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GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria: A systems biology approach towards full exploitation of their biotechnological applications.

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Presentation on theme: "GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria: A systems biology approach towards full exploitation of their biotechnological applications."— Presentation transcript:

1 GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria: A systems biology approach towards full exploitation of their biotechnological applications Juan Nogales Enrique Departament of Environmental Biology Centro de Investigaciones Biológicas (CSIC), Madrid, Spain jnogales@cib.csic.es

2 What is a GEnome-scale Metabolic REconstruction? “A GEMRE is a stequiometric representation of the metabolic capabilities of a given organism at genome-scale, which can be further translated to a mathematical format allowing the computation of its phenotype from its genotype” Biochemical Representation HEX1 PGI PFK TPI GADP PGK ENO Stequiometric Representation gene transcript protein reaction Mathematical Representation Reed JL et al, Nature Reviews Genetics: 2006

3 Phylogeny of COnstraints Based Reconstruction and Analysis Methods

4 McCloskey et al, MSB: 2013 Applications of GEMs

5 Addressing the 1,2 propanediol overproduction in Synechocystis Eric Knight Iceland University Synechocystis sp. PCC6803 Multiple problems found during the engineering and fermentation processes Low genetic stability of the synthetic pathway The fermentation process lost efficiency at long term Very low yield (≈ µg/L) mgsA dkgB gldA mgsA dkgB gldA Synechocystis sp. PCC6803 Genome-Scale Model Reconstruction and Analysis kan mgsA dkgB gldA DHAP MethylglyoxalAcetolR-1,2-PD MgsA DkgB GldA

6 Genome-Scale Metabolic Reconstruction of Synechocytis sp. PCC 6803 GenesReactionsMetabolites BOF Level a Photosynthesis Modeling Lipids Modeling Mass and Charge Balancing CompartmentsReference 678863795AdvanceComplete Yes[e],[p],[c],[u] (Nogales et al., 2012) Nd93NdBasicLumpedNo [e],[c] (Shastri and Morgan, 2005) 785672BasicLumpedNo [e],[c] (Hong and Lee, 2007) 505652701BasicLumpedNo [e],[c](Fu, 2009) Nd4629BasicLumpedNo [e],[c] (Navarro et al., 2009) 343380291IntermediateLumpedPartialNo[e],[c] (Knoop et al., 2010) 669882790IntermediateCompletePartialNo[e],[c](Montagud et all, 2010)

7 Growth Condition µ (h -1 ) q glc (mmol/g/h) q O2 (mmol/g/h) q CO2 (mmol/g/h) HeterotrophicIn vivo0.0760.85Nd1.99 iJN6780.0630.85(-)1.182.53 MixotrophicIn vivo0.0590.38Nd0.0 iJN6780.0560.381.190.0 AutotrophicIn vivo0.085-4.82(-) 3.7 iJN6780.088-5.58(-) 3.7 54.5 mmol.gDW -1.h -1 Cell mass = 0.5 pg Cell diameter = 1.75 µm Photosynthesis efficiency = 4.6-6 % 13.14 - 17.14 µE.m 2.s -1 15 µE.m 2.s -1 Model Validation

8 Carbon Flux Distribution Validation Photoautotrophic τ=0.96 Mixotrophic τ=0.92 Heterotrophic τ=0.89

9 The photosynthetic metabolism... so simple? PQ PQH 2 H+H+ PC CytC Fdrd Fdox PSI FNR NADPH O 2 + H + H2OH2O H+H+ ATPase Pi + ADP ATP CYT BF PSII NADP + H + E. coli

10 NDH-1 H+H+ Cyd BD Succ Fum O2O2 CYO H2OH2O H+H+ O2O2 H2OH2O SDH MEHLER H2OH2O O2O2 H2ase H2H2 H+H+ NADH H+H+ NDH-1 3 NADP NADPH PQ PQH 2 H+H+ CO 2 + H 2 O HCO 3 + H + NDH-2 NAD FQR Pi + ADP PQ PQH 2 PC CytC Fdrd Fdox PSI FNR NADPH O 2 + H + H2OH2O H+H+ ATPase ATP CYT BF NADP + H + PSII PQH 2 PQ Cyd BD NDH-1 4 NADP NADPH PQ PQH 2 H+H+ CO 2 + H 2 O HCO 3 + H + O2O2 H2OH2O NDH-1 NADP H2OH2O H+H+ PC CytC CYT BF CYO O2O2 H+H+ ATPase Pi + ADP ATP NADPH H+H+ SDH Fum Succ NDH-2 NADNADH

11 The photosynthetic metabolism... so simple? so complex !!!.

12 iJN678 as computational tool for studying the photoautotrophic metabolism

13 Defining a key photosynthetic parameter: Optimal photosynthesis operates at ATP/NADPH ≈ 1.5 LEF provides a ATP/NADPH = 1.28

14 Autotrophic growth as a function of Ci and light availability * 9 AEF pathways - 5 CEF pathways - 2 PCEF pathways - 2 NADPH consuming pathways * 2 Metabolic pathways - Photorespiration - NO 3 reduction NH 4 10 -3 Light input (mmol.gDW -1.h -1 ) Flux (mmol.gDW -1.h -1 )

15 Quantification and classification of alternative photosynthetic pathways. Nogales J., et al, PNAS: 2012

16 Wild typeMehler KOPHOTOR KO Double KO Hackenberg et al. Planta. 2009 Sep;230(4):625-37 Photosynthetic robustness at work Wild type Mehler KO PHOTOR KO Double KO Photorespiratory 2-phosphoglycolate metabolism and photoreduction of O2 cooperate in high-light acclimation of Synechocystis sp. strain PCC 6803.

17 Defining additional emergent properties of photosynthetic networks EssentialSynthetic Lethal Reduced metabolic robustness

18 Impact of the photosynthetic systems properties on biotechnology High Photosynthetic Robustness Low Metabolic Robustness Eric Knight Iceland University Multiple problems found during the engineering and fermentation processes Low genetic stability of the synthetic pathway The fermentation process lost efficiency at long term Very low yield (≈ µg/L) kan mgsA dkgB gldA DHAP MethylglyoxalAcetolR-1,2-PD MgsA DkgB GldA

19 Mutant A Mutant B Mutant C Wild type Multiple problems found during the engineering and fermentation processes Low genetic stability of the synthetic pathway The fermentation process lost efficiency at long term Very low yield (≈ µg/L) Computational design of growth-coupled overproducer strains Nogales et al., Bioengineered 4:3, 1–6; May/June 2013

20 Computational design of growth-coupled overproducer strains: Autotrophic conditions

21 Computational design of growth-coupled overproducer strains: Heterotrophic conditions

22 Computational design of growth-coupled overproducer strains: Mixotrophic conditions

23 Summary Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production. Vu et al, Biotechnol J. May 2013, 8(5):619-30 Multiple problems found during the engineering and fermentation processes Low genetic stability of the synthetic pathway The fermentation process lost efficiency at long term Very low yield (≈ µg/L)

24 GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria: A systems biology approach towards full exploitation of their biotechnological applications Juan Nogales Enrique Departament of Environmental Biology Centro de Investigaciones Biológicas (CSIC), Madrid, Spain jnogales@cib.csic.es

25 Prof. Bernhard O. Palsson Dr. Ines Thiele Dr. Stein Gudmundsson Thanks


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