Lecture 4 metabolic pathway eng


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Lecture 4 metabolic pathway eng

  1. 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) 1
  2. 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. 2
  3. 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 3
  4. 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 4
  5. 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) 5
  6. 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 6
  7. 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 7
  8. 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 8
  9. 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) 9
  10. 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) 10