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Analysis of Gene Regulatory Networks of Maize in Response to Nitrogen.
Jiang, Lu; Ball, Graham; Hodgman, Charlie; Coules, Anne; Zhao, Han; Lu, Chungui.
Afiliação
  • Jiang L; Provincial Key Laboratory of Agrobiology, Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 20014, China. jldeer26@163.com.
  • Ball G; The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK. graham.ball@ntu.ac.uk.
  • Hodgman C; University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK. charlie.hodgman@nottingham.ac.uk.
  • Coules A; School of Animal Rural & Environmental Sciences, Nottingham Trent University, Nottingham NG25 0QF, UK. anne.coules@ntu.ac.uk.
  • Zhao H; Provincial Key Laboratory of Agrobiology, Institute of Agricultural Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 20014, China. zhaohan@jaas.ac.cn.
  • Lu C; School of Animal Rural & Environmental Sciences, Nottingham Trent University, Nottingham NG25 0QF, UK. chungui.lu@ntu.ac.uk.
Genes (Basel) ; 9(3)2018 Mar 08.
Article em En | MEDLINE | ID: mdl-29518046
ABSTRACT
Nitrogen (N) fertilizer has a major influence on the yield and quality. Understanding and optimising the response of crop plants to nitrogen fertilizer usage is of central importance in enhancing food security and agricultural sustainability. In this study, the analysis of gene regulatory networks reveals multiple genes and biological processes in response to N. Two microarray studies have been used to infer components of the nitrogen-response network. Since they used different array technologies, a map linking the two probe sets to the maize B73 reference genome has been generated to allow comparison. Putative Arabidopsis homologues of maize genes were used to query the Biological General Repository for Interaction Datasets (BioGRID) network, which yielded the potential involvement of three transcription factors (TFs) (GLK5, MADS64 and bZIP108) and a Calcium-dependent protein kinase. An Artificial Neural Network was used to identify influential genes and retrieved bZIP108 and WRKY36 as significant TFs in both microarray studies, along with genes for Asparagine Synthetase, a dual-specific protein kinase and a protein phosphatase. The output from one study also suggested roles for microRNA (miRNA) 399b and Nin-like Protein 15 (NLP15). Co-expression-network analysis of TFs with closely related profiles to known Nitrate-responsive genes identified GLK5, GLK8 and NLP15 as candidate regulators of genes repressed under low Nitrogen conditions, while bZIP108 might play a role in gene activation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article