departments of bioengineering rice university houston, texas ka-yiu san metabolic engineering and...
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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
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