Research article

BMC Systems Biology

, 6:100

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

  • Papapit IngkasuwanAffiliated withSchool of Bioresources and Technology, King Mongkut’s University of Technology Thonburi
  • , Supatcharee NetrphanAffiliated withNational Center for Genetic Engineering and Biotechnology
  • , Sukon PrasitwattanasereeAffiliated withDepartment of Statistics, Faculty of Science, Chiang Mai University
  • , Morakot TanticharoenAffiliated withSchool of Bioresources and Technology, King Mongkut’s University of Technology Thonburi
  • , Sakarindr BhumiratanaAffiliated withSchool of Bioresources and Technology, King Mongkut’s University of Technology Thonburi
  • , Asawin MeechaiAffiliated withDepartment of Chemical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi
  • , Jeerayut ChaijaruwanichAffiliated withDepartment of Computer Science, Faculty of Science, Chiang Mai University
  • , Hideki TakahashiAffiliated withRIKEN Plant Science CenterDepartment of Biochemistry & Molecular Biology, Michigan State University
  • , Supapon CheevadhanarakAffiliated withSchool of Bioresources and Technology, King Mongkut’s University of Technology Thonburi Email author 

Abstract

Background

Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).

Results

Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines.

Conclusions

In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplasts.

Keywords

Arabidopsis thaliana Constans-like Indeterminate domain 5 Graphical Gaussian model Starch synthase 4 Transcriptional regulation