Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways.
BACKGROUND
Pathways provide topical descriptions of cellular circuitry. Comparing analogous pathways reveals intricate insights into individual functional differences among species. While previous works in the field performed genomic comparisons and evolutionary studies that were based on specific genes or proteins, whole genomic sequence, or even single pathways, none of them described a genomic system level comparative analysis of metabolic pathways. In order to properly implement such an analysis one should overcome two specific challenges: how to combine the effect of many pathways under a unified framework and how to appropriately analyze co-evolution of pathways. Here we present a computational approach for solving these two challenges. First, we describe a comprehensive, scalable, information theory based computational pipeline that calculates pathway alignment information and then compiles it in a novel manner that allows further analysis. This approach can be used for building phylogenies and for pointing out specific differences that can then be analyzed in depth. Second, we describe a new approach for comparing the evolution of metabolic pathways. This approach can be used for detecting co-evolutionary relationships between metabolic pathways.


RESULTS
We demonstrate the advantages of our approach by applying our pipeline to data from the MetaCyc repository (which includes a total of 205 organisms and 660 metabolic pathways). Our analysis revealed several surprising biological observations. For example, we show that the different habitats in which Archaea organisms reside are reflected by a pathway based phylogeny. In addition, we discover two striking clusters of metabolic pathways, each cluster includes pathways that have very similar evolution.


CONCLUSIONS
We demonstrate that distance measures that are based on the topology and the content of metabolic networks are useful for studying evolution and co-evolution.
Mano
Adi
A
Dept, of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel. amano@cs.technion.ac.il
Tuller
Tamir
T
Béjà
Oded
O
Pinter
Ron Y
RY
D001105:Archaea / Q000378:metabolism; D019143:Evolution, Molecular; D016678:Genome; D053858:Metabolic Networks and Pathways; D010802:Phylogeny; D011506:Proteins / Q000235:genetics / Q000378:metabolism; D013045:Species Specificity
D011506:Proteins