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Keywords:

  • biodegradation;
  • bioremediation;
  • systems biology;
  • metabolic networks;
  • biological databases

Abstract

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References

Biodegradation, the ability of microorganisms to remove complex chemicals from the environment, is a multifaceted process in which many biotic and abiotic factors are implicated. The recent accumulation of knowledge about the biochemistry and genetics of the biodegradation process, and its categorization and formalization in structured databases, has recently opened the door to systems biology approaches, where the interactions of the involved parts are the main subject of study, and the system is analysed as a whole. The global analysis of the biodegradation metabolic network is beginning to produce knowledge about its structure, behaviour and evolution, such as its free-scale structure or its intrinsic robustness. Moreover, these approaches are also developing into useful tools such as predictors for compounds' degradability or the assisted design of artificial pathways. However, it is the environmental application of high-throughput technologies from the genomics, metagenomics, proteomics and metabolomics that harbours the most promising opportunities to understand the biodegradation process, and at the same time poses tremendous challenges from the data management and data mining point of view.


Biodegradation: a systemic process

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References

Biodegradation is the ability of microorganisms to convert complex organic compounds into simpler ones that can be integrated into the natural biogeochemical cycles. Hundred years of industrial activity have led to the emergence of new polluting compounds artificially synthesized and increasing concentrations of toxic compounds, challenging the capacity of the microbial communities to deal with them (Diaz, 2004).

Sometimes, the release of these compounds into the environment has produced unpredicted adverse effects. The second-generation insecticide DDT [2,2-bis (p-chlorophenyl)-1,1,1-trichloroethane] (Beard, 2006) went from being a benefactor of mankind in the 1970s to public enemy in the 1980s. Thus, although in 1948, the Swiss chemist Muller received the Nobel Prize in recognition of the impressive progress that this product had represented in the fight against diseases and pests, its slow conversion to nontoxic substances and its poor water solubility, which prevents it from being eliminated in urine and leads to its accumulation along the food chain, were in the long run significant problems that led to its final prohibition.

On other occasions, damage has been caused by a large accumulation of industrial waste or accidental spills. In 2002, the tanker ‘Prestige’ dumped thousands of tonnes of fuel off the coast of Galicia, contaminating a large area of the western European coasts. The consequences of this ecological disaster not only affected the marine life and the terrestrial ecosystem but also the economy of these coastal areas (Suris-Regueiro et al., 2007).

The stability of the chemical structure of many of these contaminants contributes significantly to their persistence in the biosphere. Fortunately for us, microorganisms have adapted to the presence of these pollutants by extending their metabolic capabilities to transform toxic compounds into other less toxic ones or in some cases to completely degrade them. In this process, microorganisms obtain a relative selective advantage by using them as carbon and energy sources.

While microorganisms genetically coding for biodegradation enzymes can have the capacity to transform these pollutants, this activity can be naturally attenuated by external factors such as the lack of essential nutrients, absence of adequate electron acceptors or inappropriate environmental conditions (i.e. pH, redox potential, humidity, temperature, etc.) (Rosenberg & Zon, 1996). The presence of very toxic components in the pollutant mixture that causes stress in the cell is another factor that, in many cases, contributes towards attenuation of the biodegradation potential (King, 1997). Even when biodegradation takes place, it is a very complex process commonly mediated by coordinated microbial communities, transferring substrates and products between them, a process known as metabolic cooperation (Pelz et al., 1999; Abraham et al., 2002). Horizontal gene transfer (HGT) also plays a key role by incorporating biodegrading capabilities into bacterial communities through direct transfer of catabolic genes (reviewed in van der Meer et al., 1992). Once incorporated into the organism, these genes have to be properly induced in response to the presence of the substrates and wired to the general physiological and regulatory network of the host genome (Cases & de Lorenzo, 2005b).

Humans have attempted to enhance biodegradation in those places where nature itself has difficulties coping with contamination, overcoming the limitations of natural systems with a combination of techniques generically known as ‘bioremediation’. Potentially, bioremediation can be applied to eliminate any compound that microorganisms can capture or absorb, as are all kinds of hydrocarbons (aliphatic, aromatic, BTEX, PAHs, etc.), chlorinated hydrocarbons (PCB, TCE, PCE, pesticides, herbicides, etc.), nitro aromatic compounds (TNT), organ phosphorus compounds, cyanides, etc. (Alexander, 2001). One of the bioremediation strategies is biostimulation, which aims to enhance the local bacterial populations by changes in the environment, by providing nutrients, aeration and other factors (e.g. a more adequate pH). This approach is valid as long as indigenous microorganisms are able to degrade the pollutant. Biostimulation is particularly useful in situations where degradation in situ is necessary, for example in inaccessible contaminated areas or where it is not possible to remove the spillage from the contaminated area, for example sea oil leaks. In other situations in which the local populations lack the capability to degrade the contaminants, the possible alternative is the introduction of specific microorganisms to improve biodegradation, a technique known as ‘bioaugmentation’ (Walter, 1997; Atlas & Unterman, 1999). The concept of bioaugmentation led to an intense search for microorganisms able to degrade more and more compounds, increasing the range of potential applications. Numerous bacterial species able to use the xenobiotic chemicals as carbon and energy sources were isolated. Also, many of the enzymes responsible for the degradation of contaminants were purified and characterized, setting forth the basic principles of biodegradation biochemistry.

In the early 1980s, genes that encode those enzymes began to be cloned and characterized. These efforts led Gunsalus in 1981 and Chakrabarty to patent a modified strain of Pseudomonas able to degrade camphor, octane, salicylate and naphthalene (US Patent # 4259444). Similarly, by the end of the 1980s, Timmis and collaborators demonstrated the possibility of incorporating new biodegradation skills into microorganisms, using recombinant DNA techniques and genetic material from various species under the control of sufficiently strong promoters (Ramos et al., 1987; Rojo et al., 1987). Following this methodology, they constructed a strain of Pseudomonas that eliminated recalcitrant compounds such as alkyl-chlorobenzoate, providing the possibility of using genetically modified organisms (GMOs) in bioremediation, a possibility that raised an interesting debate about the potential ecological risks (Lindow et al., 1989). To minimize these risks, a number of genetic devices were designed to minimize the lateral transfer of foreign genes, to track genes and modified strains and, ultimately, to schedule cell death after biocatalysis (Molin et al., 1993; Diaz et al., 1994; Ramos et al., 1994; Timmis & Pieper, 1999). Although genetic engineering has produced numerous strains capable of breaking down otherwise intractable contaminants in Petri dishes or bioreactors (Reineke, 1998; Mishra et al., 2001), the in situ applications have been fairly less positive (Sayler & Ripp, 2000; Diaz, 2004). GMOs have turned out to be less efficient, competitive and adapted than their natural unmanipulated counterparts (Strong et al., 2000). Moreover, artificially introduced genes and enzymes have to be integrated into the complex regulatory and metabolic network of the host organism in order to be properly expressed (Cases & de Lorenzo, 2005a).

Systems biology, network theory and biodegradation

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References

Although originally genetic modification appeared to be the solution to environmental pollution, it appears that biodegradation processes are framed in a complex web of metabolic and regulatory interactions, difficult to approach with the traditional molecular approaches (Cases & de Lorenzo, 2005b). The recent emergence of ‘omics’ technologies (genomics, proteomics and metabolomics) and the application of ideas and methods for network analysis have offered new insights into the biodegradation process with a new ‘systems biology’ perspective.

Systems biology has been set up to examine complex biological interactions and processes using a comprehensive approach (Kitano, 2001). This field began to develop in the 1960s, but it was not institutionalized until this century. It is clear that scientists have always been aware of the need to integrate the information produced by the detailed study of individual proteins and genes and that the reductionist approach was only the first step towards understanding the entire process of life. However, for a long time, the experimental procedures only allowed the analysis of a few proteins at a time. New techniques, and in particular large-scale approaches, have made a more comprehensive view possible, in which the system is not divided into parts to study them individually, but it is possible to look into the interactions between the parts and how they influence the behaviour of the system (Noble, 2006; Sauer et al., 2007). This systems biology vision of biology is, at least initially, highly related to application of the network theory to the study of functional properties, and to deciphering of the mechanisms involved in the organization of biological systems, with the ultimate goal of modelling and predicting their response to internal and external, for example environmental, variations (Feist et al., 2007; Feist & Palsson, 2008).

In the last few years, we have seen growing activity addressing the issues related to understanding of the structure of connections between the elements of various complex systems described at the level of their interactions, including social (networks of scientific collaboration or the World Wide Web), technological (network connections between routers) and finally biological systems (networks between genes or metabolic regulation). Elements are represented by nodes, and the relations between them by edges. The number of edges connected to a node can vary (node degree) and each one of them can have an associated numerical value (weight). Edges can have a direction, such as a catalytic reaction or a signal transduction step, or can be bidirectional, as in protein–protein interaction networks. The application of this concept to biological networks, and metabolic networks in particular, has allowed an initial interpretation of their structure, behaviour and evolution. Three properties of these networks have attracted considerable attention because they reveal the basic organizational principles of biological systems. Metabolic networks are often ‘scale-free’, ‘small world’ and hierarchical. While some of these properties are still controversial (see for instance Arita, 2005; Khanin & Wit, 2006), they provide a useful conceptual framework for the analysis of global biological properties.

The ‘scale-free’ nature of a metabolic network implies a high heterogeneity in the number of connections of their nodes (defined by the chemical compounds). While most of them present a low connectivity, that is, they participate in very few metabolic reactions, a few nodes, called hubs, have a high connectivity (Barabasi & Bonabeau, 2003). A particularly important property of scale-free networks is its robustness. Random removal of nodes in these networks is more likely to affect a node connected to the few other nodes than to a very connected one, thus preserving the basic structure and behaviour. That is, mutation of a randomly selected enzyme will most likely affect a peripheral pathway, having little effect on the general metabolism or the physiology of the cell. On the other hand, the selective removal of hubs can lead to abrupt changes in the system (Albert et al., 2000), or, in other words, the mutation of a main enzyme of a central pathway, such as the Krebs cycle, can be extremely deleterious for the cell. The existence of hubs in this kind of networks has been explained by a mechanism called ‘preferential coupling’. According to this model, ‘scale-free’ networks are the result of a growth process during which new nodes are added to the system by connecting to nodes that already have many links (Jeong et al., 2000). In biological terms, this means that a novel metabolic reaction would be more likely selected by evolution if the product can in turn be used in many other metabolic reactions.

Metabolic networks are also ‘small world’. Such networks are an intermediate state between a completely randomly connected network and very regular ones (those in which all nodes have a similar number of connections). In regular networks, if one node A is connected to two others, B and C, B and C also tend to be connected. The frequency with which this occurs is called the clustering coefficient. ‘Small world’ and regular networks have large clustering coefficients, while random networks present a small one. In turn, what separates regular and ‘small world’ networks is the average distance between nodes. While regular networks tend to have long average distances, random and ‘small world’ networks present short average distances between nodes. In other words, in ‘small world’ networks, nodes tend to form highly interconnected clusters that are well connected among them. In metabolic networks, these properties translate into the easy interconversion of metabolites and also into enhanced metabolic stability, because disturbances in the concentration of a particular metabolite are rapidly buffered by the whole metabolic network (Jeong et al., 2000; Wagner & Fell, 2001).

Finally, metabolic networks are hierarchical. This means that enzymes are clustered into interconnected groups working together to perform a relatively discrete function, and that these modules are connected among them by specific nodes that acquire a critical importance to maintain the flow in the full system. These nodes are normally highly connected, that is, they are hubs, and have the additional property of presenting a low clustering coefficient, or in other words, their neighbouring nodes are not normally connected among them (Ravasz et al., 2002). In biological systems, hierarchical modularity agreed with the notion that evolution can act at several levels of organization simultaneously: at the level of particular modules and at the level of interconnection of these modules.

Additionally, evolution has operated by copying existing modules, adapting them to relatively different tasks, which increases the complexity of the system/organism. In this way, hierarchical networks arise from duplication of nodes that form clusters, a process that, in principle, could be repeated indefinitely (Barabasi & Oltvai, 2004). On the other hand, the evolution of the integration between modules remains an open question, as does their impact on the network structure and behaviour (Parter et al., 2007; Tamames et al., 2007).

All these system biology approaches have been possible due to the wealth of knowledge accumulated on cellular metabolism, and its formalization and categorization in rich databases such as KEGG (Kanehisa et al., 2006) or Metacyc (Karp et al., 2002). In the particular case of biodegradation pathways, although numerous experimental studies have provided information about the biochemical reactions for individual biodegradation pathways (Warhurst et al., 1994; Seeger et al., 1995; Casellas et al., 1997), it was not until 1995 that the University of Minnesota pioneered the compilation of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD), collecting information on many biodegradation reactions and pathways of recalcitrant chemicals (Ellis et al., 2006). In the last version, this database contains information on over 900 compounds, over 600 enzymes, nearly 1000 reactions and about 350 microorganism entries. This resource includes data about bacterial species in which reactions have been described, the conditions under which they take place and bibliographic references.

It is interesting to note that typically systems biology approaches consider the ‘complete system’ to be a single cell, a formulation that is clearly insufficient in the case of biodegradation where the ‘system’ is a complex environment including multiple biotic and abiotic components. An initial step in this direction was the work of Pazos et al. (2003), which considered all the biodegradation reactions known as part of a single supraorganism metabolic network, assuming that metabolic activities and substrates and intermediate compounds flow freely in the environment. This extreme assumption is supported by several previous observations of biodegradation scenarios. On the one hand, the coordination of microbial communities to mediate biodegradation, the transference of substrates and products between species and communities, is well documented (Pelz et al., 1999; Abraham et al., 2002). Also, the importance of HGT as a mechanism that incorporates biochemical capabilities into bacterial communities through the direct transfer of catabolic genes (Dejonghe et al., 2000) and the overall consideration of the biodegradation communities as a pool of genes needed to carry out the degradation in different parts of the pathway have also been reported. Finally, atmospheric phenomena mobilize and disperse large amounts of polluting compounds to sites distant from their place of origin (Carrera et al., 2002). All these facts make credible the model proposed by Pazos and colleagues, which considers the biodegradation process as a single interconnecting network (metabolic cooperation) where the boundaries between bacterial species are blurred (easy to incorporate new capabilities by HGT) and without a precise geographical location (dispersion of pollutants). Another concept, this time from the original UM-BBD, was to add an abstract node to the network representing the central metabolism, a node that is used as a central point towards which the distances of all the other nodes can be calculated.

The first important observation studying this global biodegradation network was that it has a free-scale structure similar to other metabolic networks, allowing a rapid degradation of many compounds: the average distance to the central metabolism is only 3.3 steps. This observation, that the biodegradation network, a supraorganism metabolism, presents properties similar to single-organism ones, poses interesting questions on how they have evolved and what are the selection mechanisms at the ecosystem level. The study of the biodegradation network topology also revealed a funnel-like structure in which several pathways converge to a set of common intermediates, which constitute the hubs of the network, and that are placed closer to the central metabolism (Fig. 1). This property is specific to the biodegradation network and diverges for the hierarchical modularity of the general metabolism. This observation confirms and generalizes the early postulates of the seminal work of Ramos & Timmis (1987); besides, their importance as basic knowledge of the biodegradation process also has relevance for bioremediation applications, and as shown by Ramos and Timmis, when designing an artificial pathway using recombinant DNA technology, it is more favourable to link it to the general metabolism through one of the hub compounds.

image

Figure 1.  The global biodegradation networks of chemical compounds. Network constructed with information from UM-BBD and Metarouter. The larger circle represents the central metabolism. The linear pathways converging on particular intermediates forming a funnel topology can be easily observed. Overlaid are the trends described in Pazos and colleagues. Reactions in exterior part of the network are more rare, and present in less bacterial species, suggesting a more recent appearance in evolution.

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These two properties produce a new feature: robustness. Again, in this case, the robustness observed in the global biodegradation network is different from the robustness observed in the general metabolic networks (Jeong et al., 2000). Random removal of reactions from the biodegradation network reduces the number of biodegradable compounds, even more than expected: on average, the removal of one node induces 1.6 compounds to lose the pathway to the central metabolism. However, the distance of each remaining compound to the central metabolism is only slightly affected. This correlates with the general topology of the biodegradation network, with long linear paths converging in a funnel structure. Thus, single mutations tend to isolate upstream compounds from the central metabolism, but leave all others unaffected.

The global analysis of the biodegradation network shows the heterogeneity of the chemical properties of the compounds along the network. As expected for any catabolic network, larger and more hydrophobic compounds tend to accumulate far from the central metabolism, and both the molecular weight and the hydrophobicity of intermediate compounds decreased when we moved along the pathways towards the central metabolism. Also, enzymatic activities are not homogenously distributed in the network. Four activities concentrate close to the central metabolism: transferases, isomerases, hydrolases and ligases, the last one easily explained by their involvement in CoA binding to small intermediates.

The availability of categorized data on biodegradative reactions also allowed interesting system-level applications. One key contribution of UM-BBD in this area is the formalization of the available knowledge in ‘reaction rules’ that abstract particular reactions in general functional groups' metabolism, allowing the prediction of the most likely metabolic pathway a given compound will follow (Ellis et al., 2008). Other databases have followed the path provided by UM-BBD. In 2005, Pazos et al. compiled a database called Metarouter from the information available at UM-BBD (Pazos et al., 2005) and other sources. Metarouter enables finding pathways between compounds and the central metabolism and between one compound and another (or between two sets of compounds). The representation of possible pathways can be restricted according to a number of criteria: the length of the pathway, all the required enzymes present in a given organism(s), all the intermediate compounds having a range of values for a given property (e.g. highly soluble), etc. Based on the information contained in the Metarouter, in 2007, Gomez et al. (2007) studied the correlation between the frequency of common chemical triads in recalcitrant compounds and the ability of microorganisms to metabolize, developing a machine-learning system for predicting the metabolic fate of these compounds.

Evolution of the biodegradation metabolic network

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References

The very recent accumulation of toxic compounds produced by human industrial activity makes it very interesting to study how microorganisms have evolved and adapted their metabolic reactions and pathways in such a short time period (van der Meer et al., 1992; Wackett, 2004a). A considerable amount of information is available for some specific mechanisms. For instance, Copley suggested that the path of degradation of the pesticide pentachlorophenol in Sphingomonas chlorophenolica had probably been formed by the recruitment of at least one enzyme from the metabolic pathway of tyrosine, and its assembly with other common environmentally related pathways for the degradation of chlorinated phenols. This work also hypothesized that the poor catalytic efficiency of the pathway and its still rudimentary control mechanism, potentially related to the inhibition of some enzymes by their substrates, are signs of its recent assembly. One more sign of its recent evolution, they suggest, would be the lack of transcriptional regulation of the pathway, where all the enzymes are constitutively expressed (Copley, 2000).

How does this fit with the current models for metabolism evolution? Several theories have been proposed that would explain the formation and evolution of metabolic pathways (Schmidt et al., 2003). In 1945, Horowitz (1945, 1965) postulated the ‘retroevolution’ model in which pathways evolve ‘backwards’ from a key metabolite. In this case, an organism will use a metabolite X until it is exhausted. Being able to produce X from an available compound Y will give the organism an advantage because the pathway is already in place, a process that would repeat once Y is exhausted. Horowitz argues that the most likely enzymes to carry out the new reactions are the ones already present and bind similar compounds, and therefore enzymes of these pathways would have a similar binding substrate although different catalytic activities, each step requiring a different metabolic transformation (Rison & Thornton, 2002; Schmidt et al., 2003). Retro-evolution requires that useful organic compounds and potential precursors accumulate in the environment, a condition that might have been present at the beginning of life. In 1976, Jensen postulated a ‘patchwork model’ in which pathways evolved from existing enzymes with poor specificity. These are then combined to give rise to new pathways (Jensen, 1976). This theory becomes more likely as life becomes more complex and the number of enzymes that can be used increases significantly. Moreover, the repertoire of available catalytic activities becomes larger thanks to the ability of many enzymes to catalyse potentially useful secondary reactions. Lastly, a third model of evolution called ‘duplication of pathways’ suggests that blocks of several consecutive reactions duplicate and later diverge to give rise to a new function (Huynen & Snel, 2000; Rison & Thornton, 2002). This mechanism is favoured by the fact that in bacteria genes coding for enzymes from the same pathway are often clustered in the genome (operons), facilitating their simultaneous duplication. Several studies, including detailed phylogenetic, structural and cross-genome analyses, have shown that actually, retro-evolution plays a small role in the formation of the central metabolic network, and that the main mechanism for metabolic network expansion is the combination of existing activities (Teichmann et al., 2001a, b; Alves et al., 2002; Rison & Thornton, 2002; Schmidt et al., 2003).

Several observations suggest that the patchwork and duplication models also play an important role in the formation of biodegradation pathways, mediated by gene transfer, mutation, transposition and recombination (reviewed in van der Meer et al., 1992). The availability of categorized information about biodegradation reactions provided by UM-BBD or Metarouter allows addressing these questions with a broader scope. For instance, it has been described that in the Global Biodegradation Network that appears when all known biodegradation enzymes are combined in a single metabolic network, enzymes that are present in more organisms, and thus considered to be older, are often located close to the central metabolism, while newer ones – present in fewer organisms – do not present this bias (Pazos et al., 2003). This suggests that the biodegradation network grows from inside to outside, adding new enzymes to the periphery of the network, allowing more compounds to be biodegraded (Fig. 1). To attain progress in the study of evolution of the biodegradation network, it is essential to obtain sufficient genomic information that, when carefully analysed, can be used to clarify the homology relations between enzymes and to trace the origin of the biodegradation pathways.

Enzymes, reactions and sequences

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References

All network formulations for the study of biodegradation described above are based on chemical compounds connected by chemical reactions, with very little or no relation to the corresponding enzymes and proteins. This is the classical approximation to metabolic networks in general and it has produced valuable insights into the chemical structure and behaviour of the biodegradation and metabolic networks. However, analysis of the network in the absence of information about the corresponding enzymes has some limitations. We already mentioned the necessity of accurate sequence information to properly reconstruct the evolutionary history of biodegradation enzymes and pathways. Other aspects, such as the biochemical and genetic organization of enzymes and pathways and their relation to their metabolic functions, or their spreading in the environment, are difficult to address in the absence of sequence and genetic information. The biological relevance of these aspects is multiple. For instance, it has been suggested that the ability of many biodegradation enzymes to perform similar chemical reactions on apparently diverse and unrelated compounds, their unspecificity, is directly related to the capacity to evolve new activities, and more importantly, to the global performance of the biodegradation communities (Copley, 2003). A systematic approach to this question would require a proper connection between enzymes and the reactions they are able to catalyse. Curiously, even if this molecular information has been obtained for the many biodegradation enzymes, which have been cloned and sequenced, it is only partially included in biodegradation databases. Currently, they provide links based on the enzyme names or the Enzyme Commission (EC) codes. The EC code is a numerical classification scheme for enzymes, based on chemical reactions that catalyse and organize into four levels of detail, from more general (oxidoreductases, transferases, hydrolases, lyases, isomerase and ligases) to more specific (the substrate that transforms). It is important to note that, in many cases, the EC code is not sufficient to identify the biological entity (the protein complex) responsible for the reaction. For instance, over 60% of the reactions in UM-BBD share their EC code with another enzyme, while most of them cannot carry out the same transformations, all of which is a consequence of the ambiguity in the definition of these codes and the different criteria used by the annotators when they were assigned.

We have created a new resource, the Bionemo database (http://bionemo.bioinfo.cnio.es), in which protein complexes and their sequences have been associated with the biodegradation reactions by a careful manual inspection of the literature. The addition of molecular information also required a conceptual change in the structure of the information, a more biologically oriented organization in which reactions are connected if the product of one is the substrate of the other (Fig. 2a). This alternative network has the same properties as the compound network, is scale-free, nonhierarchical and with a funnel structure, as was expected for a direct transformation of the compound network (Trigo et al., 2006).

image

Figure 2.  The global biodegradation networks of chemical reactions. (a) Schematic representation of the transformation from the compounds network to the reaction network. Two reactions are connected if the product of one can be the substrate of the other. Note that the overall topology of the network is preserved. (b) The network of biodegradation reactions. The larger circle represents the central metabolism. Nodes are greyed according to the level of molecular characterization, white for those reactions for which no protein sequence is known, light grey for those for which a protein sequence is known, dark grey if in addition that sequence contain a functional domain defined in the Pfam database, and black if the structure of the protein has been determined.

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With this molecular information, Bionemo covers about half of the entries included in UM-BBD or Metarouter (Carbajosa et al., 2008), but this information is not homogeneously distributed along the network (Fig. 2b). For instance, enzymes that participate in the full metabolization of a compound, that is, that connect it to the central metabolism, are more often characterized at the molecular level than those that participate in biotransformation pathways not connected to the central metabolism. Similarly, enzymes that are located closer to the central metabolism are more likely sequenced than those in the periphery of the network, as are enzymes that belong to shorter pathways compared with those that belong to longer ones (Trigo et al., 2006).

Bionemo attempts to open new opportunities for the systematic study of the biodegradation network, including the key process of transcription regulation of biodegradation pathways. Transcription regulation has been reported as one of the limiting factors in biodegradation. For instance, it is not uncommon that some chemical compounds, while being substrates of biodegradation pathways, fail to properly induce the transcription of the genes coding for the enzymes required for its metabolism, accumulating in the environment. Bionemo collects the information accumulated with years of experimental research of transcription regulation of biodegradation pathways (Tropel & van der Meer, 2004) and it offers information about specific transcription factors and inducers in the context of biochemical data (Carbajosa et al., 2006). Although this information is useful, it is known to be insufficient to understand the complex regulation of biodegradation, because other global regulatory blockages are often present, commonly related to the physiological state of the cell. For example, recently, stress has been found to be a key factor regulating biodegradation (Cases & de Lorenzo, 2005a). The interplay between biodegradation, the general physiology and the stress state of the cell is beginning to be understood thanks to the complete sequencing of biodegrading strains (Mongodin et al., 2006; Schneiker et al., 2006) and the study of global responses to the exposure to chemical contaminants (Velazquez et al., 2006).

Future directions

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References

The application of systems biology principles and approaches to the study of biodegradation is in its very early stages, and the initial results look promising. However, we can already envision some of the critical challenges that lie ahead of us towards the full understanding of the biodegradation process. We still have limited information about the enzymes and organisms involved, particularly in the real scenario in which biodegradation takes place. Until very recently, most of our knowledge was gathered under laboratory conditions, using monocultures and recalcitrant compounds as the only energy and carbon sources. New technologies such as stable isotope probing and similar methods from microbial ecology (Wackett, 2004b) and soil metabonomics using isotope distribution analysis (Villas-Boas & Bruheim, 2007) are allowing us to follow biodegradation of compounds in the natural environment, identifying intermediates and the bacterial populations that carry each step. Also, molecular fingerprinting techniques based on DNA arrays are allowing a better characterization of the environment, allowing precise profiling of the local bacterial population and their variations in time, or in response to human interventions (Yin et al., 2007). In principle, and thanks to the availability of more and more sequences, it would be possible to develop ‘diagnostic arrays’ that are able to detect the presence of particular enzymes in the environment, either by representing the sequence variability in the array (Rhee et al., 2004) or by the use of degenerate oligos designed to recognize particular enzymatic activities (Nyyssonen et al., 2006). Also, proteomic technologies are starting to be used for the enzymatic characterization of a specific environment (Zhao & Poh, 2008)

However, despite having a strong potential, it should be noted that these approaches are only able to identify the presence of previously characterized enzymes and species, However, many of the bacterial strains that were isolated in the laboratory as good ‘biodegraders’ do not seem to play such an important role under natural conditions (Wackett, 2004b), and it will not be surprising that many yet unknown species contribute to biodegradation, particularly in view of the large microbial diversity (Curtis et al., 2002), for which it has been estimated that <1% of the natural bacterial population can be cultured in the laboratory (Torsvik & Ovreas, 2002; Torsvik et al., 2002). A number of metagenomics tools allow us to access this diversity, and are bringing more and more enzymes to the table, although many times, because they are identified by sequence similarity or known enzymatic assays, they extend very little of our previous knowledge (Pieper et al., 2004; Deutschbauer et al., 2006). It might well be that the vastness of information to be obtained would require new experimental strategies for finding novel reactions, such as the utilization of intracellular biosensors (Galvao et al., 2005; Wackwitz et al., 2008). An interesting approach is the use of in vitro evolved transcription regulators able to recognize novel compounds, which can potentially also be used to increase the metabolic potential of bacterial strains (Garmendia et al., 2008).

As described above, new approaches are allowing us to gather more complete and diverse information about the biodegradation process in the real environment where it takes places. The diversity of the data, which include sequence information, metabolic data, microbial population diversity or physicochemical conditions, presents a tremendous challenge from the data management and data mining points of view. A pioneering effort to manage this rich information environment is the CAMERA project, which provides a metagenomics dataset with rich metadata that include geographical location, sampling procedures and other interesting additional information (Seshadri et al., 2007).

In summary, we have seen that the combination of high-throughput technologies, genomics, metagenomics, proteomics and metabolomics, applied in novel and imaginative ways, together with proper data management and rich metadata tagging, could in the future allow us to address even more interesting and challenging aspects of the complex process of biodegradation, and potentially enable the application of systems biology concepts to the field of biodegradation. The synergy of all these data could provide alternative tools to deal with one of the major problems in the modern world: chemical pollution.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References

This work was funded by the COMBIO, ENFIN and EMERGENCE EU projects, the pSYSMO project within the SYSMO Framework and the Fundación Banco Bilbao Vizcaya Argentaria (FBBVA-BIOCON-3). I.C. is a member of the Ramón y Cajal Program of the Spanish Ministry of Education and Science. We thank Prof. Victor de Lorenzo, Dr Florencio Pazos and Guillermo Carbajosa for useful discussions and comments.

References

  1. Top of page
  2. Abstract
  3. Biodegradation: a systemic process
  4. Systems biology, network theory and biodegradation
  5. Evolution of the biodegradation metabolic network
  6. Enzymes, reactions and sequences
  7. Future directions
  8. Acknowledgements
  9. References
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