Dataset Information

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

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 ?-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.

SUBMITTER: Ingkasuwan P

PROVIDER: S-EPMC3490714 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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

Ingkasuwan Papapit P Netrphan Supatcharee S Prasitwattanaseree Sukon S Tanticharoen Morakot M Bhumiratana Sakarindr S Meechai Asawin A Chaijaruwanich Jeerayut J Takahashi Hideki H Cheevadhanarak Supapon S

BMC systems biology 20120816

<h4>Background</h4>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).<h4>Results</h4>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 diur ...[more]

PMID: 22898356

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Project description:BACKGROUND: To understand transcriptional regulatory networks (TRNs), especially the coordinated dynamic regulation between transcription factors (TFs) and their corresponding target genes during development, computational approaches would represent significant advances in the genome-wide expression analysis. The major challenges for the experiments include monitoring the time-specific TFs' activities and identifying the dynamic regulatory relationships between TFs and their target genes, both of which are currently not yet available at the large scale. However, various methods have been proposed to computationally estimate those activities and regulations. During the past decade, significant progresses have been made towards understanding pollen development at each development stage under the molecular level, yet the regulatory mechanisms that control the dynamic pollen development processes remain largely unknown. Here, we adopt Networks Component Analysis (NCA) to identify TF activities over time course, and infer their regulatory relationships based on the coexpression of TFs and their target genes during pollen development. RESULTS: We carried out meta-analysis by integrating several sets of gene expression data related to Arabidopsis thaliana pollen development (stages range from UNM, BCP, TCP, HP to 0.5 hr pollen tube and 4 hr pollen tube). We constructed a regulatory network, including 19 TFs, 101 target genes and 319 regulatory interactions. The computationally estimated TF activities were well correlated to their coordinated genes' expressions during the development process. We clustered the expression of their target genes in the context of regulatory influences, and inferred new regulatory relationships between those TFs and their target genes, such as transcription factor WRKY34, which was identified that specifically expressed in pollen, and regulated several new target genes. Our finding facilitates the interpretation of the expression patterns with more biological relevancy, since the clusters corresponding to the activity of specific TF or the combination of TFs suggest the coordinated regulation of TFs to their target genes. CONCLUSIONS: Through integrating different resources, we constructed a dynamic regulatory network of Arabidopsis thaliana during pollen development with gene coexpression and NCA. The network illustrated the relationships between the TFs' activities and their target genes' expression, as well as the interactions between TFs, which provide new insight into the molecular mechanisms that control the pollen development.

Project description:BACKGROUND AND AIMS: Cell membranes are major targets of environmental stresses. Lipids are important membrane components, and changes in their composition may help to maintain membrane integrity and preserve cell compartmentation under water stress conditions. The aim of this work was to investigate the effects of water stress on membrane lipid composition and other aspects of lipid metabolism in the leaves of the model plant, Arabidopsis thaliana. METHODS: Arabidopsis thaliana (ecotype Columbia) plants were submitted to progressive drought stress by withholding irrigation. Studies were carried out in plants with hydration levels ranging from slight to very severe water deficit. Enzymatic activities hydrolysing MGDG, DGDG and PC were measured. Expression of several genes essential to lipid metabolism, such as genes coding for enzymes involved in lipid biosynthesis (MGDG synthase, DGDG synthase) and degradation (phospholipases D, lipoxygenase, patatin-like lipolytic-acylhydrolase), was studied. KEY RESULTS: In response to drought, total leaf lipid contents decreased progressively. However, for leaf relative water content as low as 47.5 %, total fatty acids still represented 61 % of control contents. Lipid content of extremely dehydrated leaves rapidly increased after rehydration. The time-course of the decrease in leaf lipid contents correlated well with the increase in lipolytic activities of leaf extracts and with the expression of genes involved in lipid degradation. Despite a decrease in total lipid content, lipid class distribution remained relatively stable until the stress became very severe. CONCLUSIONS: Arabidopsis leaf membranes appeared to be very resistant to water deficit, as shown by their capacity to maintain their polar lipid contents and the stability of their lipid composition under severe water loss conditions. Moreover, arabidopsis displayed several characteristics indicative of a so far unknown adaptation capacity to drought-stress at the cellular level, such as an increase in the DGDG : MGDG ratio and fatty acid unsaturation.

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Dataset Information

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

Background

Results

Conclusions

Publications

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

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