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

Frontiers in Plant Science

Volumn 6, Issue MAR, 2015, Pages

  Seaver, Samuel M D ^a,b   Bradbury, Louis M T ^c,d   Frelin, Océane ^c   Zarecki, Raphy ^e   Ruppin, Eytan ^e   Hanson, Andrew D ^c   Henry, Christopher S ^a,b  

^a ARGONNE NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

^d CITY UNIVERSITY OF NEW YORK   (United States)

^e TEL AVIV UNIVERSITY   (Israel)

Author keywords

Flux balance analysis; Metabolic networks; Plant metabolism; Systems biology; Transcriptomics; Zea mays

Indexed keywords

ZEA MAYS;

EID: 84928045239     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2015.00142     Document Type: Article

References (87)

1. Alonso, A. P., Dale, V. L., and Shachar-Hill, Y. (2010). Understanding fatty acid synthesis in developing maize embryos using metabolic flux analysis. Metab. Eng. 12,488-497. doi: 10.1016/j.ymben.2010.04.002.
2. Alonso, A. P., Val, D. L., and Shachar-Hill, Y. (2011). Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering. Metab. Eng. 13, 96-107. doi: 10.1016/j.ymben.2010.10.002.
3. Apelbaum, A., and Yang, S. F. (1981). Biosynthesis of stress ethylene induced by water deficit. PlantPhysiol. 68, 594-596. doi: 10.1104/pp.68.3.594.
4. Assabgui, R. A., Reid, L. M., Hamilton, R. I., and Arnason, J. T. (1993). Correlation of kernel (E)-ferulic acid content of maize with resistance to Fusarium graminearum. Phytopathology 83, 949-953. doi: 10.1094/Phyto-83-949.
5. Baerenfaller, K., Grossmann, J., Grobei, M. A., Hull, R., Hirsch-Hoffmann, M., Yalovsky, S., et al. (2008). Genome-scale proteomics reveals Arabidop- sis thaliana gene models and proteome dynamics. Science 320, 938-941. doi: 10.1126/science.1157956.
6. Battey, J. F., and Ohlrogge, J. B. (1990). Evolutionary and tissue-specific control of expression of multiple acyl-carrier protein isoforms in plants and bacteria. Planta 180,352-360. doi: 10.1007/BF01160390.
7. Belanger, F. C., and Kriz, A. L. (1989). Molecular characterization of the major maize embryo globulin encoded by the glb 1 gene. Plant Physiol. 91, 636-643. doi: 10.1104/pp.91.2.636.
8. Cameron, J. W., and Teas, H. J. (1948). The relation between nicotinic acid and carbohydrates in a series of maize endosperm genotypes. Proc. Natl. Acad. Sci. U.S.A. 34, 390-398. doi: 10.1073/pnas.34.8.390.
9. Caspi, R., Altman, T., Dreher, K., Fulcher, C. A., Subhraveti, P., Keseler, I. M., et al. (2012). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 40, D742-D753. doi: 10.1093/nar/gkr1014.
10. Chang, R. L., Ghamsari, L., Manichaikul, A., Hom, E. F., Balaji, S., Fu, W., et al. (2011). Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol. Syst. Biol. 7:518. doi: 10.1038/msb.2011.52.
11. Colijn, C., Brandes, A., Zucker, J., Lun, D. S., Weiner, B., Farhat, M. R., et al. (2009). Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5:e1000489. doi: 10.1371/journal.pcbi.1000489.
12. Davidson, R. M., Hansey, C. N., Gowda, M., Childs, K. L., Lin, H., Vaillancourt, B., et al. (2011). Utility of RNA sequencing for analysis of maize reproductive transcriptomes. Plant Genome 4, 191-203. doi: 10.3835/plantgenome2011. 05.0015
13. de Oliveira Dal’molin, C. G., Quek, L. E., Palfreyman, R. W., Brumbley, S. M., and Nielsen, L. K. (2010a). AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol. 152, 579-589. doi: 10.1104/pp.109.148817
14. de Oliveira Dal’molin, C. G., Quek, L. E., Palfreyman, R. W., Brumbley, S. M., and Nielsen, L. K. (2010b). C4GEM, a genome-scale metabolic model to study C-4 plantmetabolism. PlantPhysiol. 154, 1871-1885. doi: 10.1104/pp.110.166488
15. de Oliveira Dal’molin, C. G., Quek, L. E., Palfreyman, R. W., and Nielsen, L. K. (2011). AlgaGEM-a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome. BMC Genomics 12(Suppl. 4):S5. doi: 10.1186/1471-2164-12-S4-S5.
16. Dewitt, G., Richards, J., Mohnen, D., and Jones, A. M. (1999). Comparative compositional analysis of walls with two different morphologies: archetypical versus transfer-cell-like. Protoplasma 209, 238-245. doi: 10.1007/BF01453452
17. Drozak, A., and Romanowska, E. (2006). Acclimation of mesophyll and bundle sheath chloroplasts of maize to different irradiances during growth. Biochim. Biophys.Acta 1757, 1539-1546. doi: 10.1016/j.bbabio.2006.09.001.
18. Enami, K., Ozawa, T., Motohashi, N., Nakamura, M., Tanaka, K., and Hanaoka, M. (2011). Plastid-to-nucleus retrograde signals are essential for the expression of nuclear starch biosynthesis genes during amyloplast differentiation in tobacco BY-2 cultured cells. Plant Physiol. 157, 518-530. doi: 10.1104/pp.111.178897
19. Giri, K. V., Rao, N. A., Cama, H. R., and Kumar, S. A. (1960). Studies on flavinade- nine dinucleotide-synthesizing enzyme in plants. Biochem. J. 75, 381-386.
20. Grafahrend-Belau, E., Junker, A., Eschenroder, A., Muller, J., Schreiber, F., and Junker, B. H. (2013). Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale. Plant Physiol. 163, 637-647. doi: 10.1104/pp.113.224006.
21. Grafahrend-Belau, E., Schreiber, F., Koschutzki, D., and Junker, B. H. (2009). Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism. Plant Physiol. 149, 585-598. doi: 10.1104/pp.108.129635.
22. Haberer, G., Young, S., Bharti, A. K., Gundlach, H., Raymond, C., Fuks, G., et al. (2005). Structure and architecture of the maize genome. Plant Physiol. 139, 1612-1624. doi: 10.1104/pp.105.068718.
23. Hay, J., and Schwender, J. (2011a). Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to (1)(3)C metabolic flux analysis. Plant J. 67, 513-525. doi: 10.1111/j.1365-313X.2011.04611.x
24. Hay, J., and Schwender, J. (2011b). Metabolic networkreconstruction and fluxvari- ability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos. PlantJ. 67, 526-541. doi: 10.1111/j.1365-313X.2011.04613.x
25. Heller, S., McNaught, A., Stein, S., Tchekhovskoi, D., and Pletnev, I. (2013). InChl- the worldwide chemical structure identifier standard. J. Cheminform. 5:7. doi: 10.1186/1758-2946-5-7.
26. Henry, C. S., Dejongh, M., Best, A. A., Frybarger, P. M., Linsay, B., and Stevens, R. L. (2010). High-throughput generation, optimization, and analysis of genome- scale metabolic models. Nat. Biotechnol 1672, 1-6. doi: 10.1038/nbt.1672.
27. Henry, C. S., Zinner, J., Cohoon, M., and Stevens, R. (2009). iBsu1103: a new genome scale metabolic model of B. subtilis based on SEED annotations. Genome Biol. 10:R69. doi: 10.1186/gb-2009-10-6-r69.
28. Hu, W. H., Shi, K., Song, X. S., Xia, X. J., Zhou, Y. H., and Yu, J. Q. (2006). Different effects of chilling on respiration in leaves and roots of cucumber (Cucumis sativus). Plant Physiol. Biochem. 44,837-843. doi: 10.1016/j.plaphy.2006.10.016.
29. Ingle, J., Beitz, D., and Hageman, R. H. (1965). Changes in composition during development and maturation of maize seeds. Plant Physiol. 40, 835-839. doi: 10.1104/pp.40.5.835
30. Ingle, R. A. (2011). Histidine biosynthesis. Arabidopsis Book 9:e0141. doi: 10.1199/tab.0141
31. Jerby, L., Shlomi, T., and Ruppin, E. (2010). Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 6, 401. doi: 10.1038/msb.2010.56.
32. Kanehisa, M., and Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30. doi: 10.1093/nar/28.1.27.
33. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109-D114. doi: 10.1093/nar/gkr988.
34. Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M. (2014). Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199-D205. doi: 10.1093/nar/gkt1076
35. Kersey, P. J., Allen, J. E., Christensen, M., Davis, P., Falin, L. J., Grabmueller, C., et al. (2014). Ensembl Genomes 2013: scaling up access to genome-wide data. Nucleic Acids Res. 42, D546-D552. doi: 10.1093/nar/gkt979.
36. Kumar, V. S., Dasika, M. S., and Maranas, C. D. (2007). Optimization based automated curation of metabolic reconstructions. BMC Bioinform. 8:212. doi: 10.1186/1471-2105-8-212.
37. Kumar, V. S., and Maranas, C. D. (2009). GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput. Biol. 5:e1000308. doi: 10.1371/journal.pcbi.1000308.
38. Latendresse, M. (2014). Efficiently gap-filling reaction networks. BMC Bioinform. 15:225. doi: 10.1186/1471-2105-15-225.
39. Lee, D., Smallbone, K., Dunn, W. B., Murabito, E., Winder, C. L., Kell, D. B., et al. (2012). Improving metabolic flux predictions using absolute gene expression data. BMCSyst. Biol. 6:73. doi: 10.1186/1752-0509-6-73.
40. McCann, M. C., Defernez, M., Urbanowicz, B. R., Tewari, J. C., Langewisch, T., Olek, A., et al. (2007). Neural network analyses of infrared spectra for classifying cell wall architectures. Plant Physiol. 143, 1314-1326. doi: 10.1104/pp.106.093054.
41. Metz, J., Lurie, A., and Konidaris, M. (1970). A note on the folate content of uncooked maize. S. Afr. Med. J. 44, 539-541.
42. Mintz-Oron, S., Meir, S., Malitsky, S., Ruppin, E., Aharoni, A., and Shlomi, T. (2012). Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc. Natl. Acad. Sci. U.S.A. 109,339-344. doi: 10.1073/pnas.1100358109
43. Misra, P. S., Jambunathan, R., Mertz, E. T., Glover, D. V., Barbosa, H. M., and McWhirter, K. S. (1972). Endosperm protein synthesis in maize mutants with increased lysine content. Science 176, 1425-1427. doi: 10.1126/science.176.4042.1425
44. Mo, M. L., Palsson, B. O., and Herrgard, M. J. (2009). Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst. Biol. 3:37. doi: 10.1186/1752-0509-3-37.
45. Monaco, M. K., Sen, T. Z., Dharmawardhana, P. D., Ren, L., Schaeffer, M., Naithani, S., et al. (2013). Maize metabolic network construction and tran- scriptome analysis. Plant Gen. 6. doi: 10.3835/plantgenome2012.09.0025. Available online at: https://www.crops.org/publications/citation-manager/tpg/6/1/plantgenome2012.09.0025.
46. Mueller, L. A., Zhang, P., and Rhee, S. Y. (2003). AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol. 132, 453-460. doi: 10.1104/pp.102.017236.
47. Nakaya, A., Katayama, T., Itoh, M., Hiranuka, K., Kawashima, S., Moriya, Y., et al.(2013). KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Res. 41, D353-D357. doi: 10.1093/nar/gks1239
48. Naqvi, S., Zhu, C., Farre, G., Ramessar, K., Bassie, L., Breitenbach, J., et al. (2009). Transgenic multivitamin corn through biofortification of endosperm with three vitamins representing three distinct metabolic pathways. Proc. Natl. Acad. Sci. U.S.A. 106,7762-7767. doi: 10.1073/pnas.0901412106.
49. Ozsolak, F., and Milos, P. M. (2011). RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12,87-98. doi: 10.1038/nrg2934.
50. Penning de Vries, F. W., Brunsting, A. H., and van Laar, H. H. (1974). Products, requirements and efficiency of biosynthesis: a quantitative approach. J. Theor. Biol. 45, 339-377. doi: 10.1016/0022-5193(74)90119-2.
51. Perez-Lopez, U., Robredo, A., Lacuesta, M., Sgherri, C., Mena-Petite, A., Navari- Izzo, F., et al. (2010). Lipoic acid and redox status in barley plants subjected to salinity and elevated CO2. Physiol. Plant. 139, 256-268. doi: 10.1111/j.1399-3054.2010.01361.x
52. Pilalis, E., Chatziioannou, A., Thomasset, B., and Kolisis, F. (2011). An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism. Biotechnol. Bioeng. 108, 1673-1682. doi: 10.1002/bit.23107.
53. Poolman, M. G., Kundu, S., Shaw, R., and Fell, D. A. (2013). Responses to light intensity in a genome-scale model of rice metabolism. Plant Physiol. 162, 1060-1072. doi: 10.1104/pp.113.216762.
54. Poolman, M. G., Miguet, L., Sweetlove, L. J., and Fell, D. A. (2009). A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol. 151, 1570-1581. doi: 10.1104/pp.109.141267.
55. Richter, A., Peter, E., Pors, Y., Lorenzen, S., Grimm, B., and Czarnecki, O. (2010). Rapid dark repression of 5-aminolevulinic acid synthesis in green barley leaves. Plant CellPhysiol. 51, 670-681. doi: 10.1093/pcp/pcq047
56. Rolletschek, H., Koch, K., Wobus, U., and Borisjuk, L. (2005). Positional cues for the starch/lipid balance in maize kernels and resource partitioning to the embryo. PlantJ. 42, 69-83. doi: 10.1111/j.1365-313X.2005.02352.x
57. Rolletschek, H., Melkus, G., Grafahrend-Belau, E., Fuchs, J., Heinzel, N., Schreiber, F., et al. (2011). Combined noninvasive imaging and modeling approaches reveal metabolic compartmentation in the barley endosperm. Plant Cell 23, 3041-3054. doi: 10.1105/tpc.111.087015.
58. Saha, R., Suthers, P. F., and Maranas, C. D. (2011). Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism. PLoS ONE6:e21784. doi: 10.1371/journal.pone.0021784.
59. Satish Kumar, V., Dasika, M. S., and Maranas, C. D. (2007). Optimization based automated curation of metabolic reconstructions. BMC Bioinform. 8:212. doi: 10.1186/1471-2105-8-212.
60. Saulnier, L., Marot, C., Chanilaud, E., and Thibault, J.-F. (1995). Cell wall polysaccharide interactions in maize bran. Carbohydr. Polym. 26, 279-287. doi: 10.1016/0144-8617(95)00020-8.
61. Schnable, J. C., and Freeling, M. (2011). Genes identified by visible mutant phenotypes show increased bias toward one of two subgenomes of maize. PLoS ONE 6:e17855. doi: 10.1371/journal.pone.0017855.
62. Schnable, J. C., Springer, N. M., and Freeling, M. (2011). Differentiation of the maize subgenomes by genome dominance and both ancient and ongoing gene loss. Proc. Natl. Acad. Sci. U.S.A. 108, 4069-4074. doi: 10.1073/pnas.1101368108.
63. Schnable, P. S., Ware, D., Fulton, R. S., Stein, J. C., Wei, F., Pasternak, S., et al. (2009). The B73 maize genome: complexity, diversity, and dynamics. Science 326, 1112-1115. doi: 10.1126/science.1178534.
64. Schwender, J., and Hay, J. O. (2012). Predictive modeling of biomass component tradeoffs in Brassica napus developing oilseeds based on in silico manipulation of storage metabolism. Plant Physiol. 160, 1218-1236. doi: 10.1104/pp.112.203927.
65. Seaver, S. M., Gerdes, S., Frelin, O., Lerma-Ortiz, C., Bradbury, L. M., Zallot, R., et al. (2014). High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource. Proc. Natl. Acad. Sci. U.S.A. 111, 9645-9650. doi: 10.1073/pnas.1401329111.
66. Shannon, J. C., Pien, F. M., and Liu, K. C. (1996). Nucleotides and nucleotide sugars in developing maize endosperms (synthesis of ADP-glucose in brittle-1). PlantPhysiol. 110,835-843.
67. Shi, J., Wang, H., Hazebroek, J., Ertl, D. S., and Harp, T. (2005). The maize low- phytic acid 3 encodes a myo-inositol kinase that plays a role in phytic acid biosynthesis in developing seeds. Plant J. 42, 708-719. doi: 10.1111/j.1365-313X.2005.02412.x
68. Simkin, A. J., Guirimand, G., Papon, N., Courdavault, V., Thabet, I., Ginis, O., et al. (2011). Peroxisomal localisation of the final steps of the mevalonic acid pathway in planta. Planta 234, 903-914. doi: 10.1007/s00425-011-1444-6.
69. Skirycz, A., and Inze, D. (2010). More from less: plant growth under limited water. Curr. Opin. Biotechnol. 21, 197-203. doi: 10.1016/j.copbio.2010.03.002
70. Skogerson, K., Harrigan, G. G., Reynolds, T. L., Halls, S. C., Ruebelt, M., Ian- dolino, A., et al. (2010). Impact of genetics and environment on the metabolite composition of maize grain. J. Agric. Food Chem. 58, 3600-3610. doi: 10.1021/jf903705y
71. Sonnhammer, E. L., and Koonin, E. V. (2002). Orthology, paralogy and proposed classification for paralog subtypes. Trends Genet. 18, 619-620. doi: 10.1016/S0168-9525(02)02793-2.
72. Spielbauer, G., Li, L., Romisch-Margl, L., Do, P. T., Fouquet, R., Fernie, A. R., et al. (2013). Chloroplast-localized 6-phosphogluconate dehydrogenase is critical for maize endosperm starch accumulation. J. Exp. Bot. 64, 2231-2242. doi: 10.1093/jxb/ert082
73. Stitt, M., Sulpice, R., and Keurentjes, J. (2010). Metabolic networks: how to identify key components in the regulation of metabolism and growth. PlantPhysiol. 152,428-444. doi: 10.1104/pp. 109.150821.
74. Sun, Q., Zybailov, B., Majeran, W., Friso, G., Olinares, P. D., and van Wijk, K. J.(2009) PPDB, the Plant Proteomics Database at Cornell. Nucleic Acids Res. 37, D969-D974. doi: 10.1093/nar/gkn654.
75. Szal, B., Jolivet, Y., Hasenfratz-Sauder, M.-P., Dizengremel, P., and Rychter, A. M. (2003). Oxygen concentration regulates alternative oxidase expression in barley roots during hypoxia and post-hypoxia. Physiol. Plant. 119, 494-502. doi: 10.1046/j.1399-3054.2003.00161.x
76. Tanz, S. K., Castleden, I., Hooper, C. M., Vacher, M., Small, I., and Millar, H. A. (2013). SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis. Nucleic Acids Res. 41, D1185-D1191. doi: 10.1093/nar/gks1151.
77. Teas, H. J. (1954). B vitamins in starchy and sugary maize endosperms. Plant Physiol. 29, 190-194. doi: 10.1104/pp.29.2.190.
78. Topfer, N., Caldana, C., Grimbs, S., Willmitzer, L., Fernie, A. R., and Nikoloski, Z. (2013). Integration of genome-scale modeling and transcript profiling reveals metabolic pathways underlying light and temperature acclimation in Arabidop- sis. Plant Cell 25, 1197-1211. doi: 10.1105/tpc.112.108852.
79. Tumaney, A. W., Ohlrogge, J. B., and Pollard, M. (2004). Acetyl coenzyme A concentrations in plant tissues. J. Plant Physiol. 161, 485-488. doi: 10.1078/01761617-01258
80. Vilella, A. J., Severin, J., Ureta-Vidal, A., Heng, L., Durbin, R., and Birney, E. (2009). EnsemblCompara GeneTrees: complete, duplication-aware phylogenetic trees invertebrates. GenomeRes. 19, 327-335. doi: 10.1101/gr.073585.107.
81. Wallace, N. H., and Kriz, A. L. (1991). Nucleotide sequence of a cDNA clone corresponding to the maize globulin-2 gene. Plant Physiol. 95, 973-975. doi: 10.1104/pp.95.3.973
82. Wang, Y., Eddy, J. A., and Price, N. D. (2012). Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 6:153. doi: 10.1186/1752-0509-6-153.
83. Weber, E. J. (1979). The lipids of corn germ and endosperm. J. Am. Oil Chem. Soc. 56, 637-641. doi: 10.1007/BF02679340
84. Weber, E. J. (1987). Carotenoids and tocols of corn grain determined by HPLC. J.Am. Oil Chem. Soc. 64, 1129-1134. doi: 10.1007/BF02612988.
85. Wu, Y., Wang, W., and Messing, J. (2012). Balancing of sulfur storage in maize seed. BMC Plant Biol. 12:77. doi: 10.1186/1471-2229-12-77.
86. Zhang, P., Dreher, K., Karthikeyan, A., Chi, A., Pujar, A., Caspi, R., et al.(2010). Creation of a genome-wide metabolic pathway database for Populus tri- chocarpa using a new approach for reconstruction and curation of metabolic pathways for plants. Plant Physiol. 153, 1479-1491. doi: 10.1104/pp.110.157396
87. Zur, H., Ruppin, E., and Shlomi, T. (2010). iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140-3142. doi: 10.1093/bioinformatics/btq602