Identification of Gene Modules Associated with Drought Response in Rice by Network-Based Analysis

Abstract

Understanding the molecular mechanisms that underlie plant responses to drought stress is challenging due to the complex interplay of numerous different genes. Here, we used network-based gene clustering to uncover the relationships between drought-responsive genes from large microarray datasets. We identified 2,607 rice genes that showed significant changes in gene expression under drought stress; 1,392 genes were highly intercorrelated to form 15 gene modules. These drought-responsive gene modules are biologically plausible, with enrichments for genes in common functional categories, stress response changes, tissue-specific expression and transcription factor binding sites. We observed that a gene module (referred to as module 4) consisting of 134 genes was significantly associated with drought response in both drought-tolerant and drought-sensitive rice varieties. This module is enriched for genes involved in controlling the response of the plant to water and embryonic development, including a heat shock transcription factor as the key regulator in the expression of ABRE-containing genes. These results suggest that module 4 is highly conserved in the ABA-mediated drought response pathway in different rice varieties. Moreover, our study showed that many hub genes clustered in rice chromosomes had significant associations with QTLs for drought stress tolerance. The relationship between hub gene clusters and drought tolerance QTLs may provide a key to understand the genetic basis of drought tolerance in rice.

Systematic discovery and characterization of stress-related microRNA genes in Oryza sativa

“…We compared the results of these two softwares and obtained 1046 common miRNA-target pairs referring 1050 target sites. We found that 63 (28.13%) predicted miRNA target genes have been experimentally confirmed as stress tolerant or related genes via surveying the literatures (Jain et al 2014;Shaik & Ramakrishna 2012, 2013Zhang et al 2012). In the 1050 target sites, 1029 (98%) sites were belonged to cleaveage inhibition and 21 (2%) sites belonged to translation inhibition types according to the psRNATarget prediction results.…”

Computational Analysis of “-omics” Data to Identify Transcription Factors Regulating Secondary Metabolism in Rauvolfia serpentina

“…Furthermore, PPI networks are not static but dynamic entity and expression of protein is intrinsically controlled by different regulatory mechanisms (Liang and Li 2007). With an exponential growth of gene expression data, most of the current research has been shifted toward systems biology that largely include co-expression networks of transcriptome data (Yonekura-Sakakibara et al 2008;Zhang et al 2012). Identification and annotation of TFs at genome scale are the first step toward understanding the mechanism of gene expression and its regulation.…”

Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

“…For example, in a condition-independent study of A. thaliana coexpression networks, 127 of 317 modules containing three or more genes had significantly enriched GO terms [7]. In another study of a drought response-related rice gene coexpression network, gene modules were preferentially involved in the biological processes of stimulus response, photosynthesis, nucleosome assembly, embryonic development, translation, and amino acid phosphorylation, providing insights into the complex genetic basis of drought tolerance [75].…”