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Departments of Bioengineering Departments of Chemical Engineering Rice University Houston, Texas

Metabolic Engineering and Systems Biotechnology. Ka-Yiu San. Departments of Bioengineering Departments of Chemical Engineering Rice University Houston, Texas. Recombined plasmid. Restriction cleavage. mRNA. Gene of interest. Translation. Restriction sites. Ligation.

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Departments of Bioengineering Departments of Chemical Engineering Rice University Houston, Texas

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  1. Metabolic Engineering and Systems Biotechnology Ka-Yiu San Departments of Bioengineering Departments of Chemical Engineering Rice University Houston, Texas

  2. 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

  3. 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

  4. Examples of a few biopharmaceutical products in 1994 Source: Biotechnology Industry Organization, Pharmaceutical Research and Manufacturers of America, company results, analyst reports

  5. 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

  6. Protein/ enzyme Gene mRNA transcription Modern biology – central dogma translation

  7. 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

  8. 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

  9. Current Projects

  10. Cofactor engineering

  11. 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

  12. Enzymes + Cofactors Substrate Products Importance of cofactor manipulation

  13. Cofactor engineering • NAD+/NADH • CoA/acetyl-CoA

  14. 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

  15. 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.

  16. 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

  17. Lactic acid Polylactic acid(PLA) LDH Pyruvate Lactate NAD+ NADH Example: Lactic acid formation

  18. 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)

  19. 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

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

  21. Approach Systematic manipulation of cofactor levels by genetic engineering means Model systems Simple model systems, such as biosynthesis of succinate and ester, to illustrate the concept Results • increased NADH availability to the cell • increased levels of CoA and acetyl CoA • significantly change metabolite redistribution

  22. Manipulation of NADH availability

  23. Glucose NAD+ Succinate 2NADH Pyruvate Lactate NADH Acetyl-CoA Formate 2NADH Acetate Ethanol 2NAD+ NAD+ NADH 2NAD+ Fermentation Pathway of E. coli

  24. Pyruvate NAD+ NADH PFL Formate CO2 FDH1 Acetyl-CoA FDHF CO2 original NAD independent pathway (FDHF: formate dehydrogenase, NAD independent) Newly added NAD+ dependent pathway (FDH1: NAD+ dependent formate dehydrogenase FDH1 encoded by fdh1from Candida boidinii) H 2 NADH Regeneration

  25. Construction of pSBF2 Overexpressing FDH pFDH1 PCR fdh fdh pSBF2 XbaI pSBF2 fdh fdh EcoRI/XbaI pUC18 pUCFDH XbaI pDHK30 pDHK30

  26. Strain FDH activity (units/mg protein) GJT001(pSBF2) 0.42 BS1(pSBF2) 0.28 GJT001(pDHK29) Not detected BS1(pDHK30) Not detected Assay of FDH activity

  27. XbaI lacZ' KmR lacZ fdh MCS pSBF2 pDHK29 KmR Ori Ori Characterization of NADH-dependent FDH NADH-dependent FDH NADH-dependent FDH PanK PanK GJT (pDHK29) (Control strain) GJT (pSBF2) (New strain)

  28. Anaerobic Tubes :Experimental Method • Strains : Escherichia coli (MC4100 derivative) • GJT001 (pDHK29): wild type (control plasmid) • GJT001 (pSBF2): wild type (new FDH plasmid) • Media: • LB + 1g/L NaHCO3 • 100mg/L Kanamycin • 20g/L Glucose • Temperature: 37 ºC • Agitation: 250 rpm • Samples: 72 hrs after inoculation • HPLC

  29. NADH 2NAD+ NAD+ 2NAD+ Effect of Increasing NADH Availability % of Increase/Decrease for GJT001 (pSBF2) relative to GJT001 (pDHK29) Glucose Consumed NAD+ 3-fold Succinate 55% 2NADH Lactate Pyruvate 91% NADH Formate Converted Acetyl-CoA 2NADH Acetate 8-fold 43% NADH NAD+ CO2 Formate Ethanol O.D.600nm: 59% Et/Ac: 27-fold FDH1 FDHF 15-fold CO2 H 2

  30. NADH Availability 5.0 4.0 mol NADH/mol glucose 3.0 2.0 1.0 0.0 GJT(pDHK29) GJT(pSBF2)

  31. Summary of results Effect of NADH regeneration (overexpressing NAD+-dependent FDH): • Increases intracellular NADH availability • Provide a more reduced environment • Increase reduced product (such as ethanol and succinate) productivity significantly

  32. Quantitative systems biotechnology

  33. 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

  34. Motivations Observations • Traditional reductionist approach • Knowledge at the basic and fundamental level • – but mostly isolated • Information overflow • Genome database, gene expression database (functional genomic), proteomic, metabolomics, metabolic pathway database • Most of the existing data base – static • Genome database, metabolic pathway database

  35. Motivations and objectives: How can one utilize the static genomic and metabolic databases (especially when genetic/regulatory network structures are available) to describe and predict cellular functions, such as metabolic patterns?

  36. Genome Database Pathway Database FBA Metabolic Pattern Metabolic Network A priori Knowledge Traditional flux balance analysis (FBA)

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

  38. Metabolic Pattern (Illustration) 1.0 0.8 0.2 0.8: Metabolic rates (From http://www.genome.ad.jp/kegg/pathway/map/map00020.html)

  39. 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

  40. ? 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

  41. Model System • Oxygen and redox sensing/regulation system • Sugar utilization regulatory network

  42. 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

  43. 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)

  44. 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)

  45. 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.

  46. O2 ArcA FNR pfl aceEF PDH PFL CO2 CoA NADH Acetyl-CoA NAD+ pyruvate Stimulus Sensors/regulators genes enzymes formate Metabolites activation repression

  47. Work in progress To develop a model that can provide dynamic and automatic adaptation of pathway map to environmental conditions

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