AUTHOR=Redekar Neelam , Pilot Guillaume , Raboy Victor , Li Song , Saghai Maroof M. A. TITLE=Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds JOURNAL=Frontiers in Plant Science VOLUME=8 YEAR=2017 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2017.02029 DOI=10.3389/fpls.2017.02029 ISSN=1664-462X 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/.