title: The alignment of enzymatic steps reveals similar metabolic pathways and probable recruitment events in Gammaproteobacteria.
abstract: BACKGROUND
  It is generally accepted that gene duplication followed by functional divergence is one of the main sources of metabolic diversity. In this regard, there is an increasing interest in the development of methods that allow the systematic identification of these evolutionary events in metabolism. Here, we used a method not based on biomolecular sequence analysis to compare and identify common and variable routes in the metabolism of 40 Gammaproteobacteria species.
  
  
  METHODS
  The metabolic maps deposited in the KEGG database were transformed into linear Enzymatic Step Sequences (ESS) by using the breadth-first search algorithm. These ESS represent subsequent enzymes linked to each other, where their catalytic activities are encoded in the Enzyme Commission numbers. The ESS were compared in an all-against-all (pairwise comparisons) approach by using a dynamic programming algorithm, leaving only a set of significant pairs.
  
  
  CONCLUSIONS
  From these comparisons, we identified a set of functionally conserved enzymatic steps in different metabolic maps, in which cell wall components and fatty acid and lysine biosynthesis were included. In addition, we found that pathways associated with biosynthesis share a higher proportion of similar ESS than degradation pathways and secondary metabolism pathways. Also, maps associated with the metabolism of similar compounds contain a high proportion of similar ESS, such as those maps from nucleotide metabolism pathways, in particular the inosine monophosphate pathway. Furthermore, diverse ESS associated with the low part of the glycolysis pathway were identified as functionally similar to multiple metabolic pathways. In summary, our comparisons may help to identify similar reactions in different metabolic pathways and could reinforce the patchwork model in the evolution of metabolism in Gammaproteobacteria.
authors.affiliation: Departamento de Microbiología Molecular, Instituto de Biotecnología, UNAM, Av. Universidad 2001, Cuernavaca, Morelos, CP 62210, México. apoot@ibt.unam.mx.|Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, UNAM, Ciudad Universitaria, CP 04510, México D.F., México. apoot@ibt.unam.mx.
authors.forename: Augusto Cesar
authors.initials: AC
authors.lastname: Poot-Hernandez
authors.affiliation: Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, UNAM, Ciudad Universitaria, CP 04510, México D.F., México. katya.rodriguez@iimas.unam.mx.
authors.forename: Katya
authors.initials: K
authors.lastname: Rodriguez-Vazquez
authors.affiliation: Departamento de Microbiología Molecular, Instituto de Biotecnología, UNAM, Av. Universidad 2001, Cuernavaca, Morelos, CP 62210, México. erueda@ibt.unam.mx.
authors.forename: Ernesto
authors.initials: E
authors.lastname: Perez-Rueda
mesh_terms: D000465:Algorithms; D019143:Evolution, Molecular; D020563:Gammaproteobacteria / Q000201:enzymology / Q000378:metabolism*; D006019:Glycolysis; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D016415:Sequence Alignment; D013045:Species Specificity