Differences between some biological species are reflected by a unique and distinctive pattern that represents the way in which chemical interactions occur in their cells, according to new University of Michigan research that looks at metabolism of different biological species. The U-M findings appear as a cover story in the March 2013 issue of “Molecular BioSystems”.
“Our discovery may help lay the groundwork for a novel approach to study evolution, based on the differences in metabolic reactions” says one of lead authors, Santiago Schnell, Ph.D., associate professor of molecular and integrative physiology at the U-M Medical School.
“We carried out a comparative analysis of the metabolic reactions of biological species across six kingdoms of life. In our study we were able to find a unique metabolic fingerprint between species , but also across cellular organelles” says the other lead author, Charles Burant, M.D., Ph.D., professor of internal medicine at the U-M Health System.
The research team implemented a computer analysis to disentangling the complexities of metabolism, such as the way in which proteins interact in our body’s cells. The computational approach relies in a network analysis. “Networks are ubiquitous in the world; we find them in transportation systems, the internet, electronic circuits and biological interactions” says co-author Erin Shellman, Ph.D., a recent graduate of the U-M Bioinformatics Program.
The U-M scientists represented metabolism reactions as networks. The metabolic fingerprinting requires extracting the basic structural elements of the networks, which are called “motifs”. These are essentially patterns of interactions between chemical species. Motifs that occur in significantly larger numbers in the metabolic networks than in randomized networks made the metabolic fingerprint.
“Our results show that the distribution of motifs provides a unique and distinctive pattern, which characterizes molecular differences between some biological species” says the team. Importantly, their approach reveals a previously unrecognized role of metabolic chemical interactions to study the evolution of biological species.