Abstract
Transcription factor (TF) has a significant influence on the state of a cell by regulating multiple down-stream genes. Thus, experimental and computational biologists have made great efforts to construct TF gene networks for regulatory interactions between TFs and their target genes. Now, an important research question is how to utilize TF networks to investigate the response of a plant to stress at the transcription control level using time-series transcriptome data. In this article, we present a new computational network, PropaNet, to investigate dynamics of TF networks from time-series transcriptome data using two state-of-the-art network analysis techniques, influence maximization and network propagation. PropaNet uses the influence maximization technique to produce a ranked list of TFs, in the order of TF that explains differentially expressed genes (DEGs) better at each time point. Then, a network propagation technique is used to select a group of TFs that explains DEGs best as a whole. For the analysis of Arabidopsis time series datasets from AtGenExpress, we used PlantRegMap as a template TF network and performed PropaNet analysis to investigate transcriptional dynamics of Arabidopsis under cold and heat stress. The time varying TF networks showed that Arabidopsis responded to cold and heat stress quite differently. For cold stress, bHLH and bZIP type TFs were the first responding TFs and the cold signal influenced histone variants, various genes involved in cell architecture, osmosis and restructuring of cells. However, the consequences of plants under heat stress were up-regulation of genes related to accelerating differentiation and starting re-differentiation. In terms of energy metabolism, plants under heat stress show elevated metabolic process and resulting in an exhausted status. We believe that PropaNet will be useful for the construction of condition-specific time-varying TF network for time-series data analysis in response to stress. PropaNet is available at http://biohealth.snu.ac.kr/software/PropaNet.
Highlights
A transcription factor (TF) is a protein that regulates expression levels of a target gene (TG) by binding to a specific DNA sequence on the promoter regions of target genes (Latchman, 1997)
We propose PropaNet to investigate dynamics of Transcription factor (TF) networks from time-series transcriptome data through network simulation analysis that mimics the regulatory mechanism of TF
The PropaNet analysis takes three types of input data: timeseries gene expression data EX that are measured at multiple time-points, a template TF network G and a set of target genes TGset
Summary
A transcription factor (TF) is a protein that regulates expression levels of a target gene (TG) by binding to a specific DNA sequence on the promoter regions of target genes (Latchman, 1997). ARACNe (Margolin et al, 2006) uses an information theoretic framework based on the data processing inequality theorem It was successfully used for the reconstruction of context-specific transcriptional networks in multiple tissue types (Lefebvre et al, 2010). ChIP sequencing (ChIP-seq) technology was developed independently by three research groups in 2007 (Barski et al, 2007; Johnson et al, 2007; Mikkelsen et al, 2007) and it has been used to identify genomic regions that TF binds to, known as, transcription factor binding sites (TFBSs) It crosslinks DNA and associated TFs, shears DNA-TF complexes into 500 bp DNA fragments by sonication or nuclease digestion, immunoprecipitates the targeted TF complexes using an appropriate protein-specific antibody, and determines the sequence of the DNA fragments. Some of the databases are providing TF-TG relationships by predicting binding sites for the collective TFs: TRANSFAC (Matys et al, 2006) a well-known commercial database; ENCODE (Landt et al, 2012), JASPAR (Khan et al, 2017) and ChIP-Atlas (Oki et al, 2018) for model organisms; GTRD (Yevshin et al, 2016), ChIPBase (Yang et al, 2012), Cistrome (Zheng et al, 2018b) and Factorbook (Wang et al, 2012) for human and mouse species; PlantRegMap (Jin et al, 2016) for plant species
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