Departments of Bioengineering

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Metabolic Engineering and Systems Biotechnology
Ka-Yiu San
Departments of Bioengineering Rice
University Houston, Texas
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What is metabolic engineering?
Metabolic engineering is referred to as the
directed improvement of cellular properties
through the modification of specific biochemical
reactions or the introduction of new ones, with
the use of recombinant DNA technology
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Cloning for rProtein production
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Recombinant proteins by microorganisms
Some early products
Year Products Disease Company 1982 Humulin
Type 1 diabetes Genetech, Inc. (synthetic
insulin) 1985 Protropin Growth hormone
Genetech, Inc. Deficiency
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Examples of a few biopharmaceutical products in
1994
Biopharmaceutical Disease Annual Sales ( millions)
Erythropoietin (EPO) Anemia 1,650
Factor VIII Hemophilia 250
Human growth Hormones Growth deficiency, renal insufficiency 450
Insulin Diabetes 700
Source Biotechnology Industry Organization,
Pharmaceutical Research and Manufacturers of
America, company results, analyst reports
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Current projects

• Cofactor engineering of Escherichia coli
• Manipulation of NADH availability
• Manipulation of CoA/acetyl-CoA
• Plant metabolic engineering
• 3. Quantitative systems biotechnology
• A. Rational pathway design and optimization
• Metabolic flux analysis based on dynamic genomic
  information
• Design and modeling of artificial genetic
  networks
• Metabolite profiling
• Genetic networks architectures and physiology

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Modern biology central dogma
translation
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• Current metabolic engineering approaches
• Amplification of enzyme levels
• Use enzymes with different properties
• Addition of new enzymatic pathway
• Deletion of existing enzymatic pathway

Genetic manipulation
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Cofactor engineering
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Motivations and hypothesis

• Motivations
• Existing metabolic engineering methodologies
  include
• pathway deletion
• pathway addition
• pathway modification amplification, modulation
  or use of isozymes (or enzyme from directed
  evolution study) with different enzymatic
  properties
• Cofactors play an essential role in a large
  number of biochemical reactions

• Hypothesis
• Cofactor manipulation can be used as an
  additional tool to achieve desired metabolic
  engineering goals

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Importance of cofactor manipulation
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Cofactor engineering

• NAD/NADH
• CoA/acetyl-CoA

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NADH/NAD Cofactor Pair

• Important in metabolism
• Cofactor in gt 300 red-ox reactions
• Regulates genes and enzymes
• Donor or acceptor of reducing equivalents
• Reversible transformation

• Recycle of cofactors necessary for cell growth

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• Coenzyme A (CoA)
• Essential intermediates in many biosynthetic and
  energy yielding metabolic pathways
• CoA is a carrier of acyl group
• Important role in enzymatic production of
  industrially useful compounds like esters,
  biopolymers, polyketides etc.

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• Acetyl-CoA
• Entry point to Energy yielding TCA cycle
• Important component in fatty acid metabolism
• Precursor of malonyl-CoA, acetoacetyl-CoA
• Allosteric activator of certain enzymes

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Polyketide production

• Complex natural products
• gt 10,000 polyketides identified
• Broad range of therapeutic applications
• Cancer (adriamycin)
• Infection disease (tetracyclines, erythromycin)
• Cardiovascular (mevacor, lovastatin)
• Immunosuppression (rapamycin, tacrolimus)

6-deoxyerythronolide B
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Polyketide production
Precursor supply - example
Ref Precursor Supply for Polyketide
Biosynthesis The Role of Crotonyl-CoA Reductase,
Metabolic Engineering 3, 40-48 (2001)
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Approach
Systematic manipulation of cofactor levels by
genetic engineering means
Results

• increased NADH availability to the cell
• increased levels of CoA and acetyl CoA
• significantly change metabolite redistribution

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Metabolic engineering of plant tissue

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• Catharanthus roseus
• Vincristine Vinblastine
• lymphomas
• breast cancer
• testicular cancer
• Ajmalicine Serpentine
• anti-hypertension
• Hairy Roots
• model for metabolic engineering
• increased genetic stability over cell cultures
• fast differentiated growth
• higher alkaloid productivity than cell cultures

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• Transgenic C. roseus Work
• Cell Culture
• 35S Expression of ORCA3, STR, TDC

AS

• Indole Pathway
• Feedback Resistant AS
• TDC overexpression

TDC

• Terpenoid Pathway
• Appears limiting in most cases
• DXS used to increase terpenoid flux in E. coli
• G10H hypothesized to be rate limiting

• TIA Pathway
• Developmental and Environmental Reg.
• Hairy Roots produce large amounts of Tab and
  derivatives
• Vindoline is desired goal

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• Transgenic C. roseus Work
• Cell Culture
• 35S Expression of ORCA3, STR, TDC

AS

• Indole Pathway
• Feedback Resistant AS
• TDC overexpression

TDC

• Terpenoid Pathway
• Appears limiting in most cases
• DXS used to increase terpenoid flux in E. coli
• G10H hypothesized to be rate limiting

• TIA Pathway
• Developmental and Environmental Reg.
• Hairy Roots produce large amounts of Tab and
  derivatives
• Vindoline is desired goal

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Artemisia annua

• Sweet wormwood, sweet annie
• Wormwood is a hardy perennial herb native to
  Europe but now found throughout the world. The
  wormwood bush can grow to a height of 2 meters,
  and produces a number of bushy stems that are
  covered with fine, silky grey-green hairs.
  Wormwood produces small yellow-green flowers from
  Summer through to early autumn or fall

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Motivation

• The malaria parasite has developed resistance to
  most current anti-malaria drugs
• Artemisinin kills the parasite with no observed
  resistance so far, cures 90 of the people within
  days, and has few side effects
• Only half of the 60 million doses of new
  anti-malaria drugs anticipated to be needed in
  Africa will be delivered in 2005
• Plants grown on Chinese and Vietnamese farms have
  not kept up with demand
• Result cost is 10-20 times more expensive than
  existing drugs
• GOOD TARGET for Metabolic Engineering

(SCIENCE VOL 307 7 JANUARY 2005 p33)
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3-Acetyl-CoA
Pyruvate G3P
DXS
HMG-CoA
1-Deoxy-D-Xylulose-5-Phosphate
HMGR
DXR
Mevalonate
2-C-Methyl-D-erythritol-4-phosphate
IPP
DMAPP
? IPP ?
CYTOSOL
FPPS
IPP
DMAPP
FDP
PLASTID
SQS
SQC
GPP
Sesquiterpenes
Squalene
Monoterpenes, diterpenes, carotenoids, etc.
Artemisinin
Sterols
(Souret et al. 2003)
Amorpha-4,11-diene
Artemisinic Acid
FDP
Artemisinin
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Strategy for ME
m/z spectra for artemisinin

• Detect artemisinin in hairy roots using LCMS

Artemisinin (283.1)
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3-Acetyl-CoA
Pyruvate G3P
DXS
HMG-CoA
1-Deoxy-D-Xylulose-5-Phosphate
HMGR
DXR
Mevalonate
2-C-Methyl-D-erythritol-4-phosphate
IPP
DMAPP
? IPP ?
CYTOSOL
FPPS
IPP
DMAPP
FDP
PLASTID
SQS
SQC
GPP
Sesquiterpenes
Squalene
Monoterpenes, diterpenes, carotenoids, etc.
Artemisinin
Sterols
(Souret et al. 2003)
Amorpha-4,11-diene
Artemisinic Acid
FDP
Artemisinin
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Quantitative systems biotechnology
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Projects

• Metabolic flux analysis based on dynamic genomic
  information
• Rational pathway design and optimization
• feasible and realizable new network design
• Design and modeling of artificial genetic networks

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Metabolic Network
 
 
(From http//www.genome.ad.jp/kegg/pathway/map/map
00020.html)
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Metabolic Pattern (Illustration)
1.0
0.8
0.2
0.8 Metabolic rates
 
 
(From http//www.genome.ad.jp/kegg/pathway/map/map
00020.html)
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Traditional flux balance analysis (FBA)
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Proposed New Approach
Environmental Conditions
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Model System

• Oxygen and redox sensing/regulation system
• Sugar utilization regulatory network

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Simplified schematic of E. coli central metabolic
pathways
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Schematic showing selected oxygen and redox
sensing pathways in E. coli (adopted from
Sawers, 1999)
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Some example of available pathway information
Recommended Name EC number Reactions Encoded by Effect Ref
pyruvate dehydrogenase complex 1.2.4.1 Acetyl-CoA CO2 NADH CoA pyruvate NAD aceEF ArcA(-) FNR(-) 1,3 4
pyruvate formate-lyase 2.3.1.54 CoA pyruvate acetyl-CoA formate pfl ArcA() FNR() 2 1
citrate synthase 4.1.3.7 Acetyl-CoA H2O oxaloacetate citrate CoA gltA ArcA(-) 1,3
fumarate hydratase (fumarase) 4.2.1.2 fumarate H2O (S)-malate fumA FNR(0) 1
fumarate hydratase (fumerase) 4.2.1.2 (S)-malate fumarate H2O fumB FNR() 1,2
succinate dehydrogenase 1.3.99.1 Succinate acceptor fumarate reduced acceptor sdhCDAB ArcA(-) FNR(-) 1,2,3 2
fumarate reductase 1.3.1.6 Fumarate NADH succinate NAD frdABCD ArcA() FNR() 1 1,2,4
FNR active in the absence of oxygen ArcA is
activated in the absence of oxygen  Ref 1 Reg
of gene expression in fermentative and
respiratory systems in Escherichia coli and
related bacteria, E.C.E. Lin and S. Iuchi, .
Annual Rev. Genet, 1991, 25361-87Ref 2 Ref
2 O2-Sensing and o2 dependent gene regulation in
facultatively anaerobic bacteria, G. Unden, S.
Becker, J. Bongaerts, G.Holighaus, J. Schirawski,
and S. Six, Arch Microbi. (1995) 16481-90 Ref 3
Regualtion of gene expression in E. coli
E.C.C. Lin and A.S. Lynch eds. (1996) Chapman
Hall, New York (p370) Ref 4 Regualtion of
gene expression in E. coli E.C.C. Lin and A.S.
Lynch eds. (1996) Chapman Hall, New York (p322)
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pfl
fumB
aspA
ldhA
frdABCD
cyd
cyo
ArcB
aceB
mqo
ArcA
FNR
fumC
aceEF
acnB
sucCD
sucAB
fumA
icd
gltA
mdh
sdhCDAB
We have 3 sensing/regulatory components whose
activity evolves according to the Boolean mapping
coded in the figure. Here red denotes repress and
green denotes activate. When two components
regulate a third we suppose their action to be an
and. These regulatory components determine the
state of 19 structural genes via the specified
Boolean net.
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Biosystems

• Systems biology is the study of living organisms
  at the systems level rather than simply their
  individual components
• High-throughput, quantitative technologies are
  essential to provide the necessary data to
  understand the interactions among the components
• Computation tools are also required to handle and
  interpret the volumes of data necessary to
  understand complex biological systems

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Analytic tools
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Functional Genomics
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Gene ExpressionQRT-PCR
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Gene ExpressionQRT-PCR
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Metabolic flux determination using C-13 labeling
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Shimadzu LCMS 2010A
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Shimadzu QP-2010 (GCMS)
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2D-NMR spectrum
13C- glucose
Continuous culture
Samples
GC-MS spectrum
Positional Enrichments
1D-NMR spectrum
Relative intensities of multiplets
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start
Set free fluxes
Flux estimation based on stoichiometric
constraints
Simulating isotopomer distribution
Signal simulation
No
Optimal result achieved?
Yes
End
Principle of flux analysis based on 13C-labeling
experiment
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Mathematical modeling and computer simulations
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Simulation tranient from high oxygen to low
oxygen
high O2 low O2
very low
O2
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Integrated Approach

• Experiments
• Mathematical modeling and computer simulations

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Collaborators
Dr. George N. Bennett Department of
Biochemistry and Cell Biology Dr. Steve
Cox Department of Computational Applied
Math Rice University Dr. Ramon
Gonzalez Depart of Chemical and Biomolecular
Engineering Dr. Nikos Mantzaris Depart
of Chemical and Biomolecular Engineering Dr.
Kyriacos Zygourakis Depart of Chemical and
Biomolecular Engineering Dr. Jacqueline V.
Shanks Depart of Chemical and Biological
Engineering Dr. Sue I. Gibson Departmen
t of Plant Biology
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Recent Graduates
Aristos Aristidou, Ph.D. Cargill Dow NatureWorks
Chih-Hsiung Chou, Ph.D. University of Waterloo, Canada
Peng Yu, Ph.D. BMS Valentis, Inc.
Susana Joanne Berrios Ortiz, Ph.D Amgen
Erik Hughes, Ph.D Wyeth
Ravi Vadali Eli Lilly GSK
Henry Lin Amgen
Ailen Sanchez Genentech
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Metabolic Engineering and Systems Biotechnology
Laboratory
Ka-Yiu San
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Questions ?
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Strategy for ME

• Generate hairy roots
• Many reports in literature of A. annua hairy
  roots
• Followed a process similar to C. roseus hairy
  root generation
• Used pTA7002/GFP and pTA7002/DXS plasmids to
  generate hairy roots
• GFP will be used to characterize the use of the
  glucocorticoid inducible promoter
• DXS will be used to see if overexpressing DXS
  leads to an increase in artemisinin content
• We have hairy root lines 5th generation liquid
  adaptation, which are ready to begin
  characterization studies

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Functional Genomics
Metabolomics
Proteomics
Genomics
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Gene ExpressionQRT-PCR
Gene Primer Pairs PCR Products (bp)
yfiD 5-ACTAAAGCCGCTAACGACGA-3 5-TTCAATGTCACCCAGTTTGC-3 138
pflA 5-TACGATCCGGTGATTGATGA-3 5-TCACATTTTTGTTCGCCAGA-3 151
pflB 5-GCGAAATACGGCTACGACAT-3 5-CATCCAGGAAGGTGGAGGTA-3 142
pflC 5-GTCTGCACTGTGCGAAATGT-3 5-GGACGTGCGAAAGAAAATGT-3 134
pflD 5-AGCCTCGCAGAAACACATTT-3 5-AGAACGTCTGCGGCTTATGT-3 143
pdhR 5-GGAAGGTATCGCCGCTTATT-3 5-CTGGAGTACGGCGTTTGATT-3 136
aceE 5-TCTGATCGACCAACTGCTTG-3 5-GGCGTTCCAGTTCCAGATTA-3 137
fdhF 5-AAACGGACTGGCAAATCATC-3 5-GTTCGCCCATTTTCTCGTAA-3 141
fhlA 5-AGGCTCTTTCGCAACTGGTA-3 5-TGTGCCAGAACAGTTTCGTC-3 148