Field-omics-understanding large-scale molecular data from field crops

Frontiers in Plant Science

Volumn 5, Issue JUN, 2014, Pages

  Alexandersson, Erik ^a   Jacobson, Dan ^b   Vivier, Melané A ^b   Weckwerth, Wolfram ^c   Andreasson, Erik ^a  

^a SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

^b STELLENBOSCH UNIVERSITY   (South Africa)

^c UNIVERSITY OF VIENNA   (Austria)

Author keywords

Bioinformatics; Crops; Field sampling; Field trials; Field omics; Metabolomics; Proteomics; Transcriptomics

Indexed keywords

EID: 84979608085     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2014.00286     Document Type: Article

References (43)

1. Alexandersson, E., Ali, A., Resjo, S., and Andreasson, E. (2013). Plant secretome proteomics. Front. Plant Sci. 4:9. doi: 10.3389/fpls.2013.00009
2. Alm, E. K. H. (2007). “Success stories of agricultural long-term experiments,” in Kungl. Skogs-Och Lantbruksakademiens Tidskrift, 146. ISBN: 978-9185205-61-5
3. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat. Genet. 25,25-29. doi: 10.1038/75556
4. Atkinson, N. J., and Urwin, P. E. (2012). The interaction of plant biotic and abiotic stresses: from genes to the field. J. Exp. Bot. 63, 3523-3543. doi: 10.1093/jxb/ers100
5. Bläsing, O.E., Gibon, Y., Günther, M., Höhne, M., Morcuende, R., Osuna, D., et al. (2005). Sugars and circadian regulation make major contributions to the global regulation of diurnal gene expression in Arabidopsis. Plant Cell 17, 3257-3281. doi: 10.1105/tpc.105.035261
6. Borisjuk, L., Rolletschek, H., and Neuberger, T. (2012). Surveying the plant’s world by magnetic resonance imaging. Plant J. 70, 129-146. doi: 10.1111/j.1365-313X.2012.04927.x
7. Brisset, M.-N., De, D., and Thomas, B. (2013). Device for determining or studying the state of stimulation of the natural defences of plants or portions of plants, US Patent 20,130,090,261.
8. Clark, J., Ortego, L. S., and Fairbrother, A. (2004). Sources of variability in plant toxicity testing. Chemosphere 57, 1599-1612. doi: 10.1016/j.chemosphere.2004.07.044
9. Colmsee, C., Mascher, M., Czauderna, T., Hartmann, A., Schluter, U., Zellerhoff, N., et al. (2012). OPTIMAS-DW: a comprehensive transcriptomics, metabolomics, ionomics, proteomics and phenomics data resource for maize. BMCPlantBiol. 12:245. doi: 10.1186/1471-2229-12-245
10. Dal Santo, S., Tornielli, G. B., Zenoni, S., Fasoli, M., Farina, L., Anesi, A., et al. (2013). The plasticity of the grapevine berry transcriptome. Genome Biol. 14:r54. doi: 10.1186/gb-2013-14-6-r54
11. Davies, H. V., Shepherd, L. V., Stewart, D., Frank, T., Rohlig, R. M., and Engel, K.-H. (2010). Metabolome variability in crop plant species-When, where, how much and so what? Regul. Toxicol. Pharmacol. 58, S54-S61. doi: 10.1016/j.yrtph.2010.07.004
12. Diamandis, E. P. (2012). The failure of protein cancer biomarkers to reach the clinic: why, and what can be done to address the problem? BMC Med. 10:87. doi: 10.1186/1741-7015-10-87
13. Furbank, R. T., and Tester, M. (2011). Phenomics-technologies to relieve the phe-notyping bottleneck. Trends Plant Sci. 16, 635-644. doi: 10.1016/j.tplants.2011. 09.005
14. Granier, C., and Vile, D. (2014). Phenotyping and beyond: modelling the relationships between traits. Curr. Opin. Plant Biol. 18, 96-102. doi: 10.1016/j.pbi.2014.02.009
15. Hannemann, J., Poorter, H., Usadel, B., Blasing, O. E., Finck, A., Tardieu, F., et al. (2009). Xeml lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy. Plant Cell Environ. 32, 1185-1200. doi: 10.1111/j.1365-3040.2009.01964.x
16. Hartmann, A., Czauderna, T., Hoffmann, R., Stein, N., and Schreiber, F. (2011). HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12:148. doi: 10.1186/1471-2105-12-148
17. Hoehenwarter, W., Larhlimi, A., Hummel, J., Egelhofer, V., Selbig, J., van Dongen, J. T., et al. (2011). MAPA distinguishes genotype-specific variability of highly similar regulatory protein isoforms in potato tuber. J. Proteome Res. 10, 2979-2991. doi: 10.1021/pr101109a
18. Hoehenwarter, W., van Dongen, J. T., Wienkoop, S., Steinfath, M., Humme, J., Erban, A., et al. (2008). A rapid approach for phenotype-screening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment. Proteomics 8, 4214-4225. doi: 10.1002/pmic.200701047
19. Jacobson, D., and Emerton, G. (2012). GSA-PCA gene set generation by principal component analysis of the Laplacian matrix of a metabolic network. BMC Bioinformatics 13:197. doi: 10.1186/1471-2105-13-197
20. Jacoby, R. P., Millar, A. H., and Taylor, N. L. (2013). Application ofselected reaction monitoring mass spectrometry to field-grown crop plants to allow dissection of the molecular mechanisms of abiotic stress tolerance. Front. PlantSci. 4:20. doi: 10.3389/fpls.2013.00020
21. Kangasjarvi, S., Neukermans, J., Li, S., Aro, E. M., and Noctor, G. (2012). Photosynthesis, photorespiration, and light signalling in defence responses. J. Exp. Bot. 63, 1619-1636. doi: 10.1093/jxb/err402
22. Keurentjes, J. J., Angenent, G. C., Dicke, M., Dos Santos, V. A., Molenaar, J., van der Putten, W. H., et al. (2011). Redefining plant systems biology: from cell to ecosystem. Trends PlantSci. 16, 183-190. doi: 10.1016/j.tplants.2010.12.002
23. Külheim, C., Ågren, J., and Jansson, S. (2002). Rapid regulation of light harvesting and plant fitness in the field. Science 297, 91-93. doi: 10.1126/science.1072359
24. Lehmann, U., Wienkoop, S., Tschoep, H., and Weckwerth, W. (2008). If the antibody fails-a mass western approach. PlantJ. 55,1039-1046. doi: 10.1111/j.1365-313X.2008.03554.x
25. Lund, S. T., Peng, F. Y., Nayar, T., Reid, K. E., and Schlosser, J. (2008). Gene expression analyses in individual grape (Vitis vinifera L.) berries during ripening initiation reveal that pigmentation intensity is a valid indicator of developmental staging within the cluster. Plant Mol. Biol. 68, 301-315. doi: 10.1007/s11103-008-9371-z
26. Mishra, Y., Jankanpaa, H. J., Kiss, A. Z., Funk, C., Schroder, W. P., and Jansson, S. (2012). Arabidopsis plants grown in the field and climate chambers significantly differ in leaf morphology and photosystem components. BMC Plant Biol. 12:6. doi: 10.1186/1471-2229-12-6
27. Montes, J. M., Technow, F., Dhillon, B. S., Mauch, F., and Melchinger, A. E. (2011). High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res. 121, 268-273. doi: 10.1016/j.fcr.2010.12.017
28. Morgenthal, K., Wienkoop, S., Scholz, M., Selbig, J., and Weckwerth, W. (2005). Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite-protein networks and improve pattern recognition for multiple biomarker selection. Metabolomics 1, 109-121. doi: 10.1007/s11306-005-4430-9
29. Rietman, H., Bijsterbosch, G., Cano, L. M., Lee, H. R., Vossen, J. H., Jacobsen, E., et al. (2012). Qualitative and quantitative late blight resistance in the potato cultivar Sarpo Mira is determined by the perception of five distinct RXLR effectors. Mol. Plant Microbe Interact. 25, 910-919. doi: 10.1094/MPMI-01-12-0010-R
30. Scherling, C., Roscher, C., Giavalisco, P., Schulze, E. D., and Weckwerth, W. (2010). Metabolomics unravel contrasting effects of biodiversity on the performance of individual plant species. PLoS ONE 5:e12569. doi: 10.1371/jour-nal.pone.0012569
31. Setati, M. E., Jacobson, D., Andong, U. C., and Bauer, F. (2012). The vineyard yeast microbiome, a mixed model microbial map. PLoS ONE 7:e52609. doi: 10.1371/journal.pone.0052609
32. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102, 15545-15550. doi: 10.1073/pnas.0506580102
33. Tabashnik, B. E., Carriere, Y., Dennehy, T. J., Morin, S., Sisterson, M. S., Roush, R. T., et al. (2003). Insect resistance to transgenic Bt crops: lessons from the laboratory and field. J. Econ. Entomol. 96 1031-1038. doi: 10.1603/0022-049396.4.1031
34. Tavakkoli, E., Fatehi, F., Rengasamy, P., and McDonald, G. K. (2012). A comparison of hydroponic and soil-based screening methods to identify salt tolerance in the field in barley. J. Exp. Bot. 63, 3853-3867. doi: 10.1093/jxb/ers085
35. Valledor, L., Escandon, M., Meijon, M., Nukarinen, E., Canal, M. J., and Weckwerth, W. (2014). A universal protocol for the combined isolation of metabolites, DNA, long RNAs, small RNAs, and proteins from plants and microorganisms. PlantJ. doi: 10.1111/tpj.12546. [Epub ahead of print].
36. Walls, R. L., Athreya, B., Cooper, L., Elser, J., Gandolfo, M. A., Jaiswal, P., et al. (2012). Ontologies as integrative tools for plant science. Am. J. Bot. 99, 1263-1275. doi: 10.3732/ajb.1200222
37. Walter, A., Studer, B., and Kolliker, R. (2012). Advanced phenotyping offers opportunities for improved breeding of forage and turf species. Ann. Bot. 110, 1271-1279. doi: 10.1093/aob/mcs026
38. Weckwerth, W. (2011). Green systems biology-from single genomes, proteomes and metabolomes to ecosystems research and biotechnology. J. Proteomics 75, 284-305. doi: 10.1016/j.jprot.2011.07.010
39. Weckwerth, W., Wenzel, K., and Fiehn, O. (2004). Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks. Proteomics 4, 78-83. doi: 10.1002/pmic.200200500
40. Wienkoop, S., Morgenthal, K., Wolschin, F., Scholz, M., Selbig, J., and Weckwerth, W. (2008). Integration of metabolomic and proteomic phenotypes analysis of data covariance dissects starch and RFO. metabolism from low and high temperature compensation response in Arabidopsis Thaliana. Mol. Cell. Proteomics 7, 1725-1736. doi: 10.1074/mcp.M700273-MCP200
41. Yin, X., and Struik, P. (2007). Crop systems biology: an approach to connect functional genomics with crop modelling. Frontis 21, 61-71.
42. Yin, X., and Struik, P. C. (2008). Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics. New Phytol. 179, 629-642. doi: 10.1111/j.1469-8137.2008.02424.x
43. Young, P. R., Lashbrooke, J. G., Alexandersson, E., Jacobson, D., Moser, C., Velasco, R., et al. (2012). The genes and enzymes of the carotenoid metabolic pathway in Vitisvinifera L. BMC Genomics 13:243. doi: 10.1186/1471-2164-13-243