Identification of cis-regulatory elements associated with salinity and drought stress tolerance in rice from co-expressed gene interaction networks

Abstract

Rice, a staple food crop, is often subjected to drought and salinity stresses thereby limiting its yield potential. Since there is a cross talk between these abiotic stresses, identification of common and/or overlapping regulatory elements is pivotal for generating rice cultivars that showed tolerance towards them. Analysis of the gene interaction network (GIN) facilitates identifying the role of individual genes and their interactions with others that constitute important molecular determinants in sensing and signaling cascade governing drought and/or salinity stresses. Identification of the various cis-regulatory elements of the genes constituting GIN is equally important. Here, in this study graphical Gaussian model (GGM) was used for generating GIN for an array of genes that were differentially regulated during salinity and/or drought stresses to contrasting rice cultivars (salt-tolerant [CSR11], salt-sensitive [VSR156], drought-tolerant [Vandana], drought-sensitive [IR64]). Whole genome transcriptom profiling by using microarray were employed in this study. Markov Chain completed co-expression analyses of differentially expressed genes using Dynamic Bayesian Network, Probabilistic Boolean Network and Steady State Analysis. A compact GIN was identified for commonly co-expressed genes during salinity and drought stresses with three major hubs constituted by Myb2 transcription factor (TF), phosphoglycerate kinase and heat shock protein (Hsp). The analysis suggested a pivotal role of these genes in salinity and/or drought stress responses. Further, analysis of cis-regulatory elements (CREs) of commonly differentially expressed genes during salinity and drought stresses revealed the presence of 20 different motifs.

Co-expression network analysis of protein phosphatase 2A (PP2A) genes with stress-responsive genes in Arabidopsis thaliana reveals 13 key regulators

“…A meta-analysis in Arabidopsis plant unveils how perturbations preferentially affect connected, central and organised genes in modules of network 20 . Gene networks are rapidly used in economically important plant species aimed to study modules and identification of hubness since the past two years for growth and development in Bamboo 21 , stress responses in cotton 22 , graft healing in tomato 23 , flower and fruit development in wild strawberry 24 , plant height development 25 and salt and drought stress 26 in maize, identification of drought-responsive genes in rice 27 , Theanine biosynthesis in tea 28 , sesame gene functions 14 .…”

Genome-Wide Computational Identification of Biologically Significant Cis-Regulatory Elements and Associated Transcription Factors from Rice

“…Several rice microarray platforms have been developed for global gene expression studies of rice, including the National Science Foundation (NSF) 22K and 45K Rice Oligonucleotide Arrays, Agilent 44K arrays, Affymetrix arrays, and the Yale/BGI oligonucleotide array [14]. An analysis of promoters from co-expressed genes from microarray data has facilitated the prediction of rice CREs [7,9,15,16,17]. However, some of these predicted CREs could be short motifs that occur randomly throughout the genome without regulatory function.…”

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

“…Next, we asked how the DR TFs respond to drought in terms of differential expression. We re-analyzed data from seven independently published drought experiments performed on multiple varieties, growth stages, and tissues of rice plants ( Wang et al, 2011 ; Ding et al, 2013 ; Pabuayon et al, 2016 ; Mishra et al, 2018 ). We estimated genome-wide fold change values in each of these datasets, and ranked all TFs based on the absolute values of these fold changes.…”