Microarrays are a large-scale expression profiling method which has been used to study the transcriptome of plants under various environmental conditions. However, manual inspection of microarray data is difficult at the genome level because of the large number of genes (normally at least 30？000) and the many different processes that occur within any given plant. MapMan software, which was initially developed to visualize microarray data for Arabidopsis, has been adapted to other plant species by mapping other species onto MapMan ontology. This paper provides a detailed procedure and the relevant computing codes to generate a MapMan ontology mapping file for tobacco (Nicotiana tabacum L.) using potato and Arabidopsis as intermediates. The mapping file can be used directly with our custom-made NimbleGen oligoarray, which contains gene sequences from both the tobacco gene space sequence and the tobacco gene index 4 (NTGI4) collection of ESTs. The generated dataset will be informative for scientists working on tobacco as their model plant by providing a MapMan ontology mapping file to tobacco, homology between tobacco coding sequences and that of potato and Arabidopsis, as well as adapting our procedure and codes for other plant species where the complete genome is not yet available. 1. Introduction Plants, being sessile organisms, must react and acclimatize to abiotic stresses to survive in various environmental conditions. Plants have developed various stress tolerance mechanisms, such as physiological and biochemical alterations, that result in adaptive or morphological changes. In crop production, understanding how cultivated crops respond to abiotic stress is crucial in developing new varieties that could tolerate stress without affecting potential yield. With the rapid development of technologies for functional genomics research, comprehensive analyses at the mRNA, protein, and metabolites level have become possible. This is leading to increased understanding of the complex regulatory networks associated with stress adaptation and tolerance [1]. Currently, microarrays are one of the most popular technologies for large-scale expression profiling because they allow the simultaneous detection of tens of thousands of transcripts at a reasonable cost [2]. The development of gene chips for model plants like Arabidopsis and rice and other species that have a sequenced genome has led to genome-wide transcriptional profiling from diverse tissues. This is a key tool for the identification of novel target genes for functional genomics [3]. Studies using
References
[1]
K. Urano, Y. Kurihara, M. Seki, and K. Shinozaki, “'Omics' analyses of regulatory networks in plant abiotic stress responses,” Current Opinion in Plant Biology, vol. 13, no. 2, pp. 132–138, 2010.
[2]
W. Busch and J. U. Lohmann, “Profiling a plant: expression analysis in Arabidopsis,” Current Opinion in Plant Biology, vol. 10, no. 2, pp. 136–141, 2007.
[3]
S. Hafidh, K. Breznenová, P. R？？i？ka, J. Feciková, V. ？apková, and D. Honys, “Comprehensive analysis of tobacco pollen transcriptome unveils common pathways in polar cell expansion and underlying heterochronic shift during spermatogenesis,” BMC Plant Biology, vol. 12, supplement 24, Article ID 24, 2012.
[4]
A. C. Cuming, S. H. Cho, Y. Kamisugi, H. Graham, and R. S. Quatrano, “Microarray analysis of transcriptional responses to abscisic acid and osmotic, salt, and drought stress in the moss, Physcomitrella patens,” New Phytologist, vol. 176, no. 2, pp. 275–287, 2007.
[5]
M. Seki, M. Narusaka, J. Ishida et al., “Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray,” Plant Journal, vol. 31, no. 3, pp. 279–292, 2002.
[6]
Y. Oono, M. Seki, M. Satou et al., “Monitoring expression profiles of Arabidopsis genes during cold acclimation and deacclimation using DNA microarrays,” Functional and Integrative Genomics, vol. 6, no. 3, pp. 212–234, 2006.
[7]
D. Li, Z. Su, J. Dong, and T. Wang, “An expression database for roots of the model legume Medicago truncatula under salt stress,” BMC Genomics, vol. 10, article no. 517, 2009.
[8]
D. Li, Y. Zhang, X. Hu et al., “Transcriptional profiling of Medicago truncatula under salt stress identified a novel CBF transcription factor MtCBF4 that plays an important role in abiotic stress responses,” BMC Plant Biology, vol. 11, article no. 109, 2011.
[9]
S. Li, Q. Fu, W. Huang, and D. Yu, “Functional analysis of an Arabidopsis transcription factor WRKY25 in heat stress,” Plant Cell Reports, vol. 28, no. 4, pp. 683–693, 2009.
[10]
R. Stolf-Moreira, E. G. M. Lemos, L. Carareto-Alves et al., “Transcriptional profiles of roots of different soybean genotypes subjected to drought stress,” Plant Molecular Biology Reporter, vol. 29, no. 1, pp. 19–34, 2011.
[11]
M. W. Davey, N. S. Graham, B. Vanholme, R. Swennen, S. T. May, and J. Keulemans, “Heterologous oligonucleotide microarrays for transcriptomics in a non-model species; a proof-of-concept study of drought stress in Musa,” BMC Genomics, vol. 10, article no. 1471, p. 436, 2009.
[12]
A. Rotter, B. Usadel, S. Baebler, M. Stitt, and K. Gruden, “Adaptation of the MapMan ontology to biotic stress responses: application in solanaceous species,” Plant Methods, vol. 3, no. 1, article no. 10, 2007.
[13]
O. Thimm, O. Bl？sing, Y. Gibon et al., “MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes,” Plant Journal, vol. 37, no. 6, pp. 914–939, 2004.
[14]
M. ？irava, T. Sch？fer, M. Eiglsperger et al., “BioMiner—modeling, analyzing, and visualizing biochemical pathways and networks,” Bioinformatics, vol. 18, no. 2, pp. S219–S230, 2002.
[15]
B. Usadel, F. Poree, A. Nagel, M. Lohse, A. Czedik-Eysenberg, and M. Stitt, “A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, Maize,” Plant, Cell and Environment, vol. 32, no. 9, pp. 1211–1229, 2009.
[16]
S. M. Paley and P. D. Karp, “The pathway tools cellular overview diagram and Omics viewer,” Nucleic Acids Research, vol. 34, no. 13, pp. 3771–3778, 2006.
[17]
T. Tokimatsu, N. Sakurai, H. Suzuki et al., “KaPPA-view. A web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps,” Plant Physiology, vol. 138, no. 3, pp. 1289–1300, 2005.
[18]
A. Conesa, S. G？tz, J. M. García-Gómez, J. Terol, M. Talón, and M. Robles, “Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research,” Bioinformatics, vol. 21, no. 18, pp. 3674–3676, 2005.
[19]
S. Okuda, T. Yamada, M. Hamajima et al., “KEGG Atlas mapping for global analysis of metabolic pathways.,” Nucleic Acids Research, vol. 36, pp. W423–W426, 2008.
[20]
Y. Nanjo, K. Maruyama, H. Yasue, K. Yamaguchi-Shinozaki, K. Shinozaki, and S. Komatsu, “Transcriptional responses to flooding stress in roots including hypocotyl of soybean seedlings,” Plant Molecular Biology, pp. 129–144, 2011.
[21]
Y. Al-Ghazi, S. Bourot, T. Arioli, E. S. Dennis, and D. J. Llewellyn, “Transcript profiling during fiber development identifies pathways in secondary metabolism and cell wall structure that may contribute to cotton fiber quality,” Plant and Cell Physiology, vol. 50, no. 7, pp. 1364–1381, 2009.
[22]
J. A. Christianson, D. J. Llewellyn, E. S. Dennis, and I. W. Wilson, “Global gene expression responses to waterlogging in roots and leaves of cotton (Gossypium hirsutum L.),” Plant and Cell Physiology, vol. 51, no. 1, pp. 21–37, 2010.
[23]
A. Rotter, C. Camps, M. Lohse et al., “Gene expression profiling in susceptible interaction of grapevine with its fungal pathogen Eutypa lata: extending MapMan ontology for grapevine,” BMC Plant Biology, vol. 9, article no. 104, 2009.
[24]
M. Kondrák, F. Marincs, B. Kalapos, Z. Juhász, and Z. Bánfalvi, “Transcriptome analysis of potato leaves expressing the trehalose-6-phosphate synthase 1 gene of yeast,” PLoS One, vol. 6, no. 8, Article ID e23466, 2011.
[25]
C. Barsan, P. Sanchez-Bel, C. Rombaldi et al., “Characteristics of the tomato chromoplast revealed by proteomic analysis,” Journal of Experimental Botany, vol. 61, no. 9, pp. 2413–2431, 2010.
[26]
A. Pitzschke and H. Hirt, “Bioinformatic and systems biology tools to generate testable models of signaling pathways and their targets,” Plant Physiology, vol. 152, no. 2, pp. 460–469, 2010.
[27]
R. M. Twyman, “Host plants, systems and expression strategies for molecular farming,” in Molecular Farming: Plant-Made Pharmaceuticals and Technical Proteins, R. Fischer and S. Schillberg, Eds., p. 338, John Wiley & Sons, New York, NY, USA, 2006.
[28]
T. Nagata, Y. Nemoto, and S. Hasezawa, “Tobacco BY-2 cell line as the “HeLa” cell in the cell biology of higher plants,” in International Review of Cytology, W. J. Kwang and F. Martin, Eds., pp. 1–30, Academic Press, 1992.
[29]
N. Remmerie, T. De Vijlder, D. Valkenborg et al., “Unraveling tobacco BY-2 protein complexes with BN PAGE/LC-MS/MS and clustering methods,” Journal of Proteomics, vol. 74, no. 8, pp. 1201–1217, 2011.
