Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

Seaver, Samuel M.D.; Bradbury, Louis M.T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

doi:10.3389/fpls.2015.00142

Title: Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

Journal Article · Tue Mar 10 00:00:00 EDT 2015 · Frontiers in Plant Science

DOI: https://doi.org/10.3389/fpls.2015.00142 · OSTI ID:1215428

Seaver, Samuel M.D. ^[1]; Bradbury, Louis M.T. ^[2]; Frelin, Océane ^[3]; Zarecki, Raphy ^[4]; Ruppin, Eytan ^[3]; Hanson, Andrew D. ^[3]; Henry, Christopher S. ^[1]

Argonne National Lab. (ANL), Argonne, IL (United States); The Univ. of Chicago, Chicago, IL (United States)
Univ. of Florida, Gainesville, FL (United States); York College, Univ. of New York, New York, NY (United States)
Univ. of Florida, Gainesville, FL (United States)
Tel Aviv Univ., Ramat Aviv (Israel)

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1215428

Journal Information:: Frontiers in Plant Science, Journal Name: Frontiers in Plant Science Vol. 6; ISSN 1664-462X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering Alonso, Ana P.; Val, Dale L.; Shachar-Hill, Yair Metabolic Engineering, Vol. 13, Issue 1 https://doi.org/10.1016/j.ymben.2010.10.002	journal	January 2011
Impact of Genetics and Environment on the Metabolite Composition of Maize Grain Skogerson, Kirsten; Harrigan, George G.; Reynolds, Tracey L. Journal of Agricultural and Food Chemistry, Vol. 58, Issue 6 https://doi.org/10.1021/jf903705y	journal	March 2010
Computational reconstruction of tissue‐specific metabolic models: application to human liver metabolism Jerby, Livnat; Shlomi, Tomer; Ruppin, Eytan Molecular Systems Biology, Vol. 6, Issue 1 https://doi.org/10.1038/msb.2010.56	journal	January 2010
Metabolic network reconstruction of Chlamydomonas offers insight into light‐driven algal metabolism Chang, Roger L.; Ghamsari, Lila; Manichaikul, Ani Molecular Systems Biology, Vol. 7, Issue 1 https://doi.org/10.1038/msb.2011.52	journal	January 2011
High-throughput generation, optimization and analysis of genome-scale metabolic models Henry, Christopher S.; DeJongh, Matthew; Best, Aaron A. Nature Biotechnology, Vol. 28, Issue 9 https://doi.org/10.1038/nbt.1672	journal	August 2010
RNA sequencing: advances, challenges and opportunities Ozsolak, Fatih; Milos, Patrice M. Nature Reviews Genetics, Vol. 12, Issue 2 https://doi.org/10.1038/nrg2934	journal	December 2010
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Differentiation of the maize subgenomes by genome dominance and both ancient and ongoing gene loss Schnable, J. C.; Springer, N. M.; Freeling, M. Proceedings of the National Academy of Sciences, Vol. 108, Issue 10 https://doi.org/10.1073/pnas.1101368108	journal	February 2011
High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource Seaver, Samuel M. D.; Gerdes, Svetlana; Frelin, Océane Proceedings of the National Academy of Sciences, Vol. 111, Issue 26 https://doi.org/10.1073/pnas.1401329111	journal	June 2014
The Relation Between Nicotinic Acid and Carbohydrates in a Series of Maize Endosperm Genotypes Cameron, J. W.; Teas, H. J. Proceedings of the National Academy of Sciences, Vol. 34, Issue 8 https://doi.org/10.1073/pnas.34.8.390	journal	August 1948
Acetyl coenzyme A concentrations in plant tissues Tumaney, Ajay W.; Ohlrogge, John B.; Pollard, Mike Journal of Plant Physiology, Vol. 161, Issue 4 https://doi.org/10.1078/0176-1617-01258	journal	January 2004
iMAT: an integrative metabolic analysis tool Zur, Hadas; Ruppin, Eytan; Shlomi, Tomer Bioinformatics, Vol. 26, Issue 24 https://doi.org/10.1093/bioinformatics/btq602	journal	November 2010
Chloroplast-localized 6-phosphogluconate dehydrogenase is critical for maize endosperm starch accumulation Spielbauer, Gertraud; Li, Li; Römisch-Margl, Lilla Journal of Experimental Botany, Vol. 64, Issue 8 https://doi.org/10.1093/jxb/ert082	journal	March 2013
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PPDB, the Plant Proteomics Database at Cornell Sun, Qi; Zybailov, Boris; Majeran, Wojciech Nucleic Acids Research, Vol. 37, Issue suppl_1 https://doi.org/10.1093/nar/gkn654	journal	October 2008
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