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Metabolites. 2013 May 14;3(2):347-72. doi: 10.3390/metabo3020347.

Metabolic and Transcriptional Reprogramming in Developing Soybean (Glycine max) Embryos.

Author information

1
Department of Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA, USA. collakov@vt.edu.
2
Genetics, Bioinformatics and Computational Biology Program, Virginia Tech, Blacksburg, VA, USA. delasa@vt.edu.
3
Department of Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA, USA. rainfyh@vt.edu.
4
Genetics, Bioinformatics and Computational Biology Program, Virginia Tech, Blacksburg, VA, USA. curtisk@vt.edu.
5
Department of Computer Science, Virginia Tech, Blacksburg, VA, USA. fstaba2@vt.edu.
6
Huck Institutes of the Life Sciences, Penn State University, University Park, PA, USA. auk262@psu.edu.
7
Genetics, Bioinformatics and Computational Biology Program, Virginia Tech, Blacksburg, VA, USA. ESM2310@vt.edu.
8
Department of Computer Science, Virginia Tech, Blacksburg, VA, USA. heath@vt.edu.
9
Department of Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA, USA. grene@vt.edu.

Abstract

Soybean (Glycine max) seeds are an important source of seed storage compounds, including protein, oil, and sugar used for food, feed, chemical, and biofuel production. We assessed detailed temporal transcriptional and metabolic changes in developing soybean embryos to gain a systems biology view of developmental and metabolic changes and to identify potential targets for metabolic engineering. Two major developmental and metabolic transitions were captured enabling identification of potential metabolic engineering targets specific to seed filling and to desiccation. The first transition involved a switch between different types of metabolism in dividing and elongating cells. The second transition involved the onset of maturation and desiccation tolerance during seed filling and a switch from photoheterotrophic to heterotrophic metabolism. Clustering analyses of metabolite and transcript data revealed clusters of functionally related metabolites and transcripts active in these different developmental and metabolic programs. The gene clusters provide a resource to generate predictions about the associations and interactions of unknown regulators with their targets based on "guilt-by-association" relationships. The inferred regulators also represent potential targets for future metabolic engineering of relevant pathways and steps in central carbon and nitrogen metabolism in soybean embryos and drought and desiccation tolerance in plants.

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