- Research article
- Open Access
Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism
BMC Genomics volume 17, Article number: 129 (2016)
D-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.
Differentially 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.
This 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.
Inositol phosphates (IPs) are a series of phosphate esters of myo-inositol (Additional file 1: Figure S1). IPs are abundant in the natural environment and have multiple biological functions . IP3  and other inositol polyphosphates—including IP6 , IP7  and IP8 —are important messengers. IP6 also plays important roles in RNA editing , transcription , DNA repair  and the stress response . In humans, IP6 showed significant inhibition and regulation of cancer cell growth . IP5 functions as a ligand in the COI1-JAZ signaling pathway  or as a substrate for the production of other secondary metabolites, including components of the cell wall  and raffinose .
Grain crops store excess phosphate as a single compound, inositol hexaphosphate (IP6), also known as phytic acid, which accounts for ~65–85 % of the total phosphate in grain crop seeds . IP6 has been reported to be an inhibitor of the absorption of mineral nutrients such as zinc and iron  by animals including humans because of a lack of phytase in their digestive system . Moreover, the excrement of grain-fed livestock containing IP6 can lead to phosphorus pollution .
The reduction of phytic acid content is a major goal of plant-breeding programs that aim to improve the nutritional quality of crops. Efforts to breed low-phytic-acid (lpa) crops have focused on the genes involved in IP metabolism and compartmentation [17–21], while the regulatory pathways for IP synthesis and accumulation have received scant attention [22, 23]. Moreover, although high IP6 content in seeds was thought to be unnecessary for plant development , many lpa mutants showed a lethal phenotype  or recessionary agronomic traits , including reduced yield and reduced resistance to stress. However, the molecular mechanism(s) underlying these phenotypes of lpa plants, and the roles played by IPs in plant physiology remain unclear, owing to a generally poor understanding of the synthesis and biological functions of IPs in plants . Thus, further research into the functions of IPs is warranted.
The synthesis of IPs involves sequential phosphorylation of the inositol ring . Plants have two main IP synthesis pathways (Additional file 1: Figure S2). The first is the lipid-dependent pathway, which is the main source of the secondary messenger Ins(1,4,5)P3. IP3 is then phosphorylated by inositol phosphate multikinase to form IP6. The second pathway is lipid-independent and is considered the main source of IP6 in seeds. Several steps in this pathway are unknown, e.g., no genes or proteins have been identified as responsible for the conversion of IP1 into IP2. IP metabolism in maize is poorly understood. Three genes were identified as responsible for IP metabolism in maize using a reverse-genetics technique. A maize lpa2 mutant was created by mutation of the ZmIpk gene. ZmIpk encodes an inositol phosphate kinase and is homologous with Arabidopsis Ins(1, 3, 4)P3 5/6-kinase . The maize line lpa3 is a Mu-insertion mutant in which an increased level of myo-inositol and decreased levels of other IPs were caused by a mutation in MIK, which encodes a pfkB carbohydrate kinase-family protein named myo-inositol kinase. This protein catalyzes the production of myo-inositol monophosphate from myo-inositol and ATP . Another low-phytic-acid maize mutant, termed lpa1, harbored a mutation in a multidrug resistance-associated protein (MRP) ATP-binding cassette (ABC) transporter, the exact molecular function of which remains unclear [19, 25]. Such findings increase our understanding of IP metabolism. However, further information regarding the metabolic pathways and molecular functions of IPs is needed.
Some genes involved in IP metabolism are present as more than one copy or as multiple splice variants. Interestingly, only one of those copies or splice variants is important for IP accumulation in seeds [18, 30], while others play roles in physical development, e.g., the gene encoding myo-inositol 1-phosphate synthase (MIPS) is required for the suppression of cell death . Other than participating in the metabolism of IPs, some genes encode multifunctional proteins involved in the regulation of complex physiological processes [26, 32], e.g., MIPS-silenced soybean lines show inhibited seed development . Thus, it is necessary to explore the regulation of IP metabolism and establish the link between IPs and these important physiological processes.
Systems biology approaches are powerful tools for exploring the links between co-regulated genes and metabolites. Metabolite abundance is controlled by gene expression and enzyme activities. Thus metabolite data can be combined with those of gene expression by means of correlation and other distance analyses . Moreover, the coordinated expression pattern of a set of genes indicates the functional links among them and genes associated with the same metabolic pathway are likely to be co-expressed . The network method facilitates exploration of candidate genes and the regulatory mechanisms of a pathway . Modules [33, 36], which contain information regarding which biological processes are related to the candidate genes or metabolites, can be extracted from a co-expression network.
Thus, construction of a co-expression network of genes related to IPs has two advantages: first, complete coverage of all expressed functional genes, which provides information on those related to both metabolism and regulation; and second, identification of relationships between IP metabolism and other biological pathways. Weighted gene co-expression network analysis (WGCNA)  is more effective than other network construction methods, and weighted networks are more robust and accurate than un-weighted networks .
In this study, we constructed gene co-expression networks related to each IP by integrating transcriptome and metabolite data to explore the regulatory mechanism of IP metabolism. Key nodes between IP metabolism and other biological pathways were implied from the network. Three candidate genes were also predicted based on the gene co-expression network and validated by biological experiments.
Results and discussion
Determination and analysis of IP levels in maize seeds
IP6 screening in a maize germplasm collection
We screened an inbred maize collection  to select inbred lines with significant differences in phytic acid content. Four hundred twenty-five content values were obtained after removing abnormal values. The average content of phytic acid phosphors (PAP, phosphate in phytic acid) in this inbred maize collection was 3.9 mg/g, slightly higher than the 3.0 mg/g reported previously . Ultimately, 24 line-pairs with stable IP6 content ratios (~2 times) were obtained (Methods, Additional file 2: Table S1). The PAP content of low phytic acid (LPA) lines ranged from 2.4 mg/g to 2.5 mg/g, and that of high phytic acid (HPA) lines from 4.0 to 5.4 mg/g (Additional file 1: Figure S3). B73 (HPA), Lv28 (HPA), Qi319 (LPA), and CIMBL141 (LPA) were selected for further monitoring of dynamic changes in IP levels because they were elite inbred lines used for breeding and genetic research, and the IP6 content ratios in their seeds was ≥ 2.
Dynamic changes in IP levels in maize kernels at different developmental stages
To explore the dynamic changes and distributions of the various IPs, we collected whole fresh seeds and embryos of maize at different developmental stages (6, 12, 18, 21, 24, and 30 days after pollination, DAP) and determined their IP levels by LC-MS/MS. We first examined each IP in the whole seeds of four inbred lines—B73 (HPA), Lv28 (HPA), CIMBL141 (LPA) and Qi319 (LPA)—to explore the dynamic changes in IP levels as a function of developmental stage.
IP4–IP6 levels were undetectable or very low in whole seeds at earlier developmental stages. Thus, we next compared the IP1–IP3 levels in seeds at the aforementioned developmental stages (Fig. 1). The results showed that IP1 displayed similar accumulation trends in each inbred line, with peaks at 12-21DAP. Moreover, the peak value of IP1 in B73 (12DAP) was 9 days earlier than in Qi319 (21DAP). IP3 showed a steady trend in B73, but underwent significant fluctuations in CIMBL141 and Qi319. IP2 levels declined continuously in B73, but in Qi319 peaked at 12DAP. It is also worth noting that IP2 and IP3 showed a sustained increasing trend in Lv28 (Fig. 1).
We next determined IP levels in embryos (E) and compared them with the IP levels in whole seeds (W) of B73 and Qi319. The results showed that IPs accumulated mainly in embryos, as indicated by content ratios considerably greater than 1 (E/W > 1, Fig. 2). Moreover, IPs with higher phosphate numbers—such as IP4 and IP6—were not detected before 21DAP in whole seeds (Fig. 3). For example, in contrast with B73, IP4 was not detected before 21DAP in Qi319 (Fig. 3a); IP6 was first detectable in embryos at 21DAP, and its levels were significantly higher in embryos than in the whole kernels (Fig. 3b, c). Interestingly, in the fresh samples, the IP6 level in B73 was slightly lower than that in Qi319, despite B73 being a high-phytic-acid content line. IP5 was detected in all embryos but its levels were significantly lower at 21DAP in B73 and Qi319 (Additional file 1: Figure S4), which may be related to the abundances of IP4 and IP6.
In summary, our data showed that IPs accumulated mainly in embryos, and the fact that IP4 and IP6 were not detected before 21DAP in whole seeds (Fig. 3) suggests that phytic acid was synthesized sequentially in the embryo, and that the stages of IP synthesis are associated with different developmental stages of the embryo. In maize, the compartment in which phytic acid is synthesized has been reported , however, our data confirmed that, at the metabolite level, IP6 accumulates mainly in the embryo.
Taking into consideration the abundance and distribution of each IP, these results indicated clear sampling points for RNA-Seq analysis. We therefore subjected embryos of B73 and Qi319 (Lv28 and CIMBL141 were not selected because they were not growing well in the field, and were susceptible to diseases and insect pests) at 12, 21 and 30DAP to RNA-Seq and microRNA-Seq analyses to explore the regulation of IP metabolism. Data-mining workflows are shown in Additional file 1: Figure S5.
Transcriptome analysis of B73 and Qi319
Alternative splicing and differentially expressed gene (DEG) analysis
To further understand the regulation of IP metabolism, we performed RNA-Seq analyses of maize embryos at different developmental stages. To verify that the gene expression patterns were consistent with previous reports, we determined the expression levels of three known genes—MIK (GRMZM2G361593) , ITPK-1 (GRMZM2G456626)  and ABC transporter (GRMZM5G820122) —by qRT-PCR (Additional file 1: Figure S6). MIK expression in Qi319 peaked at about 21DAP, similar to the findings of Shi et al. . In contrast, MIK expression in B73 declined continuously. The expression levels of ITPK-1 and ABC transporter were similar to that of MIK, but exhibited different expression patterns. Moreover, the greater expression of the three genes in B73 at 12DAP is likely related to the earlier detection of IP4 (Additional file 1: Figure S6, Fig. 3a).
Alternative splicing (AS) plays important roles in gene functions  and is the main source of protein diversity . For instance, OsLpa1 has three splices in rice, but only the longest transcript is responsible for the phytic acid content of seeds . IP metabolism genes also have specific AS modes (Additional file 1: Figure S7). The gene encoding inositol 1,4,5-triphosphate 5-phosphatase (EC 220.127.116.11) (GRMZM2G004301) has splice sites at the first exon (alternative first exon, AFE) and the last exon (alternative last exon, ALE). This AS mode was present in B73 but not in Qi319. In B73, ITPK-2 (GRMZM2G179473) exhibited two variants: 3' alternative splicing (alternative 3' splice site, 3AS) and exon skipping (ES) at 12 and 30DAP, while at 21DAP, only 3AS was present. Interestingly, only 3AS of ITPK-2 was detected by RNA-Seq at 30DAP in Qi319. Therefore, the variation in AS modes observed among the inbred lines at different developmental stages is likely related to the metabolic differences between B73 and Qi319 (Additional file 1: Figure S7).
The different abundances of each IP among plant tissues would be due to differences in the expression levels of the corresponding genes. To gain an increased understanding of the differences in IP metabolism, we compared gene expression levels between the B73 and Qi319 lines. Genes with fold changes (FCs) > 2 and FDR < 0.01 were considered to be DEGs.
In general, a greater number of DEGs was found among the genetic backgrounds, rather than the developmental stages. In total, 1481 genes were differentially expressed in the embryos at all time points (Additional file 1: Figure S8). Gene ontology (GO) annotations of the DEGs indicated that they were enriched in the “metabolic process”, “binding”, “catalytic activity” and “transporter activity” categories (Additional file 1: Figure S9A-C). Cluster of Orthologous Groups of proteins (COG) annotation showed that ~500 DEGs were enriched in the “Carbohydrate transport and metabolism”, “Transcription” and “Signal transduction mechanism” categories (Additional file 1: Figure S9E-F).
We also mapped the DEGs to KEGG pathways (Entry: map00562, p < 0.05, multiple hypothesis-based testing) (Additional file 1: Figure S10). MIPS and ITPK-1 were differentially expressed in Qi319 and B73 at all time points. MIPS (EC 18.104.22.168) catalyzes the conversion of glucose-6-phosphate (G6P) to myo-inositol 3-phosphate (IP1), the first step in IP synthesis (Additional file 1: Figure S10A-C). The differential expression of MIPS implied that the metabolic differences between B73 and Qi319 began at the first step of IP synthesis. ITPK-1 catalyzes the conversion of IP3 to IP4 and was upregulated at 12DAP but downregulated at 21DAP and 30DAP in B73 (Additional file 1: Figure S10A-C), which was consistent with the qRT-PCR results (Additional file 1: Figure S6). This expression pattern was likely related to the appearance of IP4 in the embryo (Fig. 3a). In addition, some inositol transporter genes and inositol triphosphate 5-phosphase genes were also differentially expressed in B73 and Qi319 (Additional file 1: Figure S11).
Differentially expressed transcription factors and microRNAs
To our knowledge, no transcription factors (TFs) involved in the regulation of IP metabolism have been reported to date. We extracted 303 TF genes from the DEGs (Additional file 3), including 13 novel assembled genes.
Some of the differentially expressed TFs were consistently up- or down-regulated in B73 or differentially expressed at different developmental stages (Additional file 1: Figure S12A). Those TFs were enriched in four families according to their molecular functions (Additional file 1: Figure S12B)—WRKY, MYB, bHLH and ethylene-responsive TFs. WRKY family genes are actively involved in abiotic/biotic stress and hormone responses, e.g., gibberellic acid, GA  and ~30 WRKY genes were persistently differentially expressed during all developmental stages assessed in this study. MYB and bHLH domain-containing TFs are associated with the regulation of plant development, the stress response, and temporal regulation of gene expression . Twenty MYB genes were sustainably differentially expressed. Ethylene-responsive TFs are involved in primary and secondary metabolite regulation, and were (40 genes) differentially expressed mainly at 12DAP (Additional file 1: Figure S12B). These results provide resources for further research into IP related TFs.
MicroRNA-Seq analyses showed that five differentially expressed microRNAs in B73 and Qi319 targeted IP-related genes (according to the GO annotation and microRNA target prediction, Table 1). Three of these candidate microRNAs had predicted stem-loop structures, and their expression tended to be negatively correlated (e.g., GRMZM2G135978 was negatively correlated with zma-miR393c-5p, Pearson coefficient = −0.67) with that of their target genes (Additional file 1: Figure S13).
Weighted gene co-expression network construction and analysis
The abundance of each IP in B73 and Qi319 embryos showed similar patterns with the expression of known genes (Additional file 1: Figure S4, Figure S6). To explore the regulation of IP metabolism and discover new genes involved in IPs, we first constructed gene co-expression networks weighted with metabolite data (WGCNA method, Additional file 1: Figure S15). Ultimately, six IP-related gene co-regulation modules (correlation coefficient > 0.8, p < 0.01) were extracted from the whole transcriptome (Table 2 and Additional file 1: Figure S14, Figure S15), three of which (“magenta2”, “cornsilk” and “dodgerblue4”) were subjected to further analysis. The networks were compressed using the Power Graph method , cutting nodes with low connectivity and merging the shared edges to form power edges. The compressed ratio of each network ranged from 75 to 92 % (Additional file 2: Table S2). The networks were decoded based on guide-genes and metadata analyses using plugins embedded in Cytoscape (see Methods).
NADH: ubiquinone oxidoreductase gene co-regulated with ITPK in the “magenta2” network
ITPK-1 was co-expressed with the NADH:ubiquinone oxidoreductase (EC 22.214.171.124) gene (GRMZM2G084914, node MJ2, correlation coefficient = 0.88, p-value = 8 × 10−3), metal-binding protein gene (GRMZM2G092867, node ME2, correlation coefficient = 0.59, p-value = 2 × 10−3), UDP-glycosyltransferase gene (AC199541.4_FG004, node ME1, correlation coefficient = 0.51, p-value = 3 × 10−3), and a VQ-motif (interacting with WRKY TFs)  -containing protein gene (AC194056.3_FG008, node MG, correlation coefficient = 0.65, p-value = 1 × 10−3) in the “magenta2” network, which was negatively correlated with IP6 (−0.87, p-value = 3 × 10−4) and IP4 (−0.87, p-value = 2 × 10−3; the sub-network 1 of “magenta2”, Fig. 4a and Additional file 4). As described above, the correlation of ITPK-1 with the VQ-coding gene suggested a mechanism by which WRKY TFs regulate the expression of genes related to IP metabolism.
NADH:ubiquinone oxidoreductase consumes or produces NADH and functions as a key enzyme in oxidative phosphorylation and the electron transport chain [46, 47]. The relationships between IP metabolism and NADH have not been discussed previously. Although it is known that NADH is required for catalysis by MIPS of glucose 6-phosphate to form inositol monophosphate , why the expression of ITPK-1 and IP4/IP6 was correlated with that of the NADH:ubiquinone oxidoreductase gene is unclear. The IP3 receptor interacted with NADH to release Ca2+ [49, 50], and Voronina et al. reported that the NADH level was related to Ca2+ signaling , which was directly modulated by the NAD+/NADH redox state . Therefore, the correlation of ITPK-1 expression with that of the NADH:ubiquinone oxidoreductase gene essentially reflects the co-regulation of redox homeostasis (NADH) and Ca2+ signaling, and implies that sequential changes in IP3 levels will in turn affect IP metabolism.
Interaction of the gibberellic acid signaling pathway with IP metabolism in the “magenta2” network
We extracted another sub-network from “magenta2” using a gene (GRMZM2G154565, node Ma1) annotated as “1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase” (a member of the phospholipase C gene family) as a guide-gene (Fig. 4b, and Additional file 4, the sub-network 2 of “magenta2”). This network overlapped through four nodes (ME2, MI, MJ1, and MJ2) with the first sub-network mentioned above (Fig. 4a).
Phospholipase C (PLC, node Ma1) is involved in Ca2+ signal transduction [53, 54] and IP metabolism  by releasing IP3 from phosphatidylinositol. The PLC gene was correlated with node ME2 (correlation coefficient = 0.86, p-value = 1 × 10−2), which was also correlated with ITPK-1 (Fig. 4a). This indirect interaction is consistent with the roles of PLC and ITPK-1 in IP metabolism.
F-box protein and metal ion transporter genes lie in the regulation center between the PLC gene and the P-loop–containing protein gene (GRMZM2G123544, node Mf3). Some small molecules, e.g., auxin, can bind to F-box protein and mediate ubiquitination to regulate protein function at the posttranslational level [56, 57]. Interestingly, two gibberellic acid (GA)-related genes, i.e., node Mc1 (GRMZM2G070068) and Md1 (GRMZM2G013016), were also in contact with node Mf3 by sharing the Mb1, ME2, and MJ1 nodes, which were also in contact with the PLC gene (Fig. 4b).
The finding that the PLC gene shared nodes with GA-related genes is noteworthy. Metadata analyses showed that the GA signaling pathway has biological relationships with inositol phosphates. IP6 is hydrolyzed by phytase in the presence of GA during seed germination , and some lpa mutant crop lines show a reduced germination phenotype [27, 59]. Murthy et al.  found that phosphatidylinositol metabolism was altered by GA in the aleurone layer of barley. Microarray data analysis showed that IP metabolism genes also responded to GA and ABA induction in rice . Fleet et al.  reported that Arabidopsis 5ptase mutants were hypersensitive to paclobutrazol (a GA synthesis inhibitor), suggesting a relationship between elevated IP3 levels and decreased GA signal transduction. Therefore, rather than a direct interaction, it is reasonable that GA-related genes and IP-related genes were co-regulated by nodes Mb1 (F-box protein gene), ME2 (metal ion-binding protein gene) and MJ1 (putative phospholipase gene) in this sub-network. Thus, this network predicts the potential mechanism and key nodes of the interactions between IPs and GA.
Several other genes also contacted the guide gene by sharing nodes Mb1 and ME2, including a P-loop containing (IPR027417) protein-coding gene (GRMZM2G123544, node Mf3). A P-loop domain is also present in the Lpa1 ABC transporter [19, 63]. Node Mf12 (GRMZM2G146041) is another P-loop-containing protein-coding gene, which was further from the guide gene compared with node Mf3. Therefore, we selected Mf3 as the first candidate gene involved in IP metabolism. Mf3 likely encodes an enzyme because it lacks a predicted transmembrane domain but contains an ATPase domain (IPR027417).
Carbohydrate-related transporter genes in the “cornsilk” network
The “cornsilk” network was correlated with both IP5 (correlation coefficient = 0.91, p-value = 1 × 10−4) and IP6 (correlation coefficient = 0.86, p-value = 3 × 10−4). Interestingly, many transporter genes (10 nodes, >30 %) and transcription factor genes (15 nodes, 50 %) were in this network (Fig. 5, and Additional file 5).
Unexpectedly, the multidrug resistance-associated ATP-binding cassette (ABC) transporter gene (GRMZM5G820122, ZmMRP4) [19, 25] was not in this network, despite its similar expression pattern to those of ITPK-1 and MIK. Many identified ABC transporters are multi-role and have various substrates, including lipids, ABA, and other hormones [64–66]. The low-phytic-acid phenotype of lpa1 mutants or transgenic lines in rice , maize [19, 25, 67], and soybean [63, 68, 69] suggest that other unknown transporters are also responsible for IP transport between cells or organs. This may explain the lack of a correlation between ZmMRP4 expression and IP6 level.
Most transporter genes in this network were related to carbohydrate metabolism. Representative genes include a UDP-galactose transporter (GRMZM2G089630, node CD2), carbohydrate/inositol-transporters (GRMZM2G063824, GRMZM2G064437, nodes CA1 and CC1), and a glycerol-3-phosphate transporter (GRMZM2G078757, node CC4).
The correlation between IP6 levels and the expression of carbohydrate transporters (correlation coefficient ranged from 0.51 to 0.95, p-value < 4 × 10−3) would imply some interesting biological relationships. Metabolite profiling of rice showed that galactose and galactinol levels were increased in lpa mutant lines . In fact, MIPS consumes glucose 6-phosphate and NADPH to generate inositol monophosphate, producing UDP-glucose as a by-product. UDP-glucose is then converted into UDP-galactose. Therefore, the correlation between IPs and UDP-transporters would indicate the presence of metabolic cooperativity between IPs and UDP-galactose. IP6, or possibly other IPs, affects carbohydrate metabolism and composition by an unclear mechanism, e.g., several lpa mutants of maize exhibited decreased starch content . In a study of the pleiotropic effects in the lpa1 mutant, neither the total starch content nor the amylose/amylopectin ratio was altered, but the structure and size of granules differed from those in the wild type . Thus this network illustrates the possible interactions between IPs and carbohydrate metabolism.
Raboy hypothesized that ion transporters are involved in IP6 transport to vesicles . Indeed, an H+ transporter gene (GRMZM2G075900, node CB5) was also identified in this network. Lemtiri-Chlieh et al. found that ABA increased IP6 levels, and that IP6 was a potent Ca2+-dependent inhibitor of K+ traffic into guard cells . IP6 accumulated mainly in vacuoles and its accumulation was enhanced when cells were grown in the presence of high concentrations of inorganic phosphates containing K+, Ca2+, or Zn2+ . H+-transporters are a type of H+-translocating pyrophosphatase. Takasu et al. found that IP6 could directly interact with and inhibit H+-pyrophosphatase activity . The H+-transporter (node CB5, GRMZM2G075900) was likely involved in the translocation of IP6 into vesicles and therefore CB5 was selected as a candidate gene for further study.
Specific ABC transporters and IP compartmentation
IP1 was correlated with the “dodgerblue4” network (correlation coefficient = 0.87, p-value = 3 × 10−4), which contained the myo-inositol kinase (MIK) gene (GRMZM2G361593, node DA1) (Fig. 6a, Additional file 6, sub-network 1 of “dodgerblue4”). Similar to the “magenta2” network, MIK expression was correlated with that of an ubiquitin-related gene (GRMZM2G136313, node DB1, correlation coefficient = 0.78, p-value = 1 × 10−3). This implies that the protein ubiquitination pathway plays an important role in the regulation of IP metabolism. Two ABC transporter family genes (GRMZM2G009464, GRMZM2G072850; nodes DC2 and DC3) were also correlated with a ubiquitin-related gene (node DB1, correlation coefficients were 0.86 and 0.65 respectively, p-value < 1 × 10−2). The interaction between DB1 with the two ABC transporters (nodes DC2 and DC3) and MIK (node DA1) supports the hypothesis that specific transporters are responsible for the transport of inositol or inositol monophosphate to the embryo from other organs . In bacteria, some ABC transporters bind specifically to inositol . A soybean Mrp1 mutant exhibited reduced seed inositol levels and altered ABA sensitivity. Moreover, its Mrp2 paralog was unable to complement the mutant phenotype . This suggests that ABC transporters play multiple roles and that specific ABC transporters are responsible for inositol/inositol phosphate transport.
The purple acid phosphatase (PAP)-like gene (GRMZM5G881649, node DJ1, Fig. 6b, Additional file 6, sub-network 2 of “dodgerblue4”) was co-expressed with the OsLpa1-like gene (GRMZM2G342327, node DH3, correlation coefficient = 0.90, p-value = 5 × 10−3) and an ABC transporter-like gene (GRMZM5G874955, node DH2, correlation coefficient = 0.93, p-value = 5 × 10−3). PAP is an acid phosphatase with phytase activity . Overexpression of the PAP gene in Arabidopsis activated Ca2+ signaling , indicating a relationship between PAP and IP metabolism. Thus, the correlation between node DH2 with OsLpa1-like (DH3) and PAP (DJ1) was notable. Moreover, node DH2 was also in contact with an ethylene-responsive transcription factor (GRMZM5G830365, node DF2), which was in turn in contact with two IP-related genes (nodes DE1 and DE2). Therefore, node DH2 (GRMZM5G874955) was selected as a candidate gene for further study to confirm whether it is involved in IP transport.
Interestingly, similar to the “magenta2” network, a GA-related gene (GRMZM5G861082) was directly/indirectly in contact with that of a type C ABC transporter gene (GRMZM2G145446), a phosphatidylinositol 4-kinase gene (GRMZM2G137558), and an ABC transporter 5-like (ZmMRP5) gene (GRMZM2G105570). This suggested an interaction of IP metabolism or Ca2+ signaling with the GA pathway.
Validation of the candidate genes
To assess the network, we selected three candidate genes for further validation. We use node names to represent each candidate gene (Table 3). According to the network information, we predicted that gene Mf3 from “magenta2” would be involved in IP metabolism as an enzyme, gene DH2 from “dogerblue4” would be responsible for IP transport across organs, and gene CB5 from “cornsilk” would be related to IP co-transport into vesicles.
We analyzed the expression profiles of the three candidate genes in maize embryo (Additional file 1: Figure S16). The Mf3 and DH2 genes showed expression patterns similar to those of ITPK-1 and MIK, respectively. GFP-fused transient transformation in maize protoplasts showed that Mf3 was expressed in the cytoplasm, DH2 in the plasma membrane, and CB5 in vesicles, which is consistent with their expected molecular functions (Fig. 7).
To further validate the function of each candidate gene, we obtained T-DNA insertion mutant lines of Arabidopsis orthologs of DH2 (orthologous to AT1G15520; insertion line SALK_013945) and CB5 (orthologous to AT1G16780; insertion line SALK_044701) (Additional file 1: Figure S17). IP metabolism was found to be perturbed in these two Arabidopsis insertion lines, levels of almost all IPs—with the exception of IP3—were decreased in both mutant lines (Fig. 8). The IP6 level was significantly decreased in SALK_013945 (~30 %) and SALK_044701 (~20 %) compared with in Clo-0. Interestingly, the IP1 level in SALK_044701 was similar to that in Clo-0, but was decreased by ~23 % in SALK_013945. These differences in the IP levels in insertion lines suggest that the DH2 and CB5 genes play different roles in IP metabolism. Taken together with the GFP-fused transient transformation data, these results suggest that DH2 and CB5 participate in IP transport across organs and co-transport into vesicles, respectively.
To validate the function of Mf3, we ligated an Mf3 cDNA segment into pCAMBIA3301 with a ubiquitin (UBI) promoter. The transcript produced from the segment formed a hairpin structure (for RNA interference, RNAi). The construct was transformed into maize to produce Mf3-knockdown lines (Fig. 9a, RT-PCR validation of the knockdown levels of Mf3), and the IP levels in the knockdown lines were then determined (Fig. 9b). Results showed that, the expression level of Mf3 in the transgenic lines was reduced approximately three- to five-fold compared with the segregated negative transgenic lines. The IP1 level increased almost two-fold in the RNAi transgenic lines, but the IP2 level showed no significant difference. However, the levels of IP3–IP6 decreased significantly in the RNAi lines. That of IP6 decreased ~30 % compared to the transgenic negative lines. Taken together with the GFP-fused transient transformation results, the findings suggest that the product of the Mf3 gene functions as an enzyme in IP metabolism.
To explore the regulatory mechanism of IP metabolism, we screened two inbred lines with significantly different IP6 levels (B73 and Qi319) from a maize germplasm collection and carried out RNA-Seq and microRNA-Seq analyses.
Transcriptome analyses showed that IPK/ITPK expression was upregulated at 12DAP in B73 compared with Qi319, while most known genes were downregulated in B73 after 12DAP. However, MIPS and ITPK showed continuously different expression patterns between the two lines. The differences in gene expression patterns were related to the abundance of IPs (particularly IP4 and IP6) in embryos, suggesting a different pattern of IP metabolism regulation between the B73 and Qi319 inbred lines.
Several transcription factors, especially WRKY and ethylene-responsive transcription factors, would be involved in the regulation of IP metabolism. Moreover, three microRNAs, which would be involved in IP metabolism regulation were identified. These findings will facilitate further research into IP metabolism.
To assess the implications of our data, six co-regulated networks were constructed. These networks have the potential to uncover the function and mechanism of regulation of IP metabolism.
The networks suggest Ca2+ as the bridge and core node between IP metabolism and other pathways, and inositol-phosphate-related genes were linked with GA-related genes through ubiquitin-related genes or specific transporter genes (see networks “magenta2” and “dodgerblue4”). This information will facilitate investigation of the interactions of IPs with GA. We also inferred several carbohydrate/inositol transporters and specific ABC transporters as responsible for inositol or IP transport across organs.
Three new candidate genes were extracted from the networks and validated experimentally. Gene mutations in Arabidopsis and gene knockdown in maize showed that the candidate genes were indeed involved in IP metabolism. The Mf3 gene (“magenta2” network) encodes an enzyme, DH2 (“dodgerblue4” network) encodes an ABC transporter responsible for cross-organ transport of IP, and the product of the CB5 gene (“cornsilk” network) is related to the co-transport of IP into vesicles.
Plant materials and IP determination
Four hundred seventy-five maize inbred lines (an inbred maize collection) were cultivated in Sanya (Hainan, 2012), Sanya (Hainan, 2013) and Shunyi (Beijing, 2013), and mature seeds were harvested. IP levels were determined by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Significant differences in IP6 content were evaluated by calculating IP6 content ratios as the function Q = abs[log 2 (x i /x j )], where “abs” is the absolute value of log 2 (x i /x j ), and x i and x j are the IP6 contents of each inbred line. Then, a t-test was performed (p < 0.05 was considered to indicate an unstable Q value) by using the Q value to screen the inbred line pairs with stable Q value (Q > 0.58) in phytic acid content. Four maize inbred lines with significant differences in phytic acid (IP6) content—B73 (high in phytic acid, HPA), Qi319 (low in phytic acid, LPA), Lv28 (HPA), and CMBIA141 (LPA)—were first monitored for dynamic changes of each IP in the developmental embryo and kernels. Only B73 and Qi319 were used for RNA-Seq analysis because Lv28 and CIMBL141 were not growing well in the field and were susceptible to diseases and insect pests. Seeds and embryos used for IP monitoring and RNA-Seq analysis were collected from the field (Shunyi, Beijing, 2013) at various days after pollination (DAP, 6DAP, 12DAP, 18DAP, 21DAP, 24DAP, and 30DAP). Dynamic changes in IP levels in the fresh samples were determined by LC-MS/MS.
RNA-Seq and microRNA-Seq
Embryos of B73 and Qi319 (12DAP, 21DAP, and 30DAP) were subjected to RNA-Seq and microRNA-Seq analyses. Total RNA was isolated from embryos using an RNeasy Plant Mini Kit (74904; Qiagen, Germany) according to the manufacturer’s protocol. MicroRNA was extracted using an miRNeasy Mini Kit (217004; Qiagen, Germany).
Approximately 35 μg of total RNA were used for cDNA synthesis and library construction. All libraries were sequenced using the Illumina HiSeq 2500 platform. Small RNAs were first isolated from total RNA using 6 % agarose gels, and then purified. A library was then constructed using the Multiplex Small RNA Library Prep Set for Illumina (NEB).
Annotation and statistics
Reference maize genome sequences were downloaded from Gramene (B73 AGPv3, http://gramene.org/). Protein sequences were downloaded from Uniprot (http://www.uniprot.org/). ID mapping for DNA and protein sequence matching was accomplished using R (ver. 3.1.2). Statistical analyses were conducted using R. Gene annotation based on BLAST was performed using Blast2GO . The blast E-value was set as 1 × 10−3, and the E-Value-Hit-Filter was set as 1 × 10−6 in Blast2GO.
Read alignment and assembly
For RNA-Seq, all reads from each sample were aligned to the reference genome of maize using TopHat2 . Briefly, reads were first aligned to the reference genome by using Bowtie  to identify splice junctions between exons and then the aligned reads were subjected to Cufflinks  to assemble those reads into sample-specific transcriptomes using alignment coordinates.
For microRNA-Seq, high-quality reads were aligned to the GenBank and Rfam  databases to remove ncRNAs, with the exception of miRNAs. miRNAs were identified using miRDeep2 software . Target prediction for known and unknown miRNAs was performed as reported previously using TargetFinder software . Briefly, miRNA sequences were matched to the reference mRNA FASTA sequences and potential targets were computationally predicted by the match/mismatch-scoring ratio. Only predicted targets with scores less than four were considered reasonable.
Gene/miRNA differential expression analysis
Gene expression levels were computed and expressed as reads per Kb per million fragments (RPKM), which was defined as:
Differential gene/miRNA expression was analyzed using DESeq . DEGs were identified by calculating the fold-change ratios (FC) between samples. Genes with FC ≥ 2 and FDR < 0.01 were considered differentially expressed and their levels of up- and down-regulation were expressed as logarithms of FC (log2FC).
Alternative splicing analysis
Various types of alternative splicing (AS)—exon skipping (ES), intron retention (IR), mutually exclusive exon (MEE), alternative first exon (AFE), alternative last exon (ALE), alternative 5’ splice site (5’AS) and alternative 3’ splice site (3’AS)—were analyzed using Cufflinks. The novel spliced exons were identified by comparing the sequenced gene model with the annotated locus. The detected novel AS models were visualized using SpliceGrapher , giving a diagram view.
Novel transcripts analysis
All reads from RNA-Seq were first assembled using TopHat2 and Cufflinks. Exons and junctions that overlapped or were adjacent to the existing annotated transcripts were filtered out. Finally, the remaining exons were extended and merged. The novel assembled transcripts were future filtered according to their size (coding sequence > 150 amino acids). The novel transcripts were annotated using Blast2GO.
Network construction and analysis
RNA-Seq gene expression data were filtered such that genes with more than three missing values or 0 were filtered out. Ultimately, 15,536 genes were filtered out and 26,264 genes were retained for further processing.
Gene correlation coefficients (Spearman’s coefficient, ρ) were calculated using the WGCNA R package , but the maximum block size was set as 3500 to save running time, and the threshold for network output was set as 0.5 to achieve more stringent connectivity of nodes in the network. Co-expressed genes were clustered by applying TOM and DynamicTreeCut functions to form different co-expression modules. A unique color was assembled to name each module. Correlations of IPs with modules were calculated using the cor() function in R and the significance of the correlations was evaluated by t-tests. Correlation coefficients > 0.80 and p-values < 0.01 were considered to indicate a significant correlation.
Modules that were significantly correlated with IPs were extracted and exported to Cytoscape (ver. 2.8, http://www.cytoscape.org/) for network visualization and editing [87, 88]. Hub genes were identified using the cyotHubba plugin for Cytoscape . Network compression was conducted by the Power Graph method [44, 90, 91] and the networks were compressed according to the linkage of nodes.
To understand the implications of the gene co-expression network, we carried out a literature search using the “AgilentLiteratureSearchPlugin” (ver. 2.78, http://apps.cytoscape.org/apps/) and MiMI Plugin  for Cytoscape. Nodes and studies were sorted using the key words “inositol”, “phytic acid”, “phytate”, and “phosphatidylinositol.” Functional elements were extracted according to the GO annotation and literature containing the above-mentioned key words.
Key nodes and candidate genes were selected according to guide-genes found to be involved in IP metabolism in maize. The guide-genes were: MIK (GRMZM2G361593) , ITPK (GRMZM2G456626) , MIPS (GRMZM2G155242) , and ABC transporter (GRMZM5G820122) .
We used the following principles to screen candidate genes: first, candidate genes and inositol phosphate-related genes should share the same key node/hub gene; secondly, the distance from guide-gene/inositol phosphate-related gene to the candidate gene should be less than 4 ; and third, the co-regulated genes that correlated with the guide-genes and candidate genes should be in power nodes. It was considered optimal if conserved domains present in known proteins—including carbohydrate kinase PfkB, IPR011611; P-loop-containing, IPR005337; and P-loop containing nucleoside triphosphate hydrolase, IPR027417 domains—were also present in the proteins encoded by candidate genes. Genes differentially expressed in the B73 and Qi319 lines were also considered.
Total RNA was extracted with TRIzol reagent (TaKaRa, Dalian, China) according to the manufacturer’s instructions. Total RNA (100 ng) was used for first-strand cDNA synthesis using EasyScript One-Step gDNA Removal and cDNA Synthesis SuperMix (TRANSGEN, Beijing China). Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed using TransStart Green qPCR SuperMix (TRANSGEN, Beijing, China). Primers used for qRT-PCR are listed in Additional file 7.
Validation of candidate genes
For convenience, candidate genes were named using the node name of the containing module.
GFP fusion expression was carried out to assess the subcellular localization of each candidate gene in the maize protoplast. Protoplasts of maize B73 were prepared according to Yoo et al.  using etiolated seedlings, but the time required for protoplast isolation was reduced to 3 h, and ~30 μg of plasmid DNA was used for transient transformation. CDS of candidate genes were ligated into pRTL2 to form a C-fusion GFP construction using restriction enzymes (Additional file 7).
Due to the limited maize mutant resources available, we obtained Arabidopsis thaliana lines containing T-DNA insertions in genes orthologous to the candidate genes (Additional file 1: Figure S17) from ABRC (https://abrc.osu.edu/). IP levels in seeds of the insertion lines were determined by LC-MS/MS (at least three individuals of each insertion line were used for IP determination). Gene names and the corresponding Arabidopsis mutant lines are listed in Table 3. Mutant test primers are listed in Additional file 7. The T-border primer was LBa1: 5'-TGGTTCACGTAGTGGGCCATCG-3'. T-DNA insertion effects were tested by RT-PCR using cDNAs of siliques at 20 days after flowering. Primers used for RT-PCR are listed in Additional file 7.
The maize Mf3 gene was evaluated by RNAi technology. The conserved sequence of Mf3 was reversed and forward-linked to a rice intron to form a hairpin structure and then digested with HindIII and SacI from pTCK303 and linked into pCAMBIA3301, which had been digested with HindIII and BstEII. The 35S promoter and the GUS coding sequence were then replaced by the Mf3 RNAi segment (Additional file 1: Figure S18). Primers for Mf3 segment cloning are listed in Additional file 7. The final expression plasmid vector was transformed into GV3103 (Agrobacterium tumefaciens) and then transfected into an immature embryo of HiII, induced into callus, regenerated seedling under selection stress of glufosinate.
In total, five transgenic events were obtained, three of which (based on RT-PCR results in transgenic lines) were used for IP determination. T1 seeds (5–10) of each transgenic line were ground singly using a Geno/Grinder 2010 (Molecular Devices, Sunnyvale, CA USA). A portion of the powder (30 mg) was used for IP determination and the remainder for PCR screening of positive transgenic individuals. Mf3 knockdown levels were validated by RT-PCR in T1 seedlings of RNAi lines.
Availability of supporting data
RNA-Seq data used in this study have been deposited into the NCBI Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/sra/) under accession number SRP065818 (SRR2907733, SRR2908040, SRR2908041, SRR2908042, SRR2908043, and SRR2908044).
days after pollination
differentially expressed gene
high phytic acid content
liquid chromatography coupled with tandem mass spectrometry
low phytic acid content
low phytic acid
myo-inositol 1-phosphate synthase
multidrug resistance-associated protein
weighted gene co-expression network analysis
Bennett M, Onnebo SM, Azevedo C, Saiardi A. Inositol pyrophosphates: metabolism and signaling. Cell Mol Life Sci. 2006;63:552–64.
Stevenson JM, Perera IY, Heilmann I, Persson S, Boss WF. Inositol signaling and plant growth. Trends Plant Sci. 2000;5:252–8.
Gillaspy G. The Role of Phosphoinositides and Inositol Phosphates in Plant Cell Signaling. In: Capelluto DGS, editors. Lipid-mediated Protein Signaling. vol. 991. Springer Netherlands; 2013. p. 141–57.
York JD, Lew DJ. IP7 guards the CDK gate. Nat Chem Biol. 2008;4:16–7.
Macbeth MR, Schubert HL, VanDemark AP, Lingam AT, Hill CP, Bass BL. Inositol hexakisphosphate is bound in the ADAR2 core and required for RNA editing. Science. 2005;309:1534–9.
Thota SG, Unnikannan CP, Thampatty SR, Manorama R, Bhandari R. Inositol pyrophosphates regulate RNA polymerase I-mediated rRNA transcription in Saccharomyces cerevisiae. Biochem J. 2015;466:105-14.
Hanakahi LA, Bartlet-Jones M, Chappell C, Pappin D, West SC. Binding of inositol phosphate to DNA-PK and stimulation of double-strand break repair. Cell. 2000;102:721–9.
Munnik T, Vermeer JEM. Osmotic stress-induced phosphoinositide and inositol phosphate signalling in plants. Plant Cell Environ. 2010;33:655–69.
Kumar V, Sinha AK, Makkar HPS, Becker K. Dietary roles of phytate and phytase in human nutrition: a review. Food Chem. 2010;120:945–59.
Sheard LB, Tan X, Mao H, Withers J, Ben-Nissan G, Hinds TR, et al. Jasmonate perception by inositol-phosphate-potentiated COI1-JAZ co-receptor. Nature. 2010;468:400–5.
Kanter U, Usadel B, Guerineau F, Li Y, Pauly M, Tenhaken R. The inositol oxygenase gene family of Arabidopsis is involved in the biosynthesis of nucleotide sugar precursors for cell-wall matrix polysaccharides. Planta. 2005;221:243–54.
Hitz WD, Carlson TJ, Kerr PS, Sebastian SA. Biochemical and molecular characterization of a mutation that confers a decreased raffinosaccharide and phytic acid phenotype on soybean seeds. Plant Physiol. 2002;128:650–60.
Raboy V, Young KA, Dorsch JA, Cook A. Genetics and breeding of seed phosphorus and phytic acid. J Plant Physiol. 2001;158:489–97.
Hurrell RF. Influence of vegetable protein sources on trace element and mineral bioavailability. J Nutr. 2003;133:2973S–7S.
Brinch-Pedersen H, Sørensen LD, Holm PB. Engineering crop plants: getting a handle on phosphate. Trends Plant Sci. 2002;7:118–25.
Golovan SP, Hayes MA, Phillips JP, Forsberg CW. Transgenic mice expressing bacterial phytase as a model for phosphorus pollution control. Nat Biotech. 2001;19:429–33.
Shi J, Wang H, Wu Y, Hazebroek J, Meeley RB, Ertl DS. The maize low-phytic acid mutant lpa2 is caused by mutation in an inositol phosphate kinase gene. Plant Physiol. 2003;131:507–15.
Kim SI, Andaya CB, Goyal SS, Tai TH. The rice OsLpa1 gene encodes a novel protein involved in phytic acid metabolism. Theor Appl Genet. 2008;117:769–79.
Shi J, Wang H, Schellin K, Li B, Faller M, Stoop JM, et al. Embryo-specific silencing of a transporter reduces phytic acid content of maize and soybean seeds. Nat Biotechnol. 2007;25:930–7.
Nunes AC, Vianna GR, Cuneo F, Amaya-Farfan J, de Capdeville G, Rech EL, et al. RNAi-mediated silencing of the myo-inositol-1-phosphate synthase gene (GmMIPS1) in transgenic soybean inhibited seed development and reduced phytate content. Planta. 2006;224:125–32.
Stevenson-Paulik J, Bastidas RJ, Chiou ST, Frye RA, York JD. Generation of phytate-free seeds in Arabidopsis through disruption of inositol polyphosphate kinases. Proc Natl Acad Sci U S A. 2005;102:12612–7.
Sparvoli F, Cominelli E. Seed biofortification and phytic acid reduction: a conflict of interest for the plant? Plants. 2015;4:728–55.
Aggarwal S, Shukla V, Bhati KK, Kaur M, Sharma S, Singh A, et al. Hormonal regulation and expression profiles of wheat genes involved during phytic acid biosynthesis pathway. Plants. 2015;4:298–319.
Raboy V, Hudson SJ, Dickson DB. Reduced phytic acid content does not have an adverse effect on germination of soybean seeds. Plant Physiol. 1985;79:323–5.
Cerino Badone F, Amelotti M, Cassani E, Pilu R. Study of low phytic acid1-7 (lpa1-7), a new ZmMRP4 mutation in maize. J Hered. 2012;103:598–605.
Pilu R, Panzeri D, Cassani E, Badone FC, Landoni M, Nielsen E. A paramutation phenomenon is involved in the genetics of maize low phytic acid1-241 (lpa1-241) trait. Heredity. 2008;102:236–45.
Raboy V. Approaches and challenges to engineering seed phytate and total phosphorus. Plant Sci. 2009;177:281–96.
van Haastert PJM, van Dijken P. Biochemistry and genetics of inositol phosphate metabolism in Dictyostelium. FEBS Lett. 1997;410:39–43.
Shi J, Wang H, Hazebroek J, Ertl DS, Harp T. The maize low-phytic acid 3 encodes a myo-inositol kinase that plays a role in phytic acid biosynthesis in developing seeds. Plant J. 2005;42:708–19.
Kim SI, Tai TH. Genetic analysis of two OsLpa1-like genes in Arabidopsis reveals that only one is required for wild-type seed phytic acid levels. Planta. 2010;232:1241–50.
Donahue JL, Alford SR, Torabinejad J, Kerwin RE, Nourbakhsh A, Ray WK, et al. The Arabidopsis thaliana Myo-inositol 1-phosphate synthase1 gene is required for Myo-inositol synthesis and suppression of cell death. Plant Cell Online. 2010;22:888–903.
Bowen DE, Souza EJ, Guttieri MJ, Raboy V, Fu J. A Low phytic acid barley mutation alters seed gene expression. Crop Sci. 2007;47:S-149.
Liang Y-H, Cai B, Chen F, Wang G, Wang M, Zhong Y, et al. Construction and validation of a gene co-expression network in grapevine (Vitis vinifera. L.). Hortic Res. 2014;1:14040.
Michalak P. Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes. Genomics. 2008;91:243–8.
Mounet F, Moing A, Garcia V, Petit J, Maucourt M, Deborde C, et al. Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development. Plant Physiol. 2009;149:1505–28.
Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011;474:380–4.
Kadarmideen HN, Watson-Haigh NS. Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data. Bioinformation. 2012;8:855-61.
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17.
Yang X, Yan J, Shah T, Warburton M, Li Q, Li L, et al. Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection. Theor Appl Genet. 2010;121:417–31.
Kornblihtt AR, Schor IE, Allo M, Dujardin G, Petrillo E, Munoz MJ. Alternative splicing: a pivotal step between eukaryotic transcription and translation. Nat Rev Mol Cell Biol. 2013;14:153–65.
Ellis Jonathan D, Barrios-Rodiles M, Çolak R, Irimia M, Kim T, Calarco John A, et al. Tissue-specific alternative splicing remodels protein-protein interaction networks. Mol Cell. 2012;46:884–92.
Rushton PJ, Somssich IE, Ringler P, Shen QJ. WRKY transcription factors. Trends Plant Sci. 2010;15:247–58.
Abe H. Arabidopsis AtMYC2 (bHLH) and AtMYB2 (MYB) function as transcriptional activators in abscisic acid signaling. Plant Cell. 2002;15:63–78.
Royer L, Reimann M, Andreopoulos B, Schroeder M. Unraveling Protein Networks with Power Graph Analysis. PLoS Comput Biol. 2008;4:e1000108.
Cheng Y, Zhou Y, Yang Y, Chi YJ, Zhou J, Chen JY, et al. Structural and functional analysis of VQ motif-containing proteins in Arabidopsis as interacting proteins of WRKY transcription factors. Plant Physiol. 2012;159:810–25.
Brandt U, Kerscher S, Dröse S, Zwicker K, Zickermann V. Proton pumping by NADH:ubiquinone oxidoreductase. A redox driven conformational change mechanism? FEBS Lett. 2003;545:9–17.
Bridges HR, Birrell JA, Hirst J. The mitochondrial-encoded subunits of respiratory complex I (NADH:ubiquinone oxidoreductase): identifying residues important in mechanism and disease. Biochem Soc Trans. 2011;39:799–806.
Jin X, Geiger JH. Structures of NAD(+)- and NADH-bound 1-l-myo-inositol 1-phosphate synthase. Acta Crystallogr D Biol Crystallogr. 2003;59:1154–64.
Patterson RL, van Rossum DB, Kaplin AI, Barrow RK, Snyder SH. Inositol 1,4,5-trisphosphate receptor/GAPDH complex augments Ca2+ release via locally derived NADH. Proc Natl Acad Sci U S A. 2005;102:1357–9.
Bickler PE, Fahlman CS, Gray J, McKleroy W. Inositol 1,4,5-triphosphate receptors and NAD(P)H mediate Ca2+ signaling required for hypoxic preconditioning of hippocampal neurons. Neuroscience. 2009;160:51–60.
Voronina S, Sukhomlin T, Johnson PR, Erdemli G, Petersen OH, Tepikin A. Correlation of NADH and Ca(2+) signals in mouse pancreatic acinar cells. J Physiol. 2002;539:41–52.
Requardt RP, Hirrlinger PG, Wilhelm F, Winkler U, Besser S, Hirrlinger J. Ca(2)(+) signals of astrocytes are modulated by the NAD(+)/NADH redox state. J Neurochem. 2012;120:1014–25.
Ferguson KM, Lemmon MA, Schlessinger J, Sigler PB. Structure of the high affinity complex of inositol trisphosphate with a phospholipase C pleckstrin homology domain. Cell. 1995;83:1037–46.
Kim JK, Choi JW, Lim S, Kwon O, Seo JK, Ryu SH, et al. Phospholipase C-η1 is activated by intracellular Ca2+ mobilization and enhances GPCRs/PLC/Ca2+ signaling. Cell Signal. 2011;23:1022–9.
Majerus PW. Inositol phosphate biochemistry. Annu Rev Biochem. 1992;61:225–50.
Kepinski S, Leyser O. The Arabidopsis F-box protein TIR1 is an auxin receptor. Nature. 2005;435:446–51.
Hayashi K-i, Tan X, Zheng N, Hatate T, Kimura Y, Kepinski S, et al. Small-molecule agonists and antagonists of F-box protein–substrate interactions in auxin perception and signaling. Proc Natl Acad Sci U S A. 2008;105:5632–7.
Greiner R, Turner B, Richardson A, Mullaney E. Phytate-degrading enzymes: regulation of synthesis in microorganisms and plants. Inositol phosphates: linking agriculture and the environment. 2007. p. 78–96.
Doria E, Galleschi L, Calucci L, Pinzino C, Pilu R, Cassani E, et al. Phytic acid prevents oxidative stress in seeds: evidence from a maize (Zea mays L.) low phytic acid mutant. J Exp Bot. 2009;60:967–78.
Murthy PP, Renders JM, Keranen LM. Phosphoinositides in barley aleurone layers and gibberellic Acid-induced changes in metabolism. Plant Physiol. 1989;1266:9.
Yazaki J, Kishimoto N, Nagata Y, Ishikawa M, Fujii F, Hashimoto A, et al. Genomics approach to abscisic acid- and gibberellin-responsive genes in rice. DNA Res. 2003;10:249–61.
Fleet CM, Ercetin ME, Gillaspy GE. Inositol phosphate signaling and gibberellic acid. Plant Signal Behav. 2009;4:73–4.
Panzeri D, Cassani E, Doria E, Tagliabue G, Forti L, Campion B, et al. A defective ABC transporter of the MRP family, responsible for the bean lpa1 mutation, affects the regulation of the phytic acid pathway, reduces seed myo-inositol and alters ABA sensitivity. New Phytol. 2011;191:70–83.
Martinoia E, Klein M, Geisler M, Bovet L, Forestier C, Kolukisaoglu Ü, et al. Multifunctionality of plant ABC transporters – more than just detoxifiers. Planta. 2002;214:345–55.
Klein M, Perfus-Barbeoch L, Frelet A, Gaedeke N, Reinhardt D, Mueller-Roeber B, et al. The plant multidrug resistance ABC transporter AtMRP5 is involved in guard cell hormonal signalling and water use. Plant J. 2003;33:119–29.
Pighin JA, Zheng H, Balakshin LJ, Goodman IP, Western TL, Jetter R, et al. Plant cuticular lipid export requires an ABC transporter. Science. 2004;306:702–4.
Raboy V, Gerbasi PF, Young KA, Stoneberg SD, Pickett SG, Bauman AT, et al. Origin and seed phenotype of maize low phytic acid 1–1 and low phytic acid 2–1. Plant Physiol. 2000;124:355–68.
Walker DR, Scaboo AM, Pantalone VR, Wilcox JR, Boerma HR. Genetic mapping of loci associated with seed phytic acid content in CX1834-1-2 soybean. Crop Sci. 2006;46:390.
Gillman JD, Pantalone VR, Bilyeu K. The low phytic acid phenotype in soybean line CX1834 is due to mutations in two homologs of the maize low phytic acid gene. Plant Gen. 2009;2:179–90.
Frank T, Meuleye BS, Miller A, Shu Q-Y, Engel K-H. Metabolite profiling of two low phytic acid (lpa) rice mutants. J Agric Food Chem. 2007;55:11011–9.
Landoni M, Cerino Badone F, Haman N, Schiraldi A, Fessas D, Cesari V, et al. Low phytic acid 1 mutation in maize modifies density, starch properties, cations, and fiber contents in the seed. J Agric Food Chem. 2013;61:4622–30.
Lemtiri-Chlieh F, MacRobbie EAC, Brearley CA. Inositol hexakisphosphate is a physiological signal regulating the K + −inward rectifying conductance in guard cells. Proc Natl Acad Sci U S A. 2000;97:8687–92.
Mitsuhashi N, Ohnishi M, Sekiguchi Y, Kwon YU, Chang YT, Chung SK, et al. Phytic acid synthesis and vacuolar accumulation in suspension-cultured cells of Catharanthus roseus induced by high concentration of inorganic phosphate and cations. Plant Physiol. 2005;138:1607–14.
Takasu A, Nakanishi Y, Yamauchi T, Maeshima M. Analysis of the substrate binding site and carboxyl terminal region of vacuolar H + −pyrophosphatase of mung bean with peptide antibodies. J Biochem. 1997;122:883–9.
Herrou J, Crosson S. myo-inositol and D-ribose ligand discrimination in an ABC periplasmic binding protein. J Bacteriol. 2013;195:2379–88.
Zhang W, Gruszewski HA, Chevone BI, Nessler CL. An Arabidopsis purple acid phosphatase with phytase activity increases foliar ascorbate. Plant Physiol. 2008;146:431–40.
Conesa A, Gotz S. Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int J Plant Genomics. 2008;2008:619832.
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg S. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36.
Langmead B, Trapnell C, Pop M, Salzberg S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:1–10.
Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotech. 2010;28:511–5.
Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. Rfam: an RNA family database. Nucleic Acids Res. 2003;31:439–41.
Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40:37–52.
Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, et al. High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One. 2007;2:e219.
Anders S, McCarthy DJ, Chen Y, Okoniewski M, Smyth GK, Huber W, et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protocols. 2013;8:1765–86.
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protocols. 2012;7:562–78.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27:431–2.
Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, et al. A travel guide to Cytoscape plugins. Nat Methods. 2012;9:1069–76.
Chin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8:S11.
Ahnert SE. Generalised power graph compression reveals dominant relationship patterns in complex networks. Sci Rep. 2014;4.
Ahnert SE. Power graph compression reveals dominant relationships in genetic transcription networks. Mol Biosyst. 2013;9:2681–5.
Gao J, Ade AS, Tarcea VG, Weymouth TE, Mirel BR, Jagadish HV, et al. Integrating and annotating the interactome using the MiMI plugin for cytoscape. Bioinformatics. 2009;25:137–8.
Larson SR, Raboy V. Linkage mapping of maize and barley myo-inositol 1-phosphate synthase DNA sequences: correspondence with a low phytic acid mutation. Theor Appl Genet. 1999;99:27–36.
Newman ME, Girvan M. Finding and evaluating community structure in networks. Phys Rev E. 2004;69:026113.
Yoo SD, Cho YH, Sheen J. Arabidopsis mesophyll protoplasts: a versatile cell system for transient gene expression analysis. Nat Protoc. 2007;2:1565–72.
The authors would like to thank Professors Jun Zhao and Lei Wang for their conceptual support, and Baozhen Yang, Suzhen Li and Xianjun Li for their technical support.
This work was supported by the National Special Program for GMO Development of China (grant number 2013ZX08003-002) and the National basic research program of China (grant number 2014CB138205).
The authors declare that they have no competing interests.
YF, RC, and SZ designed the project. SZ and RC drafted the manuscript. SZ, QZ, and LJ collected and processed the maize samples. SZ, WY, QZ, XZ and SM performed all molecular experiments. SZ and WY performed the LC-MS/MS determination of metabolites and the data processing. XL and YL coordinated the maize transformation. CZ provided advice on this project and contributed towards manuscript editing. All authors read and approved the final manuscript.
Figure S1. to S18. (PDF 3864 kb)
Table S1. Table S2. (DOCX 15 kb)
Differentially expressed transcription factors. (XLSX 20 kb)
Sub-network 1 and 2 of “magenta2”, node annotation. (XLSX 10 kb)
“cornsilk” network and node annotation. (XLSX 9 kb)
Sub-network 1 and 2 of “dodgerblue4” network, node annotation. (XLSX 9 kb)
Primers used for cloning, PCR, RT-PCR and q RT-PCR. (DOCX 15 kb)
About this article
Cite this article
Zhang, S., Yang, W., Zhao, Q. et al. Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism. BMC Genomics 17, 129 (2016). https://doi.org/10.1186/s12864-016-2476-x
- Inositol phosphates
- Co-regulatory network
- Candidate gene