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Research Article

Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells

Maria Angels de Luis Balaguer, Adam P. Fisher, Natalie M. Clark, Maria Guadalupe Fernandez-Espinosa, Barbara K. Möller, Dolf Weijers, View ORCID ProfileJan U. Lohmann, Cranos Williams, Oscar Lorenzo, and Rosangela Sozzani
  1. aPlant and Microbial Biology Department, North Carolina State University, Raleigh, NC 27695;
  2. bBiomathematics Program, North Carolina State University, Raleigh, NC 27695;
  3. cDepartamento de Botánica y Fisiología Vegetal, Instituto Hispano-Luso de Investigaciones Agrarias, Facultad de Biología, Universidad de Salamanca, 37185 Salamanca, Spain;
  4. dLaboratory of Biochemistry, Wageningen University, 6703HA, Wageningen, The Netherlands;
  5. eDepartment of Stem Cell Biology, University of Heidelberg, Heidelberg D-69120, Germany;
  6. fElectrical and Computer Engineering Department, North Carolina State University, Raleigh, NC 27695

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PNAS September 5, 2017 114 (36) E7632-E7640; first published August 21, 2017; https://doi.org/10.1073/pnas.1707566114
Maria Angels de Luis Balaguer
aPlant and Microbial Biology Department, North Carolina State University, Raleigh, NC 27695;
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Adam P. Fisher
aPlant and Microbial Biology Department, North Carolina State University, Raleigh, NC 27695;
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Natalie M. Clark
aPlant and Microbial Biology Department, North Carolina State University, Raleigh, NC 27695;
bBiomathematics Program, North Carolina State University, Raleigh, NC 27695;
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Maria Guadalupe Fernandez-Espinosa
cDepartamento de Botánica y Fisiología Vegetal, Instituto Hispano-Luso de Investigaciones Agrarias, Facultad de Biología, Universidad de Salamanca, 37185 Salamanca, Spain;
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Barbara K. Möller
dLaboratory of Biochemistry, Wageningen University, 6703HA, Wageningen, The Netherlands;
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Dolf Weijers
dLaboratory of Biochemistry, Wageningen University, 6703HA, Wageningen, The Netherlands;
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Jan U. Lohmann
eDepartment of Stem Cell Biology, University of Heidelberg, Heidelberg D-69120, Germany;
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  • ORCID record for Jan U. Lohmann
Cranos Williams
fElectrical and Computer Engineering Department, North Carolina State University, Raleigh, NC 27695
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Oscar Lorenzo
cDepartamento de Botánica y Fisiología Vegetal, Instituto Hispano-Luso de Investigaciones Agrarias, Facultad de Biología, Universidad de Salamanca, 37185 Salamanca, Spain;
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Rosangela Sozzani
aPlant and Microbial Biology Department, North Carolina State University, Raleigh, NC 27695;
bBiomathematics Program, North Carolina State University, Raleigh, NC 27695;
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  • For correspondence: ross_sozzani@ncsu.edu
  1. Edited by Julia Bailey-Serres, University of California, Riverside, CA, and approved July 31, 2017 (received for review May 7, 2017)

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Significance

We developed a computational pipeline that uses gene expression datasets for inferring relationships among genes and predicting their importance. We showed that the capacity of our pipeline to integrate spatial and temporal transcriptional datasets improves the performance of inference algorithms. The combination of this pipeline with Arabidopsis stem cell-specific data resulted in networks that capture the regulations of stem cell-enriched genes in the stem cells and throughout root development. Our combined approach of molecular biology, computational biology, and mathematical biology, led to successful findings of factors that could play important roles in stem cell regulation and, in particular, quiescent center function.

Abstract

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential 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. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells.

  • root stem cell
  • root development
  • cell-type expression profile
  • gene regulatory network
  • modeling

Footnotes

  • ↵1Present addresses: Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), B-9052 Ghent, Belgium; and Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Ghent, Belgium.

  • ↵2To whom correspondence should be addressed. Email: ross_sozzani{at}ncsu.edu.
  • Author contributions: M.A.d.L.B. and R.S. designed research; M.A.d.L.B., A.P.F., N.M.C., M.G.F.-E., B.K.M., D.W., O.L., and R.S. performed research; J.U.L. and C.W. contributed new reagents/analytic tools; M.A.d.L.B., A.P.F., and N.M.C. analyzed data; and M.A.d.L.B. and R.S. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/geo (accession nos. GSE76710, GSE97792, and GSE97857).

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1707566114/-/DCSupplemental.

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Predicting GRNs in Arabidopsis root 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
Proceedings of the National Academy of Sciences Sep 2017, 114 (36) E7632-E7640; DOI: 10.1073/pnas.1707566114

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Predicting GRNs in Arabidopsis root 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
Proceedings of the National Academy of Sciences Sep 2017, 114 (36) E7632-E7640; DOI: 10.1073/pnas.1707566114
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  • Plant Biology
Proceedings of the National Academy of Sciences: 114 (36)
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