Using Computations to Reconstruct, Analyze and Redirect Metabolism by Great Lakes Bioenergy Research Center

Using Computations to Reconstruct, Analyze and Redirect Metabolism

Costas D. Maranas Penn State University University Park, PA 16802 E-mail: costas@psu.edu Web page: http://maranas.che.psu.edu

Chemical factories on the Âľm 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  Standardize: MetRxn: standardized knowledgebase of metabolites and reactions Compile and standardize genome-scale models and databases with consistent naming and balanced reactions (Kumar et al. submitted)

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)

Genome-Scale Metabolic Models Linking reactions ď&#x192; proteins ď&#x192; genes (GPRs)

Nature Reviews Genetics, 7, 130-141, 2006

Genome-scale metabolic models vs. fully sequenced genomes sequenced genomes

# completed

(genomesonline.org)

genome-scale metabolic models

year

Genome-scale metabolic models Mycoplasma genitalium

274 met., 262 rxn’s

§

Salmonella enterica

945 met., 1,964 rxn’s

§

Methanosarcina acetivorans 779 met., 776 rxn’s

§

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

G/NG

NG/G

NG/NG

Zea mays

Model plant species (vintageprintable.com)

•  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)

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)

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

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

Model reconstruction workflow STEP 2: Identify

STEP 3: Assemble into

additional biotransformations using homology searches

genome-scale metabolic model

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

STEP 1: Transfer GPR

from AraGEM via orthologs in Zea mays AraGEM (Dalâ&#x20AC;&#x2122;molin et al., Plant Physiology 2010)

(Saha et al., PLoS ONE 2011)

STEP 4: Network

connectivity analysis and restoration

Step 1: Transfer GPR from AraGEM via orthologs in Zea mays AUTOGRAPH Method (Notebaart et al., BMC Bioinformatics 2006)

A. thaliana Gene AT2G21170

Z. mays ACF85433

BLASTp (E value = 10

Protein

Tpi

Rxn R01015

-30

)

Tpi R01015

(Saha et al., PLoS ONE 2011)

Auto model: 1186 rxns

Step 2: Identify additional biotransformations using homology searches Forward BLASTp (E value = 10

Zmxxxx Z. Mays genome

-30

)

Draft model: 1667 rxns

Functionally annotated genome A. thaliana O. sativa S. bicolor T. aestivum G. max

Reverse BLASTp (E value = 10

-30

)

Osxxxx

(Saha et al., PLoS ONE 2011)

Species origin of newly added reactions in the draft model

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)

Establish GPR associations. Generate stoichiometric matrix (Sij). Protein L-alanine L-arginine L-aspartic acid L-Cystine L-glutamic acid L-glycine L-histidine L-Isoleucine L-leucine L-lysine

Carbohydrates Ribose Glucose Fructose Mannose Galactose Sucrose Cellulose Pectin

L-phenylalanine

Arabinose

L-methionine L-proline L-serine L-threonine L-tryptophan L-tyrosine L-valine

Xylose Mannose Galactose Glucose Uronic acids

Hemicellulose

Lipids Glycerotripalmitate Glycerotristearate Glycerotrioleate Glycerotrilinolate Glycerotrilinoleate Lignin 4-coumaryl alcohol Coniferyl alcohol Sinapyl alcohol

Ions Potassium Chloride

Functional model: 1821 rxns

RNA ATP GTP CTP UTP DNA dATP

Organic acids Oxalic acid Glyoxalic acid Oxalo-acetic acid Malic acid Citric acid Aconitic acid

(Saha et al., PLoS ONE 2011)

dGTP dCTP dUTP

Step 4: Network connectivity analysis and restoration Enforce network connectivity by finding & filling gaps in model (GapFind & GapFill)

(E value = 1Ď&#x2021;10-24 )

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

(Saha et al., PLoS ONE 2011)

Final model: rxns filling Example1985 of gap

Maize iRS1563 Included genes Proteins Single functional proteins Multifunctional proteins Protein complexes Isozymes Multimeric proteins Others Reactions Metabolic reactions Transport reactions Exchange reactions GPR associations Gene associated Nongene associated Nonenzyme associated Spontaneous Metabolites Cytoplasmic Plastidic Peroxisomic Mitochondrial Vacuolic Extracellular

1,563 876 463 170 4 36 148 55 1,985 1,900 70 15 Distribution of metabolites 1,668 in cytoplasm and organelles 175 86 •  All reactions are elementally and charged 41 balanced 1,825 •  42% of reaction entries have direct literature 1,744 evidence 115 •  448 reactions and 369 metabolites are unique 93 to iRS1563 compared to A. thaliana 86 •  674 reactions and 893 metabolites are unique 5 to maize iRS1563 compared to C4GEM 15

C4 Photosynthesis C4 photosynthesis Unique features of C4 PS •  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 First reaction of Calvin cycle Ribulose 1,5-bisphosphate + CO2

RuBisCO

3 phosphoglycerate

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

Photosynthesis/respiration Photorespiration Photosynthesis (PS) (PR) CO2

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

Uptake Disabled Uptake Uptake Uptake Release Unconstrained Carboxylation: Carboxylation Oxygenation = 3:1 (Wise et al., 2007)

Respiration (R) O2

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

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

Release Uptake Disabled Uptake Uptake Uptake Both disabled

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

Proposed synthetic Biology experiments

Cyanothece 51142 (Image courtesy of The Pakrasi Lab)

Validated experimental findings

•  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)

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

Synechocystis 6803 (Image courtesy of The Pakrasi Lab)

•  “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)

•  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 Cyanobacterium

Synechocystis 6803

Cyanothece 51142

Model

With balancing

Without balancing

Exp. observation

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

iRS764

0.316

0.0002

0.540

 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)

Cyanothece iRS764 model (light/dark)

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

Distinct biomass equations for lightand dark phases Upregulated pathways •  Photosynthesis •  Calvin cycle •  Reductive PPP •  Glycogen synthesis

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

Upregulated pathways

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

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

Comparison between iRS764 and iRS706 Genes

Reactions

Metabolites Cyanothece 51152 iRS764 vs Synechocystis 6803 iRS706

•  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 •  Isoprene synthesis 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) #

Lindberg et al., Met Eng 2011

•  H2 production in Cyanothece 51142 and Synechocystis 6803 Cyanobacterium

Reported production rate (mM/gDW)* Cyanothece 51142 0.193 Synechocystis 6803 6.99×10-3

Max Theoretical Yield (mM/gDW) 0.082 5.32 × 10-4

* Bandyopadhay et al., Nature Comm, 2011

MEP pathway in Synechocystis 6803

Outline q  Reconstruct: Organism-specific genome-scale models

MetRxn primary metabolite and reaction data sources

MetaCyc Metabolites : 10477 Reactions: 8711

BRENDA

Metabolites : 73659 Reactions: 50416

BKM Metabolites : 22367 Reactions: 18172

Reactions : 1686

ChEBI

KEGG

Metabolites: 63344

Metabolites : 16145 Reactions: 8123

44 Metabolic models

RHEA

322,936 Metabolite 121,236 Reactions

Reactome Reactions : 2907

HMDB

Metabolites: 7900

“Raw” dataset in MetRxn

Incongruence across databases and models 1. Naming inconsistencies Example:

AMP: adenosine 5-monophosphate or ampicillin?

Example:

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

KEGG BRENDA E. coli (iAf1260) Acinetobacter baylyi Leishmania major Mannheimia succiniciproducens

C00026 + C00041 alpha-ketoglutarate + L-alanine [c] : akg + ala-L 1 GLT + 1 PYRUVATE [m] : akg + ala-L PYR + GLU

<=> <=> --> <-> -> -->

C00022Â +Â C00025 L-glutamate + pyruvate glu-L + pyr 1 2-KETOGLUTARATE + 1 L-ALPHA-ALANINE glu-L + pyr AKG + ALA

2. Elemental and charge imbalances Balanced: (R)-Lactate + NAD+ <=> Pyruvate + NADH + H+ [c] : lac-D + nad --> h + nadh + pyr

KEGG iAF1260 E.coli

Unbalanced: 1 D-LACTATE + 1 NAD <==> 1 NADH + 1 PYRUVATE

(Feist et al. Mol Sys Biol, 2007)

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

3. Incompleteness, degeneracy, and errors in information Non-specific structural information

# of metabolites

Multiple structures associated with the same metabolite name

# 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) 42,540 Metabolite, 35,473 Reactions entities

322,936 Metabolite 121,236 Reactions entities

71,089 Metabolite, 63,243 Reactions entities

Full atomistic detail

Resolved repository 28,549 Metabolite, 27,770 Reactions entities

Initial repository

31,177 Metabolite, 7,180 Reactions entities Non-resolved (no atomistic detail; sometimes no chemical formula) lipids generics (e.g., “electron donor”) macromolecules (3,490 structural proteins and enzymes)

Partial atomistic detail generic side chains unspecified repeats

“known unknowns”

MetRxn content (Oct 2011)

42,540 Metabolite, 35,473 Reactions entities 71,089 Metabolite, 63,243 Reactions entities

Resolved repository

Full atomistic detail

28,549 Metabolite, 27,770 Reactions entities Partial atomistic detail

31,177 Metabolite, 7,180 Reactions entities

databases

models

Non-resolved (no atomistic detail)

# of metabolites

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

1. Model selection, viewing, exporting

1. GSM Re-balancing q  iAF1260 E.coli

(Feist et al. Mol Sys Biol, 2007)

1,039 metab, 2,077 rxn arbtn-fe3 Incorrect: C05554 Corrected:

MetRxn fixed link to incorrect structure

Aerobactin C22H33FeN4O13 C05554 iAF1260 Aerobactin C22H36N4O13 KEGG ferric-aerobactin C22H33FeN4O13 PubChem

q  Acinetobacter baylyi (Durot et al. BMC Systems Biology, 2008 ) 703 metab, 853 rxn

189 rxn balanced by MetRxn

Charge unbalanced: D-LACTATE + NAD <==> NADH + PYRUVATE Balanced by MetRnx: D-LACTATE + NAD <==> NADH + PYRUVATE + PROTON Elemental and charge unbalanced: GTP + 2 H2O <-> FORMATE + DIHYDRONEOPTERIN-P3 Balanced by MetRnx: GTP + 1 H2O <-> FORMATE + DIHYDRONEOPTERIN-P3 + PROTON

2. Model comparisons

ď&#x192;ź

2. Model comparisons (clostridia) C. acetobutylicum

79 140

(Lee, et al. 2008)

C. thermocellum (Roberts, et al. 2010)

Metabolites reactions

29295266 642 147 57

173 210 Reactions

61 90

B. subtilis

13779 1181 58 66 224 290 Overlaps occur in

Differences occur in

solventogenesis, CoB12 pathway

C. acetobutylicum

cellulosome rxns charged/uncharged tRNA

C. thermocellum

amino acids biosynthesis pathways carbohydrate metabolism nucleoside metabolism

Outline q  Reconstruct: Organism-specific genome-scale models

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

OptKnock OptStrain OptGene FSEOF OptORF RobustKnock Existing (Tepper and Schlomi (Pharkya (Patil (Choi et al. (Kim and Reed Strategies: et(Burgard 2010) al. 2003) et al. 2004) et al. 2005) 2010) 2010) Limitations: 1.  Generate one “redesign” at a time

Use of surrogate objective functions (e.g., max biomass or min MOMA) No direct use of MFA or other flux data

2.  3.

Wild-type flux ranges (with MFA data)

Wild-type flux ranges (without MFA data)

MFA data

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

Flux ranges required for overproduction

Vproduct > target

Min / Max vj s.t. Stoichiometry Uptake

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

Flux range classifications (MUST sets) Key Idea:

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

Wild-type phenotype

Desired phenotype

can increase can decrease must increase must decrease must knockout Sum of two fluxes v1 or v2 must increase v1 or v2 must decrease Sum of three fluxes v1, v2, or v3 must increase

: :

Flux range classifications (MUST sets) Singles v1

Pairs v3

Triples

Higher order

v5 v6 v7

....

MUSTUU MUSTU

MUSTUL

MUSTUUU MUSTUUL

MUSTL

MUSTLL

MUSTULL MUSTLLL Define Logic Relations

(V1

AND V2 ) AND

(V3

V4 ) AND

(V5

OR V6

OR V7

)

MUST sets: Encode changes that must happen in the metabolic network è Identify set of required direct genetic interventions

FORCE set Max-min Identify the minimal set of genetic interventions that problem: guarantee the imposed yield by satisfying all the MUST relations

vproduct Maximize (over MUST sets)‫‏‬ s.t.

Alternate interventions

vproduct Target yield

Minimize vproduct (over fluxes)‫‏‬ s.t. Stoichiometry Environmental conditions MUST set constraints

∑ # of direct manipulations < k 2

•

Prioritization of genetic interventions

•

Mostly additive contribution of interventions

•

Alternate minimal FORCE sets

Number of interventions (k)

Flavanone synthesis in E. coli Engr., 2011)

(Xu et al., Metab.

(Collaboration with Prof. Mattheos Koffas group)

New reactions added: 4CL: 4-coumaric acid lyase CHS: chalcone synthase CHI: chalcone isomerase Metabolic flux data for wild-type strain BL21* q

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

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

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

Results for

, and

set of reactions

Flavanone Biosynthesis PDH

PPC

FUM

ENO

PFL

ASPTA

GAPD

ACONTa

ACLS

ACCOAC

ACONTb

TKT1

FBA

ICDHyr

TALA

PGK

RPE

HSK

AKGDH

CHORS

PGI

MDH

DHAD1

SERD

PYK

MTHFD

HSK

3HAD181

HSK

3OAS181

HSK

3OAR181

THRS

3HAD181

THRS

3OAS181

THRS

3OAR181

SUCOAS

PPCSCT

RPI

PPCK

Glyceraldehyde-3-phosphate dehydrogenase (GAPD)

Enolase (ENO)

Pyruvate:Formate Lyase (PFL)

Pyruvate Dehydrogenase (PDH) Phosphoenolpyruvate Carboxylase (PPC)

Aconitase (ACONT)

Succinyl-CoA Synthase (SUCOAS) Propanoyl-CoA:SuccinylCoA transferase (PPCSCT)

FORCE set for flavanone synthesis in E. coli Glyceraldehyde-3phosphate dehydrogenase (GAPD) Phosphoglycerate Kinase (PGK)

GPR Associations fumB gapA accABD and pdh and

pgk

Δ scpC

fumC and

mdh or

acnA

Pyruvate Dehydrogenase (PDH)

Acetyl-CoA Carboxylase (ACCOAC)

or and

Δ sucC or

Δ sucD Malate Dehydrogenase mdh (MDH)

Experimental Results

Aconitase (ACONT)

Fumarase (FUM) fum ppcsct/ sucoas Succinyl-CoA

Δ Succinyl-CoA Synthetase (SUCOAS)

Δ Propanoyl-CoA:SuccinylCoA Carboxylase (PPCSCT)

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

219

157

112

accABD BL21*

•  Knock-outs

150

153

53 52

gapA

pgk

Δmdh

ΔacnA

BL21* ↑ gapA

BL21* ↑ pgk

↑pgk ↑gapA

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

155

113

BL21* Δ mdh

BL21* Δ acnA

ΔfumB ↑pgk ↑gapA BL21* Δ fumB

ΔfumC

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

ΔsucC

BL21* Δ fumC

Δ fumB

pgk

fumC and

mdh or

acnA

or and

Δ sucC or

Δ sucD

↑gapA

Δ fumB Δ fumC

Δ scpC

gapA

Δ fumC

↑pgk

BL21* Δ sucC

fumB accABD and

pdh

↑pgk ↑gapA ↑pgk ↑gapA ↑pgk ↑gapA

of mdh or acnA decreases

•

198

196

(mg / gr glucose)

yield

203

199

Naringenin yield

213

and pdh

BL21* Δ sucC ↑pdh

Summary & Acknowledgements

Rajib Saha (Maize, cyano)

Vinay Satish Kumar (GapFill, GrowMatch)

Funding Source: DOE DE-FG02-05ER25684

Akhil Kumar (MetRxn)

Sridhar Ranganathan (OptForce)

Patrick Suthers (GSM)