Transcriptomics, proteomics, and metabolomics to reveal mechanisms underlying plant secondary metabolism

Engineering in Life Sciences

Volumn 14, Issue 5, 2014, Pages 456-466

  Yang, Dongfeng ^a   Du, Xuhong ^a   Yang, Zongqi ^a   Liang, Zongsuo ^a   Guo, Zhixin ^b   Liu, Yan ^c  

^a ZHEJIANG SCI TECH UNIVERSITY   (China)

^b Tianjin Tasly Group   (China)

^c Tianjin Tasly Modern TCM Resources Co Ltd   (China)

Author keywords

Bioinformatics; Functional genomics; Metabolic engineering; Plant biology; Secondary metabolism

Indexed keywords

AGRICULTURAL ROBOTS; BIOINFORMATICS; BIOMOLECULES; GENES; METABOLIC ENGINEERING; METABOLITES; PLANTS (BOTANY); PROTEOMICS;

FUNCTIONAL GENOMICS; METABOLIC NETWORK; PLANT BIOLOGY; PLANT SECONDARY METABOLISM; PLANT SECONDARY METABOLITES; SECONDARY METABOLISM; SECONDARY METABOLITES; TRANSCRIPTOMICS;

METABOLISM;

BIOFUEL;

BIOINFORMATICS; CROP; FUNCTIONAL GENOMICS; GENE FUNCTION; METABOLIC ENGINEERING; METABOLITE; METABOLOMICS; NONHUMAN; PLANT BREEDING; PLANT GENE; PROTEOMICS; REVIEW; SECONDARY METABOLISM; TRANSCRIPTOMICS;

EID: 84907978595     PISSN: 16180240     EISSN: 16182863     Source Type: Journal    
DOI: 10.1002/elsc.201300075     Document Type: Review

References (107)

1. Keller, N. P., Turner, G., Bennett, J. W., Fungal secondary metabolism - From biochemistry to genomics. Nat. Rev. Microbiol. 2005, 3, 937-947.
2. Dixon, R. A., Strack, D., Phytochemistry meets genome analysis, and beyond. Phytochemistry 2003, 62, 815-816.
3. Verpoorte, R., Exploration of nature's chemodiversity: The role of secondary metabolites as leads in drug development. Drug Discov. Today 1998, 3, 232-238.
4. Hostettman, K., Terreaux, C., Search for new lead compounds from higher plants. Chimia 2000, 54, 652-657.
5. Kutchan, T., Dixon, R. A., Secondary metabolism: Nature's chemical reservoir under deconvolution. Curr. Opin. Plant Biol. 2005, 8, 227-229.
6. Dixon, R. A., Natural products and plant disease resistance. Nature 2001, 411, 843-847.
7. Oksman-Caldentey, K. M., Inze, D., Plant cell factories in the post-genomic era: New ways to produce designer secondary metabolites. Trends Plant Sci. 2004, 9, 433-440.
8. Hartmann, T., From waste products to ecochemicals: Fifty years research of plant secondary metabolism. Phytochemistry 2007, 68, 2831-2846.
9. Verpoorte, R., Memelink, J., Engineering secondary metabolite production in plants. Curr. Opin. Biotechnol. 2002, 13, 181-187.
10. Oksman-Caldentey, K. M., Inze, D., Oresic, M., Connecting genes to metabolites by a systems biology approach. Proc. Natl. Acad Sci. USA 2004, 101, 9949-9950.
11. Keurentjes, J. J. B., Fu, J. Y., de Vos, C. H. R., Lommen, A. et al., The genetics of plant metabolism. Nat. Genet. 2006, 38, 842-849.
12. Goossens, A., Hakkinen, S. T., Laakso, I., Seppanen-Laakso, T. et al., A functional genomics approach toward the understanding of secondary metabolism in plant cells. Proc. Natl. Acad. Sci. USA 2003, 100, 8595-8600.
13. Vuylsteke, M., Peleman, J. D., van Eijk, M. J., AFLP-based transcript profiling (cDNA-AFLP) for genome-wide expression analysis. Nat. Protoc. 2007, 2, 1399-1413.
14. Nam, M. H., Kim, S. I., Liu, J. R., Yang, D. C. et al., Proteomic analysis of Korean ginseng (Panax ginseng C.A. Meyer). J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2005, 815, 147-155.
15. Jacobs, D. I., Gaspari, M., Greef, J. V. D., Heijden, R. V. D., Verpoorte, R., Proteome analysis of the medicinal plant Catharanthus roseus. Planta 2005, 221, 690-704.
16. Lei, Z., Elmer, A. M., Watson, B. S., Dixon, R. A. et al., A two-dimensional electrophoresis proteomic reference map and systematic identification of 1367 proteins from a cell suspension culture of the model legume Medicago truncatula. Mol. Cell Proteomics 2005, 4, 1812-1825.
17. Nicholson, J. K., Lindon, J. C., Systems biology: Metabonomics. Nature 2008, 455, 1054-1056.
18. Champagne, A., Boutry, M., Proteomics of nonmodel plant species. Proteomics 2013, 13, 663-673.
19. Fiehn, O., Metabolomics-The link between genotypes and phenotypes. Plant Mol. Biol. 2002, 48, 155-171.
20. Hall, R., Beale, M., Fiehn, O., Hardy, N. et al., Plant metabolomics: The missing link in functional genomics strategies. Plant Cell 2002, 14, 1437-1440.
21. Bino, R. J., Hall, R. D., Fiehn, O., Kopka, J. et al., Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 2004, 9, 418-425.
22. Fiehn, O., Kopka, J., Dormann, P., Altmann, T. et al., Metabolite profiling for plant functional genomics. Nat. Biotechnol. 2000, 18, 1157-1161.
23. Saito, K., Phytochemical genomics-A new trend. Curr. Opin. Plant Biol. 2013, 16, 373-380.
24. Yuan, J. S., Galbraith, D. W., Dai, S. Y., Griffin, P. et al., Plant systems biology comes of age. Trends Plant Sci. 2008, 13, 165-171.
25. Oksman-Caldentey, K. M., Inze, D., Oresic, M., Connecting genes to metabolites by a systems biology approach. Proc. Natl. Acad. Sci. U S A 2004, 101, 9949-9950.
26. Berrocal-Lobo, M., Molina, A., Arabidopsis defense response against Fusarium oxysporum. Trends Plant Sci. 2008, 13, 145-150.
27. Nguyen, Q. T., Merlo, M. E., Medema, M. H., Jankevics, A. et al., Metabolomics methods for the synthetic biology of secondary metabolism. FEBS Lett. 2012, 586, 2177-2183.
28. Zeng, J. G., Liu, Y. S., Liu, W., Liu, X. B. et al., Integration of transcriptome, proteome and metabolism data reveals the alkaloids biosynthesis in Macleaya cordata and Macleaya microcarpa. PLoS One 2013, 8, e53409.
29. Hirai, M. Y., Klein, M., Fujikawa, Y., Yano, M. et al., Elucidation of gene-to-gene and metabolite-to-gene networks in Arabidopsis by integration of metabolomics and transcriptomics. J. Biol. Chem. 2005, 280, 25590-25595.
30. Hirai, M. Y., Yano, M., Goodenowe, D. B., Kanaya, S. et al., Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA 2004, 101, 10205-10210.
31. Tohge, T., Nishiyama, Y., Hirai, M. Y., Yano, M. et al., Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J. 2005, 42, 218-235.
32. Schoof, H., Ernst, R., Nazarov, V., Pfeifer, L. et al., MIPS Arabidopsis thaliana database (MAtDB): An integrated biological knowledge resource for plant genomics. Nucleic Acids Res. 2004, 32, D373-D376.
33. Memelink, J., The use of genetics to dissect plant secondary pathways. Curr. Opin. Plant Biol. 2005, 8, 230-235.
34. Goossens, A., Rischer, H., Implementation of functional genomics for gene discovery in alkaloid producing plants. Phytochemistry Rev. 2007, 6, 35-49.
35. Suzuki, H., Achnine, L., Xu, R., Matsuda, S. P. T. et al., A genomics approach to the early stages of triterpene saponin biosynthesis in Medicago truncatula. Plant J. 2002, 32, 1033-1048.
36. Yamazaki, M., Shibata, M., Nishiyama, Y., Springob, K. et al., Differential gene expression profiles of red and green forms of Perilla frutescens leading to comprehensive identification of anthocyanin biosynthetic genes. FEBS J. 2008, 275, 3494-3502.
37. Falara, V., Fotopoulos, V., Margaritis, T., Anastasaki, T. et al., Transcriptome analysis approaches for the isolation of trichome-specific genes from the medicinal plant Cistus creticus subsp creticus. Plant Mol. Biol. 2008, 68, 633-651.
38. Mercke, P., Kappers, I. F., Verstappen, F. W. A., Vorst, O. et al., Combined transcript and metabolite analysis reveals genes involved in spider mite induced volatile formation in cucumber plants. Plant Physiol. 2004, 135, 2012-2024.
39. Aharoni, A., Keizer, L. C. P., Bouwmeester, H. J., Sun, Z. K. et al., Identification of the SAAT gene involved in strawberry flavor biogenesis by use of DNA microarrays. Plant Cell 2000, 12, 647-661.
40. Achnine, L., Huhman, D. V., Farag, M. A., Sumner, L. W. et al., Genomics-based selection and functional characterization of triterpene glycosyltransferases from the model legume Medicago truncatula. Plant J. 2005, 41, 875-887.
41. Guterman, I., Shalit, M., Menda, N., Piestun, D. et al., Rose scent: Genomics approach to discovering novel floral fragrance-related genes. Plant Cell 2002, 14, 2325-2338.
42. Saito, K., Hirai, M. Y., Yonekura-Sakakibara, K., Decoding genes with coexpression networks and metabolomics - 'Majority report by precogs.' Trends Plant Sci. 2008, 13, 36-43.
43. Yonekura-Sakakibara, K., Tohge, T., Niida, R., Saito, K., Identification of a flavonol 7-O-rhamnosyltransferase gene determining flavonoid pattern in Arabidopsis by transcriptome coexpression analysis and reverse genetics. J. Biol. Chem. 2007, 282, 14932-14941.
44. Yonekura-Sakakibara, K., Tohge, T., Matsuda, F., Nakabayashi, R. et al., Comprehensive flavonol profiling and transcriptome coexpression analysis leading to decoding gene-metabolite correlations in Arabidopsis. Plant Cell 2008, 20, 2160-2176.
45. Obayashi, T., Kinoshita, K., Nakai, K., Shibaoka, M. et al., ATTED-II: A database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis. Nucleic Acids Res. 2007, 35, D863-D869.
46. Gachon, C. M. M., Langlois-Meurinne, M., Henry, Y., Saindrenan, P., Transcriptional co-regulation of secondary metabolism enzymes in Arabidopsis: Functional and evolutionary implications. Plant Mol. Biol. 2005, 58, 229-245.
47. Rischer, H., Oresic, M., Seppanen-Laakso, T., Katajamaa, M. et al., Gene-to-metabolite networks for terpenoid indole alkaloid biosynthesis in Catharanthus roseus cells. Proc. Natl. Acad. Sci. USA 2006, 103, 5614-5619.
48. Ziegler, J., Diaz-Chavez, M., Kramell, R., Ammer, C. et al., Comparative macroarray analysis of morphine containing Papaver somniferum and eight morphine free Papaver species identifies an O-methyltransferase involved in benzylisoquinoline biosynthesis. Planta 2005, 222, 458-471.
49. Potnis, N., Krasileva, K., Chow, V., Almeida, N. F. et al., Comparative genomics reveals diversity among xanthomonads infecting tomato and pepper. BMC Genomics 2011, 12, 146.
50. Broun, P., Transcription factors as tools for metabolic engineering in plants. Curr. Opin. Plant Biol. 2004, 7, 202-209.
51. Hirai, M. Y., Sugiyama, K., Sawada, Y., Tohge, T. et al., Omics-based identification of Arabidopsis Myb transcription factors regulating aliphatic glucosinolate biosynthesis. Proc. Natl. Acad. Sci. USA 2007, 104, 6478-6483.
52. Jacobs, D. I., van der Heijden, R., Verpoorte, R., Proteomics in plant biotechnology and secondary metabolism research. Phytochem. Anal. 2000, 11, 277-287.
53. Decker, G., Wanner, G., Zenk, M. H., Lottspeich, F., Characterization of proteins in latex of the opium poppy (Papaver somniferum) using two-dimensional gel electrophoresis and microsequencing. Electrophoresis 2000, 21, 3500-3516.
54. Trethewey, R. N., Gene discovery via metabolic profiling. Curr. Opin. Biotechnol. 2001, 12, 135-138.
55. Weckwerth, W., Integration of metabolomics and proteomics in molecular plant physiology - Coping with the complexity by data-dimensionality reduction. Physiol. Plant. 2008, 132, 176-189.
56. Sumner, L. W., Mendes, P., Dixon, R. A., Plant metabolomics: Large-scale phytochemistry in the functional genomics era. Phytochemistry 2003, 62, 817-836.
57. Raamsdonk, L. M., Teusink, B., Broadhurst, D., Zhang, N. S. et al., A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 2001, 19, 45-50.
58. Guo, D., Chen, F., Inoue, K., Blount, J. W. et al., Downregulation of caffeic acid 3-O-methyltransferase and caffeoyl CoA 3-O-methyltransferase in transgenic alfalfa. Impacts on lignin structure and implications for the biosynthesis of G and S lignin. Plant Cell 2001, 13, 73-88.
59. Meyermans, H., Morreel, K., Lapierre, C., Pollet, B. et al., Modifications in lignin and accumulation of phenolic glucosides in poplar xylem upon down-regulation of caffeoyl-coenzyme A O-methyltransferase, an enzyme involved in lignin biosynthesis. J. Biol. Chem. 2000, 275, 36899-36909.
60. Vigeolas, H., Chinoy, C., Zuther, E., Blessington, B. et al., Combined metabolomic and genetic approaches reveal a link between the polyamine pathway and albumin 2 in developing pea seeds. Plant Physiol. 2008, 146, 74-82.
61. Rohde, A., Morreel, K., Ralph, J., Goeminne, G. et al., Molecular phenotyping of the pal1 and pal2 mutants of Arabidopsis thaliana reveals far-reaching consequences on and carbohydrate metabolism. Plant Cell 2004, 16, 2749-2771.
62. Roberts, S. C., Production and engineering of terpenoids in plant cell culture. Nat. Chem. Biol. 2007, 3, 387-395.
63. Lichtenthaler, H. K., Non-mevalonate isoprenoid biosynthesis: Enzymes, genes and inhibitors. Biochem. Soc. Trans. 2000, 28, 785-789.
64. Rohmer, M., The discovery of a mevalonate-independent pathway for isoprenoid biosynthesis in bacteria, algae and higher plants. Nat. Prod. Rep. 1999, 16, 565-574.
65. Marcotte, E. M., The path not taken. Nat. Biotechnol. 2001, 19, 626-627.
66. Weckwerth, W., Fiehn, O., Can we discover novel pathways using metabolomic analysis? Curr. Opin. Biotechnol. 2002, 13, 156-160.
67. Farag, M. A., Huhman, D. V., Dixon, R. A., Sumner, L. W., Metabolomics reveals novel pathways and differential mechanistic and elicitor-specific responses in phenylpropanoid and isoflavonoid biosynthesis in Medicago truncatula cell cultures. Plant Physiol. 2008, 146, 387-402.
68. Broun, P., Transcriptional control of flavonoid biosynthesis: A complex network of conserved regulators involved in multiple aspects of differentiation in Arabidopsis. Curr. Opin. Plant Biol. 2005, 8, 272-279.
69. Forster, J., Famili, I., Fu, P., Palsson, B. O. et al., Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 2003, 13, 244-253.
70. Thum, K. E., Shin, M. J., Gutierrez, R. A., Mukherjee, I. et al., An integrated genetic, genomic and systems approach defines gene networks regulated by the interaction of light and carbon signaling pathways in Arabidopsis. BMC Syst. Biol. 2008, 2, 31.
71. Soranzo, N., Bianconi, G., Altafini, C., Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: Synthetic versus real data. Bioinformatics 2007, 23, 1640-1647.
72. Wang, Y., Joshi, T., Zhang, X. S., Xu, D. et al., Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 2006, 22, 2413-2420.
73. Bansal, M., Belcastro, V., Ambesi-Impiombato, A., di Bernardo, D., How to infer gene networks from expression profiles. Mol. Syst. Biol. 2007, 3, 78.
74. Weckwerth, W., Loureiro, M. E., Wenzel, K., Fiehn, O., Differential metabolic networks unravel the effects of silent plant phenotypes. Proc. Natl. Acad Sci. USA 2004, 101, 7809-7814.
75. Tunahan, C., Margriet, M. W. B. H., Johan, A. W., Age, K. S., Metabolic network discovery through reverse engineering of metabolome data. Metabolomics 2009.
76. Fiehn, O., Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genomics. 2001, 2, 155-168.
77. Fiehn, O., Weckwerth, W., Deciphering metabolic networks. Eur. J. Biochem. 2003, 270, 579-588.
78. Mounet, F., Moing, A., Garcia, V., Petit, J. et al., Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development. Plant. Physiol. 2009, 149, 1505-1528.
79. Saito, K., Dixon, R. A., Willmitzer, L., in: Saito, K., Dixon, R. A., Willmitzer, L., (Eds), Plant Metabolomics, Springer-Verlag Berlin Heidelberg, Germany 2006, pp. 111-113.
80. Lindon, J. C., Nicholson, J. K., Holmes, E., The Handbook of Metabonomics and Metabolomics, Elsevier, Amsterdam 2007.
81. Jansen, J. J., Hoefsloot, H. C. J., van der Greef, J., Timmerman, M. E. et al., Multilevel component analysis of time-resolved metabolic fingerprinting data. Anal. Chim. Acta 2005, 530, 173-183.
82. Weckwerth, W., Loureiro, M. E., Wenzel, K., Fiehn, O., Differential metabolic networks unravel the effects of silent plant phenotypes. Proc. Natl. Acad Sci. USA 2004, 101, 7809-7814.
83. Mercke, P., Kappers, I. F., Verstappen, F. W. A., Vorst, O. et al., Combined transcript and metabolite analysis reveals genes involved in spider mite induced volatile formation in cucumber plants. Plant Physiol. 2004, 135, 2012-2024.
84. Smilde, A. K., Jansen, J. J., Hoefsloot, H. C., Lamers, R. J. et al., ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics 2005, 21, 3043-3048.
85. Xia, J., Mandal, R., Sinelnikov, I. V., Broadhurst, D. et al., MetaboAnalyst 2.0 - A comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012, 40, W127-W133.
86. Tokimatsu, T., Sakurai, N., Suzuki, H., Ohta, H. et al., KaPPA-view: A web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol. 2005, 138, 1289-1300.
87. Akiyama, K., Chikayama, E., Yuasa, H., Shimada, Y. et al., PRIMe: A web site that assembles tools for metabolomics and transcriptomics. In Silico Biol. 2008, 8, 339-345.
88. Medina, I., Carbonell, J., Pulido, L., Madeira, S. C. et al., Babelomics: An integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res. 2010, 38, 210-213.
89. Wurtele, E. S., Li, J., Diao, L., Zhang, H. et al., MetNet: Software to build and model the biogenetic lattice of Arabidopsis. Comp. Funct. Genomics 2003, 4, 239-245.
90. Pan, D., Sun, N., Cheung, K. H., Guan, Z. et al., PathMAPA: A tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis. BMC Bioinformatics 2003, 4, 56.
91. Beltrame, L., Bianco, L., Fontana, P., Cavalieri, D., Pathway processor 2.0: A web resource for pathway-based analysis of high-throughput data. Bioinformatics 2013, 29, 1825-1826.
92. Zhang, P., Foerster, H., Tissier, C. P., Mueller, L. et al., MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol. 2005, 138, 27-37.
93. Thimm, O., Blasing, O., Gibon, Y., Nagel, A. et al., MAPMAN : A user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004, 37, 914-939.
94. van Moerkercke, A., Fabris, M., Pollier, J., Baart, G. J. et al., CathaCyc, a metabolic pathway database built from Catharanthus roseus RNA-Seq data. Plant Cell Physiol. 2013, 54, 673-685.
95. Oksman-Caldentey, K.-M., Saito, K., Integrating genomics and metabolomics for engineering plant metabolic pathways. Curr. Opin. Biotechnol. 2005, 16, 174-179.
96. Chandia, N. P., Matsuhiro, B., Characterization of a fucoidan from Lessonia vadosa (Phaeophyta) and its anticoagulant and elicitor properties. Int. J. Biol. Macromol. 2008, 42, 235-240.
97. Capell, T., Christou, P., Progress in plant metabolic engineering. Curr. Opin. Biotechnol. 2004, 15, 148-154.
98. Wu, S. Q., Chappell, J., Metabolic engineering of natural products in plants; tools of the trade and challenges for the future. Curr. Opin. Biotechnol. 2008, 19, 145-152.
99. Forkmann, G., Martens, S., Metabolic engineering and applications of flavonoids. Curr. Opin. Biotechnol. 2001, 12, 155-160.
100. Dudareva, N., Pichersky, E., Metabolic engineering of plant volatiles. Curr. Opin. Biotechnol. 2008, 19, 181-189.
101. Schwender, J., Metabolic flux analysis as a tool in metabolic engineering of plants. Curr. Opin. Biotechnol. 2008, 131-137.
102. Trethewey, R. N., Metabolite profiling as an aid to metabolic engineering in plants. Curr. Opin. Plant Biol. 2004, 7, 196-201.
103. Askenazi, M., Driggers, E. M., Holtzman, D. A., Norman, T. C. et al., Integrating transcriptional and metabolite profiles to direct the engineering of lovastatin-producing fungal strains. Nat. Biotechnol. 2003, 21, 150-156.
104. Arnaud, B., Elio, S., Robert, D. H., Metabolic engineering of flavonoids in tomato (Solanum lycopersicum): The potential for metabolomics. Metabolomics 2007, 3, 399-412.
105. Kizer, L., Pitera, D. J., Pfleger, B. F., Keasling, J. D., Application of functional genomics to pathway optimization for increased isoprenoid production. Appl. Environ. Microbiol. 2008, 74, 3229-3241.
106. Kirby, J., Keasling, J. D., Biosynthesis of plant isoprenoids: Perspectives for microbial engineering. Annu. Rev. Plant Biol. 2009, 60, 335-355.
107. Chang, M. C. Y., Keasling, J. D., Production of isoprenoid pharmaceuticals by engineered microbes. Nat. Chem. Biol. 2006, 2, 674-681.