Institute for Cell Institute for Cell Dynamics and Dynamics and

Biotechnology: A Center Biotechnology: A Center for Systems Biologyfor Systems Biology

Systems Biology

Systems BiologyHolistic Description of Cellular Functions

Connectionof "Modules"

Modular Aggregationof Components

Single Component Analysis

Functional Analysis

Metabolic Networks

Regulatory Networks

Signalling Networks

Biological Information/Knowledge

Deductive

Inductive

Top-DownBottom-Up

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 ofSystems 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 (Dir.)

• Barbara A. Andrews

• Juan Bacigalupo

• Bruce K. Cassels

• Carlos Conca

• Christian González

• Yedy Israel

• Carlos A. Jerez

• Marco T. Núñez

• Iván Rapaport

• Gonzalo Navarro

Young Researchers/Postdocs

• Paula Aracena• Miguel Arredondo• Francisco Chávez• Paulette Conget• Patricio Cumsille• Ricardo Delgado• Gonzalo Encina• Angélica Fierro• Ziomara Gerdtzen• Nicolas Guiliani• Patricio Iturriaga• Eduardo Karahanian• M. Elena Lienqueo• Casilda Mura

• Pablo Moisset• Rodrigo Lecaros• Álvaro Olivera-Nappa• Axel Osses• Miguel Reyes• Magdalena Sanhueza• Patricio Sáez• Julio Salazar• Oriana Salazar• Amalia Sapag• Lorena Sülz• Gerald Zapata• Cristian Salgado• Fernando Ezquer• 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 WorkshopDecember 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) Mount Isa Mines Ltd. ESSAN S.A. Punta del Cobre S.A. Grupo Bios Merck Recalcine

• Training in US Biotech Companies Chiron, Bayer, Genentech, Amgen

• Ph.D. students working in industry Avecia, IM2 (CODELCO), Diagnotec, Biosigma

Metabolomics, Biofilms

Biomining andbioremediation

Enzymes(Gene Therapy?)Gene Therapy

International Scientific Network

• Pedro Alzari (protein crystallography)

• Ioav Cabantchik (iron accumulation)

• John Caprio (neuroscience)• Douglas Clark (protein

engineering, enzymology)• Caleb E. Finch (ageing)• Peter Gray (mammalian cell culture)• Eckart D. Gundelfinger

(neuroscience)• Vassily Hatzimanikatis

(systems and mathematical biology)• Wei-Shou Hu (animal cell culture

and mathematical models)• Donald F. Hunt (high throughput

proteomics)• Jim Liao (modelling metabolism)

• Chris Lowe (protein purification and affinity, high throughput methods)

• Alan Mackay-Sim (stem cells)

• David E. Nichols (medicinal chemistry)

• Steve Oliver (yeast systems biology)

• Diego Restrepo (chemotransduction)

• Wolfgang Sand (biomining mechanisms)

• James Tiedje (environmental microbiology)

• Susan Wonnacott (nicotinic receptors)

External Advisory Board

• Roger Kornberg, Nobel Laureate, Stanford University School of Medicine, USA

• Douglas Lauffenburger, Systems Biology, MIT, USA

• F. Ivy Carroll, Director of Organic and Medicinal Chemistry, Research Triangle Institute, USA

• Angela Stevens, Mathematical Biology, University of Heidelberg, Germany

• John E. Lisman, Volen Center for Complex Systems, Brandeis University, USA

• Matthias Reuss, Systems Biology, University of Stuttgart, Germany

• Terry Papoutsakis, Department of Chemical and Biological Engineering, Northwestern University, USA

Institute for Cell Institute for Cell Dynamics and Dynamics and

Biotechnology: A Center Biotechnology: A Center for Systems Biologyfor Systems Biology

Metabolomics and Protein Metabolomics and Protein EngineeringEngineering

Protein EngineeringProtein Engineering

ColdCold--Active Active enzymesenzymes fromfromAntarcticaAntarctica

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 (directedevolution)

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

RMSD: a measure of how much each amino acid can move

0

1

2

3

4

5

6

7

8

RM

SD

Aminoácido

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

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

0 50 100 150 200 250 300 350 400

Rel

ativ

eAb

sorb

ance

Mutants Clones

Mutant Ser22125ºC 40ºC

MutationS221T

Cavity Reduction

98Å3 86Å3

ThrSer

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

Hydrogen bond networks

Electrostatic

Interactions

AtomicFluctuations

Compaction

Cel5A

L52A

1x 4x 8x

34,5 kD

0

20

40

60

80

100

120

0 10 20 30 40

Tiempo (seg-

0

20

40

60

80

100

120

140

160

0 10 20 30 40 50

Temperatura

Characterisation of mutant L52A

Activity

Time (min)

Temperature

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

Metabolomics

GLUCGLUC

GLUC6PGLUC6P

FRUC6PFRUC6P

3PG3PG

GAPGAP

PIR PIR

PEPPEPACETACETEtOHEtOH

ACAC

RIBU5PRIBU5P

XIL5PXIL5PRIB5PRIB5P

GAPGAPSED7PSED7P

FRUC6PFRUC6P

aaaa

aaaa

aaaa

aaaa

aaaaaaaaE4PE4P

CARBCARB

ATP ADPATP ADP

RNARNA

OO22EE OO22

COCO22 COCO22EE

2

3

5

LIPLIP

AcCoAAcCoAmitmit

AcCoAAcCoAcitcit

FUMFUM AKGAKG

SUCCoASUCCoASUCSUC

MALMAL ISOCITISOCIT

OACOAC

SODSOD

SODSOD

SODSOD

SODSOD

SODSOD

PROTPROTPROTPROT

PROTPROT

PROTPROT

PROTPROT

6

7

9

13

11

10

10

76

77

70-aaOAC

69

71 -aaOAC

17

16

15

14

73-AcCoA

30

70-aaAKG

71-aaAKG

70-aaPIR

PEP

PIR

74

31

3P G

28

2726

E4P

19 20

21

22

23

18 1

25

71-aaPIR

70-aa3PG

71-aaPEP

70-aaPE P

71-aa3PG

71-aaE4P

70-aaE4P

70-aaRIB5P

71-aaRIB 5P

72-nuOAC

72-nuRIB5P

72-nu3P G

NHNH44EE NHNH44

78

LIPLIP

73-GAP

PROTPROTaaaa

RNARNA SODSOD

nunu

OAC

nunu

RI B5P

aaaa

Ac CoAci t71-aaAcCoA

70-aaAcCoA

AK G

RNARNA

nunu

GLICGLIC

AcCoAAcCoAcitcit

24

75

4

8Metabolic Flux Analysis

Gonzalez, R., Andrews, B.A. Molitor, J. and Asenjo, J.A. (2003) Biotechnol.

Bioeng., 82, 152-169.

dX/dt = S v - bdX/dt = S v - b in SS: S v = b in SS: S v = b or or S r = 0 S r = 0 SScc r rcc + S + Smm r rmm = 0 = 0

Metabolic Flux AnalysisMetabolic Flux AnalysisMetabolic Flux BalanceMetabolic Flux Balance

AA

EE

BB

CC

DD FF

S r=0=S r=0=1-0100D01-010C001-1-1B

54321

5

4

3

2

1

100D010C1-1-1B321

3

2

1

1-0D01-C00B

54

5

4

+

SS StoichiometricStoichiometric Matrix Matrixrr Rate (Flux) vectorRate (Flux) vectorcc CalculatedCalculatedmm MeasuredMeasured

0

3

6

9

12

15

0 9 18 27 36 45Time, h

Glu

cose

, g/L

0.0

0.7

1.4

2.1

2.8

3.5

Cel

ls, E

than

ol a

nd S

OD

, g/L

Strain P+Strain P+ Strain PStrain P--

0

3

6

9

12

15

0 9 18 27 36 45

Time, h

Glu

cose

, g/L

0.0

0.7

1.4

2.1

2.8

3.5

Cel

ls a

nd E

than

ol, g

/L

0.0

0.3

0.6

0.9

1.2

1.5

0 9 18 27 36 45Time, h

Tota

l Pro

tein

and

Car

bohy

drat

es, g

/L

0.00

0.05

0.10

0.15

0.20

0.25

Tota

l RN

A, g

/L

Strain P+Strain P+ Strain PStrain P--

0.0

0.3

0.6

0.9

1.2

1.5

0 9 18 27 36 45Time, h

Tota

l Pro

tein

and

Car

bohy

drat

es, g

/L

0.00

0.05

0.10

0.15

0.20

0.25

Tota

l RN

A, g

/L

Microarrays of Gene ExpressionGeneChip 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, underexpressiongeneralized 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

SER

FUM

MET, ILE,THR, VAL

GLY

PYRUVATE

ACCoA

MAL

AKG

OAA

LACTATE

TYR

ASN

SUCCoA

PHE

BIOMASS

ALA

ASP

HIS, ARG,PRO

LYS, ILE, LEU, TYR

AA

CoA

CO2

PYR, OAA, AKG,MAL, GLY, HIS,ARG, VAL, TYR,LYS

GLU GLN

glc-pyr

pyr-acc

pyr-lac

oaa-akg

akg-suc

suc-fum

fum-mal

mal-oaa

gln-gluglu-akg

aa-glu

asp-oaa

ser-pyr

pyr-ala

mal-pyr

tyr-fum aa-sucCO2 Flux

aa-biom

gln-biom

glc-biom

aa-acc

aa (total) aa (cons) aa (prod)

aa-TCA

DL/DG

= -

GLUCOSE

SER

FUM

MET, ILE,THR, VAL

GLY

PYRUVATE

ACCoA

MAL

AKG

OAA

LACTATE

TYR

ASN

SUCCoA

PHE

BIOMASS

ALA

ASP

HIS, ARG,PRO

LYS, ILE, LEU, TYR

AA

CoA

CO2

PYR, OAA, AKG,MAL, GLY, HIS,ARG, VAL, TYR,LYS

Adv

GLU GLN

glc-pyr

pyr-acc

pyr-lac

oaa-akg

akg-suc

suc-fum

fum-mal

mal-oaa

gln-gluglu-akg

aa-glu

asp-oaa

ser-pyr

pyr-ala

mal-pyr

tyr-fum aa-sucCO2 Flux

aa-ab

aa-biom

gln-biom

glc-biom

aa-acc

aa (total) aa (cons) aa (prod)

aa-TCA

L/G

Conclusions

• Using Fed-batch culture and medium with low glucoseconcentration (based on MFA to lower lactate) a higher cellconcentration is obtained as lactate accumulation isminimized.

• 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 inKey steps in in vitroin vitroembryonic stem cell embryonic stem cell differentiation is differentiation is largely unknownlargely unknown

Conclusions

• Interesting correlations between metabolic fluxesand expression patters in the genes of the pyruvate tolactate reaction, notable differences between thedifferent differentiation conditions (EB: embryoid bodyformation, GEL: gelatin, and MAT: matrigel).

• A major event occurs between days 4 and 5 ofdifferentiation identified by changes in both metabolicfluxes and gene expression profiles.

Study of model dynamics

67 nodes28 genes21 enzymes18 regulators / biochemical compounds

Ficticious Regulators needed so modelreaches PhenotypesAlgorithm

Define combination of substratesGenerate105 aleatory vectorsActualize 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 precipitationof 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

1: Aerobic S0 oxidation 2: Aerobic Fe2+ oxidation3: Chemical S2- oxidation (chalcopyrite leaching)1: Aerobic S0 oxidation 2: Aerobic Fe2+ oxidation3: Chemical S2- oxidation (chalcopyrite leaching)

O2, CO2O2, CO2 O2O2

Fe3+Fe3+

Fe2+Fe2+

S0S0 S2-S2-

BiofilmBiofilm

MineralMineral

H2OH2O

Fe2+Fe2+ Fe3+Fe3+

SO42-SO42-

O2O2

22

33

11

BacteriaBacteriaO2 and CO2

diffusive gradient

O2 and CO2

diffusive gradient

LiquidLiquid

SulfurdepositsSulfur

deposits

H2OH2O

Biochemical chalcopyrite leaching:comparison of low and high iron

concentrations in bulk liquid

Low ironLarge effect of microorganisms on copper recovery

High ironSmall 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