Phylogenetic distances are encoded in networks of interacting pathways

Abstract

Motivation: Although metabolic reactions are unquestionably shaped by evolutionary processes, the degree to which the overall structure and complexity of their interconnections are linked to the phylogeny of species has not been evaluated in depth. Here, we apply an original metabolome representation, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies, to address this question. NIPs compress the information of the metabolic network exhibited by a species into much smaller networks of overlapping metabolic pathways, where nodes are pathways and links are the metabolites they exchange.

Results: Our analysis shows that a small set of descriptors of the structure and complexity of the NIPs combined into regression models reproduce very accurately reference phylogenetic distances derived from 16S rRNA sequences (10-fold cross-validation correlation coefficient higher than 0.9). Our method also showed better scores than previous work on metabolism-based phylogenetic reconstructions, as assessed by branch distances score, topological similarity and second cousins score. Thus, our metabolome representation as network of overlapping metabolic pathways captures sufficient information about the underlying evolutionary events leading to the formation of metabolic networks and species phylogeny. It is important to note that precise knowledge of all of the reactions in these pathways is not required for these reconstructions. These observations underscore the potential for the use of abstract, modular representations of metabolic reactions as tools in studying the evolution of species.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Extraction of NIPs from metabolic networks. A list of all metabolites processed is compiled for each pathway known to exist in a given species; example is given here of fictive metabolites A, B and C processed in three metabolic pathways. Pathways that use or produce metabolites found in other pathways are linked together (shaded lines). Links are weighted by the number of metabolites exchanged.

Fig. 2.

General approach. (A) For any given species, metabolic networks are extracted and descriptors of their structure and complexity are calculated. Network-based distances between all pairs of species are derived from the NIP and NIM descriptors. In this example, the three descriptor values are numerical and the resulting distances are the arithmetic difference. (B) Correlation between network-based distances and 16S-based phylogenetic distance is measured by training regression models. The models heuristically search for combinations of network-based distances best predicting the phylogenetic distance. Performance is expressed as the Pearson's correlation coefficient between known and predicted phylogenetic distances.

Fig. 3.

Example of NIP. NIPs extracted from the filtered KEGG metabolic dataset for Saccharomyces cerevisiae. Node shade is proportional to the number of metabolic pathways overlapping with the represented one. Edge shade is proportional to the number of metabolites exchanged.

Fig. 4.

Example of predicted tree. Example of phylogenetic tree predicted from descriptors of NIPs from the unfiltered version of the Ma dataset, for all 80 taxa considered in this study (right). For comparison, the tree resulting from the 16S sequences is also given (left). These two trees show minor differences (highlighted in bold) as indicated by the high similarity scores obtained on subsets of taxa (Table 2). Branch lengths are displayed as equal for the purpose of display.