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Open Access Research article

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

Papapit Ingkasuwan1, Supatcharee Netrphan2, Sukon Prasitwattanaseree3, Morakot Tanticharoen1, Sakarindr Bhumiratana1, Asawin Meechai4, Jeerayut Chaijaruwanich5, Hideki Takahashi67 and Supapon Cheevadhanarak1*

Author Affiliations

1 School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand

2 National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand

3 Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand

4 Department of Chemical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand

5 Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand

6 RIKEN Plant Science Center, Yokohama, 230-0045, Japan

7 Department of Biochemistry & Molecular Biology, Michigan State University, 603 Wilson Rd, East Lansing, MI, 48824, USA

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BMC Systems Biology 2012, 6:100  doi:10.1186/1752-0509-6-100

Published: 16 August 2012

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