- Research article
- Open Access
Maize network analysis revealed gene modules involved in development, nutrients utilization, metabolism, and stress response
© The Author(s). 2017
Received: 23 December 2016
Accepted: 19 July 2017
Published: 1 August 2017
The advent of big data in biology offers opportunities while poses challenges to derive biological insights. For maize, a large amount of publicly available transcriptome datasets have been generated but a comprehensive analysis is lacking.
We constructed a maize gene co-expression network based on the graphical Gaussian model, using massive RNA-seq data. The network, containing 20,269 genes, assembles into 964 gene modules that function in a variety of plant processes, such as cell organization, the development of inflorescences, ligules and kernels, the uptake and utilization of nutrients (e.g. nitrogen and phosphate), the metabolism of benzoxazionids, oxylipins, flavonoids, and wax, and the response to stresses. Among them, the inflorescences development module is enriched with domestication genes (like ra1, ba1, gt1, tb1, tga1) that control plant architecture and kernel structure, while multiple other modules relate to diverse agronomic traits. Contained within these modules are transcription factors acting as known or potential expression regulators for the genes within the same modules, suggesting them as candidate regulators for related biological processes. A comparison with an established Arabidopsis network revealed conserved gene association patterns for specific modules involved in cell organization, nutrients uptake & utilization, and metabolism. The analysis also identified significant divergences between the two species for modules that orchestrate developmental pathways.
This network sheds light on how gene modules are organized between different species in the context of evolutionary divergence and highlights modules whose structure and gene content can provide important resources for maize gene functional studies with application potential.
Advances over the past two decades have generated numerous transcriptome datasets. Increasingly, ever more complete transcriptome data can be merged and integrated with gene and genome structures. They provide unbiased snapshots of gene expression dynamics within organisms under various conditions. While cell-, organ-, or condition-specific expression profiles abound, it remains a key challenge to deduce the underlying gene regulatory circuits that control and give rise to the observed gene expression dynamics. To address this, gene network analysis has emerged as a tool that can filter and refine the analysis of large gene expression datasets. Such gene networks consist of genes (nodes) and connections (edges) between genes that represent co-expression dynamics or association patterns underlying the expression data. According to a ‘guilt-by-association’ paradigm, connected genes may have similar functions, be part of the same complex or pathway, or participate in the same signaling circuits . Gene networks can assign putative functions to unknown genes based on functions revealed by their associates, or to identify novel genes for existing pathways [2, 3].
Different from random networks, gene networks generally identify gene modules categorizing and tracing groups of highly inter-connected genes that share similar expression patterns and, often, are recognizable by their relationship to a function in a particular biological process. Each module can be viewed as a unit in control of one or several biological functions. As such, modules identify and bring together segments of a biological system. Gene network analysis has been used to detect gene modules associated with, for example, human diseases or plant seed germination, and to study the transcriptome landscapes and gene module organization in yeasts, plants, and animals [4–10].
Gene networks derived from microarray data have been described for plant species such as Arabidopsis, maize, and rice [6, 8, 11–13]. These networks have been constructed via a variety of approaches that employed different ways to measure interactions between genes. Most common are co-expression networks that utilize the Pearson correlation coefficient (PCC) to measure expression similarity between genes, where gene pairs with PPC larger than a chosen threshold value are considered to interact with each other. Examples included two Arabidopsis networks: one identified clusters of genes involved in processes such as photosynthesis, vitamin metabolism, or cell cycle-regulation, while a second network revealed groupings with cellular organelles and tissue-specific functions [8, 11]. Rice and maize networks were also assembled via the Weighted Gene Coexpression Network Analysis (WGCNA) method, which utilizes a power function of PCC to assess expression similarity [6, 12–14]. Specifically, Downs et al. used WGCNA to construct a maize developmental gene co-expression network that captured modules with tissue and developmental stages specificity, while Ficklin and Feltus compared WGCNA networks from maize and rice to identify conserved modules [6, 13].
Yet another way to approach co-expression network analysis uses the graphical Gaussian model (GGM), which utilizes partial correlation (Pcor) to identify association relationships between genes [15–17]. Significantly, Pcor determines the correlation that remains between two genes after removing the effects of all other genes. Pcor measures direct association between genes, while PCC often fails to differentiate between direct and indirect associations [15, 16]. Thus, Pcor is deemed as a better metric than PCC for gene network analysis [16, 18]. However, the utilization of GGM has been impeded by a requirement that the number of samples must be far larger than the number of genes. The original design was able to calculate Pcor data among a few thousand genes only . Previously, we developed a random sampling-based method to overcome this obstacle and constructed the first genome-wide GGM network for Arabidopsis, followed by an updated network model termed AtGGM2014 [7, 17]. Compared to other networks, AtGGM2014 contained more genes and identified additional modules participating in a large variety of plant processes, like development, metabolism, response to stresses, and response to hormones . For example, among many informative gene modules, it included hormonal signaling modules for phytohormones like auxin, abscisic acid, jasmonic acid, gibberellins, cytokinins, ethylene, and salicylic acid, demonstrating the network’s potential to facilitate systems biology studies on Arabidopsis gene functions.
More recently, a large collection of RNA-Seq based maize gene expression datasets have been generated by different groups and deposited in the public domain. Among these datasets are a gene expression atlas for 79 maize tissues [19, 20], as well as expression datasets for specific organs like inflorescences , leaves , ligules , embryos and endosperms , and transcriptome datasets for different compartments in the endosperm [25, 26]. Others resulted from monitoring maize responses to abiotic stresses [27, 28], fungal infections , and different nutrition regimes . These datasets provide extremely valuable information for maize functional genomics research. A maize RNA-Seq-based gene network focused exclusively on development has been constructed from expression data of 23 different tissues, which identified 19 gene modules via the WGCNA method . However, to our knowledge, missing so far is a comprehensive gene network analysis that combines the wealth of many different maize datasets and merges them into an inclusive network to outline future research opportunities.
Here, we constructed a maize GGM gene co-expression network using expression data from 787 RNA-Seq runs deposited in the NCBI SRA database. 964 gene modules were identified from this network, highlighting functions in various cell organization, development, nutrients, metabolism, and stress responses pathways. As examples, we describe in detail modules involved in the development of inflorescences, ligules, and kernels, the uptake and utilization of nitrogen and phosphate, the metabolism of benzoxazionids, oxylipins, flavonoids and wax, and the response to heat stress, endoplasmic reticulum (ER) stress, and fungal infections. These modules provide a general picture for the relevant biological processes and identified both known genes as well as potential, so far unconnected, candidate genes for future functional studies. Importantly, many of these modules contain transcription factor genes that act as potential gene expression regulator for the genes within the same modules. In addition, the maize network has been compared to a previous published Arabidopsis network . This juxtaposition revealed conserved as well as diverging modules in the two species. The identified gene modules were further used to analyze a dataset on a maize leaf developmental series , demonstrating the usefulness of this network for systems biology analysis.
Overview of the maize GGM gene network
Selected gene modules identified from the network
Number of genes within the module
Percentage of genes sharing conserved interactions with Arabidopsis
Selected genes from the modulea
Enriched GO term
microtubule motor activity
monolayer-surrounded lipid storage body
abaxial cell fate specification
nutrient reservoir activity
sequence-specific DNA binding
response to phosphate starvations
cellular response to phosphate starvation
respiratory electron transport chain
mitochondrial electron transport / ATP synthesis
wax biosynthetic process
flavonoid metabolic process
fatty acid biosynthesis
monocarboxylic acid metabolic process
benzoxazinoids and oxylipins biosynthesis
oxylipin metabolic process
flavonoid biosynthetic process
response to fungus
defense response to fungus, incompatible interaction
response to heat stress
response to heat
response to endoplasmic reticulum stress
response to endoplasmic reticulum stress
Gene modules revealing developmental features
Ligules are fringe-like tissues located at the junction of the leaf blade and leaf sheath. In maize ligules control leaf angles and affect vegetative architecture [57, 58]. Two modules involved in ligules development were identified. Module #48 (Fig. 3b, Table 1 & Additional file 4: Table S3) is specifically expressed in the pre-ligule region of the leaf primordia (Additional file 5: Fig. S2). A key gene within the module is lg1 (liguleless1), encoding a SBP TF that acts as a mater regulator of ligule development . The recessive lg1 mutations in maize erases ligules and renders leaves more upright compared to wild type leaves . Interestingly, the genes within this module have their promoters enriched with a SBP TF binding motif “CGTAC” (pValue = 1.04E-6) , indicating they might be targets of Lg1. Two other SPB genes, sbp3 and sbp28, are also included within the module, although their functions remain uncharacterized. Other potential development regulators included myb44, a homologue of Arabidopsis LOF1 that functions in organ boundary specification , GRMZM2G480687, encoding a MEMBRANE-ASSOCIATED KINASE REGULATOR (MARK) proteins, and GRMZM2G145909, homologous to an Arabidopsis atypical bHLH gene IBL1. MARKs and atypical bHLH TFs in Arabidopsis participate in developmental processes mediated through brassinosteroids (BR) [61, 62], while BR-signaling also regulates ligules development in maize . Module #210 contains 15 genes, eight of which encode TFs in the KNOX family (Table 1 & Additional file 4: Table S3). Among these TFs are gn1 (gnarley1), kn1 (knotted1), rs1 (rough sheath1), lg3 (liguleless3), and lg4 (liguleless4), none of which show any expression in the pre-ligule regions of the wild type plants (Additional file 5: Fig. S2). However, ectopic expression of any of these genes in their corresponding dominant mutant background affects and distorts leaf and ligule development [64–66]. It remains to be tested if the other 3 KNOX TFs within the module have similar functions and if and how these TFs function together to regulate ligules development.
Kernels development represents yet another critical process determining maize grain yield and quality. Five relevant gene modules are identified. Among them, Module #46 (Fig. 3c, Table 1 & Additional file 4: Table S3), including 73 genes, is specifically expressed in the endosperm (Additional file 5: Fig. S2) and enriched with genes indicating nutrient reservoir activity (GO pValue = 6.32E-64). The module contains 16 genes encoding α-, δ-, or γ-zein proteins, the major seed storage proteins in maize, and 4 genes for starch biosynthesis (bt1, bt2, sh2, and wx1) [67, 68]. Notably, also included are a bZIP TF gene o2 (opaque endosperm2) and a Dof TF gene pbf1 (prolamin-box binding factor1), two master regulators of zein gene expression [69, 70]. It is recognized that within the maize network transcription factors and their target genes are often contained within the same modules, providing an edifying way to identify those modules’ expression regulator(s). Module #42 includes other TF genes as well, such as ereb167, platz12, nrp1, nactf130, as potential transcription regulators. Module #6 is expressed in both embryo and endosperm tissues at late developmental stages (Additional file 5: Fig. S2) and enriched with genes functioning in seed maturation (pValue = 7.39E-6). The module includes many lipid storage genes (e.g. oleosin1), desiccation tolerance genes (e.g. genes encoding late embryogenesis abundant proteins), and the TF gene vp1 (viviparous1), a master regulator of seed maturation and dormancy (Additional file 4: Table S3) . 36% of the genes within this modules possess conserved interactions when compared with the Arabidopsis network, including the key seed development genes like vp1, ole1, ole3, and mlg3. Additional modules were recovered with specific expression in different compartments of the maize endosperm, such as the basal endosperm transfer layer (BETL) (#11), the embryo-surrounding region (ESR) (#32), and the placento-chalazal region (PC) (#53) (Additional file 6: Fig. S3), similar to a previous report . These examples indicate that our network delineates different modules corresponding to different functional domains of kernels development and provides a general picture of the process.
Other modules were identified that draw attention to the development of other tissues and organs (Additional file 1: Table S4, Additional file 7: Fig. S4), such as anthers (#3, 17 and 22), meiotic tassels (#18), roots (#30), carpels (#92), Casparian strip (#76), and epidermal cells (#155). Furthermore, identified were modules for primary (#543) and secondary cell wall biosynthesis (#13), and for the signaling pathways of development related hormones, such as auxin (#158) and cytokinins (#183) (Additional file 1: Table S4). These modules are valuable resources for future functional studies on distinct and related developmental processes.
Modules for nutrients uptake and utilization
Module #36 includes genes involved in the phosphate starvation response (pValue = 9.62E-17) (Fig. 4b, Table 1 & Additional file 4: Table S3). The maize module includes homologues of Arabidopsis phosphate starvation response genes, such as SPX2, SPX3, SQD1, SQD2, MGD2, and PS2 , although their functions have not been characterized in maize. A P1BS motif “GNATATNC”, the binding site of the Arabidopsis G2-like transcription factor PHR1, a master regulator of phosphate starvation response gene expression , is enriched in the promoters of the genes in this module (pValue = 9.40E-24). Interestingly, the maize module contains three G2-like TFs, glk4, glk5, glk7, which could also act as master regulators.
Maize modules involved in the uptake and utilization of iron (#43) and sulfate (#79) were also identified (Additional file 1: Table S4). It should be noted that, compared to Arabidopsis, fewer transcriptome datasets are available for maize. As more data become available, more maize modules for nutrient uptake and usage should be revealed in future analysis.
Modules for metabolic processes
Module #154 (Fig. 5b, Table 1 & Additional file 4: Table S3) is enriched with flavonoids biosynthesis pathway genes (pValue = 2.26E-08), including fht1, a1, a2, bz1, bz2 . The module also contains pl1 (purple plant1) and r1 (colored1), two TF genes controlling anthocyanin biosynthesis , and two potential regulatory TFs myb105 and wrky33. In Arabidopsis, TTG2, a homolog of wrky33, regulates tannin level in the seed coat , while the function of maize wrky33 remains to be tested. The maize module also includes the gene mrpa3, encoding a tonoplast-localized anthocyanin transporter , and two uncharacterized transporter genes mrpa6 and AC206266.3_FG001. In addition to Module #154, involvement in flavonoids biosynthesis is indicated for Module #65 as well.
Waxes, deposited on the aerial surface of plants as a water-proof layer, are essential for plants in that they not only significantly limit water loss, but also counteract environmental stresses . Module #40 (Additional file 8: Fig. S5, Table 1 & Additional file 4: Table S3) of the maize network is enriched with wax biosynthesis genes (pValue = 2.25E-13), i.e. gl1, gl2, and gl3 . Among the 84 genes within this module, 23 have conserved interactions when compared with the corresponding Arabidopsis network. Conservation extends to uncharacterized genes that are homologous to Arabidopsis wax biosynthesis genes like KCS1, KCS6, KCS12, CER3, and LACS1 . Another conserved gene pair are fdl1 (fused leaves1) in maize and MYB94 in Arabidopsis, homologous to each other, both identified recently as TF regulators of wax biosynthesis [87, 88]. The maize module also contains gl3 (glossy3), another MYB TF as a master regulator of wax production . Other potential TF regulators within the module include 3 AP-EREBP type TFs, ereb12, ereb60, and ereb143. Among them, ereb60 shows homology to Arabidopsis WRI1 that regulates the accumulation of fatty acids – precursors for wax biosynthesis . Additionally, another related module for fatty acid biosynthesis (#73) was also identified from the network (Table 1).
In addition to benzoxazinoids, oxylipins, flavonoids, wax, and fatty acids, modules were also identified for the metabolism of suberin (#42), trehalose (#61), glucose (#234), glucan (#278), and lignins (#712) (Additional file 1: Table S4). Based on these modules, promising candidate genes can be selected for future functional studies.
Modules for stress responses
Modules involved in abiotic stress responses were also identified. For example, the genes in Module #10 (Additional file 8: Fig. S6, Table 1 & Additional file 4: Table S3) show relationships to heat stress responses. Enriched are heat shock stress response genes (pValue = 8.75E-49). 74 of the 157 genes within this module share conserved gene interaction(s) with an Arabidopsis heat shock related gene module . The conserved genes include 6 heat shock transcription factor genes (hsft7/8/12/20/24) and a co-activator gene MBF1C (GRMZM2G051135), highlighting their overarching importance in regulating heat activated gene expression regulation. The splicing regulator genes SR45a (GRMZM2G073567) and SR30 (GRMZM2G331811) are also included in the list of conserved genes, suggesting alternative splicing could play important roles in the heat shock response in both species. Similarly, Module #95 (Additional file 8: Fig. S7, Table 1 & Additional file 4: Table S3) is enriched with ER stress response genes (pValue = 6.89E-15). 23 out of the 35 genes within the module shared conserved interactions with Arabidopsis, including bip1, bip2, pdi1, and der1 . Interestingly, contained within the maize module is a putative master regulatory TF gene bzip60 , whose homologue in Arabidopsis, bZIP60, is a major regulator of the ER stress response , indicating bzip60 might have similar function in maize.
Also identified from the network were modules related to biotic stress responses. For example, Module #5 (Additional file 8: Fig. S8, Table 1 & Additional file 4: Table S3) is enriched with genes functioning in defense responses to fungal infections (pValue = 3.27E-09), such as cta1, wip1, prp1, and tps6. It contains a NAC type TF gene nactf7, a homologue of the Arabidopsis NAC042 gene. In Arabidopsis, NAC042 is a master TF that regulates the biosynthesis of the anti-fungal compound camalexin . Although maize does not produce camalexin, the inclusion of nactf7 in this anti-fungal module indicates that it may modulate other secondary metabolism processes to produce anti-fungal compounds. Indeed, within the module are many metabolism-related genes, including 9 genes encoding cytochrome P450 enzymes, whose roles in maize anti-fungal defenses remain to be studied. Interestingly, the module also encompasses genes for gibberellin biosynthesis (ks1, ks4, ko2, and cpps2), consistent with previous reports that infection by certain fungal pathogens upregulates gibberellin related genes in maize .
Additional defense related modules were identified from the network, for example Module #2 and #47 (Additional file 1: Table S4). These modules provide useful targets for future functional studies.
Gene network comparison between maize and Arabidopsis
Gene expression dynamics of gene modules in maize leaves
We report on the construction of a maize GGM gene co-expression network that includes 20,269 genes based on large-scale RNA-Seq transcriptome data. The resulting gene network was then analyzed and clustered via the MCL clustering algorithm . Although the algorithm partitioned the network purely based on its topology, the analysis resulted in 964 distinct and informative gene modules that included functions in a wide range of maize physiological processes. These modules are particularly useful in that they can assign putative functions to unknown/uncharacterized genes and identify participating genes (including novel genes) for specific developmental or physiological processes, as demonstrated by the selected examples. Module structure and the nature of the genes assembled in a module may then be used to analyze individual gene expression datasets, for example the expression data on maize leaf segments.
The modules identified in our network analysis covered many aspects of maize biology. Compared to a previously published maize transcriptional network that contained 49 modules , our model defined 964 modules in total. Sizes of previously identified modules were large. Considering that 87% of all genes in previous network  were included in modules with more than 1000 genes it was difficult to pinpoint potential key regulatory genes as candidates for future studies. In contrast, our GGM network identified modules containing between 5 and 306 genes, which facilitated ranking of potentially interesting genes, as demonstrated by the presentation above for various modules. Also, we were particularly encouraged by the numerous examples in which individual GGM modules identified control genes that had previously been revealed and verified experimentally by mutants analysis, such as ra1/3 in Module #16, o2, pbf1 in Module #46, bx1/2/3/4/5/6/8 in Module #80, and pl1, r1 in Module #154 [44, 50, 69, 70, 81, 82]. In an additional contrast to Downs et al. (2013), where the focus on developmental microarray data sets recovered modules with tissue-specific expression, our maize GGM gene network is condition-independent, constructed from transcriptome datasets related to development, stresses, nutrition, as well as other treatments. This facilitated the distinction between modules involved in cell organization, development, nutrients utilization, metabolism, and stress responses. These modules could potentially play critical roles in determining important maize agronomic traits. For example, the inflorescences development related Module #16 includes 5 genes ra1, ba1, gt1, tga1, and tb1 that have been shown by mutants analysis to regulate maize inflorescences architecture, and these 5 genes are designated as domestication genes since they were subjected to selections in the history of maize domestication as they control desirable traits [44, 45, 47, 51, 52, 56], while other genes within this module might also control maize architecture and performance. As well, modules for ligules and kernels development, for nitrogen and phosphate uptake and utilization, for primary and secondary metabolisms, and for responses to fungal infections include a wealth of information potentially useful for crop improvement.
An important feature of our network is that transcription factors and their target genes are often contained within the same module, suggesting shared expression characteristics. One example is the endosperm development Module #46, which is enriched with zein storage protein genes. The regulatory genes for zein biosynthesis, TF o2 and pbf1, are contained within this modules as well. In yet another example, the master regulator TF of seed maturation, vp1, is included within the seed maturation Module #6. The ligule-related Module #48 contains the master regulator gene lg1, encoding a SPL TF. The genes within this module are enriched a SPL binding motif, indicating they are targets of Lg1. Similarly, the heat-induced Module #10 contains 6 heat shock TF genes hsft7/8/12/20/24, the ER-stress response Module #95 includes a putative maser regulator bzip60, and the phosphate starvation response Module #38 possesses three putative master regulator glk4, glk5, glk7. Significantly, many identified modules also included genes encoding unknown and uncharacterized TFs that may represent novel gene expression regulators. The modular structures, as revealed by our network, provide an expedient and edifying way to identify putative TF regulators for various maize pathways.
The maize gene network also enables cross-species comparison between maize and Arabidopsis. The comparison revealed an unexpected degree of insight into different degrees of conservation in different pathways. Not surprisingly, cell organization modules showed the highest percentage of conserved genes, indicating the evolutionary stability of such basic cellular pathways. Among development related modules, those involved in generating basic building blocks of the plant body are shown to be conserved as well, i.e. carpels, Casparian strip, epidermis, and cell walls. However, divergence was found for functions in modules related to the overall architecture of the plants, for example the development of inflorescence structures. Such comparison sheds light on pathways that might have been “hot targets” for evolutionary changes. In the near future, with RNA-Seq transcriptome datasets rapidly accumulating, such network comparison analysis can be extended into more plant species to identify steps that highlight and determine plant evolutionary trajectories. As shown here for maize and Arabidopsis, conversation and similarity in modular comparisons will assist in pinpointing key regulators in various modules that can then be analyzed in detailed studies.
In conclusion, the maize GGM network presented here - in juxtaposition with a corresponding Arabidopsis network  - sheds light on similarities and differences in the organization of gene modules between different species in the context of evolutionary separation and different life histories. Additionally, our analysis highlights modules whose structure and gene content can provide important new resources for maize gene functional studies with application potential.
Maize RNA-Seq data collection
The publicly available maize RNA-Seq transcriptome datasets deposited in the NCBI SRA database were used in the analysis. These datasets were organized by studies. The studies were manually inspected to filter out those focusing on non-coding RNAs or those measuring transcriptome of the same tissues from a large number of maize varieties. Also removed were the studies with less than five RNA-Seq runs or without published articles. As a result, 36 studies were kept and their raw data files (sra) were downloaded. For RNA-Seq data processing, adapter sequences, if present, were removed from raw sequence reads using FASTX-toolkit pipeline version 0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit/). Sequence quality was examined using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and low quality read was filtered by FASTX-toolkit. The remaining reads were then mapped to the maize genome AGP v3.22 (Ensembl Plants, http://plants.ensembl.org) using Tophat v2.0.10  with default settings. After removing files with mapping rate smaller than 70%, the bam files from 787 RNA-Seq runs were analyzed to obtain gene expression values (FPKM) via Cufflinks v2.1.1 .
Maize GGM network construction
The gene expression data were then merged into a single gene expression matrix with 787 columns, and the low expressed genes (maximum FPKM values among all samples being less than 20) were filtered out, resulting in a matrix with 29,316 genes and 787 columns. The matrix was log-transformed [32–40] via the log2 (FPKM + 1) function, a procedure that significantly reduced the dataset’s mean-variance dependency (Additional file 2: Fig. S1). The log-transformed gene expression matrix was then used for partial correlation calculation, following a method described before . Briefly, the calculation involved a procedure with 25,000 iterations. In each iteration, 2000 genes were randomly selected and the partial correlation coefficients between gene pairs were estimated via the “ggm.estimate.pcor” function in the GeneNet v1.2.13 package in R . The Pcors were recorded in every iteration. After 25,000 iterations, for every gene pair, the Pcor with the lowest absolute value was chosen as its final Pcor. The PCC between all gene pairs were also calculated. The gene pairs with Pcor > = 0.035 and PCC > = 0.35 and those with Pcor <= −0.035 and PCC < = −0.35 were selected for gene network construction (Additional file 2: Table S2), resulting in a maize GGM gene network based on the log-transformed gene expression data.
To evaluate the effect of log-transformation on the network quality, another gene network was also constructed directly from the gene expression matrix with the original FPKM values without data transformation, designated as non-transformed FPKM network, keeping all other parameters the same as in the log-transformation-based network outlined above. These two networks were then evaluated and compared via the EGAD package in R regarding their capacities to connect maize genes with shared GO terms . The results indicated the log-transformation-based network out-performed the non-transformed FPKM network (Additional file 8: Fig. S9). Additionally, the log-transformation-based network identified gene modules that were not recovered by the non-transformed FPKM network, such as those related to ER stress response (#95) and nitrate response & assimilation (#72) (data not shown). These modules identified only by the log-transformation-based network contained genes that have been identified in different analyses to be related to the modules in question [73, 74, 91, 92].
Thus, we considered the higher power of the log-transformation-based network, and only results using the log-transformation-based network, designated as the Maize GGM Network, were further analyzed and discussed.
Gene network properties and gene module identification
The R package of RBGL v 1.44.0 (http://bioconductor.org/packages/RBGL/) is used to calculate the clustering coefficient of the maize GGM network. The network was clustered via the MCL clustering algorithm, using these parameters “-I 1.5 -Scheme 7” . The genes within each module were then analyzed for Gene Ontology enrichment via GOStats , with GO annotation file downloaded from the Gramene database (ftp://ftp.gramene.org/). The maize genes and their Arabidopsis homologues were further annotated with annotation files from MaizeGDB, TAIR, and PlnTFDB [101–103]. Selected modules were also tested for promoter motifs enrichment via the binomial distribution. An R script, included in the accompanied program MaizeGGM2016, was developed to extract sub-networks for gene modules and to draw development heatmaps for the genes within selected modules, with expression data from published datasets [19–21, 23–26]. The whole GGM network and the extracted sub-networks were layout and visualized with BioLayout Express 3D and Cytoscape 3.3, respectively [104, 105].
Gene network comparison between the maize network and the Arabidopsis network
To enable comparison between the maize GGM network and the Arabidopsis network AtGGM2014, the InParanoid program (v 4.1)  was used to identify the maize genes’, if present, most similar homologues in Arabidopsis. For any gene within the maize network, if there exists a homologous gene within the Arabidopsis AtGGM2014 network, the maize gene’s immediate neighboring genes within the maize network were extracted as group A. Also extracted, as group B, were its homologous gene’s neighbors within the Arabidopsis network. If any of the gene within group A has a homologous gene in group B, the original maize gene was considered to have conserved interaction within the Arabidopsis network. For any given module, the percentage of genes with conserved interaction was calculated as an indicator of evolution conserveness or divergence.
We thank Dr. Hans Bohnert for his critical reading of the manuscript. We are indebted to his invaluable suggestions.
S.M. is supported by grants from Thousand Youth Talents Program and University of Science and Technology of China (Start-up fund). P.L. is supported by grants from NSFC (91435108), National Key Research and Development Program of China (2016YFD0101003) and Taishan Scholarship.
Availability of data and materials
The maize gene network, and a companion program MaizeGGM2016, can be downloaded from our laboratory website for academic usage (http://staff.ustc.edu.cn/~sma/maizeggm.html).
SM and PL designed experiments. SM, ZD, and PL performed experiments. SM and PL wrote and edited the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Usadel B, Obayashi T, Mutwil M, Giorgi FM, Bassel GW, Tanimoto M, Chow A, Steinhauser D, Persson S, Provart NJ. Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ. 2009;32:1633–51.PubMedView ArticleGoogle Scholar
- Obayashi T, Kinoshita K, Nakai K, Shibaoka M, Hayashi S, Saeki M, Shibata D, Saito K, Ohta H. ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis. Nucleic Acids Res. 2007;35:D863–9.PubMedView ArticleGoogle Scholar
- Persson S, Wei H, Milne J, Page GP, Somerville CR. Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets. Proc Natl Acad Sci U S A. 2005;102:8633–8.PubMedPubMed CentralView ArticleGoogle Scholar
- FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, et al. A promoter-level mammalian expression atlas. Nature. 2014;507:462–70.View ArticleGoogle Scholar
- Dekkers BJ, Pearce S, van Bolderen-Veldkamp RP, Marshall A, Widera P, Gilbert J, Drost HG, Bassel GW, Muller K, King JR, et al. Transcriptional dynamics of two seed compartments with opposing roles in Arabidopsis seed germination. Plant Physiol. 2013;163:205–15.PubMedPubMed CentralView ArticleGoogle Scholar
- Downs GS, Bi YM, Colasanti J, Wu W, Chen X, Zhu T, Rothstein SJ, Lukens LN. A developmental transcriptional network for maize defines coexpression modules. Plant Physiol. 2013;161:1830–43.PubMedPubMed CentralView ArticleGoogle Scholar
- Ma S, Bohnert HJ, Dinesh-Kumar SP. AtGGM2014, an Arabidopsis gene co-expression network for functional studies. Sci China Life Sci. 2015;58:276–86.PubMedView ArticleGoogle Scholar
- Mao L, Van Hemert JL, Dash S, Dickerson JA. Arabidopsis gene co-expression network and its functional modules. BMC Bioinformatics. 2009;10:346.PubMedPubMed CentralView ArticleGoogle Scholar
- Mistry M, Gillis J, Pavlidis P. Meta-analysis of gene coexpression networks in the post-mortem prefrontal cortex of patients with schizophrenia and unaffected controls. BMC Neurosci. 2013;14:105.PubMedPubMed CentralView ArticleGoogle Scholar
- Stuart JM, Segal E, Koller D, Kim SK. A gene-coexpression network for global discovery of conserved genetic modules. Science. 2003;302:249–55.PubMedView ArticleGoogle Scholar
- Mentzen WI, Wurtele ES. Regulon organization of Arabidopsis. BMC Plant Biol. 2008;8:99.PubMedPubMed CentralView ArticleGoogle Scholar
- Childs KL, Davidson RM, Buell CR. Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS One. 2011;6:e22196.PubMedPubMed CentralView ArticleGoogle Scholar
- Ficklin SP, Feltus FA. Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice. Plant Physiol. 2011;156:1244–56.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005;4:Article17.Google Scholar
- Wille A, Zimmermann P, Vranova E, Furholz A, Laule O, Bleuler S, Hennig L, Prelic A, von Rohr P, Thiele L, et al. Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis Thaliana. Genome Biol. 2004;5:R92.PubMedPubMed CentralView ArticleGoogle Scholar
- Schäfer J, Strimmer K. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol 2005;4:Article32.Google Scholar
- Ma S, Gong Q, Bohnert HJ. An Arabidopsis gene network based on the graphical Gaussian model. Genome Res. 2007;17:1614–25.PubMedPubMed CentralView ArticleGoogle Scholar
- Zuo Y, Yu G, Tadesse MG, Ressom HW. Biological network inference using low order partial correlation. Methods. 2014;69:266–73.PubMedPubMed CentralView ArticleGoogle Scholar
- Sekhon RS, Briskine R, Hirsch CN, Myers CL, Springer NM, Buell CR, de Leon N, Kaeppler SM. Maize gene atlas developed by RNA sequencing and comparative evaluation of transcriptomes based on RNA sequencing and microarrays. PLoS One. 2013;8:e61005.PubMedPubMed CentralView ArticleGoogle Scholar
- Stelpflug SC, Sekhon RS, Vaillancourt B, Hirsch CN, Buell CR, de Leon N, Kaeppler SM: An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development. The Plant Genome 2016;9:0.Google Scholar
- Eveland AL, Goldshmidt A, Pautler M, Morohashi K, Liseron-Monfils C, Lewis MW, Kumari S, Hiraga S, Yang F, Unger-Wallace E, et al. Regulatory modules controlling maize inflorescence architecture. Genome Res. 2014;24:431–43.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang L, Czedik-Eysenberg A, Mertz RA, Si Y, Tohge T, Nunes-Nesi A, Arrivault S, Dedow LK, Bryant DW, Zhou W, et al. Comparative analyses of C(4) and C(3) photosynthesis in developing leaves of maize and rice. Nat Biotechnol. 2014;32:1158–65.PubMedView ArticleGoogle Scholar
- Johnston R, Wang M, Sun Q, Sylvester AW, Hake S, Scanlon MJ. Transcriptomic analyses indicate that maize ligule development recapitulates gene expression patterns that occur during lateral organ initiation. Plant Cell. 2014;26:4718–32.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen J, Zeng B, Zhang M, Xie S, Wang G, Hauck A, Lai J. Dynamic transcriptome landscape of maize embryo and endosperm development. Plant Physiol. 2014;166:252–64.PubMedPubMed CentralView ArticleGoogle Scholar
- Yi G, Neelakandan AK, Gontarek BC, Vollbrecht E, Becraft PW. The naked endosperm genes encode duplicate INDETERMINATE domain transcription factors required for maize endosperm cell patterning and differentiation. Plant Physiol. 2015;167:443–56.PubMedView ArticleGoogle Scholar
- Zhan J, Thakare D, Ma C, Lloyd A, Nixon NM, Arakaki AM, Burnett WJ, Logan KO, Wang D, Wang X, et al. RNA sequencing of laser-capture microdissected compartments of the maize kernel identifies regulatory modules associated with endosperm cell differentiation. Plant Cell. 2015;27:513–31.PubMedPubMed CentralView ArticleGoogle Scholar
- Makarevitch I, Waters AJ, West PT, Stitzer M, Hirsch CN, Ross-Ibarra J, Springer NM. Transposable elements contribute to activation of maize genes in response to abiotic stress. PLoS Genet. 2015;11:e1004915.PubMedPubMed CentralView ArticleGoogle Scholar
- Ding Y, Virlouvet L, Liu N, Riethoven JJ, Fromm M, Avramova Z. Dehydration stress memory genes of Zea Mays; comparison with Arabidopsis Thaliana. BMC Plant Biol. 2014;14:141.PubMedPubMed CentralView ArticleGoogle Scholar
- Lanubile A, Ferrarini A, Maschietto V, Delledonne M, Marocco A, Bellin D. Functional genomic analysis of constitutive and inducible defense responses to Fusarium verticillioides infection in maize genotypes with contrasting ear rot resistance. BMC Genomics. 2014;15:710.PubMedPubMed CentralView ArticleGoogle Scholar
- Bi YM, Meyer A, Downs GS, Shi X, El-Kereamy A, Lukens L, Rothstein SJ. High throughput RNA sequencing of a hybrid maize and its parents shows different mechanisms responsive to nitrogen limitation. BMC Genomics. 2014;15:77.PubMedPubMed CentralView ArticleGoogle Scholar
- Walley JW, Sartor RC, Shen Z, Schmitz RJ, Wu KJ, Urich MA, Nery JR, Smith LG, Schnable JC, Ecker JR, Briggs SP. Integration of omic networks in a developmental atlas of maize. Science. 2016;353:814–8.PubMedView ArticleGoogle Scholar
- Zwiener I, Frisch B, Binder H. Transforming RNA-Seq data to improve the performance of prognostic gene signatures. PLoS One. 2014;9:e85150.PubMedPubMed CentralView ArticleGoogle Scholar
- Specht AT, Li J. LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering. Bioinformatics. 2017;33:764–6.PubMedGoogle Scholar
- Israel JW, Martik ML, Byrne M, Raff EC, Raff RA, McClay DR, Wray GA. Comparative developmental Transcriptomics reveals rewiring of a highly conserved gene regulatory network during a major life history switch in the sea urchin genus Heliocidaris. PLoS Biol. 2016;14:e1002391.PubMedPubMed CentralView ArticleGoogle Scholar
- Kim B, Suo B, Emmons SW. Gene function prediction based on developmental Transcriptomes of the two sexes in C. Elegans. Cell Rep. 2016;17:917–28.PubMedView ArticleGoogle Scholar
- Pfeifer M, Kugler KG, Sandve SR, Zhan B, Rudi H, Hvidsten TR, International Wheat Genome Sequencing C, Mayer KF, Olsen OA. Genome interplay in the grain transcriptome of hexaploid bread wheat. Science. 2014;345:1250091.PubMedView ArticleGoogle Scholar
- Sekhon RS, Hirsch CN, Childs KL, Breitzman MW, Kell P, Duvick S, Spalding EP, Buell CR, de Leon N, Kaeppler SM. Phenotypic and transcriptional analysis of divergently selected maize populations reveals the role of developmental timing in seed size determination. Plant Physiol. 2014;165:658–69.PubMedPubMed CentralView ArticleGoogle Scholar
- Fertuzinhos S, Li M, Kawasawa YI, Ivic V, Franjic D, Singh D, Crair M, Sestan N. Laminar and temporal expression dynamics of coding and noncoding RNAs in the mouse neocortex. Cell Rep. 2014;6:938–50.PubMedPubMed CentralView ArticleGoogle Scholar
- Xiao X, Moreno-Moral A, Rotival M, Bottolo L, Petretto E. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules. PLoS Genet. 2014;10:e1004006.PubMedPubMed CentralView ArticleGoogle Scholar
- Langfelder P, Horvath S: WGCNA package FAQ. https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/faq.html. Accessed 11 May 2017.
- Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998;393:440–2.PubMedView ArticleGoogle Scholar
- Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL. Hierarchical organization of modularity in metabolic networks. Science. 2002;297:1551–5.PubMedView ArticleGoogle Scholar
- van Dongen S. Graph clustering by flow simulation. Dissertation: University of Utrecht; 2000.Google Scholar
- Vollbrecht E, Springer PS, Goh L, Buckler ES, Martienssen R. Architecture of floral branch systems in maize and related grasses. Nature. 2005;436:1119–26.PubMedView ArticleGoogle Scholar
- Gallavotti A, Zhao Q, Kyozuka J, Meeley RB, Ritter MK, Doebley JF, Pe ME, Schmidt RJ. The role of barren stalk1 in the architecture of maize. Nature. 2004;432:630–5.PubMedView ArticleGoogle Scholar
- Chuck G, Muszynski M, Kellogg E, Hake S, Schmidt RJ. The control of spikelet meristem identity by the branched silkless1 gene in maize. Science. 2002;298:1238–41.PubMedView ArticleGoogle Scholar
- Doebley J, Stec A, Hubbard L. The evolution of apical dominance in maize. Nature. 1997;386:485–8.PubMedView ArticleGoogle Scholar
- Gallavotti A, Barazesh S, Malcomber S, Hall D, Jackson D, Schmidt RJ, McSteen P. Sparse inflorescence1 encodes a monocot-specific YUCCA-like gene required for vegetative and reproductive development in maize. Proc Natl Acad Sci U S A. 2008;105:15196–201.PubMedPubMed CentralView ArticleGoogle Scholar
- Hayward AP, Moreno MA, Howard TP 3rd, Hague J, Nelson K, Heffelfinger C, Romero S, Kausch AP, Glauser G, Acosta IF, et al. Control of sexuality by the sk1-encoded UDP-glycosyltransferase of maize. Sci Adv. 2016;2:e1600991.PubMedPubMed CentralView ArticleGoogle Scholar
- Satoh-Nagasawa N, Nagasawa N, Malcomber S, Sakai H, Jackson D. A trehalose metabolic enzyme controls inflorescence architecture in maize. Nature. 2006;441:227–30.PubMedView ArticleGoogle Scholar
- Whipple CJ, Kebrom TH, Weber AL, Yang F, Hall D, Meeley R, Schmidt R, Doebley J, Brutnell TP, Jackson DP. Grassy tillers1 promotes apical dominance in maize and responds to shade signals in the grasses. Proc Natl Acad Sci U S A. 2011;108:E506–12.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang H, Nussbaum-Wagler T, Li B, Zhao Q, Vigouroux Y, Faller M, Bomblies K, Lukens L, Doebley JF. The origin of the naked grains of maize. Nature. 2005;436:714–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Karim MR, Hirota A, Kwiatkowska D, Tasaka M, Aida M. A role for Arabidopsis PUCHI in floral meristem identity and bract suppression. Plant Cell. 2009;21:1360–72.PubMedPubMed CentralView ArticleGoogle Scholar
- Klucher KM, Chow H, Reiser L, Fischer RL. The AINTEGUMENTA gene of Arabidopsis required for ovule and female gametophyte development is related to the floral homeotic gene APETALA2. Plant Cell. 1996;8:137–53.PubMedPubMed CentralView ArticleGoogle Scholar
- Vroemen CW, Mordhorst AP, Albrecht C, Kwaaitaal MA, de Vries SC. The CUP-SHAPED COTYLEDON3 gene is required for boundary and shoot meristem formation in Arabidopsis. Plant Cell. 2003;15:1563–77.PubMedPubMed CentralView ArticleGoogle Scholar
- Swanson-Wagner R, Briskine R, Schaefer R, Hufford MB, Ross-Ibarra J, Myers CL, Tiffin P, Springer NM. Reshaping of the maize transcriptome by domestication. Proc Natl Acad Sci U S A. 2012;109:11878–83.PubMedPubMed CentralView ArticleGoogle Scholar
- Becraft PW, Bongard-Pierce DK, Sylvester AW, Poethig RS, Freeling M. The liguleless-1 gene acts tissue specifically in maize leaf development. Dev Biol. 1990;141:220–32.PubMedView ArticleGoogle Scholar
- Moreno MA, Harper LC, Krueger RW, Dellaporta SL, Freeling M. Liguleless1 encodes a nuclear-localized protein required for induction of ligules and auricles during maize leaf organogenesis. Genes Dev. 1997;11:616–28.PubMedView ArticleGoogle Scholar
- Birkenbihl RP, Jach G, Saedler H, Huijser P. Functional dissection of the plant-specific SBP-domain: overlap of the DNA-binding and nuclear localization domains. J Mol Biol. 2005;352:585–96.PubMedView ArticleGoogle Scholar
- Lee DK, Geisler M, Springer PS. LATERAL ORGAN FUSION1 and LATERAL ORGAN FUSION2 function in lateral organ separation and axillary meristem formation in Arabidopsis. Development. 2009;136:2423–32.PubMedView ArticleGoogle Scholar
- Jaillais Y, Hothorn M, Belkhadir Y, Dabi T, Nimchuk ZL, Meyerowitz EM, Chory J. Tyrosine phosphorylation controls brassinosteroid receptor activation by triggering membrane release of its kinase inhibitor. Genes Dev. 2011;25:232–7.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang H, Zhu Y, Fujioka S, Asami T, Li J. Regulation of Arabidopsis brassinosteroid signaling by atypical basic helix-loop-helix proteins. Plant Cell. 2009;21:3781–91.PubMedPubMed CentralView ArticleGoogle Scholar
- Kir G, Ye H, Nelissen H, Neelakandan AK, Kusnandar AS, Luo A, Inze D, Sylvester AW, Yin Y, Becraft PW. RNA interference knockdown of BRASSINOSTEROID INSENSITIVE1 in maize reveals novel functions for Brassinosteroid signaling in controlling plant architecture. Plant Physiol. 2015;169:826–39.PubMedPubMed CentralView ArticleGoogle Scholar
- Becraft PW, Freeling M. Genetic analysis of rough sheath1 developmental mutants of maize. Genetics. 1994;136:295–311.PubMedPubMed CentralGoogle Scholar
- Fowler JE, Freeling M. Genetic analysis of mutations that alter cell fates in maize leaves: dominant Liguleless mutations. Dev Genet. 1996;18:198–222.PubMedView ArticleGoogle Scholar
- Foster T, Yamaguchi J, Wong BC, Veit B, Hake S. Gnarley1 is a dominant mutation in the knox4 Homeobox gene affecting cell shape and identity. Plant Cell. 1999;11:1239–52.PubMedPubMed CentralView ArticleGoogle Scholar
- Giroux MJ, Boyer C, Feix G, Hannah LC. Coordinated transcriptional regulation of storage product genes in the maize endosperm. Plant Physiol. 1994;106:713–22.PubMedPubMed CentralView ArticleGoogle Scholar
- Beatty MK, Rahman A, Cao H, Woodman W, Lee M, Myers AM, James MG. Purification and molecular genetic characterization of ZPU1, a pullulanase-type starch-debranching enzyme from maize. Plant Physiol. 1999;119:255–66.PubMedPubMed CentralView ArticleGoogle Scholar
- Hartings H, Maddaloni M, Lazzaroni N, Di Fonzo N, Motto M, Salamini F, Thompson R. The O2 gene which regulates zein deposition in maize endosperm encodes a protein with structural homologies to transcriptional activators. EMBO J. 1989;8:2795–801.PubMedPubMed CentralGoogle Scholar
- Vicente-Carbajosa J, Moose SP, Parsons RL, Schmidt RJ. A maize zinc-finger protein binds the prolamin box in zein gene promoters and interacts with the basic leucine zipper transcriptional activator Opaque2. Proc Natl Acad Sci U S A. 1997;94:7685–90.PubMedPubMed CentralView ArticleGoogle Scholar
- McCarty DR, Carson CB, Stinard PS, Robertson DS. Molecular analysis of viviparous-1: an Abscisic acid-insensitive mutant of maize. Plant Cell. 1989;1:523–32.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu G, Fan X, Miller AJ. Plant nitrogen assimilation and use efficiency. Annu Rev Plant Biol. 2012;63:153–82.PubMedView ArticleGoogle Scholar
- Lahners K, Kramer V, Back E, Privalle L, Rothstein S. Molecular cloning of complementary DNA encoding maize nitrite reductase: molecular analysis and nitrate induction. Plant Physiol. 1988;88:741–6.PubMedPubMed CentralView ArticleGoogle Scholar
- sakakibara H, Kawabata S, Takahashi H, Hase T, Sugiyama T. Molecular Cloning of the Family of Glutamine Synthetase Genes from Maize: Expression of Genes for Glutamine Synthetase and Ferredoxin-Dependent Glutamate Synthase in Photosynthetic and Non-Photosynthetic Tissues. Plant Cell Physiol. 1992;33:49–58.Google Scholar
- Ho CH, Lin SH, Hu HC, Tsay YF. CHL1 functions as a nitrate sensor in plants. Cell. 2009;138:1184–94.PubMedView ArticleGoogle Scholar
- De Angeli A, Monachello D, Ephritikhine G, Frachisse JM, Thomine S, Gambale F, Barbier-Brygoo H. The nitrate/proton antiporter AtCLCa mediates nitrate accumulation in plant vacuoles. Nature. 2006;442:939–42.PubMedView ArticleGoogle Scholar
- Rubin G, Tohge T, Matsuda F, Saito K, Scheible WR. Members of the LBD family of transcription factors repress anthocyanin synthesis and affect additional nitrogen responses in Arabidopsis. Plant Cell. 2009;21:3567–84.PubMedPubMed CentralView ArticleGoogle Scholar
- Hammond JP, Bennett MJ, Bowen HC, Broadley MR, Eastwood DC, May ST, Rahn C, Swarup R, Woolaway KE, White PJ. Changes in gene expression in Arabidopsis shoots during phosphate starvation and the potential for developing smart plants. Plant Physiol. 2003;132:578–96.PubMedPubMed CentralView ArticleGoogle Scholar
- Rubio V, Linhares F, Solano R, Martin AC, Iglesias J, Leyva A, Paz-Ares J. A conserved MYB transcription factor involved in phosphate starvation signaling both in vascular plants and in unicellular algae. Genes Dev. 2001;15:2122–33.PubMedPubMed CentralView ArticleGoogle Scholar
- Tzin V, Fernandez-Pozo N, Richter A, Schmelz EA, Schoettner M, Schafer M, Ahern KR, Meihls LN, Kaur H, Huffaker A, et al. Dynamic maize responses to aphid feeding are revealed by a time series of Transcriptomic and Metabolomic assays. Plant Physiol. 2015;169:1727–43.PubMedPubMed CentralGoogle Scholar
- Frey M, Schullehner K, Dick R, Fiesselmann A, Gierl A. Benzoxazinoid biosynthesis, a model for evolution of secondary metabolic pathways in plants. Phytochemistry. 2009;70:1645–51.PubMedView ArticleGoogle Scholar
- Petroni K, Pilu R, Tonelli C. Anthocyanins in corn: a wealth of genes for human health. Planta. 2014;240:901–11.PubMedView ArticleGoogle Scholar
- Johnson CS, Kolevski B, Smyth DR. TRANSPARENT TESTA GLABRA2, a trichome and seed coat development gene of Arabidopsis, encodes a WRKY transcription factor. Plant Cell. 2002;14:1359–75.PubMedPubMed CentralView ArticleGoogle Scholar
- Goodman CD, Casati P, Walbot V. A multidrug resistance-associated protein involved in anthocyanin transport in Zea Mays. Plant Cell. 2004;16:1812–26.PubMedPubMed CentralView ArticleGoogle Scholar
- Post-Beittenmiller D. Biochemistry and molecular biology of wax production in plants. Annu Rev Plant Physiol Plant Mol Biol. 1996;47:405–30.PubMedView ArticleGoogle Scholar
- Lee SB, Suh MC. Advances in the understanding of cuticular waxes in Arabidopsis Thaliana and crop species. Plant Cell Rep. 2015;34:557–72.PubMedView ArticleGoogle Scholar
- La Rocca N, Manzotti PS, Cavaiuolo M, Barbante A, Dalla Vecchia F, Gabotti D, Gendrot G, Horner DS, Krstajic J, Persico M, et al. The maize fused leaves1 (fdl1) gene controls organ separation in the embryo and seedling shoot and promotes coleoptile opening. J Exp Bot. 2015;66:5753–67.PubMedPubMed CentralView ArticleGoogle Scholar
- Lee SB, Suh MC. Cuticular wax biosynthesis is up-regulated by the MYB94 transcription factor in Arabidopsis. Plant Cell Physiol. 2015;56:48–60.PubMedView ArticleGoogle Scholar
- Liu S, Yeh CT, Tang HM, Nettleton D, Schnable PS. Gene mapping via bulked segregant RNA-Seq (BSR-Seq). PLoS One. 2012;7:e36406.PubMedPubMed CentralView ArticleGoogle Scholar
- Baud S, Wuilleme S, To A, Rochat C, Lepiniec L. Role of WRINKLED1 in the transcriptional regulation of glycolytic and fatty acid biosynthetic genes in Arabidopsis. Plant J. 2009;60:933–47.PubMedView ArticleGoogle Scholar
- Kirst ME, Meyer DJ, Gibbon BC, Jung R, Boston RS. Identification and characterization of endoplasmic reticulum-associated degradation proteins differentially affected by endoplasmic reticulum stress. Plant Physiol. 2005;138:218–31.PubMedPubMed CentralView ArticleGoogle Scholar
- Li Y, Humbert S, Howell SH. ZmbZIP60 mRNA is spliced in maize in response to ER stress. BMC Research Notes. 2012;5:144.PubMedPubMed CentralView ArticleGoogle Scholar
- Iwata Y, Fedoroff NV, Koizumi N. Arabidopsis bZIP60 is a proteolysis-activated transcription factor involved in the endoplasmic reticulum stress response. Plant Cell. 2008;20:3107–21.PubMedPubMed CentralView ArticleGoogle Scholar
- Saga H, Ogawa T, Kai K, Suzuki H, Ogata Y, Sakurai N, Shibata D, Ohta D. Identification and characterization of ANAC042, a transcription factor family gene involved in the regulation of camalexin biosynthesis in Arabidopsis. Mol Plant-Microbe Interact. 2012;25:684–96.PubMedView ArticleGoogle Scholar
- Skibbe DS, Doehlemann G, Fernandes J, Walbot V. Maize tumors caused by Ustilago Maydis require organ-specific genes in host and pathogen. Science. 2010;328:89–92.PubMedView ArticleGoogle Scholar
- Li P, Ponnala L, Gandotra N, Wang L, Si Y, Tausta SL, Kebrom TH, Provart N, Patel R, Myers CR, et al. The developmental dynamics of the maize leaf transcriptome. Nat Genet. 2010;42:1060–7.PubMedView ArticleGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.PubMedPubMed CentralView ArticleGoogle Scholar
- Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks. Nat Protoc. 2012;7:562–78.PubMedPubMed CentralView ArticleGoogle Scholar
- Ballouz S, Weber M, Pavlidis P, Gillis J. EGAD: ultra-fast functional analysis of gene networks. Bioinformatics. 2017;33:612–14.Google Scholar
- Falcon S, Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics. 2007;23:257–8.PubMedView ArticleGoogle Scholar
- Andorf CM, Cannon EK, Portwood JL 2nd, Gardiner JM, Harper LC, Schaeffer ML, Braun BL, Campbell DA, Vinnakota AG, Sribalusu VV, et al. MaizeGDB update: new tools, data and interface for the maize model organism database. Nucleic Acids Res. 2016;44:D1195–201.PubMedView ArticleGoogle Scholar
- Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, et al. The Arabidopsis information resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2012;40:D1202–10.PubMedView ArticleGoogle Scholar
- Perez-Rodriguez P, Riano-Pachon DM, Correa LG, Rensing SA, Kersten B, Mueller-Roeber B. PlnTFDB: updated content and new features of the plant transcription factor database. Nucleic Acids Res. 2010;38:D822–7.PubMedView ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.PubMedPubMed CentralView ArticleGoogle Scholar
- Theocharidis A, van Dongen S, Enright AJ, Freeman TC. Network visualization and analysis of gene expression data using BioLayout express(3D). Nat Protoc. 2009;4:1535–50.PubMedView ArticleGoogle Scholar
- Sonnhammer EL, Ostlund G. InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic. Nucleic Acids Res. 2015;43:D234–9.PubMedView ArticleGoogle Scholar