Departments of Bioengineering Rice UniversityHouston, Texas

Ka-Yiu San

Metabolic Engineering and

Systems Biotechnology

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

What is metabolic engineering?

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.

Cloning for rProtein productionR

est

rict

ion

site

s

Cloning vector

Ligation

Recombined plasmid

Restrictioncleavage

Restrictioncleavage

Ge

ne

of i

nte

rest

Transformation

Tra

nsc

rip

tion

Translation

mR

NA

Pro

tein

Host cell

Recombinant proteins by microorganisms

Year Products Disease Company 1982 Humulin Type 1 diabetes Genetech, Inc.

(synthetic insulin) 1985 Protropin Growth hormone Genetech, Inc.

Deficiency

Some early products

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

Examples of a few biopharmaceutical products in 1994

Current projects

1. Cofactor engineering of Escherichia coli A. Manipulation of NADH availability B. Manipulation of CoA/acetyl-CoA

2. Plant metabolic engineering

3. Quantitative systems biotechnologyA. Rational pathway design and optimization B. Metabolic flux analysis based on dynamic genomic informationC. Design and modeling of artificial genetic networksD. Metabolite profiling

4. Genetic networks – architectures and physiology

NADH (Reduced)

NAD+

(Oxidized)

Gene

Modern biology – central dogma

mRNA

transcription

Protein/enzyme

translation

Current metabolic engineering approaches

• Amplification of enzyme levels • Use enzymes with different properties• Addition of new enzymatic pathway• Deletion of existing enzymatic pathway

Gene mRNA

transcription

Protein/enzyme

translation

Genetic manipulation

Cofactor engineering

HypothesisCofactor manipulation can be used as an additional tool to achieve desired metabolic engineering goals

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

Importance of cofactor manipulation

Enzymes

Cofactors

+

Products

Substrate

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

• Recycle of cofactors necessary for cell growth

NADH (Reduced)

NAD+

(Oxidized)

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

(PHB/PHV block copolymer)Poly(3-hydroxybutyrate- co-3-hydroxyvalerate)

Biopolymer production

Glycerol Propionate

Acetyl-CoA Propionyl-CoA

Acetoacetyl-CoA 3-Ketovaleryl-CoA

3-Hydroxybutyryl-CoA 3-Hydroxyvalery-CoA

Acetyl-CoA

HSCoA3-Ketothiolase (PhaA)

NADPH

NADP+

Acetoacetyl-CoAReductase (PhaB)

P(HB-co-HV)

HSCoAHSCoAPHA Synthase (PhaC)

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 productionPrecursor supply - example

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

Results

ApproachSystematic manipulation of cofactor levels by genetic engineering means

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

Metabolic engineeringof

plant tissue

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

Motivations

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

Transgenic C. roseus Work• Cell Culture

• 35S Expression of ORCA3, STR, TDC

Indole Pathway• Feedback Resistant AS

• TDC overexpression

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

AS

TDC

Clone Generation

Plasmid Construction

in E. coli

ATCC 15834 A. rhizogenes

Ri

Sterile Grown

Plants

(5 weeks) Infection

Desired gene

(6 weeks) Selection Media

(6 weeks)

Adapt to Liquid Media

(16 weeks)

Biosynthesis of TIAs in C. roseus Plant

Shikimate Pathway DXP Pathway

Pyruvate + GA-3P

DXS 1-Deoxy-D-Xylose-5-phosphate DXR Mevalonate 2-C-Methyl-D-erythitol-4-phosphate Chorismate DMAPP IPP AS/AS GPPS

Anthranilate GPP Geraniol G10H 10-Hydroxygeraniol Tryptophan Loganin TDC Tryptamine Secologanin

SSS (Indole Pathway) (Monoterpenoid Pathway) Strictosidine SGD 4,21-Dehydrogeissoschizine

Stemmadenine

Cathenamine

Ajamalicine Tabersonine Catharanthine T16H

Serpentine Lochnericine D4H Hörhammericine DAT

Vindoline Vinblastine Vincristine

0

500

1000

1500

2000

2500

Serpentine Catharanthine Ajamalicine Hörhammericine Lochnericine Tabersonine

Alkaloid

Con

cent

ratio

n(u

g/g

dry

wei

ght)

Uninduced Induced

*

*

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Ajm+Serp Loch+Hor+Tab Total

Alkaloid

Con

cent

ratio

n(u

g/g

dry

wei

ght)

Uninduced Induced

*

*

*

Transgenic C. roseus Work• Cell Culture

• 35S Expression of ORCA3, STR, TDC

Indole Pathway• Feedback Resistant AS

• TDC overexpression

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

AS

TDC

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• 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)

Pyruvate + G3P

1-Deoxy-D-Xylulose-5-Phosphate

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

DXS

DXR

IPP DMAPP

GPP

Monoterpenes, diterpenes, carotenoids, etc.

PLASTID

? IPP ?

3-Acetyl-CoA

HMG-CoA

Mevalonate

IPPDMAPP CYTOSOL

FDP

Sesquiterpenes

Artemisinin

Squalene

Sterols

HMGR

FPPS

SQCSQS

FDPAmorpha-4,11-diene

Artemisinic Acid Artemisinin

(Souret et al. 2003)

Strategy for ME

• Detect artemisinin in hairy roots using LCMS

m/z spectra for artemisinin

Artemisinin(283.1)

Pyruvate + G3P

1-Deoxy-D-Xylulose-5-Phosphate

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

DXS

DXR

IPP DMAPP

GPP

Monoterpenes, diterpenes, carotenoids, etc.

PLASTID

? IPP ?

3-Acetyl-CoA

HMG-CoA

Mevalonate

IPPDMAPP CYTOSOL

FDP

Sesquiterpenes

Artemisinin

Squalene

Sterols

HMGR

FPPS

SQCSQS

FDPAmorpha-4,11-diene

Artemisinic Acid Artemisinin

(Souret et al. 2003)

Quantitative systems biotechnology

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

Projects

 

Metabolic Network

 

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

 

Metabolic Pattern (Illustration)

 

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

1.0 0.8

0.2

0.8: Metabolic rates

Traditional flux balance analysis (FBA)

FBAMetabolic

PatternMetabolic Network

Pathway Database

Genome Database

A priori Knowledge

geneticperturbations(mutant strains)

Stimuli

environmentalperturbations

traditional metabolic engineering study

Cellular Responses

ORMetabolite

Patterns

genotype phenotype

Gene Protein/enzyme

TranslationTranscription

mRNA

Metabolic Flux Analysis

Proposed New Approach

Gene Chip (Array) Data

FBAMetabolic Network

Metabolic Patterns

Pathway Database

Genome Database

A priori Knowledge

GeneticNetwork

Gene Regulation Knowledge

?Genetic Structure

Expression Patterns

Environmental Conditions

Model System

• Oxygen and redox sensing/regulation system

• Sugar utilization regulatory network

Simplified schematic of E. coli central metabolic pathwaysSimplified schematic of E. coli central metabolic pathways

CO2

ldhA[1.1.1.28]

Acetyl- CoA

NADPH

NADH

NAD+

NADH

CO2

Glucose PEP

NADP+

NAD+

pfl[2.3.1.54]

sucAB[1.2.4.2]

icd[1.2.4.2]

mdh[1.1.1.37]

sdhCDAB[1.3.99.1]

pdh[1.2.4.1]

sucCD[6.2.1.5]

fumB[4.2.1.2]

Aspartate

aspC[2.6.1.1]

aspA[4.3.1.1]

acnB[4.2.1.3]

frdABCD[1.3.1.6]

Oxaloacetate

Malate

Fumarate

Succinate

fumA[4.2.1.2]

2-ketoglutarate

Isocitrate

CitrategltA

[4.1.3.7]

CO2

ppc[4.1.1.31]

Succinyl-CoA

H2 + CO2Formate

CoA

Lactate

Ethanol

Acetate

Pyruvate

NADH, CO2

NAD+,CoA

NADH

NAD+

NADH

NAD+

Cytoplasmic membrane

O2

FNRFNR

Transcription

Aer

Energy taxis

CheW,A,Y

Redox,metabolites Dos

unknown

O2

e- transport

ArcA

ArcB P

ArcA-P

Transcription

Redox?

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

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(-)

1,34

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

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) 164:81-90Ref 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)

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.

ArcB

sucCDsucAB

aceEF

cyo

fumA icd

cydpflfumBaspAldhA

aceB

mqo

fumC

acnB

gltAmdhsdhCDAB

frdABCD

ArcA FNR

• 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

Biosystems

Analytic tools

Functional Genomics

Gene ExpressionGene ExpressionQRT-PCRQRT-PCR

Gene ExpressionGene ExpressionQRT-PCRQRT-PCR

MG1655MG1655 ▲ ▲ MG1655 [MG1655 [arcA arcA fnrfnr]] MG1655 [MG1655 [arcAarcA]] MG1655 MG1655 [[fnrfnr]]

0

50

100

150

200

250

300

0 2 4 6 8 10

Oxygen Concentration in the Headspace (%)

cydAB

Ex

pre

ss

ion

Re

lati

ve

to

MG

16

55

10

% O

2

Metabolic flux determination using C-13 labeling

Shimadzu LCMS 2010A

Shimadzu QP-2010(GCMS)

Continuousculture

13C-glucose Samples

2D-NMR spectrum

1D-NMR spectrumRelative intensities

of multiplets

GC-MS spectrum

Positional Enrichments

start

Set free fluxes

Flux estimation based on stoichiometric constraints

Simulating isotopomer distribution

Signal simulation

Optimal result achieved?

No

Yes

End

Glucose

GAP

PEP

PYR

SerGly

Tyr PheTrpVal

Ala

Leu

OAA aKG Glx

Pro

Arg

Asx

Thr

MetLys

Ile

Principle of flux analysis based on 13C-labeling experiment

G6P P5P

F6P

GAP

PEP Pyr AcCoA

Acetate

CO2

PEP

Pyr

CO2

CO2

OAA

AKG

99.7399.7999.7499.80

3PG

100100100100

106.5192.9995.7565.02Lac

0.084.440.0073.85

Suc

CO2

20.9212.9912.088.55

19.869.6918.055.04

Biomass

1.931.541.951.45

CO2

7.628.530.658.45

CO2

Formate

114.90148.73111.8876.93

28.6460.7749.9229.51

Ethanol

Sucex

13.294.4611.430.11

Glucose MG1655MG1655 ΔarcA MG1655 ΔfnrMG1655 ΔarcAΔfnr

192.89194.33192.83194.66

71.1783.1672.9088.22

49.1624.3953.9332.16

24.0315.4715.2210.89

0.010.000.000.01

G6P P5P

F6P

GAP

PEP Pyr AcCoA

Acetate

CO2

PEP

Pyr

CO2

CO2

OAA

AKG

99.7399.7999.7499.80

3PG

100100100100

106.5192.9995.7565.02Lac

0.084.440.0073.85

Suc

CO2

20.9212.9912.088.55

19.869.6918.055.04

Biomass

1.931.541.951.45

CO2

7.628.530.658.45

CO2

Formate

114.90148.73111.8876.93

28.6460.7749.9229.51

Ethanol

Sucex

13.294.4611.430.11

Glucose MG1655MG1655 ΔarcA MG1655 ΔfnrMG1655 ΔarcAΔfnr

192.89194.33192.83194.66

71.1783.1672.9088.22

49.1624.3953.9332.16

24.0315.4715.2210.89

0.010.000.000.01

Mathematical modeling and computer simulations

Inactive ArcAB system – with high oxygen

Active ArcAB system – with low oxygen

Active FNR system – with low oxygen

ArcAB and FNR reaction scheme

ATP + ArcB ArcB-P + ADP Phosporylation of ArcB

ArcB-P ArcB + P Dephosporylation of ArcB

ArcB-P + ArcA ArcA-P Phosporylation of ArcA

ArcB + Q ArcBQ Sequestration of ArcBP by quinone

ArcA + ArcA-P Dimer Dimerization of ArcA

Dimer + Dimer Tetramer Tetramerization of ArcA

FNR + FNR DFNR Dimerization of FNR

ko

k4

k3+k6

k-3

k1

k2

k5

k-5

k7

k-7

k+(Q)

k-

ATP + ArcB ArcB-P + ADP Phosporylation of ArcB

ArcB-P ArcB + P Dephosporylation of ArcB

ArcB-P + ArcA ArcA-P Phosporylation of ArcA

ArcB + Q ArcBQ Sequestration of ArcBP by quinone

ArcA + ArcA-P Dimer Dimerization of ArcA

Dimer + Dimer Tetramer Tetramerization of ArcA

FNR + FNR DFNR Dimerization of FNR

koko

k4k4

k3+k6

k-3

k3+k6

k-3

k1

k2

k1

k2

k5

k-5

k5

k-5

k7

k-7

k7

k-7

k+(Q)

k-

k+(Q)

k-

ArcB

FNR

][]][[][

][]][[][

][]][[

]][)[(]][[][

][]][[

][]][[

]][)[(]][[][

ArcBQKArcBQKdt

Qd

ArcBQkArcBQkdt

ArcBQd

ArcBPkArcAPArcBk

ArcAArcBPkkATPArcBkdt

ArcBPd

ArcBQkArcBQk

ArcBPkArcAPArcBk

ArcAArcBPkkATPArcBkdt

ArcBd

o

o

21

21

43

36

21

43

36

][])[(][

DFNRkFNRQkdt

DFNRd 2

Total balance

][][][

][][][][][

][][][

][][][][

DFNRFNRFNR

TDArcAPArcAArcA

ArcBQQQ

ArcBQArcBPArcBArcB

o

o

o

o

2

42

ArcA

][][][

][][][]][[][

][]][[]][[

]][)[(][

][]][[]][[

]][)[(][

TkDkdt

Td

TkDkDkArcAPArcAkdt

Dd

DkArcAPArcAkArcAPArcBk

ArcAArcBPkkdt

ArcAPd

DkArcAPArcAkArcAPArcBk

ArcAArcBPkkdt

ArcAd

72

7

72

755

553

36

553

36

22

high O2 low O2 very low O2

Simulation – tranient from high oxygen to low oxygen

Integrated Approach

• Experiments

• Mathematical modeling and computer simulations

Dr. George N. BennettDepartment of Biochemistry and

Cell Biology

Dr. Steve CoxDepartment of Computational &

Applied Math Rice University

Dr. Ramon GonzalezDepart of Chemical and

Biomolecular Engineering

Dr. Nikos MantzarisDepart of Chemical and

Biomolecular Engineering

Dr. Kyriacos ZygourakisDepart of Chemical and

Biomolecular Engineering

Dr. Jacqueline V. ShanksDepart of Chemical and

Biological Engineering

Dr. Sue I. GibsonDepartment of Plant Biology

Collaborators

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

Recent Graduates

Ka-Yiu San

Metabolic Engineering and Systems Biotechnology Laboratory

(ksan@rice.edu)

Office: GRB E200KLab: GRB E201, E202, E210, E128, E121

Questions ?

???

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

Genomics

geneticperturbations(mutant strains)

Stimuli

environmentalperturbations

Cellular Responses

ORMetabolite

Patterns

genotype phenotype

Gene Protein/enzymemRNA

Proteomics

Functional GenomicsMetabolomics

Gene ExpressionGene ExpressionQRT-PCRQRT-PCR

GeneGene Primer PairsPrimer Pairs PCR Products (bp)PCR Products (bp)

yfiDyfiD 5’-ACTAAAGCCGCTAACGACGA-3’5’-ACTAAAGCCGCTAACGACGA-3’5’-TTCAATGTCACCCAGTTTGC-3’5’-TTCAATGTCACCCAGTTTGC-3’

138138

pflA pflA 5’-TACGATCCGGTGATTGATGA-3’ 5’-TACGATCCGGTGATTGATGA-3’ 5’-TCACATTTTTGTTCGCCAGA-3’5’-TCACATTTTTGTTCGCCAGA-3’

151151

pflB pflB 5’-GCGAAATACGGCTACGACAT-3’5’-GCGAAATACGGCTACGACAT-3’ 5’-CATCCAGGAAGGTGGAGGTA-3’5’-CATCCAGGAAGGTGGAGGTA-3’

142142

pflC pflC 5’-GTCTGCACTGTGCGAAATGT-3’ 5’-GTCTGCACTGTGCGAAATGT-3’ 5’-GGACGTGCGAAAGAAAATGT-3’5’-GGACGTGCGAAAGAAAATGT-3’

134134

pflD pflD 5’-AGCCTCGCAGAAACACATTT-3’5’-AGCCTCGCAGAAACACATTT-3’5’-AGAACGTCTGCGGCTTATGT-3’5’-AGAACGTCTGCGGCTTATGT-3’

143143

pdhRpdhR 5’-GGAAGGTATCGCCGCTTATT-3’5’-GGAAGGTATCGCCGCTTATT-3’5’-CTGGAGTACGGCGTTTGATT-3’5’-CTGGAGTACGGCGTTTGATT-3’

136136

aceEaceE 5’-TCTGATCGACCAACTGCTTG-3’ 5’-TCTGATCGACCAACTGCTTG-3’ 5’-GGCGTTCCAGTTCCAGATTA-3’5’-GGCGTTCCAGTTCCAGATTA-3’

137137

fdhFfdhF 5’-AAACGGACTGGCAAATCATC-3’5’-AAACGGACTGGCAAATCATC-3’5’-GTTCGCCCATTTTCTCGTAA-3’5’-GTTCGCCCATTTTCTCGTAA-3’

141141

fhlAfhlA 5’-AGGCTCTTTCGCAACTGGTA-3’5’-AGGCTCTTTCGCAACTGGTA-3’5’-TGTGCCAGAACAGTTTCGTC-3’5’-TGTGCCAGAACAGTTTCGTC-3’

148148