Large-scale reconstruction and phylogenetic analysis of metabolic environments

Abstract

The topology of metabolic networks may provide important insights not only into the metabolic capacity of species, but also into the habitats in which they evolved. Here we introduce the concept of a metabolic network's "seed set"--the set of compounds that, based on the network topology, are exogenously acquired--and provide a methodological framework to computationally infer the seed set of a given network. Such seed sets form ecological "interfaces" between metabolic networks and their surroundings, approximating the effective biochemical environment of each species. Analyzing the metabolic networks of 478 species and identifying the seed set of each species, we present a comprehensive large-scale reconstruction of such predicted metabolic environments. The seed sets' composition significantly correlates with several basic properties characterizing the species' environments and agrees with biological observations concerning major adaptations. Species whose environments are highly predictable (e.g., obligate parasites) tend to have smaller seed sets than species living in variable environments. Phylogenetic analysis of the seed sets reveals the complex dynamics governing gain and loss of seeds across the phylogenetic tree and the process of transition between seed and non-seed compounds. Our findings suggest that the seed state is transient and that seeds tend either to be dropped completely from the network or to become non-seed compounds relatively fast. The seed sets also permit a successful reconstruction of a phylogenetic tree of life. The "reverse ecology" approach presented lays the foundations for studying the evolutionary interplay between organisms and their habitats on a large scale.

Fig. 1.

Identifying seed compounds in metabolic networks. (A) A schematic representation of the interaction of a metabolic network with its environment. Seed compounds must be externally acquired from the environment and are highlighted in red. (B) The procedure for identifying seed compounds is illustrated in a simple synthetic network. The network is decomposed into its SCC (illustrated as contour lines surrounding sets of nodes) using Kosaraju's algorithm (19). SCC decomposition reduces the seed detection problem to the simpler problem of detecting source components (i.e., components with no incoming edges) in a directed acyclic graph, where each source component forms a collection of candidate seed compounds. The source components are highlighted in red. The color saturation of the original nodes denotes the seed's confidence level, C (Materials and Methods), with a darker red indicating a higher confidence level. Although some of the seed compounds are easily identified (e.g., those forming the first step of an isolated and directed metabolic pathway), in a complex network the full set of seed compounds cannot easily be found without such a graph-theory algorithm. (C) The metabolic network of Buchnera with the seed compounds highlighted in red as in B. The seed set in this organism (which possesses a metabolic network of 314 occurring compounds) is composed of 61 chemicals (of these, only 38 have a confidence level C = 1). See also Table S2.

Fig. 2.

The size of seed sets across different phyla. (A) The average number of reactions and seed compounds across different bacterial taxonomic phyla. The number of seed compounds is estimated by the number of source components. Evidently, phyla that include mostly obligate intracellular parasites have, on average, the smallest metabolic networks and smallest seed sets. (B) The fraction of the occurring compounds included in the seed set as a function of the ratio between the number of transcription factors and the genome size, across prokaryotic phyla. Again, phyla of intracellular parasites (e.g., Rickettsias, Mollicutes, Spirochete, and Chlamydia) inhabiting well defined and predictable environments have small seed sets (even when normalized by the number of compounds in the network) and a small number of transcription factors. The solid line represents a linear regression. The strong correlation attests to the alignment between the size of the seed set and habitat variability.

Fig. 3.

Phylogenetic tree based on seed compounds content. 〉Bac〈, Bacteria (orange squares); 〉Arc〈, Archaea (cyan triangles); 〉Pla〈, plants (light green circles); 〉Ani〈, animals (blue circles); 〉Fun〈, fungi (dark green circles); 〉Pro〈, protists (purple circles).