VCU Scholars Compass Study of Biological Complexity Publications Center for the Study of Biological Complexity 2010 Evolution of metabolic network organization

Background: Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints. Results: We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints. Conclusions: Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems. Background Networks are commonly used in biology to represent molecular mechanisms occurring in the cell, from protein-protein interactions to enzymatic reactions (metabolic networks) and gene regulations (gene regulatory networks). The use of tools derived from graph theory has led to the successful characterization of some aspects of the topology and organization of these networks and the intracellular mechanisms they represent. The study of intracellular networks so far has revealed their smallworldness [1] and scale-freeness [2], as well as the existence of network motifs, [3,4] and a strongly modular organization [5-8]-see Barabasi et al. [9] for a review. Network representations are also increasingly used to characterize the function of the objects (genes, proteins, metabolites) they interconnect. This has lead to applications such as inference of functional annotation [10] or predictions of new disease-related or longevity-related genes [11,12]-see Aittokallio et al. [13] for a review. Networks are also used in comparative studies to highlight the differences and similarities existing in the organization of the intracellular mechanisms of multiple species. Studies have been published that compare the topological features of metabolic networks for taxa sampled from all three kingdoms of life [14-17]. These investigations shed light on how these metabolic networks differ among the archaea, bacteria and eukarya, as a result of natural selection processes [18]. The results show that while some properties are shared by all taxa (e.g., their metabolic networks are all scale-free), bacteria species distinguish themselves over archaea and eukarya by having a shorter network average path length [15,16]. * Correspondence: ajmazurie@vcu.edu 1 Institut Pasteur, Systems Biology Lab, Department of Genomes and Genetics, F-75015 Paris, France Full list of author information is available at the end of the article Mazurie et al. BMC Systems Biology 2010, 4:59 http://www.biomedcentral. com/1752-0509/4/59 Page 2 of 10 Metabolic networks of archaea species also exhibit a lower average clustering coefficient, betweenness centrality and scale-freeness [17] than those of other species. Finally, the organization of the metabolism of species appears to be impacted by the species lifestyle and phenotype; e.g., by the variability of their habitat [19], or their optimal growth temperature [20]. Metabolic networks of modern organisms have also been compared to inferred ancestral ones [21]. These studies indicate that niche specialization and extreme environments tend to decrease the modularity of the metabolic networks of taxa. A comparison of intracellular networks on the basis of their topological similarity is thus a promising approach to study the evolution of species. It complements comparative genomics strategies that focus on comparison of gene sequence and genome architecture. However, while metabolic networks are a useful representation of the metabolic capabilities of the cell, they are of low organizational level, i.e., at the level of individual enzymatic reactions. We propose here to proceed from higher-level organizational principles of cell metabolism, and determine how these principles change under evolutionary pressures. Results and Discussion Comparison of metabolism organization Proceeding from publicly available annotations, different groups of taxa were selected for this study as representative of environments or lineages of particular evolutionary interest. Hence, the organization of the metabolism of taxa from the three kingdoms archaea, bacteria and eukarya was compared, with an additional comparison of the superkingdom prokarya and the kingdom eukarya. Unicellular eukarya were opposed to multicellular eukarya to reveal how the more stable environment and cellular differentiation that a multicellular body implies impacted the organization of metabolism. Free-living bacteria were compared to host-associated bacteria, as taxa from the latter group typically inhabit less demanding environments-the host providing most nutrients, while limiting competitors and predators. Immotile bacteria were compared to motile ones, motility being a key competitive advantage allowing an active search for nutrients and evasion from predators or harmful environments. Aerobic bacteria were compared to anaerobes and to facultative aerobic bacteria as a potential negative control; in Raymond et al. [22], the authors demonstrated that aerobic metabolism had little impact on the rewiring of metabolic network structure during evolution, although network size was affected. Finally, bacteria living in environments of distinct salinity (halophiles versus halotolerants) and temperature (mesophiles versus psychrophiles and thermophiles) were compared, to assess any large-scale adaptation of their metabolic network to these environments. Each group of taxa was thus selected for being a result of evolutionary pressures, and opposed to other groups either lacking their most prominent characteristics, or subject to distinct evolutionary pressures. Networks of interacting pathways (NIPs) were constructed to represent the high-level organization of cellular metabolism in all taxa as described in Mazurie et al. [23], using the latest version of the KEGG metabolic reactions database [24]. Then, for all taxa group comparisons considered, classification models were built to assess the effectiveness of a comprehensive set of 52 quantitative descriptors of the structure and complexity of NIPs in distinguishing the groups. Briefly, classification models were trained to predict the group to which a taxon belongs solely from the values of its NIP descriptors. Good predictions, as assessed by the accuracy and Kappa statistics of 10-fold cross-validation, would indicate that a sufficiently large difference exists between the NIPs of the different taxa groups to allow an accurate classification. Results are shown on Table 1. The high scores obtained for most of the taxa group comparisons confirm that distinct evolutionary pressures are reflected by distinct organizational principles of the metabolism of species. Our results confirm the finding from Raymond et al. [22] showing a lack of significant differences between metabolic networks of aerobic versus anaerobic species. We also show that no distinction could be made between bacteria living in an environment of distinct salinity or distinct temperature, based on the topology of their NIPs. In the first case, this could be explained by a lack of representative taxa for halotolerant bacteria (only 4, compared to 15 halophiles). In the second case, the large number of representative taxa suggests a genuine absence of significant differences in the structure and complexity of NIPs of psycrophilic, mesophilic and thermophilic bacteria. Significant changes of metabolism organization We first investigated the differences in the structure and complexity of the NIPs of species from different lineages or environments. Among the 52 NIP descriptors used for this investigation, we identified using feature selection algorithms (see Methods) the smallest subsets of descriptors that best discriminate taxa. The scores obtained are reported in Table 1. A list of these best discriminating descriptors of metabolic organization, as well as the average value obtained for each group of taxa, is given in Figure 1; values for all descriptors are provided as Additional file 1. Prokarya versus eukarya The transition from archaea and bacteria to eukarya is associated with an increase in the NIP size as revealed by the network radius, diameter, and the average distance between pathways. The weighted edge and vertex Mazurie et al. BMC Systems Biology 2010, 4:59 http://www.biomedcentral. com/1752-0509/4/59 Page 3 of 10 betweenness centrality (fractio