Bioinformatic analysis of Mycobacterium tuberculosis metabolism under hypoxic conditions

Descripción del Articulo

Objective: To predict by using bioinformatic tools Mycobacterium tuberculosis (MT) metabolic pathways under hypoxic conditions. Design: Biology analysis. Setting: Instituto de Química Biológica, Microbiología y Biotecnología Marco Antonio Garrido Malo Biological, Microbiologic and Biotechnologic Che...

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Detalles Bibliográficos
Autores: Solís, Christian, Negrón, Luisa
Formato: artículo
Fecha de Publicación:2008
Institución:Universidad Nacional Mayor de San Marcos
Repositorio:Revistas - Universidad Nacional Mayor de San Marcos
Lenguaje:español
OAI Identifier:oai:ojs.csi.unmsm:article/1135
Enlace del recurso:https://revistasinvestigacion.unmsm.edu.pe/index.php/anales/article/view/1135
Nivel de acceso:acceso abierto
Materia:Mycobacterium tuberculosis
hipoxia
latencia
vías metabólicas.
hypoxia
latency
metabolic pathways.
Descripción
Sumario:Objective: To predict by using bioinformatic tools Mycobacterium tuberculosis (MT) metabolic pathways under hypoxic conditions. Design: Biology analysis. Setting: Instituto de Química Biológica, Microbiología y Biotecnología Marco Antonio Garrido Malo Biological, Microbiologic and Biotechnologic Chemistry Institute, Faculty of Pharmacy and Biochemistry, UNMSM. Biologic material: Mycobacterium tuberculosis genes. Methods: The study began with the selection of 355 genes of MT H37Rv whose expression has been shown by other studies is induced by hypoxic conditions and 359 genes whose expression was repressed. Up and down expressed genes were comparatively analyzed, localizing genes of each group within the MT genome and predicting some physicochemical properties (isoelectric point and hydrophobic moment) for their protein products. In order to assign a metabolic or regulatory pathway to each gene, Kyoto Encyclopedia of Genes and Genomes (KEGG) and PATH-A sequence analysis tool were used. Main outcome measures: Metabolic pathways in Mycobacterium tuberculosis genes under hypoxia conditions. Results: From the initial 355 up expressed genes, it was possible to assign metabolic pathways to only 95 using KEGG and 57 using PATH-A. Conclusions: There were no differences between up and down expressed genes for their genome distribution and values for studied physicochemical properties of their protein products. The comparative analysis of the assigned metabolic pathways to down and up-expressed genes revealed that under hypoxic conditions several metabolic pathways related to ATP spent were down-expressed, being induced some genes whose proteins participate in central metabolism pathways such as the pyruvate metabolism, glycolysis and citric acid cycle.
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