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Bacteria • • • • Single cells Small size (1-5 mm) Rapid reproduction Genomic and genetic capabilities Bacterial Diversity • 4 billion years of evolution • Ability to thrive in extreme environments • Use nutrients unavailable to other organisms • Tremendous catalytic potential Problem to be Solved: Waste Minimization in the Chemical Industry •Most of our manufactured goods involve chemicals •Chemical industry currently based on chemicals derived from petroleum Not renewable resource Many produce hazardous wastes Use bacteria as the factories of the future Bacteria as Factories Starting materials Harnessing Catalytic Potential of Bacteria Value-added products Starting materials • Use bacteria as self-replicating multistage catalysts for chemical production • Environmentally benign • Renewable starting materials (feedstocks) Potential Feedstocks Characteristics: Inexpensive Abundant Renewable Candidates • • • • Glucose C6H12O6 Methane CH4 Methanol CH3OH Carbon dioxide/water CO2/H2O Source agricultural wastes natural gas, sewage methane atmosphere/photosynthesis Potential Products • Fuels • H2 hydrogen • CH4 methane • CH3CH2OH ethanol Potential Products • Natural products (complex synthesis) • • • • • • • Vitamins Therapeutic agents Pigments Amino acids Viscosifiers Industrial enzymes PHAs (biodegradable plastics) Potential Products • Engineered products • Starting materials for polymers (such as rubber, plastics, fabrics) • Specialty chemicals (chiral) • Bulk chemicals (C4 acids) Problem to Solve • If bacteria are such wonderful alternatives, why are our chemicals still made from environmentally hazardous feedstocks? Bacterial processes are too expensive Nature’s Design Solutions • Competitive advantage in natural niches • Optimized parameters • Low nutrients • Defense systems Opportunity Redesign bacteria with industrially-valuable parameters optimized • Redirect metabolism to specific products • Increase metabolic efficiency • Increase process efficiency This idea has been around for 30 years, why has the problem not been solved? Metabolism as a Network • Metabolism: the complex network of chemical reactions in the cell • Must redesign the network • Understand the connections to achieve end result What’s New? • Genomics • Bacterial genomes small (1000 = human) • Hundreds of bacterial genome sequences available • Provides the blueprint for the organism (the parts list) New platform for redesign What’s New? • Increased understanding of how new kinds of metabolism arose New strategies for redesign How Build Novel Metabolic Pathways? • Whole metabolic pathways: no single gene or small number of genes confer selective advantage • Cannot build a step at a time Dilemma: how were entire pathways constructed during evolution? Modular Aspect of Metabolism • Metabolic capabilities were built in blocks, like puzzle pieces Strategy: Understand the modules and their connections Redesign in blocks Methanol as an Alternative Biofeedstock • • • • Soluble in water Inexpensive Pure substrate Bacteria that use it well-studied CH3OH chemicals Methylotrophic Bacteria CH3OH (methanol) O2 CO2, H2O, cells Specified product Approach • Define functional modules by experimental and evolutionary analysis methanol MEDH MADH CH OH HCHO 3CH2 NH 3 cytcL amicyanin H4F H4MPT Assimilation Dissimilation MethyleneHCHO H4F Methylene H 4MPT NADH CO2 NADPH Methenyl NADPH H4F Methenyl H4MPT Serine N10-Formyl H4F cycle N5-Formyl H4MPT ATP Formate BIOMASS C3 Compounds Formyl MFR Purines NADH fMet-tRNA 2H x CO2 •Manipulate modules to optimize product •Optimize process parameters CO2 product CO2 Methylobacterium extorquens AM1 •Grows on one-carbon compounds (reducing power limited) •Also grows on multi-carbon compounds (ATP-limited) •Natural habitat: leaf surfaces •Substantial toolkit for genetic analyses •Genome sequence available •Whole genome microarrays available Clover leaf print showing pink Methylobacterium strains Target Product: Biodegradable Plastics CH3OH Biosynthesis (assimilation) C3 CO2 Energy metabolism (dissimilation) Biomass PHA (biodegradable plastic) Methylotrophic Metabolic Modules Methanol Methanol Oxidation PHA PHA cycle Glyoxylate Regeneration cycle Formaldehyde Serine cycle TCA cycle Methylene H4F H4MPT-linked C1 transfer H4F-linked C1 transfer Formate CELLS FDH2 FDH1 CO2 FDH3 Constraints • Understanding how the system is integrated in time and space • Changing how it works Work in Progress • Use genome-wide techniques to assess expression of genes within each module • Use metabolic modeling to make predictions about flow through each module • Use labeling techniques to measure flow through each module BIOMASS CO2 Results: redesign the metabolic network to overproduce a biodegradable plastic Multi-tiered datasets Metabolite pools Xiaofeng Guo Abundance Average spectra for serine peak 156 400 360 228 320 microarrays: mRNA 280 240 200 160 120 114 80 Yoko Okubo, Betsy Skovran 138 174 128 40 101 184 220 242 0 100 120 140 160 180 m/z 200 220 240 256 260 Enzyme activities Xiaofeng Guo CH3OH MDH H2O, 2eH4MPT H4 F HCHO Fae spont. H2 O Methylene H2 O Methylene CH2=H4MPT H4F CH2=H4F H4MPT Serine NAD(P)H MtdA, MtdB MtdA NADPH CH=H4MPT CH=H4F H2 O H2 O Mch Fch CHO-H4MPT CHO-H F H O 4 2 H2 O FtfL H4MPT Fhc H4F, ATP HCOOH FDHs NADH CO2 Fluxes Chris Marx Steve Van Dien Greg Crowther 2 NADPH G6P 0.29 R5P 0 Biomass yield: 4.98 PP Pathway 0.01 NADH F6P E4P -KG 0.09 0.03 Triose-P 0.04 Citrate 0.35 0 Ac-CoA CO2 2.27 Propionyl-CoA Butyryl-CoA Glyoxylate Ac-CoA 3.27 Serine Acetyl-CoA Conversion Pathway 1.00 FADH2 3.27 Serine Cycle NADPH Glycine 1.00 NADPH 0.62 2.92 Methylene-H4MPT CO2 NADH 2 e- Malyl-CoA 3.27 OAA 2.56 PEP 3.17 CO2 NADPH 2 e- 1.00 1.00 Malate 0 0.21 2.83 Succinate 2 NADH Pyruvate 2-PG TCA Cycle 0.09 3-PG 0.35 Succ-CoA 0 0.30 Hydroxybutyryl-CoA 3.21 0.46 6.17 5.55 HCHO 3.84 Methylene-H4F 0.62 NADPH ATP Formate Cell membrane 10.00 CH3OH NADH 0 4 H+ext 0.56 HCHO 2e- PHB CO2 NADH 2 H+ext 19.3 ATP proteomics: proteins Julia Vorholt group Murray Hackett group Global Analysis Global analysis provides indepth information •Transcription of all detectable genes •Production of all detectable proteins •Measurement of all major fluxes •Measurement of 100s of metabolites Involves a basic assumption, that all cells are roughly in the same physiological state Growing body of literature shows this is not correct Final Phase: Study Metabolism in Single Cells • Metabolic studies in averaged populations do not capture the range of metabolic events or heterogeneity in subpopulations • Difficult to study multiple metabolic parameters in single cells Need: new technologies to study living individual cells in real time Single Cell Challenges • Volume of a bacterial cell ~ fl (10-15) • Number of DNA molecules ~2-3 • Number of mRNA molecules for a specific gene ~10-10,000 • Total protein amount ~amoles (10-18) • Total moles of specific metabolites ~ amoles (10-18) • Respiration rates ~fmol/min/cell (10-15 ) New Interdisciplinary Approaches • Combine • • • • • Genomics Computational biology MEMS (microelectromechanical systems) Systems integration Nanotechnology Microscale Life Sciences Center University of Washington • Center of Excellence of Genomic Sciences funded by NIH NHGRI • Co-directed by Mary Lidstrom and Deirdre Meldrum (EE) • • Started August 2001 Goal: Study complex processes in individual living cells Chemists, biologists, engineers working together Microsystem-Based Devices for Studying Single Cells •Move, trap, image single cells (9 cell sets x 11) •Control environment, make additions •Measure 4 fluorescent protein fusions •Single-cell proteomics •Measure substrate-dependent O2 uptake (phosphorescence sensor) Multi-parameter high throughput analysis at the single-cell level, leading to understanding of metabolic networks N. Dovichi group (Chemistry); L. Burgess group (Chemistry); D. Meldrum group (Elec Engr); A. Jen group (Mat Sci Engr) Evidence for Heterogeneity • Single-cell cell cycle analysis: growth Single Cell Division Times During MeOH Growth Range: 2.5-4.3 hr 5 4.5 4 Time (hrs) 3.5 3 2.5 2 1.5 1 0.5 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 Single Cell Division Times 12 10 # cells 8 6 4 Tim Strovas, Linda Sauter 2 0 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 Time , Hr Summary • Breadth of bacterial diversity provides opportunity • Environmentally benign aspects provide impetus • New approaches provide strategies • Result: increasing number of microbiallybased products over the next several years