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Transcriptomic data of Arabidopsis thaliana hypocotyl upon suppression of expansin genes.

ABSTRACT: Expansin is a cell wall loosening protein without hydrolytic activity, which allows cell expansion by influencing cell wall extensibility. Previous studies showed that the suppression of expansin genes (EXPA1, EXPA3, EXPA5 and EXPA10) resulted in defective organ growth and altered cell wall chemical composition [1,2]. However, the molecular mechanism on how the suppression of non-enzymatic expansin expression can result in widespread effects on plant cell wall and organ growth is still unclear. In this study, we performed transcriptomic analysis on the hypocotyls of previously reported transgenic Arabidopsis line [1] to investigate the effects of expansin gene suppression on the global gene expression pattern, particularly on the cell wall related genes.

SUBMITTER: Ilias IA

PROVIDER: S-EPMC5429223 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Transcriptomic data of Arabidopsis thaliana hypocotyl upon suppression of expansin genes.

Ilias Iqmal Asyraf IA Airianah Othman Babul OB Baharum Syarul Nataqain SN Goh Hoe-Han HH

Genomics data 20170503

Expansin is a cell wall loosening protein without hydrolytic activity, which allows cell expansion by influencing cell wall extensibility. Previous studies showed that the suppression of expansin genes (EXPA1, EXPA3, EXPA5 and EXPA10) resulted in defective organ growth and altered cell wall chemical composition [1,2]. However, the molecular mechanism on how the suppression of non-enzymatic expansin expression can result in widespread effects on plant cell wall and org ...[more]

PMID: 28529882

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Project description:BackgroundTranscription factors (TFs) regulate downstream genes in response to environmental stresses in plants. Identification of TF target genes can provide insight on molecular mechanisms of stress response systems, which can lead to practical applications such as engineering crops that thrive in challenging environments. Despite various computational techniques that have been developed for identifying TF targets, it remains a challenge to make best use of available experimental data, especially from time-series transcriptome profiling data, for improving TF target identification.ResultsIn this study, we used a novel approach that combined kinetic modelling of gene expression with a statistical meta-analysis to predict targets of 757 TFs using expression data of 14,905 genes in Arabidopsis exposed to different durations and types of abiotic stresses. Using a kinetic model for the time delay between the expression of a TF gene and its potential targets, we shifted a TF's expression profile to make an interacting pair coherent. We found that partitioning the expression data by tissue and developmental stage improved correlation between TFs and their targets. We identified consensus pairs of correlated profiles between a TF and all other genes among partitioned datasets. We applied this approach to predict novel targets of known TFs. Some of these putative targets were validated from the literature, for E2F's targets in particular, while others provide explicit genes as hypotheses for future studies.ConclusionOur method provides a general framework for TF target prediction with consideration of the time lag between initiation of a TF and activation of its targets. The framework helps make significant inferences by reducing the effects of independent noises in different experiments and by identifying recurring regulatory relationships under various biological conditions. Our TF target predictions may shed some light on common regulatory networks in abiotic stress responses.

Dataset Information

Transcriptomic data of Arabidopsis thaliana hypocotyl upon suppression of expansin genes.

Publications

Transcriptomic data of <i>Arabidopsis thaliana</i> hypocotyl upon suppression of expansin genes.

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