Metabolic Engineering

Lecture # 32

Outline• Some history and definition of field• Evolution of Metabolic Engineering

– Phase 1: Mutagenesis and Screening• Example Studies

– Phase 2: Targeted Genetic Manipulations• Example Studies

– Phase 3: Systems-level Engineering• Example Studies

• Modern tools for Metabolic Engineering– Metabolic Modeling– Adaptive Evolution

• From Metabolic Engineering to Biotechnology• Summary

Biotechnology through centuries.

Biotechnology – using a biological system to make products. (16th century)

Technology

Bioreactors for producing proteins, NRC Biotechnology Research Institute, Montréal, Canada

Biotechnology – using an engineered biological system to make products. (21th century)

Metabolic Engineering

What is metabolic engineering?

“Metabolic engineering is the improvement of cellular activities by manipulation of enzymatic, transport, and regulatory functions of the cell with the use of recombinant DNA technology… At present, metabolic engineering is more a collection of examples than a codified science”

James E. Bailey, 1991

Metabolic Engineering Frontiers

Organisms used for Metabolic Engineering

• E. coli – organic acids, bio-fuels, • S. cerevisiae – bio-ethanol• B. subtilis – therapeutics, enzymes• G. metallireducens – bioremediation,

electricity• Streptomyces sp. – antibiotics, recombinant

human proteins• C. reinhardtii – bio-diesel, hydrogen gas• T. maritima – hydrogen gas• L. lactis – food industry

E. coli as a model organism for Metabolic Engineering

• Fast growth rate• Genetic amenability• Metabolism is well understood• Natural production of organic acids• Ability to grow on various substrates• Simple growth media• Plasticity of the metabolism• High theoretical yields

Rocky Mountain Laboratories, NIAID, NIH

Plasticity of E. coli Metabolismmaximum theoretical yield

Adv Biochem Eng Biotechnol. 2007;108:237-61.

Evolution of Metabolic Engineering

• Random Mutagenesis

• Heterologous Expression

• Over-expression

(Gene/Pathway)

• Genetic Manipulation

(KnockOut/KnockIn)

• Protein Engineering

• Random Mutagenesis

• Heterologous Expression

• Over-expression

(Gene/Pathway)

• Systems-Level Engineering

• Adaptive Evolution• Metabolic Modeling

• Genetic Manipulation

(KnockOut/KnockIn)

• Protein Engineering

• Random Mutagenesis

• Heterologous Expression

• Over-expression

(Gene/Pathway)

Phase 1: Mutagenesis and

screening

Phase 2: Targeted genetic

manipulations

Phase 3: Systems-level engineering

MUTAGENESIS AND SCREENINGPhase 1

• Random mutagenesis

• Heterologous expression– Individual genes

– Entire pathways

• Redirection metabolite flow

• Genetic manipulations

• Improved phenotypic traits

• Enhancing the variety of produced compounds

• Activation of new pathways– Towards the desired pathway– Metabolic regulation

• Strain design

Traditional Metabolic Engineering

Science. 1991, 252(5013):1668-75.

Strategy Result

Random Mutagenesis• Bacteria is subjected a round of mutagenesis

• Chemical mutagens• UV radiation

• Clonal analysis is conducted to identify the mutants with altered phenotypic traits

• Growth rate• Metabolic production

• Phenotypic characterization is conducted to characterized the resulted strain

• Synthesis of new products is enabled by completion of partial pathways– Example: production of the Vitamin C

precursor 2-keto-L-gulonic acid from glucose once required 2 separate fermentations, in Erwinia herbicola and Corynebacterium. Researchers cloned Corynebacterium 2,5-DKG reductase into E. herbicola, which can now carry out the entire fermentation itself.

– Example: production of human glycoproteins by Chinese Hamster Ovary (CHO) cells. When the CHO cells express the enzyme β-galactoside α2,6-sialyltransferase, they can form terminal glycosylation linkages common in human proteins.

Heterologous Expression

Science. 1991 Jun 21;252(5013):1668-75.

Redirecting Metabolite Flow• Directing traffic toward the desired branch

– Many forks in biochemical pathways, need to direct flux away from competing pathways

– Example: Production of threonine by Brevibacterium lactofermentum. Cloned homosering dehydrogenase (HD), homoserine kinase (HK), and phosphoenolpyruvate carboxylase (PEPCase) into a strain lacking feedback inhibition from threonine.

• Reducing competition for a limiting resource– Cells have a limited number of ribosomes, can limit production of desired peptides– A cloned mutant 16S ribosomal RNA makes ribosomes that only translate mRNA

with a certain Shine-Dalgarno sequence mutation.– This method separates translation of heterologous transcripts from native

transcripts, improving yield of these products.

• Revising metabolic regulation– Can upregulate biosynthetic genes to improve yields– Example: yeast with maltose permease and maltase with constitutively active

promoters to overcome glucose repression, allowing for faster CO2 production in bread baking.

Science. 1991 Jun 21;252(5013):1668-75.

Examples of Early Metabolic Engineering

Science. 1991 Jun 21;252(5013):1668-75.

TARGETED GENETIC MANIPULATIONSPhase 2

MUTAGENESIS AND SCREENINGPhase 1

L-Alanine in E. coli

Appl Microbiol Biotechnol. 2007 Nov;77(2):355-66.

• L-Alanine is produced commertially by an enzymatic decarboxylation of L-aspartic acid• World demand is on the order of 500 tons/year• L-Alanine is used as a nutrition and food additive• L-Alaninie can be produced from pyruvate by some organisms: A. oxydans, B. sphaericus, G. stearothermophilus etc.

In this study:• Lactate overproducer harboring the following mutations (pflB, frdBC, adhE, and ackA) was used to produce L-Alanine • Replaced ldhA with the alaD (alanine dehydrogenase) gene from Geobactillus stearothermophilus.

• Removal of ldhA (lactate dehydrogenase) resulted in availability of pyruvate for L-Alanine production

• alaD (homologous; on a plasmid) reaction is co-factor coupled because it uses NADH

• Knocked out mgsA gene to eliminate lactate production

• Knocked out dadX to improve chiral purity of L-Alanine

L-Alanine in E. coli

Appl Microbiol Biotechnol. 2007 Nov;77(2):355-66.

Artemisinic acid in E. coli• Artemisinic acid is a precursor of Artemisinin• Artemisinin – a drug used to treat malaria • Isolated from plant: Artemisia annua• Cost to produce is $2.40/dose – TOO expensive for

developing countries—need $0.25/dose

• In these studies:– Mevalonate pathway from S. cerevisiae was introduced in E.coli– Cytochrome p450 from A. annua was introduced in E. coli in

order to carry out the oxidation to artemisinic acid in vivo

Artemisinic acid in E. coli

S. cerevisiae A. annua

• Eukaryotic and plant biochemical pathways were introduced into E. coli

Nat Biotechnol. 2003 Jul;21(7):796-802.

Artemisinic acid in E. coli

• Engineering successful amorphadiene producing E. coli took over 3 years• Over a million fold increase in production was observed• High-throughput data together with traditional techniques (pathway overexpression) were used to successfully engineer this strain

ACS Chem Biol. 2008 Jan 18;3(1):64-76. Nat Chem Biol. 2007 May;3(5):274-7

SYSTEMS LEVEL ENGINEERINGPhase 3

TARGETED GENETIC MANIPULATIONSPhase 2

MUTAGENESIS AND SCREENINGPhase 1

Uses of the E. coli ReconstructionMetabolic Engineering:

1. Biotechnol Bioeng 84, 647 (2003)2. Biotechnol Bioeng 84, 887 (2003)3. Genome Res 14, 2367 (2004)4. Metab Eng 7, 155 (2005)5. Nat Biotechnol 23, 612 (2005)6. Appl Environ Microbiol 71, 7880 (2005)7. Metab Eng 8, 1 (2006)8. Appl Microbiol Biotechnol V73, 887 (2006)9. Biotechnol Bioeng 91, 643 (2005)10. Proc Natl Acad Sci U S A 104 7797(2007)

Nat Biotechnol. 2008 Jun;26(6):659-67.

• Based on current genome annotation

• Contains:• 1260 ORF ( ~26%)• 2,077 reactions• 1039 unique metabolites• Thermodynamic information

for chemical reactions• Computational model is

presented in a form of a stoichiometric (S) matrix

• Can be analyzed by Flux Balance Analysis

Molecular Systems Biology, 3:121 (2007)

Modeling MetabolismGenome-scale model of E. coli K-12 metabolism

Metabolic map of central metabolism of E. coli.

Succinate Production Study

Appl Environ Microbiol 71, 7880-7887 (2005).Appl Microbiol Biotechnol V73, 887-894 (2006).

• Examined the effect of selected intuitive targets to determine the best overproducer

• Network modeling was demonstrated to be more effective than comparative genomics

Production of L-threonineThree areas of analysis in strain design

Lee KH, Park JH, Kim TY, Kim HU, Lee SY. Mol Syst Biol. 3:149. (2007)

• Tuning of optimal expression levels• Mapping of high-throughput data• Simulations for gene knock-outs for by product elimination

Lycopene Production Study

Nat Biotechnol 23, 612-616 (2005).

• 2 x increase over an already high producing parental strain

• Maximum production could be designed solely using model-aidedcomputationaldesign

Computational designs vs. mixed combinatorial transposon mutagenesis

Towards Systems Level Metabolic Engineering

Phase 1: Random• Unpredicted local and global result• Low reproductively• Simple implementation

Phase 2: Targeted• Predicted local result• Unpredicted global result• Fairly reproducible• Moderately difficult to implement

Phase 3: System-Level• Predicted local and global result• Aided by computer modeling• Highly reproducible• Highly difficult to implement• Great potential

Current Opinions in Biotech., 2008, 19:454-460

• thick red: increased flux by direct overexpression

• thick blue: lrp repression

• thin red: increased flux by in silico predicted KOs

• thin blue: decreased flux by KOs

• dotted lines: feedback inhibition

• X: inhibition removed• +: gene activation• -: gene inhibition

Amino acid production in E. coliL-Valine

PNAS, 2007 May 8;104(19):7797-802

Production of L-valineSimulating sequential gene knockouts in silico

Park, J.H., Lee, K.H., Kim, T.Y. & Lee, S.Y. PNAS U S A 104(19):7797-7802 (2007)

• 2 x increase over a previously engineered strain• in silico design modifications showed the greatest

improvement over:• Relieving feedback inhibition & attenuation• Removing competing pathways• Up-regulation of the pathways

PNAS, 2007 May 8;104(19):7797-802

Amino acid production in E. coli1. Feedback inhibition removed from ilvH by site-directed mutagenesis and

transcriptional attenuation removed from ilvGM by replacement with tac promonter

2. Eliminated competing L-Leu and L-Ile pathways by knocking out ilvA, panB, and leuA

3. Enhanced valine pathway flux by amplifying ilvBN operon4. Transcriptome profiled this strain to identify additional genes for modification5. Amplified ilvCED genes to further enhance valine pathway flux6. Amplified lrp gene to overcome inhibition by L-leucine7. Knocked out ygaZH genes to test them for valine transport activity.

Discovered a new valine exporter8. Amplified the ygaZH valine transporter, discovered synergistic effects of lrp

and ygaZH9. Used constraints based analysis (MOMA) to identify additional knockouts in a

genome scale E. coli model10. Based on in silico modeling, knocked out aceF, mdh, and pfkA

PNAS. 2007 May 8;104(19):7797-802.

High-throughput data and modeling to improve production

Trends in Biotech., 2008, 26(8), 404-410

Growth Coupled Designs• mutations decrease fitness of organisms• secretion rates decrease over time

• self optimizing strains• secretion rates increase over time

Growth Coupled Designs

Growth Coupled Designs

Growth Coupled Designs

Computational Algorithms–OptKnock: Optknock is a bi-level algorithm that suggests gene deletion strategies leading to the forced overproduction of a specified growth-coupled target metabolite. Briefly, it searches the defined constraint space while simultaneously optimizing for both growth rate and target metabolite secretion rate. It has been computationally examined and suggested strain designs have been experimentally verified with success [Biotechnol Bioeng. 2003 Dec

20;84(6):647-57.].

–OptGene: OptGene is based on a genetic algorithm that can also produce growth-coupled strain designs. Its advantages include the potential for running at a higher speed than OptKnock and utilizing non-linear objectives. It has been tested using a genome-scale model of yeast, but has yet to be applied to engineer E. coli designs [Metab Eng, 2001. 3(2):

p. 111-4].

–OptStrain: OptStrain is a hierarchical computational framework incorporating mixed integer programming that identifies pathways that are targets for recombination of non-native pathways to host organisms. It is effectively similar to Optknock with the added feature that additional reactions can be added to the model to simulate a genetic addition to a cell (i.e., a knock-in). For recombinant pathways, it chooses both the pathway that will produce the greatest potential yield and require the smallest number of genetic additions [Genome Res. 2004 Nov;14(11):2367-76].

Biotechnol Bioeng. 2003 Dec 20;84(6):647-57.

OptKnock• Inner problem

• Flux calculation based on optimization of a objective function (e.g., growth)

• Outer problem• Maximizes the

bioengineering objective (e.g., overproduction) by knocking-out reactions available to the inner problem.

•genetic algorithm for identifying knockout strains

•“evolves” knockouts to maximum objective

•Not guaranteed to find global optimal solution

•Can use nonlinear objective functions

-Strength of growth coupling

-Knockout penalty

OptGene

BMC Bioinformatics. 2005 Dec 23;6:308.

OptStrain

1. Obtain and curate reactions from universal database (KEGG)

2. Calculate max theorteical yield of product using any reactions needed

3. Find alternative pathways with the highest yield and fewest non-native reactions

4. Run OptKnock to get growth coupled design

Genome Res. 2004 14: 2367-2376.

Adaptation of Metabolic Engineering Strains

• Three growth-coupled strain designs were generated

• Strains were evolved for 60 days anaerobically

• The growth rate increase lead to increase in production rate and reduction of by-product secretion

Biotechnology and Bioengineering, 91(5):643-648 (2005).

Growth Coupled Designs

Metab Eng, (2009).

Growth Coupled Designs

Anaerobic designs Aerobic designs

Aerobic and anaerobic growth-coupled strain designs were calculated using the iAF1260 model. Three substrates were tested and designs for 5 metabolites are presented

Metab Eng, (2009).

Adaptation of Metabolic Engineering Strains (cont)

Culture Conditions

Supp µProduct / Substrate

Production / Consumption

Rate

% Yp/s

Steady-state% Yp/s

g/L hr-1 mmol gDW-1 hr-1 wt% wt%

4 g/L glucose M9 1 g/L YE0.86 ± 0.00

glucose 43.1 ± 1.3

lactate 84.4 ± 1.5 97.9 ± 1.2% *98.4 ± 3.4%

succinate 4.3 ± 0.3 6.5 ± 0.3% *3.4 ± 2.8%

4 g/L xylose M9 1 g/L YE0.13 ± 0.02

xylose 9.5 ± 0.8acetate 1.0 ± 1.4 4.0 ± 5.7% 2.3 ± 3.2%lactate 13.0 ± 0.4 82.3 ± 4.1% 83.7 ± 3.0%

succinate 2.6 ± 0.8 21.4 ± 4.7% 18.7 ± 2.5%

ENGINEERED STAINS ARE PARTS OF AN OVERALL PROCESS

From Metabolic Engineering to Biotechnology

overview

Biomass

Primary refinery

Primary products

ChemicalsMaterials

Fuels

Extraction Separation

Secondary refinery

Heat energy

Thermodynamical

Biotechnological

TRENDS in Biotechnology

1. Consumables• Inexpensive

substrate

3. Post-processing• Simple• Inexpensive• High recovery• Min by-

product

2. Fermentation• High product yield• High product and

substrate tolerance• Strain stability

From Metabolic Engineering to Biotechnology

considerations

Prentice Hall, NJ, 2002

Summary• Metabolic Engineering is evolving towards the systems-level

approach• More and more organisms become genetically engineered as

genetic manipulation tools become available• New organisms with unique metabolic traits are studied in

order to be used for metabolic engineering• Genome-scale metabolic models become an important tool

for integration of the high-throughput data and prediction of the metabolic responses

• Adaptation of the metabolic engineered strains shows promise for optimization

• Engineering a regulatory network leads to global changes in the metabolism increasing production potential

• More emphasis is given to metabolic engineering due to economic reasons

References Used• Bailey JE. Toward a Science of Metabolic Engineering. Science, New Series, Vol. 252, No. 5013. Jun 21, 1991.• Martin VJ, Pitera DJ, Withers ST, Newman JD, Keasling JD. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat

Biotechnol. 2003 Jul;21(7):796-802.• Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain

optimization. Biotechnol Bioeng. 2003 Dec 20;84(6):647-57. • Alper H, Miyaoku K, Stephanopoulos G. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene

knockout targets. Nat Biotechnol. 2005 May;23(5):612-6.• Patil KR, Rocha I, Forster J, Nielsen J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics. 2005 Dec

23;6:308. • Ro DK, Paradise EM, Ouellet M, Fisher KJ, Newman KL, Ndungu JM, Ho KA, Eachus RA, Ham TS, Kirby J, Chang MC, Withers ST, Shiba Y, Sarpong R,

Keasling JD. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature. 2006 Apr 13;440(7086):940-3.• Park JH, Lee KH, Kim TY, Lee SY. Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in

silico gene knockout simulation. Proc Natl Acad Sci U S A. 2007 May 8;104(19):7797-802. Epub 2007 Apr 26.• Chang MC, Eachus RA, Trieu W, Ro DK, Keasling JD. Engineering Escherichia coli for production of functionalized terpenoids using plant P450s. Nat

Chem Biol. 2007 May;3(5):274-7.• Jantama K, Haupt MJ, Svoronos SA, Zhang X, Moore JC, Shanmugam KT, Ingram LO. Combining metabolic engineering and metabolic evolution to

develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. 2007 Oct;99(5):1140-53.• Zhang X, Jantama K, Moore JC, Shanmugam KT, Ingram LO. Production of L -alanine by metabolically engineered Escherichia coli. Appl Microbiol

Biotechnol. 2007 Nov;77(2):355-66. Epub 2007 Sep 15.• Lee KH, Park JH, Kim TY, Kim HU, Lee SY. Systems metabolic engineering of Escherichia coli for L-threonine production. Mol Syst Biol. 2007;3:149.

Epub 2007 Dec 4.• Sauer M, Porro D, Mattanovich D, Branduardi P. Microbial production of organic acids: expanding the markets. Trends Biotechnol. 2008

Feb;26(2):100-108. Epub 2008 Jan 11.• Keasling JD. Synthetic biology for synthetic chemistry. ACS Chem Biol. 2008 Jan 18;3(1):64-76.• Kim TY, Sohn SB, Kim HU, Lee SY. Strategies for systems-level metabolic engineering. Biotechnol J. 2008 May;3(5):612-23.• Kim HU, Kim TY, Lee SY. Metabolic flux analysis and metabolic engineering of microorganisms. Mol Biosyst. 2008 Feb;4(2):113-20.• Feist AM, Zielinski DC, Orth JD, Schellenberger J, Herrgard MJ, Palsson BO. Model-driven evaluation of the production potential for growth-

coupled products of Escherichia coli. Metab Eng. 2009 Oct 17. [Epub ahead of print]

Jay Keasling on the Colbert Reporthttp://www.colbertnation.com/the-colbert-report-videos/221178/march-10-2009/jay-keasling

extras

What is metabolic engineering?

• normal microbial metabolism:– high energy inputs (glucose,

fructose)– low energy outputs (ethanol,

acetate)

• can alter metabolism by genetic engineering

• production of desirable products

• consumption of new substrates

ME StrategiesA. amplifying genes in the pathway to the end product

B. manipulating regulatory genes

C. eliminating accumulation and amplifying secretion of the end product

D. removing genes leading to production of by-products

E. enhancing precursor uptake

F. disrupting other nonintuitive genes

Kim TY, Sohn SB, Kim HU, Lee SY. Strategies for systems-level metabolic engineering. Biotechnol J. 2008 Feb 1