Gene Coexpression Network Alignment and Conservation of Gene Modules between Two Grass Species: Maize and Rice    

Abstract

One major objective for plant biology is the discovery of molecular subsystems underlying complex traits. The use of genetic and genomic resources combined in a systems genetics approach offers a means for approaching this goal. This study describes a maize (Zea mays) gene coexpression network built from publicly available expression arrays. The maize network consisted of 2,071 loci that were divided into 34 distinct modules that contained 1,928 enriched functional annotation terms and 35 cofunctional gene clusters. Of note, 391 maize genes of unknown function were found to be coexpressed within modules along with genes of known function. A global network alignment was made between this maize network and a previously described rice (Oryza sativa) coexpression network. The IsoRankN tool was used, which incorporates both gene homology and network topology for the alignment. A total of 1,173 aligned loci were detected between the two grass networks, which condensed into 154 conserved subgraphs that preserved 4,758 coexpression edges in rice and 6,105 coexpression edges in maize. This study provides an early view into maize coexpression space and provides an initial network-based framework for the translation of functional genomic and genetic information between these two vital agricultural species.The combination of genomics, genetics, and systemslevel computational methods provides a powerful approach toward insight into complex biological systems. Of particular significance is the discovery of genetic interactions that lead to desirable agricultural and economic traits in the Poaceae family (grasses). The Poaceae includes valuable crops such as rice (Oryza sativa), maize (Zea mays), wheat (Triticum spp.), and sugarcane (Saccharum officinarum), which are globally some of the most agriculturally and economically important crops (FAOSTAT, 2007). Understanding complex interactions underlying agronomic traits within these species, therefore, is of great significance, in particular to help with crop improvements to meet the challenges of plant and human health but also for basic understanding of complex biological systems.In addition to their pivotal role in agriculture, grasses offer a powerful model system in that their genomes are closely conserved and functional genomic knowledge gained in one species can be hypothesized to occur in another syntenic region (translational functional genomics; Paterson et al., 2009). In cases of grass species with poorly resolved, polyploid genomes such as sugarcane, where genomic resources are not as far progressed as in other grasses (e.g. rice, sorghum [Sorghum bicolor], maize, etc.), translational functional genomics methods may be the most cost-effective strategy for crop improvement as well as for unraveling the functional consequences of polyploidy. Additionally, crops rich in genetically mapped loci deposited in sites like Gramene (Jaiswal, 2011) provide a rich source of systems genetic hypotheses that could in principle accelerate the translation of interacting gene sets ass...

Systematic Identification of Functional Plant Modules through the Integration of Complementary Data Sources

“…An important advantage of the module-based approach with respect to function prediction is that homologs are not required for a gene to receive a prediction. In agreement with a recent comparative transcriptomics study reporting conserved modules between maize and rice (Ficklin and Feltus, 2011), we observed that modules showing ultraconserved coexpression primarily cover genes that are related to energy and housekeeping functions, such as photosynthesis, ribosome biogenesis, and translation. However, the 910 modules showing significant coexpression in other angiosperms cover a broad range of biological processes and provide a valuable resource to identify new gene functions and translate biological information from model species to crops.…”

“…These networks were built combining expression and PPI data with sequence data (Kourmpetis et al, 2011), genetic and physical interaction data (Warde-Farley et al, 2010), phylogenetic profiles and gene location (Bradford et al, 2010), and the integration of functional genomics, proteomics, and comparative genomics data sets (Lee et al, 2010). Apart from studying gene modules in one species, recent studies have applied comparisons across species to identify conserved gene coexpression in plants (Ficklin and Feltus, 2011;Movahedi et al, 2011;Mutwil et al, 2011). The analysis of coexpression networks between more distantly related species exploits the assumption that predicted gene function associations, occurring by chance within one organism, will not be conserved in a multispecies context.…”

Maize Source Leaf Adaptation to Nitrogen Deficiency Affects Not Only Nitrogen and Carbon Metabolism But Also Control of Phosphate Homeostasis

“…PEPC), and tetrapyrrole metabolism (Supplemental Table S4). The high connectivity of photosynthesis-related transcripts also was recently shown along the developmental gradient of the maize leaf (Pick et al, 2011) and in the large maize microarray collection analyzed by Ficklin and Feltus (2011). In our experiment, these modules enriched for chloroplast metabolism were responding mainly to the developmental stage of the maize plants.…”

“…The weighted correlation network analysis (WGCNA) tool was developed specifically for the identification of gene coexpression networks and has so far been applied to transcriptome data from rice and maize (Ficklin et al, 2010;Ficklin and Feltus, 2011). For our experiment, we were interested in identification of modules containing genes with generally high correlation to specific metabolic processes and modules with a strong N-treatment-specific pattern.…”

Plant expressed sequence tags databases: practical uses and the improvement of their searches using network module analysis

“…To evaluate relationships between multiple elements (e.g., gene or metabolite), network module analysis is a useful approach (Saito et al 2008). Network module analyses have been applied to plant gene co-expression, in which a plant gene is related to other genes based on similar expression profiles (Aoki et al 2007;Ficklin and Feltus 2011;Huber et al 2007;Marino-Ramirez et al 2009;Ogata et al 2010;Winden et al 2011). This approach allows a co-expression module, which includes coexpressed gene to be assigned to a particular biological process.…”