Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

Simple item page
dc.contributor.authorRedekar, Neelam R.en
dc.contributor.authorPilot, Guillaumeen
dc.contributor.authorRaboy, Victoren
dc.contributor.authorLi, S.en
dc.contributor.authorSaghai-Maroof, Mohammad A.en
dc.contributor.departmentSchool of Plant and Environmental Sciencesen
dc.date.accessioned2018-01-15T15:57:51Zen
dc.date.available2018-01-15T15:57:51Zen
dc.date.issued2017-11-30en
dc.description.abstractA 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/.en
dc.description.versionPublished versionen
dc.format.extent? - ? (14) page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fpls.2017.02029en
dc.identifier.issn1664-462Xen
dc.identifier.orcidPilot, G [0000-0001-7520-1059]en
dc.identifier.orcidLi, S [0000-0002-8133-3944]en
dc.identifier.urihttp://hdl.handle.net/10919/81782en
dc.identifier.volume8en
dc.language.isoenen
dc.publisherFrontiersen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000416511600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectPlant Sciencesen
dc.subjectphytic aciden
dc.subjectsoybean seed developmenten
dc.subjectmyo-inositol metabolismen
dc.subjectunsupervised machine learningen
dc.subjectgene regulatory networken
dc.subjectGENE-EXPRESSION DATAen
dc.subjectARABIDOPSIS-THALIANAen
dc.subjectSIGNAL-TRANSDUCTIONen
dc.subjectCELL-DEATHen
dc.subjectAUXINen
dc.subjectMAIZEen
dc.subjectBIOSYNTHESISen
dc.subjectSYNTHASEen
dc.subjectREVEALSen
dc.subjectKINASEen
dc.titleInference of Transcription Regulatory Network in Low Phytic Acid Soybean Seedsen
dc.title.serialFrontiers in Plant Scienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Crop & Soil Environmental Scienceen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Plant Pathology, Physiology, & Weed Scienceen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Fralin Affiliated Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds.pdf
Size:
3.34 MB
Format:
Adobe Portable Document Format
Description:
Publisher's Version
Download
License bundle
Now showing 1 - 1 of 1
Name:
VTUL_Distribution_License_2016_05_09.pdf
Size:
18.09 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

All Faculty Deposits
Open Access Subvention Fund Articles
Scholarly Works, School of Plant and Environmental Sciences