385 However, the accumulation of xanthine in cells is not high enough to be detected by traditional methods, and thus a more accurate method is needed, such as the noninvasive in vivo sensors of metabolite production. In order to meet this demand, the stranddisplacement-based ribozyme switch that contains a sensor domain with an aptamer sequence and an actuator domain with a hammerhead ribozyme sequence, was utilized for detection of xanthine accumulation in yeast.372 This switch exhibits tunable regulation, design modularity, and target specificity in transmitting changes in metabolite accumulation to changes in reporter expression levels. When the conversion of xanthosine to xanthine was started in the yeast cells, the accumulation of xanthine was monitored by a xanthine-responsive switch coupled to the regulation of a GFP reporter. In other words, the increased xanthine accumulation was a positive correlation with an increase in GFP levels.371 Thus, these metabolite sensing ribozyme switches can be applied to screen high xanthine-producing strains. 5.3.2. Riboswitch. Recently, riboswitches have been discovered in prokaryotes and eukaryotes,370 and their function mechanisms have been clearly explained. Riboswitches contain aptamer domain sites,386 which are used to regulate cellular metabolism and gene expression through binding small molecules or ligands.387−390 When an aptamer site selectively 33


Chemical Reviews

Review

Figure 23. Schematic of cobalamin biosynthetic pathway. Cbi operon encodes the enzymes for the transformation of uroporphyrinogen III into adenosylcobyrinic acid a,c-diamide. CbiP: adenosylcobyric acid synthase; NLA: nucleotide loop assembly pathway.

Figure 24. Strategies are used for protein engineering. (A) Improving enzyme activity is achieved by moderating the binding pocket, flanking regions or encoding sequence, which can used to channel more carbon flux into central pathways. (B) Altering substrate/product specificity is realized by introducing mutagenesis to active sites or binding pocket, which can used to alleviate the limitation of substrate utilization and product formation. (C) Modifying regulatory elements are implemented by mutating transcriptional regulatory proteins and regulatory domains, which can used to eliminate feedback inhibition.

These findings present a potential way to develop B. megaterium as a cell factory for VB12 production. 5.3.3. Antisense Switch. Antisense RNA switches use the RNA chaperone Hfq to mediate binding between antisense RNA and their target mRNA, and protect antisense RNA from Rnase degradation.399,400 Based on this structure, antisense RNA switches can activate and silence gene expression by targeting specific mRNA sequences.401,402 In addition, antisense RNA switches have been expressed to improve the adaptability of conditions and stimuli, such as oxidative stress, toxins tolerant and thermal sensitivity, and to regulate metabolic pathways, such as glycolysis, TCA cycle and L-lysine biosynthesis.370 Cadaverine, an important platform chemical, serves as a component of polymers such as polyamides and polyurethane, chelating agents, and other additives.403,404 Cadaverine biosynthesis is derived from L-lysine, a direct precursor of cadaverine,405 and mainly contains three steps (Figure 4): (i) phosphorylation of aspartate catalyzed by aspartokinase III (lysC); (ii) conversion L-aspartate-semialdehyde to meso-diaminopimelate catalyzed by dihydrodipicolinate synthase (dapA); (iii) cadaverine formation from L-lysine catalyzed by L-lysine decarboxylases (cadA). Because the level of cadaverine in E. coli is mainly regulated by its biosynthesis and degradation pathways,406 E. coli has been engineered to produce 9.61 g/L cadaverine by overexpressing cadA and dapA in biosynthesis pathway, and inactivating cadaverine aminopropyltransferase (speE), spermidine acetyl-

binds to a ligand, the conformation of RNA structure is changed, thus resulting in an alteration in gene expression. In other words, such alterations in gene expression are mainly attributed to riboswitch-mediated control of translation initiation,388 transcription termination,388,390 or mRNA cleavage.387,389 Cobalamin (VB12) is one of the most alluring and fascinating molecules391 and is widely used to treat pernicious anemia and peripheral neuritis.392 VB12 biosynthesis pathway consists of approximately 30 enzymes for its de novo biosynthesis.393 Pseudomonas denitrif icans operated an aerobic route for VB12 biosynthesis, and metabolic engineering was adopted to enhance VB12 production by overexpressing enzymes at key bottleneck steps.394 Finally, a variant strain P. denitrif icans SC510 produced approximately 250−300 mg/L VB12.391 On the other hand, an anaerobic pathway is also used for VB12 biosynthesis in Bacillus megaterium,395 which contains three parts (Figure 23): VB12 biosynthetic operon (cbi), adenosylcobyric acid synthase (CbiP), and nucleotide loop assembly pathway (NLA).396 However, gene expression of the cbi operon is tightly controlled by VB12 sensing RNA cis-regulatory elements (VB12-riboswitch) in B. megaterium,397 and a VB12-riboswitch is operating at a switch-off point at approximately 5 nM VB12.398 To bypass the effects of the VB12-riboswitch, the cbi operon was cloned without these regulatory elements. Thus, the genes, such as cobA, cobI, cobG, cobJ, cobM, cobF, cobK, cobL, cobH, and cobB were upregulated, and the titer of VB12 was increased up to 200 μg/L.398 34


Chemical Reviews

Review

Figure 25. Schematic of PHA and PHB biosynthetic pathway. Pdh: pyruvate dehydrogenase; phaA: β-ketothiolase; phaB: acetoacetyl-CoA reductase; phaC: PHA synthase; SucA: 2-oxoglutarate decarboxylase; SucB: dihydrolipoyltranssuccinase; SucD: succinate semialdehyde dehydrogenase; 4HBd: 4hydroxybutyrate dehydrogenase; orfZ: CoA transferase.

feedback-resistant. These regulatory elements can be modified with chemical mutagenesis, DNA shuffling, site-saturation mutagenesis, etc.411 Currently, protein engineering has been successfully used in metabolic engineering and synthetic biology, but protein dynamics are not well-understood mechanistically or structurally. Thus, computational technologies are acquired to predict the properties of engineered proteins or design new proteins de novo, thus obtaining more effective enzymes for valuable chemicals production.412 5.4.1. Improving Enzyme Activity. When new metabolic pathways are reconstructed for producing a non-native target product, one of the potential challenges that researchers often face is low product yield, owing to low enzyme activities in heterologous systems.407 To solve the problem above, the most common treatment is to enhance enzyme specific activity by protein engineering, thus relieving bottlenecks in metabolic flux and preventing toxic intermediates accumulation to promote target metabolites production in nonideal conditions.151,155,407 Polyhydroxyalkanoate (PHA) is identified as good candidates for biodegradable plastics,413 which has been developed into applications in drug delivery carriers, printing and photographic materials, a new type of biofuel, etc.414 PHA biosynthesis occurs in a three-step reaction starting with two acetyl-CoA (Figure 25):415 (i) acetyl-CoA is condensed to 3-keto-acyl-CoA by βketothiolase (phaA); (ii) 3-keto-acyl-CoA is reduced to R-3-OHacyl-CoA by 3-keto-acyl-CoA reductase (phaB); (iii) R-3-OHacyl-CoA is esterified to PHA by PHA synthase (phaC). Recombinant E. coli harboring phaCAB has been applied to various PHA production on a large scale, such as mcl PHA, poly4-hydroxybutyrate (P4HB) homopolymer and (R)-3-hydroxyhexanoate (HHx).414 Although a high PHA productivity can be obtained under appropriate growth conditions,413 high cost of PHA has been a key limiting factor, partly due to the low activity of phaC. To reduce PHA production cost, phaC from Aeromonas punctata was engineered through in vivo evolution in the mutator strain E. coli XL1-Red, which exhibited a 5000-fold higher mutation rate than wild-type E. coli.416 Plasmid pPS2 was replicated in E. coli XL1-Red for about 200 generations. About 200 000 mutants were screened, and five variants (phaCF518I, phaC V214G , phaC SD93/94RV/S103C/F518I , phaC F362I/F518I , and phaCD459 V/A513C) exhibited elevated in vitro and in vivo phaC activity. Most notably, single mutation phaCF518I displayed a 5fold increase in phaC activity, and the corresponding PHA production was increased by 20%, compared to the wild-type phaC. Levopimaradiene is the diterpenoid precursor of pharmaceutically important ginkgolides, which occupy obvious pharmacological activities, such as specific platelet-activating factor

transferase (speG), cadaverine aminotransferase (ygjG), and glutamate-cadaverine ligase (puuA) in degradation pathway.405 To avoid the disadvantages of multiple genes knockout and improve cadaverine production, small regulatory RNAs (sRNAs) were designed to regulate expression of multiple genes in E. coli, which were composed of a scaffold sequence and a target-binding sequence.374 A library of 130 synthetic sRNAs was used to finetune the expression levels of gene targets, such as four genes for deregulating the tyrosine biosynthetic pathway and eight genes for diverting metabolic fluxes from cadaverine formation, and the final results showed that cadaverine production was increased by 55% through repression of UDP-Nacetylmuramoylalanyl-Dglutamate 2,6-diaminopimelate ligase (murE), compared with the reported strain E. coli XQ56. 5.4. Protein Engineering

Protein engineering is a powerful tool in synthetic biology for altering protein properties and tailoring protein “parts” and protein “devices” to meet the demands of synthetic metabolic pathway.407 Although synthetic biology is still in its primary stage, protein engineering has significantly driven the development of this field. The increasingly important role that protein engineering plays in modifying synthetic metabolic pathway is illustrated by three factors (Figure 24). (i) Improving enzyme activity: Overexpression of enzymes is a traditional strategy for relieving bottlenecks in biosynthetic pathways. However, this high expresssion level of heterologous proteins is easy to form inactive insoluble proteins, which can reduce its enzyme activities.155 Thus, improving enzyme activity is an essential tool for increasing chemicals production by channelling carbon flux into central pathways. This improvement in enzyme activity can be achieved by moderating the binding pocket, flanking regions or encoding sequence using error-prone PCR, mutator strain, staggered extension process, etc.408,409 (ii) Altering substrate and product specificity: The substrate specificity of native enzymes are not broad enough to convert the precursors to non-native products,408 and the low product specificity of some enzymes often induces the accumulation of undesired side products. Thus, altering substrate and product specificity can efficiently alleviate the limitation of substrate utilizing and redirect carbon flux to targets biosynthesis. This alteration in enzyme specificity can be achieved by introducing mutagenesis to active sites or binding pocket using DNA shuffling, errorprone PCR, site-directed mutagenesis, etc.410 (iii) Modifying regulatory elements: Metabolic flux toward target chemicals can be decelerated when the product concentration is increased to a threshold value. Thus, regulatory elements of enzymes, such as transcriptional regulatory proteins and regulatory domains, become versatile platforms for engineering proteins to be 35


Chemical Reviews

Review

Figure 26. Schematic of glucosamine biosynthetic pathway. GlcNAc: N-acetylglucosamine; IIGlc: glucose transporter; IINAG: GlcNAc-specific transporter; IIMan: mannose transporter; GlmS: glucosamine synthase; GlmS*: glucosamine synthase mutation; NagA: GlcNAc-6-P deacetylase; GNA1: GlcN-6-P N-Acetyltransferase.

antagonists.417 Levopimaradiene biosynthesis starts from geranylgeranyl diphosphate (GGPP), which is derived from the condensation of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) with GGPP synthase (GGPPS) in MEP pathway (Figure 5).418 After this condensation, GGPP is converted to levopimaradiene by LP synthase (LPS).418 Combined with the systematic upregulation of genes in the MEP pathway, the functional expression of GGPPS and LPS was used to redirect metabolic flux toward IPP and DMAPP, but levopimaradiene production had only a little increase.409 The threshold of levopimaradiene synthesis is probably attributed to the low GGPPS and LPS activity. Based on the construction of LPS model, the putative binding pocket in LPS consisted of 15 residues, M593, C618, L619, A620, L696, K723, A729, N838, G854, I855, Y700, A727, V731, Asn769, and Glu777, which were selected for site-saturation mutagenesis. Since a suitable structural guide for GGPPS was lacking, a random approach with error-prone PCR was used to mutate GGPPS. Subsequently, combinatorial mutagenesis of GGPPS and LPS was adopted to construct mutant library for screening the improved activities of GGPPS and LPS. The final levopimaradiene production was resulted in a 2,600-fold increase by coupling the mutants GGPPSS239C/G295D and LPSM593I/Y700F with the improvements in precursor flux, compared with that of wild-type GGPPS and LPS.409 5.4.2. Altering Substrate/Product Specificity. Synthetic biology often utilizes various enzymes in synthetic pathways to convert non-native substrates into natural or non-natural chemicals, and thus high substrate specificity of enzymes is often the bottleneck for desired chemicals biosynthesis, which may result in the reduced transmission efficiency of synthetic pathways and lower the yield of desired chemicals. Engineering enzymes can promote the development of synthetic biology by tailoring these biocatalysts to efficiently transform non-native substrates or produce the desired chemicals with high specificity.407 L-Homoalanine is a non-natural amino acid, which can be used as a key chiral intermediate for the synthesis of several important drugs,419 such as S-2-aminobutyramide and S-2-aminobutanol. A non-natural metabolic pathway for L-homoalanine production in E. coli is derived from the natural amino acid threonine (Figure 4). Next, threonine is converted into 2-ketobutyrate by threonine

dehydratase (ilvA), and then 2-ketobutyrate is aminated to Lhomoalanine.419 Since L-homoalanine can not be detected in normal cells, the existing amination enzymes may not work for this reaction. Thus, finding the right amination enzyme is the major challenge. According to the literature, the branched-chain amino acid aminotransferase (ilvE)420 and valine dehydrogenases (VDH)421,422 are likely to be a functional candidate. When ilvE from E. coli and VDH from Streptomyces avermitilis, Streptomyces coelicolor, and Streptomyces fradiae were cloned and overexpressed in E. coli BW25113, these enzymes slightly improved the L-homoalanine titer compared to that of E. coli BW25113. To further enhance L-homoalanine production, other ideal enzymes for amino acid production should be rationally selected, such as glutamate dehydrogenase (GDH). However, GDH shows its activity with α-ketoglutarate as substrate, suggesting that the substrate specificity of GDH should be engineered and expanded for L-homoalanine biosynthesis. Based on the known structure of GDH from Clostridium symbiosum (PDB ID: 1BGV), the binding pocket residues (K92, T195, V377 and S380) were modified by site-saturation mutagenesis, and two mutants GDHK92L/T195A/V377A/S380C and GDHK92 V/T195S were isolated based on growth rate in the valine auxotroph E. coli (ΔavtA, ΔilvE). The catalytic activity of GDHK92 V/T195S on 2ketobutyrate was largely improved with 2-fold increase in kcat and 4-fold decrease in Km, thus allowing a signicifant increase in Lhomoalanine production up to 5.4 g/L in a optimized threonineproducing E. coli ATCC 98082 (ΔrhtA).407 3-Dehydroshikimic acid (DHS) is often used as starting materials for synthesis of many chemicals, such as phenol, adipic acid, vanillin, indigo, and the antiviral drug Tamiflu.407 DHS biosynthesis starts from the condensation of phosphoenolpyruvate (PEP) and D-erythrose 4-phosphate (E4P) (Figure 8), which is catalyzed by three key enzymes in shikimate pathway, 3deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) synthase (aroF/G/H), 3-dehydroquinate (DHQ) synthase (aroB), DHQ dehydratase (aroD). In order to increase DHS production, a recombinant E. coli was constructed by inserting aroB and aroF genes into its genome, but aroF was sensitive to feedback inhibition. Thus, aroF was replaced with a feedback-insensitive isozyme of aroF (aroFFBR), and DHS titer was increased to 69 g/ L by coexpression of aroFFBR and transketolase (TKT). In this process, aroFFBR competes with the carbohydrate phospho36


Chemical Reviews

Review

uninhibited mutants. The feedback inhibition resistant aroG mutant (aroGfbr) has been reported and used for increasing carbon flow to aromatic biosynthesis.434 To further enhance Lphe production, protein directed evolution was adopted to screen feedback inhibition resistant CM-PDT. After two evolutionary cycles, two CM-PDT mutants, CM-PDTT259S/Y230L and CMN5S/L8P/D54N/L55M-PDTS235A, showed a large decrease in feedback inhibition by L-phe, and the yield of L-phe from glucose (YPhe/Glc) was increased to 0.21 g/g by overexpressing aroGfbr, transketolase (TKT) and a truncated CM-PDTT259S/Y230L.435 Based on these observation, E. coli W3110 was further engineered to produce L-phe at systems level by inactivating PTS system to reduce the glucose uptake rate, removing feedback inhibition of aroG, CM-PDT, tyrB, aroE, and aroK/L to increase the supply of precursors, enhancing L-phe transport system to shift the reaction equilibrium to L-phe biosynthesis, and the final L-phe titer achieved 47.0 g/L with YPhe/Glc up to 0.252 g/g.433

transferase system (PTS) for the native precursor PEP, thus limiting the concentration and yield of DHS.423 To relieve this competition, an alternative substrate, 2-keto-3-deoxy-6-phosphogalactonate (KDPGal), was added,424,425 and KDPGal aldolase (DgoA) from E. coli was engineered by directed evolution, DNA shuffling and multiple site-directed mutagenesis to conserve its native function for decomposing KDPGal into pyruvate and D-glyceraldehyde 3-phosphate as well as enhance its new function for condensing pyruvate and E4P into DAHP. Finally, a variant DgoAF33I/D58N/Q72H/A75 V/V85A/V154F/Y180F resulted in a 60-fold increase in kcat/Km relative to the wild-type enzyme.424 Fed-batch fermentation with DgoAF33I/D58N/Q72H/A75 V/V85A/V154F/Y180F in E. coli NR7 enabled a 9.7% increase in the molar yield of DHS from glucose. 5.4.3. Modifying Regulatory Elements. Microbial metabolic process in biological systems is tightly regulated by regulatory elements. These elements can regulate cells phenotype to adapt environmental changes and vary intracellular activities to maintain conducive intracellular settings. In other words, gene expression can be finely tuned by regulatory proteins of transcription factors and enzymatic activities can be efficiently adjusted by regulatory domains of metabolism enzymes. These regulatory factors can be used as versatile platforms not only for engineering signal responses of cell metabolism, but also for retarding the cumulative feedback inhibition of target metabolites.407 In addition, it can also be utilized to systematically control metabolic flux and protein expression. Glucosamine (GlcN) is the precursors of the disaccharide units in glycosaminoglycans such as hyaluronic acid, chon droitin sulfate and keratan sulfate,426 which is used in clinical trials for treatment of arthritis.427,428 The GlcN biosynthetic pathway is well characterized in E. coli and other organisms, including two key steps (Figure 26):429,430 (i) fructose-6-phosphate is converted to GlcN-6-P by GlcN synthase (GlmS); (ii) GlcN-6P is dephosphorylated to GlcN and secreted by glucose transporter IIGlc and mannose transporter IIMan. Although GlcN production was increased to 60 mg/L by overexpression of GlmS and deletion of the nag regulon,426 this production did not meet the demand for industrial production of GlcN, possibly due to the fact that GlmS is strongly inhibited by GlcN-6-P. Thus, a library of E. coli GlmS mutants was created by errorprone PCR, and screened by producing higher levels of GlcN. Five mutants, GlmS I3T/I271T/S449P , GlmS A38T/R249C/G471S , GlmSE14K/D386 V/S449P/E524G, GlmSL468P, and GlmSG471S, displayed a significant decrease in sensitivity to inhibition by GlcN-6-P, and the highest concentration of GlcN was achieved up to 17 g/L through overexpression of GlmSE14K/D386 V/S449P/E524G.431 L-Phenylalanine (L-phe) has many applications in the food and pharmaceutical industries,432 and the market volume of L-phe ascends to 11 000 tons per annum.432 L-phe biosynthesis pathway has ten reactions from PEP and D-Erythrose-4phosphate (E4P) (Figure 8), involving nine enzymes, 3-deoxyD-arabino-heptulosonate 7-phosphate (DAHP) synthase (aroG), 3-dehydroquinate (DHQ) synthase (aroB), DHQ dehydratase (aroD), shikimate dehydrogenase (aroE), shikimate kinase (aroK/L), 5-enolpyruvylshikimate 3-phosphate (EPSP) synthase (aroA), chorismate synthase (aroC), chorismate mutaseprephenate dehydratase (CM-PDT), tyrosine aminotransferase (tyrB). Among these reactions, five enzymes are feedback regulated, i.e., aroE, aroK/L, tyrB, aroG, CM-PDT.433 The feedback inhibition for aroE, aroK/L, and tyrB can be deregulated by overexpressing these genes under strong promoters, but aroG and CM-PDT must be replaced by their

5.5. Cofactor Engineering

Cofactors can act as redox carriers to meet the needs of anabolic and catabolic reactions, thus achieving energy transfer in the cell. In other words, cofactors can alter the intracellular redox state, adjust energy metabolism, control carbon flux, and so on.436 Thus, cofactors manipulation impacts a series of biochemical reactions, and as expected, cofactor engineering is an useful strategy to enhance the efficiency of metabolic pathways and maximize metabolic flux toward target products.437 It mainly includes two parts (Figure 27): (i) Cofactor specificity system

Figure 27. Strategies are used for cofactor engineering. (A) Cofactor specificity system can be built by screening heterologous enzymes to replace NAD(P)H-dependent enzymes and evoluting a target enzyme to change cofactor specificity. (B) Cofactor regeneration system can be constructed through improving cofactor pools, regenerating cofactors and interconverting cofactors.

can be built by screening various heterologous enzymes to replace NADH-dependent central metabolic enzymes with NADPH-dependent ones7 and evolving a target enzyme to change cofactor specificity with site-directed or random mutagenesis.438,439 Cofactor specificity system can be applied to balance electron-mediating organic cofactors, such as NADH and NADPH, for the production of many desired chemicals.14,440 (ii) Cofactor regeneration can be constructed through the 37


Chemical Reviews

Review

Figure 28. Schematic of vitamin C biosynthetic pathway. SLDH: D-sorbitol dehydrogenase; 2,5-DKG: 2,5-diketo-D-gluconic acid reductase.

DKGF22Y/K232G/R238H/A272G showed a 110-fold increase in activity with NADH compared with that of the wildtype 2,5-DKG. The use of an NADH-active mutant 2,5-DKGF22Y/K232G/R238H/A272G could result in a more effective one-step fermentation process for 2-KLG production. 5.5.2. Cofactor Regeneration System. Cofactor regeneration offers a direct route for keeping redox balance in cell by altering the intracellular cofactor pool. Subsequently, many enzymes, which are shown to directly affect the ratio of NAD(P)H/NAD(P)+ or ATP/ADP, have been used to regulate cofactor regeneration, such as cytoplasmic H2O-forming NADH oxidase (NoxE),313 mitochondrial alternative oxidase (AOX),452 mitochondrial NADH kinase (POS5),453 NAD(P)+ transhydrogenase (STH),454 etc. D-Lactate is an important intermediate for producing several pharmaceutical, pesticide, chemical industry products and biodegradable polymer.455 D-Lactate is the product of pyruvate dehydrogenation, which is catalyzed by lactate dehydrogenase (LDH). Consequently, D-lactate biosynthesis is directly regulated by the activity of LDH, the rate of glycolysis and the state of cellular redox (Figure 16).456 D-Lactate was enhanced by expressing LDH with the λ PR and PL promoters (as a genetic switch), deleting ackA, phosphotransacetylase (pta), PEP synthase (pps), pflB, ldhA, poxB, adhE and fumarate reductase (frdA), and the final strain E. coli B0013−070B produced 122.8 g/L D-lactate under two-phase fermentation.457 However, many redox-related gene mutations in strain E. coli B0013−070B such as ldhA, adhE, and f rdA, resulted in NADH surplus and redox imbalance.458 To solve this problem, NADH oxidase (NoxE) was used for the reoxidation of NADH to NAD+ to maintain redox balance. To finely tune D-lactate production, a constitutive promoter library was constructed to precisely regulate NoxE expression,313 and the results showed that the decreased pyruvate flux to the ALS pathway was rerouted to the LDH pathway. Thus, cofactor regeneration system may be a more direct way to manipulate metabolism and achieve redox balance.436

deletion or attenuation of glycolytic enzymes for channelling metabolic flux into the pentose phosphate pathway to increase NADPH pool,441,442 the interconversion of NADH and NADPH for regulating the NADH/NADPH ratio using transhydrogenase7 and the introduction of exogenous enzyme to directly regenerate cofactors such as NoxE, AOX, POS5, etc.436 Cofactor regeneration can be used to overcome the bottleneck of cofactor availability in cofactor dependent biosynthetic pathways.14,436 To sum up, cofactor engineering can carefully monitor the balance of NAD(P)H/NAD(P)+ or ATP/ADP ratio, thus achieving cellular redox balance to improve the physiological state of the cell factories. However, current cofactor engineering just focuses on natural metabolic pathways and enzymes, novel technology in metabolic engineering should be developed to refine ideal redox chemistry, which fits with trends in synthetic biology circuits and genome engineering.443 5.5.1. Cofactor Specificity System. Currently, synthetic biology has possessed the potential to produce many chemicals from renewable resources.407 However, biosynthetic pathways can not function optimally through simply cobbling together biological components in nature,418 partially due to the disequilibrium of cofactor utilization.436 To overcome this difficulty, advances in cofactor engineering to achieve redox balance have mainly focused on modifying cofactor specificity444 and creating bioorthogonal redox systems.445 Vitamin C is now widely employed in the pharmaceutical, food, beverages, feedstock and cosmetic industries,446 and the market volume is about 110 000 tons annually.447 Currently, vitamin C is mainly produced via a two-step fermentation process,448 which rely on a mixed fermentation step for converting L-sorbose into the precursor of vitamin C, 2-keto-Lgulonic acid (2-KLG) (Figure 28). However, this mix-culture system including B. megaterium and K. vulgare makes it difficult for strain improvement and process optimization.449 Thus, onestep fermentation process may be more effective for vitamin C production. In this process, the 2-KLG biosynthetic pathway was constructed by expressing NADPH-dependent 2,5-diketo-Dgluconic acid reductase (2,5-DKG) from Corynebacterium in Erwinia strain.450 Because NADH is more prevalent and stable than NADPH in the cell, the cofactor specificity of 2,5-DKG should be switched from NADPH to NADH for enhancing 2KLG production.451 Banta and colleagues performed a series of site-directed mutations at the cofactor binding site of the 2′phosphate group of NADPH, and five mutants, 2,5DKGF22Y/K232G/R238H/A272G, 2,5-DKGF22Y/K232G/R235G/R238E/A272G, 2,5-DKGF22Y/K232G/R235G/R238H/A272G, 2,5DKGF22Y/K232G/R235T/R238H/A272G and 2,5DKGF22Y/S233T/R235S/R238H/A272G, were able to use both NADH and NADPH as cofactors.451 Among these mutants, 2,5-

5.6. Structural Biotechnology

Structural biotechnology is an interdisciplinary field between structural biology and synthetic biology, which can provide a new perspective for designing cellular metabolic pathways and regulatory networks.459 Structural biotechnology offers a new approach to localize and enhance metabolic pathways, and creates additional opportunities for improving cellular processes.436 Currently, this technology has been successfully applied to a large class of linear and nonlinear metabolic pathways through designing and synthesizing three nanobiological devices (Figure 29): (i) DNA scaffold is designed for constructing artificial complexes of metabolic pathway enzymes 38


Chemical Reviews

Review

production.461 (iii) Protein scaffold is built for changing the transmission efficiency of metabolic pathway to improve metabolite conversion rate by increasing the local concentrations of pathway enzymes, in which each metabolic enzymes fused with one ligand can be delivered to the corresponding scaffold protein.462 Structural biotechnology is effective for localizing enzymes to enhance the concentration of local intermediates in a desired biochemical reaction, or to prevent the toxicity of intermediates from damaging the cell. Further, future advances in structural biotechnology will facilitate the design and assembly of more stable robust and configurable scaffold systems in vivo for efficiently producing chemicals.463,464 5.6.1. DNA Scaffold. DNA scaffold is an alternative method to construct artificial complexes of metabolic pathway enzymes, in which individual enzymes can specifically bind unique DNA sequences by genetic fusion to zinc-finger domains.460 Because DNA has a highly predictable local structure, this scaffold has the potential for arranging enzymes into a predefined order. This arrangement can increase the local concentration of metabolic intermediates and improve the transmission efficiency of metabolic pathways, thus enhancing the production of target metabolites. Resveratrol is used as neutraceutical, pharmaceutical and food ingredient.465 As a kind of natural antioxidants, resveratrol can reduce the blood viscosity and prevent the occurrence of cancer and the development of heart disease.466−468 Resveratrol biosynthetic pathway is a branch of the phenylpropanoid pathway469 (Figure 30), which starts from p-coumaric acid. pCoumaric acid is converted into resveratrol by 4-coumaric acid:CoA ligase (4CL)470 and stilbene synthase (STS).471 Advances in microbial production of resveratrol are mainly

Figure 29. Strategies are used for structural biotechnology. (A) Metabolic pathway is constructed for target production with no scaffold. (B) DNA scaffold is designed for constructing artificial complexes of metabolic pathway enzymes, which can be used to enhance the concentration of metabolic intermediates. (C) RNA scaffold can be assembled into complex multidimensional architecturest, which can used to block unwanted complex reactions in cell growth. (D) Protein scaffold are built for changing the transmission efficiency of metabolic pathway, which can used to increase the local concentrations of pathway enzymes.

to improve end product formation by increasing the concentration of metabolic intermediates, in which individual enzymes can specifically bind unique DNA sequences by genetic fusion to zinc-finger domains.460 (ii) RNA scaffold can be assembled into complex multidimensional architectures to enhance the yield of the target products by blocking unwanted complex reactions in cell growth, in which RNA devices harbor aptamer regions that provided docking sites for enzymes involved in target

Figure 30. Schematic of resveratrol, (2S)-pinocembrin, and (2S)-naringenin biosynthetic pathways. DAHP: 3-deoxy-D-arabinoheptulosonate-7phosphate; CM/PDH: chorismate mutase/prephenate dehydrogenase; CM/PDT: chorismate mutase/prephenate dehydratase; PAL: phenylalanine ammonia lyase; TAL: tyrosine ammonia lyase; 4CL: 4-coumarate:CoA ligase; CHS: chalcone synthase; CHI: chalcone/flavanone isomerase; STS: stilbene synthase. 39


Chemical Reviews

Review

focused on metabolic engineering E. coli and S. cerevisiae, in which resveratrol biosynthetic pathway was reconstructed by expressing 4CL and STS from different strains with the addition of pcoumaric acid in medium as a precursor.472 However, the resveratrol yield is relatively low, possibly due to the fact that little effort has been made to finely tune gene expression and optimize two enzymes ratio.473 Thus, in order to increase resveratrol production, DNA scaffold was designed and used to synthesize artificial complexes of metabolic pathway enzymes.460 The enzymes 4CL and STS were fused to the zinc finger region Zif268 and PBSII resulting in Zif268−4CL and PBSII-STS, respectively, and then both fusion proteins were simultaneously ligated to one plasmid and transformed into E. coli. Finally, the resveratrol biosynthetic pathway was optimized via DNA scaffold by rearranging enzymes into a predefined order and varying the number of enzymes into the best proportion. When the number of DNA scaffold unit was decreased from 16 to 4, the resveratrol yield was increased by 50-fold. 1,2-Propanediol (1,2-PD) is widely applied in cosmetics, adhesives, lubricants and medicines.474 In addition, 1,2-PD can act as a monomer to produce industrial polymers, such as polyesters and polyurethanes.475,476 1,2-PD production from glycolytic intermediate dihydroxyacetone phosphate (DHAP) has been well established,477−479 and this biosynthetic pathway mainly contains three key enzymes, methylglyoxal synthase (mgsA), 2,5-diketo-D-gluconic acid reductase (dkgA) and glycerol dehydrogenase (gldA)477,480 (Figure 31). Based on

this pathway, various microorganisms showed great potential to produce 1,2-PD from renewable feed stocks, e.g., S. cerevisiae,481,482 E. coli,480,483 and C. glutamicum,484,485 but the yield of 1,2-PD was low. The reasons may be due to two aspects: (i) many enzymes were simultaneously overexpressed in these studies, which resulted in protein burden; (ii) the distance of substrate transmission was relatively far apart, thus leading to ineffective flux consumption. Hence, to identify the optimal minimal set of pathway for efficient production of 1,2-PD, 1,2PD biosynthetic pathway was constructed according to the following principles that several candidate enzymes were screened for each step of the pathway, different combinations of their expression were tested for 1,2-PD production, and this minimal pathway was optimized by DNA scaffold.460 The isolated enzymes mgsA, dkgA, and gldA were fused to a zinc finger region ZFa, ZFb, and ZFc, thus forming mgsA-ZFa, dkgAZFb, and gldA-ZFc, respectively. These zinc finger enzyme chimeras could bind to specific DNA sequences corresponding to each of the ZF domains. Further, various enzyme:scaffold ratios were constructed and tested. When the ratio was (1:1:1)4, 1,2-PD titer was increased to approximately 4.5-fold higher than that of no scaffold controls. 5.6.2. RNA Scaffold. RNA scaffold can be assembled into complex multidimensional architectures, which differ from DNA and protein-based approach.461 Thus, in vivo RNA organization can be applied to engineer biological pathways with the help of spatial constraints. When RNA scaffold is mutated to prevent protein binding through the aptamer binding domain, the yield of metabolites will have no specially change; when RNA scaffold is assembled to bind with protein by aptamer region, the path of metabolites will be enhanced and its production will be increased. Hydrogen (H2) is mainly applied in the processing of fossil fuel and the production of ammonia. The synthesis of H2 mainly depends on the electrolysis of H2O, but this method has many shortcomings, such as energy waste, high cost and low efficiency. Thus, the production of H2 with biological methods attracts a lot of attention. To enhance the ability of H2 biosynthesis, E. coli BL21-star (DE3) was used and RNA scaffold was constructed to optimize the process of H2 biosynthesis.461 This scaffold consisted of the dimer (DDs) and polymer (PDs), in which PDs folded to form hairpin to protect the structure, and DDs overlapped to prevent the collapse of structures. With RNA scaffold, the reduction of protons to H2 could be achieved by simultaneously overexpressing hydrogenase (HydAEFG) and pyruvate ferredoxin oxidoreductase (PFOR) in electron transfer (Figure 16). In this process, HydAEFG and PFOR were fused to a single copy of PP7 (Hp) and dimers MS2 (Fm), respectively. Then, Hp and Fm were added to D0, thus leading to the proteinRNA assembly D0FH. When D0FH was formed in E. coli BL21-star

Figure 31. Schematic of 1,2-propanediol biosynthetic pathway. hk: hexokinase; pgi: phosphoglucose isomerase; fpk: phosphofructokinase; ald: fructose bisphosphate aldolase; tpi: triose phosphate isomerase; mgsA: methylglyoxal synthase; dkgA: 2,5- diketo-D-gluconic acid reductase; gldA: secondary alcohol dehydrogenase.

Figure 32. Schematic of butyrate biosynthetic pathway. AtoB: acetoacetyl-CoA thiolase; Hbd: 3-hydroxybutyryl-CoA dehydrogenase; Crt: 3hydroxybutyryl-CoA dehydratase; Ter: ans-enoyl-coenzyme A reductase; TesB: acyl-CoA thioesteraseII. 40


Chemical Reviews

Review

Figure 33. Schematic of D-glucaric acid and myo-inositol biosynthetic pathway. PEP: phosphoenolpyruvate; PTS: phosphoenolpyruvate-dependent phosphotransferase system; INO1: myo-inositol-1-phosphate synthase; MIMP: myo-inositol monophosphatase; MIOX: myo-inositol oxygenase; UDH: uronate dehydrogenase.

Figure 34. Strategies are used for compartmentalization engineering. (A) Mitochondria engineering can provide broad spectrum precursors for chemicals production, which can realize faster reaction rate and higher metabolite productivity in mitochondria environment. (B) Peroxisome engineering is versatile and suitable organelle, which can be designed as microcompartment for the biosynthesis of unusual metabolites. (C) Carboxysome engineering is a protein-based organelle, which provides a microcompartment for shielding metabolic reaction system from the interference of other intracellular metabolites.

5.6.3. Protein Scaffold. A significant improvement in metabolite conversion rate can be achieved by colocalizing

(DE3), H2 production was obtained a 4.0-fold increase compared with no scaffold controls. 41


Chemical Reviews

Review

5.7. Compartmentalization Engineering

spatially organized pathway enzymes into complexes to increase the local concentrations of pathway enzymes and reduce the accumulation of pathway intermediates.486−488 This desired effect can be achieved by synthetic scaffold proteins. In this strategy, a corresponding ligand is fused to the C-termini of metabolic enzymes and deliver these enzymes to the appropriate scaffold proteins. Butyrate is a short-chain fatty acid, which is used in various chemical industries such as food, pharmaceutical, and plastics.489 In addition, its derivatives have great potential as biofuels, such as ethyl butyrate or butanol.490 Thus, butyrate production using microorganisms has drawn significant attention in biotechnology industry.491−493 For this purpose, butyrate biosynthetic pathway from acetyl-CoA is engineered, mainly involving five enzymes, acetoacetyl-CoA thiolase (AtoB), 3-hydroxybutyryl-CoA dehydrogenase (Hbd), 3-hydroxybutyryl-CoA dehydratase (Crt), trans-enoyl-coenzyme A reductase (Ter), and acyl-CoA thioesteraseII (TesB) (Figure 32). Previous advances in butyrate production were mainly focused on optimizing N-terminal sequences of heterologous enzymes and refactoring redox cofactor regeneration,489 but the butyrate titer was relatively low, possibly owing to the loss of metabolic intermediates and the low catalytic efficiency of heterologous pathway. To successfully improve butyrate production, synthetic protein scaffolds were introduced to improve the efficiency of the heterologous pathway by spatially organizing enzymes Hbd, Crt, and Ter.494 The C termini of Hbd, Crt, and Ter were fused to GBD, SH3, and PDZ domains, forming Hbd-GBD, Crt-SH3, and Ter-PDZ, respectively, which were used as peptide ligands for protein scaffold. Then, the numbers of interaction domain repeats in the scaffolds were optimized, and E. coli DSM03 with scaffold protein (GBD1SH31PDZ2) realized a 3-fold increase in the concentration of butyrate compared with no scaffold controls.494 Using the optimized inducer concentration and pH adjustment, the butyrate titer was increased to 7.2 g/L. D-Glucaric acid is identified as one of top value-added chemicals from biomass,495 which is used for the therapeutic purposes such as cholesterol reduction496 and cancer chemotherapy.497,498 Because chemical synthesis for D-glucaric acid is a nonselective and expensive process, new biological catalysis systems may result in higher yield and selectivity. For this purpose, the biosynthetic pathway for D-glucaric acid has been constructed by combining biological parts from different organisms, mainly including three enzymes, myo-inositol-1phosphate synthase (INO1), myo-inositol oxygenase (MIOX), and uronate dehydrogenase (UDH)499 (Figure 33). When INO1 from S. cerevisiae, MIOX from Mus musculus and UDH from Pseudomonas syringae were simultaneously expressed in E. coli BL21-star (DE3), D-glucaric acid was produced only 1 g/L.499 To improve the productivity of D-glucaric acid, pathway enzymes, INO1 and MIOX, were colocalized using synthetic protein scaffold, and D-glucaric acid was increased by 3-fold with colocalizing INO1 and MIOX in a 1:1 ratio.462 Further, synthetic protein scaffolds were created to colocalize these three enzymes, which can independently manipulate the numbers of interaction domain repeats in the scaffolds.500 Based on the protein scaffold (GBD1SH3xPDZ2), the numbers of interaction domains binding INO1 and MIOX were optimized to regulate the effective concentration of myo-inositol at the synthetic complex, and the final D-glucaric acid titer was improved up to 2.5 g/L.

Compartmentalization engineering is a direct approach for limiting cross-talk between the engineered pathways and the cellular milieu. Compartmentalization is adopted at the molecular level to configure and control enzymes in substrate transmission channel up to organelles in eukaryotic cells.487 Compartmentalized pathways can enhance the efficiency of the targeted metabolite production via physical barriers that can prevent metabolite exchange and circumvent the undesirable interactions between heterologous enzymes and the host cell. Thus, organelles are isolated from the cytosol with specialized metabolic reactions and can be modified or mimicked to improve the engineered pathways for producing desired chemicals501 (Figure 34): (i) Mitochondria contains many central metabolic pathways, such as the citric acid cycle, amino-acid biosynthesis and fatty-acid metabolism, which can provide broad spectrum precursors for many chemicals production. The smaller volume of mitochondria can further concentrate these precursors to realize faster reaction rate and higher metabolite productivity.502 In addition, many higher enzymes activity in biosynthetic pathways can be achieved in mitochondria environment, which is different from that in cytoplasm, such as pH, oxygen concentration and redox potential. (ii) Peroxisome has been found with diverse form and function in eukaryotic cells, providing evidence for peroxisomes versatility and suitability as a synthetic organelle. Thus, peroxisome can be designed as microcompartment by clearing its endogenous matrix proteins without inhibiting cell growth, and further used as a site for the biosynthesis of unusual metabolites that are hardly accumulated in cytoplasm,503,504 such as prodeoxyviolacein and penicillin. (iii) Carboxysome is a protein-based organelle, which provides a microcompartment for carbon dioxide fixation in cyanobacteria, and further used for the specific chemicals production that can be synthesized from carbon dioxide, such as resveratrol, naringenin, and p-coumaric acid.505,506 Further, based on the sequence similarity between carboxysome shell genes and some metabolic operon genes, many compartments with diverse shapes and topologies are established for shielding metabolic reaction system from the interference of other intracellular metabolites by overexpressing individual shell proteins with different levels, thus enhancing the accumulation of target products.507 In conclusion, compartmentalization engineering has great potential for diverse chemicals production in metabolic engineering and synthetic biology, due to the specific functions in concentrating substrates and enzymes, sequestrating the toxicity of pathway intermediates, bypassing inhibitory regulatory networks and avoiding competing pathways. However, the permeability and the stability of compartmentalization need to be improved in the future. 5.7.1. Mitochondria Engineering. As an emerging strategy to maintain homeostasis between synthetic pathways and intracellular environment in host organisms, mitochondria engineering can improve the local concentrations of pathway metabolites and enzymes by colocalizing pathway enzymes into mitochondria, potentially limiting the accumulation of pathway intermediates, reducing the probability of precursor loss, and trapping the toxicity of intermediates and products.487,508 Thus, mitochondria engineering is a viable strategy for pathway engineering of eukaryotic production hosts. Isobutanol is used as an important platform compound in food, pharmaceutical and chemical industry.509 Additionally, isobutanol is also an ideal gasoline additives or substitutes.510,511 In S. cerevisiae, isobutanol biosynthesis starts from pyruvate, and 42


Chemical Reviews

Review

Figure 35. Schematic of isobutanol and isobutyraldehyde biosynthetic pathway. ILV2: acetolactate synthase; ILV5: ketol-acid reductoisomerase; ILV3: dihydroxyacid dehydratase; BAT1/BAT2: amino acid aminotransferases; α-KDC: α-ketoacid decarboxylase; ADH: alcohol dehydrogenase; CA: carbonic anhydrase; RuBisCO: ribulose 1,5-bisphosphate carboxylase/oxygenase.

metabolism, fatty acids β-oxidation, and penicillin biosynthesis.504,518−520 Violacein, deoxyviolacein and prodeoxyviolacein (VDP) have many interesting biological properties, such as antibacterial, antiviral, and anticancer activity.521−524 In addition, violacein is a biodye with good color tone and stability.525 Many bacteria can naturally accumulate the mixtures of VDP,526,527 and VDP biosynthetic pathway is encoded by the vioABCDE operon.528−530 VDP biosynthesis starts from L-tryptophan, and mainly contains three steps (Figure 20): (i) L-tryptophan is converted into indole-3-pyruvic acid imine by tryptophan 2monooxygenase (VioA); (ii) indole-3-pyruvic acid imine is condensed to protodeoxyviolaceinic acid by concomitant action of polyketide synthase (VioB) and violacein synthetase (VioE); (iii) protodeoxyviolaceinic acid is further processed into VDP by monooxygenase (VioC) and hydroxylase (VioD).531 Although VDP production was achieved in E. coli by engineering the pentose phosphate pathway and L-tryptophan route, enhancing serine supplement, and eliminating L-tryptophan repression,521,532 the titers of VDP were still low, probably due to the metabolic crosstalk and pathway inefficiency in local pathway engineering. Thus, the yeast peroxisome was selected and repurposed by the development of a sensitive high-throughput assay for detecting proteins and metabolites into peroxisome and the identification of an efficient signal peptide to target heterologous proteins to peroxisome.503 Then, prodeoxyviolacein pathway was reconstructed in a VioE-limited regime by colocalizing VioA and VioB in the peroxisome to reduce byproduct chromopyrrolic acid and increase prodeoxyviolacein production, and this compartmentalization led to a 35% increase in prodeoxyviolacein production and a 61% reduction in the offpathway byproduct chromopyrrolic acid. This work lays good foundation for using peroxisome as a synthetic organelle and highlights future challenges on the way to achieve this goal. Recently, peroxisome was also recruited for alkane production in yeast. Through targeting enzymes involved in the conversion of free fatty acids to alkanes, via fatty aldehydes, to peroxisome, it was possible to significantly improve alkane production compared with expression of the same pathway in the cytosol.533,534

this pathway contains two part512 (Figure 35): (i) the upstream isobutanol pathway is confined to mitochondria, including acetolactate synthase (ILV2), ketol-acid reductoisomerase (ILV5), and dihydroxyacid dehydratase (ILV3); (ii) the downstream isobutanol pathway is confined to cytoplasm, including α-ketoacid decarboxylase (α-KDC) and alcohol dehydrogenase (ADH). Based on this, the isobutanol pathway was partially constructed to increase isobutanol production by upregulating some enzymes in their natural compartments,513−515 but only a slight increase in isobutanol production was achieved. A possible source of this result was due to the complicated subcellular compartmentalization. In other words, the simple overexpression of enzymes probably created various bottlenecks, in which the transport of intermediates across membranes might reduce the productivity of isobutanol and enable the consumption of intermediates. To address these bottlenecks, the complete isobutanol pathway was targeted to mitochondria to avoid pathway subcompartmentalization using the N-terminal mitochondrial localization signal CoxIV,502 and the final isobutanol production was resulted in a 260% increase compared with that of overexpression in their natural compartments. This advance provides effective evidence that mitochondrial compartmentalization can at least partly enhance intermediate availability and local enzyme concentrations. 5.7.2. Peroxisome Engineering. Recently, the subcellular organelles of S. cerevisiae, such as mitochondrion, have been engineered to produce chemicals via substrates that naturally accumulate in the mitochondria, thus limiting its application to new synthetic pathways.502,516 How to select or design a flexible synthetic organelle to extend the advantage of compartmentalization to any other pathway remains to be solved. Peroxisome is presented in many eukaryotic cells, and it is composed of a protein-rich matrix surrounded with a single membrane. Unlike other organelles, peroxisome can be completely destroyed without any negative influences on cell growth at fermentative conditions, suggesting that peroxisome can be applied to establish an orthogonal subcellular compartment through clearing its endogenous matrix proteins.503,517 Although its function is often specific to species and cell type, three widely distributed functions have been identified, namely, H2O2 43


Chemical Reviews

Review

Figure 36. Strategies are used for modular pathway engineering. (A) Biochemistry-based modular is designed to optimize the production of metabolic intermediates by finely analyzing chemical and physical properties of metabolites. (B) Metabolic branch-based modular is constructed to regulate the flux ratio between the branch pathway and the central pathway by understanding the structure and function of metabolic networks. (C) Enzyme turnover rate-based modular is used to achieve the repurposed balance of pathway enzymes by organizing and controlling key enzymes into distinct modules.

5.7.3. Carboxysome Engineering. Carboxysome, a bacterial microcompartment (BMC), consists of many subunits including hexameric and pentameric proteins, which can form a shell to encapsulate carbonic anhydrase (CA) and ribulose 1,5bisphosphate carboxylase/oxygenase (RuBisCO).505 It plays a central role in the Calvin-Benson-Bassham cycle:535 HCO3− is converted to CO2 by CA, and then CO2 and ribulose bisphosphate (RuBP) are converted to 3-phosphoglycerate (3PGA) by RuBisCO. Given these BMC capabilities, further research on BMC structures, pore complexes and targeting sequences make it possible to repurpose new synthetic pathways in industrial strains.501 Isobutyraldehyde can be used to produce various hydrocarbons derived from petroleum, such as isobutanol, isobutyric acid, acetal, oxime, and imine.536 In addition, isobutyraldehyde is also used as fragrance and flavor additive.537 In order to produce isobutyraldehyde, the valine biosynthesis pathway was used to generate the precursor 2-ketoisovalerate catalyzed by three enzymes537 (Figure 35): acetolactate synthase (ILV2), ketol-acid reductoisomerase (ILV5) and dihydroxyacid dehydratase (ILV3). Then, 2-ketoisovalerate is converted to isobutyraldehyde via α-ketoacid decarboxylase (α-KDC). To enhance isobutyraldehyde production, the ILV2 gene from B. subtilis, ILV5 and ILV3 genes from E. coli, and α-KDC gene from L. lactis were integrately expressed in Synechococcus elongatus, and the engineered strain S. elongatus SA590 produced 723 mg/L isobutyraldehyde with an average production rate of 2.5 mg/L/ h.536 This poor production rate suggested that CO2 fixation might be one of the bottlenecks in isobutyraldehyde production. To compensate the inherent limitations of RuBisCO in the Calvin-Benson-Bassham cycle, the additional rbcLS gene from S. elongatus PCC6301 was integrated into the downstream of the rbcLS gene in S. elongatus SA590, and the RuBisCO activity of the resulting strain S. elongatus SA665 was found to be 1.4-fold higher than that of S. elongatus SA590.536 Further, S. elongatus SA665

could produce 1.1 g/L isobutyraldehyde with the production rate of 6.23 mg/L/h, which was about 2.0-fold higher than that of S. elongatus SA590. These results demonstrated the promise of direct bioconversion of CO2 into fuels and chemicals with carboxysome engineering. 5.8. Modular Pathway Engineering

It is challenging for strain improvement to optimize and balance multigene pathways. Generally, the end of one bottleneck is the beginning of another in traditional pathway engineering.538 In this process, multiple rounds of strain construction, selection and optimization are necessary for strain improvement, but it is inefficient and time-consuming. In order to solve the above problems, modular pathway engineering has developed as a promising strategy, which can artificially divide metabolic pathway into various modules, finely control each modules with different expression levels, and simultaneously assemble multiple modules to generate strain library.539,540 Using this strategy, high-producing strains with balanced metabolic flux can be efficiently identified through one round of screening a strain library. Recently, modular pathway engineering has been successfully used to produce various biochemicals,541 which can be done in three different ways (Figure 36). (i) Biochemistrybased modular: The accumulation of intermediates not only can result in toxicity for cell growth but also lead to feedback inhibition of pathway enzymes as well as the unexpected formation of byproducts, thus limiting the biosynthetic efficiency of product pathway. Biochemistry-based modular is designed to optimize the production of unwanted metabolic intermediates by finely analyzing chemical and physical properties of metabolites. This modular approach can efficiently improve the validity of intermediates through decreasing the competition for chemicals biosynthesis and the interference for metabolic balance.539 (ii) Metabolic branch-based modular: When the native metabolism in microbes is repurposed through the manipulation of endogenous genes and the introduction of heterologous 44


Chemical Reviews

Review

pathways, significant imbalances in pathway flux is frequently introduced. Metabolic branch-based modular is constructed to fine-tune the flux ratio between the branch pathway and the central pathway, further to achieve appropriate amounts of precursors and rational design of pathways for target chemicals production.542 (iii) Enzyme turnover rate-based modular: Some bottenecks in biosynthetic pathways are usually caused by enzymes with low or high turnover rates, which can penalize cellular fitness through rerouting the essential resources toward the nonessential metabolism. Enzyme turnover rate-based modular is used for organizing and controlling key enzymes into distinct modules to achieve the repurposed balance of pathway enzymes, thus enhancing intermediate metabolites transmission efficiency and improving target chemicals production.543 Modular pathway engineering is efficient for reconstituting metabolic balance and improving metabolites production. Further research may focus on coupling this approach with computational and analytical tools to accurately design and control the expression of synthetic pathway.541 5.8.1. Biochemistry-Based Modular. Recently, many genetic tools have been developed to combinatorially and globally optimize synthetic pathways in industrial strains. However, the accumulation of intermediates can result in toxicity for cell growth, cause the feedback inhibition of pathway enzymes, and lead to the unexpected formation of byproducts.151,409 Taxol is an antineoplastic drug with potent activity against a series of cancers, which is isolated from the bark of the Pacific yew tree.544 The taxol biosynthetic pathway from glyceraldehyde-3 phosphate and pyruvate consists of two parts539 (Figure 5): (i) an upstream isoprenoid pathway can produce two building blocks, isopentenyl diphosphate (IPP) and dimethylallyl diophosphate (DMAPP), which is catalyzed by 1-deoxy-Dxylulose-5-phosphate synthase (DXS), 1-deoxy-D-xylulose-5phosphate reductoisomerase (IspC), 4-diphosphocytidyl-2Cmethyl-D-erythritol synthase (IspD), 4-diphosphocytidyl-2-Cmethyl-D-erythritol kinase (IspE), 2C-methyl-D-erythritol-2,4cyclodiphosphate synthase (IspF), 1-hydroxy-2-methyl-2-(E)butenyl-4-diphosphate synthase (IspG), 4-hydroxy-3-methyl-2(E)-butenyl-4-diphosphate reductase (IspH), isopentenyl-diphosphate isomerase (Idi); (ii) a heterologous downstream terpenoid pathway can be designed for taxol precursor production,545 which is catalyzed by geranly geranyldiphosphate synthase (GGPPS), taxadiene synthase (TS), taxoid-5αhydroxylase (T5α−OH). The taxol precursor pathway has been reconstructed in E. coli and S. cerevisiae by coordinately overexpressing the key enzymes in the downstream and upstream pathway, but the titers were limited to less than 10 mg/L.546,547 In order to increase taxol precursor production, the taxadiene biosynthetic pathway was divided into an upstream module (DXS, IspD, IspF, and Idi) and a downstream module (GGPPS, TS, T5α−OH). Then, the gene expression levels in both modules were simultaneously optimized to reduce the accumulation of indole by regulating promoter strength and plasmid copy number, thus relieving its inhibition to isoprenoid pathway activity.539 The final taxadiene production was improved 15 000-fold over the control strain, yielding 1.02 g/L in fed-batch bioreactor fermentations. This modular approach was not only propitious to simplify the main parameters affecting pathway flux, but also beneficial to identify an optimally balanced pathway without high-throughput screen. 5.8.2. Metabolic Branch-Based Modular. Currently, metabolic engineers can reconfigure biochemical network to

direct the conversion of renewable feedstock into value-added compounds in microorganisms.548 However, when the native metabolism in microbes is repurposed through the manipulation of endogenous genes and the introduction of heterologous pathways, significant imbalances in pathway flux is frequently introduced.542,549 To circumvent this imbalance, metabolic branch-based modular is constructed through separately controlling central metabolism to increase precursor supply. (2S)-Pinocembrin acts as an antioxidative and antiapoptotic drug to reduce cerebral ischemia and blood-brain injury,550,551 and is also used as precursor for the synthesis of various flavonoids, such as galangin, dihydroflavonol and chrysin.552−554 Four catalytic steps are required for (2S)-pinocembrin production via the L-phenylalanine pathway555 (Figure 30): (i) L-phenylalanine is converted to cinnamic acid by phenylalanine ammonia lyase (PAL); (ii) cinnamic acid is transformed into cinnamoyl-CoA by 4-coumarate:CoA ligase (4CL); (iii) the condensation of 3 mol malony-CoA with 1 mol cinnamoyl-CoA forms (2S)-pinocembrin chalcone by chalcone synthase (CHS); (iv) (2S)-pinocembrin chalcone is changed to (2S)-pinocembrin by chalcone isomerase (CHI). Previous studies have made great advancements in demonstrating the feasibility of (2S)-pinocembrin biosynthesis in E. coli, and (2S)-pinocembrin production was increased to 29.9 mg/L.556 Although traditional metabolic engineering can yield a moderate increase in (2S)-pinocembrin production, it usually results in an imbalance expression of the multiple gene pathway, which can create over or underproduction of enzymes and the accumulation of intermediate metabolites.539,540 Therefore, metabolic branch-based modular pathway engineering can be adopted to achieve a balance among the multiple pathways and obtain better results in (2S)pinocembrin production. The (2S)-pinocembrin biosynthetic pathway was partitioned into four modules:555 module1 consisted of aroFwt and pheAfbr for phenylalanine production; module2 was composed of PAL and 4CL for cinnamoyl-CoA production; module3 was formed by malonate synthetase (matB) and malonate carrier protein (matC) for malonyl-CoA supply; module4 contained CHS and CHI for (2S)-pinocembrin production. Based on this, the expression levels of these four modules were finely tuned to obtain a balanced pathway by modifying plasmid copy numbers and optimizing gene codon preference, and the final (2S)-pinocembrin concentration was increased to 40.02 mg/L. 5.8.3. Enzyme Turnover Rate-Based Modular. In strain engineering process, imbalances in pathway flux can penalize cellular fitness through rerouting the essential resources for cell growth toward nonessential production of pathway enzymes.543,557 Achieving this optimal pathway balance is a vital step for producing target metabolites, due to the fact that this balance can substantially boost cellular health, product titer, yield, and productivity.558 Thus, enzyme turnover rate-based modular is optimized to enhance intermediate metabolite transmission efficiency by rearranging key enzymes via enzyme turnover rate in metabolic pathway. (2S)-Naringenin has widespread application in pharmaceutical indications, such as antioxidative, anticancer, and antiinflammatory activities.559,560 (2S)-Naringenin biosynthesis starts with Ltyrosine, and mainly contains two steps (Figure 30): (i) Ltyrosine is deaminated into p-coumaric acid by tyrosine ammonia lyase (TAL), and then p-coumaric acid is converted into coumaroyl-CoA by 4-coumarate:CoA ligase (4CL);561 (ii) coumaroyl-CoA is condensed with three malonyl-CoA to form (2S)-naringenin chalcone via chalcone synthase (CHS), which is 45


Chemical Reviews

Review

Figure 37. Strategies are used for genome-scale engineering. (A) Global transcriptional machinery engineering is an approach for reprogramming gene transcription and eliciting cellular phenotypes by introducing mutations to the master transcription factors. (B) Multiplex automated genome engineering is a way of using short oligonucleotides to scarlessly modify genomes and produce combinatorial genomic diversity by introducing oligomediated allelic replacement. (C) Trackable multiplex recombineering is a method to create specific genetic modifications and construct genomic mutant library by inserting synthetic DNA cassettes and molecular barcodes upstream of each gene.

finally transformed into (2S)-naringenin through chalcone isomerase (CHI).562 Previous studies have demonstrated the practicability of (2S)-naringenin biosynthesis by overexpressing TAL, 4CL, CHS, and CHI, and optimizing enzyme sources and gene expression levels,563 and the final strain was capable of producing 29 mg/L (2S)-naringenin. Generally, these modifications in individual pathways need additional precursors to improve carbon flux, but this flux may not be accommodated by downstream pathways, thus resulting in the accumulation of intermediate metabolites.539,555 To overcome these difficulties, enzyme turnover rate-based modular pathway engineering was used to divide the initial synthetic pathway for (2S)-naringenin production into three modules:561 (i) module1 consisted of TAL and 4CL; (ii) module2 contained CHS and CHI; (iii) module3 incorporated matB and matC. Based on this, the (2S)-naringenin biosynthetic pathway was further balanced by modifying plasmid copy numbers and promoter strengths, and the optimal strain was able to produce 100.64 mg/L (2S)-naringenin.

evolution of organisms with new and improved properties than that of the existing metabolic engineering techniques.568,569 (iii) Trackable multiplex recombineering (TRMR) is a method of creating thousands of specific genetic modifications and constructing genomic mutant library by inserting synthetic DNA cassettes and molecular barcodes upstream of each gene. This method can be used to quantitatively analyze genome-scale growth phenotypes, accurately map environmental tolerance pulse and largely enhance target product yield.570 To sum up, genome-scale engineering is effective for multiplex modification of endogenous genes and regulatory elements to study the interactions among gain-of-functions in gene networks and will enable more efforts in pushing the limits of engineered biological systems to obtain the desired functions, such as increasing the production of desired chemicals and improving the adaptation of environments. Thus, genome-scale engineering is still in need of technologies that select targeting genes more precisely, allow genetic modification approaches more diversely as well as design and construct the library more reasonably. 5.9.1. Global Transcriptional Machinery Engineering. Most cellular phenotypes are affected by many genes. As a result, a desired phenotype can be constructed by modifying transcription factor to change the combinational degree of promoter. In other words, it is important to note that target product can be significantly improved by screening strains with simultaneous modification in multiple genes at transcriptional level. gTME is an efficient method for reprogramming gene transcription to reveal cellular phenotypes.567 571 L-Tyrosine, an aromatic amino acid, is widely used as precursor for the synthesis of drugs, 571 biodegradable polymers,572 melanin, and phenylpropanoids.573 The L-tyrosine biosynthetic pathway is a branch of the shikimate pathway (Figure 8), which starts from erythrose-4-phosphate (E4P) and phosphoenolpyruvate (PEP), and mainly contains nine enzymes, 3-deoxy-D-arabino-heptulosonate (DAHP) synthase (aroG), dehydroquinate (DHQ) synthase (aroB), DHQ dehydratase (aroD), shikimate dehydrogenase (aroE/ydiB), shikimate kinase I/II (aroK/L), 5-enolpyruvoylshikimate-3-phosphate (EPSP) synthase (aroA), chorismate synthase (aroC), chorismate mutase/prephenate dehydrogenase (CM/PDH), and tyrosine

5.9. Genome-Scale Engineering

Genome-scale engineering is an art of constructing a genotype that generates a desired phenotype, and a challenge of engineering industrial strains that is influenced by genomic alterations.564 Application of genome-scale approaches to metabolic engineering can solve many biological questions through precisely modifying segments of genomic DNA, such as regulatory domains and promoter sequences. Powerful targeted mutagenesis methods have been developed, as follows (Figure 37).565,566 (i) Global transcriptional machinery engineering (gTME) is an approach for reprogramming gene transcription and eliciting cellular phenotypes by introducing mutations to the master transcription factors that mainly mediate DNA recognition. This approach can be applied to drastically enhance the stress tolerance of cells and improve the prospects of metabolite production.567 (ii) Multiplex automated genome engineering (MAGE) is a way of using short oligonucleotides to scarlessly modify genomes and produce combinatorial genomic diversity by introducing oligo-mediated allelic replacement to create mismatches, insertions, deletions in a single cell or population of cells. This way is more efficient in the design and 46


Chemical Reviews

Review

intermediate, indole, by heterologous expression of a flavincontaining monooxygenase (FMO) from Methylophaga aminisulf idivorans MPT,584 including three steps (Figure 8): (i) tryptophan is converted to indole by tryptophan synthase (trpA); (ii) indole is oxidized to 2-hydroxyindole, 3-hydroxyindole and isatin by FMO;585,586 (iii) indigo is synthesized in the presence of oxygen.587,588 Recently, several investigations have been reported for the biosynthesis of indigo with the addition of indole by expressing mono- or dioxygenase. However, the resultant yield of indigo is low,589−591 owing to the fact that indole is toxic for cell growth.592 Thus, new substrates should be explored, such as glucose.583 To optimize the complicated tryptophan pathway from glucose, MAGE was performed by introducing short functional DNA stretches into the genome of E. coli EcHW47, which the feedback regulation and allosteric inhibition associated with tryptophan biosynthesis were removed.568 In other words, 20 base pair T7 promoters were chosen for insertion upstream of 12 genomic operons involved in the biosynthesis of tryptophan in E. coli EcHW47, thus producing combinatorial libraries of variants.593 Finally, 80 unique variants with 12 T7 promoter insertions were successfully recovered, and the E. coli EcHW47 variant H33 showed a 62% increase in indigo production by combining T7 promoter insertions in aroC and trpE and overexpressing FMO. These results indicate that MAGE provides a new approach to modify endogenous genes and regulatory elements, and makes great progress in genome-scale engineering to overcome the limits of biological systems. 5.9.3. Trackable Multiplex Recombineering. Application of MAGE requires knowledge of the specific gene of the metabolic pathway. However, if the target gene is unkown, trackable multiplex recombineering (TRMR) may be an alternative. First, mutant library containing molecules barcode sequence is constructed. Then, these synthetic DNA cassettes are transformed into competent cell to produce thousands of variants. Finally, if the properties are greatly improved, the mutation site can be determined by molecular barcode tracking technology. Biofuels have a great potential and prospect to be a valuable substitute for gasoline, in particular if it can be produced from lignocellulosic materials, such as wood, agricultural, and forest residues.594 In order to use lignocellulosic feedstocks in fermentation, such as switchgrass, poplar and corn stover, the sugars in the polymer chains must be released. To achieve this, pretreatment must be adequately carried out to produce hydrolysate, that is, the sugary liquid. However, this pretreatment process also produces a variety of compounds that inhibit the fermentation performance, such as acetic acid, furan derivatives and phenolic compounds.595 Although E. coli, S. cerevisiae and Zymomonas mobilis are recognized as the most promising microorganisms for industrial biofuel production,596 each strain has limitations in native substrate utilization, production capacity and tolerance. E. coli can natively use both hexose and pentose as carbon source, but the heterogeneous ethanol production pathway must be supplemented to produce biofuel.597 In addition, E. coli is fit for genetic improvement and hopeful to improve the capability of biofuel production. Therefore, TRMR was used to construct a genome-wide library by inserting synthetic DNA cassettes and molecular barcodes upstream of each gene, thus achieving the modification of gene expression in E. coli.570 Based on microarray analysis, a minority of recombinant colonies were selected for recursive multiplex recombination by supplying synDNA oligos that were initially

aminotransferase (TyrB). Previous advances in engineering the L-tyrosine pathway was mainly focused on removing the feedback inhibition of aroG and CM/PDH,574,575 expressing the key enzymes aroK/L, ydiB, phosphoenolpyruvate synthase (PEPS) and transketolase (TKT), deleting the branch of L-phenylalanine biosynthesis, 575,576 and altering glucose transport system.423,574,577 Despite this, many important multigenic phenotypes are still difficult to achieve, probably due to the fact that the unpredictable disconnects between genotypes and phenotypes often become major roadblocks for engineering biological systems.578 To improve the efficiency of engineering phenotypes, gTME has been shown to be particularly effective in introducing phenotypic diversity by reprogramming the cellular transcriptome. 578 E. coli P2 with the aroG D146N -CM/PDH M53I/A354 V operon was selected as the starting strain for the construction of gTME-derived libraries. The RNA polymerase α subunit (rpoA) and the principal sigma factor σ70 (rpoD) in E. coli P2, which played important roles in modulating global transcriptional profiles, were mutated to form two plasmid-based mutagenesis libraries. Finally, three strains were successfully isolated, which exhibited significant increases in both L-tyrosine yields and titers. The highest L-tyrosine titer was up to 13.8 g/L in large-scale fermentations, which showed a 114% increase over the rationally engineered strain. Ethanol is one of the most important products stemming from the fermentation industry,579 and it is mainly used as a biofuel, but can also be used for ethylene production.580 S. cerevisiae is generally considered as the best producer for ethanol production.581 However, as a toxic metabolite, ethanol exhibits a strong inhibition in cell growth, which limits the productivity of ethanol.582 Although this inhibition can be reduced to some extent by high gravity fermentation technology,582 strong substrate inhibition still occurs in continuously stirred tank bioreactors and tubular bioreactors. These results indicate that the traditional methods have limited the success rate of strain improvement. Compared with the traditional approaches, gTME is a new cellular engineering concept, which aims to elicit cellular phenotypes for industrial applications by modifying transcription factor behavior and reprograming gene transcription.567 The standard haploid S. cerevisiae BY4741, which contains the endogenous, unmutated TATA-binding protein (SPT15) and its associated factors (TAF25), was used to create two gTME mutant libraries from SPT15 and TAF25. Among these mutations, SPT15F177S/Y195H/K218R showed the most desirable phenotype that conferred the elevated tolerance for ethanol and glucose, and more efficient conversion from glucose to ethanol. Thus, gTME can provide an effective channel to alter cellular phenotypes that are difficult to obtain by traditional methods. 5.9.2. Multiplex Automated Genome Engineering. Most biological products involve multiple step enzymatic reactions and therefore must simultaneously coordinate the expression of several genes to improve product formation. Multiplex automated genome engineering (MAGE) simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. This approach needs a pool of oligos and each of the oligos contains a mutation in one gene. Electroporating synthetic DNA oligos into cell may lead to mismatches, insertions, deletions in a single cell or population of cells, thus producing combinatorial genomic diversity. Indigo is recognized as the oldest textile dye,583 which has been widely used for the dyeing of cotton and wool fabrics.583 Indigo can be produced from the tryptophan-biosynthesis 47


Chemical Reviews

Review

Figure 38. Strategies are used for multiplex genome editing. Multiplex genome editing has been widely used to precisely edit genome, such as gene integration, replacement and deletion, which has opened up a new window for high-throughput rerouting biosynthetic pathways to produce chemicals. (A) ZFNs editing consists of a nonspecific endonuclease Fok I to introduce double strand breaks (DSBs) and zinc-finger proteins to bind specific target DNA. (B) TALENs editing is constituted by nonspecific endonuclease Fok I to introduce DSBs and TALEs protein to bind specific target DNA. (C) CRISPR/Cas9 editing is composed of Cas9 endonuclease to introduce DSBs and guide RNA to pair with target DNA.

present in low concentrations.569 Many modifications conferred fitness advantages to improve cell growth in hydrolysate, such as the up mutation of some key genes in primary metabolism, RNA metabolism and sugars transport.598 Among these positive mutations, four mutations conferred higher tolerance to acetate.

clustered regularly interspaced short palindromic repeats (CRISPR) editing is composed of Cas9 endonuclease to introduce DSBs and guide RNA to pair with target DNA.605,606 The above-mentioned three universal and predictable multiplex genome editing tools have opened up a new window for high-throughput editing of genomes at multiple sites, rerouting biosynthetic pathways and removing negative feedbacks, which can avoid many bottlenecks in randomized mutagenesis, Cre/loxp and Flp/FRT system. For example, the left scars in each cycle of genome manipulation with Flp/FRT system can largely reduce the efficiency of the following multigene knockout. Although genome editing has made great progress, it is still in its infancy and needs improvement in the following three directions: (i) minimizing off-target cleavage; (ii) avoiding target sequence limitation; (iii) broadening the range of genome editing. As an imperative part in system metabolic engineering, advances of multiplex genome editing in introducing synthetic routes and balancing metabolic fluxes have revealed its new applications in different fields. 5.10.1. ZFNs Editing. Zinc-finger nucleases (ZFNs) contain a hybrid protein derived from a specific DNA-binding protein and a nonspecific cleavage domain of the endonuclease Fok I. Every zinc-finger protein consists of many zinc-finger domains, and the Cys2−His2 zinc-finger domain is the most common type of DNA-binding motifs in eukaryotes. An individual zinc-finger domain is composed of approximately 30 amino acids in a conserved β−β−α configuration. By substituting amino acid residues at α helix, numerous zinc-finger domains can be

5.10. Multiplex Genome Editing

The idea of multiplex genome editing has been implemented in recent years with the development of fast whole-genome sequencing, large genome annotation and targeted genome editing tools. Multiplex genome editing is a re-engineering strategy with rational design of metabolic pathway to expand the product portfolio in a wide range of organisms, such as microorganisms, plants, and animals. In addition, multiplex genome editing can be widely used to precisely edit genome, such as gene integration, replacement and deletion. According to editing mechanism, genome editing can be divided into three approaches (Figure 38): (i) zinc-finger nucleases (ZFNs) editing consists of a nonspecific endonuclease Fok I to introduce double strand breaks (DSBs) and zinc-finger proteins to bind specific target DNA, which can be assembled by modular assembly (MA),599 oligomerized pool engineering (OPEN)600 and context dependent assembly system (CoDA);601 (ii) transcription activator-like effector nucleases (TALENs) editing is constituted by nonspecific endonuclease Fok I to introduce DSBs and TALEs protein to bind specific target DNA, which can be assembled by golden gate,602 fast ligation-based automatable solid-phase high-throughput (FLASH)603 and LIC;604 (iii) 48


Chemical Reviews

Review

Figure 39. Strategies are used for transporter engineering. Transporter engineering is one of the common strategies employed in metabolic engineering to improve chemicals production, including exporters and importers. (A) ABC transporters consist of two cytoplasmic nucleotide-binding domains that hydrolyze ATP as energy source to drive transport and two transmembrane domains that bind chemical compounds and provide a translocation channel. (B) Secondary efflux pumps are composed of a cytoplasmic membrane export protein, a periplasmic linker and an outer membrane channel, in which the membrane export protein is responsible for chemical compound recognition and proton exchange.

produced. In addition, since the Fok I nuclease functions as a dimer, two artifical zinc-finger proteins, which can recognize the corresponding sequences on the opposite strands of target DNA, are required for induction of a double strand break (DSB).607 Nonfucosylated antibody which is fucose defect, can greatly improve antibody-dependent cellular cytotoxicity activity in vitro.608 Currently, chinese hamster ovary (CHO) cells represent the most frequently used mammalian host for antibody production, owing to its capability of producing the recombinant proteins with human-like post-translational modifications. However, the native antibody produced by CHO cells is generally fucosylated by alpha-1,6-fucosyltransferase (Fut8). Thus, the disruption of Fut8 gene in CHO cells is essential for producing nonfucosylated antibody. However, the conventional gene disruption by homologous recombination is a laborious process, and the extremely low editing efficiency is cumbersome.609 To meet these needs, the site-specific genome editing tool, ZFNs editing was developed and used for CHO cells modification. In this procedure, two ZFNs that could individually recognize 15 and 18 nucleotides on Fut motif II, were assayed and transfected into CHO-K1 cells. The target cells with phenotypic modification could be screened in less than 3 weeks at a frequency of 5%. This method accelerates the progress of genetic knockouts with less time and higher efficiency compared to that of the other alternatives. Additionally, evidence also shows that there is no negative impact on the engineered CHO cells, such as cell growth and production yield.608 5.10.2. TALENs Editing. Transcription activator-like effector nucleases (TALENs) are initially discovered in plant Xanthomonas sp. The key DNA-binding domain of TALENs consists of 13−29 repeat amino acid units, which is highly similar to each other except the 12th and 13th amino acid residues. These repeat variable diresidues (RVDs) are the key modules in determining the specificity of nucleotide recognition.610 Because the relationship between RVD and its corresponding nucleotides is high-specific,611,612 TALENs editing has revealed its advantages in many fields such as easier site modification and better modular

assembly, and has achieved great success in genome engineering of rat613 and human cells.602 Triacylglycerol (TAG), which serves as the crucial storage form of energy in lipid metabolic, is the major feedstock for biodiesel production. TAG can be produced by three major steps in microalgae614 (Figure 11): (i) the carboxylation of acetyl-CoA in the plastid; (ii) the elongation of acyl chain in the plastid and cytosol; (iii) the formation of TAG in the endoplasmic reticulum. Normally, TAG accumulation is correlated with the environmental stress in microalgae, which increases the difficulty to obtain hosts with high TAG content. Thus, the large-scale harvest of TAG from microalgae suffers from low yields and biomass consumption. In addition, the reported modifications in genome to increase TAG productivity has been limited by its diploid genome and insufficient microalgae sequence information. Recently, TALENs editing was undertaken by targeting seven genes in three different modules, which were involved to lipid metabolismin in Phaeodactylum tricornutum, including:615 (i) lipid content module contained three enzymes, UDP-glucose pyrophosphorylase, glycerol-3-phosphate dehydrogenase and enoyl-ACP reductase; (ii) acyl chain length module consisted of long chain acyl-CoA elongase and putative palmitoyl-protein thioesterase; (iii) fatty acid saturation module was composed of ω-3 fatty acid desaturase and δ-12-fatty acid desaturase. After amplicon sequencing of the selected colonies, the highest gene modification efficiency of 56% could be obtained. Finally, the strain with UDP-glucose pyrophosphorylase inactivation generated by TALENs editing achieved a 45-fold increase in TAG content compared with that of the parental strain.615 These results indicate that TALENs editing has the ability to manipulate metabolic pathways, and paves the way for synthetic biology in diatoms. 5.10.3. CRISPR/Cas9 Editing. Clustered regularly interspaced short palindromic repeats (CRISPRs) editing is originally identified in prokaryotes by bioinformatics analysis.616 The type II CRISPR/Cas9 system is widely used in biotechnology owing to its simplicity and efficiency. In this system, an endonuclease 49


Chemical Reviews

Review

Cas9 from Streptococcus pyogenes is introduced, which can generate double strand breaks in complementary genomic sequences at the upstream of NGG-PAM with the guidance of a single guide RNA.617 In addition, Cas9 can also be converted into a specific effector in gene activation or repression by disrupting its nuclease activity.618,619 β-Carotene is a member of ubiquitous pigments,620 which is considered as good pharmaceuticals and nutraceuticals, and an important additive in cosmetics and food.620 The biosynthesis pathway of β-carotene consists of five modules (Figure 5): glycolysis module, mevalonate (MEV) module, 2-C-methylderythritol-4-phosphate (MEP) module, pentose phosphate (PP) module, and β-carotene synthesis module. Various strategies have been used to improve β-carotene production through precursors supplement,621 synthetic pathway amplification,622 model-based systematic gene prediction,623 modules balance.623 However, there are so many genes in metabolic pathway that it is a low-efficiency and time-consuming procedure to take metabolic modification. Recently, CRISPR/Cas9 editing was adopted to improve β-carotene production. In this system, various types of genomic modifications such as gene insertion, deletion and replacement could be accomplished within 2 days per cycle at near 100% editing efficiency. Finally, a total of 33 genomic modifications forming more than 100 genetic variants were tested, in which the engineered strain E. coli ZF237T could reach a maximum titer of 2.0 g/L β-carotene after combinatorial optimization.624

productivity of target chemicals. However, transporter engineering is not always efficient for improving production titer, owing to the fact that overexpression of certain transporters is usually detrimental to cell growth.405 In the future, the new technologies for transporter engineering should be developed to explore novel transporters with low pump toxicity and desirable substrate specificity in improving tolerance and productivity.542 5.11.1. ABC Transporters. ATP-binding cassette (ABC) transporters are the main transporters for catalyzing the translocation of substrates with ATP as energy source. ABC transporters contain two parts: (i) two transmembrane domains to bind chemical compound and provide translocation channel; (ii) two cytoplasmic nucleotide-binding domains to hydrolyze ATP and drive transport.542,631 Application of ABC transporters can decrease the intracellular accumulation of target chemicals, and at the same time increase the extracellular concentration of target chemicals.14,90,127,243,405 Avermectin are widely used as effective agricultural pesticides and antiparasitic agents in the fields of veterinary medicine and agriculture.632 Streptomyces avermitilis has been widely used to produce avermectin, but at the same time S. avermitilis produces a variety of toxic macrolide oligomycin, thus resulting in product inhibition to the enzymes in metabolic pathway. To solve this problem, the whole genome of S. avermitilis was sequenced,633 with focus on the gene cluster for avermectin biosynthesis.634 Avermectin biosynthesis can be divided into four steps (Figure 11): biosynthesis of starter units, formation of initial aglycons, modification of avermectin aglycons, glycosylation of avermectin aglycons. Several studies have been successfully used to increase avermectin production by overexpressing of S-adenosylmethionine synthetase (metK),635 constructing a mutant library of the major sigma factor (hrdB),636 controlling transcriptional regulator SAV151 and its target genes,637 but the toxicity of product was still drastically increased. Further sequence analyses indicated that there was an avtAB gene in the upstream of the gene cluster, which was highly homologous to the mammalian multidrug efflux pump. This result indicated that the avtAB pump might play an important role in secreting avermectins or transporting effector molecules for avermectin biosynthesis.626 Finally, overexpression of the AvtAB pump led to a 1.5-fold increase in avermectin productivity, and the ratio of intracellular to extracellular avermectin was reduced from 6:1 to 4.5:1.626 ABC transporters are helpful for exporting endogenous secondary metabolites, and thus preventing self-poisoning, reducing feedback inhibition and increasing metabolite production. 5.11.2. Secondary Efflux Pumps. Secondary efflux pumps can perform substrate translocation with proton or sodium gradients as energy source. They are composed of three protein subunits: a cytoplasmic membrane export protein (CMEP), a periplasmic linker and an outermembrane channel.542,628 Among these subunits, CMEP is responsible for chemical compound recognition and proton exchange. Secondary efflux pumps have been used to excrete toxic compounds from the cells, thus alleviating product toxicity and increasing metabolite productivity.542,629,630 Limonene is considered to be a safe compound,638 which can be used as precursors for several pharmaceutical and commodity chemicals.639 Limonene is synthesized from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP),638 which are from the MEV pathway in eukaryotes or the DXP pathway in prokaryotes640 (Figure 5). IPP and DMAPP are condensed into geranyl pyrophosphate by geranyl

5.11. Transporter Engineering

Engineering of transporters including exporters and importers is one of the common strategies in metabolic engineering to improve chemicals production.249 Usually, target chemicals generated in cytoplasm need to be transported out of the cells through exporters, such as secondary efflux pumps.14 This transportion is conducive to minimize the intracellular concentration of target chemicals, thus avoiding feedback inhibition and growth toxicity, and ultimately realizing maximum production of target chemicals.90,127,243 At the same time, importers such as ABC transporters should be eliminated to prevent the reintroduction of extracellular products back into the cells or expressed to improve the absorption of extracellular nutrients.249 Thus, transporter engineering can be classified into two major categories (Figure 39): (i) ABC transporters consist of four domains: two cytoplasmic nucleotide-binding domains that hydrolyze ATP as energy source to drive transport, two transmembrane domains that bind chemical compounds and provide a translocation channel.625 ABC transporters are mainly divided into two types: an exporter that pumps out the final product, preventing it from accumulating in the intracellular space;626 an importer that improves the absorption of substances, improving cell growth.627 (ii) Secondary efflux pumps are composed of three protein subunits: a cytoplasmic membrane export protein, a periplasmic linker and an outer membrane channel, in which the membrane export protein with proton or sodium gradients as energy is responsible for chemical compound recognition and proton exchange.542,628 Secondary efflux pumps can actively excrete toxic compounds from cells and thus draw much attention for its potential roles in alleviating toxicity and increasing productivity.629,630 As shown above, the chemical compound can be recognized and transported between inside and outside the cell by rationally harnessing transporters, which can endow cell factories with strong tolerance for target metabolites and other compounds, thus enhancing the 50


Chemical Reviews

Review

diphosphate synthase (ERG20), which is then converted to limonene by limonene synthase (LS). Subsequent metabolic engineering of the DXP pathway resulted in overproducing IPP and DMAPP through overexpressing AtoB, ERG13, tHMGR, ERG12, ERG8, ERG19, and Idi, and this overproduction can be readily routed to produce limonene by overexpressing the truncated and codon-optimized LS gene from Mentha spicata and ERG20 gene from Abies grandis.7,268,641,642 The final strain E. coli BL21 (DE3) (pMVAidi, pTAC:LS:AGPPS2) could produce 2.7 g/L limonene.643 However, microbial production of monoterpenes such as limonene is limited due to its toxicity and volatility.644 In order to improve the tolerance and production of limonene, bioinformatics was used to generate a list of efflux pumps from bacterial genomes and determine a subset of targets order for cloning. The resulting library of 43 pumps was heterologously expressed in the limonene-producing E. coli strain, and the results indicated that overexpression of the efflux pumps from Alcanivorax borkumensis led to a 1.6-fold improvement in limonene yield.644 These advances provide an important proof-of-principle demonstration that efflux pumps can be utilized to improve the yield of limonene in a production host by increasing tolerance and relieving toxicity to exogenous limonene. It also should be noted that an effective efflux pumps may serve as the other functions to improve metabolites production, such as relieving end product inhibition to metabolic pathway enzymes.

Figure 40. Strategies are used for morphology engineering. Morphology engineering is a framework of cell morphology regulation, in which various strategies are introduced to change the size of cell for improving the yield of desired products. (A) Fungal morphology can be regulated by microparticle-enhanced cultivation and genetic manipulation. (B) Bacterial morphology can be regulated by changing the rheology of culture and the inherent shape of cell.

5.12. Morphology Engineering

Morphology engineering, which incorporates the concepts and techniques of biochemical engineering and metabolic engineering at the macromorphogenesis level, offers a conceptual and technological framework to speed the optimization and creation of microbial cell factories for the optimal production of desired products. Generally, strain morphology can be controlled through empirical operations, such as pH, agitation speed and medium composition, but the obtained parameters such as average pellet diameter and biomass density, do not meet the requirements for clarifying the relationship between morphology and productivity, suggesting that tunable control of strain morphology is critical in fermentation industry. During fermentaion bioprocess, the morphology of strain is comprehensively influenced by multiple correlation interactions between physiological aspects of morphological development and molecular aspects of morphological control. On the one hand, fungal morphology can be regulated, from macroscopic to microscopic, by two approaches (Figure 40A): (i) microparticleenhanced cultivation (MPEC) can affect the morphology from pellets to mycelia and thus is used to enhance the yield of enzymes, such as laccase,645 glucoamylase,646 and fructofuranosidase;647 (ii) genetic manipulation can change the component of cell wall and thus is used to improve production of α-amylase648 and penicillin.649 On the other hand, bacterial morphology can also be finely controlled by two strategies (Figure 40B): (i) changing the rheology of cultures can affect particle interaction, pellet formation and pellet aggregation, and thus improve production of antibiotics;650 (ii) changing the inherent shape of cells can be genetically manipulated through three directions:651 transforming the rod-shape cell to mini-cell for high cell density fermentation by overexpressing genes involving cell division such as FtsZ, changing rod-shape cells to filamentary cells for intracellular metabolites accumulation by improving the expression of genes involving binary division such as SulA and MinCD, altering rod-shape cell to sphere cell for

expanding cell volume by modifying the genes involving shape maintenance proteins such as MreB. Using morphology engineering, it is now possible to control the morphology transformation of filamentous fungi among mycelia, clump and pellet,652 and the shape change of bacteria among bars, spheres and fibers.651 Morphology engineering has proven not only beneficial for energy reduction but also positive for improvement in chemicals production. 5.12.1. Fungal Morphology. An important aspect in metabolic engineering of filamentous fungi is the subsequent effect of morphology on product formation. However, morphology is acknowledged as being difficult to control especially with respect to superior production performance, due to the fact that many factors exert great influence on morphology, such as specific strain properties, process variables, rheology, etc.653 It is therefore necessary to search alternative approaches and techniques to manipulate the underlying macro and micromorphogenesis. Recent advances in the targeted control of fungal morphology have shown that the morphological development of filamentous fungi can be strongly influenced by adding microparticles to the culture medium.646,654 Glucoamylase (GA), also known as γ-amylase, can hydrolyze α-1,4 glycosidic linkages in starches and oligosaccharides with the inversion of the anomeric configuration to produce βglucose.655 GA can be produced by many fungal species, such as Aspergillus niger and recombinant strains of this fungus,655 but the titer of GA is merely 30 g/L.656 This low production level is possibly attributed to inefficient protein secretion, which is affected by cell morphology.656 Fungi morphology is generally affected by inoculum size, initial broth pH, agitation, medium composition, etc.652,656 Thus, the recombinant strain A. niger AB4.1 (pgpdAGLAGFP), which carried the GA-S65T GFP fusion protein, was selected to study the correlation between cell morphology and GA production, and the results indicated that smaller pellets (1 mm) produced more GA than large pellets (5 51


Chemical Reviews

Review

Figure 41. Strategies are used for consortia engineering. Consortia engineering represents a new frontier of synthetic biology to extend engineering capabilities from single-cell behaviors to multicellular microbial consortia. (A) Synthetic consortia via metabolite exchanges is a rational engineering strategy to confer biological systems on new functions and behaviors. (B) Synthetic ecosystems via QS communications offers insights into the optimal design of synthetic consortia for programing biological systems.

mm) in the bioreactor.656 To finely control the morphological development of A. niger, morphology engineering was used to improve GA production with supplemented silicate microparticles and titanate microparticles. When silicate microparticles (10 g/L, 15 μm) was added in shake flask culture of A. niger ANip7-MCS-gfp2, the final GA activity (61 U/mL) was increased by almost 4-fold compared with the control (17 U/ mL).647 Further, when titanate microparticles (25 g/L, 0.3 mm) was chosen, GA production (320 U/mL) with A. niger ANip7MCS-gfp2 was almost 7-fold higher than that of the normal cultivation (50 U/mL).646 These research advances provide further possibility to use microparticles for tailor-made morphology design, especially for pellet-based processes in industrial production.646 5.12.2. Bacterial Morphology. Many bacteria contain various inclusion bodies naturally, which can be used to make different materials, such as glycogen, polyamino acids, PHB, PHA, etc. Thus, it has great potential to explore bacteria as cell factories for efficiently producing these inclusion bodies. However, the intracellular accumulation of inclusion bodies was limited by the small size of bacteria ranging from 0.5 to 2 μm. Therefore, how to make bacterial cells larger is a key point to improve the production of inclusion bodies. In other words, a larger intracellular space is needed for accumulation of more inclusion bodies.657 Polyhydroxybutyrate (PHB) is a kind of strong, flexible, and absorbable material, which has various applications in medicine, such as tissue engineering and drug delivery.658,659 PHB can be produced by combining the PHA biosynthesis and the succinate degradation660,661 (Figure 25): (i) the PHA biosynthesis is from acetyl-CoA in two sequential reaction steps catalyzed by βketothiolase (phaA) and acetoacetyl-CoA reductase (phaB); (ii) the succinate degradation consists of two CoA transferases and two dehydrogenases, i.e., succinyl-CoA: CoA transferase (Cat1), succinic semialdehyde dehydrogenase (SucD), 4-hydroxybutyrate dehydrogenase (4HBd), 4-hydroxybutyryl-CoA: CoA transferase (Cat2) (Figure 9); (iii) the PHB polymerization is catalyzed by PHB synthase (phaC). Based on this, E. coli strain XL1-Blue was able to produce 58.5% w/w PHB by overexpressing phaC from R. eutropha and Cat2 from Clostridium kluyveri.662 Since PHB is produced as inclusion bodies in bacteria, the cell size of bacteria limits the amount of PHB granules and the quantity of PHB in each cell. Thus, E. coli JM109SG was enlarged by deleting 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (folK), and overexpressing phaCAB operon, Cat2, 4HBd, SucD, IspH, folK, and SOS cell division inhibitor (sulA), and the final 78.9% w/w PHB was obtained in the

engineered E. coli JM109SGIK (p68orfZ-ispH/pMCSH5-folK/ p15asulA).661 However, this size enlargement is not stable in cell growth period, and a better way to stabilize cell size should be found. Finally, by overexpressing the bacterial peptidoglycan cell wall and the actin-like protein (mreB) in an mreB deletion mutant under the inducible expression of sulA, Guo-Qiang Chen and co-workers obtained 86% w/w PHB in an engineered E. coli JM109SG (ΔmreB/pTK-mreB-PBAD:sulA/pBHR68).657 This morphology engineering opens a new way for the production of microbial inclusion bodies, such as PHA, proteins, and carboxysomes. 5.13. Consortia Engineering

Synthetic biology is an emerging research field for programing biological systems by rational engineering strategies, thus conferring new functions and behaviors on cells. Although many genetic circuits and metabolic pathways have been programmed in single cells, the new frontier of synthetic biology is how to extend systems engineering capabilities from single-cell behaviors to multicellular microbial consortia. Synthetic microbial consortia have been constructed and applied in diverse fields:663 (i) Engineering cell−cell communications in isogenic microbial communities to program microbial consortia composed of multiple microorganisms;664 (ii) engineering pattern formation via cell−cell communications in isogenic microbial communities to control spatiotemporal behaviors of microbial populations;665 (iii) engineering one-way communications in binary microbial consortia to achieve the coordinated behaviors in synthetic microbial communities;666 (iv) engineering microbial consortia with two-way (bidirectional) communications to program synthetic microbial consortia via metabolite exchanges667 and synthetic ecosystems via quorum-sensing (QS) communications (Figure 41);668 (v) using synthetic microbial ecosystems to address ecological questions,669,670 such as cell dispersion, spatial effects and cooperator-cheater on the stability of ecosystems. Additionally, bidirectional communications in microbial consortia have been successfully engineered for production of nutraceuticals (e.g., vitamin C,671,672 myoinositol,673 etc.), drugs (e.g., sugar nucleotides, oligosaccharides,674 etc.), and biofuels (e.g., ethanol,675 isobutanol,676 etc.). These advances show that the synergistic development of systems biology and synthetic biology will pave the way for thoroughly understanding natural microbial consortia and rationally engineering these complicated consortia to confer novel applications. 5.13.1. Synthetic Consortia. Microorganisms usually interact with each other via two main mechanisms:663 (i) the contact-based interaction for the interchanges of biomolecules 52


Chemical Reviews

Review

can regulate population density up to a critical threshold.663 Thus, QS mechanisms can help microorganisms to sense their neighboring species, gauge their cell densities, modulate their gene expressions, and coordinate their group behaviors.683,684 Based on this mechanism, synthetic ecosystems via QS communications offers insights into the optimal design of synthetic consortia for programing biological systems. Myo-inositol (MI), a six carbon cyclohexane hexitol, is used as a supplement in treating several pathologies such as PCOS,685 metabolic syndrome,686 and gestational diabetes.687 Myoinositol (MI) biosynthesis from glucose contains three steps in E. coli688 (Figure 33): (i) glucose is converted to glucose-6phosphate (G6P) by the native phosphotransferase system (PTS); (ii) G6P is isomerized to myo-inositol-1-phosphate by myo-inositol-1-phosphate synthase (INO1); (iii) myo-inositol1-phosphate is dephosphorylated to MI by myo-inositol monophosphatase (MIMP). Using this pathway, MI can be further converted into other useful products, such as glucaric acid499 and scyllo-inositol.689 In these engineered pathways, the theoretical yields of almost 100% are possible, but G6P is directed into native metabolism through glycolysis and HMP pathway, as well as into heterologous production of MI via IPS from S. cerevisiae. The split of G6P flux indicates that the relative kinetic efficiency among the competing branches determines the potential improvement in MI titers with dynamic downregulation of native metabolic flux. To regulate metabolic flux dynamically, a pathway-independent QS circuit was created to switch off gene expression at desired times and cell densities.673 First, the PesaS promoter was used to replace the native promoter of phosphofructokinase-1 (Pfk-1), and a standard SsrA degradation tag was appended to the C terminus of Pfk-1. Then, esaRI70 V was inserted in the genome under the control of a constitutive promoter from the BioFAB library (apFAB104). Next, 3-oxohexanoylhomoserine lactone synthase gene (esaI) was inserted in the genome under the control of a combinatorial library of promoter and RBS variants. Thus, Pfk-1 expression was coupled with glycolytic flux and cell growth, and this QS circuit enabled switching from “growth mode” to “production mode” at different rates in a completely autonomous fashion. Finally, the MI titers in strain E. coli L19S was increased by 5.5-fold, compared to the parent strains lacking dynamic flux control. Similarly, this QS circuit was also applied to dynamically control production of glucaric acid and shikimate.673

and electrons via physical cell−cell contact; (ii) the contactindependent interaction for the exchanges of metabolites and information signals via diffusible chemicals and physical contact.677 Microbial consortia via metabolite exchanges is a common mechanism in natural microbial ecosystems. Thus, synthetic consortia is a rational engineering strategy to confer biological systems on new functions and behaviors. Globotriose is the carbohydrate portions of globotriosylceramide that constitutes the rare Pk blood group antigen on erythrocytes and the CD77 differentiation antigen on lymphocytes.678 Globotriose biosynthesis can be divided into three steps674 (Figure 42): (i) orotic acid is converted into

Figure 42. Schematic of globotriose biosynthetic pathway. UMP: uridine 5′-monophosphate; UDP: uridine 5′-diphosphate; UTP: uridine 5′-triphosphate; PPi: pyrophosphoric acid; Pi: phosphoric acid; Glc-1P: glucose-1-phosphate; UDP-Glc: uridine 5′-diphospho-glucose; UDP-Gal: uridine 5′-diphospho-galactose; Gal-1-P: galactose-1-phosphate; Lac: lactose; galK: galactokinase; galT: galactose-1-phosphate uridylyltransferase; galU: glucose-1-phosphate uridylyltransferase; ppa: pyrophosphatase; LgtC: α-4-galactosyltransferase.

uridine 5′-triphosphate (UTP); (ii) UTP is condensed with glucose and galactose to form uridine 5′-diphospho-galactose (UDP-Gal); (iii) α-4-galactosyltransferase (LgtC) converts UDP-Gal and lactose into globotriose. Although efficient multienzyme systems have been developed to produce oligosaccharide with cofactor regeneration,679,680 this method requires expensive starting materials such as phosphoenolpyruvate, nucleoside 5′-phosphates, enzyme preparations, and LgtC.674 To reduce costs, a large-scale production system of globotriose from orotic acid, galactose, and lactose was developed using microbial consortia by coupling two metabolically engineered strains E. coli and Corynebacterium ammoniagenes.674 In this bacteria consortium, C. ammoniagenes DN510 first converted orotic acid to UTP; then, E. coli NM522/pNT25/ pNT32 converted galactose into UDP-Gal by expressing galactose-1-phosphate uridylyltransferase (galT), galactokinase (galK), glucose-1-phosphate uridylyltransferase (galU), and pyrophosphatase (ppa); next, E. coli NM522/pGT5 converted lactose and UDP-Gal into globotriose by expressing LgtC. Finally, globotriose production was increased to 188 g/L. These results indicated that synthetic microbial consortia would be good tools for the industrial manufacture of various oligosaccharide, such as CMP-NeuAc,681 3′-sialyllactose,681 and αNeup5Ac-(2 → 6)-D-GalpNAc.682 In addition, the microbial consortium of K. vulgare and B. megaterium has been successfully used for the industrial production of vitamin C in a two-step fermentation process.663 5.13.2. Synthetic Ecosystems. Contact independent interaction among microorganisms via QS signaling molecules is one of the most common manners, by which microbial cells

6. CONCLUDING REMARKS DCEO biotechnology is becoming an essential platform technology for developing cell factories to produce a wide range of chemicals, including biofuels, bulk chemicals, pharmaceuticals, nutraceuticals, etc. DCEO biotechnology not only extensively adopts the traditional tools from various disciplines, but also rationally uses the emerging tools from different fields to meet the specific needs for cell improvement. As a result, DCEO biotechnology is a system-wide concept and technique to understand “cell pathway surgery” through four technical links, i.e., pathway design, pathway construction, pathway evaluation, and pathway optimization. Taken together, many chemicals successfully produced by DCEO biotechnology provide insights as to when, what and how each of strategies should be taken. Product development depends on DCEO cycle throughput, and DCEO cycle throughput relies on biotechnological innovation. Thus, DCEO cycle throughput needs to be pursued, which is mainly influenced by two factors: cycle speed and cycle 53


Chemical Reviews

Review

bandwidth. Cycle speed determines how quickly each iteration of DCEO cycle can be completed, and cycle bandwidth reflects how many designs each iteration of DCEO cycle can be evaluated. DCEO biotechnology is an exhaustive and innovative engineering biology approach, and thus in this process, many possible genetic designs must be evaluated to find the cells that can produce high levels of desired chemicals. Recent advances in DCEO biotechnology have enabled the design and construction of billions of genetic variants per day, but the capacity of evaluation and optimization is limited to thousands of variants per day. This phenomenon suggests that cycle bandwidth can be improved to enhance overall DCEO cycle throughput, but cycle speed is limited by the rate of cell growth and cell manipulation. Thus, an entirely high-throughput DCEO cycle will allow bioengineers to address these challenges that were previously out of reach. This new era of DCEO biotechnology, in its consummate maturity, has great potential applications to achieve the sustainable production of useful chemicals from renewable resources.

research group and was appointed full Professor there in 1998. He was a Fulbright visiting professor at MIT in 1995−1996. At DTU, he founded and directed the Center for Microbial Biotechnology. In 2008, he was recruited as Professor and Director to Chalmers University of Technology, Sweden. His group is focusing on systems biology of metabolism, including metabolic engineering of industrial microorganisms for production of fuels and chemicals. Jian Chen is a Professor at Jiangnan University. His group focuses on the research of metabolic engineering, synthetic biology, and enzyme engineering. He was a visiting scholar at Osaka University, Tokyo Institute of Technology and Inha University, where he worked on the synthesis and application of various sustainable and renewable biomass materials and the process optimization and control of fermentation. He received his Ph.D. degree in Biochemical Engineering from Jiangnan University in 1990. Liming Liu is a Professor at Jiangnan University. His group focuses on the research of systems biology, metabolic engineering, and enzyme engineering. From 2009 to 2010, he worked at Chalmers University of Technology as a postdoctoral fellow, focusing on the research of systems biology. He received his Ph.D. degree in Biochemical Engineering from Jiangnan University in 2006.

AUTHOR INFORMATION Corresponding Author

*Fax: +86-510-85197875. E-mail: [email protected].

ACKNOWLEDGMENTS This work was financially supported by the National Natural Science Foundation of China (21676118, 21422602), the Provincial Natural Science Foundation of Jiangsu Province (BK20160163), the Special Foundation for State Key Research and Development Program of China (2016YFD0400801), the Major State Basic Research Development Program of China (2013CB733602), the National Science Foundation for Postdoctoral Scientists of China (2016M600362), the Fundamental Research Funds for the Central Universities (JUSRP51611A), the Novo Nordisk Foundation, and the Knut and Alice Wallenberg Foundation.

ORCID

Xiulai Chen: 0000-0002-5154-3860 Notes

The authors declare no competing financial interest. Biographies Xiulai Chen is an Associate Professor at Jiangnan University. His research focuses on natural chemicals production through systems metabolic engineering and synthetic biology. He received his Ph.D. degree in Biochemical Engineering from Jiangnan University in 2015. Cong Gao received his M.S. degree from Shihezi University and Nanjing Tech University in 2015 and conducted research on Enzyme Engineering. Currently, he is a Ph.D. candidate in the School of Biotechnology at Jiangnan University, and his research focuses on metabolic engineering for natural chemicals production.

REFERENCES (1) Li, C. J.; Trost, B. M. Green chemistry for chemical synthesis. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (36), 13197−13202. (2) Sun, X.; Shen, X.; Jain, R.; Lin, Y.; Wang, J.; Sun, J.; Wang, J.; Yan, Y.; Yuan, Q. Synthesis of chemicals by metabolic engineering of microbes. Chem. Soc. Rev. 2015, 44 (11), 3760−3785. (3) Labarre, C.; Schirawski, J.; van der Zwet, A.; Fitzgerald, G. F.; van Sinderen, D. Insertional mutagenesis of an industrial strain of Streptococcus thermophilus. FEMS Microbiol. Lett. 2001, 200 (1), 85−90. (4) Park, J. H.; Lee, S. Y. Towards systems metabolic engineering of microorganisms for amino acid production. Curr. Opin. Biotechnol. 2008, 19 (5), 454−460. (5) Park, J. H.; Lee, S. Y.; Kim, T. Y.; Kim, H. U. Application of systems biology for bioprocess development. Trends Biotechnol. 2008, 26 (8), 404−412. (6) Jones, J. A.; Toparlak, O. D.; Koffas, M. A. Metabolic pathway balancing and its role in the production of biofuels and chemicals. Curr. Opin. Biotechnol. 2015, 33, 52−59. (7) Ma, S. M.; Garcia, D. E.; Redding-Johanson, A. M.; Friedland, G. D.; Chan, R.; Batth, T. S.; Haliburton, J. R.; Chivian, D.; Keasling, J. D.; Petzold, C. J.; et al. Optimization of a heterologous mevalonate pathway through the use of variant HMG-CoA reductases. Metab. Eng. 2011, 13 (5), 588−597. (8) Jang, Y. S.; Park, J. M.; Choi, S.; Choi, Y. J.; Seung, D. Y.; Cho, J. H.; Lee, S. Y. Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches. Biotechnol. Adv. 2012, 30 (5), 989−1000.

Liang Guo received his B.E. degree in Bioengineering from HuaiBei Normal University in 2014. Currently, he is a Ph.D. candidate in the School of Biotechnology at Jiangnan University, and his research focuses on protein engineering and synthetic biology for metabolite biosynthesis. Guipeng Hu received his B.E. degree in Bioengineering from the University of Science & Technology of Anhui in 2013. Currently, he is a Ph.D. candidate in the School of Biotechnology at Jiangnan University, and his research focuses on engineering E. coli for chemicals production via systems metabolic engineering. Qiuling Luo received her M.S. degree from Jiangnan University in 2014 and conducted research on metabolic engineering of microbes for acetoin production. Currently, she is a junior engineer in Jiangnan University, and her research focuses on metabolic engineering. Jia Liu received her M.S. degree from Jiangnan University in 2014, and conducted research on metabolic engineering of microbes for polysaccharide synthesis. Currently, she is a junior engineer in Jiangnan University, and her research focuses on enzyme catalysis. Jens Nielsen has an MSc degree in Chemical Engineering and a Ph.D. degree (1989) in Biochemical Engineering from the Danish Technical University (DTU), and after that he established his independent 54


Chemical Reviews

Review

(9) Lechner, A.; Brunk, E.; Keasling, J. D. The need for integrated approaches in metabolic engineering. Cold Spring Harbor Perspect. Biol. 2016, 8, a023903. (10) Yim, H.; Haselbeck, R.; Niu, W.; Pujol-Baxley, C.; Burgard, A.; Boldt, J.; Khandurina, J.; Trawick, J. D.; Osterhout, R. E.; Stephen, R.; et al. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat. Chem. Biol. 2011, 7 (7), 445−452. (11) Rabinovitch-Deere, C. A.; Oliver, J. W.; Rodriguez, G. M.; Atsumi, S. Synthetic biology and metabolic engineering approaches to produce biofuels. Chem. Rev. 2013, 113 (7), 4611−4632. (12) Becker, J.; Wittmann, C. Advanced biotechnology: metabolically engineered cells for the bio-based production of chemicals and fuels, materials, and health-care products. Angew. Chem., Int. Ed. 2015, 54 (11), 3328−3350. (13) Luo, Y.; Li, B. Z.; Liu, D.; Zhang, L.; Chen, Y.; Jia, B.; Zeng, B. X.; Zhao, H.; Yuan, Y. J. Engineered biosynthesis of natural products in heterologous hosts. Chem. Soc. Rev. 2015, 44 (15), 5265−5290. (14) Lee, J. W.; Na, D.; Park, J. M.; Lee, J.; Choi, S.; Lee, S. Y. Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat. Chem. Biol. 2012, 8 (6), 536−546. (15) Lee, J. W.; Kim, T. Y.; Jang, Y. S.; Choi, S.; Lee, S. Y. Systems metabolic engineering for chemicals and materials. Trends Biotechnol. 2011, 29 (8), 370−378. (16) Muller, H. M.; Kenny, E. E.; Sternberg, P. W. Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS Biol. 2004, 2 (11), e309. (17) Goetz, T.; von der Lieth, C. W. PubFinder: a tool for improving retrieval rate of relevant PubMed abstracts. Nucleic Acids Res. 2005, 33, W774−778. (18) Becker, K. G.; Hosack, D. A.; Dennis, G., Jr.; Lempicki, R. A.; Bright, T. J.; Cheadle, C.; Engel, J. PubMatrix: a tool for multiplex literature mining. BMC Bioinf. 2003, 4, 61. (19) Maier, H.; Dohr, S.; Grote, K.; O’Keeffe, S.; Werner, T.; Hrabe de Angelis, M.; Schneider, R. LitMiner and WikiGene: identifying problemrelated key players of gene regulation using publication abstracts. Nucleic Acids Res. 2005, 33, W779−782. (20) Dieterich, G.; Karst, U.; Wehland, J.; Jansch, L. MineBlast: a literature presentation service supporting protein annotation by data mining of BLAST results. Bioinformatics 2005, 21 (16), 3450−3451. (21) Liu, L.; Agren, R.; Bordel, S.; Nielsen, J. Use of genome-scale metabolic models for understanding microbial physiology. FEBS Lett. 2010, 584 (12), 2556−2564. (22) Zelle, R. M.; de Hulster, E.; van Winden, W. A.; de Waard, P.; Dijkema, C.; Winkler, A. A.; Geertman, J. M.; van Dijken, J. P.; Pronk, J. T.; van Maris, A. J. Malic acid production by Saccharomyces cerevisiae: engineering of pyruvate carboxylation, oxaloacetate reduction, and malate export. Appl. Environ. Microbiol. 2008, 74 (9), 2766−2777. (23) Zhang, X.; Wang, X.; Shanmugam, K. T.; Ingram, L. O. L-malate production by metabolically engineered Escherichia coli. Appl. Environ. Microbiol. 2011, 77 (2), 427−434. (24) Chen, X.; Xu, G.; Xu, N.; Zou, W.; Zhu, P.; Liu, L.; Chen, J. Metabolic engineering of Torulopsis glabrata for malate production. Metab. Eng. 2013, 19, 10−16. (25) Moon, S. Y.; Hong, S. H.; Kim, T. Y.; Lee, S. Y. Metabolic engineering of Escherichia coli for the production of malic acid. Biochem. Eng. J. 2008, 40 (2), 312−320. (26) Xu, G.; Zou, W.; Chen, X.; Xu, N.; Liu, L.; Chen, J. Fumaric acid production in Saccharomyces cerevisiae by in silico aided metabolic engineering. PLoS One 2012, 7 (12), e52086. (27) Chen, X.; Zhu, P.; Liu, L. Modular optimization of multi-gene pathways for fumarate production. Metab. Eng. 2016, 33, 76−85. (28) Xu, G.; Chen, X.; Liu, L.; Jiang, L. Fumaric acid production in Saccharomyces cerevisiae by simultaneous use of oxidative and reductive routes. Bioresour. Technol. 2013, 148, 91−96. (29) Chen, X.; Wu, J.; Song, W.; Zhang, L.; Wang, H.; Liu, L. Fumaric acid production by Torulopsis glabrata: engineering the urea cycle and the purine nucleotide cycle. Biotechnol. Bioeng. 2015, 112 (1), 156−167.

(30) Chen, X.; Dong, X.; Wang, Y.; Zhao, Z.; Liu, L. Mitochondrial engineering of the TCA cycle for fumarate production. Metab. Eng. 2015, 31, 62−73. (31) Li, N.; Zhang, B.; Wang, Z.; Tang, Y. J.; Chen, T.; Zhao, X. Engineering Escherichia coli for fumaric acid production from glycerol. Bioresour. Technol. 2014, 174, 81−87. (32) King, Z. A.; Lu, J.; Drager, A.; Miller, P.; Federowicz, S.; Lerman, J. A.; Ebrahim, A.; Palsson, B. O.; Lewis, N. E. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 2016, 44 (D1), D515−522. (33) Feist, A. M.; Palsson, B. O. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat. Biotechnol. 2008, 26 (6), 659−667. (34) Oberhardt, M. A.; Palsson, B. O.; Papin, J. A. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 2009, 5, 320. (35) Kim, T. Y.; Sohn, S. B.; Kim, Y. B.; Kim, W. J.; Lee, S. Y. Recent advances in reconstruction and applications of genome-scale metabolic models. Curr. Opin. Biotechnol. 2012, 23 (4), 617−623. (36) Lewis, N. E.; Nagarajan, H.; Palsson, B. O. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 2012, 10 (4), 291−305. (37) Colijn, C.; Brandes, A.; Zucker, J.; Lun, D. S.; Weiner, B.; Farhat, M. R.; Cheng, T. Y.; Moody, D. B.; Murray, M.; Galagan, J. E. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 2009, 5 (8), e1000489. (38) Beg, Q. K.; Vazquez, A.; Ernst, J.; de Menezes, M. A.; Bar-Joseph, Z.; Barabasi, A. L.; Oltvai, Z. N. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (31), 12663− 12668. (39) Zhuang, K.; Vemuri, G. N.; Mahadevan, R. Economics of membrane occupancy and respiro-fermentation. Mol. Syst. Biol. 2011, 7, 500. (40) Park, J. M.; Kim, T. Y.; Lee, S. Y. Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (33), 14931−14936. (41) Lewis, N. E.; Hixson, K. K.; Conrad, T. M.; Lerman, J. A.; Charusanti, P.; Polpitiya, A. D.; Adkins, J. N.; Schramm, G.; Purvine, S. O.; Lopez-Ferrer, D.; et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 2010, 6, 390. (42) Benyamini, T.; Folger, O.; Ruppin, E.; Shlomi, T. Flux balance analysis accounting for metabolite dilution. Genome Biol. 2010, 11 (4), R43. (43) Zhuang, K.; Izallalen, M.; Mouser, P.; Richter, H.; Risso, C.; Mahadevan, R.; Lovley, D. R. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 2011, 5 (2), 305−316. (44) Varma, A.; Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 1994, 60 (10), 3724−3731. (45) Klitgord, N.; Segre, D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 2010, 6 (11), e1001002. (46) Suthers, P. F.; Dasika, M. S.; Kumar, V. S.; Denisov, G.; Glass, J. I.; Maranas, C. D. A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189. PLoS Comput. Biol. 2009, 5 (2), e1000285. (47) Varma, A.; Boesch, B. W.; Palsson, B. O. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl. Environ. Microbiol. 1993, 59 (8), 2465−2473. (48) Smallbone, K.; Simeonidis, E. Flux balance analysis: a geometric perspective. J. Theor. Biol. 2009, 258 (2), 311−315. (49) Lee, S.; Phalakornkule, C.; Domach, M. M.; Grossmann, I. E. Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput. Chem. Eng. 2000, 24 (2−7), 711−716. (50) Burgard, A. P.; Vaidyaraman, S.; Maranas, C. D. Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments. Biotechnol. Prog. 2001, 17 (5), 791−797. 55


Chemical Reviews

Review

(51) Heino, J.; Tunyan, K.; Calvetti, D.; Somersalo, E. Bayesian flux balance analysis applied to a skeletal muscle metabolic model. J. Theor. Biol. 2007, 248 (1), 91−110. (52) Burgard, A. P.; Nikolaev, E. V.; Schilling, C. H.; Maranas, C. D. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 2004, 14 (2), 301−312. (53) David, L.; Marashi, S. A.; Larhlimi, A.; Mieth, B.; Bockmayr, A. FFCA: a feasibility-based method for flux coupling analysis of metabolic networks. BMC Bioinf. 2011, 12, 236. (54) Choi, H. S.; Lee, S. Y.; Kim, T. Y.; Woo, H. M. In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol. 2010, 76 (10), 3097−3105. (55) Lun, D. S.; Rockwell, G.; Guido, N. J.; Baym, M.; Kelner, J. A.; Berger, B.; Galagan, J. E.; Church, G. M. Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 2009, 5, 296. (56) Fowler, Z. L.; Gikandi, W. W.; Koffas, M. A. Increased malonyl coenzyme A biosynthesis by tuning the Escherichia coli metabolic network and its application to flavanone production. Appl. Environ. Microbiol. 2009, 75 (18), 5831−5839. (57) Patil, K. R.; Rocha, I.; Forster, J.; Nielsen, J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinf. 2005, 6, 308. (58) Rocha, M.; Maia, P.; Mendes, R.; Pinto, J. P.; Ferreira, E. C.; Nielsen, J.; Patil, K. R.; Rocha, I. Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinf. 2008, 9, 499. (59) Kim, J.; Reed, J. L. OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol. 2010, 4, 53. (60) Pharkya, P.; Burgard, A. P.; Maranas, C. D. OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 2004, 14 (11), 2367−2376. (61) Pharkya, P.; Maranas, C. D. An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab. Eng. 2006, 8 (1), 1−13. (62) Yang, L.; Cluett, W. R.; Mahadevan, R. EMILiO: a fast algorithm for genome-scale strain design. Metab. Eng. 2011, 13 (3), 272−281. (63) Ranganathan, S.; Suthers, P. F.; Maranas, C. D. OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol. 2010, 6 (4), e1000744. (64) Tepper, N.; Shlomi, T. Computational design of auxotrophydependent microbial biosensors for combinatorial metabolic engineering experiments. PLoS One 2011, 6 (1), e16274. (65) Tepper, N.; Shlomi, T. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 2010, 26 (4), 536. (66) Feist, A. M.; Zielinski, D. C.; Orth, J. D.; Schellenberger, J.; Herrgard, M. J.; Palsson, B. O. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab. Eng. 2010, 12 (3), 173−186. (67) Burgard, A. P.; Pharkya, P.; Maranas, C. D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 2003, 84 (6), 647−657. (68) Beard, D. A.; Liang, S. D.; Qian, H. Energy balance for analysis of complex metabolic networks. Biophys. J. 2002, 83 (1), 79−86. (69) Schellenberger, J.; Lewis, N. E.; Palsson, B. O. Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys. J. 2011, 100 (3), 544−553. (70) Kummel, A.; Panke, S.; Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2006, 2, 20060034. (71) Henry, C. S.; Broadbelt, L. J.; Hatzimanikatis, V. Thermodynamics-based metabolic flux analysis. Biophys. J. 2007, 92 (5), 1792− 1805. (72) Hoppe, A.; Hoffmann, S.; Holzhutter, H. G. Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Syst. Biol. 2007, 1, 23.

(73) Holzhutter, H. G. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem. 2004, 271 (14), 2905−2922. (74) Jerby, L.; Shlomi, T.; Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 2010, 6, 401. (75) Shlomi, T.; Cabili, M. N.; Herrgard, M. J.; Palsson, B. O.; Ruppin, E. Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol. 2008, 26 (9), 1003−1010. (76) van Berlo, R. J. P.; de Ridder, D.; Daran, J. M.; Daran-Lapujade, P. A. S.; Teusink, B.; Reinders, M. J. T. Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM Trans. Comput. Biol. Bioinf. 2011, 8 (1), 206−216. (77) Jensen, P. A.; Papin, J. A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 2011, 27 (4), 541−547. (78) Becker, S. A.; Palsson, B. O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 2008, 4 (5), e1000082. (79) Chandrasekaran, S.; Price, N. D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (41), 17845−17850. (80) Lee, J. M.; Gianchandani, E. P.; Eddy, J. A.; Papin, J. A. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput. Biol. 2008, 4 (5), e1000086. (81) Covert, M. W.; Xiao, N.; Chen, T. J.; Karr, J. R. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008, 24 (18), 2044−2050. (82) Barua, D.; Kim, J.; Reed, J. L. An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Comput. Biol. 2010, 6 (10), e1000970. (83) Shlomi, T.; Eisenberg, Y.; Sharan, R.; Ruppin, E. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol. Syst. Biol. 2007, 3, 101. (84) Covert, M. W.; Schilling, C. H.; Palsson, B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 2001, 213 (1), 73−88. (85) Lee, M. H.; Lee, H. W.; Park, J. H.; Ahn, J. O.; Jung, J. K.; Hwang, Y. I. Improved L-threonine production of Escherichia coli mutant by optimization of culture conditions. J. Biosci. Bioeng. 2006, 101 (2), 127− 130. (86) Lee, J. H.; Lee, D. E.; Lee, B. U.; Kim, H. S. Global analyses of transcriptomes and proteomes of a parent strain and an L-threonineoverproducing mutant strain. J. Bacteriol. 2003, 185 (18), 5442−5451. (87) Jetten, M. S.; Sinskey, A. J. Recent advances in the physiology and genetics of amino acid-producing bacteria. Crit. Rev. Biotechnol. 1995, 15 (1), 73−103. (88) Song, K. H.; Lee, H. H.; Hyun, H. H. Characterization of salttolerant mutant for enhancement of L-threonine production in Escherichia coli. Appl. Microbiol. Biotechnol. 2000, 54 (5), 647−651. (89) Ogawa-Miyata, Y.; Kojima, H.; Sano, K. Mutation analysis of the feedback inhibition site of aspartokinase III of Escherichia coli K-12 and its use in L-threonine production. Biosci., Biotechnol., Biochem. 2001, 65 (5), 1149−1154. (90) Lee, K. H.; Park, J. H.; Kim, T. Y.; Kim, H. U.; Lee, S. Y. Systems metabolic engineering of Escherichia coli for L-threonine production. Mol. Syst. Biol. 2007, 3, 149. (91) Agarwal, S.; Rao, A. V. Tomato lycopene and its role in human health and chronic diseases. CMAJ. 2000, 163 (6), 739−744. (92) Xie, W.; Lv, X.; Ye, L.; Zhou, P.; Yu, H. Construction of lycopeneoverproducing Saccharomyces cerevisiae by combining directed evolution and metabolic engineering. Metab. Eng. 2015, 30, 69−78. (93) Kim, Y. S.; Lee, J. H.; Kim, N. H.; Yeom, S. J.; Kim, S. W.; Oh, D. K. Increase of lycopene production by supplementing auxiliary carbon sources in metabolically engineered Escherichia coli. Appl. Microbiol. Biotechnol. 2011, 90 (2), 489−497. 56


Chemical Reviews

Review

(94) Matthaus, F.; Ketelhot, M.; Gatter, M.; Barth, G. Production of lycopene in the non-carotenoid-producing yeast Yarrowia lipolytica. Appl. Environ. Microbiol. 2014, 80 (5), 1660−1669. (95) Ye, V. M.; Bhatia, S. K. Pathway engineering strategies for production of beneficial carotenoids in microbial hosts. Biotechnol. Lett. 2012, 34 (8), 1405−1414. (96) Farmer, W. R.; Liao, J. C. Improving lycopene production in Escherichia coli by engineering metabolic control. Nat. Biotechnol. 2000, 18 (5), 533−537. (97) Vadali, R. V.; Fu, Y.; Bennett, G. N.; San, K. Y. Enhanced lycopene productivity by manipulation of carbon flow to isopentenyl diphosphate in Escherichia coli. Biotechnol. Prog. 2005, 21 (5), 1558−1561. (98) Zhou, Y.; Nambou, K.; Wei, L.; Cao, J.; Imanaka, T.; Hua, Q. Lycopene production in recombinant strains of Escherichia coli is improved by knockout of the central carbon metabolism gene coding for glucose-6-phosphate dehydrogenase. Biotechnol. Lett. 2013, 35 (12), 2137−2145. (99) Alper, H.; Jin, Y. S.; Moxley, J. F.; Stephanopoulos, G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 2005, 7 (3), 155−164. (100) Alper, H.; Miyaoku, K.; Stephanopoulos, G. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nat. Biotechnol. 2005, 23 (5), 612−616. (101) Rai, A.; Saito, K. Omics data input for metabolic modeling. Curr. Opin. Biotechnol. 2016, 37, 127−134. (102) Joyce, A. R.; Palsson, B. O. The model organism as a system: integrating ’omics’ data sets. Nat. Rev. Mol. Cell Biol. 2006, 7 (3), 198− 210. (103) Cook, C. E.; Bergman, M. T.; Finn, R. D.; Cochrane, G.; Birney, E.; Apweiler, R. The European Bioinformatics Institute in 2016: Data growth and integration. Nucleic Acids Res. 2016, 44 (D1), D20−26. (104) Chatr-aryamontri, A.; Breitkreutz, B. J.; Heinicke, S.; Boucher, L.; Winter, A.; Stark, C.; Nixon, J.; Ramage, L.; Kolas, N.; O’Donnell, L.; et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 2013, 41 (D1), D816−823. (105) Zhang, Z. D.; Shen, T.; Rui, B.; Zhou, W. W.; Zhou, X. F.; Shang, C. Y.; Xin, C. W.; Liu, X. G.; Li, G.; Jiang, J. S.; et al. CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics. Nucleic Acids Res. 2015, 43 (D1), D549−557. (106) Luscombe, N. M.; Babu, M. M.; Yu, H.; Snyder, M.; Teichmann, S. A.; Gerstein, M. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 2004, 431 (7006), 308−312. (107) Han, J. D.; Bertin, N.; Hao, T.; Goldberg, D. S.; Berriz, G. F.; Zhang, L. V.; Dupuy, D.; Walhout, A. J.; Cusick, M. E.; Roth, F. P.; et al. Evidence for dynamically organized modularity in the yeast proteinprotein interaction network. Nature 2004, 430 (6995), 88−93. (108) Palsson, B. Two-dimensional annotation of genomes. Nat. Biotechnol. 2004, 22 (10), 1218−1219. (109) Patil, K. R.; Nielsen, J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (8), 2685−2689. (110) Kanehisa, M.; Goto, S.; Kawashima, S.; Okuno, Y.; Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004, 32, 277D−280. (111) Covert, M. W.; Knight, E. M.; Reed, J. L.; Herrgard, M. J.; Palsson, B. O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 2004, 429 (6987), 92−96. (112) Begley, T. J.; Rosenbach, A. S.; Ideker, T.; Samson, L. D. Hot spots for modulating toxicity identified by genomic phenotyping and localization mapping. Mol. Cell 2004, 16 (1), 117−125. (113) Lee, W.; St Onge, R. P.; Proctor, M.; Flaherty, P.; Jordan, M. I.; Arkin, A. P.; Davis, R. W.; Nislow, C.; Giaever, G. Genome-wide requirements for resistance to functionally distinct DNA-damaging agents. PLoS Genet. 2005, 1 (2), e24. (114) Boulton, S. J.; Gartner, A.; Reboul, J.; Vaglio, P.; Dyson, N.; Hill, D. E.; Vidal, M. Combined functional genomic maps of the C. elegans DNA damage response. Science 2002, 295 (5552), 127−131.

(115) Santos, H.; da Costa, M. S. Organic solutes from thermophiles and hyperthermophiles. Methods Enzymol. 2001, 334, 302−315. (116) Neves, C.; da Costa, M. S.; Santos, H. Compatible solutes of the hyperthermophile Palaeococcus ferrophilus: osmoadaptation and thermoadaptation in the order thermococcales. Appl. Environ. Microbiol. 2005, 71 (12), 8091−8098. (117) Esteves, A. M.; Chandrayan, S. K.; McTernan, P. M.; Borges, N.; Adams, M. W.; Santos, H. Mannosylglycerate and di-myo-inositol phosphate have interchangeable roles during adaptation of Pyrococcus f uriosus to heat stress. Appl. Environ. Microbiol. 2014, 80 (14), 4226− 4233. (118) Goncalves, L. G.; Borges, N.; Serra, F.; Fernandes, P. L.; Dopazo, H.; Santos, H. Evolution of the biosynthesis of di-myo-inositol phosphate, a marker of adaptation to hot marine environments. Environ. Microbiol. 2012, 14 (3), 691−701. (119) Neelon, K.; Wang, Y.; Stec, B.; Roberts, M. F. Probing the mechanism of the Archaeoglobus f ulgidus inositol-1-phosphate synthase. J. Biol. Chem. 2005, 280 (12), 11475−11482. (120) Borges, N.; Goncalves, L. G.; Rodrigues, M. V.; Siopa, F.; Ventura, R.; Maycock, C.; Lamosa, P.; Santos, H. Biosynthetic pathways of inositol and glycerol phosphodiesters used by the hyperthermophile Archaeoglobus f ulgidus in stress adaptation. J. Bacteriol. 2006, 188 (23), 8128−8135. (121) Rodrigues, M. V.; Borges, N.; Henriques, M.; Lamosa, P.; Ventura, R.; Fernandes, C.; Empadinhas, N.; Maycock, C.; da Costa, M. S.; Santos, H. Bifunctional CTP:inositol-1-phosphate cytidylyltransferase/CDP-inositol: inositol-1-phosphate transferase, the key enzyme for di-myo-inositol-phosphate synthesis in several (hyper)thermophiles. J. Bacteriol. 2007, 189 (15), 5405−5412. (122) Rodionov, D. A.; Kurnasov, O. V.; Stec, B.; Wang, Y.; Roberts, M. F.; Osterman, A. L. Genomic identification and in vitro reconstitution of a complete biosynthetic pathway for the osmolyte di-myo-inositol-phosphate. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (11), 4279−4284. (123) Leuchtenberger, W.; Huthmacher, K.; Drauz, K. Biotechnological production of amino acids and derivatives: current status and prospects. Appl. Microbiol. Biotechnol. 2005, 69 (1), 1−8. (124) Blombach, B.; Schreiner, M. E.; Bartek, T.; Oldiges, M.; Eikmanns, B. J. Corynebacterium glutamicum tailored for high-yield Lvaline production. Appl. Microbiol. Biotechnol. 2008, 79 (3), 471−479. (125) Park, J. H.; Lee, S. Y. Fermentative production of branched chain amino acids: a focus on metabolic engineering. Appl. Microbiol. Biotechnol. 2010, 85 (3), 491−506. (126) Elisakova, V.; Patek, M.; Holatko, J.; Nesvera, J.; Leyval, D.; Goergen, J. L.; Delaunay, S. Feedback-resistant acetohydroxy acid synthase increases valine production in Corynebacterium glutamicum. Appl. Environ. Microbiol. 2005, 71 (1), 207−213. (127) Park, J. H.; Lee, K. H.; Kim, T. Y.; Lee, S. Y. 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 (19), 7797−7802. (128) Nielsen, J.; Fussenegger, M.; Keasling, J.; Lee, S. Y.; Liao, J. C.; Prather, K.; Palsson, B. Engineering synergy in biotechnology. Nat. Chem. Biol. 2014, 10 (5), 319−322. (129) Medema, M. H.; van Raaphorst, R.; Takano, E.; Breitling, R. Computational tools for the synthetic design of biochemical pathways. Nat. Rev. Microbiol. 2012, 10 (3), 191−202. (130) O’Brien, E. J.; Monk, J. M.; Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 2015, 161 (5), 971−987. (131) Hatzimanikatis, V.; Li, C.; Ionita, J. A.; Henry, C. S.; Jankowski, M. D.; Broadbelt, L. J. Exploring the diversity of complex metabolic networks. Bioinformatics 2005, 21 (8), 1603. (132) Rodrigo, G.; Carrera, J.; Prather, K. J.; Jaramillo, A. DESHARKY: automatic design of metabolic pathways for optimal cell growth. Bioinformatics 2008, 24 (21), 2554. (133) Chou, C. H.; Chang, W. C.; Chiu, C. M.; Huang, C. C.; Huang, H. D. FMM: a web server for metabolic pathway reconstruction and comparative analysis. Nucleic Acids Res. 2009, 37, W129. 57


Chemical Reviews

Review

(134) Carbonell, P.; Planson, A. G.; Fichera, D.; Faulon, J. L. A retrosynthetic biology approach to metabolic pathway design for therapeutic production. BMC Syst. Biol. 2011, 5, 122. (135) Schellenberger, J.; Que, R.; Fleming, R. M.; Thiele, I.; Orth, J. D.; Feist, A. M.; Zielinski, D. C.; Bordbar, A.; Lewis, N. E.; Rahmanian, S.; et al. Quantitative prediction of cellular metabolism with constraintbased models: the COBRA Toolbox v2.0. Nat. Protoc. 2011, 6 (9), 1290−1307. (136) Gevorgyan, A.; Bushell, M. E.; Avignone-Rossa, C.; Kierzek, A. M. SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks. Bioinformatics 2011, 27 (3), 433. (137) Le Fevre, F.; Smidtas, S.; Combe, C.; Durot, M.; d’Alche-Buc, F.; Schachter, V. CycSim–an online tool for exploring and experimenting with genome-scale metabolic models. Bioinformatics 2009, 25 (15), 1987. (138) Garcia-Albornoz, M.; Thankaswamy-Kosalai, S.; Nilsson, A.; Varemo, L.; Nookaew, I.; Nielsen, J. BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data. Nucleic Acids Res. 2014, 42, W175−181. (139) Yamada, T.; Letunic, I.; Okuda, S.; Kanehisa, M.; Bork, P. iPath2.0: interactive pathway explorer. Nucleic Acids Res. 2011, 39, W412. (140) Bates, J. T.; Chivian, D.; Arkin, A. P. GLAMM: genome-linked application for metabolic maps. Nucleic Acids Res. 2011, 39, W400−405. (141) Salis, H. M.; Mirsky, E. A.; Voigt, C. A. Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 2009, 27 (10), 946−950. (142) Villalobos, A.; Ness, J. E.; Gustafsson, C.; Minshull, J.; Govindarajan, S. Gene Designer: a synthetic biology tool for constructing artificial DNA segments. BMC Bioinf. 2006, 7, 285. (143) Richardson, S. M.; Nunley, P. W.; Yarrington, R. M.; Boeke, J. D.; Bader, J. S. GeneDesign 3.0 is an updated synthetic biology toolkit. Nucleic Acids Res. 2010, 38 (8), 2603−2606. (144) Hoover, D. M.; Lubkowski, J. DNAWorks: an automated method for designing oligonucleotides for PCR-based gene synthesis. Nucleic Acids Res. 2002, 30 (10), 43e. (145) Chandran, D.; Bergmann, F. T.; Sauro, H. M. TinkerCell: modular CAD tool for synthetic biology. J. Biol. Eng. 2009, 3, 19. (146) Czar, M. J.; Cai, Y.; Peccoud, J. Writing DNA with GenoCAD. Nucleic Acids Res. 2009, 37, W40−47. (147) Weeding, E.; Houle, J.; Kaznessis, Y. N. SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs. Briefings Bioinf. 2010, 11 (4), 394−402. (148) Hadadi, N.; Hatzimanikatis, V. Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways. Curr. Opin. Chem. Biol. 2015, 28, 99−104. (149) Abdin, M. Z.; Israr, M.; Rehman, R. U.; Jain, S. K. Artemisinin, a novel antimalarial drug: biochemical and molecular approaches for enhanced production. Planta Med. 2003, 69 (4), 289−299. (150) White, N. J. Qinghaosu (artemisinin): the price of success. Science 2008, 320 (5874), 330−334. (151) Martin, V. J.; Pitera, D. J.; Withers, S. T.; Newman, J. D.; Keasling, J. D. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat. Biotechnol. 2003, 21 (7), 796−802. (152) Ro, D. K.; Paradise, E. M.; Ouellet, M.; Fisher, K. J.; Newman, K. L.; Ndungu, J. M.; Ho, K. A.; Eachus, R. A.; Ham, T. S.; Kirby, J.; et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 2006, 440 (7086), 940−943. (153) Dhingra, V.; Vishweshwar Rao, K.; Lakshmi Narasu, M. Current status of artemisinin and its derivatives as antimalarial drugs. Life Sci. 1999, 66 (4), 279−300. (154) Newman, J.; D; Marshall, J.; Chang, M.; Nowroozi, F.; Paradise, E.; Pitera, D.; Newman, K. L.; Keasling, J. D. High-level production of amorpha-4,11-diene in a two-phase partitioning bioreactor of metabolically engineered Escherichia coli. Biotechnol. Bioeng. 2006, 95 (4), 684−691.

(155) Pitera, D. J.; Paddon, C. J.; Newman, J. D.; Keasling, J. D. Balancing a heterologous mevalonate pathway for improved isoprenoid production in Escherichia coli. Metab. Eng. 2007, 9 (2), 193−207. (156) Kizer, L.; Pitera, D. J.; Pfleger, B. F.; Keasling, J. D. Application of functional genomics to pathway optimization for increased isoprenoid production. Appl. Environ. Microbiol. 2008, 74 (10), 3229−3241. (157) Tsuruta, H.; Paddon, C. J.; Eng, D.; Lenihan, J. R.; Horning, T.; Anthony, L. C.; Regentin, R.; Keasling, J. D.; Renninger, N. S.; Newman, J. D. High-level production of amorpha-4,11-diene, a precursor of the antimalarial agent artemisinin, in Escherichia coli. PLoS One 2009, 4 (2), e4489. (158) Westfall, P. J.; Pitera, D. J.; Lenihan, J. R.; Eng, D.; Woolard, F. X.; Regentin, R.; Horning, T.; Tsuruta, H.; Melis, D. J.; Owens, A.; et al. Production of amorphadiene in yeast, and its conversion to dihydroartemisinic acid, precursor to the antimalarial agent artemisinin. Proc. Natl. Acad. Sci. U. S. A. 2012, 109 (3), E111−118. (159) Shetty, R. P.; Endy, D.; Knight, T. F., Jr. Engineering BioBrick vectors from BioBrick parts. J. Biol. Eng. 2008, 2, 5. (160) Anderson, J. C.; Dueber, J. E.; Leguia, M.; Wu, G. C.; Goler, J. A.; Arkin, A. P.; Keasling, J. D. BglBricks: A flexible standard for biological part assembly. J. Biol. Eng. 2010, 4 (1), 1. (161) Xu, P.; Vansiri, A.; Bhan, N.; Koffas, M. A. ePathBrick: a synthetic biology platform for engineering metabolic pathways in E. coli. ACS Synth. Biol. 2012, 1 (7), 256−266. (162) Martinez-Garcia, E.; Aparicio, T.; Goni-Moreno, A.; Fraile, S.; de Lorenzo, V. SEVA 2.0: an update of the Standard European Vector Architecture for de-/re-construction of bacterial functionalities. Nucleic Acids Res. 2015, 43, D1183−1189. (163) Li, M. V.; Shukla, D.; Rhodes, B. H.; Lall, A.; Shu, J.; Moriarity, B. S.; Largaespada, D. A. HomeRun Vector Assembly System: a flexible and standardized cloning system for assembly of multi-modular DNA constructs. PLoS One 2014, 9 (6), e100948. (164) Chao, R.; Yuan, Y.; Zhao, H. Recent advances in DNA assembly technologies. FEMS Yeast Res. 2014, 15 (1), 1−9. (165) Casini, A.; Storch, M.; Baldwin, G. S.; Ellis, T. Bricks and blueprints: methods and standards for DNA assembly. Nat. Rev. Mol. Cell Biol. 2015, 16 (9), 568−576. (166) Engler, C.; Kandzia, R.; Marillonnet, S. A one pot, one step, precision cloning method with high throughput capability. PLoS One 2008, 3 (11), e3647. (167) Sarrion-Perdigones, A.; Vazquez-Vilar, M.; Palaci, J.; Castelijns, B.; Forment, J.; Ziarsolo, P.; Blanca, J.; Granell, A.; Orzaez, D. GoldenBraid 2.0: a comprehensive DNA assembly framework for plant synthetic biology. Plant Physiol. 2013, 162 (3), 1618−1631. (168) Weber, E.; Engler, C.; Gruetzner, R.; Werner, S.; Marillonnet, S. A modular cloning system for standardized assembly of multigene constructs. PLoS One 2011, 6 (2), e16765. (169) Chen, W. H.; Qin, Z. J.; Wang, J.; Zhao, G. P. The MASTER (methylation-assisted tailorable ends rational) ligation method for seamless DNA assembly. Nucleic Acids Res. 2013, 41 (8), e93. (170) Duportet, X.; Wroblewska, L.; Guye, P.; Li, Y.; Eyquem, J.; Rieders, J.; Rimchala, T.; Batt, G.; Weiss, R. A platform for rapid prototyping of synthetic gene networks in mammalian cells. Nucleic Acids Res. 2014, 42 (21), 13440−13451. (171) Engler, C.; Gruetzner, R.; Kandzia, R.; Marillonnet, S. Golden gate shuffling: a one-pot DNA shuffling method based on type IIs restriction enzymes. PLoS One 2009, 4 (5), e5553. (172) Li, M.; Borodina, I. Application of synthetic biology for production of chemicals in yeast Saccharomyces cerevisiae. FEMS Yeast Res. 2014, 15 (1), 1−12. (173) Nakagawa, A.; Minami, H.; Kim, J. S.; Koyanagi, T.; Katayama, T.; Sato, F.; Kumagai, H. A bacterial platform for fermentative production of plant alkaloids. Nat. Commun. 2011, 2, 326. (174) Nakagawa, A.; Minami, H.; Kim, J. S.; Koyanagi, T.; Katayama, T.; Sato, F.; Kumagai, H. Bench-top fermentative production of plant benzylisoquinoline alkaloids using a bacterial platform. Bioengineered. 2012, 3 (1), 49−53. (175) DeLoache, W. C.; Russ, Z. N.; Narcross, L.; Gonzales, A. M.; Martin, V. J.; Dueber, J. E. An enzyme-coupled biosensor enables (S)58


Chemical Reviews

Review

reticuline production in yeast from glucose. Nat. Chem. Biol. 2015, 11 (7), 465−471. (176) Horton, R. M.; Hunt, H. D.; Ho, S. N.; Pullen, J. K.; Pease, L. R. Engineering hybrid genes without the use of restriction enzymes: gene splicing by overlap extension. Gene 1989, 77 (1), 61−68. (177) Quan, J.; Tian, J. Circular polymerase extension cloning of complex gene libraries and pathways. PLoS One 2009, 4 (7), e6441. (178) Li, M. Z.; Elledge, S. J. Harnessing homologous recombination in vitro to generate recombinant DNA via SLIC. Nat. Methods 2007, 4 (3), 251−256. (179) Wang, R. Y.; Shi, Z. Y.; Guo, Y. Y.; Chen, J. C.; Chen, G. Q. DNA fragments assembly based on nicking enzyme system. PLoS One 2013, 8 (3), e57943. (180) Gibson, D. G.; Young, L.; Chuang, R. Y.; Venter, J. C.; Hutchison, C. A., 3rd; Smith, H. O. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 2009, 6 (5), 343−345. (181) Zhang, Y.; Werling, U.; Edelmann, W. SLiCE: a novel bacterial cell extract-based DNA cloning method. Nucleic Acids Res. 2012, 40 (8), e55. (182) Nour-Eldin, H. H.; Geu-Flores, F.; Halkier, B. A. USER cloning and USER fusion: the ideal cloning techniques for small and big laboratories. Methods Mol. Biol. 2010, 643, 185−200. (183) Colloms, S. D.; Merrick, C. A.; Olorunniji, F. J.; Stark, W. M.; Smith, M. C.; Osbourn, A.; Keasling, J. D.; Rosser, S. J. Rapid metabolic pathway assembly and modification using serine integrase site-specific recombination. Nucleic Acids Res. 2014, 42 (4), e23. (184) Ellis, T.; Adie, T.; Baldwin, G. S. DNA assembly for synthetic biology: from parts to pathways and beyond. Integr. Biol. (Camb) 2011, 3 (2), 109−118. (185) Nour-Eldin, H. H.; Hansen, B. G.; Norholm, M. H.; Jensen, J. K.; Halkier, B. A. Advancing uracil-excision based cloning towards an ideal technique for cloning PCR fragments. Nucleic Acids Res. 2006, 34 (18), e122. (186) Norholm, M. H. A mutant Pfu DNA polymerase designed for advanced uracil-excision DNA engineering. BMC Biotechnol. 2010, 10, 21. (187) Lund, A. M.; Kildegaard, H. F.; Petersen, M. B.; Rank, J.; Hansen, B. G.; Andersen, M. R.; Mortensen, U. H. A versatile system for USER cloning-based assembly of expression vectors for mammalian cell engineering. PLoS One 2014, 9 (5), e96693. (188) Lalko, J.; Lapczynski, A.; McGinty, D.; Bhatia, S.; Letizia, C. S.; Api, A. M. Fragrance material review on beta-ionone. Food Chem. Toxicol. 2007, 45 (1), S241−247. (189) Beekwilder, J.; van der Meer, I. M.; Simic, A.; Uitdewilligen, J.; van Arkel, J.; de Vos, R. C.; Jonker, H.; Verstappen, F. W.; Bouwmeester, H. J.; Sibbesen, O.; et al. Metabolism of carotenoids and apocarotenoids during ripening of raspberry fruit. BioFactors 2008, 34 (1), 57−66. (190) Nacke, C.; Huttmann, S.; Etschmann, M. M.; Schrader, J. Enzymatic production and in situ separation of natural beta-ionone from beta-carotene. J. Ind. Microbiol. Biotechnol. 2012, 39 (12), 1771−1778. (191) Simkin, A. J.; Underwood, B. A.; Auldridge, M.; Loucas, H. M.; Shibuya, K.; Schmelz, E.; Clark, D. G.; Klee, H. J. Circadian regulation of the PhCCD1 carotenoid cleavage dioxygenase controls emission of beta-ionone, a fragrance volatile of petunia flowers. Plant Physiol. 2004, 136 (3), 3504−3514. (192) Beekwilder, J.; van Rossum, H. M.; Koopman, F.; Sonntag, F.; Buchhaupt, M.; Schrader, J.; Hall, R. D.; Bosch, D.; Pronk, J. T.; van Maris, A. J.; et al. Polycistronic expression of a beta-carotene biosynthetic pathway in Saccharomyces cerevisiae coupled to beta-ionone production. J. Biotechnol. 2014, 192, 383−392. (193) Lopez, J.; Essus, K.; Kim, I. K.; Pereira, R.; Herzog, J.; Siewers, V.; Nielsen, J.; Agosin, E. Production of beta-ionone by combined expression of carotenogenic and plant CCD1 genes in Saccharomyces cerevisiae. Microb. Cell Fact. 2015, 14, 84. (194) Wu, G.; Yan, Q.; Jones, J. A.; Tang, Y. J.; Fong, S. S.; Koffas, M. A. Metabolic burden: Cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol. 2016, 34 (8), 652−664.

(195) Liu, H.; Lu, T. Autonomous production of 1,4-butanediol via a de novo biosynthesis pathway in engineered Escherichia coli. Metab. Eng. 2015, 29, 135−141. (196) de Kok, S.; Stanton, L. H.; Slaby, T.; Durot, M.; Holmes, V. F.; Patel, K. G.; Platt, D.; Shapland, E. B.; Serber, Z.; Dean, J.; et al. Rapid and reliable DNA assembly via ligase cycling reaction. ACS Synth. Biol. 2014, 3 (2), 97−106. (197) Shao, Z.; Zhao, H.; Zhao, H. DNA assembler, an in vivo genetic method for rapid construction of biochemical pathways. Nucleic Acids Res. 2008, 37 (2), e16. (198) Yonemura, I.; Nakada, K.; Sato, A.; Hayashi, J.; Fujita, K.; Kaneko, S.; Itaya, M. Direct cloning of full-length mouse mitochondrial DNA using a Bacillus subtilis genome vector. Gene 2007, 391 (1−2), 171−177. (199) Itaya, M.; Fujita, K.; Kuroki, A.; Tsuge, K. Bottom-up genome assembly using the Bacillus subtilis genome vector. Nat. Methods 2007, 5 (1), 41−43. (200) Zhang, Y.; Muyrers, J. P.; Testa, G.; Stewart, A. F. DNA cloning by homologous recombination in Escherichia coli. Nat. Biotechnol. 2000, 18 (12), 1314−1317. (201) Sharan, S. K.; Thomason, L. C.; Kuznetsov, S. G.; Court, D. L. Recombineering: a homologous recombination-based method of genetic engineering. Nat. Protoc. 2009, 4 (2), 206−223. (202) Liu, R.; Bassalo, M. C.; Zeitoun, R. I.; Gill, R. T. Genome scale engineering techniques for metabolic engineering. Metab. Eng. 2015, 32, 143−154. (203) Annaluru, N.; Muller, H.; Mitchell, L. A.; Ramalingam, S.; Stracquadanio, G.; Richardson, S. M.; Dymond, J. S.; Kuang, Z.; Scheifele, L. Z.; Cooper, E. M.; et al. Total synthesis of a functional designer eukaryotic chromosome. Science 2014, 344 (6179), 55−58. (204) Marsischky, G.; LaBaer, J. Many paths to many clones: a comparative look at high-throughput cloning methods. Genome Res. 2004, 14 (10B), 2020−2028. (205) Abid, S.; Jafri, W.; Mumtaz, K.; Islam, M.; Abbas, Z.; Shah, H. A.; Hamid, S. Efficacy of L-ornithine-L-aspartate as an adjuvant therapy in cirrhotic patients with hepatic encephalopathy. JCPSP 2011, 21 (11), 666−671. (206) Mitsuhashi, S. Current topics in the biotechnological production of essential amino acids, functional amino acids, and dipeptides. Curr. Opin. Biotechnol. 2014, 26, 38−44. (207) Qin, J.; Zhou, Y. J.; Krivoruchko, A.; Huang, M.; Liu, L.; Khoomrung, S.; Siewers, V.; Jiang, B.; Nielsen, J. Modular pathway rewiring of Saccharomyces cerevisiae enables high-level production of Lornithine. Nat. Commun. 2015, 6, 8224. (208) Trubitsyna, M.; Michlewski, G.; Cai, Y.; Elfick, A.; French, C. E. PaperClip: rapid multi-part DNA assembly from existing libraries. Nucleic Acids Res. 2014, 42 (20), e154. (209) Coussement, P.; Maertens, J.; Beauprez, J.; Van Bellegem, W.; De Mey, M. One step DNA assembly for combinatorial metabolic engineering. Metab. Eng. 2014, 23, 70−77. (210) Liu, D.; Liu, H.; Li, B. Z.; Qi, H.; Jia, B.; Zhou, X.; Du, H. X.; Zhang, W.; Yuan, Y. J. Multigene Pathway Engineering with Regulatory Linkers (M-PERL). ACS Synth. Biol. 2016, 5 (12), 1535−1545. (211) Wu, S. G.; Hu, C. M.; Jin, G. J.; Zhao, X.; Zhao, Z. K. Phosphatelimitation mediated lipid production by Rhodosporidium toruloides. Bioresour. Technol. 2010, 101 (15), 6124−6129. (212) Li, Q.; Du, W.; Liu, D. Perspectives of microbial oils for biodiesel production. Appl. Microbiol. Biotechnol. 2008, 80 (5), 749−756. (213) Vicente, G.; Bautista, L. F.; Rodriguez, R.; Gutierrez, F. J.; Sadaba, I.; Ruiz-Vazquez, R. M.; Torres-Martinez, S.; Garre, V. Biodiesel production from biomass of an oleaginous fungus. Biochem. Eng. J. 2009, 48 (1), 22−27. (214) Beopoulos, A.; Cescut, J.; Haddouche, R.; Uribelarrea, J. L.; Molina-Jouve, C.; Nicaud, J. M. Yarrowia lipolytica as a model for bio-oil production. Prog. Lipid Res. 2009, 48 (6), 375−378. (215) Papanikolaou, S.; Aggelis, G. Lipids of oleaginous yeasts. Part I: Biochemistry of single cell oil production. Eur. J. Lipid Sci. Technol. 2011, 113 (8), 1031−1051. 59


Chemical Reviews

Review

(216) Beopoulos, A.; Mrozova, Z.; Thevenieau, F.; Le Dall, M. T.; Hapala, I.; Papanikolaou, S.; Chardot, T.; Nicaud, J. M. Control of lipid accumulation in the yeast Yarrowia lipolytica. Appl. Environ. Microbiol. 2008, 74 (24), 7779−7789. (217) Dulermo, T.; Nicaud, J. M. Involvement of the G3P shuttle and beta-oxidation pathway in the control of TAG synthesis and lipid accumulation in Yarrowia lipolytica. Metab. Eng. 2011, 13 (5), 482−491. (218) Qiao, K.; Imam Abidi, S. H.; Liu, H.; Zhang, H.; Chakraborty, S.; Watson, N.; Kumaran Ajikumar, P.; Stephanopoulos, G. Engineering lipid overproduction in the oleaginous yeast Yarrowia lipolytica. Metab. Eng. 2015, 29, 56−65. (219) Tai, M.; Stephanopoulos, G. Engineering the push and pull of lipid biosynthesis in oleaginous yeast Yarrowia lipolytica for biofuel production. Metab. Eng. 2013, 15, 1−9. (220) Zhang, S.; Skerker, J. M.; Rutter, C. D.; Maurer, M. J.; Arkin, A. P.; Rao, C. V. Engineering Rhodosporidium toruloides for increased lipid production. Biotechnol. Bioeng. 2016, 113 (5), 1056−1066. (221) Kim, B.; Kim, W. J.; Kim, D. I.; Lee, S. Y. Applications of genome-scale metabolic network model in metabolic engineering. J. Ind. Microbiol. Biotechnol. 2015, 42 (3), 339−348. (222) Weaver, D. S.; Keseler, I. M.; Mackie, A.; Paulsen, I. T.; Karp, P. D. A genome-scale metabolic flux model of Escherichia coli K-12 derived from the EcoCyc database. BMC Syst. Biol. 2014, 8, 79. (223) Segre, D.; Vitkup, D.; Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. U. S. A. 2002, 99 (23), 15112−15117. (224) Patil, K. R.; Rocha, I.; Forster, J.; Nielsen, J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinf. 2005, 6, 308. (225) Shlomi, T.; Berkman, O.; Ruppin, E. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (21), 7695−7700. (226) Lee, T. I.; Rinaldi, N. J.; Robert, F.; Odom, D. T.; Bar-Joseph, Z.; Gerber, G. K.; Hannett, N. M.; Harbison, C. T.; Thompson, C. M.; Simon, I.; et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002, 298 (5594), 799−804. (227) Thieffry, D.; Huerta, A. M.; Perez-Rueda, E.; Collado-Vides, J. From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. BioEssays 1998, 20 (5), 433−440. (228) Haverty, P. M.; Hansen, U.; Weng, Z. Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification. Nucleic Acids Res. 2004, 32 (1), 179−188. (229) Bailey, T. L.; Williams, N.; Misleh, C.; Li, W. W. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 2006, 34, W369−373. (230) Wingender, E.; Dietze, P.; Karas, H.; Knuppel, R. TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res. 1996, 24 (1), 238−241. (231) Sandelin, A.; Alkema, W.; Engstrom, P.; Wasserman, W. W.; Lenhard, B. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 2004, 32, D91−94. (232) Marcotte, E. M.; Pellegrini, M.; Ng, H. L.; Rice, D. W.; Yeates, T. O.; Eisenberg, D. Detecting protein function and protein-protein interactions from genome sequences. Science 1999, 285 (5428), 751− 753. (233) Schwikowski, B.; Uetz, P.; Fields, S. A network of proteinprotein interactions in yeast. Nat. Biotechnol. 2000, 18 (12), 1257−1261. (234) Wu, Q.; Ye, Y.; Ng, M. K.; Ho, S. S.; Shi, R. Collective prediction of protein functions from protein-protein interaction networks. BMC Bioinf. 2014, 15 (2), S9. (235) Masoudi-Nejad, A.; Mousavian, Z.; Bozorgmehr, J. H. Drugtarget and disease networks: polypharmacology in the post-genomic era. In Silico Pharmacol. 2013, 1, 17. (236) Cho, Y. R.; Zhang, A. Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins. BMC Bioinf. 2010, 11 (3), S3.

(237) Franceschini, A.; Szklarczyk, D.; Frankild, S.; Kuhn, M.; Simonovic, M.; Roth, A.; Lin, J.; Minguez, P.; Bork, P.; von Mering, C.; et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013, 41, D808− 815. (238) Xenarios, I.; Rice, D. W.; Salwinski, L.; Baron, M. K.; Marcotte, E. M.; Eisenberg, D. DIP: the database of interacting proteins. Nucleic Acids Res. 2000, 28 (1), 289−291. (239) Stein, A.; Russell, R. B.; Aloy, P. 3did: interacting protein domains of known three-dimensional structure. Nucleic Acids Res. 2004, 33, D413−417. (240) Gilbert, D. Biomolecular interaction network database. Briefings Bioinf. 2005, 6 (2), 194−198. (241) Carrera, J.; Estrela, R.; Luo, J.; Rai, N.; Tsoukalas, A.; Tagkopoulos, I. An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol. Syst. Biol. 2014, 10, 735. (242) Karr, J. R.; Sanghvi, J. C.; Macklin, D. N.; Gutschow, M. V.; Jacobs, J. M.; Bolival, B., Jr.; Assad-Garcia, N.; Glass, J. I.; Covert, M. W. A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150 (2), 389−401. (243) Qian, Z. G.; Xia, X. X.; Lee, S. Y. Metabolic engineering of Escherichia coli for the production of putrescine: a four carbon diamine. Biotechnol. Bioeng. 2009, 104 (4), 651−662. (244) Park, J. M.; Park, H. M.; Kim, W. J.; Kim, H. U.; Kim, T. Y.; Lee, S. Y. Flux variability scanning based on enforced objective flux for identifying gene amplification targets. BMC Syst. Biol. 2012, 6, 106. (245) Bailey, J. E.; Sburlati, A.; Hatzimanikatis, V.; Lee, K.; Renner, W. A.; Tsai, P. S. Inverse metabolic engineering: a strategy for directed genetic engineering of useful phenotypes. Biotechnol. Bioeng. 2002, 79 (5), 568−579. (246) Oud, B.; van Maris, A. J.; Daran, J. M.; Pronk, J. T. Genome-wide analytical approaches for reverse metabolic engineering of industrially relevant phenotypes in yeast. FEMS Yeast Res. 2012, 12 (2), 183−196. (247) Skretas, G.; Kolisis, F. N. Combinatorial approaches for inverse metabolic engineering applications. Comput. Struct. Biotechnol. J. 2012, 3, e201210021. (248) Ghosh, C.; Gupta, R.; Mukherjee, K. J. An inverse metabolic engineering approach for the design of an improved host platform for over-expression of recombinant proteins in Escherichia coli. Microb. Cell Fact. 2012, 11, 93. (249) Choi, K. R.; Shin, J. H.; Cho, J. S.; Yang, D.; Lee, S. Y. Systems Metabolic Engineering of Escherichia coli. EcoSal Plus 2016, 7 (1).10.1128/ecosalplus.ESP-0010-2015 (250) Zomorrodi, A. R.; Suthers, P. F.; Ranganathan, S.; Maranas, C. D. Mathematical optimization applications in metabolic networks. Metab. Eng. 2012, 14 (6), 672−686. (251) Kirby, J.; Romanini, D. W.; Paradise, E. M.; Keasling, J. D. Engineering triterpene production in Saccharomyces cerevisiae-betaamyrin synthase from Artemisia annua. FEBS J. 2008, 275 (8), 1852− 1859. (252) Otero, J. M.; Vongsangnak, W.; Asadollahi, M. A.; OlivaresHernandes, R.; Maury, J.; Farinelli, L.; Barlocher, L.; Osteras, M.; Schalk, M.; Clark, A.; et al. Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications. BMC Genomics 2010, 11, 723. (253) Madsen, K. M.; Udatha, G. D.; Semba, S.; Otero, J. M.; Koetter, P.; Nielsen, J.; Ebizuka, Y.; Kushiro, T.; Panagiotou, G. Linking genotype and phenotype of Saccharomyces cerevisiae strains reveals metabolic engineering targets and leads to triterpene hyper-producers. PLoS One 2011, 6 (3), e14763. (254) Zhang, G. L.; Cao, Q.; Liu, J. Z.; Liu, B. Y.; Li, J.; Li, C. Refactoring beta-amyrin synthesis in Saccharomyces cerevisiae. AIChE J. 2015, 61 (10), 3172−3179. (255) Mooser, V.; Ordovas, J. M. ’Omic’ approaches and lipid metabolism: are these new technologies holding their promises? Curr. Opin. Lipidol. 2003, 14 (2), 115−119. 60


Chemical Reviews

Review

(256) O’Flaherty, S.; Klaenhammer, T. R. The impact of omic technologies on the study of food microbes. Annu. Rev. Food Sci. Technol. 2011, 2, 353−371. (257) Tjaden, B.; Saxena, R. M.; Stolyar, S.; Haynor, D. R.; Kolker, E.; Rosenow, C. Transcriptome analysis of Escherichia coli using highdensity oligonucleotide probe arrays. Nucleic Acids Res. 2002, 30 (17), 3732−3738. (258) Yugi, K.; Kubota, H.; Hatano, A.; Kuroda, S. Trans-Omics: How to reconstruct biochemical networks across multiple ’Omic’ layers. Trends Biotechnol. 2016, 34 (4), 276−290. (259) Vollrath, F.; Knight, D. P. Liquid crystalline spinning of spider silk. Nature 2001, 410 (6828), 541−548. (260) Xia, X. X.; Qian, Z. G.; Ki, C. S.; Park, Y. H.; Kaplan, D. L.; Lee, S. Y. Native-sized recombinant spider silk protein produced in metabolically engineered Escherichia coli results in a strong fiber. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (32), 14059−14063. (261) Lazaris, A.; Arcidiacono, S.; Huang, Y.; Zhou, J. F.; Duguay, F.; Chretien, N.; Welsh, E. A.; Soares, J. W.; Karatzas, C. N. Spider silk fibers spun from soluble recombinant silk produced in mammalian cells. Science 2002, 295 (5554), 472−476. (262) Widmaier, D. M.; Tullman-Ercek, D.; Mirsky, E. A.; Hill, R.; Govindarajan, S.; Minshull, J.; Voigt, C. A. Engineering the Salmonella type III secretion system to export spider silk monomers. Mol. Syst. Biol. 2009, 5, 309. (263) Ayoub, N. A.; Garb, J. E.; Tinghitella, R. M.; Collin, M. A.; Hayashi, C. Y. Blueprint for a high-performance biomaterial: full-length spider dragline silk genes. PLoS One 2007, 2 (6), e514. (264) Tan, G. Y.; Zhu, F.; Deng, Z.; Liu, T. In vitro reconstitution guide for targeted synthetic metabolism of chemicals, nutraceuticals and drug precursors. Synth. Syst. Biotechnol. 2016, 1 (1), 25−33. (265) Liu, T.; Khosla, C. Genetic engineering of Escherichia coli for biofuel production. Annu. Rev. Genet. 2010, 44, 53−69. (266) Yu, X.; Liu, T.; Zhu, F.; Khosla, C. In vitro reconstitution and steady-state analysis of the fatty acid synthase from Escherichia coli. Proc. Natl. Acad. Sci. U. S. A. 2011, 108 (46), 18643−18648. (267) Kuchler, A.; Yoshimoto, M.; Luginbuhl, S.; Mavelli, F.; Walde, P. Enzymatic reactions in confined environments. Nat. Nanotechnol. 2016, 11 (5), 409−420. (268) Wang, C.; Yoon, S. H.; Jang, H. J.; Chung, Y. R.; Kim, J. Y.; Choi, E. S.; Kim, S. W. Metabolic engineering of Escherichia coli for alphafarnesene production. Metab. Eng. 2011, 13 (6), 648−655. (269) Tippmann, S.; Scalcinati, G.; Siewers, V.; Nielsen, J. Production of farnesene and santalene by Saccharomyces cerevisiae using fed-batch cultivations with RQ-controlled feed. Biotechnol. Bioeng. 2016, 113 (1), 72−81. (270) Yang, X.; Nambou, K.; Wei, L.; Hua, Q. Heterologous production of alpha-farnesene in metabolically engineered strains of Yarrowia lipolytica. Bioresour. Technol. 2016, 216, 1040−1048. (271) Zhu, F.; Zhong, X.; Hu, M.; Lu, L.; Deng, Z.; Liu, T. In vitro reconstitution of mevalonate pathway and targeted engineering of farnesene overproduction in Escherichia coli. Biotechnol. Bioeng. 2014, 111 (7), 1396−1405. (272) Smolke, C. D.; Silver, P. A. Informing biological design by integration of systems and synthetic biology. Cell 2011, 144 (6), 855− 859. (273) Rogers, J. K.; Taylor, N. D.; Church, G. M. Biosensor-based engineering of biosynthetic pathways. Curr. Opin. Biotechnol. 2016, 42, 84−91. (274) Michener, J. K.; Thodey, K.; Liang, J. C.; Smolke, C. D. Applications of genetically-encoded biosensors for the construction and control of biosynthetic pathways. Metab. Eng. 2012, 14 (3), 212−222. (275) Montange, R. K.; Batey, R. T. Riboswitches: Emerging themes in RNA structure and function. Annu. Rev. Biophys. 2008, 37, 117−133. (276) Win, M. N.; Liang, J. C.; Smolke, C. D. Frameworks for programming biological function through RNA parts and devices. Chem. Biol. 2009, 16 (3), 298−310. (277) Win, M. N.; Smolke, C. D. A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (36), 14283−14288.

(278) Win, M. N.; Smolke, C. D. Higher-order cellular information processing with synthetic RNA devices. Science 2008, 322 (5900), 456− 460. (279) Dixon, N.; Duncan, J. N.; Geerlings, T.; Dunstan, M. S.; McCarthy, J. E.; Leys, D.; Micklefield, J. Reengineering orthogonally selective riboswitches. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (7), 2830− 2835. (280) Tang, S. Y.; Cirino, P. C. Design and application of a mevalonateresponsive regulatory protein. Angew. Chem., Int. Ed. 2011, 50 (5), 1084−1086. (281) Collins, C. H.; Leadbetter, J. R.; Arnold, F. H. Dual selection enhances the signaling specificity of a variant of the quorum-sensing transcriptional activator LuxR. Nat. Biotechnol. 2006, 24 (6), 708−712. (282) van Sint Fiet, S.; van Beilen, J. B.; Witholt, B. Selection of biocatalysts for chemical synthesis. Proc. Natl. Acad. Sci. U. S. A. 2006, 103 (6), 1693−1698. (283) Schwimmer, L. J.; Rohatgi, P.; Azizi, B.; Seley, K. L.; Doyle, D. F. Creation and discovery of ligand-receptor pairs for transcriptional control with small molecules. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (41), 14707−14712. (284) Lin, H.; Tao, H.; Cornish, V. W. Directed evolution of a glycosynthase via chemical complementation. J. Am. Chem. Soc. 2004, 126 (46), 15051−15059. (285) Edwards, W. R.; Busse, K.; Allemann, R. K.; Jones, D. D. Linking the functions of unrelated proteins using a novel directed evolution domain insertion method. Nucleic Acids Res. 2008, 36 (13), e78. (286) Guntas, G.; Mansell, T. J.; Kim, J. R.; Ostermeier, M. Directed evolution of protein switches and their application to the creation of ligand-binding proteins. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (32), 11224−11229. (287) Buskirk, A. R.; Ong, Y. C.; Gartner, Z. J.; Liu, D. R. Directed evolution of ligand dependence: small-molecule-activated protein splicing. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (29), 10505−10510. (288) Kaper, T.; Lager, I.; Looger, L. L.; Chermak, D.; Frommer, W. B. Fluorescence resonance energy transfer sensors for quantitative monitoring of pentose and disaccharide accumulation in bacteria. Biotechnol. Biofuels 2008, 1 (1), 11. (289) Pfleger, B. F.; Pitera, D. J.; Newman, J. D.; Martin, V. J.; Keasling, J. D. Microbial sensors for small molecules: development of a mevalonate biosensor. Metab. Eng. 2007, 9 (1), 30−38. (290) Ma, S. M.; Li, J. W.; Choi, J. W.; Zhou, H.; Lee, K. K.; Moorthie, V. A.; Xie, X.; Kealey, J. T.; Da Silva, N. A.; Vederas, J. C.; et al. Complete reconstitution of a highly reducing iterative polyketide synthase. Science 2009, 326 (5952), 589−592. (291) Hansen, C. A.; Frost, J. W. Deoxygenation of polyhydroxybenzenes: An alternative strategy for the benzene-free synthesis of aromatic chemicals. J. Am. Chem. Soc. 2002, 124 (21), 5926−5927. (292) Tang, S. Y.; Qian, S.; Akinterinwa, O.; Frei, C. S.; Gredell, J. A.; Cirino, P. C. Screening for enhanced triacetic acid lactone production by recombinant Escherichia coli expressing a designed triacetic acid lactone reporter. J. Am. Chem. Soc. 2013, 135 (27), 10099−10103. (293) Eckermann, S.; Schroder, G.; Schmidt, J.; Strack, D.; Edrada, R. A.; Helariutta, Y.; Elomaa, P.; Kotilainen, M.; Kilpelainen, I.; Proksch, P.; et al. New pathway to polyketides in plants. Nature 1998, 396 (6709), 387−390. (294) Jez, J. M.; Austin, M. B.; Ferrer, J.; Bowman, M. E.; Schroder, J.; Noel, J. P. Structural control of polyketide formation in plant-specific polyketide synthases. Chem. Biol. 2000, 7 (12), 919−930. (295) Xie, D.; Shao, Z.; Achkar, J.; Zha, W.; Frost, J. W.; Zhao, H. Microbial synthesis of triacetic acid lactone. Biotechnol. Bioeng. 2006, 93 (4), 727−736. (296) Cardenas, J.; Da Silva, N. A. Metabolic engineering of Saccharomyces cerevisiae for the production of triacetic acid lactone. Metab. Eng. 2014, 25, 194−203. (297) Nielsen, J.; Keasling, J. D. Engineering cellular metabolism. Cell 2016, 164 (6), 1185−1197. (298) Lee, S. Y.; Kim, H. U. Systems strategies for developing industrial microbial strains. Nat. Biotechnol. 2015, 33 (10), 1061−1072. 61


Chemical Reviews

Review

(299) Blazeck, J.; Alper, H. S. Promoter engineering: recent advances in controlling transcription at the most fundamental level. Biotechnol. J. 2013, 8 (1), 46−58. (300) Alper, H.; Fischer, C.; Nevoigt, E.; Stephanopoulos, G. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (36), 12678−12683. (301) Kelwick, R.; Bowater, L.; Yeoman, K. H.; Bowater, R. P. Promoting microbiology education through the iGEM synthetic biology competition. FEMS Microbiol. Lett. 2015, 362, 16. (302) Beal, J.; Haddock-Angelli, T.; Gershater, M.; de Mora, K.; Lizarazo, M.; Hollenhorst, J.; Rettberg, R. i, G. E. M. I. S. C. Reproducibility of fluorescent expression from engineered biological constructs in E. coli. PLoS One 2016, 11 (3), e0150182. (303) Radeck, J.; Kraft, K.; Bartels, J.; Cikovic, T.; Durr, F.; Emenegger, J.; Kelterborn, S.; Sauer, C.; Fritz, G.; Gebhard, S.; et al. The Bacillus BioBrick Box: generation and evaluation of essential genetic building blocks for standardized work with Bacillus subtilis. J. Biol. Eng. 2013, 7 (1), 29. (304) Lescot, M.; Dehais, P.; Thijs, G.; Marchal, K.; Moreau, Y.; Van de Peer, Y.; Rouze, P.; Rombauts, S. PlantCARE, a database of plant cisacting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res. 2002, 30 (1), 325−327. (305) Park, S. H.; Kim, H. U.; Kim, T. Y.; Park, J. S.; Kim, S. S.; Lee, S. Y. Metabolic engineering of Corynebacterium glutamicum for L-arginine production. Nat. Commun. 2014, 5, 4618. (306) Cooling, M. T.; Rouilly, V.; Misirli, G.; Lawson, J.; Yu, T.; Hallinan, J.; Wipat, A. Standard virtual biological parts: a repository of modular modeling components for synthetic biology. Bioinformatics 2010, 26 (7), 925−931. (307) Zelcbuch, L.; Antonovsky, N.; Bar-Even, A.; Levin-Karp, A.; Barenholz, U.; Dayagi, M.; Liebermeister, W.; Flamholz, A.; Noor, E.; Amram, S.; et al. Spanning high-dimensional expression space using ribosome-binding site combinatorics. Nucleic Acids Res. 2013, 41 (9), e98. (308) Na, D.; Lee, D. RBSDesigner: software for designing synthetic ribosome binding sites that yields a desired level of protein expression. Bioinformatics 2010, 26 (20), 2633−2634. (309) Pfleger, B. F.; Pitera, D. J.; Smolke, C. D.; Keasling, J. D. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat. Biotechnol. 2006, 24 (8), 1027−1032. (310) Pertea, M.; Lin, X.; Salzberg, S. L. GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res. 2001, 29 (5), 1185−1190. (311) Dogan, R. I.; Getoor, L.; Wilbur, W. J.; Mount, S. M. SplicePort– an interactive splice-site analysis tool. Nucleic Acids Res. 2007, 35, W285−291. (312) Gao, X.; Xu, N.; Li, S. B.; Liu, L. M. Metabolic engineering of Candida glabrata for diacetyl production. PLoS One 2014, 9 (3), e89854. (313) Guo, T.; Kong, J.; Zhang, L.; Zhang, C.; Hu, S. Fine tuning of the lactate and diacetyl production through promoter engineering in Lactococcus lactis. PLoS One 2012, 7 (4), e36296. (314) Liu, W.; Christenson, S. D.; Standage, S.; Shen, B. Biosynthesis of the enediyne antitumor antibiotic C-1027. Science 2002, 297 (5584), 1170−1173. (315) Schirmer, A.; Rude, M. A.; Li, X.; Popova, E.; del Cardayre, S. B. Microbial biosynthesis of alkanes. Science 2010, 329 (5991), 559−562. (316) Liu, Q.; Wu, K.; Cheng, Y.; Lu, L.; Xiao, E.; Zhang, Y.; Deng, Z.; Liu, T. Engineering an iterative polyketide pathway in Escherichia coli results in single-form alkene and alkane overproduction. Metab. Eng. 2015, 28, 82−90. (317) Alvares, T. S.; Meirelles, C. M.; Bhambhani, Y. N.; Paschoalin, V. M. F.; Gomes, P. S. C. L-Arginine as a potential ergogenic aid in healthy subjects. Sports Med. 2011, 41 (3), 233−248. (318) Siani, A.; Pagano, E.; Iacone, R.; Iacoviello, L.; Scopacasa, F.; Strazzullo, P. Blood pressure and metabolic changes during dietary Larginine supplementation in humans. Am. J. Hypertens. 2000, 13 (5), 547−551.

(319) Ikeda, M.; Mitsuhashi, S.; Tanaka, K.; Hayashi, M. Reengineering of a Corynebacterium glutamicum L-arginine and Lcitrulline producer. Appl. Environ. Microbiol. 2009, 75 (6), 1635−1641. (320) Xu, M. J.; Rao, Z. M.; Yang, J.; Xia, H. F.; Dou, W. F.; Jin, J.; Xu, Z. H. Heterologous and homologous expression of the arginine biosynthetic argC similar to H cluster from Corynebacterium crenatum for improvement of l-arginine production. J. Ind. Microbiol. Biotechnol. 2012, 39 (3), 495−502. (321) Shin, J. H.; Lee, S. Y. Metabolic engineering of microorganisms for the production of L-arginine and its derivatives. Microb. Cell Fact. 2014, 13, 166. (322) Garg, S. K.; Jain, A. Fermentative production of 2,3-Butanediol a Review. Bioresour. Technol. 1995, 51 (2−3), 103−109. (323) Syu, M. J. Biological production of 2,3-butanediol. Appl. Microbiol. Biotechnol. 2001, 55 (1), 10−18. (324) Celinska, E.; Grajek, W. Biotechnological production of 2,3butanediol–current state and prospects. Biotechnol. Adv. 2009, 27 (6), 715−725. (325) Ji, X. J.; Huang, H.; Ouyang, P. K. Microbial 2,3-butanediol production: a state-of-the-art review. Biotechnol. Adv. 2011, 29 (3), 351− 364. (326) Zhang, L.; Xu, Q.; Zhan, S.; Li, Y.; Lin, H.; Sun, S.; Sha, L.; Hu, K.; Guan, X.; Shen, Y. A new NAD(H)-dependent meso-2,3-butanediol dehydrogenase from an industrially potential strain Serratia marcescens H30. Appl. Microbiol. Biotechnol. 2014, 98 (3), 1175−1184. (327) Li, Z. J.; Jian, J.; Wei, X. X.; Shen, X. W.; Chen, G. Q. Microbial production of meso-2,3-butanediol by metabolically engineered Escherichia coli under low oxygen condition. Appl. Microbiol. Biotechnol. 2010, 87 (6), 2001−2009. (328) Shin, H. D.; Yoon, S. H.; Wu, J.; Rutter, C.; Kim, S. W.; Chen, R. R. High-yield production of meso-2,3-butanediol from cellodextrin by engineered E. coli biocatalysts. Bioresour. Technol. 2012, 118, 367−373. (329) Ui, S.; Okajima, Y.; Mimura, A.; Kanai, H.; Kudo, T. Molecular generation of an Escherichia coli strain producing only the meso-isomer of 2,3-butanediol. J. Ferment. Bioeng. 1997, 84 (3), 185−189. (330) Xu, Y.; Chu, H.; Gao, C.; Tao, F.; Zhou, Z.; Li, K.; Li, L.; Ma, C.; Xu, P. Systematic metabolic engineering of Escherichia coli for high-yield production of fuel bio-chemical 2,3-butanediol. Metab. Eng. 2014, 23, 22−33. (331) Zhou, P.; Ye, L.; Xie, W.; Lv, X.; Yu, H. Highly efficient biosynthesis of astaxanthin in Saccharomyces cerevisiae by integration and tuning of algal crtZ and bkt. Appl. Microbiol. Biotechnol. 2015, 99 (20), 8419−8428. (332) Lemuth, K.; Steuer, K.; Albermann, C. Engineering of a plasmidfree Escherichia coli strain for improved in vivo biosynthesis of astaxanthin. Microb. Cell Fact. 2011, 10, 29. (333) Wang, C. W.; Oh, M. K.; Liao, J. C. Engineered isoprenoid pathway enhances astaxanthin production in Escherichia coli. Biotechnol. Bioeng. 1999, 62 (2), 235−241. (334) Ukibe, K.; Hashida, K.; Yoshida, N.; Takagi, H. Metabolic engineering of Saccharomyces cerevisiae for astaxanthin production and oxidative stress tolerance. Appl. Environ. Microbiol. 2009, 75 (22), 7205− 7211. (335) Chi, S.; He, Y.; Ren, J.; Su, Q.; Liu, X.; Chen, Z.; Wang, M.; Li, Y.; Li, J. Overexpression of a bifunctional enzyme, CrtS, enhances astaxanthin synthesis through two pathways in Phaf f ia rhodozyma. Microb. Cell Fact. 2015, 14, 90. (336) Lennen, R. M.; Pfleger, B. F. Microbial production of fatty acidderived fuels and chemicals. Curr. Opin. Biotechnol. 2013, 24 (6), 1044− 1053. (337) Lu, X.; Vora, H.; Khosla, C. Overproduction of free fatty acids in E. coli: implications for biodiesel production. Metab. Eng. 2008, 10 (6), 333−339. (338) Davis, M. S.; Solbiati, J.; Cronan, J. E., Jr. Overproduction of acetyl-CoA carboxylase activity increases the rate of fatty acid biosynthesis in Escherichia coli. J. Biol. Chem. 2000, 275 (37), 28593− 28598. 62


Chemical Reviews

Review

(339) Wu, H.; Karanjikar, M.; San, K. Y. Metabolic engineering of Escherichia coli for efficient free fatty acid production from glycerol. Metab. Eng. 2014, 25, 82−91. (340) Lennen, R. M.; Braden, D. J.; West, R. A.; Dumesic, J. A.; Pfleger, B. F. A process for microbial hydrocarbon synthesis: Overproduction of fatty acids in Escherichia coli and catalytic conversion to alkanes. Biotechnol. Bioeng. 2010, 106 (2), 193−202. (341) Zhang, X.; Li, M.; Agrawal, A.; San, K. Y. Efficient free fatty acid production in Escherichia coli using plant acyl-ACP thioesterases. Metab. Eng. 2011, 13 (6), 713−722. (342) Li, M.; Zhang, X.; Agrawal, A.; San, K. Y. Effect of acetate formation pathway and long chain fatty acid CoA-ligase on the free fatty acid production in E. coli expressing acy-ACP thioesterase from Ricinus communis. Metab. Eng. 2012, 14 (4), 380−387. (343) Xu, P.; Gu, Q.; Wang, W.; Wong, L.; Bower, A. G.; Collins, C. H.; Koffas, M. A. Modular optimization of multi-gene pathways for fatty acids production in E. coli. Nat. Commun. 2013, 4, 1409. (344) Lynen, F. Biosynthetic pathways from acetate to natural products. Pure Appl. Chem. 1967, 14 (1), 137. (345) Tabata, K.; Hashimoto, S. Production of mevalonate by a metabolically-engineered Escherichia coli. Biotechnol. Lett. 2004, 26 (19), 1487−1491. (346) Takagi, M.; Kuzuyama, T.; Takahashi, S.; Seto, H. A gene cluster for the mevalonate pathway from Streptomyces sp. Strain CL190. J. Bacteriol. 2000, 182 (15), 4153−4157. (347) Kuzuyama, T.; Dairi, T.; Yamashita, H.; Shoji, Y.; Seto, H. Heterologous mevalonate production in Streptomyces lividans TK23. Biosci., Biotechnol., Biochem. 2004, 68 (4), 931−934. (348) Kaberdin, V. R. Probing the substrate specificity of Escherichia coli RNase E using a novel oligonucleotide-based assay. Nucleic Acids Res. 2003, 31 (16), 4710−4716. (349) Capell, T.; Christou, P. Progress in plant metabolic engineering. Curr. Opin. Biotechnol. 2004, 15 (2), 148−154. (350) Beerli, R. R.; Barbas, C. F., 3rd Engineering polydactyl zincfinger transcription factors. Nat. Biotechnol. 2002, 20 (2), 135−141. (351) Courchesne, N. M.; Parisien, A.; Wang, B.; Lan, C. Q. Enhancement of lipid production using biochemical, genetic and transcription factor engineering approaches. J. Biotechnol. 2009, 141 (1− 2), 31−41. (352) Pireyre, M.; Burow, M. Regulation of MYB and bHLH transcription factors: a glance at the protein level. Mol. Plant 2015, 8 (3), 378−388. (353) Memelink, J.; Verpoorte, R.; Kijne, J. W. ORCAnization of jasmonate-responsive gene expression in alkaloid metabolism. Trends Plant Sci. 2001, 6 (5), 212−219. (354) Greisman, H. A.; Pabo, C. O. A general strategy for selecting high-affinity zinc finger proteins for diverse DNA target sites. Science 1997, 275 (5300), 657−661. (355) Negi, S.; Imanishi, M.; Matsumoto, M.; Sugiura, Y. New redesigned zinc-finger proteins: design strategy and its application. Chem. - Eur. J. 2008, 14 (11), 3236−3249. (356) Segal, D. J.; Stege, J. T.; Barbas, C. F., 3rd. Zinc fingers and a green thumb: manipulating gene expression in plants. Curr. Opin. Plant Biol. 2003, 6 (2), 163−168. (357) Stege, J. T.; Guan, X.; Ho, T.; Beachy, R. N.; Barbas, C. F. Controlling gene expression in plants using synthetic zinc finger transcription factors. Plant J. 2002, 32 (6), 1077−1086. (358) Kawaoka, A.; Kaothien, P.; Yoshida, K.; Endo, S.; Yamada, K.; Ebinuma, H. Functional analysis of tobacco LIM protein Ntlim1 involved in lignin biosynthesis. Plant J. 2000, 22 (4), 289−301. (359) Kawaoka, A.; Kawamoto, T.; Sekine, M.; Yoshida, K.; Takano, M.; Shinmyo, A. A cis-acting element and a trans-acting factor involved in the wound-induced expression of a horseradish peroxidase gene. Plant J. 1994, 6 (1), 87−97. (360) Vom Endt, D.; Kijne, J. W.; Memelink, J. Transcription factors controlling plant secondary metabolism: what regulates the regulators? Phytochemistry 2002, 61 (2), 107−114. (361) Mol, J.; Grotewold, E.; Koes, R. How genes paint flowers and seeds. Trends Plant Sci. 1998, 3 (6), 212−217.

(362) Harborne, J. B.; Williams, C. A. Advances in flavonoid research since 1992. Phytochemistry 2000, 55 (6), 481−504. (363) Jaakola, L.; Maatta, K.; Pirttila, A. M.; Torronen, R.; Karenlampi, S.; Hohtola, A. Expression of genes involved in anthocyanin biosynthesis in relation to anthocyanin, proanthocyanidin, and flavonol levels during bilberry fruit development. Plant Physiol. 2002, 130 (2), 729−739. (364) Grotewold, E.; Chamberlin, M.; Snook, M.; Siame, B.; Butler, L.; Swenson, J.; Maddock, S.; St Clair, G.; Bowen, B. Engineering secondary metabolism in maize cells by ectopic expression of transcription factors. Plant Cell 1998, 10 (5), 721−740. (365) Bruce, W.; Folkerts, O.; Garnaat, C.; Crasta, O.; Roth, B.; Bowen, B. Expression profiling of the maize flavonoid pathway genes controlled by estradiol-inducible transcription factors CRC and P. Plant Cell 2000, 12 (1), 65−80. (366) Zhu, J.; Wang, M.; Wen, W.; Yu, R. Biosynthesis and regulation of terpenoid indole alkaloids in Catharanthus roseus. Pharmacogn. Rev. 2015, 9 (17), 24−28. (367) Aerts, R. J.; Gisi, D.; Decarolis, E.; Deluca, V.; Baumann, T. W. Methyl jasmonate vapor increases the developmentally controlled synthesis of alkaloids in Catharanthus and Cinchona seedlings. Plant J. 1994, 5 (5), 635−643. (368) Gantet, P.; Imbault, N.; Thiersault, M.; Doireau, P. Necessity of a functional octadecanoic pathway for indole alkaloid synthesis by Catharanthus roseus cell suspensions cultured in an auxin-starved medium. Plant Cell Physiol. 1998, 39 (2), 220−225. (369) van der Fits, L.; Memelink, J. ORCA3, a jasmonate-responsive transcriptional regulator of plant primary and secondary metabolism. Science 2000, 289 (5477), 295−297. (370) Isaacs, F. J.; Dwyer, D. J.; Collins, J. J. RNA synthetic biology. Nat. Biotechnol. 2006, 24 (5), 545−554. (371) Liang, J. C.; Bloom, R. J.; Smolke, C. D. Engineering biological systems with synthetic RNA molecules. Mol. Cell 2011, 43 (6), 915− 926. (372) Win, M. N.; Smolke, C. D. A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (36), 14283−14288. (373) Mellin, J. R.; Cossart, P. Unexpected versatility in bacterial riboswitches. Trends Genet. 2015, 31 (3), 150−156. (374) Na, D.; Yoo, S. M.; Chung, H.; Park, H.; Park, J. H.; Lee, S. Y. Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs. Nat. Biotechnol. 2013, 31 (2), 170−174. (375) Doudna, J. A.; Cech, T. R. The chemical repertoire of natural ribozymes. Nature 2002, 418 (6894), 222−228. (376) Nissen, P.; Hansen, J.; Ban, N.; Moore, P. B.; Steitz, T. A. The structural basis of ribosome activity in peptide bond synthesis. Science 2000, 289 (5481), 920−930. (377) Steitz, T. A.; Moore, P. B. RNA, the first macromolecular catalyst: the ribosome is a ribozyme. Trends Biochem. Sci. 2003, 28 (8), 411−418. (378) Weinger, J. S.; Parnell, K. M.; Dorner, S.; Green, R.; Strobel, S. A. Substrate-assisted catalysis of peptide bond formation by the ribosome. Nat. Struct. Mol. Biol. 2004, 11 (11), 1101−1106. (379) Nahas, M. K.; Wilson, T. J.; Hohng, S.; Jarvie, K.; Lilley, D. M.; Ha, T. Observation of internal cleavage and ligation reactions of a ribozyme. Nat. Struct. Mol. Biol. 2004, 11 (11), 1107−1113. (380) Hampel, A.; Cowan, J. A. A unique mechanism for RNA catalysis: the role of metal cofactors in hairpin ribozyme cleavage. Chem. Biol. 1997, 4 (7), 513−517. (381) Narlikar, G. J.; Herschlag, D. Mechanistic aspects of enzymatic catalysis: lessons from comparison of RNA and protein enzymes. Annu. Rev. Biochem. 1997, 66, 19−59. (382) Stasolla, C.; Katahira, R.; Thorpe, T. A.; Ashihara, H. Purine and pyrimidine nucleotide metabolism in higher plants. J. Plant Physiol. 2003, 160 (11), 1271−1295. (383) Smith, P. M.; Atkins, C. A. Purine biosynthesis. Big in cell division, even bigger in nitrogen assimilation. Plant Physiol. 2002, 128 (3), 793−802. (384) Ogawa, J.; Takeda, S.; Xie, S. X.; Hatanaka, H.; Ashikari, T.; Amachi, T.; Shimizu, S. Purification, characterization, and gene cloning 63


Chemical Reviews

Review

of purine nucleosidase from Ochrobactrum anthropi. Appl. Environ. Microbiol. 2001, 67 (4), 1783−1787. (385) Ashihara, H. Xanthosine metabolism in plants: Metabolic fate of exogenously supplied C-14-labelled xanthosine and xanthine in intact mungbean seedlings. Phytochem. Lett. 2012, 5 (1), 100−103. (386) Hermann, T.; Patel, D. J. Adaptive recognition by nucleic acid aptamers. Science 2000, 287 (5454), 820−825. (387) Winkler, W. C.; Breaker, R. R. Regulation of bacterial gene expression by riboswitches. Annu. Rev. Microbiol. 2005, 59, 487−517. (388) Winkler, W.; Nahvi, A.; Breaker, R. R. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature 2002, 419 (6910), 952−956. (389) Winkler, W. C.; Nahvi, A.; Roth, A.; Collins, J. A.; Breaker, R. R. Control of gene expression by a natural metabolite-responsive ribozyme. Nature 2004, 428 (6980), 281−286. (390) Winkler, W. C.; Cohen-Chalamish, S.; Breaker, R. R. An mRNA structure that controls gene expression by binding FMN. Proc. Natl. Acad. Sci. U. S. A. 2002, 99 (25), 15908−15913. (391) Martens, J. H.; Barg, H.; Warren, M. J.; Jahn, D. Microbial production of vitamin B12. Appl. Microbiol. Biotechnol. 2002, 58 (3), 275−285. (392) Clemens, T. L. Vitamin B12 deficiency and bone health. N. Engl. J. Med. 2014, 371 (10), 963−964. (393) Warren, M. J.; Raux, E.; Schubert, H. L.; Escalante-Semerena, J. C. The biosynthesis of adenosylcobalamin (vitamin B12). Nat. Prod. Rep. 2002, 19 (4), 390−412. (394) Debussche, L.; Thibaut, D.; Cameron, B.; Crouzet, J.; Blanche, F. Biosynthesis of the corrin macrocycle of coenzyme B12 in Pseudomonas denitrif icans. J. Bacteriol. 1993, 175 (22), 7430−7440. (395) Raux, E.; Lanois, A.; Warren, M. J.; Rambach, A.; Thermes, C. Cobalamin (vitamin B12) biosynthesis: identification and characterization of a Bacillus megaterium cobI operon. Biochem. J. 1998, 335 (1), 159−166. (396) Moore, S. J.; Lawrence, A. D.; Biedendieck, R.; Deery, E.; Frank, S.; Howard, M. J.; Rigby, S. E.; Warren, M. J. Elucidation of the anaerobic pathway for the corrin component of cobalamin (vitamin B12). Proc. Natl. Acad. Sci. U. S. A. 2013, 110 (37), 14906−14911. (397) Nahvi, A.; Barrick, J. E.; Breaker, R. R. Coenzyme B12 riboswitches are widespread genetic control elements in prokaryotes. Nucleic Acids Res. 2004, 32 (1), 143−150. (398) Moore, S. J.; Mayer, M. J.; Biedendieck, R.; Deery, E.; Warren, M. J. Towards a cell factory for vitamin B12 production in Bacillus megaterium: bypassing of the cobalamin riboswitch control elements. New Biotechnol. 2014, 31 (6), 553−561. (399) Majdalani, N.; Vanderpool, C. K.; Gottesman, S. Bacterial small RNA regulators. Crit. Rev. Biochem. Mol. Biol. 2005, 40 (2), 93−113. (400) Masse, E.; Escorcia, F. E.; Gottesman, S. Coupled degradation of a small regulatory RNA and its mRNA targets in Escherichia coli. Genes Dev. 2003, 17 (19), 2374−2383. (401) Good, L. Translation repression by antisense sequences. Cell. Mol. Life Sci. 2003, 60 (5), 854−861. (402) Wagner, E. G.; Simons, R. W. Antisense RNA control in bacteria, phages, and plasmids. Annu. Rev. Microbiol. 1994, 48, 713−742. (403) Ikeda, N.; Miyamoto, M.; Adachi, N.; Nakano, M.; Tanaka, T.; Kondo, A. Direct cadaverine production from cellobiose using betaglucosidase displaying Escherichia coli. AMB Express 2013, 3 (1), 67. (404) Ma, W. C.; Cao, W. J.; Zhang, H.; Chen, K. Q.; Li, Y.; Ouyang, P. K. Enhanced cadaverine production from L-lysine using recombinant Escherichia coli co-overexpressing CadA and CadB. Biotechnol. Lett. 2015, 37 (4), 799−806. (405) Qian, Z. G.; Xia, X. X.; Lee, S. Y. Metabolic engineering of Escherichia coli for the production of cadaverine: a five carbon diamine. Biotechnol. Bioeng. 2011, 108 (1), 93−103. (406) Soksawatmaekhin, W.; Kuraishi, A.; Sakata, K.; Kashiwagi, K.; Igarashi, K. Excretion and uptake of cadaverine by CadB and its physiological functions in Escherichia coli. Mol. Microbiol. 2004, 51 (5), 1401−1412.

(407) Foo, J. L.; Ching, C. B.; Chang, M. W.; Leong, S. S. The imminent role of protein engineering in synthetic biology. Biotechnol. Adv. 2012, 30 (3), 541−549. (408) Wang, C.; Oh, M. K.; Liao, J. C. Directed evolution of metabolically engineered Escherichia coli for carotenoid production. Biotechnol. Prog. 2000, 16 (6), 922−926. (409) Leonard, E.; Ajikumar, P. K.; Thayer, K.; Xiao, W. H.; Mo, J. D.; Tidor, B.; Stephanopoulos, G.; Prather, K. L. Combining metabolic and protein engineering of a terpenoid biosynthetic pathway for overproduction and selectivity control. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (31), 13654−13659. (410) Zhang, K.; Sawaya, M. R.; Eisenberg, D. S.; Liao, J. C. Expanding metabolism for biosynthesis of nonnatural alcohols. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (52), 20653−20658. (411) Tang, S. Y.; Fazelinia, H.; Cirino, P. C. AraC regulatory protein mutants with altered effector specificity. J. Am. Chem. Soc. 2008, 130 (15), 5267−5271. (412) Widersten, M. Protein engineering for development of new hydrolytic biocatalysts. Curr. Opin. Chem. Biol. 2014, 21, 42−47. (413) Dias, J. M.; Lemos, P. C.; Serafim, L. S.; Oliveira, C.; Eiroa, M.; Albuquerque, M. G.; Ramos, A. M.; Oliveira, R.; Reis, M. A. Recent advances in polyhydroxyalkanoate production by mixed aerobic cultures: from the substrate to the final product. Macromol. Biosci. 2006, 6 (11), 885−906. (414) Chen, G. Q. A microbial polyhydroxyalkanoates (PHA) based bio- and materials industry. Chem. Soc. Rev. 2009, 38 (8), 2434−2446. (415) Zinn, M.; Witholt, B.; Egli, T. Occurrence, synthesis and medical application of bacterial polyhydroxyalkanoate. Adv. Drug Delivery Rev. 2001, 53 (1), 5−21. (416) Amara, A. A.; Steinbuchel, A.; Rehm, B. H. In vivo evolution of the Aeromonas punctata polyhydroxyalkanoate (PHA) synthase: isolation and characterization of modified PHA synthases with enhanced activity. Appl. Microbiol. Biotechnol. 2002, 59 (4−5), 477−482. (417) Zeng, Z.; Zhu, J.; Chen, L.; Wen, W.; Yu, R. Biosynthesis pathways of ginkgolides. Pharmacogn. Rev. 2013, 7 (1), 47−52. (418) Li, Y. Beyond protein engineering: its applications in synthetic biology. Enzyme Eng. 2012, 1, e103. (419) Zhang, K.; Li, H.; Cho, K. M.; Liao, J. C. Expanding metabolism for total biosynthesis of the nonnatural amino acid L-homoalanine. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (14), 6234−6239. (420) Inoue, K.; Kuramitsu, S.; Aki, K.; Watanabe, Y.; Takagi, T.; Nishigai, M.; Ikai, A.; Kagamiyama, H. Branched-chain amino acid aminotransferase of Escherichia coli: overproduction and properties. J. Biochem. 1988, 104 (5), 777−784. (421) Priestley, N. D.; Robinson, J. A. Purification and catalytic properties of L-valine dehydrogenase from Streptomyces cinnamonensis. Biochem. J. 1989, 261 (3), 853−861. (422) Turnbull, A. P.; Baker, P. J.; Rice, D. W. Analysis of the quaternary structure, substrate specificity, and catalytic mechanism of valine dehydrogenase. J. Biol. Chem. 1997, 272 (40), 25105−25111. (423) Yi, J.; Li, K.; Draths, K. M.; Frost, J. W. Modulation of phosphoenolpyruvate synthase expression increases shikimate pathway product yields in E. coli. Biotechnol. Prog. 2002, 18 (6), 1141−1148. (424) Ran, N.; Frost, J. W. Directed evolution of 2-keto-3-deoxy-6phosphogalactonate aldolase to replace 3-deoxy-D-arabino-heptulosonic acid 7-phosphate synthase. J. Am. Chem. Soc. 2007, 129 (19), 6130−6139. (425) Ran, N.; Draths, K. M.; Frost, J. W. Creation of a shikimate pathway variant. J. Am. Chem. Soc. 2004, 126 (22), 6856−6857. (426) Deng, M. D.; Severson, D. K.; Grund, A. D.; Wassink, S. L.; Burlingame, R. P.; Berry, A.; Running, J. A.; Kunesh, C. A.; Song, L.; Jerrell, T. A.; et al. Metabolic engineering of Escherichia coli for industrial production of glucosamine and N-acetylglucosamine. Metab. Eng. 2005, 7 (3), 201−214. (427) Hungerford, D. S.; Jones, L. C. Glucosamine and chondroitin sulfate are effective in the management of osteoarthritis. J. Arthroplasty 2003, 18, 5−9. (428) Towheed, T. E. Current status of glucosamine therapy in osteoarthritis. Arthritis Rheum. 2003, 49 (4), 601−604. 64


Chemical Reviews

Review

(429) Plumbridge, J. Coordinated regulation of amino sugar biosynthesis and degradation: the NagC repressor acts as both an activator and a repressor for the transcription of the glmUS operon and requires two separated NagC binding sites. EMBO J. 1995, 14 (16), 3958−3965. (430) Plumbridge, J.; Pellegrini, O. Expression of the chitobiose operon of Escherichia coli is regulated by three transcription factors: NagC, ChbR and CAP. Mol. Microbiol. 2004, 52 (2), 437−449. (431) Deng, M. D.; Grund, A. D.; Wassink, S. L.; Peng, S. S.; Nielsen, K. L.; Huckins, B. D.; Burlingame, R. P. Directed evolution and characterization of Escherichia coli glucosamine synthase. Biochimie 2006, 88 (5), 419−429. (432) Bongaerts, J.; Kramer, M.; Muller, U.; Raeven, L.; Wubbolts, M. Metabolic engineering for microbial production of aromatic amino acids and derived compounds. Metab. Eng. 2001, 3 (4), 289−300. (433) Liu, S. P.; Liu, R. X.; Xiao, M. R.; Zhang, L.; Ding, Z. Y.; Gu, Z. H.; Shi, G. Y. A systems level engineered E. coli capable of efficiently producing L-phenylalanine. Process Biochem. 2014, 49 (5), 751−757. (434) Gosset, G.; Yong-Xiao, J.; Berry, A. A direct comparison of approaches for increasing carbon flow to aromatic biosynthesis in Escherichia coli. J. Ind. Microbiol. 1996, 17 (1), 47−52. (435) Baez-Viveros, J. L.; Osuna, J.; Hernandez-Chavez, G.; Soberon, X.; Bolivar, F.; Gosset, G. Metabolic engineering and protein directed evolution increase the yield of L-phenylalanine synthesized from glucose in Escherichia coli. Biotechnol. Bioeng. 2004, 87 (4), 516−524. (436) Chen, X.; Li, S.; Liu, L. Engineering redox balance through cofactor systems. Trends Biotechnol. 2014, 32 (6), 337−343. (437) Akhtar, M. K.; Jones, P. R. Cofactor engineering for enhancing the flux of metabolic pathways. Front. Bioeng. Biotechnol. 2014, 2, 30. (438) Bastian, S.; Liu, X.; Meyerowitz, J. T.; Snow, C. D.; Chen, M. M.; Arnold, F. H. Engineered ketol-acid reductoisomerase and alcohol dehydrogenase enable anaerobic 2-methylpropan-1-ol production at theoretical yield in Escherichia coli. Metab. Eng. 2011, 13 (3), 345−352. (439) Ehsani, M.; Fernandez, M. R.; Biosca, J. A.; Dequin, S. Reversal of coenzyme specificity of 2,3-butanediol dehydrogenase from Saccharomyces cerevisae and in vivo functional analysis. Biotechnol. Bioeng. 2009, 104 (2), 381−389. (440) Martinez, I.; Zhu, J.; Lin, H.; Bennett, G. N.; San, K. Y. Replacing Escherichia coli NAD-dependent glyceraldehyde 3-phosphate dehydrogenase (GAPDH) with a NADP-dependent enzyme from Clostridium acetobutylicum facilitates NADPH dependent pathways. Metab. Eng. 2008, 10 (6), 352−359. (441) Auriol, C.; Bestel-Corre, G.; Claude, J. B.; Soucaille, P.; MeynialSalles, I. Stress-induced evolution of Escherichia coli points to original concepts in respiratory cofactor selectivity. Proc. Natl. Acad. Sci. U. S. A. 2011, 108 (4), 1278−1283. (442) Chemler, J. A.; Fowler, Z. L.; McHugh, K. P.; Koffas, M. A. Improving NADPH availability for natural product biosynthesis in Escherichia coli by metabolic engineering. Metab. Eng. 2010, 12 (2), 96− 104. (443) Wang, Y.; San, K. Y.; Bennett, G. N. Cofactor engineering for advancing chemical biotechnology. Curr. Opin. Biotechnol. 2013, 24 (6), 994−999. (444) Hoelsch, K.; Suhrer, I.; Heusel, M.; Weuster-Botz, D. Engineering of formate dehydrogenase: synergistic effect of mutations affecting cofactor specificity and chemical stability. Appl. Microbiol. Biotechnol. 2013, 97 (6), 2473−2481. (445) Ji, D.; Wang, L.; Hou, S.; Liu, W.; Wang, J.; Wang, Q.; Zhao, Z. K. Creation of bioorthogonal redox systems depending on nicotinamide flucytosine dinucleotide. J. Am. Chem. Soc. 2011, 133 (51), 20857− 20862. (446) Hancock, R. D. Recent patents on vitamin C: opportunities for crop improvement and single-step biological manufacture. Recent Pat. Food Nutr. Agric. 2009, 1 (1), 39−49. (447) Bremus, C.; Herrmann, U.; Bringer-Meyer, S.; Sahm, H. The use of microorganisms in L-ascorbic acid production. J. Biotechnol. 2006, 124 (1), 196−205.

(448) Zou, W.; Liu, L.; Chen, J. Structure, mechanism and regulation of an artificial microbial ecosystem for vitamin C production. Crit. Rev. Microbiol. 2013, 39 (3), 247−255. (449) Gao, L.; Hu, Y.; Liu, J.; Du, G.; Zhou, J.; Chen, J. Stepwise metabolic engineering of Gluconobacter oxydans WSH-003 for the direct production of 2-keto-L-gulonic acid from D-sorbitol. Metab. Eng. 2014, 24, 30−37. (450) Banta, S.; Boston, M.; Jarnagin, A.; Anderson, S. Mathematical modeling of in vitro enzymatic production of 2-Keto-L-gulonic acid using NAD(H) or NADP(H) as cofactors. Metab. Eng. 2002, 4 (4), 273−284. (451) Banta, S.; Swanson, B. A.; Wu, S.; Jarnagin, A.; Anderson, S. Optimizing an artificial metabolic pathway: engineering the cofactor specificity of Corynebacterium 2,5-diketo-D-gluconic acid reductase for use in vitamin C biosynthesis. Biochemistry 2002, 41 (20), 6226−6236. (452) Vemuri, G. N.; Eiteman, M. A.; McEwen, J. E.; Olsson, L.; Nielsen, J. Increasing NADH oxidation reduces overflow metabolism in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (7), 2402−2047. (453) Lee, W. H.; Kim, J. W.; Park, E. H.; Han, N. S.; Kim, M. D.; Seo, J. H. Effects of NADH kinase on NADPH-dependent biotransformation processes in Escherichia coli. Appl. Microbiol. Biotechnol. 2013, 97 (4), 1561−1569. (454) van der Donk, W. A.; Zhao, H. Recent developments in pyridine nucleotide regeneration. Curr. Opin. Biotechnol. 2003, 14 (4), 421−426. (455) Okano, K.; Tanaka, T.; Ogino, C.; Fukuda, H.; Kondo, A. Biotechnological production of enantiomeric pure lactic acid from renewable resources: recent achievements, perspectives, and limits. Appl. Microbiol. Biotechnol. 2010, 85 (3), 413−423. (456) Zhou, L.; Cui, W. J.; Liu, Z. M.; Zhou, Z. M. Metabolic engineering strategies for D-lactate over production in Escherichia coli. J. Chem. Technol. Biotechnol. 2016, 91 (3), 576−584. (457) Zhou, L.; Niu, D. D.; Tian, K. M.; Chen, X. Z.; Prior, B. A.; Shen, W.; Shi, G. Y.; Singh, S.; Wang, Z. X. Genetically switched D-lactate production in Escherichia coli. Metab. Eng. 2012, 14 (5), 560−568. (458) Kim, H. J.; Hou, B. K.; Lee, S. G.; Kim, J. S.; Lee, D. W.; Lee, S. J. Genome-wide analysis of redox reactions reveals metabolic engineering targets for D-lactate overproduction in Escherichia coli. Metab. Eng. 2013, 18, 44−52. (459) Chen, Z.; Wilmanns, M.; Zeng, A. P. Structural synthetic biotechnology: from molecular structure to predictable design for industrial strain development. Trends Biotechnol. 2010, 28 (10), 534− 542. (460) Conrado, R. J.; Wu, G. C.; Boock, J. T.; Xu, H.; Chen, S. Y.; Lebar, T.; Turnsek, J.; Tomsic, N.; Avbelj, M.; Gaber, R.; et al. DNAguided assembly of biosynthetic pathways promotes improved catalytic efficiency. Nucleic Acids Res. 2012, 40 (4), 1879−1889. (461) Delebecque, C. J.; Lindner, A. B.; Silver, P. A.; Aldaye, F. A. Organization of intracellular reactions with rationally designed RNA assemblies. Science 2011, 333 (6041), 470−474. (462) Dueber, J. E.; Wu, G. C.; Malmirchegini, G. R.; Moon, T. S.; Petzold, C. J.; Ullal, A. V.; Prather, K. L.; Keasling, J. D. Synthetic protein scaffolds provide modular control over metabolic flux. Nat. Biotechnol. 2009, 27 (8), 753−759. (463) Lin, C.; Rinker, S.; Wang, X.; Liu, Y.; Seeman, N. C.; Yan, H. In vivo cloning of artificial DNA nanostructures. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (46), 17626−17631. (464) Seo, S. W.; Yang, J.; Min, B. E.; Jang, S.; Lim, J. H.; Lim, H. G.; Kim, S. C.; Kim, S. Y.; Jeong, J. H.; Jung, G. Y. Synthetic biology: tools to design microbes for the production of chemicals and fuels. Biotechnol. Adv. 2013, 31 (6), 811−817. (465) Vos, T.; de la Torre Cortes, P.; van Gulik, W. M.; Pronk, J. T.; Daran-Lapujade, P. Growth-rate dependency of de novo resveratrol production in chemostat cultures of an engineered Saccharomyces cerevisiae strain. Microb. Cell Fact. 2015, 14, 133. (466) Jang, M.; Cai, L.; Udeani, G. O.; Slowing, K. V.; Thomas, C. F.; Beecher, C. W.; Fong, H. H.; Farnsworth, N. R.; Kinghorn, A. D.; Mehta, R. G.; et al. Cancer chemopreventive activity of resveratrol, a natural product derived from grapes. Science 1997, 275 (5297), 218−220. 65


Chemical Reviews

Review

(467) Fremont, L.; Belguendouz, L.; Delpal, S. Antioxidant activity of resveratrol and alcohol-free wine polyphenols related to LDL oxidation and polyunsaturated fatty acids. Life Sci. 1999, 64 (26), 2511−2521. (468) Smoliga, J. M.; Baur, J. A.; Hausenblas, H. A. Resveratrol and health–a comprehensive review of human clinical trials. Mol. Nutr. Food Res. 2011, 55 (8), 1129−1141. (469) Sparvoli, F.; Martin, C.; Scienza, A.; Gavazzi, G.; Tonelli, C. Cloning and molecular analysis of structural genes involved in flavonoid and stilbene biosynthesis in grape (Vitis vinifera L.). Plant Mol. Biol. 1994, 24 (5), 743−755. (470) Ehlting, J.; Buttner, D.; Wang, Q.; Douglas, C. J.; Somssich, I. E.; Kombrink, E. Three 4-coumarate:coenzyme A ligases in Arabidopsis thaliana represent two evolutionarily divergent classes in angiosperms. Plant J. 1999, 19 (1), 9−20. (471) Becker, J. V.; Armstrong, G. O.; van der Merwe, M. J.; Lambrechts, M. G.; Vivier, M. A.; Pretorius, I. S. Metabolic engineering of Saccharomyces cerevisiae for the synthesis of the wine-related antioxidant resveratrol. FEMS Yeast Res. 2003, 4 (1), 79−85. (472) Mei, Y. Z.; Liu, R. X.; Wang, D. P.; Wang, X.; Dai, C. C. Biocatalysis and biotransformation of resveratrol in microorganisms. Biotechnol. Lett. 2015, 37 (1), 9−18. (473) Beekwilder, J.; Wolswinkel, R.; Jonker, H.; Hall, R.; de Vos, C. H.; Bovy, A. Production of resveratrol in recombinant microorganisms. Appl. Environ. Microbiol. 2006, 72 (8), 5670−5672. (474) Wang, L.; Lin, Q.; Loxton, R.; Teo, K. L.; Cheng, G. M. Optimal 1,3-propanediol production: Exploring the trade-off between process yield and feeding rate variation. J. Process Control 2015, 32, 1−9. (475) Kaur, G.; Srivastava, A. K.; Chand, S. Debottlenecking product inhibition in 1,3-propanediol fermentation by In-Situ Product Recovery. Bioresour. Technol. 2015, 197, 451−457. (476) Kraus, G. A. Synthetic methods for the preparation of 1,3propanediol. Clean: Soil, Air, Water 2008, 36 (8), 648−651. (477) Altaras, N. E.; Cameron, D. C. Metabolic engineering of a 1,2propanediol pathway in Escherichia coli. Appl. Environ. Microbiol. 1999, 65 (3), 1180−1185. (478) Berrios-Rivera, S. J.; San, K. Y.; Bennett, G. N. The effect of carbon sources and lactate dehydrogenase deletion on 1,2-propanediol production in Escherichia coli. J. Ind. Microbiol. Biotechnol. 2003, 30 (1), 34−40. (479) Altaras, N. E.; Cameron, D. C. Enhanced production of (R)-1,2propanediol by metabolically engineered Escherichia coli. Biotechnol. Prog. 2000, 16 (6), 940−946. (480) Jain, R.; Sun, X.; Yuan, Q.; Yan, Y. Systematically engineering Escherichia coli for enhanced production of 1,2-propanediol and 1propanol. ACS Synth. Biol. 2015, 4 (6), 746−756. (481) Jung, J. Y.; Choi, E. S.; Oh, M. K. Enhanced production of 1,2propanediol by tpi1 deletion in Saccharomyces cerevisiae. J. Microbiol. Biotechnol. 2008, 18 (11), 1797−1802. (482) Jung, J. Y.; Yun, H. S.; Lee, J.; Oh, M. K. Production of 1,2propanediol from glycerol in Saccharomyces cerevisiae. J. Microbiol. Biotechnol. 2011, 21 (8), 846−853. (483) Clomburg, J. M.; Gonzalez, R. Metabolic engineering of Escherichia coli for the production of 1,2-propanediol from glycerol. Biotechnol. Bioeng. 2011, 108 (4), 867−879. (484) Siebert, D.; Wendisch, V. F. Metabolic pathway engineering for production of 1,2-propanediol and 1-propanol by Corynebacterium glutamicum. Biotechnol. Biofuels 2015, 8, 91. (485) Niimi, S.; Suzuki, N.; Inui, M.; Yukawa, H. Metabolic engineering of 1,2-propanediol pathways in Corynebacterium glutamicum. Appl. Microbiol. Biotechnol. 2011, 90 (5), 1721−1729. (486) Chen, A. H.; Silver, P. A. Designing biological compartmentalization. Trends Cell Biol. 2012, 22 (12), 662−670. (487) Lee, H.; DeLoache, W. C.; Dueber, J. E. Spatial organization of enzymes for metabolic engineering. Metab. Eng. 2012, 14 (3), 242−251. (488) Conrado, R. J.; Varner, J. D.; DeLisa, M. P. Engineering the spatial organization of metabolic enzymes: mimicking nature’s synergy. Curr. Opin. Biotechnol. 2008, 19 (5), 492−499.

(489) Lim, J. H.; Seo, S. W.; Kim, S. Y.; Jung, G. Y. Refactoring redox cofactor regeneration for high-yield biocatalysis of glucose to butyric acid in Escherichia coli. Bioresour. Technol. 2013, 135, 568−573. (490) Dwidar, M.; Park, J. Y.; Mitchell, R. J.; Sang, B. I. The future of butyric acid in industry. Sci. World J. 2012, 2012, 471417. (491) Zhang, C.; Yang, H.; Yang, F.; Ma, Y. Current progress on butyric acid production by fermentation. Curr. Microbiol. 2009, 59 (6), 656−663. (492) Lee, S. M.; Cho, M. O.; Park, C. H.; Chung, Y. C.; Kim, J. H.; Sang, B. I.; Um, Y. Continuous butanol production using suspended and immobilized Clostridium beijerinckii NCIMB 8052 with supplementary butyrate. Energy Fuels 2008, 22 (5), 3459−3464. (493) Joung, S. M.; Kurumbang, N. P.; Sang, B. I.; Oh, M. K. Effects of carbon source and metabolic engineering on butyrate production in Escherichia coli. Korean J. Chem. Eng. 2011, 28 (7), 1587−1592. (494) Baek, J. M.; Mazumdar, S.; Lee, S. W.; Jung, M. Y.; Lim, J. H.; Seo, S. W.; Jung, G. Y.; Oh, M. K. Butyrate production in engineered Escherichia coli with synthetic scaffolds. Biotechnol. Bioeng. 2013, 110 (10), 2790−2794. (495) Werpy, T.; Petersen, G. Top value added chemicals from biomass: Volume I. Results of screening for potential candidates from sugars and synthesis gas 2004.10.2172/926125 (496) Walaszek, Z.; Szemraj, J.; Hanausek, M.; Adams, A. K.; Sherman, U. D-glucaric acid content of various fruits and vegetables and cholesterol-lowering effects of dietary D-glucarate in the rat. Nutr. Res. (N. Y., NY, U. S.) 1996, 16 (4), 673−681. (497) Singh, J.; Gupta, K. P. Calcium glucarate prevents tumor formation in mouse skin. Biomed. Environ. Sci. 2003, 16 (1), 9−16. (498) Singh, J.; Gupta, K. P. Induction of apoptosis by calcium Dglucarate in 7,12-dimethyl benz [a] anthracene-exposed mouse skin. J. Environ. Pathol., Toxicol. Oncol. 2007, 26 (1), 63−73. (499) Moon, T. S.; Yoon, S. H.; Lanza, A. M.; Roy-Mayhew, J. D.; Prather, K. L. Production of glucaric acid from a synthetic pathway in recombinant Escherichia coli. Appl. Environ. Microbiol. 2009, 75 (3), 589−595. (500) Moon, T. S.; Dueber, J. E.; Shiue, E.; Prather, K. L. Use of modular, synthetic scaffolds for improved production of glucaric acid in engineered E. coli. Metab. Eng. 2010, 12 (3), 298−305. (501) Agapakis, C. M.; Boyle, P. M.; Silver, P. A. Natural strategies for the spatial optimization of metabolism in synthetic biology. Nat. Chem. Biol. 2012, 8 (6), 527−535. (502) Avalos, J. L.; Fink, G. R.; Stephanopoulos, G. Compartmentalization of metabolic pathways in yeast mitochondria improves the production of branched-chain alcohols. Nat. Biotechnol. 2013, 31 (4), 335−341. (503) DeLoache, W. C.; Russ, Z. N.; Dueber, J. E. Towards repurposing the yeast peroxisome for compartmentalizing heterologous metabolic pathways. Nat. Commun. 2016, 7, 11152. (504) Meijer, W. H.; Gidijala, L.; Fekken, S.; Kiel, J. A.; van den Berg, M. A.; Lascaris, R.; Bovenberg, R. A.; van der Klei, I. J. Peroxisomes are required for efficient penicillin biosynthesis in Penicillium chrysogenum. Appl. Environ. Microbiol. 2010, 76 (17), 5702−5709. (505) Cameron, J. C.; Wilson, S. C.; Bernstein, S. L.; Kerfeld, C. A. Biogenesis of a bacterial organelle: the carboxysome assembly pathway. Cell 2013, 155 (5), 1131−1140. (506) Ni, J.; Tao, F.; Wang, Y.; Yao, F.; Xu, P. A photoautotrophic platform for the sustainable production of valuable plant natural products from CO2. Green Chem. 2016, 18 (12), 3537−3548. (507) Parsons, J. B.; Dinesh, S. D.; Deery, E.; Leech, H. K.; Brindley, A. A.; Heldt, D.; Frank, S.; Smales, C. M.; Lunsdorf, H.; Rambach, A.; et al. Biochemical and structural insights into bacterial organelle form and biogenesis. J. Biol. Chem. 2008, 283 (21), 14366−14375. (508) Borodina, I.; Nielsen, J. Advances in metabolic engineering of yeast Saccharomyces cerevisiae for production of chemicals. Biotechnol. J. 2014, 9 (5), 609−620. (509) Karabektas, M.; Hosoz, M. Performance and emission characteristics of a diesel engine using isobutanol-diesel fuel blends. Renewable Energy 2009, 34 (6), 1554−1559. 66


Chemical Reviews

Review

(530) Shinoda, K.; Hasegawa, T.; Sato, H.; Shinozaki, M.; Kuramoto, H.; Takamiya, Y.; Sato, T.; Nikaidou, N.; Watanabe, T.; Hoshino, T. Biosynthesis of violacein: a genuine intermediate, protoviolaceinic acid, produced by VioABDE, and insight into VioC function. Chem. Commun. 2007, 40, 4140−4142. (531) August, P. R.; Grossman, T. H.; Minor, C.; Draper, M. P.; MacNeil, I. A.; Pemberton, J. M.; Call, K. M.; Holt, D.; Osburne, M. S. Sequence analysis and functional characterization of the violacein biosynthetic pathway from Chromobacterium violaceum. J. Mol. Microbiol. Biotechnol. 2000, 2 (4), 513−519. (532) Rodrigues, A. L.; Becker, J.; de Souza Lima, A. O.; Porto, L. M.; Wittmann, C. Systems metabolic engineering of Escherichia coli for gram scale production of the antitumor drug deoxyviolacein from glycerol. Biotechnol. Bioeng. 2014, 111 (11), 2280−2289. (533) Zhou, Y. J.; Buijs, N. A.; Zhu, Z.; Gomez, D. O.; Boonsombuti, A.; Siewers, V.; Nielsen, J. Harnessing yeast peroxisomes for biosynthesis of fatty-acid-derived biofuels and chemicals with relieved side-pathway competition. J. Am. Chem. Soc. 2016, 138 (47), 15368− 15377. (534) Zhou, Y. J.; Buijs, N. A.; Zhu, Z.; Qin, J.; Siewers, V.; Nielsen, J. Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories. Nat. Commun. 2016, 7, 11709. (535) Cheng, S.; Liu, Y.; Crowley, C. S.; Yeates, T. O.; Bobik, T. A. Bacterial microcompartments: their properties and paradoxes. BioEssays 2008, 30 (11−12), 1084−1095. (536) Atsumi, S.; Higashide, W.; Liao, J. C. Direct photosynthetic recycling of carbon dioxide to isobutyraldehyde. Nat. Biotechnol. 2009, 27 (12), 1177−1180. (537) Rodriguez, G. M.; Atsumi, S. Isobutyraldehyde production from Escherichia coli by removing aldehyde reductase activity. Microb. Cell Fact. 2012, 11, 90. (538) Liu, Y.; Shin, H. D.; Li, J.; Liu, L. Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects. Appl. Microbiol. Biotechnol. 2015, 99 (3), 1109−1118. (539) Ajikumar, P. K.; Xiao, W. H.; Tyo, K. E.; Wang, Y.; Simeon, F.; Leonard, E.; Mucha, O.; Phon, T. H.; Pfeifer, B.; Stephanopoulos, G. Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science 2010, 330 (6000), 70−74. (540) Juminaga, D.; Baidoo, E. E.; Redding-Johanson, A. M.; Batth, T. S.; Burd, H.; Mukhopadhyay, A.; Petzold, C. J.; Keasling, J. D. Modular engineering of L-tyrosine production in Escherichia coli. Appl. Environ. Microbiol. 2012, 78 (1), 89−98. (541) Biggs, B. W.; De Paepe, B.; Santos, C. N.; De Mey, M.; Kumaran Ajikumar, P. Multivariate modular metabolic engineering for pathway and strain optimization. Curr. Opin. Biotechnol. 2014, 29, 156−162. (542) Lo, T. M.; Teo, W. S.; Ling, H.; Chen, B.; Kang, A.; Chang, M. W. Microbial engineering strategies to improve cell viability for biochemical production. Biotechnol. Adv. 2013, 31 (6), 903−914. (543) Yadav, V. G.; De Mey, M.; Lim, C. G.; Ajikumar, P. K.; Stephanopoulos, G. The future of metabolic engineering and synthetic biology: towards a systematic practice. Metab. Eng. 2012, 14 (3), 233− 241. (544) Dejong, J. M.; Liu, Y.; Bollon, A. P.; Long, R. M.; Jennewein, S.; Williams, D.; Croteau, R. B. Genetic engineering of taxol biosynthetic genes in Saccharomyces cerevisiae. Biotechnol. Bioeng. 2006, 93 (2), 212− 224. (545) Ajikumar, P. K.; Tyo, K.; Carlsen, S.; Mucha, O.; Phon, T. H.; Stephanopoulos, G. Terpenoids: Opportunities for biosynthesis of natural product drugs using engineered microorganisms. Mol. Pharmaceutics 2008, 5 (2), 167−190. (546) Huang, Q.; Roessner, C. A.; Croteau, R.; Scott, A. I. Engineering Escherichia coli for the synthesis of taxadiene, a key intermediate in the biosynthesis of taxol. Bioorg. Med. Chem. 2001, 9 (9), 2237−2242. (547) Engels, B.; Dahm, P.; Jennewein, S. Metabolic engineering of taxadiene biosynthesis in yeast as a first step towards Taxol (Paclitaxel) production. Metab. Eng. 2008, 10 (3−4), 201−206. (548) Xu, P.; Bhan, N.; Koffas, M. A. G. Engineering plant metabolism into microbes: from systems biology to synthetic biology. Curr. Opin. Biotechnol. 2013, 24 (2), 291−299.

(510) Atsumi, S.; Hanai, T.; Liao, J. C. Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 2008, 451 (7174), 86−89. (511) Nielsen, D. R.; Leonard, E.; Yoon, S. H.; Tseng, H. C.; Yuan, C.; Prather, K. L. Engineering alternative butanol production platforms in heterologous bacteria. Metab. Eng. 2009, 11 (4−5), 262−273. (512) Hazelwood, L. A.; Daran, J. M.; van Maris, A. J.; Pronk, J. T.; Dickinson, J. R. The Ehrlich pathway for fusel alcohol production: a century of research on Saccharomyces cerevisiae metabolism. Appl. Environ. Microbiol. 2008, 74 (8), 2259−2266. (513) Chen, X.; Nielsen, K. F.; Borodina, I.; Kielland-Brandt, M. C.; Karhumaa, K. Increased isobutanol production in Saccharomyces cerevisiae by overexpression of genes in valine metabolism. Biotechnol. Biofuels 2011, 4, 21. (514) Kondo, T.; Tezuka, H.; Ishii, J.; Matsuda, F.; Ogino, C.; Kondo, A. Genetic engineering to enhance the Ehrlich pathway and alter carbon flux for increased isobutanol production from glucose by Saccharomyces cerevisiae. J. Biotechnol. 2012, 159 (1−2), 32−37. (515) Lee, W. H.; Seo, S. O.; Bae, Y. H.; Nan, H.; Jin, Y. S.; Seo, J. H. Isobutanol production in engineered Saccharomyces cerevisiae by overexpression of 2-ketoisovalerate decarboxylase and valine biosynthetic enzymes. Bioprocess Biosyst. Eng. 2012, 35 (9), 1467−1475. (516) Bayer, T. S.; Widmaier, D. M.; Temme, K.; Mirsky, E. A.; Santi, D. V.; Voigt, C. A. Synthesis of methyl halides from biomass using engineered microbes. J. Am. Chem. Soc. 2009, 131 (18), 6508−6515. (517) Purdue, P. E.; Lazarow, P. B. Peroxisome biogenesis. Annu. Rev. Cell Dev. Biol. 2001, 17, 701−752. (518) Subramani, S. Components involved in peroxisome import, biogenesis, proliferation, turnover, and movement. Physiol. Rev. 1998, 78 (1), 171−188. (519) van den Bosch, H.; Schutgens, R. B.; Wanders, R. J.; Tager, J. M. Biochemistry of peroxisomes. Annu. Rev. Biochem. 1992, 61, 157−197. (520) van der Klei, I. J.; Yurimoto, H.; Sakai, Y.; Veenhuis, M. The significance of peroxisomes in methanol metabolism in methylotrophic yeast. Biochim. Biophys. Acta, Mol. Cell Res. 2006, 1763 (12), 1453−1462. (521) Rodrigues, A. L.; Trachtmann, N.; Becker, J.; Lohanatha, A. F.; Blotenberg, J.; Bolten, C. J.; Korneli, C.; de Souza Lima, A. O.; Porto, L. M.; Sprenger, G. A.; et al. Systems metabolic engineering of Escherichia coli for production of the antitumor drugs violacein and deoxyviolacein. Metab. Eng. 2013, 20, 29−41. (522) Ferreira, C. V.; Bos, C. L.; Versteeg, H. H.; Justo, G. Z.; Duran, N.; Peppelenbosch, M. P. Molecular mechanism of violacein-mediated human leukemia cell death. Blood 2004, 104 (5), 1459−1464. (523) Kodach, L. L.; Bos, C. L.; Duran, N.; Peppelenbosch, M. P.; Ferreira, C. V.; Hardwick, J. C. Violacein synergistically increases 5fluorouracil cytotoxicity, induces apoptosis and inhibits Akt-mediated signal transduction in human colorectal cancer cells. Carcinogenesis 2006, 27 (3), 508−516. (524) Duran, N.; Justo, G. Z.; Ferreira, C. V.; Melo, P. S.; Cordi, L.; Martins, D. Violacein: properties and biological activities. Biotechnol. Appl. Biochem. 2007, 48 (3), 127−133. (525) Shirata, A.; Tsukamoto, T.; Yasui, H.; Hata, T.; Hayasaka, S.; Kojima, A.; Kato, H. Isolation of bacteria producing bluish-purple pigment and use for dyeing. Jpn. Agric. Res. Q 2000, 34 (2), 131−140. (526) Mendes, A. S.; de Carvalho, J. E.; Duarte, M. C. T.; Duran, N.; Bruns, R. E. Factorial design and response surface optimization of crude violacein for Chromobacterium violaceumi production. Biotechnol. Lett. 2001, 23 (23), 1963−1969. (527) Nakamura, Y.; Asada, C.; Sawada, T. Production of antibacterial violet pigment by psychrotropic bacterium RT102 strain. Biotechnol. Bioprocess Eng. 2003, 8 (1), 37−40. (528) Asamizu, S.; Kato, Y.; Igarashi, Y.; Onaka, H. VioE, a prodeoxyviolacein synthase involved in violacein biosynthesis, is responsible for intramolecular indole rearrangement. Tetrahedron Lett. 2007, 48 (16), 2923−2926. (529) Sanchez, C.; Brana, A. F.; Mendez, C.; Salas, J. A. Reevaluation of the violacein biosynthetic pathway and its relationship to indolocarbazole biosynthesis. ChemBioChem 2006, 7 (8), 1231−1240. 67


Chemical Reviews

Review

(549) Solomon, K. V.; Prather, K. L. The zero-sum game of pathway optimization: emerging paradigms for tuning gene expression. Biotechnol. J. 2011, 6 (9), 1064−1070. (550) Liu, R.; Gao, M.; Yang, Z. H.; Du, G. H. Pinocembrin protects rat brain against oxidation and apoptosis induced by ischemia-reperfusion both in vivo and in vitro. Brain Res. 2008, 1216, 104−115. (551) Shi, L. L.; Chen, B. N.; Gao, M.; Zhang, H. A.; Li, Y. J.; Wang, L.; Du, G. H. The characteristics of therapeutic effect of pinocembrin in transient global brain ischemia/reperfusion rats. Life Sci. 2011, 88 (11− 12), 521−528. (552) Fowler, Z. L.; Koffas, M. A. Biosynthesis and biotechnological production of flavanones: current state and perspectives. Appl. Microbiol. Biotechnol. 2009, 83 (5), 799−808. (553) Horinouchi, S. Combinatorial biosynthesis of plant medicinal polyketides by microorganisms. Curr. Opin. Chem. Biol. 2009, 13 (2), 197−204. (554) Kaneko, M.; Hwang, E. I.; Ohnishi, Y.; Horinouchi, S. Heterologous production of flavanones in Escherichia coli: potential for combinatorial biosynthesis of flavonoids in bacteria. J. Ind. Microbiol. Biotechnol. 2003, 30 (8), 456−461. (555) Wu, J.; Du, G.; Zhou, J.; Chen, J. Metabolic engineering of Escherichia coli for (2S)-pinocembrin production from glucose by a modular metabolic strategy. Metab. Eng. 2013, 16, 48−55. (556) Cao, W.; Ma, W.; Zhang, B.; Wang, X.; Chen, K.; Li, Y.; Ouyang, P. Improved pinocembrin production in Escherichia coli by engineering fatty acid synthesis. J. Ind. Microbiol. Biotechnol. 2016, 43 (4), 557−566. (557) Keasling, J. D. Synthetic biology for synthetic chemistry. ACS Chem. Biol. 2008, 3 (1), 64−76. (558) Woolston, B. M.; Edgar, S.; Stephanopoulos, G. Metabolic engineering: past and future. Annu. Rev. Chem. Biomol. Eng. 2013, 4, 259−288. (559) Hermenean, A.; Ardelean, A.; Stan, M.; Herman, H.; Mihali, C. V.; Costache, M.; Dinischiotu, A. Protective effects of naringenin on carbon tetrachloride-induced acute nephrotoxicity in mouse kidney. Chem.-Biol. Interact. 2013, 205 (2), 138−147. (560) Xu, C.; Chen, J.; Zhang, J.; Hu, X.; Zhou, X.; Lu, Z.; Jiang, H. Naringenin inhibits angiotensin II-induced vascular smooth muscle cells proliferation and migration and decreases neointimal hyperplasia in balloon injured rat carotid arteries through suppressing oxidative stress. Biol. Pharm. Bull. 2013, 36 (10), 1549−1555. (561) Wu, J.; Zhou, T.; Du, G.; Zhou, J.; Chen, J. Modular optimization of heterologous pathways for de novo synthesis of (2S)-naringenin in Escherichia coli. PLoS One 2014, 9 (7), e101492. (562) Miyahisa, I.; Kaneko, M.; Funa, N.; Kawasaki, H.; Kojima, H.; Ohnishi, Y.; Horinouchi, S. Efficient production of (2S)-flavanones by Escherichia coli containing an artificial biosynthetic gene cluster. Appl. Microbiol. Biotechnol. 2005, 68 (4), 498−504. (563) Santos, C. N.; Koffas, M.; Stephanopoulos, G. Optimization of a heterologous pathway for the production of flavonoids from glucose. Metab. Eng. 2011, 13 (4), 392−400. (564) Esvelt, K. M.; Wang, H. H. Genome-scale engineering for systems and synthetic biology. Mol. Syst. Biol. 2013, 9, 641. (565) Si, T.; Xiao, H.; Zhao, H. Rapid prototyping of microbial cell factories via genome-scale engineering. Biotechnol. Adv. 2015, 33 (7), 1420−1432. (566) Gagarinova, A.; Emili, A. Genome-scale genetic manipulation methods for exploring bacterial molecular biology. Mol. BioSyst. 2012, 8 (6), 1626−1638. (567) Alper, H.; Moxley, J.; Nevoigt, E.; Fink, G. R.; Stephanopoulos, G. Engineering yeast transcription machinery for improved ethanol tolerance and production. Science 2006, 314 (5805), 1565−1568. (568) Wang, H. H.; Kim, H.; Cong, L.; Jeong, J.; Bang, D.; Church, G. M. Genome-scale promoter engineering by coselection MAGE. Nat. Methods 2012, 9 (6), 591−593. (569) Wang, H. H.; Isaacs, F. J.; Carr, P. A.; Sun, Z. Z.; Xu, G.; Forest, C. R.; Church, G. M. Programming cells by multiplex genome engineering and accelerated evolution. Nature 2009, 460 (7257), 894−898.

(570) Warner, J. R.; Reeder, P. J.; Karimpour-Fard, A.; Woodruff, L. B. A.; Gill, R. T. Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides. Nat. Biotechnol. 2010, 28 (8), 856−862. (571) Chavez-Bejar, M. I.; Lara, A. R.; Lopez, H.; Hernandez-Chavez, G.; Martinez, A.; Ramirez, O. T.; Bolivar, F.; Gosset, G. Metabolic engineering of Escherichia coli for L-tyrosine production by expression of genes coding for the chorismate mutase domain of the native chorismate mutase-prephenate dehydratase and a cyclohexadienyl dehydrogenase from Zymomonas mobilis. Appl. Environ. Microbiol. 2008, 74 (10), 3284− 3290. (572) Bourke, S. L.; Kohn, J. Polymers derived from the amino acid Ltyrosine: polycarbonates, polyarylates and copolymers with poly(ethylene glycol). Adv. Drug Delivery Rev. 2003, 55 (4), 447−466. (573) Sariaslani, F. S. Development of a combined biological and chemical process for production of industrial aromatics from renewable resources. Annu. Rev. Microbiol. 2007, 61, 51−69. (574) Lutke-Eversloh, T.; Stephanopoulos, G. L-tyrosine production by deregulated strains of Escherichia coli. Appl. Microbiol. Biotechnol. 2007, 75 (1), 103−110. (575) Olson, M. M.; Templeton, L. J.; Suh, W.; Youderian, P.; Sariaslani, F. S.; Gatenby, A. A.; Van Dyk, T. K. Production of tyrosine from sucrose or glucose achieved by rapid genetic changes to phenylalanine-producing Escherichia coli strains. Appl. Microbiol. Biotechnol. 2007, 74 (5), 1031−1040. (576) Lutke-Eversloh, T.; Stephanopoulos, G. Combinatorial pathway analysis for improved L-tyrosine production in Escherichia coli: identification of enzymatic bottlenecks by systematic gene overexpression. Metab. Eng. 2008, 10 (2), 69−77. (577) Yi, J.; Draths, K. M.; Li, K.; Frost, J. W. Altered glucose transport and shikimate pathway product yields in E. coli. Biotechnol. Prog. 2003, 19 (5), 1450−1459. (578) Santos, C. N.; Xiao, W.; Stephanopoulos, G. Rational, combinatorial, and genomic approaches for engineering L-tyrosine production in Escherichia coli. Proc. Natl. Acad. Sci. U. S. A. 2012, 109 (34), 13538−13543. (579) Nissen, T. L.; Kielland-Brandt, M. C.; Nielsen, J.; Villadsen, J. Optimization of ethanol production in Saccharomyces cerevisiae by metabolic engineering of the ammonium assimilation. Metab. Eng. 2000, 2 (1), 69−77. (580) Zaldivar, J.; Nielsen, J.; Olsson, L. Fuel ethanol production from lignocellulose: a challenge for metabolic engineering and process integration. Appl. Microbiol. Biotechnol. 2001, 56 (1−2), 17−34. (581) Liu, H.; Yan, M.; Lai, C.; Xu, L.; Ouyang, P. gTME for improved xylose fermentation of Saccharomyces cerevisiae. Appl. Biochem. Biotechnol. 2010, 160 (2), 574−582. (582) Bai, F. W.; Chen, L. J.; Zhang, Z.; Anderson, W. A.; Moo-Young, M. Continuous ethanol production and evaluation of yeast cell lysis and viability loss under very high gravity medium conditions. J. Biotechnol. 2004, 110 (3), 287−293. (583) Han, G. H.; Shin, H. J.; Kim, S. W. Optimization of bio-indigo production by recombinant E. coli harboring fmo gene. Enzyme Microb. Technol. 2008, 42 (7), 617−623. (584) Choi, H. S.; Kim, J. K.; Cho, E. H.; Kim, Y. C.; Kim, J. I.; Kim, S. W. A novel flavin-containing monooxygenase from Methylophaga sp strain SK1 and its indigo synthesis in Escherichia coli. Biochem. Biophys. Res. Commun. 2003, 306 (4), 930−936. (585) Rui, L. Y.; Reardon, K. F.; Wood, T. K. Protein engineering of toluene ortho-monooxygenase of Burkholderia cepacia G4 for regiospecific hydroxylation of indole to form various indigoid compounds. Appl. Microbiol. Biotechnol. 2005, 66 (4), 422−429. (586) McClay, K.; Boss, C.; Keresztes, I.; Steffan, R. J. Mutations of toluene-4-monooxygenase that alter regiospecificity of indole oxidation and lead to production of novel indigoid pigments. Appl. Environ. Microbiol. 2005, 71 (9), 5476−5483. (587) Eaton, R. W.; Chapman, P. J. Formation of indigo and related compounds from indolecarboxylic acids by aromatic acid-degrading bacteria: chromogenic reactions for cloning genes encoding dioxygenases that act on aromatic acids. J. Bacteriol. 1995, 177 (23), 6983−6988. 68


Chemical Reviews

Review

(588) Maugard, T.; Enaud, E.; de la Sayette, A.; Choisy, P.; Legoy, M. D. Beta-glucosidase-catalyzed hydrolysis of indican from leaves of Polygonum tinctorium. Biotechnol. Prog. 2002, 18 (5), 1104−1108. (589) Murdock, D.; Ensley, B. D.; Serdar, C.; Thalen, M. Construction of metabolic operons catalyzing the de novo biosynthesis of indigo in Escherichia coli. Bio/Technology 1993, 11 (3), 381−386. (590) Doukyu, N.; Nakano, T.; Okuyama, Y.; Aono, R. Isolation of an Acinetobacter sp. ST-550 which produces a high level of indigo in a water-organic solvent two-phase system containing high levels of indole. Appl. Microbiol. Biotechnol. 2002, 58 (4), 543−546. (591) Bae, J. W.; Han, J. H.; Park, M. S.; Lee, S. G.; Lee, E. Y.; Jeong, Y. J.; Park, S. Development of recombinant Pseudomonas putida containing homologous styrene monooxygenase genes for the production of (S)styrene oxide. Biotechnol. Bioprocess Eng. 2006, 11 (6), 530−537. (592) Doukyu, N.; Aono, R. Biodegradation of indole at high concentration by persolvent fermentation with Pseudomonas sp. ST-200. Extremophiles 1997, 1 (2), 100−105. (593) Jeong, J.; Cho, N.; Jung, D.; Bang, D. Genome-scale genetic engineering in Escherichia coli. Biotechnol. Adv. 2013, 31 (6), 804−810. (594) Kumar, P.; Barrett, D. M.; Delwiche, M. J.; Stroeve, P. Methods for pretreatment of lignocellulosic biomass for efficient hydrolysis and biofuel production. Ind. Eng. Chem. Res. 2009, 48 (8), 3713−3729. (595) Olsson, L.; HahnHagerdal, B. Fermentation of lignocellulosic hydrolysates for ethanol production. Enzyme Microb. Technol. 1996, 18 (5), 312−331. (596) Dien, B. S.; Cotta, M. A.; Jeffries, T. W. Bacteria engineered for fuel ethanol production: current status. Appl. Microbiol. Biotechnol. 2003, 63 (3), 258−266. (597) Ohta, K.; Beall, D. S.; Mejia, J. P.; Shanmugam, K. T.; Ingram, L. O. Genetic improvement of Escherichia coli for ethanol production: chromosomal integration of Zymomonas mobilis genes encoding pyruvate decarboxylase and alcohol dehydrogenase II. Appl. Environ. Microbiol. 1991, 57 (4), 893−900. (598) Sandoval, N. R. Genome engineering to improve acetate and cellulosic hydrolysate tolerance in E. coli for improved cellulosic biofuel production. University of Colorado, 2011. (599) Wright, D. A.; Thibodeau-Beganny, S.; Sander, J. D.; Winfrey, R. J.; Hirsh, A. S.; Eichtinger, M.; Fu, F.; Porteus, M. H.; Dobbs, D.; Voytas, D. F.; et al. Standardized reagents and protocols for engineering zinc finger nucleases by modular assembly. Nat. Protoc. 2006, 1 (3), 1637− 1652. (600) Maeder, M. L.; Thibodeau-Beganny, S.; Osiak, A.; Wright, D. A.; Anthony, R. M.; Eichtinger, M.; Jiang, T.; Foley, J. E.; Winfrey, R. J.; Townsend, J. A.; et al. Rapid ″open-source″ engineering of customized zinc-finger nucleases for highly efficient gene modification. Mol. Cell 2008, 31 (2), 294−301. (601) Sander, J. D.; Dahlborg, E. J.; Goodwin, M. J.; Cade, L.; Zhang, F.; Cifuentes, D.; Curtin, S. J.; Blackburn, J. S.; Thibodeau-Beganny, S.; Qi, Y.; et al. Selection-free zinc-finger-nuclease engineering by contextdependent assembly (CoDA). Nat. Methods 2011, 8 (1), 67−69. (602) Zhang, F.; Cong, L.; Lodato, S.; Kosuri, S.; Church, G. M.; Arlotta, P. Efficient construction of sequence-specific TAL effectors for modulating mammalian transcription. Nat. Biotechnol. 2011, 29 (2), 149−153. (603) Reyon, D.; Tsai, S. Q.; Khayter, C.; Foden, J. A.; Sander, J. D.; Joung, J. K. FLASH assembly of TALENs for high-throughput genome editing. Nat. Biotechnol. 2012, 30 (5), 460−465. (604) Schmid-Burgk, J. L.; Schmidt, T.; Kaiser, V.; Honing, K.; Hornung, V. A ligation-independent cloning technique for highthroughput assembly of transcription activator-like effector genes. Nat. Biotechnol. 2012, 31 (1), 76−81. (605) Hsu, P. D.; Lander, E. S.; Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 2014, 157 (6), 1262−1278. (606) Hsu, P. D.; Scott, D. A.; Weinstein, J. A.; Ran, F. A.; Konermann, S.; Agarwala, V.; Li, Y.; Fine, E. J.; Wu, X.; Shalem, O.; et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 2013, 31 (9), 827−832.

(607) Smith, J.; Bibikova, M.; Whitby, F. G.; Reddy, A. R.; Chandrasegaran, S.; Carroll, D. Requirements for double-strand cleavage by chimeric restriction enzymes with zinc finger DNArecognition domains. Nucleic Acids Res. 2000, 28 (17), 3361−3369. (608) Malphettes, L.; Freyvert, Y.; Chang, J.; Liu, P. Q.; Chan, E.; Miller, J. C.; Zhou, Z.; Nguyen, T.; Tsai, C.; Snowden, A. W.; et al. Highly efficient deletion of FUT8 in CHO cell lines using zinc-finger nucleases yields cells that produce completely nonfucosylated antibodies. Biotechnol. Bioeng. 2010, 106 (5), 774−783. (609) Yamane-Ohnuki, N.; Kinoshita, S.; Inoue-Urakubo, M.; Kusunoki, M.; Iida, S.; Nakano, R.; Wakitani, M.; Niwa, R.; Sakurada, M.; Uchida, K.; et al. Establishment of FUT8 knockout Chinese hamster ovary cells: an ideal host cell line for producing completely defucosylated antibodies with enhanced antibody-dependent cellular cytotoxicity. Biotechnol. Bioeng. 2004, 87 (5), 614−622. (610) Li, T.; Huang, S.; Jiang, W. Z.; Wright, D.; Spalding, M. H.; Weeks, D. P.; Yang, B. TAL nucleases (TALNs): hybrid proteins composed of TAL effectors and FokI DNA-cleavage domain. Nucleic Acids Res. 2011, 39 (1), 359−372. (611) Boch, J.; Scholze, H.; Schornack, S.; Landgraf, A.; Hahn, S.; Kay, S.; Lahaye, T.; Nickstadt, A.; Bonas, U. Breaking the code of DNA binding specificity of TAL-type III effectors. Science 2009, 326 (5959), 1509−1512. (612) Moscou, M. J.; Bogdanove, A. J. A simple cipher governs DNA recognition by TAL effectors. Science 2009, 326 (5959), 1501. (613) Tesson, L.; Usal, C.; Menoret, S.; Leung, E.; Niles, B. J.; Remy, S.; Santiago, Y.; Vincent, A. I.; Meng, X.; Zhang, L.; et al. Knockout rats generated by embryo microinjection of TALENs. Nat. Biotechnol. 2011, 29 (8), 695−696. (614) Niu, Y. F.; Wang, X.; Hu, D. X.; Balamurugan, S.; Li, D. W.; Yang, W. D.; Liu, J. S.; Li, H. Y. Molecular characterization of a glycerol-3phosphate acyltransferase reveals key features essential for triacylglycerol production in Phaeodactylum tricornutum. Biotechnol. Biofuels 2016, 9, 60. (615) Daboussi, F.; Leduc, S.; Marechal, A.; Dubois, G.; Guyot, V.; Perez-Michaut, C.; Amato, A.; Falciatore, A.; Juillerat, A.; Beurdeley, M.; et al. Genome engineering empowers the diatom Phaeodactylum tricornutum for biotechnology. Nat. Commun. 2014, 5, 3831. (616) Jansen, R.; van Embden, J. D. A.; Gaastra, W.; Schouls, L. M. Identification of genes that are associated with DNA repeats in prokaryotes. Mol. Microbiol. 2002, 43 (6), 1565−1575. (617) Jinek, M.; Chylinski, K.; Fonfara, I.; Hauer, M.; Doudna, J. A.; Charpentier, E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 2012, 337 (6096), 816−821. (618) La Russa, M. F.; Qi, L. S. The new state of the art: Cas9 for gene activation and repression. Mol. Cell. Biol. 2015, 35 (22), 3800−3809. (619) Zalatan, J. G.; Lee, M. E.; Almeida, R.; Gilbert, L. A.; Whitehead, E. H.; La Russa, M.; Tsai, J. C.; Weissman, J. S.; Dueber, J. E.; Qi, L. S.; et al. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 2015, 160 (1−2), 339−350. (620) Das, A.; Yoon, S. H.; Lee, S. H.; Kim, J. Y.; Oh, D. K.; Kim, S. W. An update on microbial carotenoid production: application of recent metabolic engineering tools. Appl. Microbiol. Biotechnol. 2007, 77 (3), 505−512. (621) Nam, H. K.; Choi, J. G.; Lee, J. H.; Kim, S. W.; Oh, D. K. Increase in the production of beta-carotene in recombinant Escherichia coli cultured in a chemically defined medium supplemented with amino acids. Biotechnol. Lett. 2013, 35 (2), 265−571. (622) Yang, J.; Guo, L. Biosynthesis of beta-carotene in engineered E. coli using the MEP and MVA pathways. Microb. Cell Fact. 2014, 13, 160. (623) Zhao, J.; Li, Q.; Sun, T.; Zhu, X.; Xu, H.; Tang, J.; Zhang, X.; Ma, Y. Engineering central metabolic modules of Escherichia coli for improving beta-carotene production. Metab. Eng. 2013, 17, 42−50. (624) Li, Y.; Lin, Z.; Huang, C.; Zhang, Y.; Wang, Z.; Tang, Y. J.; Chen, T.; Zhao, X. Metabolic engineering of Escherichia coli using CRISPRCas9 meditated genome editing. Metab. Eng. 2015, 31, 13−21. (625) Rea, P. A. Plant ATP-binding cassette transporters. Annu. Rev. Plant Biol. 2007, 58, 347−375. 69


Chemical Reviews

Review

(626) Qiu, J.; Zhuo, Y.; Zhu, D.; Zhou, X.; Zhang, L.; Bai, L.; Deng, Z. Overexpression of the ABC transporter AvtAB increases avermectin production in Streptomyces avermitilis. Appl. Microbiol. Biotechnol. 2011, 92 (2), 337−345. (627) Davidson, A. L.; Dassa, E.; Orelle, C.; Chen, J. Structure, function, and evolution of bacterial ATP-binding cassette systems. Microbiol. Mol. Biol. Rev. 2008, 72 (2), 317−364. (628) Koronakis, V.; Sharff, A.; Koronakis, E.; Luisi, B.; Hughes, C. Crystal structure of the bacterial membrane protein TolC central to multidrug efflux and protein export. Nature 2000, 405 (6789), 914−919. (629) Higgins, C. F. Multiple molecular mechanisms for multidrug resistance transporters. Nature 2007, 446 (7137), 749−757. (630) Huffer, S.; Roche, C. M.; Blanch, H. W.; Clark, D. S. Escherichia coli for biofuel production: bridging the gap from promise to practice. Trends Biotechnol. 2012, 30 (10), 538−545. (631) Rees, D. C.; Johnson, E.; Lewinson, O. ABC transporters: the power to change. Nat. Rev. Mol. Cell Biol. 2009, 10 (3), 218−227. (632) Ikeda, H.; Omura, S. Avermectin Biosynthesis. Chem. Rev. 1997, 97 (7), 2591−2610. (633) Ikeda, H.; Ishikawa, J.; Hanamoto, A.; Shinose, M.; Kikuchi, H.; Shiba, T.; Sakaki, Y.; Hattori, M.; Omura, S. Complete genome sequence and comparative analysis of the industrial microorganism Streptomyces avermitilis. Nat. Biotechnol. 2003, 21 (5), 526−531. (634) Ikeda, H.; Nonomiya, T.; Omura, S. Organization of biosynthetic gene cluster for avermectin in Streptomyces avermitilis: analysis of enzymatic domains in four polyketide synthases. J. Ind. Microbiol. Biotechnol. 2001, 27 (3), 170−176. (635) Zhao, X. J.; Wang, Q. X.; Guo, W. Q.; Cai, Y. J.; Wang, C.; Wang, S. W.; Xiang, S. Y.; Song, Y. Overexpression of metK shows different effects on avermectin production in various Streptomyces avermitilis strains. World J. Microbiol. Biotechnol. 2013, 29 (10), 1869−1875. (636) Zhuo, Y.; Zhang, W.; Chen, D.; Gao, H.; Tao, J.; Liu, M.; Gou, Z.; Zhou, X.; Ye, B. C.; Zhang, Q.; et al. Reverse biological engineering of hrdB to enhance the production of avermectins in an industrial strain of Streptomyces avermitilis. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (25), 11250−11254. (637) He, F.; Liu, W.; Sun, D.; Luo, S.; Chen, Z.; Wen, Y.; Li, J. Engineering of the TetR family transcriptional regulator SAV151 and its target genes increases avermectin production in Streptomyces avermitilis. Appl. Microbiol. Biotechnol. 2014, 98 (1), 399−409. (638) Alonso-Gutierrez, J.; Chan, R.; Batth, T. S.; Adams, P. D.; Keasling, J. D.; Petzold, C. J.; Lee, T. S. Metabolic engineering of Escherichia coli for limonene and perillyl alcohol production. Metab. Eng. 2013, 19, 33−41. (639) Keasling, J. D. Manufacturing molecules through metabolic engineering. Science 2010, 330 (6009), 1355−1358. (640) Boucher, Y.; Doolittle, W. F. The role of lateral gene transfer in the evolution of isoprenoid biosynthesis pathways. Mol. Microbiol. 2000, 37 (4), 703−716. (641) Peralta-Yahya, P. P.; Ouellet, M.; Chan, R.; Mukhopadhyay, A.; Keasling, J. D.; Lee, T. S. Identification and microbial production of a terpene-based advanced biofuel. Nat. Commun. 2011, 2, 483. (642) Redding-Johanson, A. M.; Batth, T. S.; Chan, R.; Krupa, R.; Szmidt, H. L.; Adams, P. D.; Keasling, J. D.; Lee, T. S.; Mukhopadhyay, A.; Petzold, C. J. Targeted proteomics for metabolic pathway optimization: application to terpene production. Metab. Eng. 2011, 13 (2), 194−203. (643) Willrodt, C.; David, C.; Cornelissen, S.; Buhler, B.; Julsing, M. K.; Schmid, A. Engineering the productivity of recombinant Escherichia coli for limonene formation from glycerol in minimal media. Biotechnol. J. 2014, 9 (8), 1000−1012. (644) Dunlop, M. J.; Dossani, Z. Y.; Szmidt, H. L.; Chu, H. C.; Lee, T. S.; Keasling, J. D.; Hadi, M. Z.; Mukhopadhyay, A. Engineering microbial biofuel tolerance and export using efflux pumps. Mol. Syst. Biol. 2011, 7, 487. (645) Antecka, A.; Blatkiewicz, M.; Bizukojc, M.; Ledakowicz, S. Morphology engineering of basidiomycetes for improved laccase biosynthesis. Biotechnol. Lett. 2016, 38 (4), 667−672.

(646) Driouch, H.; Hansch, R.; Wucherpfennig, T.; Krull, R.; Wittmann, C. Improved enzyme production by bio-pellets of Aspergillus niger: targeted morphology engineering using titanate microparticles. Biotechnol. Bioeng. 2012, 109 (2), 462−471. (647) Driouch, H.; Sommer, B.; Wittmann, C. Morphology engineering of Aspergillus niger for improved enzyme production. Biotechnol. Bioeng. 2010, 105 (6), 1058−1068. (648) Muller, C.; McIntyre, M.; Hansen, K.; Nielsen, J. Metabolic engineering of the morphology of Aspergillus oryzae by altering chitin synthesis. Appl. Environ. Microbiol. 2002, 68 (4), 1827−1836. (649) Liu, H.; Wang, P.; Gong, G.; Wang, L.; Zhao, G.; Zheng, Z. Morphology engineering of Penicillium chrysogenum by RNA silencing of chitin synthase gene. Biotechnol. Lett. 2013, 35 (3), 423−429. (650) O’Cleirigh, C.; Casey, J. T.; Walsh, P. K.; O’Shea, D. G. Morphological engineering of Streptomyces hygroscopicus var. geldanus: regulation of pellet morphology through manipulation of broth viscosity. Appl. Microbiol. Biotechnol. 2005, 68 (3), 305−310. (651) Jiang, X. R.; Chen, G. Q. Morphology engineering of bacteria for bio-production. Biotechnol. Adv. 2016, 34 (4), 435−440. (652) Walisko, R.; Krull, R.; Schrader, J.; Wittmann, C. Microparticle based morphology engineering of filamentous microorganisms for industrial bio-production. Biotechnol. Lett. 2012, 34 (11), 1975−1982. (653) McIntyre, M.; Muller, C.; Dynesen, J.; Nielsen, J. Metabolic engineering of the morphology of Aspergillus. Adv. Biochem. Eng. Biotechnol. 2001, 73, 103−128. (654) Kaup, B. A.; Ehrich, K.; Pescheck, M.; Schrader, J. Microparticleenhanced cultivation of filamentous microorganisms: increased chloroperoxidase formation by Caldariomyces f umago as an example. Biotechnol. Bioeng. 2008, 99 (3), 491−498. (655) Norouzian, D.; Akbarzadeh, A.; Scharer, J. M.; Moo Young, M. Fungal glucoamylases. Biotechnol. Adv. 2006, 24 (1), 80−85. (656) Talabardon, M.; Yang, S. T. Production of GFP and glucoamylase by recombinant Aspergillus niger: effects of fermentation conditions on fungal morphology and protein secretion. Biotechnol. Prog. 2005, 21 (5), 1389−1400. (657) Jiang, X. R.; Wang, H.; Shen, R.; Chen, G. Q. Engineering the bacterial shapes for enhanced inclusion bodies accumulation. Metab. Eng. 2015, 29, 227−237. (658) Le Meur, S.; Zinn, M.; Egli, T.; Thony-Meyer, L.; Ren, Q. Poly(4-hydroxybutyrate) (P4HB) production in recombinant Escherichia coli: P4HB synthesis is uncoupled with cell growth. Microb. Cell Fact. 2013, 12, 123. (659) Chen, G. Q.; Patel, M. K. Plastics derived from biological sources: present and future: a technical and environmental review. Chem. Rev. 2012, 112 (4), 2082−2099. (660) Valentin, H. E.; Dennis, D. Production of poly(3-hydroxybutyrate-co-4-hydroxybutyrate) in recombinant Escherichia coli grown on glucose. J. Biotechnol. 1997, 58 (1), 33−38. (661) Wang, Y.; Wu, H.; Jiang, X.; Chen, G. Q. Engineering Escherichia coli for enhanced production of poly(3-hydroxybutyrate-co-4-hydroxybutyrate) in larger cellular space. Metab. Eng. 2014, 25, 183−193. (662) Hein, S.; Sohling, B.; Gottschalk, G.; Steinbuchel, A. Biosynthesis of poly(4-hydroxybutyric acid) by recombinant strains of Escherichia coli. FEMS Microbiol. Lett. 1997, 153 (2), 411−418. (663) Song, H.; Ding, M. Z.; Jia, X. Q.; Ma, Q.; Yuan, Y. J. Synthetic microbial consortia: from systematic analysis to construction and applications. Chem. Soc. Rev. 2014, 43 (20), 6954−6981. (664) You, L.; Cox, R. S., 3rd; Weiss, R.; Arnold, F. H. Programmed population control by cell-cell communication and regulated killing. Nature 2004, 428 (6985), 868−871. (665) Liu, C.; Fu, X.; Liu, L.; Ren, X.; Chau, C. K.; Li, S.; Xiang, L.; Zeng, H.; Chen, G.; Tang, L. H.; et al. Sequential establishment of stripe patterns in an expanding cell population. Science 2011, 334 (6053), 238−241. (666) Basu, S.; Gerchman, Y.; Collins, C. H.; Arnold, F. H.; Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 2005, 434 (7037), 1130−1134. 70


Chemical Reviews

Review

(667) Shou, W.; Ram, S.; Vilar, J. M. Synthetic cooperation in engineered yeast populations. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (6), 1877−1882. (668) Balagadde, F. K.; Song, H.; Ozaki, J.; Collins, C. H.; Barnet, M.; Arnold, F. H.; Quake, S. R.; You, L. A synthetic Escherichia coli predatorprey ecosystem. Mol. Syst. Biol. 2008, 4, 187. (669) Kerr, B.; Riley, M. A.; Feldman, M. W.; Bohannan, B. J. Local dispersal promotes biodiversity in a real-life game of rock-paper-scissors. Nature 2002, 418 (6894), 171−174. (670) Chuang, J. S.; Rivoire, O.; Leibler, S. Simpson’s paradox in a synthetic microbial system. Science 2009, 323 (5911), 272−275. (671) Zhou, J.; Ma, Q.; Yi, H.; Wang, L. L.; Song, H.; Yuan, Y. J. Metabolome profiling reveals metabolic cooperation between Bacillus megaterium and Ketogulonicigenium vulgare during induced swarm motility. Appl. Environ. Microbiol. 2011, 77 (19), 7023−7030. (672) Du, J.; Bai, W.; Song, H.; Yuan, Y. J. Combinational expression of sorbose/sorbosone dehydrogenases and cofactor pyrroloquinoline quinone increases 2-keto-L-gulonic acid production in Ketogulonigenium vulgare-Bacillus cereus consortium. Metab. Eng. 2013, 19, 50−56. (673) Gupta, A.; Reizman, I. M.; Reisch, C. R.; Prather, K. L. Dynamic regulation of metabolic flux in engineered bacteria using a pathwayindependent quorum-sensing circuit. Nat. Biotechnol. 2017, 35 (3), 273−279. (674) Koizumi, S.; Endo, T.; Tabata, K.; Ozaki, A. Large-scale production of UDP-galactose and globotriose by coupling metabolically engineered bacteria. Nat. Biotechnol. 1998, 16 (9), 847−850. (675) Park, E. Y.; Naruse, K.; Kato, T. One-pot bioethanol production from cellulose by co-culture of Acremonium cellulolyticus and Saccharomyces cerevisiae. Biotechnol. Biofuels 2012, 5 (1), 64. (676) Minty, J. J.; Singer, M. E.; Scholz, S. A.; Bae, C. H.; Ahn, J. H.; Foster, C. E.; Liao, J. C.; Lin, X. N. Design and characterization of synthetic fungal-bacterial consortia for direct production of isobutanol from cellulosic biomass. Proc. Natl. Acad. Sci. U. S. A. 2013, 110 (36), 14592−14597. (677) Phelan, V. V.; Liu, W. T.; Pogliano, K.; Dorrestein, P. C. Microbial metabolic exchange-the chemotype-to-phenotype link. Nat. Chem. Biol. 2011, 8 (1), 26−35. (678) Antoine, T.; Bosso, C.; Heyraud, A.; Samain, E. Large scale in vivo synthesis of globotriose and globotetraose by high cell density culture of metabolically engineered Escherichia coli. Biochimie 2005, 87 (2), 197−203. (679) Chi, H. W.; Wang, R.; Ichikawa, Y. Regeneration of sugar nucleotide for enzymic oligosaccharide synthesis: use of Gal-1phosphate uridyltransferase in the regeneration of UDP-galactose, UDP-2-deoxygalactose, and UDP-galactosamine. J. Org. Chem. 1992, 57, 4343−4344. (680) Ichikawa, Y.; Wang, R.; Wong, C. H. Regeneration of sugar nucleotide for enzymatic oligosaccharide synthesis. Methods Enzymol. 1994, 247, 107−127. (681) Endo, T.; Koizumi, S.; Tabata, K.; Ozaki, A. Large-scale production of CMP-NeuAc and sialylated oligosaccharides through bacterial coupling. Appl. Microbiol. Biotechnol. 2000, 53 (3), 257−261. (682) Endo, T.; Koizumi, S.; Tabata, K.; Kakita, S.; Ozaki, A. Largescale production of the carbohydrate portion of the sialyl-Tn epitope, alpha-Neup5Ac-(2 -> 6)-D-GalpNAc, through bacterial coupling. Carbohydr. Res. 2001, 330 (4), 439−443. (683) Schuster, M.; Sexton, D. J.; Diggle, S. P.; Greenberg, E. P. Acylhomoserine lactone quorum sensing: From evolution to application. Annu. Rev. Microbiol. 2013, 67 (1), 43−63. (684) Waters, C. M.; Bassler, B. L. Quorum sensing: Cell-to-cell communication in bacteria. Annu. Rev. Cell Dev. Biol. 2005, 21, 319−346. (685) Unfer, V.; Carlomagno, G.; Dante, G.; Facchinetti, F. Effects of myo-inositol in women with PCOS: a systematic review of randomized controlled trials. Gynecol. Endocrinol. 2012, 28 (7), 509−515. (686) Santamaria, A.; Giordano, D.; Corrado, F.; Pintaudi, B.; Interdonato, M. L.; Di Vieste, G.; Di Benedetto, A.; D’Anna, R. Oneyear effects of myo-inositol supplementation in postmenopausal women with metabolic syndrome. Climacteric 2012, 15 (5), 490−495.

(687) Pintaudi, B.; Di Vieste, G.; Bonomo, M. The effectiveness of myo-inositol and D-chiro inositol treatment in type 2 diabetes. Int. J. Endocrinol. 2016, 24, 1−5. (688) Shiue, E.; Prather, K. L. J. Improving D-glucaric acid production from myo-inositol in E. coli by increasing MIOX stability and myoinositol transport. Metab. Eng. 2014, 22, 22−31. (689) Yamaoka, M.; Osawa, S.; Morinaga, T.; Takenaka, S.; Yoshida, K. A cell factory of Bacillus subtilis engineered for the simple bioconversion of myo-inositol to scyllo-inositol, a potential therapeutic agent for Alzheimer’s disease. Microb. Cell Fact. 2011, 10, 10. (690) Zhu, Y.; Eiteman, M. A.; DeWitt, K.; Altman, E. Homolactate fermentation by metabolically engineered Escherichia coli strains. Appl. Environ. Microbiol. 2007, 73 (2), 456−464. (691) Niu, D. D.; Tian, K. M.; Prior, B. A.; Wang, M.; Wang, Z. X.; Lu, F. P.; Singh, S. Highly efficient L-lactate production using engineered Escherichia coli with dissimilar temperature optima for L-lactate formation and cell growth. Microb. Cell Fact. 2014, 13, 7810.1186/ 1475-2859-13-78 (692) Saini, M.; Wang, Z. W.; Chiang, C. J.; Chao, Y. P. Metabolic engineering of Escherichia coli for production of butyric acid. J. Agric. Food Chem. 2014, 62 (19), 4342−4348. (693) Kataoka, N.; Vangnai, A. S.; Pongtharangkul, T.; Yakushi, T.; Matsushita, K. Butyrate production under aerobic growth conditions by engineered Escherichia coli. J. Biosci. Bioeng. 2017, 123 (5), 562−568. (694) Yu, J. L.; Xia, X. X.; Zhong, J. J.; Qian, Z. G. Direct biosynthesis of adipic acid from a synthetic pathway in recombinant Escherichia coli. Biotechnol. Bioeng. 2014, 111 (12), 2580−2586. (695) Gao, Q.; Liu, J.; Liu, L. M. Relationship between morphology and itaconic acid production by Aspergillus terreus. J. Microbiol. Biotechnol. 2014, 24 (2), 168−176. (696) Chu, H. S.; Kim, Y. S.; Lee, C. M.; Lee, J. H.; Jung, W. S.; Ahn, J. H.; Song, S. H.; Choi, I. S.; Cho, K. M. Metabolic engineering of 3hydroxypropionic acid biosynthesis in Escherichia coli. Biotechnol. Bioeng. 2015, 112 (2), 356−364. (697) Deng, Y.; Mao, Y.; Zhang, X. J. Metabolic engineering of E. coli for efficient production of glycolic acid from glucose. Biochem. Eng. J. 2015, 103, 256−262. (698) Park, J. H.; Jang, Y. S.; Lee, J. W.; Lee, S. Y. Escherichia coli W as a new platform strain for the enhanced production of L-valine by systems metabolic engineering. Biotechnol. Bioeng. 2011, 108 (5), 1140−1147. (699) Yuan, P. P.; Cao, W. J.; Wang, Z.; Chen, K. Q.; Li, Y.; Ouyang, P. K. Enhancement of L-phenylalanine production by engineered Escherichia coli using phased exponential L-tyrosine feeding combined with nitrogen source optimization. J. Biosci. Bioeng. 2015, 120 (1), 36− 40. (700) Nakamura, C. E.; Whited, G. M. Metabolic engineering for the microbial production of 1,3-propanediol. Curr. Opin. Biotechnol. 2003, 14 (5), 454−459. (701) Park, S. Y.; Kim, B.; Lee, S.; Oh, M.; Won, J. I.; Lee, J. Increased 2,3-butanediol production by changing codon usages in Escherichia coli. Biotechnol. Appl. Biochem. 2014, 61 (5), 535−540. (702) Baez, A.; Cho, K. M.; Liao, J. C. High-flux isobutanol production using engineered Escherichia coli: a bioreactor study with in situ product removal. Appl. Microbiol. Biotechnol. 2011, 90 (5), 1681−1690. (703) Dellomonaco, C.; Clomburg, J. M.; Miller, E. N.; Gonzalez, R. Engineered reversal of the beta-oxidation cycle for the synthesis of fuels and chemicals. Nature 2011, 476 (7360), 355−359. (704) Tan, Z.; Yoon, J. M.; Nielsen, D. R.; Shanks, J. V.; Jarboe, L. R. Membrane engineering via trans unsaturated fatty acids production improves Escherichia coli robustness and production of biorenewables. Metab. Eng. 2016, 35, 105−113. (705) Kajiwara, S.; Fraser, P. D.; Kondo, K.; Misawa, N. Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid biosynthesis in Escherichia coli. Biochem. J. 1997, 324, 421− 426. (706) Sun, T.; Miao, L.; Li, Q.; Dai, G.; Lu, F.; Liu, T.; Zhang, X.; Ma, Y. Production of lycopene by metabolically-engineered Escherichia coli. Biotechnol. Lett. 2014, 36 (7), 1515−1522. 71


Chemical Reviews

Review

(707) Chang, M. C.; Eachus, R. A.; Trieu, W.; Ro, D. K.; Keasling, J. D. Engineering Escherichia coli for production of functionalized terpenoids using plant P450s. Nat. Chem. Biol. 2007, 3 (5), 274−277. (708) Leonard, E.; Yan, Y.; Fowler, Z. L.; Li, Z.; Lim, C. G.; Lim, K. H.; Koffas, M. A. Strain improvement of recombinant Escherichia coli for efficient production of plant flavonoids. Mol. Pharmaceutics 2008, 5 (2), 257−265. (709) Xu, P.; Ranganathan, S.; Fowler, Z. L.; Maranas, C. D.; Koffas, M. A. Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Metab. Eng. 2011, 13 (5), 578−587. (710) Lim, C. G.; Fowler, Z. L.; Hueller, T.; Schaffer, S.; Koffas, M. A. High-yield resveratrol production in engineered Escherichia coli. Appl. Environ. Microbiol. 2011, 77 (10), 3451−3460. (711) Jung, Y. K.; Kim, T. Y.; Park, S. J.; Lee, S. Y. Metabolic engineering of Escherichia coli for the production of polylactic acid and its copolymers. Biotechnol. Bioeng. 2010, 105 (1), 161−171. (712) Li, Z. J.; Qiao, K.; Shi, W.; Pereira, B.; Zhang, H.; Olsen, B. D.; Stephanopoulos, G. Biosynthesis of poly(glycolate-co-lactate-co-3hydroxybutyrate) from glucose by metabolically engineered Escherichia coli. Metab. Eng. 2016, 35, 1−8. (713) Buschke, N.; Becker, J.; Schafer, R.; Kiefer, P.; Biedendieck, R.; Wittmann, C. Systems metabolic engineering of xylose-utilizing Corynebacterium glutamicum for production of 1,5-diaminopentane. Biotechnol. J. 2013, 8 (5), 557−570. (714) Collins, T.; Barroca, M.; Branca, F.; Padrao, J.; Machado, R.; Casal, M. High level biosynthesis of a silk-elastin-like protein in E. coli. Biomacromolecules 2014, 15 (7), 2701−2708. (715) Li, S.; Liu, L.; Chen, J. Compartmentalizing metabolic pathway in Candida glabrata for acetoin production. Metab. Eng. 2015, 28, 1−7.

72


DCEO Biotechnology - ACS Publications - American Chemical Society

Recommend Documents