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Title: Departments of Bioengineering


1
Metabolic Engineering and Systems Biotechnology
Ka-Yiu San
Departments of Bioengineering Departments of
Chemical Engineering Rice University Houston,
Texas
2
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3
Cloning for rProtein production
4
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
5
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
6
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
7
Modern biology central dogma
translation
8
  • 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
9
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

10
Current Projects
I Pathway and Cofactor Metabolic Engineering
1 2 An integrated metabolic engineering study of evolved alcohol acetyl transferase enzymes in flavor compound formation in E. coli (with Dr. Bennett) NSF BES-0118815 USDA 2002-35505-11638
II Plant Metabolic Engineering
3 Collaborative research Metabolic engineering of hairy roots for alkaloid production (with Dr. Gibson of UM and Dr. Shanks of Iowa State University) NSF BES-0224593
III Quantitative Biosystems Engineering
4 Experimental driven computational analysis of E. coli global redox sensing/ regulatory networks and cellular responses (with Drs. Bennett amd Cox) NSF BES-0222691
5 Collaborative research Metabolic engineering of E. coli sugar-utilization regulatory systems for the consumption of plant biomass sugars (with Drs. Gonzalez and Shanks of Iowa State University) EPA RD-83144101
6 Modeling and design of gene switching networks for optimal control of PHA nanostructures (with Drs. Mantzaris and Bennett,) NSF BES0331324
7 From Genetic Architecture to Adaptation Dynamics (with Drs. Mantzaris PI, Bennett, and Zygourakis). NIH R01GM071888
IV Instrumentation
8 MRI Acquisition of Multiple Instruments for Research and Education NSF BES-0420840
9 Shimadzu Instrumentation Grant
11
Cofactor engineering
12
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

13
Importance of cofactor manipulation
14
Cofactor engineering
  • NAD/NADH
  • CoA/acetyl-CoA

15
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

16
  • 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.

17
  • 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

18
Example Lactic acid formation
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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
21
Polyketide production
Precursor supply - example
Ref Precursor Supply for Polyketide
Biosynthesis The Role of Crotonyl-CoA Reductase,
Metabolic Engineering 3, 40-48 (2001)
22
Approach
Systematic manipulation of cofactor levels by
genetic engineering means
Model systems
Simple model systems, such as biosynthesis of
succinate and ester, to illustrate the concept
Results
  • increased NADH availability to the cell
  • increased levels of CoA and acetyl CoA
  • significantly change metabolite redistribution

23
Manipulation of NADH availability
24
Fermentation Pathway of E. coli
25
NADH Regeneration
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Assay of FDH activity
28
Characterization of NADH-dependent FDH
NADH-dependent FDH
NADH-dependent FDH
PanK
PanK
GJT (pDHK29) (Control strain)
GJT (pSBF2) (New strain)
29
Anaerobic Tubes Experimental Method
  • Strains Escherichia coli (MC4100 derivative)
  • GJT001 (pDHK29) wild type (control plasmid)
  • GJT001 (pSBF2) wild type (new FDH plasmid)
  • Media
  • LB 1g/L NaHCO3
  • 100mg/L Kanamycin
  • 20g/L Glucose
  • Temperature 37 ºC
  • Agitation 250 rpm
  • Samples 72 hrs after inoculation
  • HPLC

30
Effect of Increasing NADH Availability
of Increase/Decrease for GJT001 (pSBF2)
relative to GJT001 (pDHK29)
Glucose Consumed
NAD
3-fold
Succinate
55
2NADH
Lactate
Pyruvate
91
NADH
Formate Converted
Acetyl-CoA
2NADH
Acetate
8-fold
43
NAD
NADH
CO2
Formate
Ethanol
O.D.600nm 59 Et/Ac 27-fold
FDH1
FDHF
15-fold
CO2
H
2
31
NADH Availability
5.0
4.0
mol NADH/mol glucose
3.0
2.0
1.0
0.0
GJT(pDHK29)
GJT(pSBF2)
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Summary of results
  • Effect of NADH regeneration (overexpressing
    NAD-dependent FDH)
  • Increases intracellular NADH availability
  • Provide a more reduced environment
  • Increase reduced product (such as ethanol and
    succinate) productivity significantly

35
Quantitative systems biotechnology
36
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

37
Motivations
Observations
  • Traditional reductionist approach
  • Knowledge at the basic and fundamental level
  • but mostly isolated
  • Information overflow
  • Genome database, gene expression database
    (functional genomic), proteomic, metabolomics,
    metabolic pathway database
  • Most of the existing data base static
  • Genome database, metabolic pathway database

38

Motivations and objectives
How can one utilize the static genomic and
metabolic databases (especially when
genetic/regulatory network structures are
available) to describe and predict cellular
functions, such as metabolic patterns?
39
Traditional flux balance analysis (FBA)
40
Metabolic Network
 
 
(From http//www.genome.ad.jp/kegg/pathway/map/map
00020.html)
41
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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Proposed New Approach
Environmental Conditions
44
Model System
  • Oxygen and redox sensing/regulation system
  • Sugar utilization regulatory network

45
Simplified schematic of E. coli central metabolic
pathways
46
Schematic showing selected oxygen and redox
sensing pathways in E. coli (adopted from
Sawers, 1999)
47
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)
48
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.
49
Stimulus
Sensors/regulators
genes
enzymes
formate
Metabolites
activation
repression
50
Work in progress
To develop a model that can provide dynamic and
automatic adaptation of pathway map to
environmental conditions
51
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

52
Functional Genomics
Metabolomics
Proteomics
Genomics
53
Functional Genomics
54
Proteinomics
  • 2D gel electrophoresis
  • Mass spectrometry
  • Bioinformatics
  • Protein "chips"

55
2D gel electrophoresis
  • IEF
  • Size

56
Protein Chips
  • The basic construction of such protein chips has
    some similarities to DNA chips, such as the use
    of a glass or plastic surface dotted with an
    array of molecules.
  • Known proteins are analyzed using functional
    assays that are on the chip. For example, chip
    surfaces can contain enzymes, receptor proteins,
    or antibodies that enable researchers to conduct
    protein-protein interaction studies, ligand
    binding studies, or immunoassays
  • High-end quadruple TOF tandem mass spectrometers
    enable high-performance protein identification,
    epitope and phosphorylation mapping, and
    protein-interaction analyses.

57
Metabolomics
  • Metabolomics is a relatively new discipline and
    techniques for high-throughput metabolic
    profiling are still under development.
  • No single technique is suitable for the analysis
    of all different types of molecule, so a mixture
    of techniques is used.
  • Methods such as gas chromatography, high-pressure
    liquid chromatography and capillary
    electrophoresis are used to separate metabolites
    according to various chemical and physical
    properties. The molecules are then identified
    using methods such as mass spectrometry.

58
Shimadzu LCMS 2010A
59
Shimadzu QP-2010
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61
Recent Graduates
Aristos Aristidou, Ph.D. Cargill Dow
Chih-Hsiung Chou, Ph.D. University of Waterloo, Canada
Peng Yu, Ph.D. BMS Valentis, Inc.
Derek Sykes, M.S. Life Technology
Irena Ying Chen, M.S. Kellog
Yea-Tyng Yang, Ph.D. M.I.T.
Susana Joanne Berrios Ortiz, Ph.D Shell Development
Erik Hughes, Ph.D Wyeth
Ravi Vadali Eli Lilly
62
Current Lab Members
Name Project
Christie Peebles Plant Metabolic Engineering
Sagit Shalel-Levanon Quantitative Systems Biotechnology
Randeep Singh Quantitative Systems Biotechnology
Ailen Sanchez Cofactor Metabolic Engineering NAD/NADH
Cheryl Dittrich Cofactor Metabolic Engineering
Henry Lin Pathway design and analysis
Stephanie Portle Genetic networks
63
Metabolic Engineering and Systems Biotechnology
Laboratory
Ka-Yiu San
64
Questions ?
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