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. 2017 Jun 15;8:1044.
doi: 10.3389/fpls.2017.01044. eCollection 2017.

Transcriptional Network Analysis Reveals Drought Resistance Mechanisms of AP2/ERF Transgenic Rice

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Free PMC article

Transcriptional Network Analysis Reveals Drought Resistance Mechanisms of AP2/ERF Transgenic Rice

Hongryul Ahn et al. Front Plant Sci. .
Free PMC article

Abstract

This study was designed to investigate at the molecular level how a transgenic version of rice "Nipponbare" obtained a drought-resistant phenotype. Using multi-omics sequencing data, we compared wild-type rice (WT) and a transgenic version (erf71) that had obtained a drought-resistant phenotype by overexpressing OsERF71, a member of the AP2/ERF transcription factor (TF) family. A comprehensive bioinformatics analysis pipeline, including TF networks and a cascade tree, was developed for the analysis of multi-omics data. The results of the analysis showed that the presence of OsERF71 at the source of the network controlled global gene expression levels in a specific manner to make erf71 survive longer than WT. Our analysis of the time-series transcriptome data suggests that erf71 diverted more energy to survival-critical mechanisms related to translation, oxidative response, and DNA replication, while further suppressing energy-consuming mechanisms, such as photosynthesis. To support this hypothesis further, we measured the net photosynthesis level under physiological conditions, which confirmed the further suppression of photosynthesis in erf71. In summary, our work presents a comprehensive snapshot of transcriptional modification in transgenic rice and shows how this induced the plants to acquire a drought-resistant phenotype.

Keywords: NGS data analysis; drought stress; drought tolerance; network analysis; rice; transcription factors.

Figures

Figure 1
Figure 1
Transcription factor (TF) network analysis workflow. A template TF network was constructed by selecting strongly co-expressed TF-target gene pairs in 1,893 public domain microarrays. A dehydration TF network was then constructed by selecting strongly co-expressed TF-target gene pairs in eight dehydration experiment mRNA sequencing data sets. Phenotype-differential dehydration networks were instantiated by mapping gene expression differences to node values. Clustering analysis was then performed. Differentially expressed gene modules were selected by t-test, and the biological function of gene modules was characterized by gene ontology (GO) analysis.
Figure 2
Figure 2
Gene expression profiles under dehydration stress. (A) Gene expression levels in FPKM (Fragments Per Kilobase of exon per Million fragments mapped) for six samples (i.e., two plants for three time points): WT (white) and erf71 (gray) for 0, 1, and 6 h after treatment (HAT). Medians of gene expression (black lines in the center of the boxes) decrease as the dehydration stress continued. (B) The number of differentially expressed genes (DEGs) in WT (white) and erf71 (gray). In the 0-to-1 HAT period, the number of DEGs in the WT (925 genes) is much greater than that in erf71 (220 genes).
Figure 3
Figure 3
Phenotype-differential dehydration transcription factor (TF) networks. The three dehydration differential networks were instantiated by mapping gene expression differences to node values of the dehydration TF network. The node values are represented by red-white-blue-gradation. The two time-point differential networks (A,B) were instantiated by mapping gene expression differences between time points, such as log2(W1/W0) and log2(E1/E0), respectively, where “W” and “E” stand for WT and erf71, and “0”, “1”, and “6” stand for 0, 1, and 6 h after treatment. In these networks, the red/blue color represents up-/down-regulation of gene expression under dehydration stress. A phenotype-differential network (C) was instantiated by mapping gene expression differences between the two rice plants, such as log2(E1/E0)-log2(W1/W0). In this network, the red/blue color represents relative up-/down-regulation of gene expression in erf71 compared with WT. The dehydration differential TF networks showed gene cluster structures distinctively, where a gene cluster indicates a sub-network with member genes highly connected to each other. They also showed a trend that member genes within gene clusters had common gene expression difference patterns.
Figure 4
Figure 4
(A) Gene expression levels of the five differentially expressed modules. The y-axis shows the mean log2 fold change in gene expression level with respect to the 0 h after treatment (HAT) time point. Error bars are standard error of the means (SEMs). Module 1 was up-regulated in both types of rice but less so in erf71. Modules 2, 3, and 4 were down-regulated in both types of rice but less down-regulated in erf71. Module 5 was down-regulated in both types of rice but more down-regulated in erf71. (B) Module structures of the five modules with different colors in the dehydration network. Numbers in circles represents each module. (C) Results of the differential expression test and the gene ontology (GO) enrichment test of the five gene modules. The differential expression test of each module was performed using a t-test at 0-to-1 HAT between WT and erf71. The GO enrichment test was performed by Fisher's exact test. A p-value cutoff (p < 10−9) was used to decide differential expression and enriched GO terms.
Figure 5
Figure 5
Differences in net photosynthesis levels in WT and erf71 plants under drought stress treatment. The net photosynthesis levels were measured for WT and erf71 at four time points under drought stress and then normalized with respect to the control sample (i.e., stress treated sample–control sample). Error bars are pooled standard error of the means (pooled SEMs). The net photosynthesis level was down-regulated in both types of rice but more so in erf71.
Figure 6
Figure 6
OsERF71 cascade tree. This tree structure network was created by transforming the transcription factor (TF) regulatory network in Figure 3 into an OsERF71-rooted cascade tree. Nodes (n = 5,804) are TF or nonTF genes that are colored according to modules. Edges are TF regulatory relationships between pairs of genes that are colored according to positive/negative correlation. The cascade depth level on the left indicates the number of edges in the shortest path from OsERF71 to that point. Hub TFs that have more than 200 genes in their downstream are highlighted by using large node sizes with gene numbers from 1 to 18, whose corresponding gene IDs are Os06g0194000, Os07g0583700, Os08g0157900, Os04g0543500, Os03g0854500, Os06g0105800, Os03g0711100, Os03g0680800, Os03g0795900, Os06g0140400, Os01g0211800, Os12g0597700, Os03g0318600, Os04g0676700, Os10g0561400, Os06g0152200, Os11g0544700, and Os07g0496300, respectively. This network shows a holistic picture of potential regulatory paths to the five gene modules.
Figure 7
Figure 7
Profiles of miRNA and DNA methylation. (A) Average DNA methylation level of CpG sites in genomic regions. This shows that DNA was hypomethylated in erf71 but not significantly changed as the dehydration stress continued. (B) The number of differentially expressed micro RNAs (DEmiRNAs) at 0-to-1 and 0-to-6 h after treatment periods.

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