Lecture # 32 Metabolic Engineering

• • • • •

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.

Technology Bioreactors for producing proteins, NRC Biotechnology Research Institute, Montréal, Canada Biotechnology – using a biological system to make products. (16 th century) Metabolic Engineering Biotechnology – using an

engineered

biological system to make products. (21 th century)

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 S. cerevisiae

– organic acids, bio-fuels, – bio-ethanol

B. subtilis

– therapeutics, enzymes G. metallireducens – bioremediation, electricity

Streptomyces sp.

C. reinhardtii T. maritima L. lactis

– antibiotics, recombinant human proteins – bio-diesel, hydrogen gas – hydrogen gas – 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 Metabolism

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

Evolution of Metabolic Engineering

• Genetic Manipulation (KnockOut/KnockIn) • Random • Mutagenesis Heterologous • Expression Over-expression (Gene/Pathway) • Random • Mutagenesis Heterologous • Expression Over-expression (Gene/Pathway) • Protein Engineering Phase 1: Mutagenesis and screening Phase 2: Targeted genetic manipulations • Systems-Level Engineering • Genetic Manipulation (KnockOut/KnockIn) • Random • Mutagenesis Heterologous • Expression Over-expression (Gene/Pathway) • Protein Engineering • • Adaptive Evolution Metabolic Modeling Phase 3: Systems-level engineering

Phase 1

MUTAGENESIS AND SCREENING

Traditional Metabolic Engineering

• Strategy Random mutagenesis • • Heterologous expression – Individual genes – Entire pathways Redirection metabolite flow • Genetic manipulations • • Result Improved phenotypic traits Enhancing the variety of produced compounds • Activation of new pathways – Towards the desired pathway – Metabolic regulation • Strain design Science. 1991, 252(5013):1668-75.

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

Heterologous Expression

• 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.

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 CO 2 bread baking.

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

Examples of Early Metabolic Engineering

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

Phase 1

MUTAGENESIS AND SCREENING

Phase 2

TARGETED GENETIC MANIPULATIONS

L-Alanine in E. coli

• 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.

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

L-Alanine in E. coli

• 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 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

• Eukaryotic and plant biochemical pathways were introduced into

E. coli S. cerevisiae A. annua

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

Phase 1

MUTAGENESIS AND SCREENING

Phase 2

TARGETED GENETIC MANIPULATIONS

Phase 3

SYSTEMS LEVEL ENGINEERING

Uses of the E. coli Reconstruction

Metabolic 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.

• • • •

Modeling Metabolism

Genome-scale model of

E. coli

K-12 metabolism 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 Metabolic map of central metabolism of

E. coli

.

Molecular Systems Biology

, 3:121 (2007)

Succinate Production Study

• Examined the effect of selected intuitive targets to determine the best overproducer • Network modeling was demonstrated to be more effective than comparative genomics Appl Environ Microbiol 71, 7880-7887 (2005).

Appl Microbiol Biotechnol V73, 887-894 (2006).

Production of L-threonine

Three 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

Computational designs vs. mixed combinatorial transposon mutagenesis • 2 x increase over an already high producing parental strain • Maximum production could be designed solely using model-aided computational design Nat Biotechnol 23, 612-616 (2005).

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

Amino acid production in E. coli L-Valine

•

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

Production of L-valine

Simulating 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. coli

3.

4.

5.

6.

7.

1.

2.

8.

Feedback inhibition removed from ilvH by site-directed mutagenesis and transcriptional attenuation removed from ilvGM by replacement with tac promonter Eliminated competing L-Leu and L-Ile pathways by knocking out ilvA, panB, and leuA Enhanced valine pathway flux by amplifying ilvBN operon Transcriptome profiled this strain to identify additional genes for modification Amplified ilvCED genes to further enhance valine pathway flux Amplified lrp gene to overcome inhibition by L-leucine Knocked out ygaZH genes to test them for valine transport activity. Discovered a new valine exporter Amplified the ygaZH valine transporter, discovered synergistic effects of lrp and ygaZH 9.

Used constraints based analysis (MOMA) to identify additional knockouts in a genome scale E. coli model 10. 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

Growth Coupled Designs

• • self optimizing strains secretion rates increase over time

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 ].

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.

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

OptGene

•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 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

Aerobic and anaerobic growth-coupled strain designs were calculated using the

i

AF1260 model. Three substrates were tested and designs for 5 metabolites are presented Anaerobic designs Metab Eng

,

(2009

).

Aerobic designs

Adaptation of Metabolic Engineering Strains (cont)

Culture Conditions Supp g/L

µ

hr -1

4 g/L glucose M9 1 g/L YE 0.86 ± 0.00

4 g/L xylose M9 1 g/L YE 0.13 ± 0.02

Product / Substrate

glucose lactate succinate xylose acetate lactate succinate

Production / Consumption Rate mmol gDW -1 hr -1 43.1

±

1.3

84.4 ± 1.5

4.3 ± 0.3

9.5 ± 0.8

1.0 ± 1.4

13.0 ± 0.4

2.6 ± 0.8

% Y p/s Steady-state wt%

97.9 ± 6.5 ± 4.0 ± 82.3 ± 21.4 ± 1.2% 0.3% 5.7% 4.1% 4.7%

% Y p/s wt%

*98.4 ± 3.4% *3.4 ± 2.8% 2.3 ± 3.2% 83.7 ± 3.0% 18.7 ± 2.5%

ENGINEERED STAINS ARE PARTS OF AN OVERALL PROCESS

From Metabolic Engineering to Biotechnology

overview Primary refinery Secondary refinery Thermodynamical Biomass Extraction Separation Chemicals Materials Fuels Biotechnological Primary products Heat energy

TRENDS in Biotechnology

From Metabolic Engineering to Biotechnology

considerations 1. Consumables • Inexpensive substrate 2. Fermentation • High product yield • High product and • substrate tolerance Strain stability 3. Post-processing • Simple • Inexpensive • High recovery • Min by product 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 Report

http://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

A.

B.

C.

D.

E.

F.

ME Strategies

amplifying genes in the pathway to the end product manipulating regulatory genes eliminating accumulation and amplifying secretion of the end product removing genes leading to production of by-products enhancing precursor uptake disrupting other nonintuitive genes Kim TY, Sohn SB, Kim HU, Lee SY. Strategies for systems-level metabolic engineering. Biotechnol J. 2008 Feb 1