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    ICDB ICDB Presentation Transcript

    • Institute for Cell Dynamics and Biotechnology: A Center for Systems Biology
    • Systems Biology
    • Systems Biology Holistic Description of Cellular Functions Functional Analysis Inductive Metabolic Networks Regulatory Networks Bottom-Up Top-Down Connection Signalling Networks of "Modules" Modular Aggregation Deductive of Components Single Component Analysis Biological Information/Knowledge
    • Goal of the Institute • To conduct frontier research in cell function and dynamics and to develop models of important biological systems using a modern Systems Biology approach • A multidisciplinary team of bioengineers, cell and molecular biologists, mathematicians, biochemists, chemists and computer scientists
    • Key Features of the Institute • Development of novel approaches in the field of Systems Biology aimed at reaching original solutions to traditional biological problems • Impact on important scientific problems (Basic Research) • Application of the know-how of the different groups of the Institute to the solution of problems important to society (Applied Research)
    • Applied Research • Development of enzymes with high activity at low temperatures • Development of mammalian cell culture for production of monoclonal antibodies and therapeutic proteins • Development of improved microorganisms for biomining • Development of methods for the mass production of cells for transplant and adenoviral vectors for gene therapy • Development of medications for the treatment of alcoholism and nicotine dependence • Development of fluorescent microbial sensors to monitor arsenic and other toxic heavy metals
    • Key Associate Scientists • Juan A. Asenjo • Yedy Israel (Dir.) • Carlos A. Jerez • Barbara A. Andrews • Marco T. Núñez • Juan Bacigalupo • Iván Rapaport • Bruce K. Cassels • Gonzalo Navarro • Carlos Conca • Christian González
    • Young Researchers/Postdocs • Paula Aracena • Pablo Moisset • Miguel Arredondo • Rodrigo Lecaros • Francisco Chávez • Álvaro Olivera-Nappa • Paulette Conget • Axel Osses • Miguel Reyes • Patricio Cumsille • Magdalena Sanhueza • Ricardo Delgado • Patricio Sáez • Gonzalo Encina • Julio Salazar • Angélica Fierro • Oriana Salazar • Ziomara Gerdtzen • Amalia Sapag • Nicolas Guiliani • Lorena Sülz • Patricio Iturriaga • Gerald Zapata • Eduardo Karahanian • Cristian Salgado • M. Elena Lienqueo • Fernando Ezquer • Casilda Mura • Javier Wolnitzky
    • How • Multidisciplinary collaborations • Improved interdisciplinary training • Extensive international network with state-of-the- art experimental facilities • During the second year the institute exceeded all its main objectives including the support and training of 71 Ph.D. students, postdocs and young scientists (58 the first year).
    • Second Workshop December 2008 at Marbella
    • We haven’t the money, so we’ve got to think Ernest Lord Rutherford, 1871 - 1937
    • Training and Interactions with Industry • Enzymes • Biomining and bioremediation • Gene and cancer therapy • Inhibition of iron uptake • Interactions with Industry in Chile and overseas  BiosChile  Ph.D. students carrying out their work together with company scientists (M. Salamanca, F. Reyes, A. Olivera-Nappa, M. Paz Merino)  Ph.D. students writing US patents (F. Reyes, J. P. Acevedo, L. Parra)  Collaboration of post-docs and young scientists (O. Salazar, A. Olivera-Nappa)
    • Training and Interactions with Industry • Interactions with Industry in Chile and overseas  Biosigma (CODELCO) Metabolomics, Biofilms  Mount Isa Mines Ltd. Biomining and  ESSAN S.A. bioremediation  Punta del Cobre S.A.  Grupo Bios Enzymes  Merck (Gene Therapy?)  Recalcine Gene Therapy • Training in US Biotech Companies  Chiron, Bayer, Genentech, Amgen • Ph.D. students working in industry  Avecia, IM2 (CODELCO), Diagnotec, Biosigma
    • International Scientific Network • Pedro Alzari (protein • Chris Lowe (protein purification and crystallography) affinity, high throughput methods) • Ioav Cabantchik (iron • Alan Mackay-Sim (stem cells) accumulation) • John Caprio (neuroscience) • David E. Nichols (medicinal chemistry) • Douglas Clark (protein engineering, enzymology) • Steve Oliver (yeast systems biology) • Caleb E. Finch (ageing) • Diego Restrepo • Peter Gray (mammalian cell culture) (chemotransduction) • Eckart D. Gundelfinger • Wolfgang Sand (biomining (neuroscience) mechanisms) • Vassily Hatzimanikatis • James Tiedje (environmental (systems and mathematical biology) microbiology) • Wei-Shou Hu (animal cell culture • Susan Wonnacott (nicotinic and mathematical models) receptors) • Donald F. Hunt (high throughput proteomics) • Jim Liao (modelling metabolism)
    • External Advisory Board • Roger Kornberg, Nobel • John E. Lisman, Volen Center Laureate, Stanford University for Complex Systems, Brandeis School of Medicine, USA University, USA • Douglas • Matthias Reuss, Systems Lauffenburger, Systems Biology, University of Stuttgart, Biology, MIT, USA Germany • F. Ivy Carroll, Director of • Terry Papoutsakis, Organic and Medicinal Chemistry, Research Triangle Department of Chemical and Institute, USA Biological Engineering, Northwestern University, USA • Angela Stevens, Mathematical Biology, University of Heidelberg, Germany
    • Institute for Cell Dynamics and Biotechnology: A Center for Systems Biology
    • Metabolomics and Protein Engineering
    • Protein Engineering
    • Cold- Cold-Active enzymes from Antarctica 1. Trypsin-like Protease from Krill – US Patent granted. Medical applications. 2. Subtilisin-like Protease fron Pseudomonas sp. – US Patent filed. Use in detergent industry. 3. Xylanase from Psychrobacter sp. - US Patent filed. Use in biofuels industry.
    • Cryophilic Enzymes • Protease with High Activity at low Temperature for Detergents • 12% of the Market • = 81.000.000 dollars
    • Protein Engineering: Random Mutagenesis (Directed Evolution) “error-prone” PCR
    • Activity vs. Assay used for screening
    • Random Mutagenesis (directed evolution) Saturation Mutagenesis Gene Shuffling 3-D Models (homology) Site-Directed Mutagenesis
    • Increasing the Thermostability of a Xylanase using a Homology model • Background • Phsycrophilic xylanase, complete sequence obtained, cloned and expressed in E. coli BL21(DE3)/pET22b(+). • Active at temperatures between 5ºC-40ºC, pH Optimum → 6 - 8 • Patent filed • Problem • Using directed evolution the Kcat was increased 3 times but there was no increase in thermostability. • Using a homolgy model of structure appropriate regions for mutations were found by simulation of molecular dynamics and degree of compaction.
    • Results of simulation of molecular dynamics 8 7 6 5 RMSD 4 3 2 1 0 Aminoácido RMSD: a measure of how much each amino acid can move
    • Selection of amino acids to mutate using a model of comparative compaction • the program compares the density of contact between equivalent residues in 2 groups of enzymes. • The density of contact is the number of atoms which can make contact with a residue. • Distance < 4,5 Å • Negative results indicate that the compaction in the cryophilic protein is smaller than in the mesophilic counterpart and these amino acids are therefore targets for mutagenesis. • The most promising target was SER221 as it is near to the active site and in a highly conserved region.
    • Ser221
    • 4,5 Mutant Ser221 4 25ºC 40ºC 3,5 Relative Absorbance 3 2,5 2 1,5 1 Mutation 0,5 S221T 0 0 50 100 150 200 250 300 350 400 Mutants Clones 98Å3 Cavity 86Å3 Reduction Ser Thr
    • Effect of structural flexibility on the cryophilicity of enzymes • The aim is to identify elements related to structural dynamics in enzyme molecules which could be responsible for their activity at low temperatures using algorithms to compare proteins with structural homology. • Model enzyme: Celulase from Bacillus agaradherans (Cel5A) • Comparison of structural and dynamic aspects • Electrostatic Interactions: salt bridges, hydrogen bonds • Compactation: density of contact • Average Atomic Fluctuations
    • Electrostatic Compaction Interactions Atomic Hydrogen bond Fluctuations networks
    • Characterisation of mutant L52A 160 140 120 Cel5A 100 80 L52A Activity 60 40 20 0 0 10 20 30 40 50 Temperatura Temperature 120 100 80 34,5 kD 60 40 20 0 1x 4x 8x 0 10 20 30 40 Time (min) - Tiempo (seg
    • Metabolomics of Recombinant Yeast • Metabolic Flux Analysis • Microarrays of Gene Expression • Integration of Gene Expression and Regulation with Metabolic Fluxes • Modelling Metabolic Fluxes and Gene Regulation
    • RNA SOD  71 -aa RIB 5P  72 -nuRIB5 P  70 -aa R IB 5P nu aa PRO T GLUC  RI B5 P RIBU5P  1 18  19  20  74 GLUC6P CARB RIB5P  21 XIL5P 2 FRUC6P LIP  23 SED7P GAP 4 3  22 GA P RNA Metabolomics FRUC6P  E4 P E4P  31 GAP 5  73 - nu  72 -nu3P G SOD  3P G aa GL IC 3PG aa  71 -aa 3 PG  70 -aa E 4P  71 -aa E 4P 6  70 -aa 3 PG PROT SOD PRO T  PEP  70 -aa PE P  27 PEP aa EtO H ACET  26 7  71 -aa PE P SOD  28 AC  PIR Metabolic Flux Analysis  73 -AcCoA  30 8 PIR aa  71 -aa PIR  70 -aa PIR LIP AcCoAcit 9 PROT  Ac CoAci t  71 -aa AcCo A 71 aa AcCoAmit SOD - aa O AC  75  78 Gonzalez, R., Andrews, B.A. Molitor, J.  70 -aa AcCo A OA C aa  25  10 NH4 E NH4  70 -a a  OA C and Asenjo, J.A. (2003) Biotechnol. PRO T OAC CO2  76 CO2 E  10 Bioeng., 82, 152-169. MAL  17 ISOCIT  72 -nuOA C AcCoAcit RNA nu  11  16  24 FUM AKG  AK G  69 PROT ATP ADP  13  70 -aa AKG  15 aa O2 E  77 O2  71 -aa A KG SOD SUC SUCCoA  14
    • Metabolic Flux Analysis Metabolic Flux Balance dX/dt = S v - b in SS: S v = b or S r = 0  S c rc + Sm rm = 0 S Stoichiometric Matrix  C E r Rate (Flux) vector   A B c Calculated  m Measured  D F 1  2  3  4  5 1 1  2  3 1 4 5 4 B 1 -1 -1 0 0 B 1 -1 -1 B 0 0 S r=0= C 0 1 0 -1 0 2 C 0 1 0 2 + C -1 0 5 D 0 0 1 0 -1 3 D 0 0 1 3 D 0 -1 4 5
    • Strain P+ Strain P- P- 15 3.5 15 3.5 12 2.8 12 2.8 Cells, Ethanol and SOD, g/L Cells and Ethanol, g/L Glucose, g/L Glucose, g/L 9 2.1 9 2.1 6 1.4 6 1.4 3 0.7 3 0.7 0 0.0 0 0.0 0 9 18 27 36 45 0 9 18 27 36 45 Time, h Time, h Strain P+ Strain P- P- 1.5 0.25 1.5 0.25 Total Protein and Carbohydrates, g/L Total Protein and Carbohydrates, g/L 1.2 0.20 Total RNA, g/L 1.2 0.20 0.9 0.15 Total RNA, g/L 0.9 0.15 0.6 0.10 0.6 0.10 0.3 0.05 0.3 0.05 0.0 0.00 0 9 18 27 36 45 0.0 0.00 Time, h 0 9 18 27 36 45 Time, h
    • Microarrays of Gene Expression GeneChip from Affimetrix (6,871 genes of S. cerevisiae)
    • Conclusions • (Glucose Ethanol): It is CLEARLY not possible to correlate quantitative mRNA expression levels with cell function shown by MFA • Comparing the P- (and P+) when Stat/Eth, underexpression generalized as biosynthetic machinery of the cell shuts down. • Comparing P+/P- on Ethanol, in P+ underexpression in many genes in central pathways indicating a decrease in respiratory metabolism compared to P-. • When growing on ethanol, the PPP and amino acid biosynthesis pathways show repression of genes important in the synthesis of glutamate, glutamine, proline and glycine. This is evidence that there will be less protein synthesis in P+ compared to P-.
    • Viral Vectors for the Treatment of Alcoholism: use of Metabolic Flux Analysis for Cell Cultivation and Vector Production • Ponga aquí su texto
    • • Human Embryo Kidney (HEK) cells • Adenovirus: vectors for gene therapy • 26% of clinical trials • Advantages : concentration, size of insert, infectivity • Design of culture medium based on cellular requeriments using MFA (minimize Lactate synthesis) • Design of culture medium based on MFA for synthesis of adenoviral vectors based on virus composition/stoichiometry
    • Cell Growth MFA and MFA for virus synthesis GLUCOSE GLUCOSE GLY SER GLY SER ser-pyr glc-biom glc-biom ser-pyr glc-pyr glc-pyr pyr-lac pyr-ala pyr-lac pyr-ala LACTATE PYRUVATE ALA LACTATE PYRUVATE ALA BIOMASS BIOMASS ASN ASP ASN ASP pyr-acc pyr-acc aa-acc LYS, ILE, aa-acc asp-oaa asp-oaa LYS, ILE, ACCoA ACCoA LEU, TYR LEU, TYR CoA gln-biom gln-biom CoA mal-pyr mal-pyr OAA aa-biom OAA aa-biom AA AA oaa-akg glu-akg gln-glu oaa-akg glu-akg gln-glu AKG GLU GLN AKG GLU GLN mal-oaa mal-oaa MAL MAL aa-TCA aa-ab aa-TCA akg-suc akg-suc Adv fum-mal aa-glu fum-mal aa-glu HIS, ARG, suc-fum SUCCoA HIS, ARG, FUM PRO suc-fum SUCCoA FUM PRO PYR, OAA, AKG, CO2 Flux aa-suc MAL, GLY, HIS, CO2 PYR, OAA, AKG, CO2 Flux tyr-fum PHE TYR MET, ILE, ARG, VAL, TYR, tyr-fum aa-suc MAL, GLY, HIS, CO2 THR, VAL LYS PHE TYR MET, ILE, ARG, VAL, TYR, THR, VAL LYS = - = - aa (total) aa (cons) aa (prod) DL/DG aa (total) aa (cons) aa (prod) L/G
    • Conclusions • Using Fed-batch culture and medium with low glucose concentration (based on MFA to lower lactate) a higher cell concentration is obtained as lactate accumulation is minimized. • Comparison of cells in suspension culture in low-glucose medium fed-batch vs. batch culture in original medium  DLac/DGluc similar • Specific growth rate similar • Maximum cell concentration 160% more • Specific glucose consumption rate 50% lower • Improved Medium for Adenovirus Production
    • Mouse Embryonic Stem Cell Differentiation Key steps in in vitro embryonic stem cell differentiation is largely unknown
    • Conclusions • Interesting correlations between metabolic fluxes and expression patters in the genes of the pyruvate to lactate reaction, notable differences between the different differentiation conditions (EB: embryoid body formation, GEL: gelatin, and MAT: matrigel). • A major event occurs between days 4 and 5 of differentiation identified by changes in both metabolic fluxes and gene expression profiles.
    • Study of model dynamics 67 nodes 28 genes 21 enzymes 18 regulators / biochemical compounds Ficticious Regulators needed so model reaches Phenotypes Algorithm Define combination of substrates Generate105 aleatory vectors Actualize in parallel way Find atractor
    • Different colours represent different genetic regulation mechanisms: Blue: Glucose repression (gluconeogenic genes) Red: Positive regulation (glycolytic genes) Green: Repression (shift from glucose to ethanol) - Glycolytic genes are mainly constitutive with few exceptions: eg. enolase2. - Other genes from Microarray data: (-) gluc to eth.: pyk1, pyk2, pdc1, pdc5, pda2, adh1 (x10). - Rec. strain genes: protein and recombinant protein: eg. pdc1 (- ), 1lv6, ilv2, glt1, aat1 (+), aat2. - PPP gene: zwf1 (-) in gluc.
    • MFA of Bioleaching Microorganisms • Acidithiobacillus ferrooxidans (62 reactions) • Leptospirilum ferrooxidans • Leptospirilum ferriphilum • Ferroplasma acidiphilum
    • Leptospirilum ferrooxidans (82 reactions/equations)
    • Development of a novel biofilm model for bioleaching Objectives • Understanding the kinetics of leaching and bioleaching • Finding theoretically optimal microorganism parameters able to successfully recover metals to obtain more efficient microorganisms. Modelling approach: non-homogeneous biofilms • Simultaneous space and time scales for biofilm formation and growth, chalcopyrite leaching and passivation and precipitation of insoluble matter • Possible existence of non-homogeneous cross-gradient diffusional limitation mechanisms • Obligated inclusion of inorganic precipitates • Presence of contact chemical reaction phenomena (sulfur leaching)
    • Scheme of the proposed model O2, CO2 Liquid H2O O2 O2 and SO42- Bacteria CO2 Fe3+ 2 diffusive H2O O2 Fe2+ Biofilm gradient 1 Fe2+ Fe3+ Sulfur deposits S0 3 S2- Mineral 1: Aerobic S0 oxidation 2: Aerobic Fe2+ oxidation 3: Chemical S2- oxidation (chalcopyrite leaching)
    • Biochemical chalcopyrite leaching: comparison of low and high iron concentrations in bulk liquid Low iron Large effect of microorganisms on copper recovery High iron Small effect of microorganisms on copper recovery
    • Typical simulation of simultaneous chalcopyrite leaching and microorganism growth • Fe3+ is more abundant beneath the biofilm, and iron diffusion to the mineral surface is hindered by thicker sulfur layers, decreasing the concentration of Fe3+ near the mineral surface and slowing down the leaching rate. • Corrosion-like pits are observed in the sulfur layer beneath the microorganism colonies (biofilm) at later stages.
    • Main Conclusions • Embedded microorganisms are responsible of decreasing diffusion limitations in the solid layer by increasing its porosity, forming corrosion pits • A flat layer of microorganisms on the mineral surface acts by accelerating sulfur dissolution over iron oxidation • A flat biofilm morphology can be favored by low iron and high oxygen conc. • This morphology guarantees maximum supply of energy simultaneously for all the cells (biofilm and planctonic cells). Most convenient symbiotic association between sulfur-oxidizing biofilm bacteria and iron-oxidizing planctonic cells • It provides an explanation of natural evolutive tendency of bioleaching bacteria to form flat biofilms