Báo cáo khoa học: Prediction of missing enzyme genes in a bacterial metabolic network Reconstruction of the lysine-degradation pathway ofPseudomonas aeruginosa doc

The proposed method con-sists of two steps: a estimation of the functional association between the genes with respect to chromosomal proximity and evolutionary association, using supervi

metabolic network

Reconstruction of the lysine-degradation pathway of Pseudomonas aeruginosa

Yoshihiro Yamanishi1, Hisaaki Mihara2, Motoharu Osaki2, Hisashi Muramatsu3, Nobuyoshi Esaki2, Tetsuya Sato1, Yoshiyuki Hizukuri1, Susumu Goto1and Minoru Kanehisa1

1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan

2 Division of Environmental Chemistry, Institute for Chemical Research, Kyoto University, Japan

3 Department of Biology, Graduate School of Science, Osaka University, Japan

Most biological functions involve the coordinated

actions of many proteins, and the complexity of living

systems arises as a result of such interactions It is

there-fore important to understand biological systems by

analyzing the relationships among many proteins A

challenge in recent genome science is to computationally

predict the systemic functional behaviors of proteins

from genomic and molecular information for industrial and other practical applications [1,2] Recent sequence projects and developments in biotechnology have con-tributed to an increasing amount of high-throughput genomic data for biomolecules and their interactions These data are useful sources from which to computa-tionally infer many types of biological networks [3–6]

Keywords

kernel methods; lysine degradation

pathway; metabolic network; missing

enzymes; network inference

Correspondence

Y Yamanishi, Bioinformatics Center,

Institute for Chemical Research, Kyoto

University, Gokasho, Uji, Kyoto 611-0011,

Japan

Fax: +81 774 38 3269

Tel: +81 774 38 3270

E-mail: yoshi@kuicr.kyoto-u.ac.jp

(Received 6 December 2006, revised 17

February 2007, accepted 1 March 2007)

doi:10.1111/j.1742-4658.2007.05763.x

The metabolic network is an important biological network which consists

of enzymes and chemical compounds However, a large number of meta-bolic pathways remains unknown, and most organism-specific metameta-bolic pathways contain many missing enzymes We present a novel method to identify the genes coding for missing enzymes using available genomic and chemical information from bacterial genomes The proposed method con-sists of two steps: (a) estimation of the functional association between the genes with respect to chromosomal proximity and evolutionary association, using supervised network inference; and (b) selection of gene candidates for missing enzymes based on the original candidate score and the chemical reaction information encoded in the EC number We applied the proposed methods to infer the metabolic network for the bacteria Pseudomonas aeru-ginosafrom two genomic datasets: gene position and phylogenetic profiles Next, we predicted several missing enzyme genes to reconstruct the lysine-degradation pathway in P aeruginosa using EC number information As a result, we identified PA0266 as a putative 5-aminovalerate aminotransferase (EC 2.6.1.48) and PA0265 as a putative glutarate semialdehyde dehydro-genase (EC 1.2.1.20) To verify our prediction, we conducted biochemical assays and examined the activity of the products of the predicted genes, PA0265 and PA0266, in a coupled reaction We observed that the predi-cted gene products catalyzed the expepredi-cted reactions; no activity was seen when both gene products were omitted from the reaction

Abbreviations

OGC, ortholog gene cluster; ROC, receiver operating curve.

The metabolic network is an important class of

biolo-gical network, consisting of enzymes and chemical

com-pounds Recent developments in pathway databases,

such as KEGG PATHWAY [7] and EcoCyc [8], enable

us to analyze known metabolic networks

Unfortu-nately, most organism-specific metabolic networks

con-tain many ‘missing enzymes’ in their known pathways

Because the experimental determination of metabolic

networks remains challenging, even for the most basic

organisms, there is a need to develop methods to infer

the unknown parts of metabolic networks and identify

genes coding for missing enzymes in known metabolic

pathways [9–11] Thanks to the development of

homol-ogy detection tools [12–14], enzyme genes can be easily

found from fully sequenced genomes using comparative

genomics [15], but it can be difficult to assign them a

precise biological role within a pathway

Missing enzymes are an obstacle to understanding

the functional behavior of enzymes in metabolic

path-ways There are two research directions for finding the

genes of missing enzymes The first is to use genomic

information to predict candidate genes coding for the

missing enzymes Examples include using information

about the gene order along the chromosome in

bacter-ial genomes [16], gene fusion [17,18], genomic context

[19,20], gene-expression patterns [21,22], statistical

methods [23] and multiple genomic datasets [5,6,24]

The second approach is to use information about the

chemical compounds with which the enzymes are

involved An example is the path-computation

approach [25], in which all possible paths between two

compounds are searched by losing the

substrate-specif-icity restriction However, this system tends to produce

too many candidates and it is difficult to select reliable

paths It is more natural to use both genomic data and

chemical information simultaneously, rather than to

use each individually

This study presents a novel method to identify genes

coding for missing enzymes from genomic data and

chemical information for bacterial genomes First, we

designed kernel-similarity measures [26] between genes

based on gene positions and phylogenetic profiles This

is motivated by the interesting observation that

func-tionally related genes tend to be closely located along

bacterial chromosomes [16,27] or evolve in a correlated

manner [28–30] Next, we predict a global gene

net-work applying supervised netnet-work inference using the

kernels based on the genomic datasets, which are

based on a previously developed network inference

algorithm [24,31] Finally, we collect genes that have

potential functional relations with enzyme genes

adja-cent to the target missing enzyme using the original

candidate score, and select genes based on the enzyme

commission (EC) numbers of the target enzymes in the pathway Figure 1 illustrates this procedure

We applied the proposed method to the metabolic network of Pseudomonas aeruginosa and attempted to find several missing enzyme genes We focused on the lysine-degradation pathway of P aeruginosa (Fig 2) because it contains many missing enzymes for which the coding genes have not yet been identified Our sur-vey of missing enzymes in the KEGG PATHWAY database suggests that the lysine-degradation pathway map for P aeruginosa is missing 28 of its 62 enzymes (45%) Lysine catabolism is notable for its biochemical diversity across organisms Enzymatic reactions in the lysine pathway in bacteria are completely different from those seen in eukaryotes and archaea, and there

is also variation within bacteria We also focused on the lysine-degradation pathway because the substrates and intermediates of the pathway are structurally sim-ple, and so the reactions can be easily examined bio-chemically Thus, the computational prediction of missing enzymes can be verified using relatively simple biochemical experiments

We selected gene candidates for some of the missing enzymes in the lysine-degradation pathway based on the candidate scores, which in turn were based on association scores with known enzymes that catalyzed similar reactions based on EC number For example,

we identified PA0266 as a putative 5-aminovalerate aminotransferase (EC 2.6.1.48) and PA0265 as a putative glutarate semialdehyde dehydrogenase (EC 1.2.1.20) To verify the prediction, we conducted wet-lab experiments, in which PA0265 and PA0266 genes were cloned and expressed in Escherichia coli, and the proteins purified The activity of PA0265 and PA0266 was examined, and we found that the enzymes catalyzed the expected reactions Therefore,

we concluded that PA0265 is glutarate semialdehyde dehydrogenase and PA0266 is 5-aminovalerate amino-transferase This is how we successfully reconstructed the metabolic pathway for lysine degradation

Results Inference of potential gene network First, we attempted to infer a global network consist-ing of the potential functional relationship between the genes of P aeruginosa from two genomic datasets: the gene position along the genome, and phylogenetic pro-files Details of our network inference method are given in the Experimental procedures and the original references [24,31] In previous studies, the usefulness of the network inference method was confirmed by a

cross-validation experiment which attempted to recover

the metabolic network in the KEGG PATHWAY

database as follows In each cross-validation step, the

known enzyme genes were randomly divided into two

sets: the training set and the test set, in the proportion

of nine to one First, we used the training dataset for a

learning process Second, we predicted the network

involving the enzyme genes in the test set Finally, we

evaluated the accuracy of the prediction using ROC

scores, defined as the area under the receiver operating

curve (ROC), that is, the area under the plot of true

positives as a function of false positives, normalized

to 1 for a perfect prediction and 0.5 for a random

prediction

To evaluate the biological relevance of the gene

position and the phylogenetic profile with metabolic

networks, we computed ROC scores by applying the

cross-validation test as in previous studies Table 1

shows the ROC scores for gene position, phylogenetic

profile, and the integration of both datasets Both gene

position and phylogenetic profile seemed to capture

information for reconstructing the metabolic network

We evaluated the biological relevance of each data

source by ROC score) 0.5, and used them to weight

the data integration process The resulting weights for

gene position and phylogenetic profile are 0.48 and

0.52, respectively, where the sum of the weights is nor-malized to 1 We also observed a significant effect of integrating the two genomic datasets into a single set via the sum of the kernel-similarity matrices Finally,

we predicted a global network for all the genes of

P aeruginosa In the inference process, we used all the current knowledge about the metabolic network as training data The predicted network enabled us to predict unknown functional relations between genes The results of the predicted gene network can be obtained from http://web.kuicr.kyoto-u.ac.jp/supp/ yoshi/pae/ A web server to carry out the network inference procedure is in preparation

Missing enzyme gene prediction There are many missing enzymes whose coding genes have not been identified in known pathways In this study, we focused on the reconstruction of the lysine-degradation pathway of P aeruginosa, because this pathway contains many missing enzymes and our understanding of the detailed enzymatic behavior

in this pathway is far from complete Figure 2 shows the lysine-degradation pathway stored in the KEGG PATHWAY database, where a green box indicates that the enzyme’s gene has been identified for

1 2

9

3 4

8

6

5

7

Gene Location

Predicted Gene Network

PATHWAY Database

Phylogenetic Profile

Gene 1 (1 0 1 0 0 0 1 0 1 1 1 0)

Gene 2 (1 0 1 0 0 0 1 0 1 1 1 0)

Gene 3 (1 0 1 0 0 0 1 0 1 1 1 0)

Gene 4 (1 0 1 0 0 0 1 0 1 1 1 0)

Gene 5 (0 0 0 0 0 0 1 0 1 1 1 0)

Gene 6 (1 1 1 1 1 1 1 1 1 1 1 0)

Gene 7 (1 0 1 0 0 1 1 1 1 1 1 1)

Gene 8 (1 0 1 0 0 0 0 0 0 0 1 0)

Gene 9 (1 0 1 0 0 0 0 0 0 0 1 0)

1.1.1.79

4.1.1.40

1.1.1.77

5

6 8

3

1

9

Fig 1 Procedure for predicting missing enzyme genes First, we estimated the functional associations between genes by predicting a global gene network from the chromosomal proximity and phylogenetic profiles, using the supervised network inference method Second, we looked for sets of genes sharing high association scores with the neighbors of missing enzymes Finally, we selected candidates for the missing enzymes based on the chemical reaction information encoded in the first three digits of the EC numbers.

P aeruginosa, and the red color indicates missing

enzymes for which genes have yet to be identified

Based on the predicted gene network, we attempted to

predict the candidate genes corresponding to missing

enzymes in this pathway There are two paths from

l-lysine to glutarate in the lysine-degradation pathway:

l-lysine fi 5-amino pentanamide fi 5-amino

pen-tanoate fi glutarate semialdehyde fi glutarate and

l-lysine fi cadaverine fi 1-piperideine fi 5-amino

pentanoate fi glutarate semialdehyde fi glutarate

The second pathway is known to exist for P

aerugi-nosa [32] However, several of the enzyme genes

involved have not been identified, therefore we

foc-used on the second pathway, which is illustrated in

Fig 3

(EC 1.3.99.7) as seed genes, because they are adjacent

to the missing enzymes These enzyme genes PA1586 and PA0447 are known to work in the lysine-degrada-tion pathway We looked for genes with high graphical association scores to PA1586 and PA0447 in our pre-dicted gene network, using our original candidate score (see Experimental procedures for more details) Table 2 shows a list of the top 50 high-scoring genes Several of these high-scoring genes may be functionally related to PA1586 and PA0447 Taking into account the first three digits of the EC numbers, we assigned the high-scoring genes to each missing enzyme For example, the first three digits of the EC number for PA1589 (EC 6.2.1.5) are the same as those for the missing enzyme (EC 6.2.1.6), therefore we predicted that PA1589 is a candidate for the enzyme gene corres-ponding to EC 6.2.1.6 In a similar manner, we predi-cted PA0265 (EC 1.2.1.16) and PA0266 (EC 2.6.1.19)

as enzyme gene candidates for EC 1.2.1.20 and

EC 2.6.1.48 The chemical reactions between cadave-rine, 1-piperideine and 5-amino pentanoate have not been assigned an EC number in the International

Table 1 Prediction accuracy for gene network inference: ROC

scores.

LYSINE DEGRADATION

Penicillins and

cephalos-porins biosynthesis

2-Oxoadipate

S

-Glutaryl-dihydrolipoamide

Crotonoyl-CoA

2.6.1.39 1.2.1.31

1.5.3.7

1.5.1.21

1.4.3.14

2.6.1.21

5.1.1.5 5.1.1.9

5.4.3.4 1.4.1.12

1.4.1.11

6.3.2.27

2.3.1.102

5.4.3.3 5.4.3.2

1.14.13.59

2.6.1.65

3.5.1.17 2.3.1.32

1.5.1.16

3.5.1.17 1.5.1.1

1.5.99.3

1.2.4.2

2.3.1.61 1.3.99.7

4.2.1.17

1.1.1.35

2.3.1.9

Acetoacetyl-

CoA

Protein-

lysine

erythro-5- Hydroxylysine

Protein- N6-Me-lysine

Glutaryl- CoA

Glutarate Glutarate

semialdehyde

5-Amino pentanoate

1-Piperideine

5-Amino- pentanamide

Cadaverine

N2-( D -1-Carboxy- ethyl)- L -lysine

L -2-Aminoadipate 6-semialdehyde

L -2-Amino- adipate

Saccharopine

6-Amino-2-oxohexanoate

6-Acetoamido-2-oxohexanoate

D-Lysine

2,5-Diamino-hexanoate 3,5-Diamino-hexanoate

L -β-Lysine

L -Lysine N6-Hydroxy-lysine

N6-Acetyl-N6-hydroxy-lysine Aerobactin

2-Amino-5-oxohexanoate 5-Amino-3-oxohexanoate

N

-Acetyl-lysine

L -Pipecolate

Δ1-Piperideine-6- L -carboxylate

Δ1-Piperideine-2-carboxylate

1.5.1.7 1.5.1.9

1.5.1.10 1.5.1.8

3.5.1.30 1.13.122

2.3.1.–

2.6.1.39 1.1.1.–

3.5.1.63

5-Acetanmido- pentanoate

6-Acetanmido- 2-oxohexanoate

N -Acetyllysine

Glycine 4-Trimethyl-

ammoniobutanoate

Biotin metabolism Lysine biosynthesis

4-Trimethyl- ammoniobutanal

N6-Hydroxy- trimethyl-lysine

Protein-N -

trimethyl-lysine 3.4.–.–

2.1.1.43 2.1.1.59 2.1.1.60

Protein-N, N -

Me2-lysine 5-Phosphonooxy-lysine

Trimethyl- lysine

5-Galactosyloxy-lysine

Camitine

3-Dehydroxy- camitine

1.14.11.1 1.14.11.1 1.2.1.47

1.2.1.3 2.1.2.1

1.14.11.8

Citrate cycle

2.1.1.43 2.1.1.59 2.1.1.60

1.14.11.4

2.4.1.50 2.7.1.81 2.1.1.60 2.1.1.59 2.1.1.43 Acetyl-CoA

(S)-3-Hydroxy-

butanoyl-CoA

Fig 2 Lysine-degradation pathway of P aeruginosa A small circle corresponds to one chemical compound and a rectangle corresponds to one enzyme protein Green indicates that the coding enzyme genes have been identified, and red indicates that the coding enzyme genes have not yet been identified ‘?’ indicates that the enzyme has not been assigned an EC number.

Union of Biochemistry and Molecular Biology

(IUBMB) at the time of writing

To obtain putative EC number information, we used

the E-zyme system [24], which is an automatic EC

number assignment system developed in the KEGG

database Using the E-zyme system, we carried out EC

number predictions based on the chemical structures

of 1-piperideine and 5-amino pentanoate As a result, the E-zyme system returned EC 1.1.1.- for the chemical reaction The list of high-scoring genes contains PA1576 (EC 1.1.1.31), so we assigned it to the missing enzyme involved in the reaction between 1-piperideine and 5-amino pentanoate Unfortunately, the current version of the E-zyme system could not generate a pre-diction for the reaction between cadaverine and 1-pip-erideine, because there is no template information describing the target reaction in the current system For EC 4.1.1.18, the list of high-scoring genes does not contain any genes whose first three EC number digits match Therefore, we were not able to assign any specific gene to the missing enzyme EC 4.1.1.18 However, there are many hypothetical proteins with high candidate scores in the list given in Table 2, so there is a possibility that one of these hypothetical pro-teins might work as an enzyme in the target chemical reaction Table 3 summarizes our gene assignment for the corresponding missing enzymes in the lysine-degra-dation pathway

Expression and purification of recombinant enzymes

Finally, we conducted a wet-lab experiment based on biological assays in order to verify that our predicted genes were involved in the target chemical reactions

We focused on a successive reaction: 4-amino pentano-atefi glutarate semialdehyde fi glutarate Recall that

we predicted that PA0266 was a putative 5-amino-valerate aminotransferase (EC 2.6.1.48) and PA0265

a putative glutarate semialdehyde dehydrogenase (EC 1.2.1.20) The PA0265 and PA0266 genes were cloned by PCR and expressed in E coli, and the pro-teins were purified to homogeneity as a C-terminal histidine-tagged fusion protein SDS⁄ PAGE analysis of the purified PA0265 and PA0266 proteins gave single bands with subunit molecular masses of 53 and

46 kDa, respectively, in good agreement with those calculated from the amino acid sequences (53 142 and

46 285 Da, respectively) Purified PA0266 exhibits a yellow color and UV-visible spectra characteristic of a pyridoxal 5¢-phosphate-dependent enzyme (data not shown)

Enzymatic activity of predicted genes The activity of PA0265 and PA0266 was examined in

a coupled reaction, in which conversion of 5-amino pentanoate fi glutarate semialdehyde fi glutarate was monitored by the increase in the amount of

O

O

L -Lysine

EC:4.1.1.18

EC: ?

N

Cadaverine

1–Pierideine

5–Aminopentanoate

H HO

Glutarate semialdehyde

Glutarate

Glutaryl–CoA

O OH

O

HO

EC: 1.2.1.20

EC: 6.2.1.6

O

HO

A

B

C

D

E

F

O CoA

EC: ?

EC: 2.6.1.48

HO

NH2

NH2

NH2

H2N

NH2 HO

O

Fig 3 A series of chemical reactions focused on in this study.

Cadaverin-based path from L -lysine to glutarate via cadaverine,

1-piperideine, 5-amino pentanoate, and glutarate semialdehyde.

NADPH at 340 nm (Fig 4A) We found that the

enzymes catalyzed the expected reactions (Fig 4B),

and no activity was seen when both enzymes were

omitted from the reaction Reaction mixture contain-ing only PA0266 showed a slight increase A340, due to the formation of pyridoxamine 5’-phosphate from

Table 2 Top 50 high-scoring genes in our candidate scores.

pyridoxal 5’-phosphate via a transamination reaction

catalyzed by PA0266 Therefore, we concluded that

PA0265 is glutarate semialdehyde dehydrogenase and

PA0266 is 5-aminovalerate aminotransferase

Discussion

Here, we have proposed a novel method to predict

genes coding for missing enzymes in metabolic

path-ways using genomic data and chemical information for

bacterial genomes As an application of this technique,

we attempted to reconstruct the enzyme gene network

of the lysine-degradation pathway in P aeruginosa

We filled in some of the enzyme genes in the

lysine-degradation pathway, for example, by predicting

PA0266 as a putative 5-aminovalerate

aminotrans-ferase and PA0265 as a putative glutarate semialdehyde

dehydrogenase Recently, a report has suggested

candi-date genes for 5-aminovalerate aminotransferase and

glutarate semialdehyde dehydrogenase in the

lysine-degradation pathway of P putida [34] These genes have an orthologous relationship with those predicted for P aeruginosa, so this is additional evidence for our prediction We also confirmed the validity of our pre-diction by conducting biochemical assays We exam-ined enzyme activity in successive enzymatic reactions, and observed that the genes PA0266 and PA0265 work

as 5-aminovalerate aminotransferase and glutarate semialdehyde dehydrogenase, catalyzing successive chemical reactions from 5-amino pentanoate to gluta-rate There is a hypothesis that the predicted gene products PA0266 and PA0265 might have broad sub-strate specificity For example, the E coli gene of

EC 2.6.1.19 (on which many experimental studies have been performed) has high sequence similarity with

P aeruginosa gene PA0266, and the corresponding gene cluster structure is well conserved

To date, techniques for reconstructing metabolic networks have depended heavily on sequence homol-ogy detection [35] A typical computational approach

to reconstructing the metabolic network from the gen-ome sequence of a certain organism is as follows: (a) Assign an EC number to enzyme candidate genes

by detecting homology based on comparative genomics across different organisms (b) Obtain compound information such as substrates and products, in which the enzyme genes are involved, from reaction know-ledge based on the EC number (c) Assign each enzyme gene to appropriate positions in metabolic pathway maps, created from current biochemical knowledge for many organisms (d) Visualize metabolic

Table 3 Assignment of genes to missing enzymes in the

lysine-degradation pathway of P aeruginosa.

A EC:6.2.1.6 PA1589 (succinyl-CoA synthetase; EC 6.2.1.5)

B EC:1.2.1.20 PA0265 (dehydrogenase; EC 1.2.1.16)

C EC:2.6.1.48 PA0266 (amino-transferase; EC 2.6.1.19)

D 5ami.1-pip PA1576 (dehydrogenase; EC 1.1.1.31)

E Cadav.Delta not specified

F EC:4.1.1.18 not specified

0 0.05 0.10 0.15 0.20

Glutamate

Time (s)

A

B

a

b

c d

Fig 4 Enzymatic activity of predicted genes (A) Schematic drawing of reactions catalyzed by aminovalerate aminotrans-ferase (PA0266) and glutarate semialdehyde dehydrogenase (PA0265) (B) Activity of PA0265 and PA0266 The reaction was car-ried out in the presence of PA0266 and PA0265 (a), PA0266 (b), PA0265 (c), or in the absence of the enzymes (d).

pathways that are specific to a target organism

How-ever, this procedure does not always work well in

reconstructing the correct metabolic pathways, and

tends to lead to many missing enzymes or gaps in

known metabolic pathways If we cannot detect a

sig-nificant sequence homology with enzyme genes whose

pathway information is known in other organisms, it

is not possible to identify candidate genes for missing

enzymes This has been one cause of missing enzymes

or pathway gaps in the predicted metabolic network,

as suggested previously [9–11]

There are two possible reasons for missing enzymes

in predicted pathways First, there may be alternative

paths between the two compounds either side of the

gap To solve this, a path computation approach has

been proposed [25] This method searches all possible

pathways between two compounds if the enzyme

link-ing the compounds is misslink-ing However, it has been

pointed out that this system tends to show too many

possible pathways Second, the EC number annotation

might be wrong for the enzyme linking the

com-pounds We often observe that the sequence homology

for enzymes sharing the first three digits of the EC

number is well conserved across different organisms,

however, sequence homology corresponding to the

substrate specificity represented by the fourth digit of

the EC number is not strongly conserved Therefore, it

is suspected that wrongly annotated genes may have

been the cause of some of the pathway gaps or missing

enzymes It is also suspected that many genes have

been assigned incorrect EC numbers and assigned the

wrong biological roles Even so, the first three digits of

the EC number remain useful for predicting potential

enzyme genes, and if the first three digits in the EC

number are the same between two enzymes, those

enzymes can be considered to catalyze similar types of

chemical reactions Therefore, our gene-selection

method for missing enzymes can be regarded as

rea-sonable from a chemical viewpoint It should also be

pointed out that our method is applicable to any

reac-tion, even when no EC numbers are assigned to the

reactions, because our procedure includes the process

of estimating the possible EC subsubclass for the

reac-tions based on biochemical structure transformation

patterns [33] There are many reactions for which EC

numbers have not been assigned, especially in

secon-dary metabolism We expect that our approach works

well for such complex metabolic pathways

From a technical viewpoint, we transformed all the

predictor datasets into kernel-similarity matrices in

order to estimate functional associations between

genes In this study, we used the gene position and

phylogenetic profiles because they reflect the following

two properties of bacterial genomes First, functionally interacting genes in metabolic pathways tend to be clo-sely located along the chromosome, as seen in operon structures [16,27] Second, functionally interacting genes in metabolic pathways tend to evolve in a corre-lated manner [28–30] Performance depends on the design of the kernel-similarity measure, so there remains room in the evaluation for gene–gene similar-ities based on each data source For gene position data, the incorporation of directed information of genes into the similarity would be interesting For phylo-genetic profiles, the use of a real-valued phylophylo-genetic profile [36] might improve the performance Additional use of other genomic information, such as gene fusion [17,18], in the framework of kernel methods will be studied in future

Another solution to the problem of missing enzymes would be to use other experimental data such as gene-expression data [21,22] The pattern of gene gene-expression based on several experimental conditions makes it possible to observe the expression behavior of thou-sands of genes and estimate potential functional associ-ations between them It has been confirmed that the gene-expression pattern of successively working enzyme pairs is more similar than that of randomly selected enzyme pairs [21] Therefore, gene-expression data would be a useful source of additional data in our study However, microarray technology is expen-sive, so the information is not always available for the target organism, and we were not able to obtain the microarray gene expression data for P aeruginosa Another problem is that the microarray data tend to contain considerable noise By contrast, our method brings about a new possibility for the systematic pre-diction of potential functional relationships between genes Our predicted network enables us to suggest unknown gene–gene relations and estimate missing enzyme genes using just the adjacency information and comparative genomics

The originality of this study is also seen in the colla-borative work between both computational prediction and experimental validation In this study, the biologi-cal validity of the prediction was confirmed by con-ducting a biochemical assay, and it was observed that the enzymes corresponding to the predicted genes cata-lyzed successive reactions in the target metabolic path-way This type of collaborative work will become a standard in research in near future Furthermore, we expect to identify more missing enzyme genes in other pathways by a similar application of our approach Comprehensive identification of missing enzyme genes

in the entire metabolic network will be carried out in the future

Experimental procedures

Datasets

In this study, we focused on the metabolic pathways of

P aeruginosa.As a gold standard for the enzyme gene

net-work, we used the KEGG PATHWAY database [7] The

resulting enzyme network contains 799 nodes and 2782

edges Note that this network is based on biological

phenom-ena and represents known molecular interaction networks in

various cellular processes We obtained information about

enzyme genes from the KEGG GENES database, in which

EC numbers are assigned to candidate enzyme genes At the

time of writing, in P aeruginosa, 1133 genes have been

assigned at least one EC number, but only 799 have been

assigned at least one precise role in metabolic pathways

The dataset for the gene position on the genome was

con-structed from the KEGG GENES database We obtained

information about the start and end positions of each gene

region (ORF region), and we computed all pair-wise

distan-ces between the genes The gene position data can be

regar-ded as a dataset representing the spatial association between

genes along chromosomes Phylogenetic profiles were

con-structed from a set of ortholog gene clusters (OGCs)

obtained from comprehensive cluster analysis for all the

genes of fully sequenced organisms in KEGG GENES A

group of genes identified as a quasi-clique in the graph of

the KEGG SSDB (sequence similarity database) is thought

to be a candidate for the OGC The concept of OGC is

sim-ilar to that of the COG database [37] In this study, we

focus on organisms with fully sequenced genomes, including

11 eukaryotes, 16 archaea, and 118 bacteria Each

phylo-genetic profile consists of a string of bits, in which the

pres-ence and abspres-ence of an orthologuous gene is coded 1 and 0,

respectively, across the above 145 organisms

We obtained chemical information for the enzymes, for

example chemical reactions, substrates and products, from

their EC numbers, using the KEGG LIGAND database [38],

which contains 11 817 compounds and 6349 reactions at the

time of writing EC numbers are a numerical classification

scheme for enzymes, based on the chemical reactions they

cat-alyze We focused on the first three digits in the EC number,

because the fourth digit in the EC number is often just a serial

number In cases where a target reaction has not been

assigned an EC number, we used the E-zyme system, which

was recently developed in the KEGG database The E-zyme

system is an EC number assignment system for chemical

reac-tions, which enabled us to estimate the first three digits of the

EC number for the target reaction by taking into account the

structural information of two given chemical compounds [33]

Data representation and integration

To deal with the heterogeneity of genomic datasets, we

pro-pose to transform all the datasets into kernel-similarity

matrices [26] In recent years, kernel methods such as support vector machine have received much attention in computational biology An advantage of using kernel meth-ods is that we can apply a variety of statistical analyses to any structured data, for example graphs, strings and trees Suppose that we have a set of genesfxigni¼1;where n is the number of genes For the gene position data, we computed all the pair-wise distances between genes along the chromo-some, where the distance dij between gene i and gene j is defined by the number of nucleotides between the end of the i-th gene and the start of the j-th gene along the chro-mosomes We then derived a distance kernel using the for-mula Kposition(xi,xj)¼ exp(–dij⁄ h) for i,j ¼ 1,2, ,n where h

is a positive constant parameter In this study the param-eter h is set to 105 This means that, the larger the distance between two genes along the chromosome, the smaller the value of the similarity score The resulting kernel matrix (similarity matrix) is denoted as Kposition The phylogenetic profiles are sets of numerical vectors Suppose that we have

ngenes and q organisms Let us define x as the phylogene-tic profile for each gene (145 dimensional vector) and y as the phylogenetic profile for each organism (5525 dimen-sional vector) Here we used a weighted linear kernel (weighted inner product) as follows:

Kphylogeneticðxi; xjÞ ¼ xT

iW xj; for i; j¼ 1; 2; ; n; where W is an diagonal matrix whose elements are given as ðWÞkk¼ 1  corrðypae; ykÞ; for k¼ 1; 2; ; q

where q is the number of organisms, ypaeis the phylogenetic profile for P aeruginosa, and corr(.) refers to Peason’s corre-lation coefficient This means that the more similar the gene inheritance pattern between two genes, the larger the value of the similarity score The resulting kernel-similarity matrix is denoted as Kphylogenetic The weight is introduced to reduce the effect of related organisms with P aeruginosa All the kernel-similarity matrices are supposed to be normalized so that the diagonal elements are all 1 This means that the maximum value of the similarity score is 1 and the minimum value of the similarity score is 0 To integrate the above infor-mation of gene position and phylogenetic profile into a single one, we constructed a new kernel-similarity matrix by taking the weighted sum of the above kernel matrices as follows:

Kgenomic¼ w1Kposition+w2Kphylogenetic The usefulness of this type of data integration has been shown previously [24,39]

Network inference

A straightforward approach to network reconstruction is a similarity-based approach, which is based on an assumption that functionally related enzyme pairs are likely to share high similarity with respect to a given dataset Intuitively, the kernel value K(xi,xj) can often be considered as a meas-ure of similarity between gene xiand gene xj This strategy

is therefore to predict an edge between two genes whenever

the kernel value between these genes is above a threshold

to be determined We refer to this approach as the direct

approach The discrete version of this approach

corres-ponds to the joint graph method [17] However, we

some-times meet cases in which gene pairs sharing high similarity

based on the data do not always have any functional

relation

In this study, we used a recently proposed algorithm to

perform the supervised inference of the metabolic gene

net-work [24,31] As opposed to the direct approach, these

methods require a partial knowledge of the true metabolic

network An advantage of using the supervised network

inference method is that we can distinguish functionally

related gene pairs as being different from functionally

meaningless gene pairs, which have numerically high

simi-larity values based on the data This formalism is more

suitable to our current situation, because we can obtain

partially known networks from, for example, the KEGG

PATHWAY database

Here, we make a brief review of the supervised network

inference method This algorithm involves a training

pro-cess, where a mapping of all genes to a low-dimensional

space is learned by exploiting the partial knowledge of

the network, and a test process where new edges are

inferred Roughly speaking, the training process finds a

projection f(Æ) which minimizes the following criterion:

P

ijfðxiÞ  fðxjÞ2

where i j means gene i and gene j are adjacent on the training network Note that f(x)¼

(f(1)(x), f(2)(x), , f(L)(x))T and L are the number of

features of interest The test process is simply the

direct approach performed after genes are mapped to

the low-dimensional feature space, that is, pairs of

genes with short interdistances are connected

Follow-ing the spirit of the direct approach, we use a

similar-ity measure to evaluate the closeness between genes in

the feature space In this study, the Pearson’s

correla-tion coefficient between f(xi) for gene i and f(xj) for

gene j is used as an indicator of the presence or

absence of edges This is referred to as graphical

association score, and the resulting matrix whose

ele-ments represent the graphical association scores is

denoted as S For example, S(xi,xj) represents a

graph-ical association score between genes xi and xj High

scoring gene pairs are expected to be connected in the

target network, therefore the output of this algorithm

is thought of as a weighted graph

In this study, we adopted the kernel CCA-based algorithm

[24], and set the number of features L (dimension of the

fea-ture space) to 50, the regularization parameter k (trade-off

parameter to avoid over-fitting in the training process) to 0.1

in the application, because the usefulness of those parameter

values had been confirmed through systematic

cross-valida-tion experiments in our previous studies [24]

Selecting candidate genes coding for missing enzymes

Missing enzymes in metabolic pathways are found visually

by looking at the connectivity between the enzyme genes on the pathway map reflecting current pathway knowledge Suppose that there is a pathway hole between known enzyme gene a and known enzyme gene b, and this path-way hole consists of missing enzymes To find genes coding for such missing enzymes, we search set of genes having high graphical association score with the known enzyme genes a and b in our predicted network

More generally, suppose that there are multiple known enzyme genes around a target pathway hole as A¼ {a1,a2, .,a|A|}, where |A| is the number of known enzyme genes that are adjacent to missing enzymes in a target path-way hole We define candidate score defined as follows: 1

jAj

PjAj p¼1Sðx; apÞ; where S is the graphical association matrix whose elements correspond to weighted edges in the predic-ted network High-scoring genes are chosen as candidates for target missing enzymes

We then select genes for which the first three digits of the

EC number are the same as that of the corresponding miss-ing enzymes This strategy is based on the followmiss-ing proper-ties of the EC numbers The first three digits of the EC number represent the chemical reaction types with which

an enzyme is involved, while the fourth digit represents the substrate specificity or serial number [24] Therefore, a set

of enzymes, whose the first three digits of the EC number are the same, are suspected of catalyzing similar reactions

Cloning and gene expression DNA fragments containing the PA0265 and PA0266 genes were amplified by PCR from the genomic DNA of

P aeruginosa: PAO1 (M Olson, University of Washington, Seattle, WA) and cloned into pET21a(+) (Novagen, Madi-son, WI) The primers used for the PCR cloning for PA0265 were as follows: 5’-GGAATTCCATATGCAACT CAAAGATGCCAAGCTG)3’ and 5’-CCCAAGCTTGA TACCGCCCAGGCAGAGGTACTTG-3’

The primers used for the PCR cloning of PA0266 were

as follows: 5’-GGAATTCCATATGAGCAAGACCAACG AATCCC-3’ and 5’-CCGCTCGAGAGCGAGTTCGTCG AAGCACTCGG-3’ PCR was performed using KOD-plus DNA polymerase (Toyobo Co., Ltd, Osaka, Japan) with 30 cycles of 94C for 30 s, 60 C for 30 s, and 68 C for

120 s The resulting PA0265 DNA fragment was digested with NdeI and HindIII, and the PA0266 fragment was digested with NdeI and XhoI Each digested fragment was ligated into the corresponding sites of pET21a(+) (Nova-gen) to obtain pETPA0265 and pETPA0266 The proteins with a C-terminal His6-tag were overexpressed in the E coli BL21(DE3) cells carrying pETPA0265 or pETPA0266 at