Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice

Abstract

MYB transcription factor (TF) is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by “top-down” and “guide-gene” approaches. More than 50% of OsMYBs were strongly correlated under 50 experimental conditions with 51 hub genes via “top-down” approach. Further, clusters were identified using Markov Clustering (MCL). To maximize the clustering performance, parameter evaluation of the MCL inflation score (I) was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by “guide-gene” approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought response in rice. Thus, the co-regulatory network analysis facilitated the identification of complex OsMYB regulatory networks, and candidate target regulon genes of selected guide MYB genes. The results contribute to the candidate gene screening, and experimentally testable hypotheses for potential regulatory MYB TFs, and their targets under stress conditions.

Transcriptional Regulation of Sorghum Stem Composition: Key Players Identified Through Co-expression Gene Network and Comparative Genomics Analyses

“…To the same extent as for NAC genes, these numbers globally match to the ones previously described for these species. One hundred ninety-seven (Katiyar et al, 2012), 198 (Yanhui et al, 2006, and 133 (Li et al, 2016) MYB genes were identified in Arabidopsis, 191 in poplar (Wilkins et al, 2009),141 in eucalyptus (Soler et al, 2015), 155 in barrel medic (Zhong and Ye, 2015), 157 in maize (Du et al, 2012), 209 in foxtail millet (Muthamilarasan et al, 2014), 233 (Smita et al, 2015), 155 (Katiyar et al, 2012), and 163 (Yanhui et al, 2006) MYB in rice and 122 in brome (Zheng et al, 2017).…”

Genome-Wide Analysis and Expression Profiling of the Heat Shock Factor Gene Family in Phyllostachys edulis during Development and in Response to Abiotic Stresses

“…The expression correlation of the PheHsfs was calculated by Pearson correlation coefficient (PCC; R-value) using gene expression RPKM values from the high-throughput transcriptome data in R. Expression correlation data were used for the correlation network, and co-expressed gene pairs were filtered with a PCC cut-off of 0.85 as previously described [50]. Cytoscape version 3.4.0 were used to analyze and visualize the network [51].…”

Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities

“…NLP3 (LOC_Os01g13540) played a major role in nitrate uptake, translocation and signaling in rice root [66]. Both LOC_Os01g13570 [67] and LOC_Os01g13740 [68] participated in abiotic stress response. Similarly, in the qFLL 2.1 region, three candidate genes including OsbZIP19 (LOC_Os02g14910), OsCNGC1…”