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Modular Architecture of Metabolic Pathways Revealed by Conserved Sequences of Reactions

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Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan

*Phone: +81-774-38-4521. Fax: +81-774-38-3269. E-mail: [email protected]

Cite this: J. Chem. Inf. Model. 2013, 53, 3, 613–622

Publication Date (Web):February 5, 2013

https://doi.org/10.1021/ci3005379

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Abstract

The metabolic network is both a network of chemical reactions and a network of enzymes that catalyze reactions. Toward better understanding of this duality in the evolution of the metabolic network, we developed a method to extract conserved sequences of reactions called reaction modules from the analysis of chemical compound structure transformation patterns in all known metabolic pathways stored in the KEGG PATHWAY database. The extracted reaction modules are repeatedly used as if they are building blocks of the metabolic network and contain chemical logic of organic reactions. Furthermore, the reaction modules often correspond to traditional pathway modules defined as sets of enzymes in the KEGG MODULE database and sometimes to operon-like gene clusters in prokaryotic genomes. We identified well-conserved, possibly ancient, reaction modules involving 2-oxocarboxylic acids. The chain extension module that appears as the tricarboxylic acid (TCA) reaction sequence in the TCA cycle is now shown to be used in other pathways together with different types of modification modules. We also identified reaction modules and their connection patterns for aromatic ring cleavages in microbial biodegradation pathways, which are most characteristic in terms of both distinct reaction sequences and distinct gene clusters. The modular architecture of biodegradation modules will have a potential for predicting degradation pathways of xenobiotic compounds. The collection of these and many other reaction modules is made available as part of the KEGG database.

Introduction

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Metabolism is the most basic aspect of life. It represents a chemical system generating all necessary chemical substances in living cells through chemical reactions. It also represents a genetic system in the sense that chemical reactions are catalyzed by genome-encoded enzymes. The dual aspect of metabolism has been utilized for metabolic reconstruction, where the repertoire of enzyme genes in the genome is used to infer chemical capacity of an organism, such as biosynthetic and biodegradation potentials and environmental adaptability. The procedure for metabolic reconstruction generally involves finding orthologous genes in the genome that match the network of enzymes in known metabolic pathways. (1-4) This type of analysis has revealed conserved functional units in metabolic pathways; for example, sets of enzyme genes adjacent on the chromosome encoded in operon-like structures were linked to functional units of successive reaction steps. (5) In this paper we focus on the chemistry of metabolic reactions. Our working hypothesis is that the functional units revealed by enzyme clusters must also reflect chemical units of organic reactions. We have thus developed a method to extract, what we call, reaction modules without using the data on enzymes and enzyme genes.

The metabolic pathway reconstruction problem is a special case of the pathway alignment problem, where the pathway similarity is defined by the sequence similarity of orthologous enzyme genes. (3) In another example of pathway alignment, Tohsato et al. (6) used the EC number (Enzyme Commission number) similarity to find similar reaction steps in the well-characterized metabolic network. Pinter et al. (7) followed Tohsato’s definition of EC number similarity, and developed methods to detect conserved metabolic pathways among different organisms and divergent pathways within an organism. Wernicke et al. (8) followed Pinter’s definition of aligning metabolic pathways and provided a faster algorithm. In yet another example of pathway alignment, Tohsato and Nishimura (9) used the similarity of substructure changes to detect similar sequences of metabolites along known metabolic pathways. Ay et al. (10) developed a method that combined EC number similarity and metabolite similarity, as well as the metabolic network topology similarity to conduct the pathway alignment.

Not surprisingly, the outputs of these different pathway alignment studies are different. The assignment of orthologous enzyme genes can only deal with the pathways that consist of the same reactions catalyzed by the same enzymes. The use of EC number similarity allows not only the same reactions but also somewhat different reactions to be considered because of the EC number hierarchy. However, since the EC numbers are manually given to experimentally characterized enzymes with varying standards, the EC number similarity is not a reliable measure for systematic analysis, for example, comparison of genomic diversity of enzyme genes and chemical diversity of enzyme-catalyzed reactions. Furthermore, since the EC numbers are annotated to genes in a genome according to sequence similarity, their use is not much different from ortholog-based approaches in many types of analyses.

Here we introduce a new similarity measure for pathway alignment. It is based on the similarity of chemical structure transformation patterns along the metabolic pathways. This is a purely chemical similarity measure without incorporating any protein sequence information or the EC number information, enabling the analysis of reactions with no EC numbers assigned or even with no enzymes identified. The new measure applies to localized structural changes extracted from compound pairs (substrate–product pairs) accommodating global structure differences of individual compounds. Our data set is derived from the KEGG database, (11) more specifically the KEGG RPAIR (reactant pair) database (12) and the KEGG RCLASS (reaction class) database. KEGG RPAIR contains chemical structure transformation patterns in all known enzyme-catalyzed reactions, and KEGG RCLASS consists of reaction class (RC) entries defined by the identity of chemical transformation patterns in the “main” reactant pairs that generally correspond to the main substrate and product pairs shown on the KEGG metabolic pathway maps. The reaction modules, which are conserved sequences of similar reactions, are systematically searched in the KEGG metabolic pathways using the similarity scoring scheme between reaction class entries. Extracted reaction modules are then compared with the pathway modules in the KEGG MODULE database, which are defined as sets of enzyme orthologs represented by the KEGG Orthology (KO) entries. (11)

Materials and Methods

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Metabolic Pathway Database

The present analysis is based on the KEGG database (http://www.kegg.jp/) release 62.0+ (May 24, 2012). We used the metabolic pathways stored in the Metabolism section of the KEGG PATHWAY database, a collection of manually drawn pathway maps representing knowledge on the molecular interaction and reaction networks summarized from literature. Generally, KEGG pathway maps are not organism specific and contain integrated information from different organisms. The KEGG “reference” pathway maps are drawn in a generic form in terms of orthologs, so that they can be expanded to organism-specific pathways by combining with the gene information in individual genomes. A typical KEGG metabolic pathway map depicts how small molecules (drawn as circles) are converted by reactions catalyzed by enzymes (drawn as rectangles). The dual aspect of metabolism is annotated in KEGG by linking each rectangle in the KEGG reference metabolic pathway map to both the enzyme ortholog entry (identified by K number) in the KEGG Orthology (KO) database and the reaction entry (identified by R number) in the KEGG REACTION database. The KEGG pathway maps in an XML format (KGML files) contain information about the relationship of neighboring rectangles, which has been utilized to extract consecutive reaction steps as sequences of R numbers.

Reactant Pairs

The KEGG REACTION database contains all known enzymatic reactions taken from the Enzyme Nomenclature (13) and also from the metabolic pathway section of the KEGG PATHWAY database. The KEGG release that we used contains 8990 reactions including 4321 enzyme nomenclature reactions. Among them, 6238 reactions including 2595 enzyme nomenclature reactions appear on the KEGG metabolic pathways. Generally, one reaction consists of multiple substrates and products. Reactant pairs are defined as one-to-one relationships of substrate–product pairs by considering the flow of atoms (other than hydrogen atoms) in enzymatic reactions as well as the six EC number classes. (12) There were 13 448 reactant pairs stored in the KEGG RPAIR database.

Each reactant pair is associated with the chemical transformation pattern in the RDM notation consisting of the KEGG atom type changes at the reaction center (R), the difference substructure (D), and the matching substructure (M) atoms (see the Supporting Information for more details). The RDM notation for the pair of reactants A and B is as follows:

For example, a typical acyltransferase reaction on primary amine is described as

The KEGG atom type (14) generally consists of three characters, such as N1a for primary amine nitrogen and C5a for ketone carbon. The first (upper case letter) indicates the atomic species, the second (numeral) represents the predefined class of atomic bonding for each atomic species, and the third (lower case letter) represents the predefined class of topological information, e.g., the number of substituted groups. The total of 68 atom types have been defined to distinguish important functional groups in biological small molecules. In the above notation, * corresponds to a hydrogen atom (not defined in the KEGG atom types) for substitution reaction. Note that * sometimes means that there is no atom in the D-substructure. Note also that the RDM notation generally represents a single chemical bond that changes during a reaction; in the case where a reaction generates or degrades more than one chemical bond, then more than one RDM notation is required. (12)

Reaction Class

The KEGG RCLASS database has been developed to classify chemical structure transformation patterns associated with all the reactions that appear in the KEGG metabolic pathway maps. The database is a collection of reaction class entries (identified by RC numbers), each representing a unique RDM chemical structure transformation pattern for a group of “main” reactant pairs in the KEGG RPAIR database. In other words, each RCLASS entry indicates a collection of substrate–product pairs that appear on the KEGG metabolic pathway maps and whose chemical transformation patterns are identical. The RCLASS entry is computationally generated from the KEGG RPAIR database and manually annotated with a diagram of chemical transformation pattern and other information. There were 2481 RCLASS entries in this study.

Results

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Similarity Grouping of RCLASS Entries

Because the RDM chemical transformation patterns and the resulting RCLASS entries are too finely classified, we first introduced a similarity scoring scheme for RCLASS entries in order to detect similar (in addition to identical) chemical transformation patterns. This is based on the fingerprint representation of KEGG atom types using twelve keys. The keys indicate the presence or absence of a carbon atom, a carbon atom having a π bond, a carbon atom in a carbonyl group, an oxygen atom, an oxygen atom with unpaired electron, a nitrogen atom, a phosphorus atom, a sulfur atom, a halogen atom, other atoms, an atom in aromatic ring, and an atom in any ring (see the Supporting Information for more details). Figure 1 illustrates how the RDM notation is converted into the 72-bit fingerprint notation. For example, methyl (C1a), methylene (C1b), and other sp3 carbon atoms (C1c and C1d) are given different KEGG atom types, but they are the same in the fingerprint representation.

Figure 1. Fingerprint representation of the RDM pattern. Two reaction class entries RC00106 and RC01192 are shown for the reactant pairs of pyruvate and acetolactate (upper) and oxobutanoate and 2-aceto-2-hydroxybutanoate (lower). The RDM notation for these reaction class entries is converted to the fingerprint representation, which reveals that they are the same in the fingerprint representation despite the difference in the M atom (circled) in the RDM notation.

The similarity score S between two RCLASS entries, each consisting of a single RDM pattern, is defined as

where the fingerprint v is compared separately for the R, D, and M atoms, and the average Jaccard’s coefficient Ja is weighted depending on, for example, whether the D atom is missing (see the Supporting Information for more details). In the present analysis, we use the similarity threshold score of 1.0; namely, we simply use the criterion of the same fingerprint to group RCLASS entries into a similarity group. As a result, 2481 RCLASS entries were grouped into 376 similarity groups with more than one member and the remaining 1190 singletons.

Extraction of Conserved RCLASS Sequence Patterns

On the basis of the similarity measure among RCLASS entries as defined above, we extracted “reaction modules”, which in our definition are consecutive reaction steps (reaction sequences) with conserved RCLASS sequence patterns that are observed in different metabolic pathways. We used the following procedure to extract such conserved patterns (see the Supporting Information for more details). Known metabolic pathways in the KEGG PATHWAY database are split into all possible subsequences of 2–8 consecutive reactions. The pathways involving branches are split into all combinations of linear reaction sequences. The direction of the reaction pathway is taken into consideration when generating consecutive reaction sequences that contain irreversible reactions. When the pathway forms a cycle, all possible reaction sequences that do not include the same metabolite more than once are generated.

For a given length between 2 and 8, the reaction (R number) sequences thus generated were first converted to the RCLASS (RC number) sequences. Two RCLASS sequences are considered to be identical when the corresponding RC numbers are the same, and to be similar when they belong to the same similarity group in the fingerprint representation. For each given length, conserved RCLASS sequence patterns consisting of such similarity groups were extracted from the entire collection of KEGG metabolic pathways. The result is shown in Table 1 indicating roughly one-half of the pathways correspond to conserved reaction sequence patterns. After computationally removing shorter patterns embedded in longer patterns, we manually examined the results to identify reaction modules.

Table 1. Number of Conserved RCLASS Sequence Patterns Found in the KEGG Metabolic Pathways

length	no. of conserved patterns	no. of reactions included	coverage^a
2	928	3479	0.599
3	770	2503	0.431
4	534	1662	0.286
5	338	1074	0.185
6	218	765	0.132
7	140	527	0.091
8	88	399	0.069
total	3016

The ratio to 5805 reactions, the total number of reactions with RC assignment in the KEGG pathways.

Table 2. List of Reaction Modules Discussed in This Paper^a

RC module	description	length
RM001	2-oxocarboxylic acid chain extension by tricarboxylic acid pathway	5
RM002	caboxyl to amino conversion using protective N-acetyl group	5
RM032	caboxyl to amino conversion	3
RM033	branched chain addition	4
RM030	glucosinolate biosynthesis	5
RM021	fatty acid synthesis using malonyl-CoA	4
RM020	fatty acid synthesis using acetyl-CoA (reversal of beta oxidation)	4
RM018	beta oxidation in acyl-CoA degradation	4
RM003	methyl to carboxyl conversion on aromatic ring, aerobic	3
RM004	dihydroxylation of aromatic ring, type 1 (dioxygenase and dehydrogenase reactions)	2
RM005	dihydroxylation of aromatic ring, type 1a (dioxygenase and decarboxylating dehydrogenase reactions)	2
RM006	dihydroxylation of aromatic ring, type 2 (two monooxygenase reactions)	2
RM008	ortho-cleavage of catechol (beta-ketoadipate pathway)	4
RM009	meta-cleavage of catechol	6
RM010	dihydroxylation and meta-cleavage of aromatic ring, type 1	4
RM015	oxidation of methyl group on aromatic ring, anaerobic	6
RM016	aromatic ring cleavage via beta oxidation, anaerobic	3
RM025	conversion of amino acid moiety to carboxyl group	3
RM022	nucleotide sugar biosynthesis, type 1	3
RM023	nucleotide sugar biosynthesis, type 2	2
RM027	hydroxylation and methylation motif	2

See http://www.kegg.jp/kegg/reaction/rmodule.html for the full list of reaction modules.

General Characteristics of Reaction Modules

The list of manually refined reaction modules is partially shown in Table 2 and fully shown at http://www.kegg.jp/kegg/reaction/rmodule.html. We found three general characteristics of reaction modules. First, reaction modules are repeatedly used in different pathways to generate different chemical substances. Second, reaction modules are used in combination as if they are building blocks of the metabolic network. Third, and most importantly, reaction modules (also called RC modules) derived from chemical properties of substrate–product structure transformation patterns tend to correspond to KEGG pathway modules (also called KO modules) defined as sets of enzyme orthologs in the genome, especially gene clusters in operon-like structures coding for the enzymes. The total of 26 corresponding KO modules were found for 16 out 21 RC modules shown in Table 2, and all the KO modules except one contained operon-like gene clusters in some genomes (for an updated list, see http://www.kegg.jp/brite/ko00003). Thus, we confirmed our working hypothesis mentioned in the Introduction; the functional units revealed by enzyme clusters reflect chemical units of organic reactions. Here we report detailed analysis of the reaction modules for 2-oxocarboxylic acid chain extension and modification, fatty acid synthesis and beta-oxidation, and aromatic ring cleavage.

2-Oxocarboxylic Acid Chain Extension

One of the most characteristic reaction modules was RM001 for the chain extension of 2-oxocarboxylic acids, an important class of precursor metabolites. This module corresponds to the well-known sequence of reactions involving citrate and other tricarboxylic acids in the TCA cycle (map00020 in KEGG), where an acetyl-CoA derived carbon is used to extend the 2-oxocarboxylic acid chain from oxaloacetate (2-oxobutanedioate) to 2-oxoglutarate, namely, from a four-carbon (C4) compound to a five-carbon (C5) compound. This is in fact the only part in the TCA cycle that involves tricarboxylic acids. Interestingly, we identified three more examples of the same reaction module RM001. One is a further extension from 2-oxoglutarate (C5) to 2-oxoadipate (C6) in lysine biosynthesis pathway (map00300). Another is found in valine, leucine, and isoleucine biosynthesis pathway (map00290) where pyruvate (2-oxopropanoate) is extended to 2-oxobutanoate, and 2-oxoisovalerate is extended to 2-oxoisocaproate. (15) Furthermore, in the biosynthesis pathway of glucosinolates (map00966), which are plant secondary metabolites, a six tandem repeat of RM001 is found from 2-oxo-4-methylthiobutanoate to 2-oxo-10-methylthiodecanoate (see Supporting Information Figure S1).

These additional examples were found by considering not only the similarity grouping of reaction class entries, but also the matching of multistep reactions to an overall reaction. In the KEGG pathway map for the citrate cycle (map00020), the conversion from oxaloacetate to 2-oxoglutarate (RM001) is shown as follows: oxaloacetate and acetyl-CoA generating citrate (RC00067), converting to cis-aconitate (RC00498), converting to isocitrate (RC00618), and converting to 2-oxoglutarate in two reaction steps (RC00084+RC00626) or in one step (RC00114). The variation of the last conversion originates from the definition of EC numbers: EC 1.1.1.42 defined as two step reactions and EC 1.1.1.41 defined as one overall reaction (RC00114). Furthermore, the second and third reactions (RC00498+RC00618) are catalyzed by the same enzyme EC 4.2.1.3, although the overall one-step reaction from citrate to isocitrate (RC00497) is not included in the KEGG map. As shown in Table 3, the reaction module RM001 apparently consists of different RCLASS sequences, but they are actually the same with these considerations.

Table 3. Reaction Modules Involving 2-Oxocarboxylic Acids

RC module	pathway	overall reaction	RCLASS sequence
RM001	citrate cycle (map00020)	oxaloacetate → 2-oxoglutarate	RC00067 RC00498 RC00618 RC00084+RC00626
	lysine biosynthesis (map00300)	2-oxoglutarate → 2-oxoadipate	RC00067 RC00498 RC00618 RC00114
	isoleucine biosynthesis (map00290)	pyruvate → 2-oxobutanoate	RC01205 RC00976 RC00977 RC00417
	leucine biosynthesis (map00290)	2-oxoisovalerate → 2-oxoisocaproate	RC00470 RC01041 RC01046 RC00084+RC00577
	glucosinolate biosynthesis (map00966)	2-oxo-4-methylthiobutanoate → 2-oxo-10-methylthiodecanoate	RC00067 RC00497 RC00114 (six repeats)
RM002	lysine biosynthesis (map00300)	2-aminoadipate → lysine	RC00064 RC00043 RC00684 RC00062 RC00064
RM002	arginine biosynthesis (map00330)	glutamate → ornithine	RC00064 RC00043 RC00684 RC00062 RC00064
RM032	ectoine biosynthesis (map00260)	aspartate → 2,4-diaminobutanoate	RC00043 RC00684 RC00062
RM033	valine biosynthesis (map00290)	pyruvate → 2-oxoisovalerate	RC01192 RC00837 RC00726 RC00468
RM033	isoleucine biosynthesis (map00290)	2-oxobutanoate → 3-methyl-2-oxopentanoate	RC01192 RC01726 RC00726 RC01714
RM030	glucosinolate biosynthesis (map00966)	homomethionine → glucoiberverin	RC02295 RC02210 RC02265 RC00882 RC00883

Modification of 2-Oxocarboxylic Acids

The reaction module RM001 for 2-oxocarboxylic acid chain extension by tricarboxylic acid pathway was found to be used in combination with three modification modules, RM002 (including RM032), RM033, and RM030, together with a reductive amination step (RC00006 or RC00036). In Figure 2, RM002 is for conversion of carboxyl group to amino group in the biosynthesis of basic amino acids (ornithine and lysine), and RM033 is for addition of branched chains in the biosynthesis of branched-chain amino acids (valine, leucine, and isoleucine). RM030 in glucosinolate biosynthesis pathway is for conversion to oxime followed by addition of thio-glucose moiety (see Supporting Information Figure S2).

Figure 2. Architecture of reaction modules consisting of 2-oxocarboxylic acid chain extension and modification, generating (A) basic amino acids and (B) branched-chain amino acids. Vertical arrows indicate the extension modules RM001. Horizontal arrows indicate the modification modules RM002, RM032, and RM033 together with the reductive amination step (RC00006 and RC00036).

For the chemical modification module RM002 in Table 3 and Figure 2, the reaction sequence contains addition and removal of N-acetyl group as a protective group in the first and last steps (RC00064). We found the same overall reaction sequence without the protective group (RM032) in the conversion from aspartate to 4-diaminobutanoate in ectoine biosynthesis pathway (map00260) (see Figure 2 and Table 3). This seems to suggest an interesting variation, possibly an evolution, of reaction modules. While RM032 (RC00043 RC00684 RC00062) is used for shorter carboxylic acid chains, RM002 (RC00064 RC00043 RC00684 RC00062 RC00064), containing N-acetylation and N-deacetylation steps surrounding the core sequence of RM032, is used for longer chains. Furthermore, in the lysine biosynthesis pathway from 2-aminoadipate to lysine, the surrounding steps are proteinaceous N-terminal modifications. (16) Thus, basically the same overall reaction is achieved with varying degrees of complexity, namely the involvement of a small modified group and a modified group attached to a carrier protein, which may indicate a chemical evolution of the metabolic network to cope with longer chains (see Supporting Information Figure S3).

Reaction Modules Encoded in Enzyme Gene Clusters

The KEGG pathway modules (KO modules) in the KEGG MODULE database are represented by manually defined sets of enzyme orthologs, which often correspond to operon-like gene clusters in prokaryotic genomes. We examined relationships between the reaction modules (RC modules) extracted in the present analysis and the previously defined KO modules, especially the gene clusters that constitute the KO modules, to exclude the ambiguity of manual definition. We found, for example, that the RC module RM001 coincided well with the KO modules M00010, M00432, and M00535. As shown in Table 4, in the genome of Pyrococcus furiosus, (17) two gene clusters correspond to the reaction module RM001: the gene cluster (PF0203 PF0201 PF0202) for the RCLASS sequence (RC00067 RC00498+RC00618 RC00084+RC00626) in citrate cycle and the gene cluster (PF0937 PF0938+PF0939 PF0940) for the RCLASS sequence (RC00470 RC01041+RC01046 RC00084+RC00577) in leucine biosynthesis. It is interesting to note that although the first enzymes (citrate synthase PF0203 and 2-isopropylmalate synthase PF0937) do not share any sequence similarity, the second hydratases (PF0201 and PF0938+PF0939) and the third dehydrogenases (PF0202 and PF0940) form paralogous gene groups with sequence identity of around 35%, suggesting a link between genomic diversity and chemical diversity. Table 4 shows only a few examples of enzyme gene clusters for RM001 and RM002. Many more examples can be found in the KEGG database from the Ortholog table view of the KEGG MODULE entries (each entry is accessible at http://www.kegg.jp/module/M00010, etc.).

Table 4. Reaction Modules Corresponding to Enzyme Gene Clusters

RC module	overall reaction	KO module	gene cluster example^a
RM001	oxaloacetate → 2-oxoglutarate	M00010	(pfu) PF0203 PF0201 PF0202
	2-oxoisovalerate → 2-oxoisocaproate	M00432	(pfu) PF0937 PF0938+PF0939 PF0940
	pyruvate → 2-oxobutanoate	M00535	(bth) BT_1858 BT_1860+BT_1859 BT_1857
RM002	2-aminoadipate → lysine	M00028	(bsu) BSU11200 BSU11210+BSU11190 BSU11220
RM002	glutamate → ornithine	M00031	(ttr) Tter_0315+Tter_0316 Tter_0320 Tter_0319 Tter_0321 Tter_0317

KEGG organism codes are shown in parentheses: pfu (T00075), Pyrococcus furiosus DSM 3638; bth (T00122), Bacteroides thetaiotaomicrometer VPI-5482; bsu (T00010), Bacillus subtilis 168; ttr (T01134), Thermobaculum terrenum ATCC BAA-798.

Fatty Acid Synthesis and Beta Oxidation

Fatty acids are long-chain carboxylic acids. It is well-known that fatty acid synthesis is a repetition of the four-step reaction sequence consisting of ketoacyl synthase (KS), ketoreductase (KR), dehydratase (DH), and enoylreductase (ER) reactions and each sequence increasing the acyl chain length by two. The four enzymes may come from four separate genes, two genes containing KS + KR and DH + ER domains or a single gene containing all domains. Thus, there seems to be an evolution of the genetic aspect of fatty acid synthesis. It is less known that there is a minor pathway for fatty acid biosynthesis. In contrast to the major pathway (map00061) that involves acyl carrier protein and uses malonyl-CoA as a carbon source, the minor pathway (map00062) in mitochondria does not involve acyl carrier protein and uses acetyl-CoA as a carbon source. The minor pathway is essentially the reversal of beta-oxidation (RM018) in fatty acid degradation (see below). The reaction modules RM021 and RM020, for the major and minor pathways respectively, consist of the following RCLASS sequences (see also Supporting Information Figure S4).

Except for the first KS reaction step distinguishing malonyl-[acp] and acetyl-CoA, the two modules RM021 and RM020 are essentially the same. We consider that the involvement of the acyl carrier protein is an evolution of the chemical aspect of fatty acid synthesis, possibly increasing the specificity and efficiency of reactions.

The beta oxidation module RM018 (the reversal of RM020) consists of the following RCLASS sequences:

In addition to the fatty acid degradation pathway (map00071), RM018 is found in caprolactum degradation (map00930) from adipyl-CoA to succinyl-CoA and isoleucine degradation (map00280) from 2-methylbutanoyl-CoA to propanoyol-CoA (see Supporting Information Figure S5). A variation of RM018, with a somewhat different cleavage reaction, was found in the anaerobic benzoate degradation pathway (map00362) from pimeloyl-CoA to glutaryl-CoA and in primary bile acid synthesis (map00120) leading to chenodeoxycholoyl-CoA and choloyl-CoA.

More interestingly a distantly related module, RM016 (RC02034 RC00154 RC01429), is found in the anaerobic benzoate degradation pathway (map00362) and the limonene and pinene degradation pathway (map00903). RM016 is for the aromatic ring cleavage (hydrolysis) rather than the acyl chain cleavage (thiolysis), but the strategy to add oxygen preceding the cleavage reaction is similar. The RC similarity score was 0.8 for RC02034 and RC01095 (or RC00831) and 0.7 for RC00154 and RC00029 (or RC00117). Furthermore, the genes for RC00154 in the anaerobic benzoate degradation pathway and for RC00029 in the fatty acid biosynthesis pathway share sequence similarity. (18)

Aromatic Ring Cleavage in Microbial Biodegradation Pathways

Microorganisms are known to be capable of degrading diverse chemical substances including nonbiological chemicals in the environment that are mostly aromatic compounds. Figure 3 illustrates a representative set of reaction modules for biodegradation of aromatic compounds consisting of ring dihydroxylation modules (RM004–RM006), cleavage modules (RM008 and RM009), and a preprocessing module (RM003) for oxyfunctionalization, i.e., converting methyl group to carboxyl group on the aromatic ring. RM003 consists of a monooxygenase reaction (EC 1.14.13) and two steps of dehydrogenase reactions (EC 1.1.1 and 1.2.1) and is followed by a dihydroxylation module, as shown in Figure 3 for toluene to benzoate and p-cymene to p-cumate conversions.

Figure 3. Aromatic ring cleavage modules in microbial degradation pathways. Aromatic rings are cleaved in the following three steps. The first step is an occasional preprocessing step (RM003) converting a methyl group into a carboxylic group on the aromatic ring. The second step is the main step of dihydroxylation, which is classified into three types (RM006, RM004, and RM005) depending on how two hydroxyl groups are added on the aromatic ring. The third step is either ortho-cleavage (RM008) or meta-cleavage (RM009) followed by characteristic reaction patterns leading to TCA cycle intermediates.

On the basis of the similarity grouping of RCLASS entries, we categorized dihydroxylation reaction steps into three main modules (RM004, RM005, and RM006 in Figure 3) and several minor variant modules (not shown). The three main modules are most abundant and are different in the steps for generating two hydroxyl groups. RM004 (type 1) and RM005 (type 1a) use a dioxygenase reaction (EC 1.14.12) followed by a dehydrogenase reaction (EC 1.3.1), while RM006 (type 2) uses two steps of monooxygenase reactions (EC 1.14.13). In RM005, which often results from the preprocessing module RM003, one of the hydroxyl groups is attached together with the carboxyl group; thus, the dehydrogenase reaction is decarboxylating. Each dihydroxylation module is followed by a ring-cleavage dioxygenase reaction (EC 1.13.11) either at the meta- or ortho-position of the two hydroxyl groups.

RM008 and RM009 are the ortho- and meta-cleavage modules, respectively, for catechol and related compounds, containing the cleavage reaction and the subsequent reactions that generate TCA cycle intermediates. In the ortho-cleavage pathway, also known as beta-ketoadipate pathway, (19) catechol is converted to succinyl-CoA and acetyl-CoA, while catechol is converted to pyruvate and acetyl-CoA in the meta-cleavage pathway. It appears that the meta-cleavage is a more general processing strategy because it is a partial processing applicable to larger molecules. We defined the reaction module RM010 (Figure 3) consisting of the dihydroxylation module RM004 and a portion of the catechol meta-cleavage module RM009. A variety of chemicals including trans-cinnamate, p-cumate, ethylbenzene, styrene, and dioxin are degraded by this module resulting in 2-oxopent-4-enoate, which is further processed by RM009, and the remaining part, which may be processed differently. For example, two-ring containing biphenyl is partially processed to generate benzoate, which may further be degraded by other degradation modules.

The beta oxidation-like module RM016 mentioned above is an anaerobic version of the aromatic ring cleavage module. We have also identified an anaerobic version (20) of the preprocessing methyl oxidation module RM015 involving CoA (see Supporting Information Figure S6 for a comparison of the aerobic module RM003 and the anaerobic module RM015 for toluene degradation).

Table 5. Biodegradation Reaction Modules Corresponding to KEGG Modules

RC module	KO module	overall reaction
RM003	M00538	toluene → benzoate
	M00537	o-xylene → o-methylbenzoate
	M00419	p-cymene → p-cumate
RM004	M00547	benzoate → catechol
RM005	M00551	benzoate → catechol
RM006	M00548	benzene → catechol
RM008	M00568	catechol → 3-oxoadipate
RM009	M00569	catechol → pyruvate + acetaldehyde
RM010	M00539	p-cumate → 2-oxopent-4-enoate + methylpropanoate
RM010	M00543	biphenyl → 2-oxopent-4-enoate + benzoate
RM015	M00418	toluene → benzoyl-CoA

The biodegradation capacity of xenobiotic compounds, such as benzene, toluene, ethylbenzene, and xylene (BTEX compounds), is limited to specific organisms with appropriate sets of genes that are often carried by plasmids. It is therefore expected that the reaction modules identified here correspond to operon-like gene clusters in the genome. This is in fact the case. Table 5 shows the correspondence of the reaction modules (RC modules) and the KEGG modules (KO modules) for microbial biodegradation pathways, where the majority of KO modules are encoded in operon-like gene clusters (examine the Ortholog table link from each KEGG module entry, such as http://www.kegg.jp/module/M00548). For example, Pseudoxanthomonas spadix BD-a59 (21) (KEGG organism code: psd, T01643) has preprocessing modules M00538 and M00537 for toluene and xylene, respectively, both dioxygenase and monooxygenase-catalyzed dihydroxylation modules M00551 and M00548, meta-cleave module M00569, and dioxin degradation modules M00543 and M00544, confirming its BTEX degradation potentials.

Discussion

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Our analysis has shown that the metabolic network contains chemical modules of conserved reaction patterns and that these chemical reaction modules (RC modules) tend to correspond to traditional pathway modules (KO modules) and gene clusters in prokaryotic genomes. The modularity of the metabolic network has been mentioned in previous works, (22-25) but we believe that this is the first report on the modularity of the dual aspect of the metabolic network suggesting a coevolution of the chemical unit and the genetic unit. In contrast to the previous models of metabolic pathway evolution, such as the retrograde model, (26) the patchwork model, (27, 15) and pathway duplication, (15) the chemical and genetic units are clearly defined in our approach allowing more detailed analysis. For example, as mentioned for the KO modules of M00010 and M00432 that correspond to the RC module of RM001 (Table 4), only the second and the third enzymes in P. furiosus are paralogs, while the reactions are similar in all the three steps. The first dissimilar enzymes may thus reflect the constraint of specific substrate recognition. The collection of KO modules and RC modules will continue to be updated in the KEGG database. Here some other reaction modules that we did not include in the Results section are briefly discussed.

We identified the reaction module RM025 for generating biogenic amines and other important metabolites from amino acids (see Supporting Information Figure S7). We already mentioned the presence and absence of a protective group in the reaction modules RM002 and RM032, respectively, where the key reaction sequence (RC00684 RC00062) is for converting a carboxyl group to an amino group following a phosphorylation step (RC00043). In contrast, RM025 contains the reverse reaction sequence (RC00062 RC00080), in which RC00080 is identical to RC00684 in the fingerprint representation, for converting an amino group to a carboxyl group following a decarboxylation step (RC00299). The overall reaction of RM025 effectively removes the main chain part of an amino acid leaving only the side chain part with a newly introduced carboxylic group. From these examples, it appears that the reaction modules contain design principles of a series of organic reactions including, for example, how to introduce a protective group, how to achieve an activated transition state, and how to increase specificity for macromolecules.

The RCLASS entries that contain the largest numbers of reactant pairs were related to phosphorylation and glycosylation. Although our approach focusing on localized structure patterns was successful to characterize possible evolution of chemical reactions with varying structural complexity, it may be necessary to consider overall structural classes, in addition to localized reaction classes, to better characterize reaction modules containing phosphorylation or glycosylation. There are cases, however, in which a special class of overall structures can be effectively extracted from the pattern of phosphorylation sequences. One such example was found in nucleotide sugar biosynthesis (map00520). Nucleotide sugars are activated forms of monosaccharides used for biosynthesis of glycans and glycosylated substances. We found two types of phosphorylation reaction modules RM022 (RC00017 RC00408 RC00002) and RM023 (RC00078 RC00002), where the first module includes an isomerization step, and the phosphate group on carbon 6 is linked to a nucleotide (see Supporting Information Figure S8).

The metabolic network may be viewed as consisting of the conserved core (primary metabolism) for maintaining life and the variable surface (secondary metabolism) for interaction with the environment. Microbial biodegradation modules (RM003 to RM016) represent convergent pathways of secondary metabolism, feeding xenobiotic compounds with varying structures to a limited number of compounds in primary metabolism. In contrast, biosynthetic pathways of secondary metabolites, especially in plants, are divergent pathways for generating a large number of structures from a limited number of compounds. We analyzed such plant biosynthetic pathways but could not identify modules as significant as the microbial degradation modules that often correspond to gene clusters in chromosomes or plasmids. Possibly, gene clusters in plants are generated from different mechanisms. (28) More importantly, the strategy to generate divergent compounds appears to be different and involves a combination of short characteristic reaction steps. The reaction module RM027 (Table 2), a hydroxylation and methylation motif, is a well-known reaction sequence involving cytochrome P450 monooxygenase. The phenylpropanoid biosynthesis pathway as represented in KEGG (map00940) is a mesh-like architecture of reactions consisting of the repeat of RM027 in the horizontal direction and the CoA involved acid–aldehyde–alcohol conversion in the vertical direction (see Supporting Information Figure S9).

The EC (Enzyme Commission) number system is a classification of enzymatic reactions, where the first three numbers in the four number system classify reactions in a hierarchical manner, namely, into class, subclass, and subsubclass. The fourth number is a sequential number given to distinguish substrate specificity. There were 4321 fully assigned EC numbers in our study, compared to 8990 reactions in KEGG. The coverage of the EC system is much more limited because the EC numbers are given only to those reactions with published records of biochemically verified enzymes. In contrast the KEGG database contains additional reactions that are supported by pathways of less characterized enzymes, in many cases the presence of genes in the genomes. We compared the EC subsubclass, which is the most detailed reaction classification in the EC system, and KEGG RCLASS (RC) that is a grouping of reactions accommodating overall structural differences of substrates. There are 249 EC subsubclasses and 2481 RC entries, and on average 13 RC entries are found for those EC subsubclasses that appear on the KEGG pathways. Obviously, the RC system presents a much finer classification of reactions than the EC system. This is shown in the list of EC subsubclasses that contain the largest numbers of RC entries (Table 6), where the most notable example is monooxygenases. In the EC system, monooxygenases (1.14) are further classified based on the cofactors (1.14.13, 1.14.14, 1.14.15, etc.), but the distinction is not made for reaction mechanisms. For example, CYP monooxygenases may further be classified into hydroxylases, epoxidases (epoxygenases), Baeyer–Villiger monooxygenases, and so on (see Supporting Information Figure S10). It is interesting to note that the full EC number 1.14.14.1 (single reaction with the same substrate specificity in the EC system) corresponds to 75 RC entries (different reactions in the RC system).

Table 6. List of EC Numbers Containing the Largest Numbers of RC Entries

EC subsubclass	no. of RC entries	enzymes involved
1.14.13	150	monooxygenases
4.2.1	129	hydratases/dehydratases, terpene cyclases (hydrating)
1.1.1	117	alcohol:NAD(P)+ dehydrogenases
4.2.3	86	phospho-lyases, terpene cyclases (diphosphate-eliminating)
1.14.14	83	monooxygenases
2.1.1	79	methyltransferases
1.13.11	72	dioxygenases
1.3.1	68	saturases/desaturases
4.1.1	65	carboxylases/decarboxylases
2.5.1	60	prenyltransferases, 1-carboxyvinyltransferases, aminocarboxyethyltransferases, aminocarboxypropyltransferases, adenosyltransferases

There is an important practical value of the dual aspect of metabolism. Knowledge of genes and proteins can be used to make predictions about chemical compounds and reactions and, conversely, knowledge of chemical compounds and reactions can be used to make predictions about genes and proteins. The present analysis has already resulted in the improvement of the KEGG pathway maps for chlorocyclohexane and chlorobenzene degradation (map00361), benzoate degradation (map00362), toluene degradation (map00623), and xylene degradation (map00622) in terms of both identifying additional reactions and improving genome annotations. In the KEGG database, the repertoire of genes in all available complete genomes are categorized by the KEGG Orthology (KO) system, an attempt to understand the genomic space of life. Here the RC system has been introduced to categorize all known reactions, an attempt to understand the chemical space of life and the surrounding environment. We are trying to establish empirical relationships between the KO system and the RC system, especially in terms of the modular structures of the metabolic network represented by the KO modules and the RC modules. The logic of chemical reactions is so straightforward, compared to, for example, protein–protein interactions or protein–DNA interactions, that these empirical relationships can be safely extended to make predictions about metabolism resulting either from new genes or from new reactions. This type of predictive annotation is already implemented in the KEGG database toward better understanding of metabolic features encoded in the genome.

Supporting Information

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KEGG atom types, RDM patterns, fingerprint representation, and similarity scoring of RC entries (Methods); tricarboxylic acid pathway (Figure S1); glucosinolate biosynthesis pathway (Figure S2); carboxyl to amino conversion (Figure S3); fatty acid biosynthesis (Figure S4); beta oxidation (Figure S5); methyl to carboxyl conversion on aromatic ring (Figure S6); amino to carboxyl conversion (Figure S7); nucleotide sugar biosynthesis (Figure S8); phenylpropanoid biosynthesis (Figure S9); and classification of monooxygenases (Figure S10). This information is available free of charge via the Internet at http://pubs.acs.org.

ci3005379_si_001.pdf (2.36 MB)

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Author Information

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Corresponding Author
- Minoru Kanehisa - Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan; Email: [email protected]
Authors
- Ai Muto - Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
- Masaaki Kotera - Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
- Toshiaki Tokimatsu - Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
- Zenichi Nakagawa - Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
- Susumu Goto - Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
Notes
The authors declare no competing financial interest.

Acknowledgment

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We thank Dr. Nicolas Joannin for critical reading of the manuscript. This work was supported by the Japan Science and Technology Agency. Computational resources were provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University.

References

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A gene, badH, whose predicted product is a member of the short-chain dehydrogenase/reductase family of enzymes, was recently discovered during studies of anaerobic benzoate degrdn. by the photoheterotrophic bacterium Rhodopseudomonas palustris. Purified histidine-tagged BadH protein catalyzed the oxidn. of 2-hydroxycyclohexanecarboxyl CoA (2-hydroxychc-CoA) to 2-ketocyclohexanecarboxyl-CoA. These compds. are proposed intermediates of a series of three reactions that are shared by the pathways of cyclohexanecarboxylate and benzoate degrdn. used by R. palustris. The 2-hydroxychc-CoA dehydrogenase activity encoded by badH was dependent on the presence of NAD+; no activity was detected with NADP+ as a cofactor. The dehydrogenase activity was not sensitive to oxygen. The enzyme has apparent Km values of 10 and 200 μM for 2-hydroxychc-CoA and NAD+, resp. Western blot anal. with antisera raised against purified His-BadH identified a 27-kDa protein that was present in benzoate- and cyclohexanecarboxylate-grown but not in succinate-grown R. palustris cell exts. The active form of the enzyme is a homotetramer. BadH was detd. to be the first gene in an operon, termed the cyclohexanecarboxylate degrdn. operon, contg. genes required for both benzoate and cyclohexanecarboxylate degrdn. A nonpolar R. palustris badH mutant was unable to grow on benzoate or cyclohexanecarboxylate but had wild-type growth rates on succinate. Cells blocked in expression of the entire cyclohexanecarboxylate degrdn. operon excreted cyclohex-1-ene-1-carboxylate into the growth medium when given benzoate. This confirms that cyclohex-1-ene-1-carboxyl-CoA is an intermediate of anaerobic benzoate degrdn. by R. palustris. This compd. had previously been shown not to be formed by Thauera aromatica, a denitrifying bacterium that degrades benzoate by a pathway that is slightly different from the R. palustris pathway. 2-Hydroxychc-CoA dehydrogenase does not participate in anaerobic benzoate degrdn. by T. aromatica and thus may serve as a useful indicator of an R. palustris-type benzoate degrdn. pathway.

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Harwood, C. S.; Parales, R. E. The beta-ketoadipate pathway and the biology of self-identity Annu. Rev. Microbiol. 1996, 50, 553– 590

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The β-ketoadipate pathway and the biology of self-identity

Harwood, Caroline S.; Parales, Rebecca E.

Annual Review of Microbiology (1996), 50 (), 553-590CODEN: ARMIAZ; ISSN:0066-4227. (Annual Reviews)

A review with 163 refs. The β-ketoadipate pathway is a chromosomally encoded convergent pathway for arom. compd. degrdn. that is widely distributed in soil bacteria and fungi. One branch converts protocatechuate, derived from phenolic compds. including p-cresol, 4-hydroxybenzoate and numerous lignin monomers, to β-ketoadipate. The other branch converts catechol, generated from various arom. hydrocarbons, amino aroms., and lignin monomers, also to β-ketoadipate. Two addnl. steps accomplish the conversion of β-ketoadipate to tricarboxylic acid cycle intermediates. Enzyme studies and amino acid sequence data indicate that the pathway is highly conserved in diverse bacteria, including Pseudomonas putida, Acinetobacter calcoaceticus, Agrobacterium tumefaciens, Rhodococcus erythropolis, and many others. The catechol branch of the β-ketoadipate pathway appears to be the evolutionary precursor for portions of the plasmid-borne ortho-pathways for chlorocatechol degrdn. However, accumulating evidence points to an independent and convergent evolutionary origin for the eukaryotic β-ketoadipate pathway. In the face of enzyme conservation, the β-ketoadipate pathway exhibits many permutations in different bacterial groups with respect to enzyme distribution (isoenzymes, points of branch convergence), regulation (inducing metabolites, regulatory proteins), and gene organization. Diversity is also evident in the behavioral responses of different bacteria to β-ketoadipate pathway-assocd. arom. compds. The presence and versatility of transport systems encoded by β-ketoadipate pathway regulons is just beginning to be explored in various microbial groups. It appears that in the course of evolution, natural selection has caused the β-ketoadipate pathway to assume a characteristic set of features or identity in different bacteria. Presumably such identities have been shaped to optimally serve the diverse lifestyles of bacteria.

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Rabus, R.; Kube, M.; Heider, J.; Beck, A.; Heitmann, K.; Widdel, F.; Reinhardt, R. The genome sequence of an anaerobic aromatic-degrading denitrifying bacterium, strain EbN1 Arch. Microbiol. 2005, 183, 27– 36

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Lee, S. H.; Jin, H. M.; Lee, H. J.; Kim, J. M.; Jeon, C. O. Complete genome sequence of the BTEX-degrading bacterium Pseudoxanthomonas spadix BD-a59 J. Bacteriol. 2012, 194, 544

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Complete genome sequence of the BTEX-degrading bacterium Pseudoxanthomonas spadix BD-a59

Lee, Seung Hyeon; Jin, Hyun Mi; Lee, Hyo Jung; Kim, Jeong Myeong; Jeon, Che Ok

Journal of Bacteriology (2012), 194 (2), 544CODEN: JOBAAY; ISSN:0021-9193. (American Society for Microbiology)

Pseudoxanthomonas spadix BD-a59, able to metabolize all six BTEX (benzene, toluene, ethylbenzene, and o-, m-, and p-xylene) compds., was isolated from gasoline-contaminated sediment. This report presents the complete 3.45-Mb genome sequence and annotation of strain BD-a59. These advance the understanding of strain BD-a59's genomic properties and pollutant metabolic versatility. The complete genome sequence (3,452,554 bp, 3150 predicted protein-coding sequences) is deposited in GenBank/EMBL/DDBJ with accession no. CP003093.

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Papin, J. A.; Reed, J. L.; Palsson, B. O. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks Trends Biochem. Sci. 2004, 29, 641– 647

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Schuster, S.; Pfeiffer, T.; Moldenhauer, F.; Koch, I.; Dandekar, T. Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae Bioinformatics 2002, 18, 351– 361

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Yamada, T.; Kanehisa, M.; Goto, S. Extraction of phylogenetic network modules from the metabolic network BMC Bioinf. 2006, 7, 130

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Extraction of phylogenetic network modules from the metabolic network

Yamada Takuji; Kanehisa Minoru; Goto Susumu

BMC bioinformatics (2006), 7 (), 130 ISSN:.

BACKGROUND: In bio-systems, genes, proteins and compounds are related to each other, thus forming complex networks. Although each organism has its individual network, some organisms contain common sub-networks based on function. Given a certain sub-network, the distribution of organisms common to it represents the diversity of its function. RESULTS: We extracted such "common" sub-networks, defined as "phylogenetic network modules," using phylogenetic profiles and cluster analysis. The enzymes in the same "phylogenetic network module" have similar phylogenetic profiles and related functions. These modules are shown to be phylogenetic building blocks. Furthermore, the network of the modules illustrated hierarchical feature as well as the network of enzymes involved in the metabolism. CONCLUSION: We conclude that phylogenetic network modules are evolutionary conserved functional units in the metabolic network. We claim that our concept of phylogenetic modules provides a more accurate understanding of the evolution of biological networks.

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Earlier states of the biochemical system

Ycas, Martynas

Journal of Theoretical Biology (1974), 44 (1), 145-60CODEN: JTBIAP; ISSN:0022-5193.

Similarities between essential enzymes indicate homology and therefore origin from a smaller no. of ancestral genes, but there are also indications that the immediately preceding biochem. system was of about the same complexity as the present one. In order to maintain function with a smaller no. of enzymes, earlier enzymes must have been less specific, catalyzing classes of reactions. Lower specificity also resulted in ambiguous translation, each cistron producing a family of related proteins. Though individual protein mols. need not have been less specific, each family as a whole functioned as a catalyst of lower specificity. The no. of kinds of amino acids incorporated into proteins may have been larger than at present. The evidence supporting this, some of its implications, and the kinds of addnl. data that would be useful in such problems are discussed.

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Chu, H. Y.; Wegel, E.; Osbourn, A. From hormones to secondary metabolism: the emergence of metabolic gene clusters in plants Plant J. 2011, 66, 66– 79

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From hormones to secondary metabolism: The emergence of metabolic gene clusters in plants

Chu, Hoi Yee; Wegel, Eva; Osbourn, Anne

Plant Journal (2011), 66 (1), 66-79CODEN: PLJUED; ISSN:0960-7412. (Wiley-Blackwell)

A review. Gene clusters for the synthesis of secondary metabolites are a common feature of microbial genomes. Well known examples include clusters for the synthesis of antibiotics in actinomycetes, and also for the synthesis of antibiotics and toxins in filamentous fungi. Until recently, it was thought that genes for plant metabolic pathways were not clustered, and this is certainly true in many cases; however, five plant secondary metabolic gene clusters have now been discovered, all of them implicated in synthesis of defense compds. An obvious assumption might be that these eukaryotic gene clusters have arisen by horizontal gene transfer from microbes, but there is compelling evidence to indicate that this is not the case. This raises intriguing questions about how widespread such clusters are, what the significance of clustering is, why genes for some metabolic pathways are clustered and those for others are not, and how these clusters form. In answering these questions we may hope to learn more about mechanisms of genome plasticity and adaptive evolution in plants. It is noteworthy that for the five plant secondary metabolic gene clusters reported so far, the enzymes for the first committed steps all appear to have been recruited directly or indirectly from primary metabolic pathways involved in hormone synthesis. This may or may not turn out to be a common feature of plant secondary metabolic gene clusters as new clusters emerge.

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Figure 1

Figure 1. Fingerprint representation of the RDM pattern. Two reaction class entries RC00106 and RC01192 are shown for the reactant pairs of pyruvate and acetolactate (upper) and oxobutanoate and 2-aceto-2-hydroxybutanoate (lower). The RDM notation for these reaction class entries is converted to the fingerprint representation, which reveals that they are the same in the fingerprint representation despite the difference in the M atom (circled) in the RDM notation.

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Figure 2

Figure 2. Architecture of reaction modules consisting of 2-oxocarboxylic acid chain extension and modification, generating (A) basic amino acids and (B) branched-chain amino acids. Vertical arrows indicate the extension modules RM001. Horizontal arrows indicate the modification modules RM002, RM032, and RM033 together with the reductive amination step (RC00006 and RC00036).

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Figure 3

Figure 3. Aromatic ring cleavage modules in microbial degradation pathways. Aromatic rings are cleaved in the following three steps. The first step is an occasional preprocessing step (RM003) converting a methyl group into a carboxylic group on the aromatic ring. The second step is the main step of dihydroxylation, which is classified into three types (RM006, RM004, and RM005) depending on how two hydroxyl groups are added on the aromatic ring. The third step is either ortho-cleavage (RM008) or meta-cleavage (RM009) followed by characteristic reaction patterns leading to TCA cycle intermediates.

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This article references 28 other publications.
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  Bono, H.; Ogata, H.; Goto, S.; Kanehisa, M. Reconstruction of amino acid biosynthesis pathways from the complete genome sequence Genome Res. 1998, 8, 203– 210
  
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  Reconstruction of amino acid biosynthesis pathways from the complete genome sequence
  
  Bono, Hidemasa; Ogata, Hiroyuki; Goto, Susumu; Kanehisa, Minoru
  
  Genome Research (1998), 8 (3), 203-210CODEN: GEREFS; ISSN:1088-9051. (Cold Spring Harbor Laboratory Press)
  
  The complete genome sequence of an organism contains information that has not been fully utilized in the current prediction methods of gene functions, which are based on piece-by-piece similarity searches of individual genes. A method is presented a method that utilizes a higher level information of mol. pathways to reconstruct a complete functional unit from a set of genes. Specifically, a genome-by-genome comparison is first made for identifying enzyme genes and assigning EC nos., which is followed by the reconstruction of selected portions of the metabolic pathways by use of the reconstructed pathway is an indicator of the correctness of the initial gene function assignment. This feature has become possible because of efforts to computerize the current knowledge of metabolic pathways under the KEGG project. The biosynthesis pathway of all 20 amino acids were completely reconstructed in Escherichia coli, Haemophilus influenzae, and Bacillus subtilis, and probably in Synechocystis and Saccharomyces cerevisiae as well, although it was necessary to assume wilder substrate specificity for aspartate aminotransferases.
  
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  Galperin, M. Y.; Koonin, E. V. Functional genomics and enzyme evolution. Homologous and analogous enzymes encoded in microbial genomes Genetica 1999, 106, 159– 170
  
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  Dandekar, T.; Schuster, S.; Snel, B.; Huynen, M.; Bork, P. Pathway alignment: application to the comparative analysis of glycolytic enzymes Biochem. J. 1999, 343, 115– 124
  
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  Journal of Computational Biology (1999), 6 (3/4), 343-360CODEN: JCOBEM; ISSN:1066-5277. (Mary Ann Liebert, Inc.)
  
  The abundance of information provided by completely sequenced genomes defines a starting point for new insights in the multilevel organization of organisms and their evolution. At the lowest level enzymes and other protein complexes are formed by aggregating multiple polypeptides. At a higher level enzymes group conceptually into metabolic pathways as part of a dynamic information-processing system, and substrates are processed by enzymes yielding other substrates. A method based on a combination of sequence information with graph topol. of the underlying pathway is presented. With this approach pathways of different organisms are related to each other by phylogenetic anal., extending conventional phylogenetic anal. of individual enzymes. The new method is applied to pathways related to electron transfer and to the Krebs citric acid cycle. In addn. to providing a more comprehensive understanding of similarities and differences between organisms, this method indicates different evolutionary rates between substrates and enzymes.
  
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  Ogata, H.; Fujibuchi, W.; Goto, S.; Kanehisa, M. A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters Nucleic Acids Res. 2000, 28, 4021– 4028
  
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  Tohsato, Y.; Matsuda, H.; Hashimoto, A. A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy Proc. Int. Conf. Intell. Syst. Mol. Biol. 2000, 8, 376– 383
  
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  A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy
  
  Tohsato Y; Matsuda H; Hashimoto A
  
  Proceedings / ... International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology (2000), 8 (), 376-83 ISSN:1553-0833.
  
  In many of the chemical reactions in living cells, enzymes act as catalysts in the conversion of certain compounds (substrates) into other compounds (products). Comparative analyses of the metabolic pathways formed by such reactions give important information on their evolution and on pharmacological targets (Dandekar et al. 1999). Each of the enzymes that constitute a pathway is classified according to the EC (Enzyme Commission) numbering system, which consists of four sets of numbers that categorize the type of the chemical reaction catalyzed. In this study, we consider that reaction similarities can be expressed by the similarities between EC numbers of the respective enzymes. Therefore, in order to find a common pattern among pathways, it is desirable to be able to use the functional hierarchy of EC numbers to express the reaction similarities. In this paper, we propose a multiple alignment algorithm utilizing information content that is extended to symbols having a hierarchical structure. The effectiveness of our method is demonstrated by applying the method to pathway analyses of sugar, DNA and amino acid metabolisms.
  
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  Pinter, R. Y.; Rokhlenko, O.; Yeger-Lotem, E.; Ziv-Ukelson, M. Alignment of metabolic pathways Bioinformatics 2005, 21, 3401– 3408
  
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8. 8
  Wernicke, S.; Rasche, F. Simple and fast alignment of metabolic pathways by exploiting local diversity Bioinformatics 2007, 23, 1978– 1985
  
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9. 9
  Tohsato, Y.; Nishimura, Y. Reaction similarities focusing substructure changes of chemical compounds and metabolic pathway alignments Inf. Media Technol. 2009, 4, 390– 399
  
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10. 10
  Ay, Y.; Kellis, M.; Kahveci, T. SubMAP: aligning metabolic pathways with subnetwork mappings J. Comput. Biol. 2011, 18, 219– 235
  
  10
  SubMAP: aligning metabolic pathways with subnetwork mappings
  
  Ay, Ferhat; Kellis, Manolis; Kahveci, Tamer
  
  Journal of Computational Biology (2011), 18 (3), 219-235CODEN: JCOBEM; ISSN:1557-8666. (Mary Ann Liebert, Inc.)
  
  We consider the problem of aligning two metabolic pathways. Unlike traditional approaches, we do not restrict the alignment to one-to-one mappings between the mols. (nodes) of the input pathways (graphs). We follow the observation that, in nature, different organisms can perform the same or similar functions through different sets of reactions and mols. The no. and the topol. of the mols. in these alternative sets often vary from one organism to another. With the motivation that an accurate biol. alignment should be able to reveal these functionally similar mol. sets across different species, we develop an algorithm that first measures the similarities between different nodes using a mixt. of homol. and topol. similarity. We combine the two metrics by employing an eigenvalue formulation. We then search for an alignment between the two input pathways that maximizes a similarity score, evaluated as the sum of the similarities of the mapped subnetworks of size at most a given integer k, and also does not contain any conflicting mappings. Here we prove that this maximization is NP-hard by a redn. from the max. wt. independent set (MWIS) problem. We then convert our problem to an instance of MWIS and use an efficient vertex-selection strategy to ext. the mappings that constitute our alignment. We name our algorithm SubMAP (Subnetwork Mappings in Alingment of Pathways). We evaluate its accuracy and performance on real datasets. Our empirical results demonstrate that SubMAP can identify biol. relevant mappings that are missed by traditional alignment methods. Furthermore, we observe that SubMAP is scalable for metabolic pathways of arbitrary topol., including searching for a query pathway of size 70 against the complete KEGG database of 1,842 pathways.
  
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  Kanehisa, M.; Goto, S.; Sato, Y.; Furumichi, M.; Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets Nucleic Acids Res. 2012, 40, D109– D114
  
  11
  KEGG for integration and interpretation of large-scale molecular data sets
  
  Kanehisa, Minoru; Goto, Susumu; Sato, Yoko; Furumichi, Miho; Tanabe, Mao
  
  Nucleic Acids Research (2012), 40 (D1), D109-D114CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
  
  Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chem. and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing exptl. knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the mol. networks through the KEGG Orthol. system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG mol. networks. Finally, we describe recent enhancements to the KEGG content, esp. the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
  
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12. 12
  Kotera, M.; Okuno, Y.; Hattori, M.; Goto, S.; Kanehisa, M. Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions J. Am. Chem. Soc. 2004, 126, 16487– 16498
  
  12
  Computational Assignment of the EC Numbers for Genomic-Scale Analysis of Enzymatic Reactions
  
  Kotera, Masaaki; Okuno, Yasushi; Hattori, Masahiro; Goto, Susumu; Kanehisa, Minoru
  
  Journal of the American Chemical Society (2004), 126 (50), 16487-16498CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  
  The EC (Enzyme Commission) nos. represent a hierarchical classification of enzymic reactions, but they are also commonly utilized as identifiers of enzymes or enzyme genes in the anal. of complete genomes. This duality of the EC nos. makes it possible to link the genomic repertoire of enzyme genes to the chem. repertoire of metabolic pathways, the process called metabolic reconstruction. Unfortunately, there are numerous reactions known to be present in various pathways, but they will never get EC nos. because the EC no. assignment requires published articles on full characterization of enzymes. Here we report a computerized method to automatically assign the EC nos. up to the sub-subclasses, i.e., without the fourth serial no. for substrate specificity, given pairs of substrates and products. The method is based on a new classification scheme of enzymic reactions, named the RC (reaction classification) no. Each reaction in the current dataset of the EC nos. is first decompd. into reactant pairs. Each pair is then structurally aligned to identify the reaction center, the matched region, and the difference region. The RC no. represents the conversion patterns of atom types in these three regions. We examd. the correspondence between computationally assigned RC nos. and manually assigned EC nos. by the jackknife cross-validation test and found that the EC sub-subclasses could be assigned with the accuracy of about 90%. Furthermore, we examd. the correlation with genomic information as represented by the KEGG ortholog clusters (OC) and confirmed that the RC nos. are correlated not only with elementary reaction mechanisms but also with protein families.
  
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13. 13
  McDonald, A. G.; Boyce, S.; Tipton, K. F. ExplorEnz: the primary source of the IUBMB enzyme list Nucleic Acids Res. 2009, 37, D593– D597
  
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14. 14
  Hattori, M.; Okuno, Y.; Goto, S.; Kanehisa, M. Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways J. Am. Chem. Soc. 2003, 125, 11853– 11865
  
  14
  Development of a Chemical Structure Comparison Method for Integrated Analysis of Chemical and Genomic Information in the Metabolic Pathways
  
  Hattori, Masahiro; Okuno, Yasushi; Goto, Susumu; Kanehisa, Minoru
  
  Journal of the American Chemical Society (2003), 125 (39), 11853-11865CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  
  Cellular functions result from intricate networks of mol. interactions, which involve not only proteins and nucleic acids but also small chem. compds. Here we present an efficient algorithm for comparing two chem. structures of compds., where the chem. structure is treated as a graph consisting of atoms as nodes and covalent bonds as edges. On the basis of the concept of functional groups, 68 atom types (node types) are defined for carbon, nitrogen, oxygen, and other at. species with different environments, which has enabled detection of biochem. meaningful features. Maximal common subgraphs of two graphs can be found by searching for maximal cliques in the assocn. graph, and we have introduced heuristics to accelerate the clique finding and to detect optimal local matches (simply connected common subgraphs). Our procedure was applied to the comparison and clustering of 9383 compds., mostly metabolic compds., in the KEGG/LIGAND database. The largest clusters of similar compds. were related to carbohydrates, and the clusters corresponded well to the categorization of pathways as represented by the KEGG pathway map nos. When each pathway map was examd. in more detail, finer clusters could be identified corresponding to subpathways or pathway modules contg. continuous sets of reaction steps. Furthermore, it was found that the pathway modules identified by similar compd. structures sometimes overlap with the pathway modules identified by genomic contexts, namely, by operon structures of enzyme genes.
  
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15. 15
  Jensen, R. A. Enzyme recruitment in evolution of new function Annu. Rev. Microbiol. 1976, 30, 409– 425
  
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  Enzyme recruitment in evolution of new function
  
  Jensen, Roy A.
  
  Annual Review of Microbiology (1976), 30 (), 409-25CODEN: ARMIAZ; ISSN:0066-4227.
  
  A review with 96 refs., emphasizing the prospects for defining evolutionary relations between enzymes that coexist in the same organism but that no longer serve the same function.
  
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16. 16
  Horie, A.; Tomita, T.; Saiki, A.; Kono, H.; Taka, H.; Mineki, R.; Fujimura, T.; Nishiyama, C.; Kuzuyama, T.; Nishiyama, M. Discovery of proteinaceous N-modification in lysine biosynthesis of Thermus thermophilus Nat. Chem. Biol. 2009, 5, 673– 679
  
  16
  Discovery of proteinaceous N-modification in lysine biosynthesis of Thermus thermophilus
  
  Horie, Akira; Tomita, Takeo; Saiki, Asako; Kono, Hidetoshi; Taka, Hikari; Mineki, Reiko; Fujimura, Tsutomu; Nishiyama, Chiharu; Kuzuyama, Tomohisa; Nishiyama, Makoto
  
  Nature Chemical Biology (2009), 5 (9), 673-679CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
  
  Although the latter portion of lysine biosynthesis, the conversion of α-aminoadipate (AAA) to lysine, in Thermus thermophilus is similar to the latter portion of arginine biosynthesis, enzymes homologous to ArgA and ArgJ are absent from the lysine pathway. Because ArgA and ArgJ are known to modify the amino group of glutamate to avoid intramol. cyclization of intermediates, their absence suggests that the pathway includes an alternative N-modification system. We reconstituted the conversion of AAA to lysine and found that the amino group of AAA is modified by attachment to the γ-carboxyl group of the C-terminal Glu54 of a small protein, LysW; that the side chain of AAA is converted to the lysyl side chain while still attached to LysW; and that lysine is subsequently liberated from the LysW-lysine fusion. The fact that biosynthetic enzymes recognize the acidic globular domain of LysW indicates that LysW acts as a carrier protein or protein scaffold for the biosynthetic enzymes. This study thus reveals the previously unknown function of a small protein in primary metab.
  
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17. 17
  Maeder, D. L.; Weiss, R. B.; Dunn, D. M.; Cherry, J. L.; González, J. M.; DiRuggiero, J.; Robb, F. T. Divergence of the hyperthermophilic archaea Pyrococcus furiosus and P. horikoshii inferred from complete genomic sequences Genetics 1999, 152, 1299– 1305
  
  There is no corresponding record for this reference.
18. 18
  Pelletier, D. A.; Harwood, C. S. 2-Hydroxycyclohexanecarboxyl coenzyme A dehydrogenase, an enzyme characteristic of the anaerobic benzoate degradation pathway used by Rhodopseudomonas palustris J. Bacteriol. 2000, 182, 2753– 2760
  
  18
  2-Hydroxycyclohexanecarboxyl coenzyme A dehydrogenase, an enzyme characteristic of the anaerobic benzoate degradation pathway used by Rhodopseudomonas palustris
  
  Pelletier, Dale A.; Harwood, Caroline S.
  
  Journal of Bacteriology (2000), 182 (10), 2753-2760CODEN: JOBAAY; ISSN:0021-9193. (American Society for Microbiology)
  
  A gene, badH, whose predicted product is a member of the short-chain dehydrogenase/reductase family of enzymes, was recently discovered during studies of anaerobic benzoate degrdn. by the photoheterotrophic bacterium Rhodopseudomonas palustris. Purified histidine-tagged BadH protein catalyzed the oxidn. of 2-hydroxycyclohexanecarboxyl CoA (2-hydroxychc-CoA) to 2-ketocyclohexanecarboxyl-CoA. These compds. are proposed intermediates of a series of three reactions that are shared by the pathways of cyclohexanecarboxylate and benzoate degrdn. used by R. palustris. The 2-hydroxychc-CoA dehydrogenase activity encoded by badH was dependent on the presence of NAD+; no activity was detected with NADP+ as a cofactor. The dehydrogenase activity was not sensitive to oxygen. The enzyme has apparent Km values of 10 and 200 μM for 2-hydroxychc-CoA and NAD+, resp. Western blot anal. with antisera raised against purified His-BadH identified a 27-kDa protein that was present in benzoate- and cyclohexanecarboxylate-grown but not in succinate-grown R. palustris cell exts. The active form of the enzyme is a homotetramer. BadH was detd. to be the first gene in an operon, termed the cyclohexanecarboxylate degrdn. operon, contg. genes required for both benzoate and cyclohexanecarboxylate degrdn. A nonpolar R. palustris badH mutant was unable to grow on benzoate or cyclohexanecarboxylate but had wild-type growth rates on succinate. Cells blocked in expression of the entire cyclohexanecarboxylate degrdn. operon excreted cyclohex-1-ene-1-carboxylate into the growth medium when given benzoate. This confirms that cyclohex-1-ene-1-carboxyl-CoA is an intermediate of anaerobic benzoate degrdn. by R. palustris. This compd. had previously been shown not to be formed by Thauera aromatica, a denitrifying bacterium that degrades benzoate by a pathway that is slightly different from the R. palustris pathway. 2-Hydroxychc-CoA dehydrogenase does not participate in anaerobic benzoate degrdn. by T. aromatica and thus may serve as a useful indicator of an R. palustris-type benzoate degrdn. pathway.
  
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19. 19
  Harwood, C. S.; Parales, R. E. The beta-ketoadipate pathway and the biology of self-identity Annu. Rev. Microbiol. 1996, 50, 553– 590
  
  19
  The β-ketoadipate pathway and the biology of self-identity
  
  Harwood, Caroline S.; Parales, Rebecca E.
  
  Annual Review of Microbiology (1996), 50 (), 553-590CODEN: ARMIAZ; ISSN:0066-4227. (Annual Reviews)
  
  A review with 163 refs. The β-ketoadipate pathway is a chromosomally encoded convergent pathway for arom. compd. degrdn. that is widely distributed in soil bacteria and fungi. One branch converts protocatechuate, derived from phenolic compds. including p-cresol, 4-hydroxybenzoate and numerous lignin monomers, to β-ketoadipate. The other branch converts catechol, generated from various arom. hydrocarbons, amino aroms., and lignin monomers, also to β-ketoadipate. Two addnl. steps accomplish the conversion of β-ketoadipate to tricarboxylic acid cycle intermediates. Enzyme studies and amino acid sequence data indicate that the pathway is highly conserved in diverse bacteria, including Pseudomonas putida, Acinetobacter calcoaceticus, Agrobacterium tumefaciens, Rhodococcus erythropolis, and many others. The catechol branch of the β-ketoadipate pathway appears to be the evolutionary precursor for portions of the plasmid-borne ortho-pathways for chlorocatechol degrdn. However, accumulating evidence points to an independent and convergent evolutionary origin for the eukaryotic β-ketoadipate pathway. In the face of enzyme conservation, the β-ketoadipate pathway exhibits many permutations in different bacterial groups with respect to enzyme distribution (isoenzymes, points of branch convergence), regulation (inducing metabolites, regulatory proteins), and gene organization. Diversity is also evident in the behavioral responses of different bacteria to β-ketoadipate pathway-assocd. arom. compds. The presence and versatility of transport systems encoded by β-ketoadipate pathway regulons is just beginning to be explored in various microbial groups. It appears that in the course of evolution, natural selection has caused the β-ketoadipate pathway to assume a characteristic set of features or identity in different bacteria. Presumably such identities have been shaped to optimally serve the diverse lifestyles of bacteria.
  
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20. 20
  Rabus, R.; Kube, M.; Heider, J.; Beck, A.; Heitmann, K.; Widdel, F.; Reinhardt, R. The genome sequence of an anaerobic aromatic-degrading denitrifying bacterium, strain EbN1 Arch. Microbiol. 2005, 183, 27– 36
  
  There is no corresponding record for this reference.
21. 21
  Lee, S. H.; Jin, H. M.; Lee, H. J.; Kim, J. M.; Jeon, C. O. Complete genome sequence of the BTEX-degrading bacterium Pseudoxanthomonas spadix BD-a59 J. Bacteriol. 2012, 194, 544
  
  21
  Complete genome sequence of the BTEX-degrading bacterium Pseudoxanthomonas spadix BD-a59
  
  Lee, Seung Hyeon; Jin, Hyun Mi; Lee, Hyo Jung; Kim, Jeong Myeong; Jeon, Che Ok
  
  Journal of Bacteriology (2012), 194 (2), 544CODEN: JOBAAY; ISSN:0021-9193. (American Society for Microbiology)
  
  Pseudoxanthomonas spadix BD-a59, able to metabolize all six BTEX (benzene, toluene, ethylbenzene, and o-, m-, and p-xylene) compds., was isolated from gasoline-contaminated sediment. This report presents the complete 3.45-Mb genome sequence and annotation of strain BD-a59. These advance the understanding of strain BD-a59's genomic properties and pollutant metabolic versatility. The complete genome sequence (3,452,554 bp, 3150 predicted protein-coding sequences) is deposited in GenBank/EMBL/DDBJ with accession no. CP003093.
  
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  Papin, J. A.; Reed, J. L.; Palsson, B. O. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks Trends Biochem. Sci. 2004, 29, 641– 647
  
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  Ravasz, E.; Somera, A. L.; Mongru, D. A.; Oltvai, Z. N.; Barabási, A. L. Hierarchical organization of modularity in metabolic networks Science 2002, 297, 1551– 1555
  
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  Schuster, S.; Pfeiffer, T.; Moldenhauer, F.; Koch, I.; Dandekar, T. Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae Bioinformatics 2002, 18, 351– 361
  
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  Yamada, T.; Kanehisa, M.; Goto, S. Extraction of phylogenetic network modules from the metabolic network BMC Bioinf. 2006, 7, 130
  
  25
  Extraction of phylogenetic network modules from the metabolic network
  
  Yamada Takuji; Kanehisa Minoru; Goto Susumu
  
  BMC bioinformatics (2006), 7 (), 130 ISSN:.
  
  BACKGROUND: In bio-systems, genes, proteins and compounds are related to each other, thus forming complex networks. Although each organism has its individual network, some organisms contain common sub-networks based on function. Given a certain sub-network, the distribution of organisms common to it represents the diversity of its function. RESULTS: We extracted such "common" sub-networks, defined as "phylogenetic network modules," using phylogenetic profiles and cluster analysis. The enzymes in the same "phylogenetic network module" have similar phylogenetic profiles and related functions. These modules are shown to be phylogenetic building blocks. Furthermore, the network of the modules illustrated hierarchical feature as well as the network of enzymes involved in the metabolism. CONCLUSION: We conclude that phylogenetic network modules are evolutionary conserved functional units in the metabolic network. We claim that our concept of phylogenetic modules provides a more accurate understanding of the evolution of biological networks.
  
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  Horowitz, N. H. On the evolution of biochemical synthesis Proc. Natl. Acad. Sci USA 1945, 31, 153– 157
  
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  Ycas, M. On earlier states of the biochemical system J. Theor. Biol. 1974, 44, 145– 160
  
  27
  Earlier states of the biochemical system
  
  Ycas, Martynas
  
  Journal of Theoretical Biology (1974), 44 (1), 145-60CODEN: JTBIAP; ISSN:0022-5193.
  
  Similarities between essential enzymes indicate homology and therefore origin from a smaller no. of ancestral genes, but there are also indications that the immediately preceding biochem. system was of about the same complexity as the present one. In order to maintain function with a smaller no. of enzymes, earlier enzymes must have been less specific, catalyzing classes of reactions. Lower specificity also resulted in ambiguous translation, each cistron producing a family of related proteins. Though individual protein mols. need not have been less specific, each family as a whole functioned as a catalyst of lower specificity. The no. of kinds of amino acids incorporated into proteins may have been larger than at present. The evidence supporting this, some of its implications, and the kinds of addnl. data that would be useful in such problems are discussed.
  
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  Chu, H. Y.; Wegel, E.; Osbourn, A. From hormones to secondary metabolism: the emergence of metabolic gene clusters in plants Plant J. 2011, 66, 66– 79
  
  28
  From hormones to secondary metabolism: The emergence of metabolic gene clusters in plants
  
  Chu, Hoi Yee; Wegel, Eva; Osbourn, Anne
  
  Plant Journal (2011), 66 (1), 66-79CODEN: PLJUED; ISSN:0960-7412. (Wiley-Blackwell)
  
  A review. Gene clusters for the synthesis of secondary metabolites are a common feature of microbial genomes. Well known examples include clusters for the synthesis of antibiotics in actinomycetes, and also for the synthesis of antibiotics and toxins in filamentous fungi. Until recently, it was thought that genes for plant metabolic pathways were not clustered, and this is certainly true in many cases; however, five plant secondary metabolic gene clusters have now been discovered, all of them implicated in synthesis of defense compds. An obvious assumption might be that these eukaryotic gene clusters have arisen by horizontal gene transfer from microbes, but there is compelling evidence to indicate that this is not the case. This raises intriguing questions about how widespread such clusters are, what the significance of clustering is, why genes for some metabolic pathways are clustered and those for others are not, and how these clusters form. In answering these questions we may hope to learn more about mechanisms of genome plasticity and adaptive evolution in plants. It is noteworthy that for the five plant secondary metabolic gene clusters reported so far, the enzymes for the first committed steps all appear to have been recruited directly or indirectly from primary metabolic pathways involved in hormone synthesis. This may or may not turn out to be a common feature of plant secondary metabolic gene clusters as new clusters emerge.
  
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KEGG atom types, RDM patterns, fingerprint representation, and similarity scoring of RC entries (Methods); tricarboxylic acid pathway (Figure S1); glucosinolate biosynthesis pathway (Figure S2); carboxyl to amino conversion (Figure S3); fatty acid biosynthesis (Figure S4); beta oxidation (Figure S5); methyl to carboxyl conversion on aromatic ring (Figure S6); amino to carboxyl conversion (Figure S7); nucleotide sugar biosynthesis (Figure S8); phenylpropanoid biosynthesis (Figure S9); and classification of monooxygenases (Figure S10). This information is available free of charge via the Internet at http://pubs.acs.org.
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Modular Architecture of Metabolic Pathways Revealed by Conserved Sequences of Reactions

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Abstract

Introduction

Materials and Methods

Metabolic Pathway Database

Reactant Pairs

Reaction Class

Results

Similarity Grouping of RCLASS Entries

Figure 1

Extraction of Conserved RCLASS Sequence Patterns

General Characteristics of Reaction Modules

2-Oxocarboxylic Acid Chain Extension

Modification of 2-Oxocarboxylic Acids

Figure 2

Reaction Modules Encoded in Enzyme Gene Clusters

Fatty Acid Synthesis and Beta Oxidation

Aromatic Ring Cleavage in Microbial Biodegradation Pathways

Figure 3

Discussion

Supporting Information

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Acknowledgment

References

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Abstract

Figure 1

Figure 2

Figure 3

References

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Supporting Information

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