Instituto de Biotecnología Universidad Nacional Autónoma de México A network per

Instituto de Biotecnología Universidad Nacional Autónoma de México A network per www.phwiki.com

Instituto de Biotecnología Universidad Nacional Autónoma de México A network per

Zimney, Jon, News Director has reference to this Academic Journal, PHwiki organized this Journal Instituto de Biotecnología Universidad Nacional Autónoma de México A network perspective on the evolution of metabolism by gene duplication J. Javier Díaz-Mejía, Ernesto Pérez-Rueda & Lorenzo Segovia NetSci 2007 NY, USA How metabolic networks have been originated in addition to evolve http://genomebiology.com/2007/8/2/R26 Gene duplication is recognized as a main source of biological variation in addition to innovation duplication http://genomebiology.com/2007/8/2/R26

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a b c d Metabolic pathway 1 “stepwise” (Horowitz, 1945) Two pioneer models linking gene duplication in addition to evolution of metabolism 2.3.4.5 1.2.2.1 6.3.1.1 http://genomebiology.com/2007/8/2/R26 The peptidoglycan biosynthesis, stepwise or patchwork UDP-N-acetylmuramoyl-L-alanyl-D-glutamate UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate 6.3.2.8 6.3.2.9 6.3.2.13 6.3.2.15 UDP-N-acetylmuramate UDP-N-acetylmuramoyl-L-alanine UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate- D-alanyl-D-alanine D-alanyl-D-alanine + ATP L-alanine + ATP D-glutamate + ATP meso-diaminopimelate + ATP The biosynthesis of peptidoglycan stepwise or patchwork http://genomebiology.com/2007/8/2/R26 Z-score (Zi) = (Nreali – )/std(Nr in addition to i) Reaction type 1 (EC:a.b.-.-) Reaction type 2 (EC:w.x.-.-) The origin of several preferentially coupled reactions could be explained by both stepwise in addition to patchwork http://genomebiology.com/2007/8/2/R26

Question: Whether both the distance in addition to the chemical similarity between reactions influence the retention of duplicates as long as get the names of models http://genomebiology.com/2007/8/2/R26 Methodology E8 E6 E2 a E1 E1 b E4 E6 c E1 E1 c E6 E4 d E3 E7 E2 E3 E1 E4 E7 http://genomebiology.com/2007/8/2/R26 The preferential coupling of reactions partially explains the increased retention of duplicates between closer reactions http://genomebiology.com/2007/8/2/R26

The increased retention of duplicates between closer reactions is reflected in lower evolutionary distances within modules 1.- Detection of functional modules http://genomebiology.com/2007/8/2/R26 Summary http://genomebiology.com/2007/8/2/R26 In metabolic networks the closer two reactions are, the greater the probability (~2-3 folds) that their enzymes are duplicates This can be partially explained by the preferential biochemical coupling of reactions This is reflected (or caused) in (by) a high retention of duplicates within modules Retention of duplicates between chemically similar reactions is greater (~7 folds) than between chemically dissimilar ones. In both cases the observed frequencies are, however, significantly greater than expected These two properties are additive. Hence, the retention of duplicates catalyzing consecutive, chemically similar reactions is ~ 35 % In metabolic networks the closer two reactions are, the greater the probability (~2-3 folds) that their enzymes are duplicates This can be partially explained by the preferential biochemical coupling of reactions This is reflected (or caused) in (by) a high retention of duplicates within modules Retention of duplicates between chemically similar reactions is greater (~7 folds) than between chemically dissimilar ones. In both cases the observed frequencies are, however, significantly greater than expected These two properties are additive. Hence, the retention of duplicates catalyzing consecutive, chemically similar reactions is ~ 35 % In silico modeling of the origin in addition to evolution of metabolism is improved by the inclusion of specific functional constraints, such as the preferential biochemical coupling of reactions We suggest that the stepwise in addition to patchwork models are not independent of each other: in fact, the network perspective enables us to reconcile in addition to combine these models Conclusions http://genomebiology.com/2007/8/2/R26

Acknowledgments Lic. Gerardo May (Univ. Aut. Yucatán, México) Dr. L. Segovia’s lab (UNAM, México) Dr. Sergio Encarnación (UNAM, México) Dr. A-L Barabási’s lab (Univ. Notre Dame) Dr. Virginia Walbot (Univ. of Stan as long as d) Sponsors National Science in addition to Technology Council (México) UNAM Graduate Student Office More details http://genomebiology.com/2007/8/2/R26 jdime@ibt.unam.mx Shen-Orr SS et al. (2002) Nat Genet Retention of duplicates (%) This phenomenon is characteristic of enzymatic networks Distance between proteins (transcription factor regulated gene) ALL EC-EC P-P ALL EC-EC P-P ALL EC-EC P-P ALL EC-EC P-P ALL EC-EC P-P ALL EC-EC P-P ALL EC-EC P-P ALL EC-EC P-P ALL EC-EC P-P 2 3 4 5 6 7 8 All 1 2 3 4 5 6 7 8 All 6 4 2 0 Gene transcriptional regulatory network from E. coli Retention of duplicates (%) Distance between proteins 10 5 0 Protein-protein interactions network from E. coli Butl in addition to G et al. (2005) Nature ALL: all interactions EC-EC: enzyme-enzyme interactions P-P: non-enzymimatic interactions

Nodes in addition to Edges Minimal Path Length Modularity Some basic network topological properties murE murF mraY ftsW murD murG murC ddlB ddlA E. coli K12 folC The peptidoglycan biosynthesis, stepwise or patchwork UDP-N-acetylmuramoyl-L-alanyl-D-glutamate UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate 6.3.2.8 6.3.2.9 6.3.2.13 6.3.2.15 UDP-N-acetylmuramate UDP-N-acetylmuramoyl-L-alanine UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate- D-alanyl-D-alanine D-alanyl-D-alanine + ATP L-alanine + ATP D-glutamate + ATP meso-diaminopimelate + ATP From a network perspective traditional models stepwise Vs patchwork are conceptually flawed EC:2.4.2.14 PurF EC:2.7.6.1 PrsA ATP AMP 5-phosphoribosylamine L-glutamate EC:2.4.2.22 Gpt xanthosine-5-phosphate Pi L-glutamine Pi D-ribose-5- phosphate 5-phosphoribosyl 1-pyrophosphate H2O xanthine salvage pathways of guanine, xanthine, in addition to their nucleosides 5-phosphoribosyl 1-pyrophosphate biosynthesis I purine nucleotides de novo biosynthesis I

R CH2 CH2 C=O O- R CH2 CH2 C=O SCoA R CH HC C=O SCoA R CHOH CH2 C=O SCoA R C=O CH2 C=O SCoA CoA FAD FADH H2O NAD NADH R (n-2) CH2 CH2 C=O SCoA R (n+2) CH2 CH2 C=O S[ACP] R CH HC C=O S[ACP] R CHOH CH2 C=O S[ACP] R C=O CH2 C=O S[ACP] FAD FADH H2O NADP NADPH R CH2 CH2 C=O S[ACP] R CH2 CH2 C=O SCoA Phospholipids biosynthesis ATP synthesis DEGRADATION BIOSYNTHESIS 1.1.1.35 1.1.1.100 4.2.1.61 4.2.1.17 1.3.99.3 1.3.1.9 2.3.1.16 6.2.1.3 6.2.1.20 CoA ACP Acetil-CoA Retention of duplicates as groups in addition to single entities Fatty acids metabolism 2.3.1.41 2.3.1.41 Both groups in addition to single duplicates are significantly retained E1 { { E6 I II III IV V } } } Gene duplication No gene duplication Retention of duplicates (%) EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg 100 80 60 40 20 0 (I) (II) (III) (IV) (V) E4′ E5′ E5 E2 E2′ E3′ E3 E4′ E4 Null models generation (Maslov-Sneppen)

New null models now include the preferential biochemical coupling of reactions Rewiring Real network Null model EC:1.1.1.5 EC:1.1.4.7 EC:1.1.1.5 EC:2.1.1.1 EC:2.1.4.3 EC:3.5.4.1 EC:1.1.1.5 EC:1.1.4.7 EC:1.1.1.5 EC:2.1.1.1 EC:2.1.4.3 EC:3.5.4.1 Hub influence on gene duplication Enzyme recruitment rate (%) Distance between nodes (enzymes) EcoKegg EcoCyc MetaCyc RefKegg Enzyme recruitment rate (%) Distance between nodes (enzymes) Enzyme recruitment rate (%) Distance between nodes (enzymes) Enzyme recruitment rate (%) Distance between nodes (enzymes) Metabolic networks can be represented by diverse graph types G6P NADPH NADP+ 6PGL H2O GPG R5P X5P zwf pgl gnd rpe compound centric enzyme centric bipartite G6P NADPH NADP+ 6PGL H2O GPG X5P zwf pgl gnd rpe R5P

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Barabási y Oltvai (2004) Nat Rev Genet Duplication inheritance divergence By this way scale free networks have been generated, but the potential functionality of such networks is not assessed Pastor-Satorras et al, (2003) J Theor Biol In silico models have successfully simulated the grow of networks by gene duplication Pfeiffer, Soyer y Bonhoeffer (2005) Plos Biol Duplication inheritance Divergence Multifunctional enzymes in addition to transporters Potential biomass production Reaction coupling better fits connectivity properties of real networks (existence of hubs) In silico models have successfully simulated the grow of networks by gene duplication Becker, Price y Palsson (2006) BMC Bioin as long as matics There are biases in the coupling of specific metabolites These biases follow a power law distribution Metabolite coupling is significant in metabolic networks

Barabási y Oltvai (2004) Nat Rev Genet scale free clustering hierarchical Some network emerging topological properties Papp et al (2004) Nature Lemke et al (2004) Bioin as long as matics Essentiality in addition to damage in metabolic networks Clustering (C) = Watts y Strogatz (1998) Nature Short distance between nodes High clustering coefficient The small world into large networks r in addition to om small-world 2ni ki(ki – 1) ni : direct edges between i neighbors ki : number of i neighbors C C = = 1 C = = 0.05 20 5(4) 1 5(4)

The analysis of biological systems from a network perspective have had a great increase in last years Scale free Modularity Jeong et al (2000) Nature Ravasz et al (2002) Science Some topological properties of metabolic networks small world universality scale free hub elimination modularity + – – + + + e a b c d

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