Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period

被引:186
作者
Allwood, J. William [1 ]
Ellis, David I. [1 ]
Goodacre, Royston [1 ]
机构
[1] Univ Manchester, Sch Chem, Manchester Interdisciplinary Bioctr, Manchester M1 7DN, Lancs, England
关键词
D O I
10.1111/j.1399-3054.2007.01001.x
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Metabolomics is perhaps the ultimate level of post-genomic analysis as it can reveal changes in metabolite fluxes that are controlled by only minor changes within gene expression measured using transcriptomics and/or by analysing the proteome that elucidates post-translational control over enzyme activity. Metabolic change is a major feature of plant genetic modification and plant interactions with pathogens, pests, and their environment. In the assessment of genetically modified plant tissues, metabolomics has been used extensively to explore by-products resulting from transgene expression and scenarios of substantial equivalence. Many studies have concentrated on the physiological development of plant tissues as well as on the stress responses involved in heat shock or treatment with stress-eliciting molecules such as methyl jasmonic acid, yeast elicitor or bacterial lipopolysaccharide. Plant-host interactions represent one of the most biochemically complex and challenging scenarios that are currently being assessed by metabolomic approaches. For example, the mixtures of pathogen-colonised and non-challenged plant cells represent an extremely heterogeneous and biochemically rich sample; there is also the further complication of identifying which metabolites are derived from the plant host and which are from the interacting pathogen. This review will present an overview of the analytical instrumentation currently applied to plant metabolomic analysis, literature within the field will be reviewed paying particular regard to studies based on plant-host interactions and finally the future prospects on the metabolomic analysis of plants and plant-host interactions will be discussed.
引用
收藏
页码:117 / 135
页数:19
相关论文
共 121 条
[61]   GC-EI-TOF-MS analysis of in vivo carbon-partitioning into soluble metabolite pools of higher plants by monitoring isotope dilution after 13CO2 labelling [J].
Huege, Jan ;
Sulpice, Ronan ;
Gibon, Yves ;
Lisec, Jan ;
Koehl, Karin ;
Kopka, Joachim .
PHYTOCHEMISTRY, 2007, 68 (16-18) :2258-2272
[62]   Metabolic profiling of saponins in Medicago sativa and Medicago truncatula using HPLC coupled to an electrospray ion-trap mass spectrometer [J].
Huhman, DV ;
Sumner, LW .
PHYTOCHEMISTRY, 2002, 59 (03) :347-360
[63]   Application of a high-throughput HPLC-MS/MS assay to Arabidopsis mutant screening;: evidence that threonine aldolase plays a role in seed nutritional quality [J].
Jander, G ;
Norris, SR ;
Joshi, V ;
Fraga, M ;
Rugg, A ;
Yu, SX ;
Li, LL ;
Last, RL .
PLANT JOURNAL, 2004, 39 (03) :465-475
[64]   Metabolic fingerprinting of salt-stressed tomatoes [J].
Johnson, HE ;
Broadhurst, D ;
Goodacre, R ;
Smith, AR .
PHYTOCHEMISTRY, 2003, 62 (06) :919-928
[65]  
Jolliffe I. T., 1986, PRINCIPAL COMPONENT, DOI DOI 10.1016/0169-7439(87)80084-9
[66]   A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS [J].
Jonsson, P ;
Gullberg, J ;
Nordström, A ;
Kusano, M ;
Kowalczyk, M ;
Sjöström, M ;
Moritz, T .
ANALYTICAL CHEMISTRY, 2004, 76 (06) :1738-1745
[67]   High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses [J].
Jonsson, P ;
Johansson, AI ;
Gullberg, J ;
Trygg, J ;
A, J ;
Grung, B ;
Marklund, S ;
Sjoström, M ;
Antti, H ;
Moritz, T .
ANALYTICAL CHEMISTRY, 2005, 77 (17) :5635-5642
[68]   Exploring the temperature-stress metabolome of Arabidopsis [J].
Kaplan, F ;
Kopka, J ;
Haskell, DW ;
Zhao, W ;
Schiller, KC ;
Gatzke, N ;
Sung, DY ;
Guy, CL .
PLANT PHYSIOLOGY, 2004, 136 (04) :4159-4168
[69]   Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era [J].
Kell, DB ;
Oliver, SG .
BIOESSAYS, 2004, 26 (01) :99-105
[70]   Genomic computing. Explanatory analysis of plant expression profiling data using machine learning [J].
Kell, DB ;
Darby, RM ;
Draper, J .
PLANT PHYSIOLOGY, 2001, 126 (03) :943-951