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Instituto de Biotecnología Universidad Nacional Autónoma de México A network perspective on the evolution of metabolism

A. T. T. A. G. C. C. G. A. T. C. G. T. A. G. C. G. C. T. A. A. T. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 1. Instituto de Biotecnología

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Instituto de Biotecnología Universidad Nacional Autónoma de México A network perspective on the evolution of metabolism

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  1. A T T A G C C G A T C G T A G C G C T A A T 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 1 1 1 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

  2. How metabolic networks have been originated and evolve? http://genomebiology.com/2007/8/2/R26

  3. Gene duplication is recognized as a main source of biological variation and innovation Malate dehydrogenase (MDH) Lactate dehydrogenase (LDH) Enzyme Commission reactions classification 1 .- Oxidoreductases 2 .- Transferases 3 .- Hydrolases 4 .- Lyases 5 .- Isomerases 6 .- Ligases EC: 1.1.1.28 EC: 1.1.1.37 NAD+ NAD+ NADH NADH duplication http://genomebiology.com/2007/8/2/R26

  4. Two pioneer models linking gene duplication and evolution of metabolism “patchwork” (Jensen, 1976) e f g Metabolic pathway 2 2.3.7.8 4.5.6.7 “stepwise” (Horowitz, 1945) a b c d 6.3.1.1 2.3.4.5 1.2.2.1 Metabolic pathway 1 http://genomebiology.com/2007/8/2/R26

  5. The peptidoglycan biosynthesis, stepwise or patchwork? The biosynthesis of peptidoglycan stepwise or patchwork? UDP-N-acetylmuramate L-alanine + ATP 6.3.2.8 UDP-N-acetylmuramoyl-L-alanine D-glutamate + ATP 6.3.2.9 UDP-N-acetylmuramoyl-L-alanyl-D-glutamate meso-diaminopimelate + ATP 6.3.2.13 UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate D-alanyl-D-alanine + ATP 6.3.2.15 UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate- D-alanyl-D-alanine http://genomebiology.com/2007/8/2/R26

  6. The origin of several preferentially coupled reactions could be explained by both stepwise and patchwork Reaction type 1 (EC:a.b.-.-) Z-score (Zi) = (Nreali - <Nrandi>)/std(Nrandi) Reaction type 2 (EC:w.x.-.-) http://genomebiology.com/2007/8/2/R26

  7. Question: Whether both the distance and the chemical similarity between reactions influence the retention of duplicates?... ... forget the names of models http://genomebiology.com/2007/8/2/R26

  8. Methodology 4500 sequences 1880 EC numbers EC:1.1.1.35 EC:1.1.1.100 EC:4.2.1.17 Detection of duplicates comparing enzyme sequences Determining the Minimal Path Length Pair MPL E4 E1 E2 E2 a 4 E8 E3 a 2 E6 E4 E7 E6 E8 E6 a 1 E3 E4 E2  a  E1 E1  b  E4 E6  c  E1 E1  c  E6 E4  d  E3 E7 ... E1 E2 E3 E7 E6 E8 http://genomebiology.com/2007/8/2/R26

  9. The preferential coupling of reactions partially explains the increased retention of duplicates between closer reactions 40 30 20 10 0 Real network Null models Real network Null models Retention of duplicates (%) Reaction type 1 (EC:a.b.-.-) Distance between nodes (enzymes) Distance between nodes (enzymes) ALL: all-against-all reactions CDR: chemically dissimilar readtions CSR: chemically ssimilar readtions Null model (Maslov-Sneppen ) Real network Null model (functionally similar) Real Network Rewiring Rewiring Reaction type 2 (EC:w.x.-.-) http://genomebiology.com/2007/8/2/R26

  10. The increased retention of duplicates between closer reactions is reflected in lower evolutionary distances within modules 1.00 0.67 0.33 0.00 2.- A greater retention of duplicates between pathways implies a lower evolutionary distance (ED) (ED) 3.- Significance of (ED) values Z-score > 3 2 1 0 -1 -2 < -3 Protein domain content random shuffling 1.- Detection of functional modules http://genomebiology.com/2007/8/2/R26

  11. Summary • 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 (~7folds) 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 % ? A.B.c.d A.B.e.f A.B.g.h • 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 (~7folds) 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 (~7folds) 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 % http://genomebiology.com/2007/8/2/R26

  12. Conclusions • In silico modeling of the origin and 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 and patchwork models are not independent of each other: in fact, the network perspective enables us to reconcile and combine these models http://genomebiology.com/2007/8/2/R26

  13. A T T A G C C G A T C G T A G C G C T A A T 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 1 1 1 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 Stanford) Sponsors National Science and Technology Council (México) UNAM Graduate Student Office More details http://genomebiology.com/2007/8/2/R26 jdime@ibt.unam.mx

  14. This phenomenon is characteristic of enzymatic networks Gene transcriptional regulatory network from E. coli 6 4 2 0 Retention of duplicates (%) 1 2 3 4 5 6 7 8 All Distance between proteins (transcription factor  regulated gene) Shen-Orr SS et al. (2002) Nat Genet Protein-protein interactions network from E. coli 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 10 5 0 Retention of duplicates (%) • 2 3 4 5 6 7 8 All ALL: all interactions EC-EC: enzyme-enzyme interactions P-P: non-enzymimatic interactions Distance between proteins Butland G et al. (2005) Nature

  15. Some basic network topological properties Mexico city’s subway network • Nodes and Edges • Minimal Path Length • Modularity

  16. The peptidoglycan biosynthesis, stepwise or patchwork? UDP-N-acetylmuramate murE murF mraY ftsW murD murG murC ddlB ddlA L-alanine + ATP 6.3.2.8 E. coli K12 UDP-N-acetylmuramoyl-L-alanine D-glutamate + ATP 6.3.2.9 folC UDP-N-acetylmuramoyl-L-alanyl-D-glutamate meso-diaminopimelate + ATP 6.3.2.13 UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate D-alanyl-D-alanine + ATP 6.3.2.15 UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate- D-alanyl-D-alanine

  17. From a network perspective traditional models stepwise Vs patchwork are conceptually flawed salvage pathways of guanine, xanthine, and their nucleosides 5-phosphoribosyl1-pyrophosphatebiosynthesis I purine nucleotides de novo biosynthesis I AMP ATP D-ribose-5- phosphate EC:2.7.6.1 PrsA 5-phosphoribosyl 1-pyrophosphate L-glutamine EC:2.4.2.14 PurF H2O EC:2.4.2.22 Gpt xanthine L-glutamate Pi Pi 5-phosphoribosylamine xanthosine-5-phosphate

  18. Retention of duplicates as groups and single entities Fatty acids metabolism DEGRADATION ATP synthesis 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 R (n-2) | CH2 | CH2 | C=O | SCoA 6.2.1.3 1.3.99.3 4.2.1.17 1.1.1.35 2.3.1.16 Acetil-CoA CoA FAD FADH H2O NAD NADH R | CH || HC | C=O | S[ACP] R | CHOH | CH2 | C=O | S[ACP] R (n+2) | CH2 | CH2 | C=O | S[ACP] R | C=O | CH2 | C=O | S[ACP] R | CH2 | CH2 | C=O | S[ACP] FADH R | CH2 | CH2 | C=O | SCoA FAD H2O NADP NADPH CoA ACP 2.3.1.41 2.3.1.41 1.3.1.9 4.2.1.61 1.1.1.100 6.2.1.20 Phospholipids biosynthesis BIOSYNTHESIS

  19. Both groups and single duplicates are significantly retained 100 80 60 40 20 0 EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg EcoCyc EcoKegg MetaCyc RefKegg } E1 I { E2' E2 II Retention of duplicates (%) } E3' E3 III { E4' E4 E4' IV } E5' E5 V E6 (I) (II) (III) (IV) (V) Gene duplication No gene duplication

  20. Null models generation (Maslov-Sneppen) Real network Maslov-Sneppen model Rewiring

  21. New null models now include the preferential biochemical coupling of reactions Null model Real network EC:3.5.4.1 EC:3.5.4.1 EC:1.1.1.5 EC:1.1.1.5 EC:2.1.1.1 EC:2.1.1.1 Rewiring EC:1.1.1.5 EC:1.1.1.5 EC:2.1.4.3 EC:2.1.4.3 EC:1.1.4.7 EC:1.1.4.7

  22. Hub influence on gene duplication EcoKegg EcoCyc Enzyme recruitment rate (%) Enzyme recruitment rate (%) Distance between nodes (enzymes) Distance between nodes (enzymes) RefKegg MetaCyc Enzyme recruitment rate (%) Enzyme recruitment rate (%) Distance between nodes (enzymes) Distance between nodes (enzymes)

  23. Metabolic networks can be represented by diverse graph types compound centric enzyme centric bipartite G6P NADPH G6P NADPH zwf zwf pgl 6PGL H2O H2O NADP+ NADP+ 6PGL pgl gnd rpe gnd X5P R5P GPG X5P rpe R5P GPG

  24. In silico models have successfully simulated the grow of networks by gene duplication • 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 Barabási y Oltvai (2004) Nat Rev Genet

  25. In silico models have successfully simulated the grow of networks by gene duplication • Duplication  inheritance  Divergence • Multifunctional enzymes and transporters • Potential biomass production • Reaction coupling better fits connectivity properties of real networks (existence of hubs) Pfeiffer, Soyer y Bonhoeffer (2005) Plos Biol

  26. Metabolite coupling is significant in metabolic networks • There are biases in the coupling of specific metabolites • These biases follow a power law distribution Becker, Price y Palsson (2006) BMC Bioinformatics

  27. Some network emerging topological properties • scale free • clustering • hierarchical Barabási y Oltvai (2004) Nat Rev Genet

  28. Essentiality and damage in metabolic networks Papp et al (2004) Nature Lemke et al (2004) Bioinformatics

  29. The small world into large networks • Short distance between nodes • High clustering coefficient small-world 2ni C Clustering (C) = ki(ki - 1) random ni : direct edges between i neighbors ki : number of i neighbors 20 5(4) C = = 1 1 5(4) Watts y Strogatz (1998) Nature C = = 0.05

  30. The analysis of biological systems from a network perspective have had a great increase in last years

  31. Some topological properties of metabolic networks • small world • universality • scale free • hub elimination • modularity a b c d e Scale free Modularity + - - + Jeong et al (2000) Nature + + Ravasz et al (2002) Science

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