Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism

Abstract

BackgroundD-myo-inositol phosphates (IPs) are a series of phosphate esters. Myo-inositol hexakisphosphate (phytic acid, IP6) is the most abundant IP and has negative effects on animal and human nutrition. IPs play important roles in plant development, stress responses, and signal transduction. However, the metabolic pathways and possible regulatory mechanisms of IPs in maize are unclear. In this study, the B73 (high in phytic acid) and Qi319 (low in phytic acid) lines were selected for RNA-Seq analysis from 427 inbred lines based on a screening of IP levels. By integrating the metabolite data with the RNA-Seq data at three different kernel developmental stages (12, 21 and 30 days after pollination), co-regulatory networks were constructed to explore IP metabolism and its interactions with other pathways.ResultsDifferentially expressed gene analyses showed that the expression of MIPS and ITPK was related to differences in IP metabolism in Qi319 and B73. Moreover, WRKY and ethylene-responsive transcription factors (TFs) were common among the differentially expressed TFs, and are likely to be involved in the regulation of IP metabolism.Six co-regulatory networks were constructed, and three were chosen for further analysis. Based on network analyses, we proposed that the GA pathway interacts with the IP pathway through the ubiquitination pathway, and that Ca2+ signaling functions as a bridge between IPs and other pathways. IP pools were found to be transported by specific ATP-binding cassette (ABC) transporters. Finally, three candidate genes (Mf3, DH2 and CB5) were identified and validated using Arabidopsis lines with mutations in orthologous genes or RNA interference (RNAi)-transgenic maize lines. Some mutant or RNAi lines exhibited seeds with a low-phytic-acid phenotype, indicating perturbation of IP metabolism. Mf3 likely encodes an enzyme involved in IP synthesis, DH2 encodes a transporter responsible for IP transport across organs and CB5 encodes a transporter involved in IP co-transport into vesicles.ConclusionsThis study provides new insights into IP metabolism and regulation, and facilitates our development of a better understanding of the functions of IPs and how they interact with other pathways involved in plant development and stress responses. Three new genes were discovered and preliminarily validated, thereby increasing our knowledge of IP metabolism.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2476-x) contains supplementary material, which is available to authorized users.

Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

“…The differential expression of transcription factor genes such as WRKY and CAMTA (Calmodulin-binding Transcription Activator), was linked to phytic acid biosynthesis pathway, suggesting a complex regulatory mechanism (Redekar et al, 2015 ). Association of WRKY transcription factors and Ca 2+ binding activity with inositol metabolism was also confirmed by another independent study with maize low phytic acid breeding line Qi319 (Zhang et al, 2016 ). Zhang et al ( 2016 ) also identified ABC transporter gene candidates associated with low phytic acid phenotype in maize using co-regulatory network.…”

“…Association of WRKY transcription factors and Ca 2+ binding activity with inositol metabolism was also confirmed by another independent study with maize low phytic acid breeding line Qi319 (Zhang et al, 2016 ). Zhang et al ( 2016 ) also identified ABC transporter gene candidates associated with low phytic acid phenotype in maize using co-regulatory network. In this article, we focus on the discovery of transcription regulatory networks to further investigate the inositol metabolism in soybean.…”

Multilevel Regulation of Abiotic Stress Responses in Plants

“…The goal of data-driven modeling is to identify biologically meaningful signals from genome-scale data. Data-driven modeling includes identification of causal SNPs in genome-wide association analysis ( Li et al, 2010 ; Thoen et al, 2017 ), identification of DE genes ( Geng et al, 2013 ; Bechtold et al, 2016 ), proteins ( Lumba et al, 2014 ; Mostafa et al, 2016 ), metabolites ( Töpfer and Niokoloski, 2013 ; Töpfer et al, 2014 ; Zhang S. et al, 2016 ), and the reflection of those changes in gene regulatory networks ( Zaag et al, 2015 ; Landeghem et al, 2016 ). To validate modeling results, wet-bench experiments should be performed for candidate loci.…”

Co-expression network analysis of the transcriptomes of rice roots exposed to various cadmium stresses reveals universal cadmium-responsive genes

“…Through integrating transcriptome and metabolite data, gene co-expression networks related to inositol phosphate in maize [ 41 ] and fruit anthocyanin content in apple [ 42 , 43 ] were constructed by WGCNA. Recently, three types of omics data including 31,447 mRNAs, 13,175 proteins and 4267 phosphoproteins were grouped individually by WGCNA into co-expression modules, and then integrated into omics networks in a developmental atlas of maize [ 29 ].…”