Quality assessment of ginseng by (1)H NMR metabolite fingerprinting and profiling analysis.

Metabolite profiling and fingerprint analysis by (1)H NMR spectroscopy were used to identify potential biomarkers capable of distinguishing different ginseng species, varieties, and commercial products with the aim of establishing quality control code protocol based on biochemical phenotype. Principal component (PC) analyses of (1)H NMR spectra reliably discriminated between the various ginseng samples, demonstrating the potential utility of metabolomics in the natural health products industry. Four Asian ginseng varieties separated along the PC1 and PC2 axes, and four different Korean ginseng products were divided into two groups by PC1. A strong separation was also revealed between Asian ginseng (Panax ginseng) and American ginseng (Panax quinquefolius). Glutamine, arginine, sucrose, malate, and myo-inositol were the major metabolites in ginseng samples tested in this study. Combined metabolite fingerprinting and profiling suggested that several compounds including glucose, fumarate, and various amino acids could serve as biomarkers for quality assurance in ginseng.

Quantitative 1H nuclear magnetic resonance metabolite profiling as a functional genomics platform to investigate alkaloid biosynthesis in opium poppy.

Opium poppy (Papaver somniferum) produces a diverse array of bioactive benzylisoquinoline alkaloids and has emerged as a versatile model system to study plant alkaloid metabolism. The plant is widely cultivated as the only commercial source of the narcotic analgesics morphine and codeine. Variations in plant secondary metabolism as a result of genetic diversity are often associated with perturbations in other metabolic pathways. As part of a functional genomics platform, we used (1)H nuclear magnetic resonance (NMR) metabolite profiling for the analysis of primary and secondary metabolism in opium poppy. Aqueous and chloroform extracts of six different opium poppy cultivars were subjected to chemometric analysis. Principle component analysis of the (1)H NMR spectra for latex extracts clearly distinguished two varieties, including a low-alkaloid variety and a high-thebaine, low-morphine cultivar. Distinction was also made between pharmaceutical-grade opium poppy cultivars and a condiment variety. Such phenotypic differences were not observed in root extracts. Loading plots confirmed that morphinan alkaloids contributed predominantly to the variance in latex extracts. Quantification of 34 root and 21 latex metabolites, performed using Chenomx NMR Suite version 4.6, showed major differences in the accumulation of specific alkaloids in the latex of the low-alkaloid and high-thebaine, low-morphine varieties. Relatively few differences were found in the levels of other metabolites, indicating that the variation was specific for alkaloid metabolism. Exceptions in the low-alkaloid cultivar included an increased accumulation of the alkaloid precursor tyramine and reduced levels of sucrose, some amino acids, and malate. Real-time polymerase chain reaction analysis of 42 genes involved in primary and secondary metabolism showed differential gene expression mainly associated with alkaloid biosynthesis. Reduced alkaloid levels in the condiment variety were associated with the reduced abundance of transcripts encoding several alkaloid biosynthetic enzymes.

Quantitative 1H NMR metabolomics reveals extensive metabolic reprogramming of primary and secondary metabolism in elicitor-treated opium poppy cell cultures.

BACKGROUND: Opium poppy (Papaver somniferum) produces a diverse array of bioactive benzylisoquinoline alkaloids and has emerged as a model system to study plant alkaloid metabolism. The plant is cultivated as the only commercial source of the narcotic analgesics morphine and codeine, but also produces many other alkaloids including the antimicrobial agent sanguinarine. Modulations in plant secondary metabolism as a result of environmental perturbations are often associated with the altered regulation of other metabolic pathways. As a key component of our functional genomics platform for opium poppy we have used proton nuclear magnetic resonance (1H NMR) metabolomics to investigate the interplay between primary and secondary metabolism in cultured opium poppy cells treated with a fungal elicitor. RESULTS: Metabolite fingerprinting and compound-specific profiling showed the extensive reprogramming of primary metabolic pathways in association with the induction of alkaloid biosynthesis in response to elicitor treatment. Using Chenomx NMR Suite v. 4.6, a software package capable of identifying and quantifying individual compounds based on their respective signature spectra, the levels of 42 diverse metabolites were monitored over a 100-hour time course in control and elicitor-treated opium poppy cell cultures. Overall, detectable and dynamic changes in the metabolome of elicitor-treated cells, especially in cellular pools of carbohydrates, organic acids and non-protein amino acids were detected within 5 hours after elicitor treatment. The metabolome of control cultures also showed substantial modulations 80 hours after the start of the time course, particularly in the levels of amino acids and phospholipid pathway intermediates. Specific flux modulations were detected throughout primary metabolism, including glycolysis, the tricarboxylic acid cycle, nitrogen assimilation, phospholipid/fatty acid synthesis and the shikimate pathway, all of which generate secondary metabolic precursors. CONCLUSION: The response of cell cultures to elicitor treatment involves the extensive reprogramming of primary and secondary metabolism, and associated cofactor biosynthetic pathways. A high-resolution map of the extensive reprogramming of primary and secondary metabolism in elicitor-treated opium poppy cell cultures is provided.

Evaluating low-intensity unknown signals in quantitative proton NMR mixture analysis.

Analytical analyses of highly complex mixtures, such as biofluids or liquid food products, often give rise to signals for unknown compounds, particularly for compounds at low concentration. Here we compare two conventional chemometric approaches for NMR spectral analysis ("spectral binning" and "high-resolution analysis") with a novel library-based method ("targeted profiling of unknowns", TPU). The three methods were applied to a proton NMR spectral data set of ultrafiltered mouse serum typical of those examined in metabolomics/metabonomics studies. The advantages of high-resolution analysis of typical NMR peaks have been well described previously, and as a result we examined low intensity unknowns peaks (LIUPs). A total of 25 LIUPs were assessed based on their significance to multivariate statistical analysis of the data set using the TPU method. The linearity of NMR signals at low incremental concentration changes (< 10 microM) was determined by titration of endogenously occurring metabolites into filtered mouse serum. Carbon-13 decoupling of the NMR spectra was used to ensure isotope-satellite peaks were eliminated. Four peaks were noted as significant to separation between arthritic and diseased animals. The conventional spectral methods were hampered by baseline noise or overlap with high concentration metabolites and were not able to identify these LIUPs reliably. In general, conventional methods, particularly high-resolution analysis, are recommended for peaks with moderate signal-to-noise. The TPU method is recommended for peaks with low signal-to-noise or when compression of spectral data with high fidelity is desirable, such as integration of NMR data into cross-platform studies.

An inflammatory arthritis-associated metabolite biomarker pattern revealed by 1H NMR spectroscopy.

Rheumatoid arthritis, a debilitating, systemic inflammatory joint disease, is likely accompanied by alterations in circulating metabolites. Here, an 1H NMR spectroscopy-based metabolomics approach was developed to establish a metabolic 'biomarker pattern' in a model of rheumatoid arthritis, the K/BxN transgenic mouse. Sera obtained from arthritic K/BxN mice (N = 15) and a control population (N = 19) having the same genetic background, but lacking the arthritogenic T-cell receptor KRN transgene, were compared by 1H NMR spectroscopy. A unique method was developed by combining technologies such as ultrafiltration to remove proteins from serum samples, quantitative 'targeted profiling' of known metabolites, pseudo-quantitative profiling of unknown resonances, a supervised O-PLS-DA pattern recognition analysis, and a metabolic-pathway based network analysis for interpretation of results. In total, 88 spectral features were profiled (59 metabolites and 28 unknown resonances). A highly significant subset of 18 spectral features (15 known compounds and 3 unknown resonances) was identified (p = 0.00075 using MANOVA) that we term a 'metabolic bioprofile'. We identified metabolites relating to nucleic acid, amino acid, and fatty acid metabolism, as well as lipolysis, reactive oxygen species generation, and methylation. Pathway analysis suggested a shift from metabolites involved in numerous reactions (hub-metabolites) toward intermediates and metabolic endpoints associated with arthritis. The results attest to the metabolic complexity of systemic inflammation and to the power of the experimental approach for identifying a wide variety of disease-associated marker candidates. The diagnostic and prognostic implications of monitoring a spectrum of metabolic events simultaneously using serum samples is discussed with respect to the potential for individualized medicine.