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Metabolic Fingerprint (metabolic + fingerprint)
Selected AbstractsMetabolomic analysis of Echinacea spp. by 1H nuclear magnetic resonance spectrometry and multivariate data analysis technique,PHYTOCHEMICAL ANALYSIS, Issue 1 2010Michel Frédérich Abstract Introduction , The genus Echinacea (Asteraceae) comprises about 10 species originally distributed in North America. Three species are very well known as they are used worldwide as medicinal plants: Echinacea purpurea, E. pallida, E. angustifolia. Objective , To discriminate between these three Echinacea species and E. simulata by 1H NMR-based metabolomics. Methodology , 1H NMR and multivariate analysis techniques were applied to diverse Echinacea plants including roots and aerial parts, authentic plants, commercial plants and commercial dry extracts. Results , Using the 1H NMR metabolomics, it was possible, without previous evaporation or separation steps, to obtain a metabolic fingerprint to distinguish between species. Conclusion , A clear distinction between the three pharmaceutical species was possible and some useful metabolites were identified. Copyright © 2009 John Wiley & Sons, Ltd. [source] Metabolomic approaches reveal that phosphatidic and phosphatidyl glycerol phospholipids are major discriminatory non-polar metabolites in responses by Brachypodium distachyon to challenge by Magnaporthe griseaTHE PLANT JOURNAL, Issue 3 2006J. William Allwood Summary Metabolomic approaches were used to elucidate some key metabolite changes occurring during interactions of Magnaporthe grisea, the cause of rice blast disease , with an alternate host, Brachypodium distachyon. Fourier-transform infrared (FT-IR) spectroscopy provided a high-throughput metabolic fingerprint of M. grisea interacting with the B. distachyon accessions ABR1 (susceptible) and ABR5 (resistant). Principal component,discriminant function analysis (PC-DFA) allowed the differentiation between developing disease symptoms and host resistance. Alignment of projected ,test-set' on to ,training-set' data indicated that our experimental approach produced highly reproducible data. Examination of PC-DFA loading plots indicated that fatty acids were one chemical group that discriminated between responses by ABR1 and ABR5 to M. grisea. To identify these, non-polar extracts of M. grisea -challenged B. distachyon were directly infused into an electrospray ionization mass spectrometer (ESI-MS). PC-DFA indicated that M. grisea -challenged ABR1 and ABR5 were differentially clustered away from healthy material. Subtraction spectra and PC-DFA loadings plots revealed discriminatory analytes (m/z) between each interaction and seven metabolites were subsequently identified as phospholipids (PLs) by ESI-MS-MS. Phosphatidyl glycerol (PG) PLs were suppressed during both resistant and susceptible responses. By contrast, different phosphatidic acid PLs either increased or were reduced during resistance or during disease development. This suggests considerable and differential PL processing of membrane lipids during each interaction which may be associated with the elaboration/suppression of defence mechanisms or developing disease symptoms. [source] Metabolomics-based systematic prediction of yeast lifespan and its application for semi-rational screening of ageing-related mutantsAGING CELL, Issue 4 2010Ryo Yoshida Summary Metabolomics , the comprehensive analysis of metabolites , was recently used to classify yeast mutants with no overt phenotype using raw data as metabolic fingerprints or footprints. In this study, we demonstrate the estimation of a complicated phenotype, longevity, and semi-rational screening for relevant mutants using metabolic profiles as strain-specific fingerprints. The fingerprints used in our experiments are profiled data consisting of individually identified and quantified metabolites rather than raw spectrum data. We chose yeast replicative lifespan as a model phenotype. Several yeast mutants that affect lifespan were selected for analysis, and they were subjected to metabolic profiling using mass spectrometry. Fingerprinting based on the profiles revealed a correlation between lifespan and metabolic profile. Amino acids and nucleotide derivatives were the main contributors to this correlation. Furthermore, we established a multivariate model to predict lifespan from a metabolic profile. The model facilitated the identification of putative longevity mutants. This work represents a novel approach to evaluate and screen complicated and quantitative phenotype by means of metabolomics. [source] Application of 31P NMR spectroscopy and chemical derivatization for metabolite profiling of lipophilic compounds in human serumMAGNETIC RESONANCE IN CHEMISTRY, Issue S1 2009M. Aruni DeSilva Abstract New methods for obtaining metabolic fingerprints of biological samples with improved resolution and sensitivity are highly sought for early disease detection, studies of human health and pathophysiology, and for better understanding systems biology. Considering the complexity of biological samples, interest in biochemical class selection through the use of chemoselective probes for improved resolution and quantitation is increasing. Considering the role of lipids in the pathogenesis of a number of diseases, in this study fingerprinting of lipid metabolites was achieved by 31P labeling using the derivatizing agent 2-chloro-4,4,5,5-tetramethyldioxaphospholane. Lipids containing hydroxyl, aldehyde and carboxyl groups were selectively tagged with 31P and then detected with good resolution using 31P NMR by exploiting the 100% natural abundance and wide chemical shift range of 31P. After standardizing the reaction conditions using representative compounds, the derivatization approach was used to profile lipids in human serum. The results show that the 31P derivatization approach is simple, reproducible and highly quantitative, and has the potential to profile a number of important lipids in complex biological samples. Copyright © 2009 John Wiley & Sons, Ltd. [source] | |||||||||||||