Metabolic profiling of strawberry (Fragaria x ananassa Duch.) during fruit development and maturation

Strawberry (Fragaria × ananassa Duch), a fruit of economic and nutritional importance, is also a model species for fleshy fruits and genomics in Rosaceae. Strawberry fruit quality at different harvest stages is a function of the fruit's metabolite content, which results from physiological changes during fruit growth and ripening. In order to investigate strawberry fruit development, untargeted (GC-MS) and targeted (HPLC) metabolic profiling analyses were conducted. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to explore the non-polar and polar metabolite profiles from fruit samples at seven developmental stages. Different cluster patterns and a broad range of metabolites that exerted influence on cluster formation of metabolite profiles were observed. Significant changes in metabolite levels were found in both fruits turning red and fruits over-ripening in comparison with red-ripening fruits. The levels of free amino acids decreased gradually before the red-ripening stage, but increased significantly in the over-ripening stage. Metabolite correlation and network analysis revealed the interdependencies of individual metabolites and metabolic pathways. Activities of several metabolic pathways, including ester biosynthesis, the tricarboxylic acid cycle, the shikimate pathway, and amino acid metabolism, shifted during fruit growth and ripening. These results not only confirmed published metabolic data but also revealed new insights into strawberry fruit composition and metabolite changes, thus demonstrating the value of metabolomics as a functional genomics tool in characterizing the mechanism of fruit quality formation, a key developmental stage in most economically important fruit crops.     

Plant metabolomics reveals conserved and divergent metabolic responses to salinity

New metabolic profiling technologies provide data on a wider range of metabolites than traditional targeted approaches. Metabolomic technologies currently facilitate acquisition of multivariate metabolic data using diverse, mostly hyphenated, chromatographic detection systems, such as GC-MS or liquid chromatography coupled to mass spectrometry, Fourier-transformed infrared spectroscopy or NMR-based methods. Analysis of the resulting data can be performed through a combination of non-supervised and supervised statistical methods, such as independent component analysis and analysis of variance, respectively. These methods reduce the complex data sets to information, which is relevant for the discovery of metabolic markers or for hypothesis-driven, pathway-based analysis. Plant responses to salinity involve changes in the activity of genes and proteins, which invariably lead to changes in plant metabolism. Here, we highlight a selection of recent publications in the salt stress field, and use gas chromatography time-of-flight mass spectrometry profiles of polar fractions from the plant models, Arabidopsis thaliana, Lotus japonicus and Oryza sativa to demonstrate the power of metabolite profiling. We present evidence for conserved and divergent metabolic responses among these three species and conclude that a change in the balance between amino acids and organic acids may be a conserved metabolic response of plants to salt stress.     

Metabolomics of aerobic metabolism in mice selected for increased maximal metabolic rate

Maximal aerobic metabolic rate (MMR) is an important physiological and ecological variable that sets an upper limit to sustained, vigorous activity. How the oxygen cascade from the external environment to the mitochondria may affect MMR has been the subject of much interest, but little is known about the metabolic profiles that underpin variation in MMR. We tested how seven generations of artificial selection for high mass-independent MMR affected metabolite profiles of two skeletal muscles (gastrocnemius and plantaris) and the liver. MMR was 12.3% higher in mass selected for high MMR than in controls. Basal metabolic rate was 3.5% higher in selected mice than in controls. Artificial selection did not lead to detectable changes in the metabolic profiles from plantaris muscle, but in the liver amino acids and tricarboxylic acid cycle (TCA cycle) metabolites were lower in high-MMR mice than in controls. In gastrocnemius, amino acids and TCA cycle metabolites were higher in high-MMR mice than in controls, indicating elevated amino acid and energy metabolism. Moreover, in gastrocnemius free fatty acids and triacylglycerol fatty acids were lower in high-MMR mice than in controls. Because selection for high MMR was associated with changes in the resting metabolic profile of both liver and gastrocnemius, the result suggests a possible mechanistic link between resting metabolism and MMR. In addition, it is well established that diet and exercise affect the composition of fatty acids in muscle. The differences that we found between control lines and lines selected for high MMR demonstrate that the composition of fatty acids in muscle is also affected by genetic factors.     

Metabolite profiling for analysis of yeast stress response during very high gravity ethanol fermentations

A laboratory strain and an industrial strain of Saccharomyces cerevisiae were grown at high substrate concentration, so-called very high gravity (VHG) fermentation. Simultaneous saccharification and fermentation (SSF) was applied in a batch process using 280 g/L maltodextrin as carbon source. It was shown that known ethanol and osmotic stress responses such as decreased growth rate, lower viability, higher energy consumption, and intracellular trehalose accumulation occur in VHG SSF for both strains when compared with standard laboratory medium (20 g/L glucose). The laboratory strain was the most affected. GC-MS metabolite profiling was applied for assessing the yeast stress response influence on cellular metabolism. It was found that metabolite profiles originating from different strains and/or fermentation conditions were unique and could be distinguished with the help of multivariate data analysis. Several differences in the metabolic responses to stressing conditions were revealed, particularly the increased energy consumption of stressed cells was also reflected in increased intracellular concentrations of pyruvate and related metabolites.     

Alteration in Metabolic Signature and Lipid Metabolism in Patients with Angina Pectoris and Myocardial Infarction

Lipid metabolites are indispensable regulators of physiological and pathological processes, including atherosclerosis and coronary artery disease (CAD). However, the complex changes in lipid metabolites and metabolism that occur in patients with these conditions are incompletely understood. We performed lipid profiling to identify alterations in lipid metabolism in patients with angina and myocardial infarction (MI). Global lipid profiling was applied to serum samples from patients with CAD (angina and MI) and age-, sex-, and body mass index-matched healthy subjects using ultra-performance liquid chromatography/quadruple time-of-flight mass spectrometry and multivariate statistical analysis. A multivariate analysis showed a clear separation between the patients with CAD and normal controls. Lysophosphatidylcholine (lysoPC) and lysophosphatidylethanolamine (lysoPE) species containing unsaturated fatty acids and free fatty acids were associated with an increased risk of CAD, whereas species of lysoPC and lyso-alkyl PC containing saturated fatty acids were associated with a decreased risk. Additionally, PC species containing palmitic acid, diacylglycerol, sphingomyelin, and ceramide were associated with an increased risk of MI, whereas PE-plasmalogen and phosphatidylinositol species were associated with a decreased risk. In MI patients, we found strong positive correlation between lipid metabolites related to the sphingolipid pathway, sphingomyelin, and ceramide and acute inflammatory markers (high-sensitivity C-reactive protein). The results of this study demonstrate altered signatures in lipid metabolism in patients with angina or MI. Lipidomic profiling could provide the information to identity the specific lipid metabolites under the presence of disturbed metabolic pathways in patients with CAD.     

Metabolic profiling using combined GC-MS and LC-MS provides a systems understanding of aristolochic acid-induced nephrotoxicity in rat

We present here a combined GC-MS and LC-MS metabolic profiling approach to unraveling the pathological outcomes of aristolochic acid (AA)-induced nephrotoxicity. Urine samples were analyzed by GC-MS and LC-MS in combination with pattern recognition techniques, e.g. principal component analysis (PCA), orthogonal projection to latent structure-discriminant analysis. The work indicates that AA-induced acute renal toxicity as evidenced by histopathological examinations could be characterized by systemic disturbance of metabolic network involving free fatty acids generation, energy and amino acids metabolism, and alteration in the structure of gut microbiota. Therefore, this method is potentially applicable to the toxicological study, providing a comprehensive understanding of systems response to xenobiotic intervention.     

Metabolic profiling of a Schistosoma mansoni infection in mouse tissues using magic angle spinning-nuclear magnetic resonance spectroscopy

In order to enhance our understanding of physiological and pathological consequences of a patent Schistosoma mansoni infection in the mouse, we examined the metabolic responses of different tissue samples recovered from the host animal using a metabolic profiling strategy. Ten female NMRI mice were infected with ∼80 S. mansoni cercariae each, and 10 uninfected age- and sex-matched animals served as controls. At day 74 post infection (p.i.), mice were killed and jejunum, ileum, colon, liver, spleen and kidney samples were removed. We employed ¹H magic angle spinning-nuclear magnetic resonance spectroscopy to generate tissue-specific metabolic profiles. The spectral data were analyzed using multivariate modelling methods including an orthogonal signal corrected-projection to latent structure analysis and hierarchical principal component analysis to assess the differences and/or similarities in metabolic responses between infected and non-infected control mice. Most tissues obtained from S. mansoni-infected mice were characterized by high levels of amino acids, such as leucine, isoleucine, lysine, glutamine and asparagine. High levels of membrane phospholipid metabolites, including glycerophosphoryl choline and phosphoryl choline were found in the ileum, colon, liver and spleen of infected mice. Additionally, low levels of energy-related metabolites, including lipids, glucose and glycogen were observed in ileum, spleen and liver samples of infected mice. Energy-related metabolites in the jejunum, liver and renal medulla were found to be positively correlated with S. mansoni worm burden upon dissection. These findings show that a patent S. mansoni infection causes clear disruption of metabolism in a range of tissues at a molecular level, which can be interpreted in relation to the previously reported signature in a biofluid (i.e. urine), giving further evidence of the global effect of the infection.     

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

Metabolomic Profiling of Faecal Extracts from Cryptosporidium parvum Infection in Experimental Mouse Models

Cryptosporidiosis is a gastrointestinal disease in humans and animals caused by infection with the protozoan parasite Cryptosporidium. In healthy individuals, the disease manifests mainly as acute self-limiting diarrhoea, but may be chronic and life threatening for those with compromised immune systems. Control and treatment of the disease is challenged by the lack of sensitive diagnostic tools and broad-spectrum chemotherapy. Metabolomics, or metabolite profiling, is an emerging field of study, which enables characterisation of the end products of regulatory processes in a biological system. Analysis of changes in metabolite patterns reflects changes in biochemical regulation, production and control, and may contribute to understanding the effects of Cryptosporidium infection in the host environment. In the present study, metabolomic analysis of faecal samples from experimentally infected mice was carried out to assess metabolite profiles pertaining to the infection. Gas-chromatography mass spectrometry (GC-MS) carried out on faecal samples from a group of C. parvum infected mice and a group of uninfected control mice detected a mean total of 220 compounds. Multivariate analyses showed distinct differences between the profiles of C. parvum infected mice and uninfected control mice,identifying a total of 40 compounds, or metabolites that contributed most to the variance between the two groups. These metabolites consisted of amino acids (n = 17), carbohydrates (n = 8), lipids (n = 7), organic acids (n = 3) and other various metabolites (n = 5), which showed significant differences in levels of metabolite abundance between the infected and uninfected mice groups (p < 0.05). The metabolites detected in this study as well as the differences in abundance between the C. parvum infected and the uninfected control mice, highlights the effects of the infection on intestinal permeability and the fate of the metabolites as a result of nutrient scavenging by the parasite to supplement its streamlined metabolism.     

Metabolomics Analysis of Metabolic Effects of Nicotinamide Phosphoribosyltransferase (NAMPT) Inhibition on Human Cancer Cells

Nicotinamide phosphoribosyltransferase (NAMPT) plays an important role in cellular bioenergetics. It is responsible for converting nicotinamide to nicotinamide adenine dinucleotide, an essential molecule in cellular metabolism. NAMPT has been extensively studied over the past decade due to its role as a key regulator of nicotinamide adenine dinucleotide–consuming enzymes. NAMPT is also known as a potential target for therapeutic intervention due to its involvement in disease. In the current study, we used a global mass spectrometry–based metabolomic approach to investigate the effects of FK866, a small molecule inhibitor of NAMPT currently in clinical trials, on metabolic perturbations in human cancer cells. We treated A2780 (ovarian cancer) and HCT-116 (colorectal cancer) cell lines with FK866 in the presence and absence of nicotinic acid. Significant changes were observed in the amino acids metabolism and the purine and pyrimidine metabolism. We also observed metabolic alterations in glycolysis, the citric acid cycle (TCA), and the pentose phosphate pathway. To expand the range of the detected polar metabolites and improve data confidence, we applied a global metabolomics profiling platform by using both non-targeted and targeted hydrophilic (HILIC)-LC-MS and GC-MS analysis. We used Ingenuity Knowledge Base to facilitate the projection of metabolomics data onto metabolic pathways. Several metabolic pathways showed differential responses to FK866 based on several matches to the list of annotated metabolites. This study suggests that global metabolomics can be a useful tool in pharmacological studies of the mechanism of action of drugs at a cellular level.     

Apolipoprotein A-I mimetic peptide inhibits atherosclerosis by altering plasma metabolites in hypercholesterolemia

An apolipoprotein A-I mimetic peptide, D-4F, has been shown to improve vasodilation and inhibit atherosclerosis in hypercholesterolemic low-density lipoprotein receptor-null (LDLr(-/-)) mice. To study the metabolic variations of D-4F ininhibiting atherosclerosis, metabonomics, a novel system biological strategy to investigate the pathogenesis, was developed. Female LDLr(-/-) mice were fed a Western diet and injected with or without D-4F intraperitoneally. Atherosclerotic lesion formation was measured, whereas plasma metabolic profiling was obtained on the basis of ultra-high-performance liquid chromatography in tandem with time-of-flight mass spectrometry operating in both positive and negative ion modes. Data were processed by multivariate statistical analysis to graphically demonstrate metabolic changes. The partial least-squares discriminate analysis model was validated with cross-validation and permutation tests to ensure the model's reliability. D-4F significantly inhibited the formation of atherosclerosis in a time-dependent manner. The metabolic profiling was altered dramatically in hypercholesterolemic LDLr(-/-) mice, and a significant metabolic profiling change in response to D-4F treatment was observed in both positive and negative ion modes. Thirty-six significantly changed metabolites were identified as potential biomarkers. A series of phospholipid metabolites, including lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE), phosphatidylcholine (PC), phatidylethanolamine (PE), sphingomyelin (SM), and diacylglycerol (DG), particularly the long-chain LysoPC, was elevated dramatically in hypercholesterolemic LDLr(-/-) mice but reduced by D-4F in a time-dependent manner. Quantitative analysis of LysoPC, LysoPE, PC, and DG using HPLC was chosen to validate the variation of these potential biomarkers, and the results were consistent with the metabonomics findings. Our findings demonstrated that D-4F may inhibit atherosclerosis by regulating phospholipid metabolites specifically by decreasing plasma long-chain LysoPC.     

基于GC-MS代谢组学技术委陵菜与星毛委陵菜的初生代谢比较研究

为明确药用植物委陵菜与其同属植物星毛委陵菜在不同基原之间的代谢差异，更有效的利用药用植物资源。基于气相色谱与质谱联用技术对两药用植物间的小分子初生代谢物进行非靶向代谢组学分析，利用主成分分析和偏最小二乘法分析，寻找可区分两物种的差异代谢物以及在初生代谢途径上的差异。结果：利用GC-MS在两植物中共检测到134个色谱峰，通过与数据库比对，共鉴定出43种初生代谢产物。PCA分析结果显示代谢物在委陵菜和星毛委陵菜间的分布有较大的差异；采用OPLS-DA筛选获得导致2物种区分的差异代谢物共33种，两物种在半乳糖代谢、淀粉和蔗糖代谢、氨酰生物合成、氨基糖和核苷酸糖代谢和果糖和甘露糖代谢途径有显著的差异。委陵菜和星毛委陵菜的初生代谢方面存在较大差异，虽然两植物来源于同科同属植物，但是星毛委陵菜不适合代替委陵菜作为药材使用。     

Metabolic profile associated with glucose and cholesterol lowering effects of berberine in Sprague–Dawley rats

In this study, we present an integrated strategy to deconvolute the metabolic signatures associated with the cholesterol lowering effect of berberine in the livers of Sprague?CDawley rats. The rats were dosed with berberine at 50?mg/kg. Urine samples and liver tissues were collected for the analysis of metabolite contents, while livers and kidneys were collected for histopathology. Metabolites such as fatty acids, cholesterol, glucose and others in liver were analyzed by gas chromatography/mass spectrometry. The urinary metabolites were analyzed using targeted profiling with liquid chromatography/tandem mass spectrometry and non-targeted profiling with proton nuclear magnetic resonance (¹H NMR). Our results demonstrated that analysis of metabolites in rat urine samples using liquid chromatography/mass spectrometry (LC/MS) and ¹H NMR produced complementary, consistent and reliable results. The administration of berberine resulted in a reduction of glucose, maltose, fatty acids (saturated and unsaturated) and cholesterol in the rat liver samples. The analysis of urinary metabolic profiles on different days showed that before the cholesterol reduction in the rat livers, a high rate of carbohydrate usage was found to be an early event (day 2). The results suggested that the animals utilized alternative energy sources by altering the synthesis and consumption of amino acids and fatty acids. In addition, changes in the level of glutamine for the treated animals on day 2 suggested that glutamine and glutamate metabolism could be affected. Since glutamine is a precursor for nucleotides synthesis and nucleotides are required for cell growth and replication, the results are consistent with the observed cholesterol lowering effect and weight reduction. Finally, our results demonstrated that the combination of LC/MS and ¹H NMR provided a unique metabolic profile associated with the cholesterol lowering effect of berberine in rat livers.     

Optimization of a sample preparation method for the metabolomic analysis of clinically relevant bacteria

Metabolomics, or metabolite profiling, is an approach that is increasingly used to study the metabolism of diverse organisms, elucidate biological processes and/or find characteristic biomarkers of physiological states. Here, we describe the optimization of a method for global metabolomic analysis of bacterial cultures, with the following steps. Cells are grown to log-phase, starting from an overnight culture and bacterial concentrations are monitored by measuring the optical density of the cultures at 600 nm. At an appropriate density they are harvested by centrifugation, washed three times with NaCl solution and metabolites are extracted using methanol and a bead-mill. Dried extracts are methoxymated and derivatized with methyltrimethylsilyltrifluoroacetamide (MSTFA) then analyzed using gas chromatography coupled to time-of-flight mass spectrometry (GC-MS/TOF). Finally, patterns in the acquired data are examined by multivariate data modeling. This method enabled us to obtain reproducible metabolite profiles of Yersinia pseudotuberculosis, with about 25% compound identification, based on comparison with entries in available GC-MS libraries. To assess the potential utility of the method for comparative analysis of other bacterial species we analyzed cultures of Pseudomonas aeruginosa, Salmonella typhimurium, Escherichia coli and methicillin-sensitive Staphylococcus aureus (MSSA). Multivariate analysis of the acquired data showed that it was possible to differentiate the species according to their metabolic profiles. Our results show that the presented procedure can be used for metabolomic analysis of a wide range of bacterial species of clinical interest.     

Differences in the metabolite profiles of spinach (Spinacia oleracea L.) leaf in different concentrations of nitrate in the culture solution

The nitrogen (N) status of a plant determines the composition of its major components (amino acids, proteins, carbohydrates and organic acids) and, directly or indirectly, affects the quality of agricultural products in terms of their calorific value and taste. Although these effects are guided by changes in metabolic pathways, no overall metabolic analysis has previously been conducted to demonstrate such effects. Here, metabolite profiling using gas chromatography-mass spectrometry (GC-MS) was used to evaluate the effect of N levels on spinach tissue, comparing two cultivars that differed in their ability to use N. Wide variation in N content was observed without any distinct inhibition of growth in either cultivar. Principal component analysis (PCA) and self-organizing mapping (SOM) were undertaken to describe changes in the metabolites of mature spinach leaves. In PCA, the first component accounted for 44.5% of the total variance, the scores of which was positively correlated with the plant's N content, and a close relationship between metabolite profiles and N status was observed. Both PCA and SOM revealed that metabolites could be broadly divided into two types, correlating either positively or negatively with plant N content. The simple and co-coordinated metabolic stream, containing both general and spinach-specific aspects of plant N content, will be useful in future research on such topics as the detection of environmental effects on spinach through comprehensive metabolic profiling.     

Characterization of the biochemical variability of bovine milk using metabolomics

Bovine milk contains a complex mixture of metabolites, which were identified by liquid and gas chromatography mass spectrometry methods. With the aid of new software methods, spectral assignment was carried out, enabling the identification of 223 metabolites. Those included amino acids, lipids, carbohydrates, nucleotides, energy metabolites, vitamins, cofactors and short peptides. Metabolite concentrations were compared between 10 bovine milk varieties, differing in brand, fat content, expiry date, package type and farming method. Principal components analysis showed a clear separation of the different milk varieties. Whole milk could be distinguished from reduced fat and fat free milk by higher lipid metabolites like free fatty acids, cholesterol and 1,2-dipalmitoylglycerol. But also, the reduced fat varieties had lower levels of vitamin E. In comparing organic to conventional milk, 14 named metabolites were statistically different between the two farming methods. This shows the potential of identifying farming-method-specific biomarkers upon analysis and validation of a larger sample size. Finally, high biochemical variability was shown in conventional whole milk derived from different producers. The distinct biochemical profiles of milk varieties shows the utility of metabolic profiling for authentication of milk varieties, and for deriving potential markers that can serve as signatures for a particular milk.     

Global Metabolomic Profiling of Mice Brains following Experimental Infection with the Cyst-Forming Toxoplasma gondii

The interplay between the Apicomplexan parasite Toxoplasma gondii and its host has been largely studied. However, molecular changes at the metabolic level in the host central nervous system and pathogenesis-associated metabolites during brain infection are largely unexplored. We used a global metabolomics strategy to identify differentially regulated metabolites and affected metabolic pathways in BALB/c mice during infection with T. gondii Pru strain at 7, 14 and 21 days post-infection (DPI). The non-targeted Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics analysis detected approximately 2,755 retention time-exact mass pairs, of which more than 60 had significantly differential profiles at different stages of infection. These include amino acids, organic acids, carbohydrates, fatty acids, and vitamins. The biological significance of these metabolites is discussed. Principal Component Analysis and Orthogonal Partial Least Square-Discriminant Analysis showed the metabolites’ profile to change over time with the most significant changes occurring at 14 DPI. Correlated metabolic pathway imbalances were observed in carbohydrate metabolism, lipid metabolism, energetic metabolism and fatty acid oxidation. Eight metabolites correlated with the physical recovery from infection-caused illness were identified. These findings indicate that global metabolomics adopted in this study is a sensitive approach for detecting metabolic alterations in T. gondii-infected mice and generated a comparative metabolic profile of brain tissue distinguishing infected from non-infected host.