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An inference approach combines spatial and temporal gene expression data to predict gene regulatory networks in Arabidopsis stem cells

Maria Angels de Luis Balaguer, Adam P. Fisher, Natalie M. Clark, Maria Guadalupe Fernandez-Espinosa, Barbara K. Möller, Dolf Weijers, Jan U. Lohmann, Cranos Williams, Oscar Lorenzo, Rosangela Sozzani
doi: https://doi.org/10.1101/140269
Maria Angels de Luis Balaguer
1Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA.
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Adam P. Fisher
1Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA.
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Natalie M. Clark
1Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA.
2Biomathematics Program, North Carolina State University, Raleigh, NC, USA.
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  • For correspondence: ross_sozzani@ncsu.edu
Maria Guadalupe Fernandez-Espinosa
3Departamento de Botánica y Fisiología Vegetal, Instituto Hispano-Luso de Investigaciones Agrarias (CIALE), Facultad de Biología, Universidad de Salamanca, C/Río Duero 12, 37185 Salamanca, Spain
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Barbara K. Möller
4Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703HA, Wageningen, The Netherlands.
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Dolf Weijers
4Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703HA, Wageningen, The Netherlands.
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Jan U. Lohmann
5Department of Stem Cell Biology, University of Heidelberg, Heidelberg D-69120, Germany
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Cranos Williams
6Electrical and Computer Engineering Department, North Carolina State University, Raleigh, NC, USA.
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Oscar Lorenzo
3Departamento de Botánica y Fisiología Vegetal, Instituto Hispano-Luso de Investigaciones Agrarias (CIALE), Facultad de Biología, Universidad de Salamanca, C/Río Duero 12, 37185 Salamanca, Spain
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Rosangela Sozzani
1Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA.
2Biomathematics Program, North Carolina State University, Raleigh, NC, USA.
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  • For correspondence: ross_sozzani@ncsu.edu
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Abstract

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is key for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. For this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network (GRN) inference algorithm that combines clustering with Dynamic Bayesian Network (DBN) inference. We leveraged the topology of our networks to infer potential key regulators. The results presented in this work show that our combination of molecular biology approaches, computational biology and mathematical modeling was key to identify candidate factors that function in the stem cells. Specifically, through experimental validation and mathematical modeling, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center (QC) function.

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Posted May 19, 2017.
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An inference approach combines spatial and temporal gene expression data to predict gene regulatory networks in Arabidopsis stem cells
Maria Angels de Luis Balaguer, Adam P. Fisher, Natalie M. Clark, Maria Guadalupe Fernandez-Espinosa, Barbara K. Möller, Dolf Weijers, Jan U. Lohmann, Cranos Williams, Oscar Lorenzo, Rosangela Sozzani
bioRxiv 140269; doi: https://doi.org/10.1101/140269
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An inference approach combines spatial and temporal gene expression data to predict gene regulatory networks in Arabidopsis stem cells
Maria Angels de Luis Balaguer, Adam P. Fisher, Natalie M. Clark, Maria Guadalupe Fernandez-Espinosa, Barbara K. Möller, Dolf Weijers, Jan U. Lohmann, Cranos Williams, Oscar Lorenzo, Rosangela Sozzani
bioRxiv 140269; doi: https://doi.org/10.1101/140269

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