1. A T C G T A G C A T C G A G C T C G T A A T 1 0 0 0 1 0 1 1 0 0 1 0 1 1 1 1 1 1 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. H ow metabolic networks have been originated and evolve? http://genomebiology.com/2007/8/2/R26

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

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

5. T he 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 stepwisepatchwork distanceconsecutivedistantly chemistrydissimilarsimilar T he biosynthesis of peptidoglycan stepwise or patchwork? http://genomebiology.com/2007/8/2/R26

6. Z-score (Z i ) = (Nreal i - )/std(Nrand i ) Reaction type 1 (EC:a.b.-.-) Reaction type 2 (EC:w.x.-.-) T he origin of several preferentially coupled reactions could be explained by both stepwise and patchwork http://genomebiology.com/2007/8/2/R26

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

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

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

11. S ummary 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 % A.B.c.d A.B.e.fA.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 ( ~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 % ?

12. 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 C onclusions http://genomebiology.com/2007/8/2/R26

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

15. Shen-Orr SS et al. (2002) Nat Genet Retention of duplicates (%) T his 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 1 2 3 4 5 6 7 8 All 64206420 Gene transcriptional regulatory network from E. coli Retention of duplicates (%) Distance between proteins 10 5 0 Protein-protein interactions network from E. coli Butland G et al. (2005) Nature ALL: all interactions EC-EC: enzyme-enzyme interactions P-P: non-enzymimatic interactions

16. Nodes and Edges Minimal Path Length Modularity Mexico city’s subway network S ome basic network topological properties

17. murE murF mraY ftsW murD murG murC ddlB ddlA E. coli K12 folC T he 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

18. F rom a network perspective traditional models stepwise Vs patchwork are conceptually flawed EC:2.4.2.14 PurF EC:2.7.6.1 PrsA ATPAMP 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 H2OH2O xanthine salvage pathways of guanine, xanthine, and their nucleosides 5-phosphoribosyl 1-pyrophosphate biosynthesis I purine nucleotides de novo biosynthesis I

19. R | CH 2 | CH 2 | C=O | O - R | CH 2 | CH 2 | C=O | SCoA R | CH || HC | C=O | SCoA R | CHOH | CH 2 | C=O | SCoA R | C=O | CH 2 | C=O | SCoA CoAFADFADHH2OH2ONADNADH R (n-2) | CH 2 | CH 2 | C=O | SCoA R (n+2) | CH 2 | CH 2 | C=O | S[ACP] R | CH || HC | C=O | S[ACP] R | CHOH | CH 2 | C=O | S[ACP] R | C=O | CH 2 | C=O | S[ACP] FAD FADH H2OH2O NADPNADPH R | CH 2 | CH 2 | C=O | S[ACP] R | CH 2 | CH 2 | C=O | SCoA Phospholipids biosynthesis ATP synthesis DEGRADATION BIOSYNTHESIS 1.1.1.35 1.1.1.100 4.2.1.61 4.2.1.171.3.99.3 1.3.1.9 2.3.1.166.2.1.3 6.2.1.20 CoAACP Acetil-CoA R etention of duplicates as groups and single entities Fatty acids metabolism 2.3.1.41

20. B oth groups and single duplicates are significantly retained E1 { { E6 I II III IV V } } } Gene duplicationNo 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

21. N ull models generation (Maslov-Sneppen) Real networkMaslov-Sneppen model Rewiring

22. N ew 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

23. H ub influence on gene duplication Enzyme recruitment rate (%) Distance between nodes (enzymes) EcoKeggEcoCyc MetaCycRefKegg Enzyme recruitment rate (%) Distance between nodes (enzymes) Enzyme recruitment rate (%) Distance between nodes (enzymes) Enzyme recruitment rate (%) Distance between nodes (enzymes)

24. M etabolic networks can be represented by diverse graph types G6P NADPH NADP + 6PGL H2OH2O GPG R5P X5P zwfpgl gndrpe compound centricenzyme centricbipartite G6P NADPH NADP + 6PGL H2OH2O GPG X5P zwf pgl gnd rpe R5P

25. 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 I n silico models have successfully simulated the grow of networks by gene duplication

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

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

28. Barabási y Oltvai (2004) Nat Rev Genet scale free clustering hierarchical S ome network emerging topological properties

29. Papp et al (2004) Nature Lemke et al (2004) Bioinformatics E ssentiality and damage in metabolic networks

30. Clustering (C) = Watts y Strogatz (1998) Nature Short distance between nodes High clustering coefficient T he small world into large networks random small-world 2n i k i (k i - 1) n i : direct edges between i neighbors k i : number of i neighbors C C = = 1 C = = 0.05 20 5(4) 1 5(4)

31. T he analysis of biological systems from a network perspective have had a great increase in last years

32. Scale free Modularity Jeong et al (2000) Nature Ravasz et al (2002) Science S ome topological properties of metabolic networks small world universality scale free hub elimination modularity + - -+-+ + e a b cd