The document discusses metabolic pathway engineering and metabolic engineering. It provides an overview of four commercially important fermentation products, including the microorganism used, annual production levels, and applications. It then discusses the core concepts of metabolic engineering, including manipulating enzymatic and regulatory functions using recombinant DNA to improve cellular activities. Examples of applications include strain improvement for biocatalysis and bioprocessing, increasing productivity, and developing novel biosynthetic routes.

1. Metabolic Pathway Engineering
A schematic of the simplified core metabolic network
Prof. S.T. Yang
Department of Chemical and Biomolecular Engineering
The Ohio State University
Industrial fermentation products Fermentation
Four Types of Commercially Important
Fermentation Products
Production Microorganism Separation Applications
(metric tons) method
Citric acid 1,200,000 A. niger Extraction Food • Microbial cells (biomass)
Ethanol 26,000,000 S. cerevisiae Distillation Fuel
Glutamate 1,000,000 C. glutamicum Crystallization Flavoring • Microbial enzymes (cell components)
Lactic acid 400,000 Lactobacillus sp. Extraction Food, Plastics
Lysine 800,000 C. glutamicum Crystallization Feed • Microbial metabolites
Penicillin 60,000 P. chrysogenum Extraction Drug – Primary metabolites (ethanol, citric acid)
Xanthan gum 100,000 X. campestris Precipitation Food, Oil
drilling – Secondary metabolites (antibiotics)
• Microbial transformation (steroids)
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2. Regulation of gene expression in
Metabolic Engineering the metabolic network
substrates
Environment nutrients
oxygen temperature
Metabolome
pH
ions
Proteome
Transcriptome
Genome
DNA
mRNA
Protein
Metabolite
Regulatory mechanisms constrain network functions and produce a small range of
physiologically meaningful behaviors from all allowable network functions. Reduce
extreme pathways from 80 to 2 ~ 26. J. Theor. Biol., 221: 309-325 (2003)
JBC 277: 28058–64, 2002
Metabolic Engineering
• A living cell is a complex chemical reactor in
which more than 1000 independent highly
coupled enzyme-catalyzed reactions and
selective membrane transport occur.
• ME is “the improvement of cellular activities by
manipulating enzymatic, regulatory and transport
functions of the cell with the use of recombinant
DNA technology” (Jay Bailey, 1991)
Combined regulatory/metabolic network for central metabolism in E. coli. All of the metabolic genes
considered are shown. The genes that are regulated are indicated by the color code shown in the legend.
Genes or reactions regulated by multiple regulatory proteins or molecules are shown with multiple arrows.
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3. Metabolic Engineering Metabolic Engineering
Classical strain improvement (CSI)
Applications
Random mutagenesis to accumulate genomic
alterations and screening for the phenotypes with Biocatalysis and bioprocessing (fermentation strain
desirable process characteristics improvement and metabolite overproduction)
Functional genomics, signal transduction, drug
Rational metabolic engineering discovery, gene therapy (biological discovery and
medical research)
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
Metabolic Engineering Metabolic Engineering
Recruiting heterologous activities for
Bioprocessing Applications
strain improvement
• Increase Productivity by improving cell
metabolism • Completion of partial pathways - Vit. C synthesis
– Product yield • Hybrid metabolic networks
– Production rate • Construct new array of enzymatic activities to
produce new products - novel antibiotics
– Cell growth efficiency (energy efficiency)
• Perfecting strains by altering nutrient uptake and
• Eliminate (reduce) undesirable byproducts metabolite flow - eliminating end product inhibition
• Eliminate (reduce) feedback inhibition • Transferring of promising natural motifs -
• Help media design enhanced oxygen transfer with cloned hemoglobin
gene
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4. Metabolic Engineering Metabolic Engineering in
Purpose (Fermentation) Industrial Biotechnology
To optimize a biotechnologically important process
carried out by organisms by genetic manipulations to
affect the distribution of intracellular chemical reactions
(flux)
Some Applications
Improvement of yield and productivity – amino acids
Production of novel compounds - polyketides
Extension of substrate range – ethanol from xylose
Development of novel biosynthetic routes – indene
Improving cell growth and fermentation kinetics
Some Examples
Anaerobic central Glucose Succinic acid Glucose
PEP
PEP
metabolic pathway ptsG production in ptsG X
Pyruvate
Pyruvate Glucose-6-P
of E. coli Glucose-6-P
E. coli
2 NAD+
Phosphoenolpyruvate
2 NADH
poxB
Phosphoenolpyruvate
ppc Pyruvate X Acetate
ppc CO2
CO2 NADH NAD+ pdc
ldhA pyc ackA
Oxaloacetate Pyruvate D(−)-Lactate Acetyl-CoA pta X
pyc L(+)-Lactate X
NADH ldhL Oxaloacetate Acetyl-P
pfl
NAD+ CoA
Formate H2
Malate Acetyl-CoA Citrate X
Malate iclR aceBAK
Acetyl-CoA CO2
pta aceB Glyoxylate
aceA
Fumarate Acetyl-P Fumarate Isocitrate
NADH 2 NADH ackA aceA
adhE sdhAB X icd CO2
frd NAD+ 2 NAD+ Acetate 2-Ketoglutarate
Succinate
Succinate Ethanol
Succinyl- CO2
CoA
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5. Ethanol production from xylose in yeasts Sorona from Corn derived
Glucose
1,3-Propanediol
Xylose J. Polymers and the Environment,
XR Vol. 13, No. 2, April 2005
XYL1 NAD(P)H
NAD(P)+
Glucose-6P Fru-6P Ery-4P XI
Xylitol XylA
ZWF1 TAL1 NAD+
GND1 XDH NADH
CO2 XYL2
Gly-3P Sed-7P
Ribulose-5P Xylulose
TKL1
RKI1
Ribose-5P XYL3, XKS1 • DuPont’s Sorona fermentation plant
Xylulose-5P
• E. coli (10-year genetic engineering work)
Pyruvate • Reactor: Bubble column (30 m tall)
TCA Other • Capacity: 100,000 lbs/yr
cycle metabolites • Fermentation performance:
Ethanol
– Volumetric productivity: 3.5 g/L/h
– Product concentration: 135 g/L
– Yield: 0.51 g/g glucose
1,3-Propanediol from Glucose in E. coli
1,3-
Pure L-(+)-Lactic Acid from
Glucose
PEP-dependent
glucose transport
ATP-dependent
glucose transport
L. helveticus
PEP, ATP X 2 ATP
tpi
DHAP GAP
DAR1 NADH gap
GPP2
glpK gldA TCA cycle and
Glycerol x
respiration
dhaB1-3
(Cell mass and NADH, etc.)
3-hydroxypropionaldehyde Replacement of the ldhD structural gene with ldhL. The overlapping
oligonucleotides used in constructing the mRNA joint between the ldhD
NADPH
yqhD promoter region and the ldhL structural gene are shown. PldhD and tslpA refer
to the ldhD promoter region and slpA transcription terminator, respectively.
1,3-propanediol APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 66: 3835–3841 (2000)
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6. Lactic acid production in yeast Polyhydroxybutyrate (PHB)
Glucose
HMP
EMP
2 NAD+ Ethanol
2 NADH NAD+
NAD+ NADH ADH
NADH
Lactate Pyruvate X Acetaldehyde
LDH PDC
X PDH NAD(P)+
AldDH
transport NAD(P)H
Plasmid pEPL2
Acetyl-CoA between Acetate
cytosol and
mitochondria
ACS
TCA cycle Acetyl-CoA
Kluyveromyces lactis Biosynthetic pathway of poly(3-hydroxybutyrate). P(3HB) is synthesized by the
successive action of b-ketoacyl-CoA thiolase (phbA), acetoacetyl-CoA reductase (phbB)
Simple media
and PHB polymerase (phbC) in a three-step pathway. The genes of the phbCAB operon
Low pH encode the three enzymes. The promoter (P) upstream of phbC transcribes the complete
operon (phbCAB). Bioresource Technology 87 (2003) 137–146.
PHA Glycerol
Glucose Alkanoates
Indigo
Fatty acids
Propionic acid Acetic acid FadD Glucose
PEP is
PP pathway Tryptophan
id es Transketolase (tktA)
CoA ac nth
tty sy
EMP
Oxalo- Pyruvate Fa vo pathway
E4P
CoA Acyl-CoA Tryptophanase
acetate no (tnaA) Tryptophan
synthase (trpB)
de Tryptophan
FadA FadE synthase
Acetyl-CoA DAHP synthase (trpA) Indole
Fatty acid (aroGfbr)
Indole 3-glycerol Naphthalene
TCA cycle PhaA 3-Keto β-oxidation Trans-2- PEP phosphate dioxygenase (NDO)
Citrate acyl-CoA Enoyl-CoA Pyruvate kinase DAHP
(pykA, pykF)
Succinyl-CoA Acetoacetyl-CoA
FadB FadB Indoxyl
Sbm [O2]
(S)-3-Hydroxy PhaJ
PhaB NADPH
(R)-Mythyl-malonyl-CoA acyl-CoA YfcX Pyruvate
Isatin
YgfG PhaB epimerase
MaoC Isatin hydrolase
(R)-3-Hydroxy FadG
Propionyl-CoA
PhaA butyryl-CoA (R)-3-Hydroxy TCA cycle
3-Keto-valeryl-CoA acyl-CoA Indigo
Isatic acid Indirubin
PhaB PhaC PhaC
3-Hydroxyl-valeryl-CoA
PhaC
P(3HB) PHAMCL Indigo biosynthetic pathway created by the merger of indole biosynthesis and NDO
P(3HB-co-3HV) P(3HB-co-3HAMCL) activity in one organism
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7. Histidine Ribose-5-P
NADPH Glucose
Amino Acids 3-PG
β-lactame Antibiotics
PP pathway
Tryptophan Erythrose-4-P NADPH
Phenylalanine Phosphoenolpyruvate NADP+
NADPH
Tyrosine PK lat
PEPC Lysine P6C α-AAA L-Cysteine L-Valine Pyruvate
Pyruvate PDH
biotin
Acetyl-CoA Fatty acid LAT
PC DtsR NADP+
pcbAB ACV synthetase
Aspartate Oxaloacetate ACV
lysC AsK CS pcbC IPN
Citrate α-AAA PAA synthase POA α-AAA
4-Aspartylphosphate Malate
Penicillin G Isopenicillin N Penicillin V
TCA cycle penDE
Aspartate-4-semialdehyde AA cefD
Penicillin N
2-Oxoglutarate Chemical ring
dapA HDH expansion cefE
2,3-Dihydrodipicolinate hom NH3 NADPH Ad-6-APA
ODHC odhA
DAOC
NH3 Homoserine GDH Phenylacetyl-7-ADCA cefE cefEF
Succinyl-CoA cefF
NADPH cefG
thrB HK Penicillin DAC Cephalosporin C
Glutamate acylase Ad-7-ADCA Ad-7-ACA
cmcH
NADP+ Homoserine-P Acylase
Methionine OCDAC
7-ACA cmcI
2,6-diaminopimelate TD
Threonine Isoleucine 7-ADCA cmcJ
HOCDAC Cephamycin C
Lysine
Metabolic Engineering Metabolic Engineering
Procedures
Redirecting metabolite flow • Determine target gene (genes)
• Genetic modifications
• Directing traffic toward the desired • Analysis of metabolic consequences of the changes
branch • Choice of next gene modifications
• Reducing competition for a limiting
resource Challenges
• Revising metabolic regulation • Difficult to target the gene (or genes) and to predict
the consequences of the changes in the metabolic
pathway
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8. Metabolic Engineering Metabolic Engineering
The goal is to develop some principles and engineering
• Uncertain results due to complicated metabolic tools (mathematical models) that can guide the choice of
pathways that are highly regulated by a myriad useful genetic alteration and predict its consequences
of genes and enzymes of which many may still
not known Approaches / Tools
• Success usually came from many trials after • Stoichiometric analysis of metabolic
long research and hard development efforts –
costly and time consuming (fermentation) pathway (mass balance)
• It is more challenging when there is limited • Thermodynamic analysis of energetics of
knowledge on the organism and its genomics enzyme reactions (energy balance)
and metabolic pathway • Metabolic control (flux) analysis (reaction
kinetics)
Metabolic Engineering Metabolic Flux Analysis
Zhang et al., Biochem. Eng. J. 2003;16:211-220
Genetic Modifications
Gene targeting
Hypothesis Mutant strains
Overexpression of native genes
Gene knock-out
Expression of heterologous genes
Modeling and Analysis Metabolic Characterization
Metabolic flux analysis Metabolite profiling
Data
Metabolic control analysis - extracellular metabolites
Metabolic network analysis - isotopomer intracellular metabolites
- Flux control analysis Transcriptomics - cDNA microarrays
- Pathway analysis Proteomics - 2D-gel electrophoresis
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9. Comparison of gene expression profiles
Proteome Profiling
Comparison of the expression profiles of genes for enzymes that participate in key metabolic processes
involved in the utilization of metabolites during glucose exhaustion in T. reesei and S. cerevisiae. Red
and green boxes represent those genes whose expression increases and decreases, respectively, upon glucose Han, M.-J., S.Y. Lee. 2003. Proteome profiling and its use in metabolic and
exhaustion. White boxes indicate those genes that are unaffected. Yellow boxes represent genes that have yet cellular engineering. Proteomics 3: 2317-2324.
been not isolated from T. reesei. THE JOURNAL OF BIOLOGICAL CHEMISTRY, 277: 13983–13988, 2002.
In Silico Modeling
In Silico Modeling
In silico modeling of metabolism and transcriptional regulation using the constraints-based
approach. A, the constraints based approach to metabolic modeling. Flux-balance analysis can be
used to identify particular optimal solutions (such as optimization of growth) within the space (blue
point). B, transcriptional regulation reduces the steady-state solution space. (JBC 277: 28058–64, 2002)
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10. Genome-Based Modeling Genome Shuffling
In Silico Analysis
Metabolic network reconstruction
Methodology of genome-based reconstruction of a classically derived production strain. Candidates for the relevant
mutations are introduced one by one from the relevant terminal pathways to central metabolism into the wild-type genome by
allelic replacement. Only the relevant mutations (open squares) are saved to generate a defined mutant with the minimal
mutation set that is necessary and sufficient for high-level production (minimal mutation strain)
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