Characterization of dandelion species using 1H NMR- and GC-MS-based metabolite profiling

Taraxacum, known as dandelion, is a large genus of plants in the family Asteraceae. Pharmacological studies have shown that these plants display a wide variety of medicinal properties because Taraxacum extracts contain many pharmacologically active metabolites that display anti-inflammatory, antinociceptive, antioxidant, and anticancer activity. Each plant species displays several different natural constituents, the majority of which have not been studied as no global metabolite screen of the diverse Taraxacum species has been performed. In this study, we investigated the metabolite difference in three species of Taraxacum (T. coreanum, T. officinale, and T. platycarpum) by (1)H NMR spectroscopy and gas chromatography-mass spectrometry (GC-MS) coupled with multivariate statistical analyses. The aim of this study was to identify the different chemical compositions of the polar and nonpolar extracts in these species. A partial least-squares discriminant analysis showed a significantly higher separation among nonpolar extracts (mainly fatty acids and sterols) compared to polar extracts (mainly amino acids, organic acids, and sugars) between these species. A one-way ANOVA was performed to statistically certify the metabolite differences of these nonpolar extracts. Taken together, these data suggest that a metabolomic approach using combined (1)H NMR and GC-MS analysis is an effective analytical method to differentiate biochemical compositions among different species in plants.     

Quantitative metabolic profiling of 21 endogenous corticosteroids in urine by liquid chromatography-triple quadrupole-mass spectrometry

Since corticosteroid metabolism may be affected by disease states, the accurate and precise measurement of endogenous corticosteroids in urine is necessary to understand their biochemical roles. An efficient quantitative profiling of 21 endogenous corticosteroids in urine has been validated by liquid chromatography-tandem mass spectrometry (LC-MS/MS). After enzymatic hydrolysis with β-glucuronidase, samples were purified using a solid-phase extraction cartridge and then separated through a sub-2 μm particle C18 column (2.1 mm × 50 mm, 1.9 μm) and quantified within 12.1 min using a triple quadrupole MS with electrospray ionization in positive ion mode. All corticosteroids resulted in the base-line separation, which is even achieved for stereo-isomers, such as α-/β-cortol, α-/β-cortolone, and allo-tetrahydrocortisol/tetrahydrocortisol. Overall recoveries ranged from 85% to 106% with limit of quantification ranged from 0.5 to 2.0 ng mL⁻¹ for the corticosteroids examined. The precision (% CV) and accuracy (% bias) of the assay were 1.7-7.8% and 95.1-105.4%, respectively, in 0.5-200 ng mL⁻¹ calibration ranges (r² > 0.9903), for quality-control samples containing 21 endogenous corticosteroids at three different urinary concentrations. Clinical application included quantitative analysis from patients with both prostate cancer and benign prostatic hyperplasia with altered cortisol concentrations. The described LC-MS/MS method eliminates interference from other urine components, has excellent chromatographic resolution achieved by a small particle LC column with a sufficient sensitivity to allow the profiling of both gluco- and mineralo-corticosteroids at a time.     

Comparison of different water-miscible solvents for the preparation of plasma and urine samples in metabolic profiling studies

The Lowry method and a capillary electrophoresis method were used to analyse protein residues in the supernatant after solvent deproteination of plasma. Acetonitrile and acetone were much more effective than methanol and ethanol at reducing the levels of proteins in plasma. The ability of different solvents to decrease levels of phospholipids in plasma samples was assessed using electrospray ionisation mass spectrometry (MS). Phospholipid signals can obscure differences between samples in general metabolite profiling (i.e. non-target compound) studies. Acetonitrile was much more effective than methanol in reducing the MS signal due to phospholipids in plasma which is a consequence of the poor solubility of phospholipids in acetonitrile. The capability of the solvents at reducing salts in urine samples was also studied by using an amperometric method. Using this approach little difference was detected between methanol, ethanol, acetonitrile and acetone in their ability to desalt urine samples.     

Filter paper saturated by urine sample in metabolic disorders detection by proton magnetic resonance spectroscopy

NMR spectroscopy of urine samples is able to diagnose many inborn errors of metabolism (IEM). However, urinary metabolites have a poor stability, requiring special care for routine analysis (storage of urine at −20 or −80 °C, fast transport). The aim of our study was to investigate the reliability of dried urine filter paper for urine storage and transport and to evaluate the ability of NMR to detect several IEM using this method. Urine samples from five healthy subjects were analyzed by ¹H NMR following different storage conditions (−20 vs 4 °C vs dried on filter paper) and at different time points (24 h, 48 h, 96 h, and 7 days). Urine pattern of fresh urine was considered as a reference. We analyzed the conservation of some amino acids and organic acids using Bland and Altman plot with intraclass correlation coefficient determination. Then, we evaluated the use of filter paper to detect four different IEM (methylmalonic and isovaleric acidurias, ornithine transcarbamylase deficiency, and cystinuria). Analysis of urine samples from healthy subjects revealed a high stability of studied molecules (ICC > 0.8) even after 7 days of storage on filter paper. Moreover, an excellent preservation of metabolites specifically accumulated in IEM was observed when analysis of dried urine filter paper was compared to fresh urine (coefficient of variation < 15%). This preliminary study demonstrates that storage of dried urine on filter paper is reliable for ¹H NMR spectroscopy analysis. Preservation of urine molecules over time using that method is convenient for routine clinical practice.      

Nontarget metabolomics profiling of neuromyelitis optica spectrum disorder

To investigate the characteristic of neuromyelitis optica spectrum disorder (NMOSD) in plasma and cerebrospinal fluid, UHPLC–MS was used to identify metabolites by metabolite extraction and on‐machine detection. Multivariate analysis methods were used to complete differential metabolite screening and metabolic pathway analysis. The content of eight substances, such as tryptophan and l ‐glutamic acid in the plasma of NMOSD patients, was higher than that of the healthy control group. Moreover, no differential metabolite was found in the plasma of patients with AQP‐4 antibody‐positive and antibody‐negative NMOSD. The content of five substances including 3‐hydroxybutyric acid in cerebrospinal fluid of patients with NMOSD was reduced. We demonstrated that the distribution of metabolites in plasma between NMOSD patients and healthy counterparts was significantly different. However, there is no significant difference in plasma metabolites between AQP‐4 antibody positive and negative NMOSD. There were some differences in metabolites between the cerebrospinal fluid of NMOSD patients and that of healthy controls. A variety of amino‐acid abnormalities, sphingomyelin dysfunction, energy metabolism and mitochondrial dysfunction are involved in the pathogenesis of NMOSD.     

Determination of the spectrum of low molecular mass organic acids in urine by capillary electrophoresis with contactless conductivity and ultraviolet photometric detection--an efficient tool for monitoring of inborn metabolic disorders

A mixture of 29 organic acids (OAs) occurring in urine was analyzed by capillary electrophoresis (CE) with capacitively coupled contactless conductivity detection (C⁴D) and UV photometric detection. The optimized analytical system involved a 100 cm long polyacrylamide-coated capillary (50 μm i.d.) and the background electrolyte of 20 mM 2-morpholinoethanesulfonic acid (MES)/NaOH + 10% (v/v) methanol, pH 6.0 (pH is related to the 20 mM MES/NaOH buffer in water). The LOD values obtained by C⁴D for the OAs which do not absorb UV radiation range from 0.6 μM (oxalic acid) to 6.8 μM (pyruvic acid); those obtained by UV photometry for the remaining OAs range from 2.9 μM (5-hydroxy-3-indoleacetic acid) to 10.2 μM (uric acid). The repeatability of the procedure developed is characterized by the coefficients of variation, which vary between 0.3% (tartaric acid) and 0.6% (5-hydroxy-3-indoleacetic acid) for the migration time and between 1.3% (tartaric acid) and 3.5% (lactic acid) for the peak area. The procedure permitted quantitation of 20 OAs in a real urine sample and was applied to monitoring of the occurrence of the inborn metabolic fault of methylmalonic aciduria.     

Proteomic profiling of human urine using multi-dimensional protein identification technology

Human urine samples are ideal for proteomic profiling and have tremendous potential as sources of biomarkers. Multi-dimensional protein identification technology (MudPIT) is an effective approach to analyzing human urine or other fluids dominated by diverse metabolites. MudPIT analysis was used to identify 87 proteins in just 15 ml of human urine. A high throughput, reproducible, and sensitive technology, MudPIT may soon be used for more proteomic analyses of metabolites.     

Development of a metabonomic approach based on LC-ESI-HRMS measurements for profiling of metabolic changes induced by recombinant equine growth hormone in horse urine

Despite the worldwide existing regulation banning the use of the recombinant equine growth hormone (reGH) as growth promoter, it is suspected to be used in horseracing to improve performances. Various analytical methods previously developed to screen for its misuse have encountered some limitations in terms of detection timeframes, in particular during the first days following reGH administration. A novel strategy involving the characterization of global metabolomic fingerprints in urine samples of non-treated and reGH-treated horses by liquid chromatography–electrospray–high-resolution mass spectrometry (LC-ESI-HRMS) is described and assessed in this paper in order to develop a new screening tool for growth hormone abuse in horseracing. The strategy involves a limited sample preparation of the urine samples and the use of appropriate software for data processing and analysis. As preliminary work, reproducibility of both sample preparation and mass spectrometry (MS) measurements was evaluated in order to demonstrate the reliability of the method. Application of the developed protocol on two horses demonstrated the suitability of the developed strategy and preliminary results showed significant modifications of the metabolome after treatment with reGH.       

A pilot proteomic study of protein markers in autism spectrum disorder

Autism spectrum disorder (ASD) diagnosis is increasing, with 1/88 children believed to be affected by the disorder, with a most recent survey suggesting numbers as high as 1/50. Treatment and understanding of ASD causes is a pressing health concern. ASD protein biomarkers may provide clues about ASD cause. Protein biomarkers for ASDs could be used for ASD diagnosis, subtyping, treatment monitoring, and identifying therapeutic targets. Here, we analyzed the sera from seven children with ASD and seven matched controls using Tricine gel electrophoresis (Tricine‐PAGE) and LC‐MS/MS. Overall, we found increased levels of apolipoproteins ApoA1 and ApoA4, involved in cholesterol metabolism and of serum paraoxanase/arylesterase 1, involved in preventing oxidative damage, in the sera of children with ASD, compared with their matched controls. All three proteins are predicted to interact with each other and are parts of high‐density lipoproteins. Further studies are needed to validate these findings in larger subject numbers.     

Review: Microfluidic applications in metabolomics and metabolic profiling

Metabolomics is an emerging area of research focused on measuring small molecules in biological samples. There are a number of different types of metabolomics, ranging from global profiling of all metabolites in a single sample to measurement of a selected group of analytes. Microfluidics and related technologies have been used in this research area with good success. The aim of this review article is to summarize the use of microfluidics in metabolomics. Direct application of microfluidics to the determination of small molecules is covered first. Next, important sample preparation methods developed for microfluidics and applicable to metabolomics are covered. Finally, a summary of metabolomic work as it relates to analysis of cellular events using microfluidics is covered.     

Application of pathways activity profiling to urine metabolomics for screening Qi‐tonifying biomarkers and metabolic pathways of honey‐processed Astragalus

Honey‐processed Astragalus, a widely used Qi‐tonifying and immunomodulating herb in traditional Chinese medicine, has strengthened the tonic effects and achieved fewer side effects compared with astragali radix in clinical application. Here, we focus on Qi‐tonifying biomarkers and pathways of honey‐processed Astragalus using urine metabolomics that provide the basis for building the linkage between metabolites in rat urine and its symptoms. The spleen Qi deficiency model group, normal group, astragali radix group, and honey‐processed Astragalus group were implemented to evaluate Qi‐tonifying effects. Twelve potential biomarkers were screened by multivariate statistical analysis by using ultra high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry. Furthermore, pathways activity profiling showed unique pathways that are primarily involved in tryptophan metabolism, tricarboxylic acids cycle, and methionine metabolism. The results demonstrated that metabolomics coupled with pathway activity profiling were promising tools. It might serve as a novel methodological clue to systematically dissect the underlying Qi‐tonifying mechanism of honey‐processed Astragalus.     

Gas chromatography-mass spectrometric method for metabolic profiling of tobacco leaves

A gas chromatography-mass spectrometric method was developed for profiling of tobacco leaves. The differentiation among tobacco leaves planted in two different regions was investigated. Prior to analysis, the extraction solvent formulation was optimized and a combination of water, methanol and acetonitrile with a volume ratio of 3:1:1 was found to be optimal. The reproducibility of the method was satisfactory. Kendall tau-b rank correlation coefficients were equal to 1 (p<0.05) for 82% of the resolved peaks (up to 95% of the overall peak areas), indicating the good response correlation. Forty-four compounds including 9 saccharides, 9 alcohols, 9 amino acids, 16 organic acids and phosphoric acid were identified based on standard compounds. The method was successfully applied for profiling of tobacco leaves from Zimbabwe and Yunnan of China. Our result revealed that levels of saccharides and their derivatives including xylose, ribose, fructose, glucose, turanose, xylitol and glyceric acid were more abundant while sucrose, glucitol and D-gluconic acid were less abundant in tobacco leaves from Yunnan as compared to those from Zimbabwe. Amino acids such as L-alanine, L-tyrosine and L-threonine were found to be richer in Zimbabwe tobacco than in Yunnan tobacco.     

Targeted projection NMR spectroscopy for unambiguous metabolic profiling of complex mixtures

Unambiguous identification of individual metabolites present in complex mixtures such as biofluids constitutes a crucial prerequisite for quantitative metabolomics, toward better understanding of biochemical processes in living systems. Increasing the dimensionality of a given NMR correlation experiment is the natural solution for resolving spectral overlap. However, in the context of metabolites, natural abundance acquisition of ¹H and ¹³C NMR data virtually excludes the use of higher dimensional NMR experiments (3D, 4D, etc.) that would require unrealistically long acquisition times. Here, we introduce projection NMR techniques for studies of complex mixtures, and we show how discrete sets of projection spectra from higher dimensional NMR experiments are obtained in a reasonable time frame, in order to capture essential information necessary to resolve assignment ambiguities caused by signal overlap in conventional 2D NMR spectra. We determine optimal projection angles where given metabolite resonances will have the least overlap, to obtain distinct metabolite assignment in complex mixtures. The method is demonstrated for a model mixture composition made of ornithine, putrescine and arginine for which acquisition of a single 2D projection of a 3D ¹H–¹³C TOCSY‐HSQC spectrum allows to disentangle the metabolite signals and to access to complete profiling of this model mixture in the targeted 2D projection plane. Copyright © 2010 John Wiley & Sons, Ltd.     

Critical aspects of urine profiling for the selection of potential biomarkers using UPLC-TOF-MS

Biomarker selection through the metabolomics approach involves the acquisition of nontargeted metabolic profiles. In this study, some critical factors that may affect this process were investigated using urine test samples and a UPLC‐TOF system. Repeated injections of a single sample demonstrated that the percentage of undetected and poorly repeatable measurements (intensity RSD > 15%) decreased from 22.5 to 5.8% and from 32.9 to 14.7%, respectively, as the scan time was increased up to 0.6 s (approximately 11 data points per peak). An additional critical factor was identified in the presence of broad concentration differences between the samples; the application of a dilution scheme that minimized these differences reduced the number of missing values in the final datasets by 36%. The impact of missing values was further investigated in the study of two groups of samples produced by using a spike as artificial marker. Missing values weakened the models used for the interpretation of the metabolic profiles, and greatly hindered the identification of possible markers. Finally, a simple strategy for an effective analysis of urine samples was outlined; it proved to limit the need for the post‐acquisition elaboration of the data. The same strategy can easily be adapted to other matrices. Copyright © 2011 John Wiley & Sons, Ltd.     

Metabolite profiling of human urine by CE-ESI-MS using separation electrolytes at low pH

We investigated the potential of CE coupled to electrospray MS (CE-ESI-MS) in metabolite profiling of human urine without any sample prefractionation step. A heterogeneous mixture of biologically relevant compounds covering a broad range of physicochemical properties was used to optimize separation conditions in fused-silica capillaries. A running electrolyte containing 50 mM of acetic acid and 50 mM of formic acid at pH 2.5 was used for the CE separations. A sheath-flow electrospray interface was employed for CE-ESI-MS analysis. Sheath liquids containing 80:20 v/v methanol/water with 0.1% v/v of acetic acid or 60:40 v/v isopropanol/water with 0.5% v/v of ammonia were selected for optimum detection in positive and negative ESI modes, respectively. Reproducibility and sensitivity were studied, and strategies for identification of the separated urinary compounds are suggested. We report major advantages and disadvantages of CE-ESI-MS for metabolite profiling of human body fluids. This work may be regarded as a first step in the use of CE-ESI-MS for reliable differential analysis of body fluids from healthy and diseased individuals.     

Probing genetic algorithms for feature selection in comprehensive metabolic profiling approach

Six different clones of 1-year-old loblolly pine (Pinus taeda L.) seedlings grown under standardized conditions in a green house were used for sample preparation and further analysis. Three independent and complementary analytical techniques for metabolic profiling were applied in the present study: hydrophilic interaction chromatography (HILIC-LC/ESI-MS), reversed-phase liquid chromatography (RP-LC/ESI-MS), and gas chromatography all coupled to mass spectrometry (GC/TOF-MS). Unsupervised methods, such as principle component analysis (PCA) and clustering, and supervised methods, such as classification, were used for data mining. Genetic algorithms (GA), a multivariate approach, was probed for selection of the smallest subsets of potentially discriminative classifiers. From more than 2000 peaks found in total, small subsets were selected by GA as highly potential classifiers allowing discrimination among six investigated genotypes. Annotated GC/TOF-MS data allowed the generation of a small subset of identified metabolites. LC/ESI-MS data and small subsets require further annotation. The present study demonstrated that combination of comprehensive metabolic profiling and advanced data mining techniques provides a powerful metabolomic approach for biomarker discovery among small molecules. Utilizing GA for feature selection allowed the generation of small subsets of potent classifiers.     

In vitro metabolic profiling of synthetic cannabinoids by pooled human liver microsomes,cytochrome P450 isoenzymes,and Cunninghamella elegans and their detection in urine samples

As synthetic cannabinoids are extensively metabolized, there is an urgent need for data on which metabolites can be used for successful urine screening. This study examines the in vitro metabolism of EG-018 and its 5F-analogue EG-2201 by means of comparing three different in vitro models: pooled human liver microsomes, cytochrome P450 isoenzymes, and a fungal approach utilizing the filamentous fungus Cunninghamella elegans LENDNER, which is known for its ability to mimic human biotransformation of xenobiotics. In addition, this study includes the screening of two authentic urine samples from individuals with proven EG-018 consumption, for the evaluation of in vitro–in vivo extrapolations made in the study. Incubation with pooled human liver microsomes yielded 15 metabolites of EG-018 belonging to six different metabolite subgroups, and 21 metabolites of EG-2201 belonging to seven different metabolite subgroups, respectively. Incubation with cytochrome P450 isoenzymes incubation yielded a further three EG-018 and five EG-2201 metabolites. With reference to their summed metabolite peak abundancies, the isoenzymes CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5 were shown to contribute most to the microsomal metabolism of EG-018 and EG-2201. CYP2B6 was shown to make the lowest contribution, by far. As the phase I metabolism of both synthetic cannabinoids was shown to be distributed over a substantial number of different cytochrome P450 isoenzymes, it was concluded that it is likely to not be significantly affected by co-consumption of other drugs. Although fungal incubation with Cunninghamella elegans yielded an additional three EG-018 and four EG-2201 metabolites not observed after microsomal incubation, metabolites generated by Cunninghamella elegans were in good correlation with those generated by microsomal incubations. The fungal model demonstrated its ability to be an independent in vitro model in synthetic cannabinoid metabolism research. The three tested in vitro models enable sufficient predictive in vitro–in vivo extrapolations, comparable to those obtained from hepatocyte incubation published in the literature. In addition, with regard to the screening of authentic urine samples and comparison with the literature, one monohydroxylated EG-018 metabolite and two monohydroxylated EG-2201 metabolites can be recommended as urinary targets, on the basis of the tested in vitro models.

Graphical abstract