title: Correlation of Metabolic Variables with the Number of ORFs in Human Pathogenic and Phylogenetically Related Non- or Less-Pathogenic Bacteria.
abstract: To date, a few works have performed a correlation of metabolic variables in bacteria; however specific correlations with these variables have not been reported. In this work, we included 36 human pathogenic bacteria and 18 non- or less-pathogenic-related bacteria and obtained all metabolic variables, including enzymes, metabolic pathways, enzymatic steps and specific metabolic pathways, and enzymatic steps of particular metabolic processes, from a reliable metabolic database (KEGG). Then, we correlated the number of the open reading frames (ORF) with these variables and with the proportions of these variables, and we observed a negative correlation with the proportion of enzymes (r = -0.506, p < 0.0001), metabolic pathways (r = -0.871, p < 00.0001), enzymatic reactions (r = -0.749, p < 00.0001), and with the proportions of central metabolism variables as well as a positive correlation with the proportions of multistep reactions (r = 0.650, p < 00.0001) and secondary metabolism variables. The proportion of multifunctional reactions (r: -0.114, p = 0.41) and the proportion of enzymatic steps (r: -0.205, p = 0.14) did not present a significant correlation. These correlations indicate that as the size of a genome (measured in the number of ORFs) increases, the proportion of genes that encode enzymes significantly diminishes (especially those related to central metabolism), suggesting that when essential metabolic pathways are complete, an increase in the number of ORFs does not require a similar increase in the metabolic pathways and enzymes, but only a slight increase is sufficient to cope with a large genome.
authors.affiliation: Departamento de Fisiología, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara, Sierra Mojada #800, Colonia Independencia, 44340, Guadalajara, Jalisco, Mexico. anieljessica@hotmail.com.|Departamento de Ingeniería en Sistemas Computacionales y Automatización, Instituto de Investigación en Matemáticas Aplicadas y Sistemas (IIMAS), Universidad Nacional Autónoma de México (UNAM), Av. Universidad #3000, Ciudad Universitaria, C.P. 04510, Mexico City, Mexico. anieljessica@hotmail.com.
authors.forename: Aniel Jessica Leticia
authors.initials: AJ
authors.lastname: Brambila-Tapia
authors.affiliation: Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México (UNAM), C.P. 62210, Cuernavaca, Morelos, Mexico.
authors.forename: Augusto Cesar
authors.initials: AC
authors.lastname: Poot-Hernández
authors.affiliation: Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México (UNAM), C.P. 62210, Cuernavaca, Morelos, Mexico.
authors.forename: Jose Fernando
authors.initials: JF
authors.lastname: Garcia-Guevara
authors.affiliation: Departamento de Ingeniería en Sistemas Computacionales y Automatización, Instituto de Investigación en Matemáticas Aplicadas y Sistemas (IIMAS), Universidad Nacional Autónoma de México (UNAM), Av. Universidad #3000, Ciudad Universitaria, C.P. 04510, Mexico City, Mexico. katya.rodriguez@iimas.unam.mx.
authors.forename: Katya
authors.initials: K
authors.lastname: Rodríguez-Vázquez
mesh_terms: D001419:Bacteria / Q000145:classification / Q000235:genetics* / Q000378:metabolism* / Q000472:pathogenicity; D001424:Bacterial Infections / Q000382:microbiology*; D001426:Bacterial Proteins / Q000235:genetics* / Q000378:metabolism; D018628:Gene Dosage; D006801:Humans; D016366:Open Reading Frames; D010802:Phylogeny; D014774:Virulence
chemical_list: D001426:Bacterial Proteins