evolution of enzymes in metabolism: a network perspective

Evolution of Enzymes in Metabolism:A Network Perspective

Rui Alves1,2, Raphael A. G. Chaleil2 and Michael J. E. Sternberg1,2*

1Department of BiologicalSciences, StructuralBioinformatics GroupBiochemistry BuildingImperial College of ScienceTechnology and MedicineLondon SW7 2AZ, UK

2Biomolecular ModellingLaboratory, Imperial CancerResearch Fund, 44 Lincoln’sInn Fields, London WC2A 3PXUK

Several models have been proposed to explain the origin and evolution ofenzymes in metabolic pathways. Initially, the retro-evolution model pro-posed that, as enzymes at the end of pathways depleted their substratesin the primordial soup, there was a pressure for earlier enzymes in path-ways to be created, using the later ones as initial template, in order toreplenish the pools of depleted metabolites. Later, the recruitment modelproposed that initial templates from other pathways could be used aslong as those enzymes were similar in chemistry or substrate specificity.These two models have dominated recent studies of enzyme evolution.These studies are constrained by either the small scale of the study or theartificial restrictions imposed by pathway definitions. Here, a networkapproach is used to study enzyme evolution in fully sequenced genomes,thus removing both constraints. We find that homologous pairs ofenzymes are roughly twice as likely to have evolved from enzymes thatare less than three steps away from each other in the reaction networkthan pairs of non-homologous enzymes. These results, together with theconservation of the type of chemical reaction catalyzed by evolutionarilyrelated enzymes, suggest that functional blocks of similar chemistry haveevolved within metabolic networks. One possible explanation for theseobservations is that this local evolution phenomenon is likely to causeless global physiological disruptions in metabolism than evolution ofenzymes from other enzymes that are distant from them in the metabolicnetwork.

q 2002 Elsevier Science Ltd. All rights reserved

Keywords: sequence analysis; protein structure; enzyme classification;metabolic databases; comparative genomics*Corresponding author

Introduction

Cellular metabolism is a complex network ofphysico-chemical processes, most of them cata-lyzed by enzymes, that allows the survival andreproduction of cells. In the early stages ofevolution of metabolism, it is likely that a smallnumber of enzymes with low effectiveness andbroad specificity existed. Under natural selection,these enzymes were duplicated, becoming increas-ingly specialized and effective. The broadspecificity of many of these enzymes must have

been lost. A “second wave” of enzyme evolutionwould then have started with even more special-ized enzymes evolving from pre-existing enzymesthat already had high specificity and efficiency.There are two prevalent models currently used toexplain enzyme evolution. Retro-evolution,1

originally suggested by Horowitz, proposes thatenzymes at the beginning of pathways evolvefrom the enzymes at the end of pathways, byduplication and mutation of the latter. As sub-strates from the end of the pathway were depletedin the primordial soup, there was a selectivepressure for new enzymes to produce these sub-strates from other pre-existing compounds. Later,Jensen proposed the recruitment model of enzymeevolution,2 suggesting that enzymes are likely tohave evolved by duplication and mutations ofsimilar enzymes from other pathways. One way toquantify this similarity is by using the enzymecommission (EC) classification scheme, based on anumber hierarchy of four digits.3 The first digit

0022-2836/02/$ - see front matter q 2002 Elsevier Science Ltd. All rights reserved

Present address: R. Alves, Departament CienciesMediques Basiques, Universidad de Lleida, Av RouviraRoure 44, 25198 Lleida, Spain.

E-mail address of the corresponding author:[email protected]

Abbreviations used: EC, enzyme commission;PSIBLAST, position-specific iterated basic localalignment search tool; SCOP, structural classification ofprotein structures.

doi:10.1016/S0022-2836(02)00546-6 available online at http://www.idealibrary.com onBw

J. Mol. Biol. (2002) 320, 751–770

describes the class of the enzyme, namely 1 foroxyreductases, 2 for tranferases, 3 for hydrolases,4 for lyases, 5 for isomerases and 6 for synthetases.Subsequent digits define further details of functionand reactants. If two enzymes belong to the sameclass in this classification, they are considered tohave similar chemical function. Recent studies,4 – 10

using both sequence and structure similarity toidentify probable homology, have examined theevolution of enzymes in pathways. Copley &Bork10 studied the evolution of 23 different TIMbarrel SCOP superfamilies and found indicationsthat at least 12 of these had a common evolutionaryorigin. Tsoka & Ouzounis7 have studied theenzymes of Escherichia coli and found that enzymesfrom the same family are distributed amongdifferent pathways. Teichmann et al.5 found thathomologues are twice as likely to be found indifferent pathways than in the same pathway.These studies suggest, as a general model, thatnew enzymes are more likely to have evolvedfrom enzymes belonging to the same enzyme classthan from enzymes in the same pathway, thusfollowing the recruitment model.

One constraint of studying enzyme evolution ina pathway context is that metabolism is a veryintricate network, with pathways branching intoeach other. There are several databases that pro-vide annotated metabolic pathways for differentorganisms, like EcoCYC,11 KEGG12 and WIT13.Many enzymes that are thought to have evolvedby recruitment may have evolved by retro

evolution, being only one or two reactions awayfrom their homologues in a different pathway.Here, we study enzyme evolution in the context ofthe metabolic network, thus avoiding this problem.This change of context also makes it useful toredefine the retro-evolution and recruitmentmodels as local (homologous enzymes being closeto each other in the reaction network, independentof the pathway they belong to) evolution andlong-distance (homologous enzymes being farfrom each other in the reaction network, indepen-dent of the pathway they belong to) evolution,respectively.

Another constraint of those studies is the smallscale of the study, either in the sense of using alimited number of proteins9 or that they studyonly one organism,4 – 8,10 usually E. coli. There arenow several fully sequenced and annotated gen-omes that lend themselves to the same kinds ofstudy and evolutionary results from several organ-isms are needed to build a more accurate picture ofthe evolution of metabolism. Here, a networkapproach is used to study enzyme evolution in thefully sequenced genomes of 12 organisms, thusremoving both constraints.

Building and AnalyzingMetabolic Networks

Our approach is summarized in Figure 1. Weparsed the information in the LIGAND part of

Figure 1. Experimental procedure. See the text for details.

752 Evolution of Enzymes in Metabolism

KEGG 12 and BRENDA† databases to construct anew database, with all of the known enzyme activi-ties defined by the enzyme commission accordingto their EC number. BRENDA is a database thatwas used to complete the information in theLIGAND regarding substrates, products andstoichiometry of enzyme reactions. We includedinformation about the substrates, products andstoichiometry for each of the reactions catalyzedby an enzyme, building connectivity matricesbetween all the enzymes in the network. The con-nectivity matrices allow us to treat metabolism asa directed-graph and study the topological charac-teristics of the network. Several connectivitymatrices were derived from the database. Onematrix considered all metabolites as vertices of thenetwork graph. This matrix includes promiscuousmetabolites that are involved in very many reac-tions, such as water or ATP. Inclusion of thesemetabolites in the connectivity network will, forexample, make it possible for homologousenzymes that have nothing in common but the useof water as a reactant to be only one step apart inthe metabolic network. This would lead to spur-

ious suggestion of local evolution. To address thisproblem, we created other connectivity matrices,considering as vertices of the network those metab-olites that are involved in less than 10, 50 or 100reactions (i.e. with promiscuity indexes lower than10, 50 or 100, respectively), thus excluding metab-olites at different levels of promiscuity. There is acorrelation between these connectivity numbersand the number up to which the typical metabolicnetwork is scale-free. Figure 2 shows a typical con-nectivity plot, which shows that, in logarithmicspace, there is an inverse proportion between thenumber of enzymes using a metabolite and thepercentage of metabolites that is used by a givennumber of enzymes, for metabolites with connec-tivity index higher than 10 and lower than 100.This proportionality indicates that the metabolicnetwork is a scale-free network; that is, a networkthat has the same average degree of branching atdifferent levels of detail, for metabolites that areused by more than approximately ten enzymesand less than approximately 100.

The next step in connecting this network per-spective and the evolution of enzymes is to obtainthe protein sequences for the enzymes in the meta-bolic networks of individual organisms. We there-fore searched SWISSPROT,14 WIT13 and KEGG12

Figure 2. Representative example of a log–log plot of the frequency of the number of enzymes that use any givenmetabolite. The network is close to scale-free for metabolites being used in less than 100 and more than ten differentreactions. Metabolites used in more than 100 reactions are H2O, H2O2, AMP/ADP/ATP, phosphate, NAD(H),NADP(H), H, O2, CO2, CoA/Acetyl CoA, acetate, UMP/UDP/UTP, CMP/CDP/CTP, GMP/GDP/GTP, NH3 andFAD(H).

† http://www.brenda.uni-koeln.de/

Evolution of Enzymes in Metabolism 753

http://www.brenda.uni-koeln.de/

Table 1. Homology information for the different organisms that were studied

Organisms

% Enzymeswith at leastone homol-

ogue

% Enzymes withat least onehomologueusing SCOP

Number ofenzymes

with hom-ologues

Number ofenzymes withhomologuesusing SCOP

Totalnumber

ofenzymes

Number ofpairs of

homologues

Number of pairs ofhomologues withpromiscuity index

,100


,50


,10

Archea Aquifex aeolicus 40.62 43.01 78 83 192 464 405 383 161Archaeoglobus fulgi-dus

38.58 38.58 49 49 127 309 263 263 96

Methanobacteriumthermoautotrophicum

37.14 40.43 52 57 140 258 222 213 81

Thermotoga mari-tima

22.22 26.28 30 36 135 237 196 182 79

Eukaryota Arabidopsis thaliana 22.35 27.37 40 49 179 345 222 213 168Caenorhabditis ele-gans

26.49 34.76 49 65 185 438 298 282 179

Schizosaccharomycespombe

26.42 27.57 56 59 212 389 269 250 161

Bacteria Bacillus subtilis 46.53 50.35 201 218 432 1909 1246 1140 748Escherichia coli 50.62 53.34 326 343 644 4289 2749 2574 2006Haemophilus influ-enzae

32.99 38.18 127 147 385 1306 983 957 566

Pseudomonas aerugi-nosa

18.52 21.48 25 29 135 175 88 80 69

Salmonella typhi-murium

18.6 19.82 40 43 215 395 363 313 297

Included is the percentage of enzymes that has homologues as well as the total number of enzymes found in each organism. We include information about the added value of using the structureinformation in SCOP to determine homologues. Using this information, homologues are found for a further 2% to 8% of the enzymes in each genome, with the exception of Archaeoglobus fulgidus,for which there is no information added by using SCOP. The last four columns show the number of homologue pairs for each connectivity matrix, i.e. if we consider as homologues only proteinsthat are homologous in domains that are not involved in the binding of metabolites with promiscuity index higher than that for the relevant homology matrix.

for the sequences of each of the EC numbers infully sequenced genomes of representative organ-isms that are publicly available. These organismsare indicated in Table 1. We present results for 12species with fully sequenced genomes, with atleast 100 identified enzyme sequences for each.These species include four Archaea, threeEukaryota and five Bacteria. The choice of notusing databases that are committed to one specificorganism (e.g. EcoCYC) to obtain proteinsequences associated with EC numbers is deliber-ate and justifiable. These databases are, in prin-ciple, more complete and better annotated butthere are very few of them available. Thus, wewould have had to use data that have differentbias for different organisms. By using general data-bases we aim at decreasing the differences in thebias of genome annotation between differentorganisms, thus making the data more readilycomparable. Because the assignment of protein

sequences from these different genomes to meta-bolic steps is continually being updated, it will beimportant to repeat this analysis once the availabledata are augmented substantially.

We ran the sensitive sequence similarity searchprogram PSIBLAST15 to find the homologuesamong the different enzymes within each organ-ism. E-values smaller than 0.001 were set assequence homology filters. PSIBLAST identifies alocal sequence similarity and thus a significantsequence similarity between a domain in oneprotein and all or part of the sequence of anotherprotein would be interpreted as a homologybetween these two proteins. To increase the powerof the homology search, we also ran PSIBLASTagainst the SCOP database.16 This database collectsproteins into hierarchical groups that take intoaccount sequence, 3-D structure and function, thusallowing the determination of homologies thatwould have been missed if only sequence

Figure 3. Genome information for the different organisms that were analyzed. (a) Plot including information aboutthe number of genes annotated in each genome and the number and percentage of genes associated with an EC num-ber. (b) Plot of the percentage of the genome that is associated with and EC number as a function of the genome size.


information had been considered. Depending onthe organism, the percentage of homologues thatwould have been missed without the use of SCOPis roughly between 1% and 8%, being lower, on

average, for Archaea than for Bacteria orEukaryota. Table 1 shows that, on average, there isa higher percentage of enzymes that have homo-logues in Archaea than in Bacteria or Eukaryota.

Figure 4. Plot of the frequency of homologue pairs within one, two and three or more steps in the metabolic networkof different organisms using different connectivity matrices. In each plot, we present a non-homologous column forArchaea, another for Bacteria and a final one for Eukaryota. These columns give the median distribution for pairs ofnon-homologues within each of these groups for the different connectivity matrices. The maximum deviation fromthese values is always smaller than 5%. (a) Connectivity matrix excluding metabolites that are used by more than 100enzymes (i.e. with promiscuity index .100). (b) Connectivity matrix excluding metabolites with promiscuity index.50. (c) Connectivity matrix excluding metabolites with promiscuity index .10.


Figure 3(a) shows the total number of genes identi-fied for each studied organism in SWISSPROT, aswell as the number and percentage of those genesthat have been associated with an EC number. Wefound that, in general, the percentage of genesassociated with an EC number is inversely pro-portional to the number of genes in the databasefor genomes smaller than 1500–2000, and appearsto remain constant at approximately 20% as thenumber of genes in the genome of an organismincreases above that value. Similar trends areobserved for the percentage of genes that is associ-ated with each enzyme class except hydrolasesand synthetases. These two classes appear to rep-resent a roughly constant percentage of the totalnumber of genes (data not shown).

Evolution of Enzymes in aMetabolic Network

Figure 4 shows that, on average, long-distanceevolution is at least as common as local evolutionin the metabolic network. However, local evol-ution, as measured by the percentage of pairs ofhomologues that are less than three steps awayfrom each other in the network, is always signifi-cantly higher than would be expected by chancealone. This is independent of the promiscuityindex that is set as threshold in the connectivitymatrix. Random shuffling (see Materials andMethods for an explanation) shows that the pat-terns of distance in the network have a likelihoodof less than 1% of occurring by chance. Figure 4(a)shows the results for a connectivity matrix that

includes metabolites with promiscuity indicessmaller than 100. Each of the first 12 columns rep-resents an organism and it is shown that a veryhigh percentage of homologues is close to eachother in the metabolic network. The last threecolumns present the average results of thenumber of steps between members of pairs ofnon-homologous enzymes for Archaea, Eukaryotaand Bacteria, respectively, and show that thedistance pattern of pairs of homologues is not justa result of the network connectivity.

If we consider only the network that is scale-free(i.e. Figure 4, with any of the three promiscuityindices), we find that, for each of the threebranches in the tree of life, the percentage ofenzymes that has evolved locally is higher thanwould be expected by chance. In general, localevolution of enzymes is more often observed inArchaea than in Bacteria or Eukaryota. When con-sidering pairs of non-homologous enzymes in allspecies, we find that, on average, these are furtheraway from each other in the network than pairs ofhomologues.

Figure 4 also provides an example of the short-comings of using only one organism to study gen-eral evolutionary issues. Most of the evolutionarystudies of enzymes have been done using the gen-ome of E. coli. The percentage of homologues thatare less than three steps away from each other inthe metabolic network of E. coli is below average.Thus, the picture that one gets regarding local evol-ution of enzymes by studying only that organismis skewed.

To compare how the percentage of pairs ofenzyme homologues that are less than three steps

Figure 5. Plot representing the percentage of homologue pairs that would have been missed as being less than threesteps away from each other if we had used the KEGG pathway definition for each organism. The different curvesrepresent the results when using different connectivity matrices. The matrix that includes all metabolites is labeled asPromiscuous. The matrix that excludes metabolites with promiscuity index .100 is labeled as Promiscuity index,100. The matrix that excludes metabolites with promiscuity index .50 is labeled as Promiscuity index ,50. Thematrix that excludes metabolites with promiscuity index .10 is labeled as Promiscuity index ,10.


away from each other in the network is affected bythe use of traditionally defined metabolic path-ways, we performed the same studies using theKEGG pathway scheme. The percentage of homo-logues that are less than three steps away fromeach other in the network is higher with the net-work approach than it is when we use the pathwayapproach. Figure 5 shows the percentage of pairsthat would have been missed to be at less thanthree reaction steps from each other, had we usedthe traditional pathway definitions of KEGG. Forthe connectivity matrices with promiscuity indexsmaller than 100 and smaller than 50, this percen-

tage is between approximately 10% and 30% of allhomologue pairs. This is a measure of the addedinformation that we obtain by using the networkapproach.

Figures 4 and 5 show that the decrease of thenumber of homologue pairs that are within twosteps when comparing a connectivity matrix withpromiscuity index .100 with a connectivity matrixwith promiscuity index ,100 is larger for Bacteriaand Eukaryota than for Archaea. This is due to thepeculiar metabolism of Archaea. These organismsuse a set of enzymes in their anabolism and energymetabolism that is not similar to those used by

Figure 6 (legend on page 763)


Eukaryota and Bacteria, thus making the connec-tivity of the Archaea metabolic network less sensi-tive to the removal of metabolite nodes that arepromiscuous in the other two realms.

We examined the association between enzymeclass and distance in the network betweenhomologues. Figure 6 shows that transferases(class 2) and synthetases (class 6) have a very highpercentage of homologue pairs within two reactionsteps of each other, independent of the connectivitymatrix that is used to measure the distance. Forconnectivity matrices excluding metabolites withpromiscuity indices higher than 10 there is still an

average percentage of 60% of homologue pairswith members that are less than three reactionsteps away from each other. In general, enzymesin these classes do not use reactants that havehigh promiscuity indices and thus, the distancebetween them is not affected when metaboliteswith high promiscuity indices are excluded fromthe network connectivity matrix. What thissuggests is that the percentage of enzymes fromeither the synthetases or the transferases classeswithin three steps of each other is significantlyhigher than it would be if enzymes had been dis-tributed randomly in the network. This is true



also for hydrolases (class 3) and lyases (class 4),although to a lesser extent, with approximately40% of homologue pairs having members that areless than three steps away from each other in thereaction network.

In contrast, oxyreductases (class 1) and iso-merases (class 5) have a very high percentage ofhomologue pairs that are more than two stepsaway from each other, with less than 20% of homo-logue pairs having members that are less thanthree steps away from each other for connectivity

matrices with a promiscuity index equal to orlower than 50. Thus, these enzymes are spreadthroughout the metabolic network more evenlythan the other four classes. Oxyreductases areenzymes that, in general, use metabolites withhigh promiscuity indices (e.g. NAD(P) or FAD).When these are removed from the connectivity net-work, the average distance between homologueswill increase sharply. However, isomerases stillhave a lower percentage of homologues less thanthree steps away from each other even when all



metabolites are used in the connectivity matrix.The average distance in the network betweenhomologues is less affected by the decreasingpromiscuity indexes in connectivity matrices thanfor oxyreductases.

Chemistry Conservation in Evolution

Recent general surveys of the structure and func-tion of homologous proteins have highlighted thefact that chemistry tends to be more conserved

than substrate specificity.4,5,17,18 However, as thedegree of sequence identity between pairs ofenzymes decreases, the extent of commonality offunction decreases. Todd et al.6 have shown thatsome fairly close homologues can differ inchemistry, sometimes having greater similarity ofsubstrate specificity. In keeping with this study,we considered that pairs of enzymes with thesame first digit of their four-digit EC number havebroadly similar chemistry, since they performedthe same general class of reaction. Our data sup-port their result, that homologous enzymes tend



to have greater conservation of chemistry thannon-homologues. Figure 4 shows that there is ahigh likelihood for enzymes to evolve from otherenzymes that are close by in the reaction networkof metabolism. Figure 7 shows, for a connectivitymatrix with promiscuity index smaller than 50,that the likelihood of two homologues belongingto the same enzyme class is, in general, at leasttwice as high as that of having homologues withdifferent first digits in their EC numbers. Randomshuffling shows that this is the approximate aver-age probability for any random pair of enzymes toshare the same first digit in their EC numbers.

To study the association between homology, dis-tance between enzymes in the network and chem-istry conservation, we use Table 2, using apromiscuity index equal to or smaller than 50.Table 2 shows measures of how much more orless likely (i.e. the odds) it is for a pair of enzymesto have a given characteristic if they are less thanthree steps away from each other in the networkthan if they are three or more steps away fromeach other in the network. For example, for Aquifexaeolicus, the odds that members of a pair ofhomologues are less than three steps away fromeach other if they belong to the same enzyme class

Figure 6 (legend opposite)


is approximately three times higher than those ofbeing three or more steps away from each other inthe network. These odds can be found in Table 2,under same first EC number digit/homologuesand they are the ratio between the proportion ofhomologous pairs that belong to the same classand are less than three steps away from each otherto the proportion of pairs of homologues that arethree or more steps away from each other andbelong to the same enzyme class. Monte Carlosimulations have shown that the average randomvalue for the odds presented in Table 2 is around0.15. The odds from the homologues columnshave a probability of occurring by chance that issmaller than 0.01 in these simulations. On the

other hand, the values for the odds of the entriesnon-homologues/different first EC number digitare always about the average values from thesimulations.

To investigate how the distance in the network isassociated with homology and chemistry, we usethe odds ratios, also given in Table 2. An oddsratio larger than 1 demonstrates an associationbetween proximity of enzymes in the network andhomology (odds ratio 2) or chemistry conservation(odds ratio 1). The higher the odds ratio, thestronger is the association.

In general, three types of associations areobserved. For Caenorhabditis elegans, Bacillussubtillis, Haemophilus influenza, Pseudomonas

Figure 6. Plots of the frequency ofhomologue pairs within one, twoand three or more steps in the meta-bolic network of different organ-isms for the six different enzymeclasses using different connectivitymatrices. Plots labeled Promiscuityindex ,100 have been obtainedusing a connectivity matrix thatexcludes metabolites with a promis-cuity index .100. Plots labeledPromiscuity index ,50 have beenobtained using a connectivitymatrix that excludes metaboliteswith a promiscuity index .50.


Figure 7 (legend opposite)

aeruginosa and Salmonella typhimurium, the twovalues for odds ratio 2 are similar and greaterthan 1.0 irrespective of the chemistry. For thesame organisms, the values for odds ratio 1 arealso similar and greater than 1.0 irrespective ofhomology. The values for odds ratio 2 are greaterthan for odds ratio 1. This shows that both hom-ology and chemistry are associated with proximity(less than three steps in the network) but thathomology has the stronger association.

The second type of association is observed inA. aeolicus, Archaeoglobus fulgidus, Methanobacteriumthermoautotrophicum and Thermotoga maritima. Theodds ratio 1 for pairs of homologues is higherthan that for pairs of non-homologues, and so weinfer that there is a stronger association betweenchemistry conservation and proximity of enzymein the network if the enzymes are homologuesthan if they are not. This pattern is repeated forthe odds ratio 2, showing that the associationbetween homology and proximity of enzymes inthe network is strong. When comparing the oddsratio 1 to the odds ratio 2, the latter is higher, show-ing a stronger association between distance in thenetwork and homology than between distance inthe network and chemistry. Nevertheless, weobserve a strong association between all the threevariables, because the odds ratios for the columnhomologues and the row same first EC numberdigit are much higher than the remaining oddsratios. For C. elegans and S. typhimurium, thisassociation of both homology and chemistry isobserved but only to a small extent and for thesetwo species we consider, to a first-order approxi-mation, that homology and chemistry are notassociated.

The final grouping is observed in Arabidopsisthaliana, Schizosaccharomyces pombe and E. coli. Simi-lar values greater than 1.0 are observed for all fourodds ratios. Thus, there are associations betweennetwork proximity and homology, and betweennetwork proximity and chemistry. These associ-ations have approximately the same strength andthere is no marked reinforcement of the associationbetween proximity in the network and homology(similar chemistry) if enzymes have similarchemistry (are homologous).

It is interesting to note that the type of associ-ation between chemistry, distance and homologyin C. elegans is the same as in Bacteria and distinct

from that of the remaining eukaryotes. Similarly,the type of correlation between distance and hom-ology in E. coli is the same as that of yeast andcress, and different from that of the other bacteria.The reasons for this association of organisms arenot clear and we could not find any biologicallyrelevant reason that could explain these groupings.

In all species we find that there is an associationbetween similar chemistry (as defined by the samefirst EC digit) and proximity in the network irre-spective of homology. This is shown by an oddsratio 1 always being greater than 1. One mightexpect that this is a trivial consequence of proxi-mate enzymes acting on metabolites with similarchemical structure. However, the definition of thefirst EC digit is in terms of the general class ofchemistry (e.g. oxyreductase) and this is not a prioriconnected with the chemical nature of themetabolite. Furthermore, our results still holdwhen promiscuous metabolites are not consideredin the connectivity of the network. Our observationsuggests that it would be useful to undertake acomprehensive analysis of the relationshipbetween conservation of the chemistry ofmetabolites and the nature of catalyzed steps.

These inter-species differences of associationbetween network proximity, homology andchemistry are complex, and merit more detailedanalysis that would be more appropriate for sub-sequent papers.

Discussion and Conclusions

Our key results are: (1) the percentage of pairs ofhomologous enzymes that are less than three stepsaway from each other in the metabolic network issignificantly higher than what is expected had thenetwork evolved randomly. (2) There often is aclustering effect of enzymes belonging to the sameclass in metabolic networks. (3) In several species,these two effects are linked and there is a particu-larly strong tendency for homologous enzymeswith similar chemistry to be found less than threesteps away from each other in the network.

Why should local evolution have a higher occur-rence than what is expected to occur randomly?Organisms evolve to adapt to their environmentand improve their fitness. This is true at everylevel, including the cellular and metabolic

Figure 7. Plots of the frequency of homologue pairs that belong to the same enzyme class (i.e. same first digit in theEC number; equal first digit in the plot legends) or to different enzyme classes (different first digit in the plot legends)in the metabolic network of different organisms. This provides a measure of chemistry conservation among homol-ogues. (a) Plot of chemistry conservation for pairs of homologues that are less than three steps away from each otherin the metabolic network. (b) Plot of chemistry conservation for pairs of non-homologous enzymes that are less thanthree steps away from each other in the metabolic network. (c) Plot of chemistry conservation for pairs of homologuesthat are three or more steps away from each other in the metabolic network. (d) Plot of chemistry conservation forpairs of non-homologous enzymes that are three or more steps away from each other in the metabolic network. Com-paring (a) and (c) to (b) and (d), respectively, suggests that chemistry conservation is significant in the evolution ofenzymes. This is confirmed by Monte Carlo simulations (data not shown).


Table 2. Odds ratios for the correlation between chemistry conservation and distance in the network for the 12 differ-ent organisms

Aquifex aeolicus (odds n(,3 steps; M ¼ 283 þ 6137)/n($3 steps; M ¼ 100 þ 11912))

Homologues ðN ¼ 253 þ 130Þ Non-homologues ðN ¼ 3610 þ 14313Þ Odds ratio 2

Same first EC number digit 3.13 0.33 9.48Different first EC number digit 0.61 0.18 3.39Odds ratio 1 5.13 1.83

Archaeoglobus fulgidus (odds n(,3 steps; M ¼ 187 þ 2809)/n($3 steps; M ¼ 76 þ 4993))

Homologues ðN ¼ 187 þ 76Þ Non-homologues ðN ¼ 1482 þ 6320Þ Odds ratio 2Same first EC number digit 4.46 0.38 11.74Different first EC number digit 0.26 0.14 1.86Odds ratio 1 17.15 2.71

Methanobacterium thermoautotrophicum (odds n(,3 steps; M ¼ 155 þ 2684)/n($3 steps; M ¼ 58 þ 6903))


Thermotoga maritima (odds n(,3 steps; M ¼ 118 þ 2654)/n($3 steps; M ¼ 64 þ 7177))

Homologues ðN ¼ 113 þ 69Þ Non-homologues ðN ¼ 1786 þ 7145Þ Odds ratio 2Same first EC number digit ðN ¼ 1899Þ 2.05 0.25 8.20Different first EC number digit ðN ¼ 7214Þ 0.52 0.13 4Odds ratio 1 3.94 1.92

Arabidopsis thaliana (odds n(,3 steps; M ¼ 70 þ 5375)/n($3 steps; M ¼ 143 þ 10433))


Caenorhabditis elegans (odds n(,3 steps; M ¼ 155 þ 4713)/n($3 steps; M ¼ 127 þ 12118))


Schizosaccharomyces pombe (odds n(,3 steps; M ¼ 140 þ 5333)/n($3 steps; M ¼ 110 þ 16889))


Bacillus subtilis (odds n(,3 steps; M ¼ 570 þ 25040)/n($3 steps; M ¼ 570 þ 67702))


Escherichia coli (odds n(,3 steps; M ¼ 978 þ 61438)/n($3 steps; M ¼ 1596 þ 143356))


Haemophilus influenza (odds n(,3 steps; M ¼ 536 þ 23410)/n($3 steps; M ¼ 421 þ 49746))


Pseudomonas aeruginosa (odds n(,3 steps; M ¼ 26 þ 2168)/n($3 steps; M ¼ 54 þ 6865))


Salmonella typhimurium (odds n(,3 steps; M ¼ 147 þ 6612)/n($3 steps; M ¼ 166 þ 16188))

(continued)


levels.19– 24 One should therefore consider theadvantage that such a localized evolution mightbring to the physiology of the cell. New enzymesappear in a metabolic network by duplication ofanother enzyme in the genome. It is likely that thenew enzyme will retain some partial originalactivity. This will disrupt the metabolism becausethere will now be two enzymes producing andconsuming the reactants that had been producedand consumed by just one, even though now wealso have an enzyme that is necessary for thereturn of the network to a balanced state. If theoriginal enzyme is close to the new one in themetabolic network, disruption of the physiologyof the cell is likely to be restricted to a part ofmetabolism that is already disrupted. However, ifthe old enzyme is not close to the new enzyme inthe network, then there will be a disruption of thephysiological state of another part of metabolism,further decreasing the fitness of the cell. This argu-ment is less compelling if we consider enzymesthat use metabolites with a high degree of promis-cuity. Imbalanced levels of such a metabolite arelikely to disrupt many parts of metabolism, mak-ing it less relevant whether an enzyme has evolvedlocally in the network or not. For example, manyoxyreductases use NAD(P) as a reactant. Indeed,once we eliminate this promiscuous metabolitefrom the connectivity matrix, the average distancebetween homologues in class 1 increases signifi-cantly, as can be seen in Figure 6. These physiologi-cal considerations do not explain the clusteringeffect of chemical function in metabolic networksthat our results suggest as a feature in the meta-bolic networks of many organisms. The clusteringcan be explained more readily if we consider theconstraints that probably exist for enzyme evol-ution. It seems more likely that an enzyme can bemutated productively into another enzyme thatperforms a similar function than into one that per-forms a totally different function. This, combinedwith the physiological arguments presented

above, provides a good background for the evol-ution of enzymes that have similar chemistry closeto each other in the metabolic network, thus lead-ing to clustering of chemical function. We note,however, that our analysis does not take intoaccount complexities such as when enzymes arephysically distant from one another by virtue ofcellular location or the effects of differentialexpression during development and/or cell type.Once systematic and reliable database informationabout these aspects is available, it will be import-ant to take this into account and include it in ouranalysis.

The present work has highlighted the limits ofstudying the evolution of enzymes in the contextof pathways. Figure 8 shows an example of anenzyme that would have been missed as havingevolved locally if we had used the pathway struc-tures in KEGG. Serine tRNA synthetase would nothave been determined to be close to prephenatedehydratase when we use the pathway schemepresented in KEGG. Many more examples likethis exist. The median percentage of homologuesthat would not have been found to be close by inthe network had we used the KEGG pathway defi-nitions for well-represented genomes is close to30% or higher if promiscuous metabolites areconsidered.

The network approach we used in this work pro-vided valuable insight into the evolution ofenzymes, at the same time allowing us to lookinto the topological aspects of metabolism, extend-ing previous results by other groups.25 –29 We deter-mined that approximately 18% of the enzymesrepresent bridges or bottlenecks in the superset ofmetabolic networks (i.e. the network that includesall possible enzyme reactions), connecting parts ofmetabolism that do not exchange material if thebottleneck enzyme is knocked out of the genome.The number of bridges is higher for individualorganisms, because organisms do not have all theenzymes in the superset. A more detailed analysis

Table 2 Continued

Aquifex aeolicus (odds n(,3 steps; M ¼ 283 þ 6137)/n($3 steps; M ¼ 100 þ 11912))

Homologues ðN ¼ 253 þ 130Þ Non-homologues ðN ¼ 3610 þ 14313Þ Odds ratio 2


The Table gives odds n(,3 steps; M ¼ a þ b)/n($3 steps; M ¼ a þ b) for specific organisms, where a represents the number of pairsof homologues and b represents the number of pairs of non-homologues. In the homologues/non-homologues boxes, N ¼ c þ d;where c represents the number of pairs of enzymes that belong to the same class and d represents the number of pairs of enzymesthat belong to a different class. The column labeled homologues presents results for pairs of enzymes that are homologues, whilethe column labeled non-homologues presents results for pairs of enzymes that are not homologues. The rows labeled same first ECnumber digit present the results for pairs of enzymes that belong to the same class, while the rows labeled different first EC numberdigit present the results for pairs of enzymes that do not belong to the same class. Each entry in the Table compares the odds that apair of enzymes has members that are less than three steps away in the metabolic network compared with those of the pair havingmembers that are three or more steps away form each other. For example, the number under same first digit/homologues gives usthe odds that the members of the pair are less than three steps away from each other. The column labeled odds ratio 2 presents theodds ratio between pairs of homologues and non-homologues. The row labeled odds ratio 1 shows the odds ratio between pairs ofenzymes that belong to the same class and pairs of enzymes that belong to different classes.


Figure 8. Example of local evolution that would have been missed if the analysis was done using KEGG pathway definitions instead of a network approach: EC 5.4.99.5,chorismate mutase; EC 4.2.1.51, prephenate dehydratase; EC 2.6.1.21, D-alanine transaminase; EC 4.2.1.13, L-serine dehydratase; EC 6.1.1.11, serine-tRNA synthetase. Homol-ogues are presented in gray boxes. EC 6.1.1.11 is only two reactions away from EC 4.2.1.51 but it would not have been detected as being close because it is in a different KEGGpathway.

of each organism identifies a larger number ofenzymes that may be organism-specific bottle-necks. However, one must be careful with theseorganism-specific bottlenecks, because they maybe due to errors and limitations in genome annota-tion. The 18% general bottleneck enzymes arelikely to be important targets for mutations causingmetabolic diseases and a more detailed study ofthese enzymes should prove useful.

The use of several fully sequenced genomes inour work circumvents the problem of using onlyone genome to study the evolution of metabolicnetworks. The replacement of a pathway approachwith a network approach leads to a complex pic-ture of evolution in metabolic networks with ahigh percentage of non-local evolution but also asignificant over-occurrence of local evolution withlocal islands of homologous enzymes that sharesimilar chemistry.

Materials and Methods

Database construction

We used LIGAND (version 19.0) and BRENDA (Inter-net version of August 2001†) for Metabolic Network gen-eral reconstruction. Version 4.01 of Mathematica30 wasused to analyze the characteristics of metabolic networkgraphs. We used SWISSPROT (version 39), WIT (versionDecember 1999) and KEGG (current version) for recon-structing the enzyme sequence of each organism andSCOP (version 1.50) for structure-based homology. Weran PSIBLAST version 4.2.3 with the default parametersand an E-value cut off of 0.001 to find the homologuesamong the different enzymes within each organism.

Distance between enzymes and local evolution

Whether two homologues have evolved by retro-evolution or by recruitment is traditionally decided bydetermining whether they belong to the same path-way(s). Once we remove the traditional definition ofpathways, the problem of determining what is local andwhat is long-distance evolution arises. An extremely con-servative approach would consider that local evolutionoccurs only when homologues are consecutive enzymesin the network. However, this criterion is much stricterthan what is used in traditional studies, where enzymesare considered to have retro-evolved if they belong tothe same pathway, independent of their distance in thepathway. Connectivity studies (Jeong et al.,26 and thisstudy, data not shown) show that the average numberof reactions for a given metabolite to be transformedinto itself again is between 3 and 5. Thus, we definedthe threshold distance for local evolution in the networkas being 2, which is the only integer larger than 1 andsmaller than 3.

Random shuffling

Random shuffling was done using Mathematica.30 Todetermine how significant the results for distancebetween enzymes, chemistry conservation analysis,

odds and odds ratios are for each organism, we built asquare matrix where each row (column) represents anenzyme (indexed in our database). The entries in therow (column) are 1 if the enzyme in the column (row) ishomologous to the enzyme in the row (column) and 0otherwise. The index that identifies the columns is thenshuffled randomly (and the row index is made consistentwith it). We then analyze distance and chemistry conser-vation in the random network. This random procedurewas repeated 1000 times for each organism and eachtime the appropriate numbers were stored. Histogramswere built with those numbers and proportions to com-pare with the actual values of the odds and odds ratios.Note that tests such as x2 or contingency tables wouldbe inappropriate due to non-independence of the num-bers. If enzymes A, B and C are homologues, and A isone step from B, and B is one step from C, then A andC must be less than three steps apart and thus the resultfrom A–C in not independent of the results for A–Band B–C.

Acknowledgments

R.A. was supported by the Imperial Cancer ResearchFund (ICRF) and the BBSRC. R.A.G.C. was supportedby the ICRF. M.J.E.S. was supported by the ICRF andImperial College. We thank Dr Peter Sasieni (ICRF) forstatistical advice.

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Edited by F. E. Cohen

(Received 3 January 2002; received in revised form 20 March 2002; accepted 16 May 2002)


evolution of enzymes in metabolism: a network perspective

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