Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

Abstract

A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS) gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.

Keywords: gene regulatory network; myo-inositol metabolism; phytic acid; soybean seed development; unsupervised machine learning.

Figure 1

Experimental design and computational pipeline. Samples used in this study include both published (Redekar et al., 2015) and newly generated data. Differential expression analysis and clustering analysis were used to produce the initial candidate genes and gene modules. Machine learning algorithms were used to construct gene regulatory networks.

Figure 2

Venn diagrams. S1, S2, S3, S4, and S5 represent five developmental stages. (A) Number of genes differentially expressed between consecutive stages in 3mlpa seeds. (B) Number of genes differentially expressed for each stage when comparing 3mlpa to 3MWT seeds. (C) Number of genes differentially expressed for different genotypes for between stage comparisons.

Figure 3

Clustering and gene function analysis. (A) Gene expression clusters. Color indicates normalized expression levels. Genes were clustered based on the K-means clustering algorithm. Hierarchical clustering was performed on the rows of the average expression levels for each cluster. Numbers on the right of the heatmap represent cluster id (CID, 1 to 60). (B) GO functional enrichment analysis. Rows of this heatmap is organized as the same order as the expression clusters. Each column represents a GO category. Color represents -log10 FDR from enrichment analysis. Some enriched categories were summarized and annotated under the heat map. The complete set of enriched GO categories is provided as supplementary information.

Figure 4

Gene regulatory networks in low phytic acid mutants and non-mutant seeds. Directed edges (with arrowheads) represent predicted regulatory interactions. Undirected edges (without arrowheads) connect each transcription factor and the co-expression module to which the transcription factor belongs. Black arrows: the TFs are differentially expressed in between stage comparisons in both mutants (3mlpa and 1mlpa) and both non-mutants (3MWT and 1MWT). Green arrows: the TFs are differentially expressed in between stage comparisons for non-mutants but not in mutants. Blue arrows: the TFs are not differentially expressed in the non-mutants but are differentially expressed in either one of the mutants. Red arrows: the TFs are differentially expressed when comparing 3mlpa to 3MWT at one or more developmental stages. Grey arrows: interactions that do not belong any of the above four categories.

Figure 5

Schematic diagram of regulation of inositol pathway in low phytic acid soybean mutants. Black arrows represent the flow of myo-inositol in multiple pathways in non-mutant plants. Red solid arrows with mips1 label represent mutation in the rate-limiting first step of inositol pathway, catalyzed by myo-inositol phosphate synthase. Red dashed double arrows represent mutation in MRP-type ABC transporters (mrp-l/mrp-n) that block the last step in the inositol pathway, which is the movement of phytic acid to storage vacuoles. The myo-inositol pathway is blocked in single mutant (mips1 or 1mlpa) at the first step, and in triple mutant (mips1/mrp-l/mrp-n) at both first and last steps. Blue triangles represent predicted positive regulation in non-mutants. Red triangles represent predicted gene regulations in both single and triple mutants. For example, a bZIP transcription factor (Glyma.02G131700) is homologous to the well-known ABF1, and is involved in ABA signaling. This transcription factor is predicted to positively regulate raffinose synthase in non-mutant genotypes. A DOF transcription factor (Glyma.17G101000) is predicted to regulate inositol phosphatase in mutants. This enzyme is involved in breakdown of inositol pathway intermediates to form myo-inositol. A MYB transcription factor (Glyma.13G309200) is predicted to regulate myo-inositol transporter in mutants.