Transcript What is metabolic engineering?

Metabolic Engineering
and
Systems Biotechnology
Ka-Yiu San
Departments of Bioengineering
Departments of Chemical Engineering
Rice University
Houston, Texas
SOME MILESTONES
1968 Nirenberg, Khorana, and Holley awarded Nobel Prize
for elucidating genetic code.
1970 First restriction endonuclease isolated.
1972 DNA ligase joins two DNA fragments, creating first
recombinant DNA molecules.
1973 DNA inserted into plasmid vector and transferred to
host E. coli cell for propagation; cloning methods
established in bacteria. Potential hazards of
recombinant DNA technology raise concerns.
1976 National Institutes of Health prepares first guidelines
for physical and biological containment; DNA
sequencing methods developed.
1977 Genentech, the first biotechnology firm, established.
Introns discovered.
mRNA
Protein
Restriction
cleavage
Recombined
plasmid
Transcription
Restriction sites
Restriction
cleavage
Gene of interest
Cloning for rProtein production
Cloning vector
Host cell
Recombinant proteins by microorganisms
Some early products
Year
1982
Products
Humulin
(synthetic insulin)
Disease
Type 1 diabetes
Company
Genetech, Inc.
1985
Protropin
Growth hormone
Deficiency
Genetech, Inc.
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
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
Modern biology – central dogma
Gene
Protein/
enzyme
mRNA
transcription
translation
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
Gene
Protein/
enzyme
mRNA
transcription
translation
Current projects
1. Cofactor engineering of Escherichia coli
A. Manipulation of NADH availability
B. Manipulation of CoA/acetyl-CoA
NADH
(Reduced)
NAD+
(Oxidized)
2. Plant metabolic engineering
3. Quantitative systems biotechnology
A.
B.
C.
D.
Rational pathway design and optimization
Metabolic flux analysis based on dynamic genomic information
Design and modeling of artificial genetic networks
Metabolite profiling
4. Genetic networks – architectures and physiology
Current Projects
Pathway and Cofactor Metabolic Engineering
I
1
2
II
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
Plant Metabolic Engineering
3
III
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
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)
6
Modeling and design of gene switching networks for optimal control of PHA
nanostructures (with Drs. Mantzaris and Bennett,)
BES0331324
From Genetic Architecture to Adaptation Dynamics (with Drs. Mantzaris –
PI, Bennett, and Zygourakis).
NIH
R01GM071888
7
IV
EPA
RD-83144101
NSF
Instrumentation
8
MRI: Acquisition of Multiple Instruments for Research and Education
9
Shimadzu Instrumentation Grant
NSF
BES-0420840
Cofactor engineering
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
Importance of cofactor manipulation
Enzymes + Cofactors
Substrate
Products
Cofactor engineering
• NAD+/NADH
• CoA/acetyl-CoA
NADH/NAD+ Cofactor Pair
• Important in metabolism
– Cofactor in > 300 red-ox reactions
– Regulates genes and enzymes
• Donor or acceptor of reducing equivalents
• Reversible transformation
NADH
(Reduced)
NAD+
(Oxidized)
• Recycle of cofactors necessary for cell growth
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.
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
Example: Lactic acid formation
Lactic acid
Polylactic acid (PLA)
LDH
Pyruvate
NADH
Lactate
NAD+
Biopolymer production
Poly(3-hydroxybutyrate- co-3(PHB/PHV block copolymer)
hydroxyvalerate)
Glycerol
Propionate
Acetyl-CoA
Propionyl-CoA
Acetyl-CoA
3-Ketothiolase (PhaA)
HSCoA
Acetoacetyl-CoA
3-Ketovaleryl-CoA
NADPH
Acetoacetyl-CoA
Reductase (PhaB)
NADP+
3-Hydroxybutyryl-CoA
3-Hydroxyvalery-CoA
PHA Synthase (PhaC)
HSCoA
P(HB-co-HV)
HSCoA
Polyketide production
• Complex natural products
• > 10,000 polyketides identified
• Broad range of therapeutic applications
• Cancer (adriamycin)
• Infection disease (tetracyclines, erythromycin)
• Cardiovascular (mevacor, lovastatin)
• Immunosuppression (rapamycin, tacrolimus)
6-deoxyerythronolide B
Polyketide production
Precursor supply - example
Ref: Precursor Supply for Polyketide Biosynthesis: The Role of Crotonyl-CoA Reductase, Metabolic Engineering 3,
40-48 (2001)
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
Manipulation of NADH availability
Fermentation Pathway of E. coli
Glucose
NAD+
NADH
Succinate
2NAD+ 2NADH
Pyruvate
Lactate
NADH
Formate
NAD+
Acetyl-CoA
2NADH
2NAD+
Ethanol
Acetate
NADH Regeneration
Pyruvate
NADH
NAD+
Formate
CO2
FDH1
PFL
Acetyl-CoA
FDHF
CO2
H2
original NAD independent pathway
(FDHF: formate dehydrogenase, NAD independent)
Newly added NAD+ dependent pathway
(FDH1: NAD+ dependent formate dehydrogenase
FDH1 encoded by fdh1 from Candida boidinii)
Construction of pSBF2 Overexpressing FDH
pFDH1
PCR
fdh
fdh
pSBF2
XbaI
pSBF2
fdh
fdh
EcoRI/XbaI
pUC18
pUCFDH
XbaI
pDHK30
pDHK30
Assay of FDH activity
Strain
FDH activity (units/mg protein)
GJT001(pSBF2)
0.42
BS1(pSBF2)
0.28
GJT001(pDHK29)
Not detected
BS1(pDHK30)
Not detected
Characterization of NADH-dependent FDH
PanK
NADH-dependent
FDH
PanK
NADH-dependent
FDH
XbaI
lacZ'
lacZ
MCS
KmR
pDHK29
pSBF2
fdh
KmR
Ori
Ori
GJT (pDHK29)
(Control strain)
GJT (pSBF2)
(New strain)
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
Effect of Increasing NADH Availability
% of Increase/Decrease for GJT001 (pSBF2) relative to GJT001 (pDHK29)
3-fold Glucose
Consumed
Succinate
55%
NAD+
NADH
2NAD+ 2NADH
Pyruvate
NAD
H
Formate
Converted
8-fold
NADH
CO2
NAD+
Lactate
91%
Acetyl-CoA
2NADH
2NAD+
Acetate
43%
NAD+
Formate
FDH1
FDHF
CO2
H2
Ethanol
15-fold
O.D.600nm: 59%
Et/Ac: 27-fold
mol NADH/mol glucose
NADH Availability
5.0
4.0
3.0
2.0
1.0
0.0
GJT(pDHK29)
GJT(pSBF2)
Ethanol Concentration (mM)
Ethanol Concentration
(reduced product)
200
180
160
140
120
100
80
60
40
20
0
GJT001(pDHK29)
GJT001(pSBF2)
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
Quantitative systems biotechnology
Projects
1. Metabolic flux analysis based on
dynamic genomic information
2. Rational pathway design and optimization
- feasible and realizable new network
design
3. Design and modeling of artificial genetic
networks
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
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?
Traditional flux balance analysis (FBA)
Genome
Database
Pathway
Database
A priori
Knowledge
Metabolic
Network
FBA
Metabolic
Pattern
Metabolic Network
(From http://www.genome.ad.jp/kegg/pathway/map/map00020.html)
Metabolic Pattern (Illustration)
1.0
0.8
0.2
0.8: Metabolic rates
(From http://www.genome.ad.jp/kegg/pathway/map/map00020.html)
genotype
phenotype
genetic
environmental perturbations
perturbations (mutant strains)
Transcription
Translation
Metabolic Flux Analysis
Gene
mRNA
Protein/
enzyme
Stimuli
traditional metabolic
engineering study
Cellular
Responses
OR
Metabolite
Patterns
Proposed New Approach
Genome
Database
Pathway
Database
A priori
Knowledge
Genetic
Structure
Metabolic
Network
FBA Metabolic
Patterns
?
Expression
Patterns
Genetic
Network
Environmental
Conditions
Gene Regulation
Knowledge
Gene Chip (Array) Data
Model System
• Oxygen and redox sensing/regulation system
• Sugar utilization regulatory network
Simplified schematic of E. coli central metabolic pathways
Glucose
PEP
Pyruvate
ppc
CoA
NADH, CO2
Formate
[4.1.1.31]
pdh
[1.1.1.28]
H2 + CO2
pfl
[1.2.4.1]
CO2
Lactate
ldhA
NAD+,CoA
[2.3.1.54]
Acetyl- CoA
Ethanol
gltA
aspC
[4.1.3.7]
Oxaloacetate
NADH
[2.6.1.1] NAD+
[1.1.1.37]
NAD+
Aspartate
acnB
mdh
NADH
Acetate
Citrate
[4.2.1.3]
Isocitrate
Malate
NADP+
aspA
fumB
fumA
icd
[4.3.1.1]
[4.2.1.2]
[4.2.1.2]
[1.2.4.2]
NADPH
Fumarate
frdABCD
[1.3.1.6]
sdhCDAB
NADH
NAD+
CO2
[1.3.99.1]
Succinate
sucCD
[6.2.1.5]
sucAB
2-ketoglutarate
[1.2.4.2]
NAD+
NADH
Succinyl-CoA
CO2
Schematic showing selected oxygen and redox sensing
pathways in E. coli (adopted from Sawers, 1999)
Cytoplasmic
membrane
FNR FNR
e- transport
Redox,
metabolites
ArcB
P
Redox?
Aer
Dos
ArcA
O2
ArcA-P
CheW,A,Y
Transcription
O2
unknown
Energy taxis
Transcription
Some example of available pathway information
Recommended Name
EC
number
Reactions
pyruvate dehydrogenase
complex
1.2.4.1
Acetyl-CoA + CO2 +NADH
= CoA + pyruvate + NAD
aceEF
ArcA(-)
FNR(-)
1,3
4
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
pyruvate formate-lyase
Encoded
by
Effect
Ref
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, 25:361-87Ref 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) 164:81-90
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)
ldhA
aceB
mqo
aspA
fumB
frdABCD
pfl
cyd
cyo
ArcB
ArcA
FNR
fumC
aceEF
acnB
sdhCDAB
fumA
mdh
gltA
icd
sucAB
sucCD
We have 3 sensing/regulatory components whose
activity evolves according to the Boolean mapping
coded in the figure. Here
green
red
denotes repress and
denotes activate. When two components
regulate a third we suppose their action to be an
“and”. These regulatory components determine the
O2
ArcA
Stimulus
FNR
Sensors/regulators
aceEF
pfl
genes
PDH
PFL
enzymes
CO2
CoA
NADH
NAD+
Acetyl-CoA
formate
Metabolites
pyruvate
activation
repression
Work in progress
To develop a model that can provide dynamic
and automatic adaptation of pathway map to
environmental conditions
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
genotype
phenotype
genetic
environmental perturbations
perturbations (mutant strains)
Gene
mRNA
Protein/
enzyme
Stimuli
Functional Genomics
Genomics
Cellular
Responses
OR
Metabolite
Patterns
Metabolomics
Proteomics
Functional Genomics
Proteinomics
• 2D gel electrophoresis
• Mass spectrometry
• Bioinformatics
• Protein "chips"
2D gel electrophoresis
• IEF
• Size
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.
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.
Shimadzu LCMS 2010A
Shimadzu QP-2010
Collaborators
Dr. George N. Bennett
Department of Biochemistry and Cell Biology
Rice University
Dr. Steve Cox
Department of Computational & Applied Math
Dr. Nikos Mantzaris
Department of Chemical Engineering
Dr. Kyriacos Zygourakis
Department of Chemical Engineering
Dr. Jacqueline V. Shanks
Department of Chemical Engineering
Dr. Ramon Gonzalez
Department of Chemical Engineering
Dr. Sue Gibson
Department of Plant Biology
Recent Graduates
Aristos Aristidou, Ph.D.
Cargill Dow
Chih-Hsiung Chou, Ph.D.
University of Waterloo, Canada
Peng Yu, Ph.D.
BMS
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
Valentis, Inc.
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
Metabolic Engineering and
Systems Biotechnology Laboratory
Ka-Yiu San
([email protected])
Office:
Lab:
GRB E200K
GRB E201, E202, E210, E128
Questions ?
???