Transcriptional regulatory networks inArabidopsis thalianaduring single and combined stresses

Abstract

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

Principles and characteristics of the Arabidopsis WRKY regulatory network during early MAMP‐triggered immunity

“…However, whether this difference in binding site numbers alone is sufficient to explain this discrepancy remains doubtful. Clustering of TF binding motifs for the same TFs, so‐called homotypic clusters, within promoter‐proximal regions and enhancers has been suggested to maintain transcriptional robustness and to preserve stress responsiveness against cis‐ regulatory mutations (Barah et al ., ). Interestingly, our ChIP‐seq data show that there is not only a higher number of W‐boxes in the promoters of induced WRKY genes, but also that in many cases several of these sites were bound by the WRKY factors (i.e.…”

Predictive Models of Spatial Transcriptional Response to High Salinity

“…Although the in vitro TF binding data sets are extensive, binding information is available only for ;38% of the known Arabidopsis TFs (Weirauch et al, 2014;Barah et al, 2016;O'Malley et al, 2016); thus, some relevant CREs might not be included in the models. In addition, it is worth noting that the performance of models for root salt up-regulated genes is not as good as that of models for shoot salt up-regulated genes (Fig.…”

Complex interactions between day length and diurnal patterns of gene expression drive photoperiodic responses in a perennial C4 grass

“…Quantifying the transcriptome profile with RNA-sequencing (RNAseq) under different combinations of environmental cues is commonly used to investigate the regulatory mechanisms in response to those conditions (Barah et al, 2016;Coolen et al, 2016;Lasky et al, 2014;Suzuki, Rivero, Shulaev, Blumwald, & Mittler, 2014). However, the current cost of RNA-seq remains a barrier for high-throughput applications.…”