What can genome-scale metabolic network reconstructions do for prokaryotic systematics?
It has recently been proposed that in addition to Nomenclature, Classification and Identification, Comprehending Microbial Diversity may be considered as the fourth tenet of microbial systematics [Staley JT (2010) The Bulletin of BISMiS, 1(1): 1-5]. As this fourth goal implies a fundamental understanding of microbial speciation, this perspective article argues that translation of bacterial genome sequences into metabolic features may contribute to the development of modern polyphasic taxonomic approaches. Genome-scale metabolic network reconstructions (GSMRs), which are the result of computationally predicted and experimentally confirmed stoichiometric matrices incorporating all enzyme and metabolite components encoded by a genome sequence, provide a platform that can illustrate bacterial speciation. As the topology and the composition of GSMRs are expected to be the result of adaptive evolution, the features of these networks may provide the prokaryotic taxonomist with novel tools for reaching the fourth tenet of microbial systematics. Through selected examples from the Actinobacteria, which have been inferred from GSMRs and experimentally confirmed after phenotypic characterisation, it will be shown that this level of information can be incorporated into modern polyphasic taxonomic approaches. In conclusion, three specific examples are illustrated to show how GSMRs will revolutionize prokaryotic systematics, as has previously occurred in many other fields of microbiology.
Evolution of Metabolic Diversity Laboratory, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), CINVESTAV-IPN, Km 9.6 Libramiento Norte, Carretera Irapuato-León, Irapuato, Mexico. fbarona@langebio.cinvestav.mx
Francisco
F
Barona-Gómez
Pablo
P
Cruz-Morales
Lianet
L
Noda-García
D039903:Actinobacteria / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D002965:Classification / Q000379:methods*; D016680:Genome, Bacterial; D053858:Metabolic Networks and Pathways / Q000235:genetics*