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Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network

Joke Baute (UGent) , Dorota Herman (UGent) , Frederik Coppens (UGent) , Jolien De Block (UGent) , Bram Slabbinck (UGent) , Matteo Dell'Acqua, Mario Enrico Pe, Steven Maere (UGent) , Hilde Nelissen (UGent) and Dirk Inzé (UGent)
(2016) PLANT PHYSIOLOGY. 170(3). p.1848-1867
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Biotechnology for a sustainable economy (Bio-Economy)
Abstract
Leaves are vital organs for biomass and seed production because of their role in the generation of metabolic energy and organic compounds. A better understanding of the molecular networks underlying leaf development is crucial to sustain global requirements for food and renewable energy. Here, we combined transcriptome profiling of proliferative leaf tissue with indepth phenotyping of the fourth leaf at later stages of development in 197 recombinant inbred lines of two different maize (Zea mays) populations. Previously, correlation analysis in a classical biparental mapping population identified 1,740 genes correlated with at least one of 14 traits. Here, we extended these results with data from a multiparent advanced generation intercross population. As expected, the phenotypic variability was found to be larger in the latter population than in the biparental population, although general conclusions on the correlations among the traits are comparable. Data integration from the two diverse populations allowed us to identify a set of 226 genes that are robustly associated with diverse leaf traits. This set of genes is enriched for transcriptional regulators and genes involved in protein synthesis and cell wall metabolism. In order to investigate the molecular network context of the candidate gene set, we integrated our data with publicly available functional genomics data and identified a growth regulatory network of 185 genes. Our results illustrate the power of combining in-depth phenotyping with transcriptomics in mapping populations to dissect the genetic control of complex traits and present a set of candidate genes for use in biomass improvement.
Keywords
RIBOSOMAL-PROTEINS, DNA METHYLATION, PLANT DEVELOPMENT, LEAF DEVELOPMENT, ZEA-MAYS L., BINDING DOMAIN PROTEINS, CELLULOSE SYNTHASE-LIKE, ARABIDOPSIS GENE FAMILY, CHLOROPLAST DEVELOPMENT, CELL ELONGATION

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Chicago
Baute, Joke, Dorota Herman, Frederik Coppens, Jolien De Block, Bram Slabbinck, Matteo Dell’Acqua, Mario Enrico Pe, Steven Maere, Hilde Nelissen, and Dirk Inzé. 2016. “Combined Large-scale Phenotyping and Transcriptomics in Maize Reveals a Robust Growth Regulatory Network.” Plant Physiology 170 (3): 1848–1867.
APA
Baute, J., Herman, D., Coppens, F., De Block, J., Slabbinck, B., Dell’Acqua, M., Pe, M. E., et al. (2016). Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network. PLANT PHYSIOLOGY, 170(3), 1848–1867.
Vancouver
1.
Baute J, Herman D, Coppens F, De Block J, Slabbinck B, Dell’Acqua M, et al. Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network. PLANT PHYSIOLOGY. 2016;170(3):1848–67.
MLA
Baute, Joke, Dorota Herman, Frederik Coppens, et al. “Combined Large-scale Phenotyping and Transcriptomics in Maize Reveals a Robust Growth Regulatory Network.” PLANT PHYSIOLOGY 170.3 (2016): 1848–1867. Print.
@article{7244025,
  abstract     = {Leaves are vital organs for biomass and seed production because of their role in the generation of metabolic energy and organic compounds. A better understanding of the molecular networks underlying leaf development is crucial to sustain global requirements for food and renewable energy. Here, we combined transcriptome profiling of proliferative leaf tissue with indepth phenotyping of the fourth leaf at later stages of development in 197 recombinant inbred lines of two different maize (Zea mays) populations. Previously, correlation analysis in a classical biparental mapping population identified 1,740 genes correlated with at least one of 14 traits. Here, we extended these results with data from a multiparent advanced generation intercross population. As expected, the phenotypic variability was found to be larger in the latter population than in the biparental population, although general conclusions on the correlations among the traits are comparable. Data integration from the two diverse populations allowed us to identify a set of 226 genes that are robustly associated with diverse leaf traits. This set of genes is enriched for transcriptional regulators and genes involved in protein synthesis and cell wall metabolism. In order to investigate the molecular network context of the candidate gene set, we integrated our data with publicly available functional genomics data and identified a growth regulatory network of 185 genes. Our results illustrate the power of combining in-depth phenotyping with transcriptomics in mapping populations to dissect the genetic control of complex traits and present a set of candidate genes for use in biomass improvement.},
  author       = {Baute, Joke and Herman, Dorota and Coppens, Frederik and De Block, Jolien and Slabbinck, Bram and Dell'Acqua, Matteo and Pe, Mario Enrico and Maere, Steven and Nelissen, Hilde and Inz{\'e}, Dirk},
  issn         = {0032-0889},
  journal      = {PLANT PHYSIOLOGY},
  keyword      = {RIBOSOMAL-PROTEINS,DNA METHYLATION,PLANT DEVELOPMENT,LEAF DEVELOPMENT,ZEA-MAYS L.,BINDING DOMAIN PROTEINS,CELLULOSE SYNTHASE-LIKE,ARABIDOPSIS GENE FAMILY,CHLOROPLAST DEVELOPMENT,CELL ELONGATION},
  language     = {eng},
  number       = {3},
  pages        = {1848--1867},
  title        = {Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network},
  url          = {http://dx.doi.org/10.1104/pp.15.01883},
  volume       = {170},
  year         = {2016},
}

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