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Topological assessment of metabolic networks reveals evolutionary information.
Machicao, Jeaneth; Filho, Humberto A; Lahr, Daniel J G; Buckeridge, Marcos; Bruno, Odemir M.
Afiliación
  • Machicao J; São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, PO Box 369, 13560-970, Brazil.
  • Filho HA; São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, PO Box 369, 13560-970, Brazil.
  • Lahr DJG; Institute of Biosciences, University of São Paulo, São Paulo, SP, Brazil.
  • Buckeridge M; Institute of Biosciences, University of São Paulo, São Paulo, SP, Brazil.
  • Bruno OM; São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, PO Box 369, 13560-970, Brazil. bruno@ifsc.usp.br.
Sci Rep ; 8(1): 15918, 2018 10 29.
Article en En | MEDLINE | ID: mdl-30374088
Evolutionary information was inferred from the topology of metabolic networks corresponding to 17 plant species belonging to major plant lineages Chlorophytes, Bryophytes, Lycophytes and Angiosperms. The plant metabolic networks were built using the substrate-product network modeling based on the metabolic reactions available on the PlantCyc database (version 9.5), from which their local topological properties such as degree, in-degree, out-degree, clustering coefficient, hub-score, authority-score, local efficiency, betweenness and eigencentrality were measured. The topological measurements corresponding to each metabolite within the networks were considered as a set of metabolic characters to compound a feature vector representing each plant. Our results revealed that some local topological characters are able to discern among plant kinships, since similar phylogenies were found when comparing dendrograms obtained by topological metrics to the one obtained by DNA sequences of chloroplast genes. Furthermore, we also found that even a smaller number of metabolic characters is able to separate among major clades with high bootstrap support (BS > 95), while for some suborders a bigger content has been required.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Plantas / Evolución Molecular / Redes y Vías Metabólicas Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2018 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Plantas / Evolución Molecular / Redes y Vías Metabólicas Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2018 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido