Metabolic Engineering and Systems Biotechnology Ka-Yiu San Departments
Metabolic Engineering and Systems Biotechnology Ka-Yiu San Departments of Bioengineering Departments of Chemical Engineering Rice University Houston, Texas 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. a rm ti o Protein s fo an mRNA Transcription n Tr on a ti
Restriction sites io slat Lig Restriction cleavage Recombined plasmid n Tra Restriction cleavage Gene of interest
Cloning for rProtein production n Cloning vector Host cell Recombinant proteins by microorganisms Some early products Year 1982 Products Humulin (synthetic insulin) Disease Type 1 diabetes Company Genetech, Inc.
1985 Protropin Growth hormone Deficiency Genetech, Inc. Examples of a few biopharmaceutical products in 1994 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 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 Modern biology central dogma Gene Protein/ enzyme 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 Gene Protein/ enzyme mRNA transcription
translation Current projects 1. Cofactor engineering of Escherichia coli A. Manipulation of NADH availability B. Manipulation of CoA/acetyl-CoA NADH (Reduced) NAD+ (Oxidized) 2. Plant metabolic engineering 3. Quantitative systems biotechnology A. Rational pathway design and optimization B. Metabolic flux analysis based on dynamic genomic information C. Design and modeling of artificial genetic networks D. Metabolite profiling 4. Genetic networks architectures and physiology Current Projects
I Pathway and Cofactor Metabolic Engineering 1 2 II An integrated metabolic engineering study of evolved alcohol acetyl transferase enzymes in flavor compound formation in E. coli (with Dr. Bennett) NSF BES-0118815 USDA 2002-35505-11638 Plant Metabolic Engineering
3 III Collaborative research: Metabolic engineering of hairy roots for alkaloid production (with Dr. Gibson of UM and Dr. Shanks of Iowa State University) NSF BES-0224593 Quantitative Biosystems Engineering 4 Experimental driven computational analysis of E. coli global redox sensing/ regulatory networks and cellular responses (with Drs. Bennett amd Cox) 5 Collaborative research: Metabolic engineering of E. coli sugar-utilization regulatory systems for the consumption of plant biomass sugars (with Drs. Gonzalez and Shanks of Iowa State University)
6 Modeling and design of gene switching networks for optimal control of PHA nanostructures (with Drs. Mantzaris and Bennett,) BES0331324 7 From Genetic Architecture to Adaptation Dynamics (with Drs. Mantzaris PI, Bennett, and Zygourakis). NIH R01GM07188 8 IV NSF BES-0222691
EPA RD-83144101 NSF Instrumentation 8 MRI: Acquisition of Multiple Instruments for Research and Education 9 Shimadzu Instrumentation Grant NSF BES-0420840 Cofactor engineering 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 Importance of cofactor manipulation Enzyme + Cofactor s s Substr ate
Produc ts 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 NADH (Reduced) NAD+
(Oxidized) Recycle of cofactors necessary for cell growth 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 Example: Lactic acid formation Lactic acid Polylactic acid (PLA)
HSCoA 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 production Precursor supply - example Ref: Precursor Supply for Polyketide Biosynthesis: The Role of Crotonyl-CoA Reductase, Metabolic Engineering 3, 40-48 (2001) 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 Manipulation of NADH availability Fermentation Pathway of E. coli Glucose NAD+ NADH Succinate 2NAD+ 2NADH Pyruvate
NAD+ FDH1 Formate PFL Acetyl-CoA FDHF CO2 H2 original NAD independent pathway (FDHF: formate dehydrogenase, NAD independent) Newly added NAD+ dependent pathway (FDH1: NAD+ dependent formate dehydrogenase FDH1 encoded by fdh1 from Candida boidinii) Construction of pSBF2 Overexpressing FDH pFDH1
100 80 60 40 20 0 GJT001(pDHK29) GJT001(pSBF2) Succinate production from xylose (with overexpression of FDH and PEPC) 50 45 Concentration(mM) 40 35 114%
30 25 20 15 10 5 0 GJT (PDHK29 +pKK313) GJT (pSBF2+pKK313) 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 Quantitative systems biotechnology Projects
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 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
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? Traditional flux balance analysis (FBA) Genome Database Pathway Database A priori Knowledge Metabolic Network FBA
Patterns Genetic Network Environmental Conditions Gene Regulation Knowledge Gene Chip (Array) Data Model System Oxygen and redox sensing/regulation system Sugar utilization regulatory network Simplified schematic of E. coli central metabolic pathways Glucose PEP Pyruvate
ppc CoA NADH, CO2 Formate [22.214.171.124] pdh [126.96.36.199] H2 + CO2 pfl [188.8.131.52] CO2
Lactate ldhA NAD ,CoA + [184.108.40.206] Acetyl- CoA Ethanol gltA aspC [220.127.116.11] Oxaloacetate NADH
NAD+ NADH Succinyl-CoA CO2 Schematic showing selected oxygen and redox sensing pathways in E. coli (adopted from Sawers, 1999) Cytoplasmic membrane FNR FNR e- transport Redox, metabolites ArcB P Redox?
Aer Dos ArcA O2 ArcA-P CheW,A,Y Transcription O2 unknown Energy taxis Transcription Some example of available pathway information Recommended Name
EC number Reactions pyruvate dehydrogenase complex 18.104.22.168 Acetyl-CoA + CO2 +NADH = CoA + pyruvate + NAD aceEF ArcA(-) FNR(-) 1,3 4
1,2,4 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) ldhA aceB mqo aspA fumB frdABCD pfl
icd sucAB sucCD We have 3 sensing/regulatory components whose activity evolves according to the Boolean mapping coded in the figure. Here green red denotes repress and denotes activate. When two components regulate a third we suppose their action to be an O2 ArcA
Stimulus FNR Sensors/regulators aceEF pfl genes PDH PFL enzymes CO2 CoA NADH NAD+
Acetyl-CoA pyruvate formate Metabolites activation repression Work in progress To develop a model that can provide dynamic and automatic adaptation of pathway map to environmental conditions Biosystems 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 genotype phenotype genetic environmental perturbations perturbations (mutant strains) Gene mRNA Protein/ enzyme Stimuli
Functional Genomics Genomics Cellular Responses OR Metabolite Patterns Metabolomics Proteomics Functional Genomics Proteinomics 2D gel electrophoresis Mass spectrometry Bioinformatics Protein "chips" 2D gel electrophoresis
IEF Size Protein Chips The basic construction of such protein chips has some similarities to DNA chips, such as the use of a glass or plastic surface dotted with an array of molecules. Known proteins are analyzed using functional assays that are on the chip. For example, chip surfaces can contain enzymes, receptor proteins, or antibodies that enable researchers to conduct protein-protein interaction studies, ligand binding studies, or immunoassays High-end quadruple TOF tandem mass spectrometers enable high-performance protein identification, epitope and phosphorylation mapping, and protein-interaction analyses. Metabolomics Metabolomics is a relatively new discipline and techniques for
high-throughput metabolic profiling are still under development. No single technique is suitable for the analysis of all different types of molecule, so a mixture of techniques is used. Methods such as gas chromatography, high-pressure liquid chromatography and capillary electrophoresis are used to separate metabolites according to various chemical and physical properties. The molecules are then identified using methods such as mass spectrometry. Shimadzu LCMS 2010A Shimadzu QP-2010 Collaborators Rice University Dr. George N. Bennett Department of Biochemistry and Cell Biology Dr. Steve Cox Department of Computational & Applied Math Dr. Nikos Mantzaris
Department of Chemical Engineering Dr. Kyriacos Zygourakis Department of Chemical Engineering Dr. Jacqueline V. Shanks Department of Chemical Engineering Dr. Ramon Gonzalez Department of Chemical Engineering Dr. Sue Gibson Department of Plant Biology Recent Graduates Aristos Aristidou, Ph.D. Cargill Dow Chih-Hsiung Chou, Ph.D. University of Waterloo, Canada Peng Yu, Ph.D. BMS
Derek Sykes, M.S. Life Technology Irena Ying Chen, M.S. Kellog Yea-Tyng Yang, Ph.D. M.I.T. Susana Joanne Berrios Ortiz, Ph.D Shell Development Erik Hughes, Ph.D Wyeth Ravi Vadali
Eli Lilly Valentis, Inc. Current Lab Members Name Project Christie Peebles Plant Metabolic Engineering Sagit Shalel-Levanon Quantitative Systems Biotechnology Randeep Singh Quantitative Systems Biotechnology
Ailen Sanchez Cofactor Metabolic Engineering NAD+/NADH Cheryl Dittrich Cofactor Metabolic Engineering Henry Lin Pathway design and analysis Stephanie Portle Genetic networks Metabolic Engineering and Systems Biotechnology Laboratory Ka-Yiu San ([email protected]) Office:
Lab: GRB E200K GRB E201, E202, E210, E128 Questions ? ???
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