Evolution of metabolic network organization

Abstract

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.

Figure 1

Values of the NIP descriptors (abridged). For each group comparison, the descriptors of the structure and complexity of NIPs reported are those shown to best discriminate the different groups of taxa considered. Bars represent the average value and standard deviation of a given descriptor for each group of taxa, based on metabolic networks extracted from KEGG. The hypothesis that the descriptor value is the same over all groups was evaluated for each metabolic dataset either by a Kruskal-Wallis test (comparisons of three groups) or Mann-Whitney U test (comparisons of two groups). Resulting p-values were corrected for multiple testing using Bonferroni correction. Values for all 52 descriptors are available in Additional file 1. A.I., N.I., T.I.: Average, Normalized and Total Information, respectively.

Figure 2

Effects of lineage and environment on pathway frequency, connectivity and centrality. The amplitude of variation of six scores of frequency, connectivity and centrality, and a p-value evaluating the significance of this variation are reported for all metabolic pathways. P-values were calculated by either a Fisher's Exact Test (frequency) or Mann-Whitney U-text (connectivity and centrality), and corrected for multiple testing using the Benjamini-Hochberg method [31]. The median variation and the p-value for pathways in the same functional category were pictured as either a triangle or a diamond. A triangle pointing left (◀) means the score increases from left to right (e.g., from Prokarya to Eukarya), while a triangle pointing right (▶) means the score decreases from left to right. A diamond means the score does not change. The position of the symbol is proportional to the median corrected p-value, i.e. False Discovery Rate, and its size is proportional to the amplitude of variation. All values are available in Additional file 2. B.C.: Betweenness Centrality.