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EMBO reports 4, 10, 994–999 (2003)
doi:10.1038/sj.embor.embor933
AOP Published online: 5 September 2003
The organization of the microbial biodegradation network from a systems-biology perspective
Florencio Pazos1, 3, Alfonso Valencia2 & Víctor De Lorenzo2
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1 ALMA Bioinformatics, Centro Empresarial Euronova,
Ronda de Poniente 4, Tres Cantos, 28760 Madrid,
Spain
2 National Center for Biotechnology
(CNB–CSIC), Cantoblanco, 28049 Madrid,
Spain
3 Present address: Structural Bioinformatics Group,
Department of Biological Sciences, Imperial College, London
SW7 2AZ, UK
To whom correspondence should be addressed
Alfonso Valencia Tel: +34 91 585 4500; Fax: +34 91 585 4506;
valencia@cnb.uam.es
Received 28 March 2003; Accepted 24 July 2003; Published online 5 September 2003.
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Abstract
Microbial biodegradation of environmental pollutants is a field of
growing importance because of its potential use in bioremediation and
biocatalysis. We have studied the characteristics of the global biodegradation
network that is brought about by all the known chemical reactions that are
implicated in this process, regardless of their microbial hosts. This
combination produces an efficient and integrated suprametabolism, with
properties similar to those that define metabolic networks in single organisms.
The characteristics of this network support an evolutionary scenario in which
the reactions evolved outwards from the central metabolism. The properties of
the global biodegradation network have implications for predicting the fate of
current and future environmental pollutants.
EMBO reports 4, 10, 994–999 (2003)
doi:10.1038/sj.embor.embor933
AOP Published online: 5 September 2003
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Introduction
The thousands of tonnes of petroleum that were discharged off the
coasts of Galicia, Spain, by the oil tanker Prestige in 2002 is just one of the
many examples of environmental catastrophes caused by the spillage of toxic
chemicals in natural ecosystems. The ultimate fate of such compounds, as well
as the natural capacity of the afflicted sites to respond to environmental
insults, is a matter of growing concern. Some microorganisms and microbial
communities have developed the ability to process recalcitrant, often
xenobiotic compounds that do not form part of their central metabolism (CM) by
transforming them into compounds that can enter into their CM (Parales et al., 2002). Such biodegradation processes
have enormous potential for environmental cleanup (bioremediation;
Dua et al., 2002) and in biocatalysis (green
chemistry; Schmid et al., 2001).
The increasing knowledge about individual metabolic reactions and
protein interactions has allowed the assembly of complete metabolic and
protein-interaction networks (Gavin et al.,
2002; Ho et al., 2002;
Ito et al., 2000; Kanehisa et al., 2002; Rain et
al., 2001; Uetz et al.,
2000). In the same way, the increasing amount of information
available about the strains, compounds, enzymes and reactions implicated in
microbial biodegradation of toxic pollutants provides us with the building
blocks for formulating a 'biodegradation network'. This issue is directly
connected to systems biology, which complements the traditional study of genes
and proteins as isolated entities with a new perspective that regards
biological systems as consisting of components in a network of complex
relationships. In these systems, the whole is more than the sum of the parts,
and some of the properties of the system cannot be understood from the
properties of its individual components, thus requiring the study of the
network as a whole. The first studies of the properties of biological networks,
for example, metabolic networks, protein-interaction networks and genetic
control networks (Jeong et al., 2000,
2001; Ravasz et al.,
2002), revealed new facets of living systems (Alves
et al., 2002; Fraser et al.,
2002; Guelzim et al., 2002;
Ideker et al., 2001; Jeong et al., 2001; Maslov &
Sneppen, 2002; Rison & Thornton,
2002). One of the main ideas to come out of this research is that the
topology of these biological networks is not random, but has a typical
structure, known as 'scale-free'. In these networks, the distribution of
connectivity is not homogeneous, but follows a power law: there are a few
highly connected nodes (hubs) and the rest have low connectivity (Barabási & Albert, 1999). This is in contrast
with random networks, where connectivity follows a Poisson distribution.
Scale-free networks have two main properties: the pathway between any two nodes
is always short because the hubs act as shortcuts, and they are tolerant
against random perturbations (elimination of components) because there are
always alternative pathways through the hubs.
Here, we present the first systematic study of the microbial
biodegradation of environmental pollutants from a systems-biology perspective.
We examined the structure of the biodegradation network, its connectivity, the
characteristics of chemical compounds and enzymes depending on their network
context, and other descriptors of the network.
Results
Topology of the network
We put together the chemical reactions that are implicated in
biodegradation in a single graph, in which the reactions are the edges and the
chemical compounds are the nodes (see the Methods
section for details of construction; see
http://pdg.cnb.uam.es/biodeg_net for representations of the graph).
When the graph is assembled, it is possible to study its topological
characteristics. The log–log plots of connectivity against the number of
nodes (chemical compounds in this case) at each level of connectivity reveal a
clear scale-free structure (Fig. 1). This behaviour is
seen when all the connections are considered and also when the plots are
limited to the incoming or outgoing connections. In all three cases, the
relationship between the number of compounds (p(k)) and the
number of connections (k) can be expressed as p(k) 
k- . This indicates the non-random structure
of the network and the presence of a few highly connected compounds connecting
the bulk of poorly connected compounds.
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Figure 1
Log–log plots of the number of compounds versus connectivity.
(A) All connections in which the chemical compound is implicated (either
as a substrate or as a product) are counted. (B) Only the incoming
connections (with the compound as a product) are counted. (C) Only the
outgoing connections (with the compound as a substrate) are counted. The
exponents of the power law distributions are shown ( ). k,
connectivity; p(k), number of compounds.
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The 'exponent' of the network () ranges between 2 and
3 (Fig. 1) and is similar to that reported for metabolic
networks ( = 2.2; Jeong et al.,
2000). The diameter of the network (average distance between
compounds; see Methods) is 5.5, which is also
similar to that reported for metabolic networks (between 2 and 5;
Jeong et al., 2000). An interesting
parameter is the distance to the CM, the length of the shortest biodegradative
pathway for a given compound. This distance ranges from 0 (compounds that
already belong to the CM) to 14 (compounds that need many transformation steps
to enter the CM). The average is 3.3, indicating that most of the compounds
need just 3 or 4 steps to be biodegraded. These short pathways are possible
because of the scale-free structure of the network.
Other topological properties are specific to the biodegradation
network. The relationship between the number of incoming (ci)
and outgoing (co) connections for each compound reveals a
'concentrating' structure, in which several key compounds have many more
incoming than outgoing connections (Fig. 2A). There are
no nodes with high ci and high co, in
contrast with metabolic networks in which there are metabolites such as
pyruvate that participate in many reactions as reactants or as products. The
network acts as a 'funnel', concentrating the 'flux' of compounds to the CM.
There is a clear tendency for the highly connected compounds (hubs) to be close
to the CM, whereas poorly connected compounds are distributed throughout the
whole network (Fig. 2B). Compounds that cannot reach the
CM have few connections (Fig. 2B).
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Figure 2
Connections of the chemical compounds. (A) Relationship
between the number of incoming connections (ci) and the
number of outgoing ones (co) for the chemical compounds.
(B) Relationship between the total number of connections for a compound
(c) and its distance (d) to the central metabolism (CM). 'No CM'
indicates that there is no pathway to the CM. In both cases, the radii of the
circles are proportional to the number of elements (in a logarithmic
scale).
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Properties of the chemical compounds
We studied the properties of the compounds that are present at
different distances to the CM. There is a relationship between the molecular
weight and water solubility of the compounds and their distance to the CM.
Large and insoluble compounds tend to be far away from the CM (Fig. 3). This obvious fact, known before and quantified here,
is related to the difficulty in degrading large and poorly soluble compounds.
In other words, there are no reactions that can connect them directly to the
highly connected nodes because of their chemical structure and properties, and
hence more complex pathways are needed.
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Figure 3
Relationship between chemical properties of the compounds and their
position in the network. (A) Relationship between molecular weights of
the compounds and their distance to the central metabolism (CM).
(B,C) Relationship between water solubility of the compounds and
their distance to the CM. (B) Compounds for which it was possible to
obtain a numerical value for solubility. (C) Compounds for which the
solubility was described qualitatively (soluble, slightly soluble and
insoluble). The bars represent the number of compounds in each category of
solubility, at a given distance from the CM. (d = -1 indicates
that there is no pathway to the CM).
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Properties of the enzymes
We have studied the relationship between the distance of the enzymes
to the CM and their enzymatic activities (represented by the first number of
their Enzyme Commission (EC) codes). Ligases (EC 6.-.-.-) are only present
close to the CM, mainly because of the many reactions that involve coenzyme A
(CoA) binding. Three other activities, transferases (EC 2.-.-.-), isomerases
(EC 5.-.-.-) and, to a lesser extent, hydrolases (EC 3.-.-.-) are also mainly
concentrated in the region close to the CM (data not shown).
Two hypotheses can explain the scale-free structure of a network.
First, the network might have evolved from a random network to a scale-free one
(by adding and deleting connections) because of the advantages of the
scale-free topology (for example, tolerance to perturbations). Second, the
network might have evolved from a seed by adding new connections, not randomly,
but in such a way that the probability of a new connection ending in a hub is
higher than that of its ending in a poorly connected node (that is the
explanation, for example, for the scale-free structure of the World Wide Web
connections, in which new links are added preferentially to popular websites).
Two observations favour the second model for the generation of the
biodegradation network. First, the biodegradation network seems to be less
tolerant of errors than the metabolic network (Jeong et
al., 2000). This has been simulated by removing up to 200
connections (reactions) randomly (see Methods). On
average, the effect of introducing n mutations is that 1.6n
compounds lose their pathway to the CM, although the increase in the pathway
length for the remaining compounds is small (Fig. 4).
Second, the most 'ancient' enzymes (those present in many organisms) are only
present close to the CM, suggesting that this is the most ancient part of the
network (Fig. 5). 'Newer' enzymes are present both close
to and far away from the CM.
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Figure 4
Simulation of perturbations in the network. The x axes
represent the number of mutations introduced (reactions removed). (A)
Number of compounds that lost their pathways to the central metabolism (CM)
after that number of mutations. The continuous line represents the loss of one
compound for each mutation introduced. (C) Increase in the distance to
the CM for the remaining compounds ( d).
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Figure 5
Relationship between the antiquity of the enzymes, measured as the
number of organisms in which their genes are present, and their distance to the
central metabolism (CM). d = -1 indicates that there are no
pathways to the CM. The radii of the circles are proportional to the number of
elements (in logarithmic scale).
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Thus, the scale-free structure of the biodegradation network seems
to be related to its historical evolution: the network evolved from the CM
outwards, and the connection of new compounds preferentially to highly
connected ones seems to be favoured by the selection of shorter and more
efficient pathways to the CM.
Discussion
In this work, we have studied a large set of chemical reactions that
are implicated in biodegradation to obtain quantitative insights in its
organization and possible mechanism of evolution.
The main properties found include the position of central hubs and
basic ancient functions close to the CM, with large and difficult-to-degrade
compounds more concentrated in the periphery. All the analyses point to a model
of growth from the CM towards the more diversified reactions, a model that may
be connected with the history of biodegradation on Earth. These characteristics
are summarized in a model for the structure and evolution of the biodegration
network (Fig. 6). In this study, we have quantified and
given support to many facts that were previously suspected.
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Figure 6
Structure, properties and evolution of the biodegradation network.
Chemical compounds are represented by circles, the areas of which are
proportional to molecular weight. Reactions are represented by arrows, the
widths of which are proportional to the antiquity of the catalysing enzyme.
Blue arrows represent the ligase (Enzyme Commission 6.-.-.-) enzymatic activity
that is often found close to the central metabolism. The red circle represents
a new compound to be degraded. If two possibilities exist for attaching it to
the network, it is preferentially connected to a node that is already highly
connected (a hub).
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This analysis of the biodegradation network has two main differences
to equivalent studies of full metabolic or protein-interaction networks. First,
we are close to knowing the full metabolic and protein-interaction networks for
some model organisms (despite some important limitations (Lakey & Raggett, 1998; Legrain et
al., 2001) that are not discussed here), whereas this is not the
case for biodegradation, for which only part of the network is known. Moreover,
we might have knowledge of as little as 5% of the microbial diversity of the
biosphere (Curtis et al., 2002). Second,
reactions implicated in biodegradation are carried out by organisms that live
in different environments, oxygen tensions and physico-chemical conditions,
whereas we mix all of these together in the same network. Although this would
be a major problem if such a diversity of carriers were static in space and
time, the reality is that microbial communities move and evolve over time, not
only in terms of species composition, but also in tems of massive horizontal
gene-transfer events (Wilkins, 2002).
The biodegradation network presented in this article is an authentic
network only if we consider the whole microbial ecosystem of the biosphere as a
non-compartmentalized global reality, thus allowing the free movement of
strains. Although this is not entirely true, there is increasing evidence that
bacterial species with unexpected degradative abilities can be found in even
the most pristine sites (Fulthorpe et al.,
1998). The biodegradation of xenobiotic compounds by microbial
communities, which transfer substrates and products between each other and
cooperate metabolically, has been known for a long time (Abraham et al., 2002; Pelz et
al., 1999). In addition, intra-species and inter-species
horizontal transfer of DNA is far more frequent than was anticipated (Wilkins, 2002). This is exemplified by the worldwide spread
of antibiotic-resistance genes (Leverstein-van Hall et
al., 2002) and by the bioaugmentation of the biodegradative
abilities of microbial communities through directed catabolic gene transfer
(Dejonghe et al., 2000). Finally,
atmospheric phenomena mobilize and spread considerable amounts of environmental
pollutants to sites far from their original sources (Carrera
et al., 2002).
Several models have been proposed for explaining the evolution of
metabolism. The main ones are the 'retroevolution' model (Horowitz, 1945), which assumes that enzymes evolved from
other enzymes that function at subsequent stages in a pathway to replenish the
exhausted substrates of the latter, and the 'recruitment' model (Jensen, 1976), which proposes that new enzymes are created
by duplication and adaptation of similar enzymes from other pathways. In most
of the cases analysed in detail using whole-genome information, it seems that
the most common situation is the assembly of pathways from a series of gene
duplication events, followed by their later specialization. The biodegradation
network described here seems to fit this model, growing by the recruitment of
new enzymes in the periphery of the network to degrade compounds as close as
possible to well-connected hubs. This model arises from the partial data that
we are dealing with. Further studies and the continuous expansion of databases
will provide a clearer picture of the evolutionary scenario.
These facts make our network model an instrument for understanding the
evolution of new pathways for the degradation of xenobiotics and the global
capacities of the microbial world to face existing and future environmental
insults. The properties of the network provide the basis for predicting the
abilities of existing (and even not-yet synthesized) chemicals to undergo
biological degradation and for quantifying the evolutionary rate for their
elimination in the future. The properties presented here could also help in the
design of 'biodegradative genomes' from scratch (Zimmer,
2003).
Figure 6 illustrates all the described
characteristics of the biodegradation network (see
http://pdg.cnb.uam.es/biodeg_net for full representations of the
real network, coloured according to some of the parameters discussed).
Methods
The main source of information for constructing the biodegradation
network was the University of Minnesota Biocatalysis/Biodegradation Database
(UMBBD; March 2002 version; http://umbbd.ahc.umn.edu/;
Ellis et al., 2001). Other sources of
information on enzymes and compounds were the ENZYME database
(http://www.expasy.ch/enzyme/; Bairoch,
2000) and the ChemFinder database
(http://chemfinder.cambridgesoft.com/), respectively.
The biodegradation network is a directed graph in which the nodes are
the chemical compounds and the edges are the reactions, leading from the
substrate to the product. When a reaction has more than one substrate or
product, all the possible connections between substrates and products are
constructed. Commonly available chemical compounds that are not limiting
factors in the reactions, such as water and ions, are not included in the
network. The compounds have associated properties (molecular weight,
solubility, and so on) as do the reactions (enzyme, EC code, organisms in which
that enzyme is present, and so on). All the reactions in UMBBD are included in
the network, regardless of their aerobic or anaerobic nature, the organisms in
which the enzymes are present, and so on. The final network was composed of 740
compounds connected by 821 reactions, of which 678 had associated enzymatic
activity and 308 had specific enzymatic activity (values in the four positions
of the EC code, not a generic enzymatic class).
Mutations in the network were simulated by removing reactions
randomly. The result for every mutation experiment was averaged for 100
repetitions.
The distance of a given compound to the CM is defined as the minimum
number of reactions required for getting from that compound to any compounds
that belong to the CM (the shortest biodegradative pathway, or number of edges
visited in the directed graph described above). The assignation of compounds to
the CM was taken from UMBBD. The distance between two compounds is defined in
the same way. The diameter of the network is defined as the average distance
for all pairs of compounds. The distance of a given reaction (or enzyme) to the
CM is defined as the distance of its substrates to the CM.
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Acknowledgements
We acknowledge the maintainers of the databases used in this study,
which were invaluable sources of information for this work. We also acknowledge
U. Bastolla (Center for Astrobiology (INTA-CSIC)), M. Tress and R. Hoffmann
(Protein Design Group (CNB-CSIC)) for critical reading of and suggestions on
the manuscript. This work was supported by European contracts
QLK3-CT-2002-01933, QLK3-CT-2002-01923, QLRT-2001-00015 and INCO-CT-2002-1001,
by grants BIO2001-2274 and BIO2000-1358-C02-01 from the Spanish Comisión
Interministerial de Ciencia y Tecnología (CICYT) and by the Strategic
Research Groups Program of the Autonomous Community of Madrid.
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