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Metabolic Engineering and Systems Biotechnology. Ka-Yiu San. Departments of Bioengineering Rice University Houston, Texas. What is metabolic engineering?.

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slide1
Metabolic Engineering

and

Systems Biotechnology

Ka-Yiu San

Departments of Bioengineering

Rice University

Houston, Texas

slide2
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

slide4
Recombined

plasmid

Restriction

cleavage

mRNA

Gene of interest

Translation

Restriction sites

Ligation

Transcription

Protein

Restriction

cleavage

Transformation

Cloning vector

Host cell

Cloning for rProtein production

slide5
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

slide6
Examples of a few biopharmaceutical products in 1994

Source: Biotechnology Industry Organization, Pharmaceutical Research and Manufacturers of America, company results, analyst reports

slide7
NADH

(Reduced)

NAD+

(Oxidized)

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
slide8
Protein/

enzyme

Gene

mRNA

transcription

Modern biology – central dogma

translation

slide9
Protein/

enzyme

Gene

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

motivations and hypothesis
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

slide13
Cofactor engineering
  • NAD+/NADH
  • CoA/acetyl-CoA
nadh nad cofactor pair
NADH (Reduced)

NAD+

(Oxidized)

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
  • Recycle of cofactors necessary for cell growth
slide15
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.
slide16
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
slide17
Biopolymer production

Poly(3-hydroxybutyrate- co-3-hydroxyvalerate)

(PHB/PHV block copolymer)

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

HSCoA

P(HB-co-HV)

slide18
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

slide19
Polyketide production

Precursor supply - example

Ref: Precursor Supply for Polyketide Biosynthesis: The Role of Crotonyl-CoA Reductase, Metabolic Engineering 3, 40-48 (2001)

slide20
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
slide21
Metabolic engineering

of

plant tissue

slide22
Motivations

To improve the production of some important plant compounds though metabolic engineering

slide23
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
slide24
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
slide25
Clone Generation

Adapt to Liquid Media

(16 weeks)

Plasmid Construction in E. coli

ATCC 15834 A. rhizogenes

Desired gene

Ri

Sterile Grown Plants (5 weeks)

Infection

(6 weeks)

Selection Media

(6 weeks)

slide27
*

*

slide28
*

*

*

slide29
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
artemisia annua
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
motivation
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)

slide32
3-Acetyl-CoA

Pyruvate + G3P

DXS

HMG-CoA

1-Deoxy-D-Xylulose-5-Phosphate

HMGR

DXR

Mevalonate

2-C-Methyl-D-erythritol-4-phosphate

DMAPP

IPP

? IPP ?

CYTOSOL

FPPS

IPP

DMAPP

FDP

PLASTID

SQS

SQC

GPP

Squalene

Sesquiterpenes

Monoterpenes, diterpenes, carotenoids, etc.

Artemisinin

Sterols

(Souret et al. 2003)

Amorpha-4,11-diene

Artemisinic Acid

FDP

Artemisinin

strategy for me
Strategy for ME

m/z spectra for artemisinin

  • Detect artemisinin in hairy roots using LCMS

Artemisinin

(283.1)

slide35
3-Acetyl-CoA

Pyruvate + G3P

DXS

HMG-CoA

1-Deoxy-D-Xylulose-5-Phosphate

HMGR

DXR

Mevalonate

2-C-Methyl-D-erythritol-4-phosphate

DMAPP

IPP

? IPP ?

CYTOSOL

FPPS

IPP

DMAPP

FDP

PLASTID

SQS

SQC

GPP

Squalene

Sesquiterpenes

Monoterpenes, diterpenes, carotenoids, etc.

Artemisinin

Sterols

(Souret et al. 2003)

Amorpha-4,11-diene

Artemisinic Acid

FDP

Artemisinin

slide37
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
slide38
Metabolic Network

(From http://www.genome.ad.jp/kegg/pathway/map/map00020.html)

slide39
Metabolic Pattern (Illustration)

1.0

0.8

0.2

0.8: Metabolic rates

(From http://www.genome.ad.jp/kegg/pathway/map/map00020.html)

slide40
Genome

Database

Pathway

Database

FBA

Metabolic

Pattern

Metabolic

Network

A priori

Knowledge

Traditional flux balance analysis (FBA)

slide41
genotype

phenotype

environmental

genetic

perturbations

perturbations

(mutant strains)

Cellular

Responses

Transcription

Translation

Metabolic Flux Analysis

OR

Metabolite

Patterns

Protein/

enzyme

Gene

mRNA

Stimuli

traditional metabolic engineering study

slide42
?

Genome

Database

Pathway

Database

Genetic

Structure

Expression

Patterns

Genetic

Network

A priori

Knowledge

FBA

Metabolic

Network

Metabolic

Patterns

Gene Regulation

Knowledge

Gene Chip (Array) Data

Proposed New Approach

Environmental

Conditions

slide43
Model System
  • Oxygen and redox sensing/regulation system
  • Sugar utilization regulatory network
slide44
Simplified schematic of E. coli central metabolic pathways

Glucose

PEP

Pyruvate

Lactate

ldhA

[1.1.1.28]

NAD+,CoA

ppc

[4.1.1.31]

NADH, CO2

pdh

[1.2.4.1]

CoA

pfl

[2.3.1.54]

H2 + CO2

Formate

CO2

Acetyl- CoA

Ethanol

gltA

[4.1.3.7]

Acetate

Citrate

Oxaloacetate

aspC

[2.6.1.1]

NADH

acnB

[4.2.1.3]

mdh

[1.1.1.37]

NADH

NAD+

NAD+

Isocitrate

Aspartate

Malate

aspA

[4.3.1.1]

fumB

[4.2.1.2]

fumA

[4.2.1.2]

icd

[1.2.4.2]

NADP+

NADPH

Fumarate

sdhCDAB

[1.3.99.1]

CO2

NADH

frdABCD

[1.3.1.6]

2-ketoglutarate

NAD+

sucAB

[1.2.4.2]

Succinate

sucCD

[6.2.1.5]

NAD+

NADH

Succinyl-CoA

CO2

Simplified schematic of E. coli central metabolic pathways

slide45
e- transport

Cytoplasmic

membrane

ArcB

P

FNR

FNR

Redox,

metabolites

Aer

Redox?

Dos

ArcA

O2

O2

CheW,A,Y

ArcA-P

Transcription

unknown

Energy taxis

Transcription

Schematic showing selected oxygen and redox sensing pathways in E. coli (adopted from Sawers, 1999)

slide46
Some example of available pathway information

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)

slide47
ldhA

aspA

fumB

frdABCD

pfl

cyd

cyo

ArcB

aceB

mqo

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 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.

slide48
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
slide53
MG1655

MG1655 [arcA]

MG1655 [fnr]

▲ MG1655 [arcA fnr]

slide57
2D-NMR spectrum

13C-

glucose

Continuous

culture

Samples

GC-MS spectrum

Positional

Enrichments

Relative

intensities

of multiplets

1D-NMR spectrum

slide58
Glucose

Gly

Ser

GAP

Val

Tyr

Trp

Phe

PEP

Ala

Ile

PYR

Leu

Lys

Met

Glx

Asx

aKG

OAA

Pro

Thr

Arg

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

slide65
ArcB

FNR

slide66
ArcA

Total balance

slide68
Integrated Approach
  • Experiments
  • Mathematical modeling and computer simulations
slide69
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

Department of Plant Biology

slide71
([email protected])

Office:

GRB E200K

Lab:

GRB E201, E202, E210, E128, E121

Metabolic Engineering and

Systems Biotechnology Laboratory

Ka-Yiu San

slide72
???

Questions ?

strategy for me73
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
slide74
genotype

phenotype

environmental

genetic

perturbations

perturbations

(mutant strains)

Cellular

Responses

OR

Metabolite

Patterns

Protein/

enzyme

Gene

mRNA

Stimuli

Functional Genomics

Metabolomics

Proteomics

Genomics

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