Using Computations to Reconstruct, Analyze and Redirect Metabolism

Using Computations to Reconstruct, Analyze and Redirect Metabolism

E-mail: [email protected] Web page: http://maranas.che.psu.edu

Penn State University University Park, PA 16802

Costas D. Maranas

Chemical factories on the µm scale

Escherichia coli Chemical Process Plant

Outline q  Reconstruct: Organism-specific genome-scale models

Automated assembly and curation of genome-scale models of metabolism (Suthers et al., PLoS Comp Biol, 2009; M. AbuOun et al., J Biol Chem, 2009; Satish Kumar et al., BMS Sys. Biol., 2011; Saha et al., PLoS ONE, 2011)

q  Redesign: Computational strain design Pathway prospecting and identification engineering strategies leading to targeted overproductions (Ranganathan et al., PLoS Comp Biol, 2010; Ranganathan and Maranas, Biotech. J., 2010; Xu et al., Metab. Engr., 2011)

Compile and standardize genome-scale models and databases with consistent naming and balanced reactions (Kumar et al. submitted)

q  Standardize: MetRxn: standardized knowledgebase of metabolites and reactions

Nature Reviews Genetics, 7, 130-141, 2006

Genome-Scale Metabolic Models Linking reactions proteins genes (GPRs)

Genome-scale metabolic models vs. fully sequenced genomes

sequenced genomes (genomesonline.org)

genome-scale metabolic models

year

# co

mpl

eted

Genome-scale metabolic models §  Mycoplasma genitalium 274 met., 262 rxn’s

(Suthers et al., PloS Comp Biol, 2009) (Collaboration with J.C. Venter Inst.) §  Salmonella enterica 945 met., 1,964 rxn’s

(M. AbuOun et al., J Biol Chem, 2009) (Collaboration with M.F. Anjum and M.J. Woodward @ Veterinary Laboratories Agency-Weybridge (UK))

§  Methanosarcina acetivorans 779 met., 776 rxn’s (Satish Kumar et al., BMC Sys. Biol., 2011) (Collaboration with J. Ferry @ PSU)

§  Zeo mays (maize) 1,825 met., 1,983 rxn’s (Saha et al., PLoS ONE, 2011)

Tools for reconstruction §  GapFind/GapFill Network connectivity analysis and

restoration (Satish Kumar, et al., BMC Bioinformatics, 2008)

§  GrowMatch Reconcile consistency with growth/no growth experiments upon genetic and/or environmental perturbations

(Satish Kumar and Maranas, PLoS Comp Biol, 2009;Zomorrodi and Maranas, BMC Sys. Biol., 2010)

?

G/G

NG/G

G/NG

NG/NG

Zea mays

•  Major food crop 31% of the world production of cereals (Sanchez and Cardona, Bioresource Technology 2008)

•  Important source of biofuels 3.4 billion gallons of ethanol in 2004 accounting 99% of all biofuels in the USA (Farrell et al., Science 2006)

Model plant species (vintageprintable.com) Zea mays genome

(Schnable et al., Science 2009)

•  Zea mays genome 2.3 gigabase pairs – 14 χ A. thaliana

genome (Schnable et al., Science 2009)

•  Filtered Gene Set (FGS) 32,540 genes and 53,764 transcripts (Schnable et al., Science 2009)

Important to study Zea mays as a food crop, biofuels production platform and a model for studying plant genetics

•  Functional annotation 54% of total genes associated with specific metabolic functions (Schnable et al., Science 2009)

Model reconstruction workflow

STEP 3: Assemble into genome-scale metabolic model

STEP 1: Transfer GPR from AraGEM via orthologs in Zea mays

(Saha et al., PLoS ONE 2011)

STEP 4: Network connectivity analysis and restoration

STEP 2: Identify additional biotransformations using homology searches

Zea mays genome (Schnable et al., Science 2009)

AraGEM (Dal’molin et al., Plant Physiology 2010)

Step 1: Transfer GPR from AraGEM via orthologs in Zea mays


Tpi

AT2G21170

R01015

ACF85433

AUTOGRAPH Method (Notebaart et al., BMC Bioinformatics 2006)

Tpi

R01015

Gene

Protein

Rxn

A. thaliana Z. mays

BLASTp (E value = 10 -30 )

Auto model: 1186 rxns

Step 2: Identify additional biotransformations using homology searches


Functionally annotated genome

Zmxxxx

Osxxxx

Forward BLASTp

(E value = 10 -30 )

Reverse BLASTp

(E value = 10 -30 )

Z. Mays genome

A. thaliana

O. sativa

S. bicolor

T. aestivum

G. max

Species origin of newly added reactions in the

draft model

Draft model: 1667 rxns

Step 3: Assemble into genome-scale metabolic model


Generate biomass equation from biomass composition of young and vegetative Maize plants. (Penningd et al., Journal of Theoretical Biology 1974)

Generate stoichiometric matrix (Sij).

Establish GPR associations.

Protein Carbohydrates Lipids Ions L-alanine Ribose Glycerotripalmitate Potassium L-arginine Glucose Glycerotristearate Chloride L-aspartic acid Fructose Glycerotrioleate L-Cystine Mannose Glycerotrilinolate RNA L-glutamic acid Galactose Glycerotrilinoleate ATP L-glycine Sucrose GTP L-histidine Cellulose Lignin CTP L-Isoleucine Pectin 4-coumaryl alcohol UTP L-leucine Coniferyl alcohol L-lysine Hemicellulose Sinapyl alcohol DNA

L-phenylalanine Arabinose dATP

L-methionine Xylose Organic acids dGTP L-proline Mannose Oxalic acid dCTP L-serine Galactose Glyoxalic acid dUTP L-threonine Glucose Oxalo-acetic acid L-tryptophan Uronic acids Malic acid L-tyrosine Citric acid L-valine Aconitic acid

Functional model: 1821 rxns

Step 4: Network connectivity analysis and restoration


Enforce network connectivity by finding & filling gaps in model (GapFind & GapFill)

?

(Satish Kumar, et al., BMC Bioinformatics, 2008)

Example of gap filling

(E value = 1χ10-24 )

Final model: 1985 rxns

Maize iRS1563 Included genes 1,563 Proteins 876 Single functional proteins 463 Multifunctional proteins 170 Protein complexes 4 Isozymes 36 Multimeric proteins 148 Others 55 Reactions 1,985 Metabolic reactions 1,900 Transport reactions 70 Exchange reactions 15 GPR associations Gene associated 1,668 Nongene associated 175 Nonenzyme associated 86 Spontaneous 41 Metabolites 1,825 Cytoplasmic 1,744 Plastidic 115 Peroxisomic 93 Mitochondrial 86 Vacuolic 5 Extracellular 15

Distribution of metabolites in cytoplasm and organelles

•  All reactions are elementally and charged balanced •  42% of reaction entries have direct literature evidence •  448 reactions and 369 metabolites are unique to iRS1563 compared to A. thaliana •  674 reactions and 893 metabolites are unique to maize iRS1563 compared to C4GEM

C4 Photosynthesis

C4 photosynthesis

Secondary metabolism

•  Phenylpropanoid metabolism H, G, S lignins •  Flavonoid biosynthesis Fungal defense •  Others include steroid biosynthesis, caffeine metabolism, streptomycin biosynthesis, etc.

•  CO2 fixation is carried out in mesophyll cell •  The Calvin cycle (RuBisCO) works in bundle sheath cell •  Photo respiration is impeded due to separation •  It requires more energy (ATP) to power additional steps

Unique features of C4 PS

Ribulose 1,5-bisphosphate + CO2 3 phosphoglycerate RuBisCO

First reaction of Calvin cycle

Photosynthesis/respiration

CO2

O2

CO2 transport Uptake Sucrose transport Disabled Photon transport Uptake H2O transport Uptake Inorganic nutrient transport Uptake O2 transport RUBISCO: EC 4.1.1.39

Photosynthesis (PS)

O2

Biomass

CO2

Biomass CO2 transport Release Sucrose transport Uptake Photon transport Disabled H2O transport Uptake Inorganic nutrient transport Uptake O2 transport Uptake RUBISCO: EC 4.1.1.39 Both disabled

Respiration (R)

Photorespiration (PR)

Unconstrained Release Carboxylation:

Oxygenation = 3:1 Carboxylation

(Wise et al., 2007)

Models (maize iRS1563 & A. thaliana iRS1597) available at http://maranas.che.psu.edu/models.htm

Cyanothece 51142 and Synechocystis 6803 Collaboration with H. Pakrasi lab (Wash. U.)

Cyanothece 51142 (Image courtesy of The Pakrasi Lab)

Synechocystis 6803 (Image courtesy of The Pakrasi Lab)

•  Efficient nitrogen fixation Highest fixation rate than many filamentous cyanobacteria

(Zehr et al., 2005; Montoya et al., Nature 2004) •  Biofuel producer

Fermentative pathways for the production of butanol and other organic acids (Stal and Moelzaar, FEMS Microbiol Rev 1997)

Proposed synthetic Biology experiments

Validated experimental findings

•  “Chassis” for synthetic biology Useful for performing gene manipulations and building synthetic pathways (Ng et al. Arcg Microbiol 2000)

•  Source of valuable bioproducts Existence of pathways leading to the production of ethanol and alkane (Schirmer et al., J Bacteriol 1997)

•  Cyanothece 51142 genome 5.46 Mbp and 5304 ORFs (Welsh et al., PNAS 2008)

•  Synechocystis 6803 genome 3.57 Mbp and 3168 ORFs (Kaneko et al., DNA Res 1996)

Elemental and charge balancing

•  Impact of elemental and charge balancing

Accurate prediction •  Growth •  Product yield

Balanced genome-scale model

•  Test: Prediction of biomass yield (M/M CO2) with and without elemental and charge balancing under high light intensity and phototrophic condition

368 and 454 rxns were rebalanced for iRS706 and iRS764, respectively. (Fu, Journal of Chemical Technology and Biotechnology 2009; Knoop et al., Plant Physiology 2010; Montagud et al., Bmc Systems Biology 2010)

Cyanobacterium Model With balancing

Without balancing

Exp. observation

Synechocystis 6803

iRS706 0.098 0.0007 0.120

Fu’s model - 0.0024 0.120

Knoop’s model - 0.0012 0.120

Montagud’s model - 0.0037 0.120

Cyanothece 51142 iRS764 0.316 0.0002 0.540

Cyanothece iRS764 model (light/dark)

Cyanothece central metabolism (Stockel et al., PNAS 2008)

Coexpression network of strongly cycling genes (Stockel et al., PNAS 2008)

+

Cyanothece 51142 (light) model Cyanothece 51142 (dark) model

+ Distinct biomass equations for

lightand dark phases

•  Photosynthesis •  Calvin cycle •  Reductive PPP •  Glycogen synthesis

•  Glycogen degradation •  Glycolysis •  Nitrogen fixation •  Oxidative PPP •  TCA cycle •  AA biosynthesis

Upregulated pathways

Upregulated pathways

Comparison between iRS764 and iRS706

Cyanothece 51152 iRS764 vs

Synechocystis 6803 iRS706

Genes Reactions Metabolites

•  282 unique reactions in Cyanothece 51142 iRS764 compared to Synechocystis 6803 iRS706 q  Primary metabolism (i.e., central metabolism, nitrogen metabolism, amino acid biostnthesis, etc)

q  Secondary metabolism (i.e., biosynthesis of terpenoid, glucosinolate, porphyrin, etc.)

•  216 unique reactions in Synechocystis 6803 iRS706 with no counterpart in Cyanothece 51142 iRS764 q  202 from a wide range of primary metabolism pathways such as central metabolism, benjoate degradtion, starch, sucrose and lipid metabolism, amino acid and fatty acid biosynthesis

q  14 from secondary metabolism such as brassinosteroid metabolism and fluorene degradation

Comparison between iRS764 and iRS706 •  Fermentative butanol pathway

•  Citramalate pathway

Preliminary models testing

MEP pathway in Synechocystis 6803

•  Isoprene is a precursor chemical and biofuel candidate •  Upon inclusion of lspS to model the maximum isoprene theoretical yield is found to be (1.2 χ 10-5 mM/gDW-24hr) •  This value is in the same order of magnitude of the experimentally achieved# (3.0 χ 10-5 mM/gDW-24hr)

•  Isoprene synthesis in Synechocystis 6803

•  H2 production in Cyanothece 51142 and Synechocystis 6803

Cyanobacterium Reported production Max Theoretical rate (mM/gDW)* Yield (mM/gDW)

Cyanothece 51142 0.193 0.082 Synechocystis 6803 6.99×10-3 5.32 × 10-4

* Bandyopadhay et al., Nature Comm, 2011

# Lindberg et al., Met Eng 2011






MetRxn primary metabolite and reaction data sources

“Raw” dataset in MetRxn

KEGG

MetaCyc BRENDA RHEA

ChEBI

Reactome

HMDB

44 Metabolic

models

322,936 Metabolite 121,236 Reactions

Metabolites : 73659 Reactions: 50416



Reactions : 2907

Metabolites: 63344

Reactions : 1686

Metabolites: 7900

BKM Metabolites : 22367

Reactions: 18172

Incongruence across databases and models

Example: 2-Oxoglutarate + L-Alanine <=> Pyruvate + L-Glutamate

1. Naming inconsistencies

KEGG C00026 + C00041 <=> C00022 + C00025 BRENDA alpha-ketoglutarate + L-alanine <=> L-glutamate + pyruvate E. coli (iAf1260) [c] : akg + ala-L --> glu-L + pyr Acinetobacter baylyi 1 GLT + 1 PYRUVATE <-> 1 2-KETOGLUTARATE + 1 L-ALPHA-ALANINE Leishmania major [m] : akg + ala-L -> glu-L + pyr Mannheimia succiniciproducens PYR + GLU --> AKG + ALA

AMP: adenosine 5-monophosphate or ampicillin? Example:

Balanced: (R)-Lactate + NAD+ <=> Pyruvate + NADH + H+ KEGG [c] : lac-D + nad --> h + nadh + pyr iAF1260 E.coli (Feist et al. Mol Sys Biol, 2007)

Unbalanced: 1 D-LACTATE + 1 NAD <==> 1 NADH + 1 PYRUVATE Acinetobacter baylyi (Durot et al. BMC Systems Biology, 2008 )

2. Elemental and charge imbalances

Non-specific structural information Multiple structures associated with the same metabolite name

3. Incompleteness, degeneracy, and errors in information

R

# of

met

abol

ites

# of structures

Workflow Metabolite & reaction

information extraction

Download / identify metabolite bond

connectivity information

Metabolite identity analysis

Reaction identity analysis

Disambiguation of metabolites using

structure & phonetic comparisons

Canonical / isomeric SMILES at pH 7.2

Elemental & charge balancing

MetRxn (as of October 2011)

322,936 Metabolite 121,236 Reactions

entities

71,089 Metabolite, 63,243 Reactions

entities


entities

Initial repository

Non-resolved (no atomistic detail;

sometimes no chemical formula)

Resolved repository


entities


entities

Full atomistic detail

Partial atomistic detail generic side chains unspecified repeats

“known unknowns”

lipids generics (e.g., “electron donor”)

macromolecules (3,490 structural proteins and enzymes)

MetRxn content (Oct 2011)


entities


entities

Non-resolved (no atomistic

detail)

Resolved repository


entities


entities

Full atomistic detail

Partial atomistic detail

# of metabolites

mod

els

data

base

s

MetRxn Home (http://metrxn.che.psu.edu)

1. Model selection, viewing, exporting

1. GSM Re-balancing

Charge unbalanced: D-LACTATE + NAD <==> NADH + PYRUVATE Balanced by MetRnx: D-LACTATE + NAD <==> NADH + PYRUVATE + PROTON

q  iAF1260 E.coli (Feist et al. Mol Sys Biol, 2007)

q  Acinetobacter baylyi (Durot et al. BMC Systems Biology, 2008 )

1,039 metab, 2,077 rxn

703 metab, 853 rxn

arbtn-fe3 Aerobactin C22H33FeN4O13 C05554 iAF1260

189 rxn balanced by MetRxn

Elemental and charge unbalanced: GTP + 2 H2O <-> FORMATE + DIHYDRONEOPTERIN-P3 Balanced by MetRnx: GTP + 1 H2O <-> FORMATE + DIHYDRONEOPTERIN-P3 + PROTON

MetRxn fixed link to incorrect structure

Incorrect:

Corrected:

C05554 Aerobactin C22H36N4O13 KEGG ferric-aerobactin C22H33FeN4O13 PubChem

2. Model comparisons

2. Model comparisons (clostridia)

C. acetobutylicum C. thermocellum

solventogenesis, CoB12 pathway

cellulosome rxns

charged/uncharged tRNA

58

173

224 66

37

1181 79

29

79

57 90

61

642 266

B. subtilis

amino acids biosynthesis pathways carbohydrate metabolism nucleoside metabolism

Overlaps occur in

C. thermocellum (Roberts, et al. 2010)

C. acetobutylicum (Lee, et al. 2008)

reactions

295

140

147

137

210

290

Differences occur in

Reactions

Metabolites






Computational strain design: OptForce (Ranganathan et al., PLoS Comput. Biol., 2010)

Limitations: 1.  Generate one “redesign” at a time 2.  Use of surrogate objective functions (e.g., max

biomass or min MOMA) 3.  No direct use of MFA or other flux data

Existing Strategies:

Wild-type flux ranges (with MFA data)

Wild-type flux ranges (without MFA data)

Flux ranges required for overproduction

Min / Max vj s.t. MFA data Stoichiometry Uptake

Min / Max vj s.t. Stoichiometry Uptake

Min / Max vj s.t. Stoichiometry Uptake Vproduct > target

MFA data Vproduct > target

OptKnock (Burgard

et al. 2003)

OptStrain (Pharkya

et al. 2004)

FSEOF (Choi et al.

2010)

OptGene (Patil

et al. 2005)

OptORF (Kim and Reed

2010)

RobustKnock (Tepper and Schlomi

2010)

Identify all individual reactions and combinations thereof whose total flux value MUST increase, decrease or be knocked out to meet a newly imposed production target

Key Idea:

Flux range classifications (MUST sets)

Wild-type phenotype

must increase must decrease must knockout

can increase

Sum of two fluxes

v1 or v2 must increase

v1 or v2 must decrease

v1, v2, or v3 must increase

Sum of three fluxes

: :

can decrease

Desired phenotype

Flux range classifications (MUST sets)

Singles Pairs Triples Higher order

v1 v2

MUSTU

MUSTL

v3 v4

MUSTUU

MUSTUL

MUSTLL

v5 v6 v7

MUSTUUU

MUSTULL

MUSTUUL

MUSTLLL

. . . .

Define

Logic Relations

(V1 AND V2 ) (V3 OR V4 ) AND AND (V5 OR V6 OR V7 )

Encode changes that must happen in the metabolic network MUST sets:

è Identify set of required direct genetic interventions

Identify the minimal set of genetic interventions that guarantee the imposed yield by satisfying all the MUST relations

Max-min problem:

FORCE set

Maximize (over MUST sets)‏

s.t. Minimize (over fluxes)‏ s.t. Stoichiometry

Environmental conditions

MUST set constraints

vproduct

•  Prioritization of genetic interventions

•  Mostly additive contribution of interventions

•  Alternate minimal FORCE sets

vproduct

vproduct

Number of interventions (k)

4 6 8

Target yield

2

∑ # of direct manipulations < k

Alternate interventions

New reactions added: 4CL: 4-coumaric acid lyase CHS: chalcone synthase CHI: chalcone isomerase

Flavanone synthesis in E. coli (Xu et al., Metab. Engr., 2011)

Fowler Z.L. and M.A.G. Koffas, Applied Microbiology and Biotechnology, 2009, 83 (5)

(Collaboration with Prof. Mattheos Koffas group)

(van de Walle and Shiloach J, 1998; Noronha et. al, 2000)

q  Metabolic flux data for wild-type strain BL21*

è Use OptForce to identify minimal interventions (FORCE set) for malonyl-CoA availability

Results for , and set of reactions Flavanone Biosynthesis

Phosphoenolpyruvate Carboxylase (PPC)

PDH

ENO

GAPD

ACCOAC

FBA

PGK

HSK

PGI

SERD

PPC

PFL

ACONTa

ACONTb

ICDHyr

CD

AKGDH

MDH

PYK

FUM

ASPTA

ACLS

TKT1

TALA

RPE

CHORS

DHAD1

MTHFD

Pyruvate:Formate Lyase (PFL)

Aconitase (ACONT)

Glyceraldehyde-3-phosphate dehydrogenase (GAPD)

Enolase (ENO)

Pyruvate Dehydrogenase (PDH)

HSK

HSK

HSK

THRS

THRS

THRS

3HAD181

3OAS181

3OAR181

3HAD181

3OAS181

3OAR181

SUCOAS

RPI

PPCSCT

PPCK

Succinyl-CoA Synthase (SUCOAS)

Propanoyl-CoA:Succinyl-CoA transferase (PPCSCT)

FORCE set for flavanone synthesis in E. coli

fumB

Δ sucC

acnA

GPR Associations

accABD mdh

gapA

pgk pdh

Δ scpC

and and and and

or

or or or

mdh

fum

Succinyl-CoA

ppcsct/sucoas

Experimental Results

Glyceraldehyde-3-phosphate dehydrogenase (GAPD)

Phosphoglycerate Kinase (PGK)

Pyruvate Dehydrogenase (PDH)

Acetyl-CoA Carboxylase (ACCOAC)

Malate Dehydrogenase (MDH)

Fumarase (FUM)

Aconitase (ACONT)

Δ Propanoyl-CoA:Succinyl-CoA Carboxylase (PPCSCT)

Δ Succinyl-CoA Synthetase (SUCOAS)

fumC or

or Δ sucD

Experimental Results (Koffas lab, RPI) (Xu et al., Metab. Engr., 2011)

BL21*

Naringenin yield

(mg / gr glucose)

accABD gapA pgk ΔfumB ΔfumC ΔsucC

57

112 113

157 153

199

55

Δmdh

BL21* ↑ gapA

BL21* ↑ pgk

↑pgk

•  Up-regulation of pgk and/or gapA increases yield by about 98%

•  Knock-outs of mdh or acnA decreases yield

•  Knock-outs of fumB or fumC and sucC further increases yield by about 76%

•  Overexpression of pdh boosts yield by 8% resulting in a final yield of 504 mg/L

fumB

Δ sucC

acnA

accABD mdh

gapA

pgk pdh

Δ scpC

and and and and

or

or or or fumC

or

or Δ sucD

53

ΔacnA

52

BL21* Δ mdh

52

155 150

↑gapA ↑pgk

BL21* Δ acnA

↑gapA ↑pgk

BL21* Δ fumB

↑gapA ↑pgk

BL21* Δ fumC

↑gapA ↑pgk

BL21* Δ sucC

↑gapA

196

203

198

↑pgk ↑gapA

Δ fumB Δ fumC

BL21* Δ sucC ↑pdh

↑pgk ↑gapA

Δ fumC

219 213

Δ fumB

pdh

Summary & Acknowledgements

Funding Source: DOE DE-FG02-05ER25684

Patrick Suthers (GSM)

Rajib Saha (Maize, cyano)

Akhil Kumar (MetRxn)

Sridhar Ranganathan (OptForce)

Vinay Satish Kumar (GapFill,

GrowMatch)

Documents

Using Computations to Reconstruct, Analyze and Redirect Metabolism