NEW YORK (GenomeWeb News) – In a proof-of-principle paper in today's issue of Science, European researchers reported that they have developed a "reactome" array for assessing metabolic patterns in cells independently of genome sequence or gene expression data.
"We describe a sensitive metabolite array for genome sequence-independent functional analysis of metabolic phenotypes and networks, the reactomes, of cell populations and communities," co-senior author Manuel Ferrer, a researcher with the Institute of Catalysis in Madrid, Spain, and his colleagues wrote.
Untangling metabolic networks in cells or communities often relies on the availability of genome sequence data combined with bioinformatics approaches for finding homology between sequences in the organism of interest and previously characterized genes and proteins, the team noted.
But this approach is hindered by incomplete or inaccurate gene annotations, they noted, and it limits researchers' ability to rapidly assess metabolic profiles, particularly for organisms that have not been well characterized genetically.
"Functionally associating the metabolic profile obtained with the enzymes and pathways responsible still depends heavily on sequence-based metabolic reconstructions," Ferrer and his co-authors wrote. "There is thus a need for a new method to causally link metabolites with cognate enzymes, which, in addition to delivering global descriptions of metabolic responses to give environmental conditions, simultaneously provides annotation of the enzymes featured."
In an effort to simplify reactome analyses, the researchers developed an array comprised of 1,676 dye-linked substrates on a glass slide, representing components of major metabolic pathways shared by most organisms.
By applying bacterial cell extracts to the array, they could detect which metabolites were present based on telltale fluorescent signals generated in response to interactions between enzymes from the cells and metabolite substrates on the slide.
After showing that they could reconstruct metabolic pathways for the well-characterized bacteria Pseudomonas putida and Streptomyces coelicolor, the team tested the approach on microbial communities from three environments: an acidic volcanic pool, a deep-sea brine lake, and seawater polluted with hydrocarbons.
Indeed, the researchers were able to characterize and compare metagenome reactomes for each of the three sites. And, they say, a similar approach may also prove useful for assessing and reconstructing metabolic pathways in tissues, organs, organisms, or consortiums between different types of cells.
The reactome array "provides a 'metabolic barcode' that can be compared with those of other samples," the authors wrote. "Where genomic information is available, the array forges a link between metabolome and genome."