Transcript Slide 1

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. (16th century)
Metabolic
Engineering
– using an
engineered biological system
to make products. (21th century)
Biotechnology
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
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E. coli
S. cerevisiae
B. subtilis
G. metallireducens
Streptomyces sp.
• C. reinhardtii
• T. maritima
• L. lactis
– organic acids, bio-fuels,
– bio-ethanol
– therapeutics, enzymes
– bioremediation, electricity
– antibiotics, recombinant
human proteins
– bio-diesel, hydrogen gas
– hydrogen gas
– food industry
E. coli as a model organism for
Metabolic Engineering
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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
• Systems-Level
Engineering
• Genetic
Manipulation
(KnockOut/KnockIn)
• Random
Mutagenesis
• Heterologous
Expression
• Over-expression
(Gene/Pathway)
• Protein
Engineering
• Adaptive Evolution
• Metabolic Modeling
Phase 1:
Mutagenesis and
screening
Phase 2:
Targeted genetic
manipulations
Phase 3:
Systems-level
engineering
Phase 1
MUTAGENESIS AND SCREENING
Traditional Metabolic Engineering
Strategy
• Random mutagenesis
• Heterologous expression
– Individual genes
Result
• Improved phenotypic
traits
• Enhancing the variety of
produced compounds
– Entire pathways
• Redirection metabolite
flow
• Activation of new
pathways
– Towards the desired pathway
– Metabolic regulation
• Genetic manipulations
• 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 βgalactoside 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 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.
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 cofactor 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
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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
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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 operon
4. Transcriptome profiled this strain to identify additional genes for modification
5. Amplified ilvCED genes to further enhance valine pathway flux
6. Amplified lrp gene to overcome inhibition by L-leucine
7. Knocked out ygaZH genes to test them for valine transport activity.
Discovered a new valine exporter
8. 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
iAF1260 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
4 g/L glucose M9
4 g/L xylose M9
Supp
µ
g/L
hr-1
1 g/L YE
0.86 ±
0.00
1 g/L YE
0.13 ±
0.02
glucose
Production /
Consumption
Rate
mmol gDW-1
hr-1
43.1 ± 1.3
lactate
84.4 ± 1.5
97.9 ± 1.2%
*98.4 ± 3.4%
succinate
xylose
acetate
lactate
succinate
4.3 ± 0.3
9.5 ± 0.8
1.0 ± 1.4
13.0 ± 0.4
2.6 ± 0.8
6.5 ± 0.3%
*3.4 ± 2.8%
4.0 ± 5.7%
82.3 ± 4.1%
21.4 ± 4.7%
2.3 ± 3.2%
83.7 ± 3.0%
18.7 ± 2.5%
Product /
Substrate
% Yp/s
Steady-state
% Yp/s
wt%
wt%
ENGINEERED STAINS ARE PARTS OF
AN OVERALL PROCESS
From Metabolic Engineering to
Biotechnology
overview
Primary refinery
Secondary refinery
Thermodynamical
Biomass
Extraction
Separation
Biotechnological
Primary products
Chemicals
Materials
Fuels
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 byproduct
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
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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-102009/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 Strategies
A.
B.
C.
D.
E.
F.
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