Urinary profiling investigation of metabolites withcis-diol structure from cancer patients based on UPLC-MS and HPLC-MS as well as multivariate statistical analysis

Chemo-Selective Single-Cell Metabolomics Reveals the Spatiotemporal Behavior of Exogenous Pollutants During Xenopus Laevis Embryogenesis

In-depth profiling of embryogenesis-associated endogenous and exogenous metabolic changes can reveal potential bio-effects resulting from human-made chemicals and underlying mechanisms. Due to the lack of potent tools for monitoring spatiotemporal distribution and bio-transformation behavior of dynamic metabolites at single-cell resolution, however, how and to what extent environmental chemicals may influence or interfere embryogenesis largely remain unclear. Herein, a zero-sample-loss micro-biopsy-based mass spectrometric platform is presented for quantitative, chemo-selective, high-coverage, and minimal-destructive profiling of development-associated cis-diol metabolites, which are critical for signal transduction and epigenome regulation, at both cellular level and tissue level of Xenopus laevis. Using this platform, three extraordinary findings that are otherwise hard to achieve are revealed: 1) there are characteristically different cis-diol metabolic signatures among oocytes, anterior and posterior part of tailbud-stage embryos; 2) halogenated cis-diols heavily accumulate at the posterior part of tailbud-stage embryos of Xenopus laevis; 3) dimethachlon, a kind of exogenous fungicide that is widely used as pesticide, may be bio-transformed and accumulated in vertebrate animals in environment. Thus, this study opens a new avenue to simultaneously monitoring intercellular and intraembryonic heterogeneity of endogenous and exogenous metabolites, providing new insights into metabolic remolding during embryogenesis and putting a warning on potential environmental risk.

Evaluation of liver tissue extraction protocol for untargeted metabolomics analysis by ultra-high-performance liquid chromatography/tandem mass spectrometry

The aim of the untargeted metabolomics study is to obtain a global metabolome coverage from biological samples. Therefore, a comprehensive and systematic protocol for tissue metabolite extraction is highly desirable. In this study, we evaluated a comprehensive liver pretreatment strategy based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry to obtain more metabolites using four different protocols. These protocols included (A) methanol protein precipitation, (B) two-step extraction of dichloromethane-methanol followed by methanol-water, (C) two-step extraction of methyl tert-butyl ether-methanol followed by methanol-water, and (D) two-step extraction of isopropanol-methanol followed by methanol-water. Our results showed that protocol D was superior to the others due to more extracted features, annotated metabolites, and better reproducibility. And then, the stability and extraction sequence of protocol D were evaluated. The results showed that extraction with isopropanol-methanol followed by methanol-water was the optimum preparation sequence, which offered higher extraction efficiency, satisfactory repeatability, and acceptable stability. Furthermore, the optimal protocol was successfully applied by liver samples of rats after high-fat intervention. In summary, our protocol enabled a comprehensive and systematic evaluation of liver pretreatment to obtain more medium-polar and nonpolar metabolites and was suitable for high-throughput metabolomics analysis.

Determination of Oxyphylla A Enantiomers in the Fruits of Alpinia oxyphylla by a Chiral High-Performance Liquid Chromatography-Multiple Reaction Monitoring-Mass Spectrometry Method and Comparison of Their In Vivo Biological Activities

(R)-Oxyphylla A, a natural product isolated from Alpinia oxyphylla Miquel as a food and medicinal plant, has been reported previously as a novel chiral compound that possesses a potential therapeutic value for Parkinson's disease (PD). A chiral high-performance liquid chromatography-multiple reaction monitoring-mass spectrometry method was developed to separate oxyphylla A enantiomers and to identify the presence of natural (S)-oxyphylla A for the first time. Twelve samples of dried A. oxyphylla fruits were analyzed in which a large variation in the abundance of enantiomers was observed. Moreover, (S)-oxyphylla A was less abundant in all tested samples, whereas fruits harvested from Hainan and Guangdong tended to have relatively higher total concentrations of enantiomers. Additionally, enantiomers exhibited comparable neuroprotective effects in the zebrafish model of PD without observed toxicity phenotype. The optimized enantioseparation method will be crucial for the quality control of A. oxyphylla and research on bioactivities facilitates the development of oxyphylla A as a potential therapeutic for neurodegenerative diseases.

Recent development and trends in sample extraction and preparation for mass spectrometric analysis of nucleotides, nucleosides, and proteins

This review describes the recent developments in sample extraction and preparation techniques for mass spectrometric analysis of nucleotides, nucleosides, and proteins. Unique materials and techniques have been developed for highly selective extraction of nucleotides and nucleosides by solid-phase extraction strategies using various affinities. However, for proteins, the analysis of small-scale sections of diseased tissues (formalin-fixed, paraffin-embedded tissues) and the direct analysis of an exact lesion on the surface of diseased tissues (liquid extraction surface analysis) have become important advances in this field. In this review, we focus on the latest developments of these techniques and strategies.

Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds

The MolFind application has been developed as a nontargeted metabolomics chemometric tool to facilitate structure identification when HPLC biofluids analysis reveals a feature of interest. Here synthetic compounds are selected and measured to form the basis of a new, more accurate, HPLC retention index model for use with MolFind. We show that relatively inexpensive synthetic screening compounds with simple structures can be used to develop an artificial neural network model that is successful in making quality predictions for human metabolites. A total of 1955 compounds were obtained and measured for the model. A separate set of 202 human metabolites was used for independent validation. The new ANN model showed improved accuracy over previous models. The model, based on relatively simple compounds, was able to make quality predictions for complex compounds not similar to training data. Independent validation metabolites with feature combinations found in three or more training compounds were predicted with 97% sensitivity while metabolites with feature combinations found in less than three training compounds were predicted with >90% sensitivity. The study describes the method used to select synthetic compounds and new descriptors developed to encode the relationship between lipophilic molecular subgraphs and HPLC retention. Finally, we introduce the QRI (qualitative range of interest) modification of neural network backpropagation learning to generate models simultaneously based on quantitative and qualitative data.

Urinary Nucleosides and Deoxynucleosides

Urinary nucleosides and deoxynucleosides are mainly known as metabolites of RNA turnover and oxidative damage of DNA. For several decades these metabolites have been examined for their potential use in disease states including cancer and oxidative stress. Subsequent improvements in analytical sensitivity and specificity have provided a reliable means to measure these unique molecules to better assess their relationship to physiologic and pathophysiologic conditions. In fact, some are currently used as antiviral and antitumor agents. In this review we provide insight into their molecular characteristics, highlight current separation techniques and detection methods, and explore potential clinical usefulness.

Statistical-based approach in potential diagnostic application of urinary nucleosides in urogenital tract cancer

Aim: We aimed at evaluation the potential diagnostic role of urinary nucleosides in urogenital tract cancer. Materials & methods: Concentrations of 12 nucleosides determined by LC-MS/MS were subjected to correlation, association and interaction analyses. Results: We identified six pairs of nucleosides differently correlated in the group of patients and controls (p < 0.05). N-2-methylguanosine (odds ratio: 4.82; 95% CI: 1.78–12.93; p = 0.002) and N,N-dimethylguanosine (odds ratio: 5.45; 95% CI: 1.78–16.44; p = 0.003), were significantly associated with the disease risk (p-corrected = 0.004). Interaction between N-2-methylguanosine and adenosine (p-interaction = 0.019) suggested their multiplicative effect on the outcome. Conclusion: Urinary nucleosides, namely N,N-dimethylguanosine and N-2-methylguanosine may have the potential to serve as prognostic biomarkers. Gender-specific differences in urogenital tract cancer are likely to occur.

Review of mass spectrometry-based metabolomics in cancer research

Metabolomics, the systematic investigation of all metabolites present within a biologic system, is used in biomarker development for many human diseases, including cancer. In this review, we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase, and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification, or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including less than 100 patients. Standardization is required especially concerning sample preparation and data analysis. In the second part of this review, we reconstructed a metabolic network of patients with cancer by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane, and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed.

Metabolomics in cancer biomarker discovery: current trends and future perspectives

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted.

Development and validation of a hydrophilic interaction chromatography-tandem mass spectrometry method with on-line polar extraction for the analysis of urinary nucleosides. Potential application in clinical diagnosis

The present paper describes the development, validation and application of a quantitative method for the determination of endogenous nucleosides and nucleobases in urine based on the on-line coupling of a solid-phase extraction step with hydrophilic interaction chromatography-tandem mass spectrometry. The method combines the use of a highly polar restricted-access material (RAM), based on an N-vinylacetamide copolymer, for efficient analyte extraction and matrix removal, with separation by zwitterionic hydrophilic interaction chromatography (ZIC-HILIC), that revealed a satisfactory retention of the polar analytes studied. Detection using a triple quadrupole analyser allowed reliable identification and high-sensitivity quantitation of the target compounds. The on-line configuration developed, RAM-ZIC-HILIC-MS/MS, provides a convenient approach to automate the application to urine analysis, with minimum sample manipulation. The whole method was validated according to European Legislation for bioanalytical methods. The validation steps included the verification of matrix effects, calibration curve, precision, accuracy, selectivity, stability and carry-over in real samples. The results of the validation process revealed that the proposed method is suitable for the reliable determination of nucleosides and nucleobases in human urine, showing limits of detection from 0.1 to 1.3 ng mL(-1). The application to clinical samples was also checked; the results obtained in analyses of urine samples from healthy volunteers and cancer patients using Principal Component Analysis, Hierarchical Cluster Analysis and Soft Independent Modeling of Class Analogy are also shown.

Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method

Late diagnosis of hepatocarcinoma (HCC) is one of the most primary factors for the poor survival of patients. Thereby, identification of sensitive and specific biomarkers for HCC early diagnosis is of great importance in biological medicine to date. In the present study, serum metabolites of the HCC patients and healthy controls were investigated using the improved liquid chromatography-mass spectrometry (LC/MS). A wavelet-based method was utilized to find and align peaks of LC-MS. The characteristic peaks were selected by performing a two-sample t test statistics (p value <0.05). Clustering analysis based on principal component analysis showed a clear separation between HCC patients and healthy individuals. The serum metabolite, namely 1-methyladenosine, was identified as the characteristic metabolite for HCC. Moreover, receiver-operator curves were calculated with 1-methyladenosine and/or alpha fetal protein (AFP). The higher area under curve value was achieved in 1-methyladenosine group than AFP group (0.802 vs. 0.592), and the diagnostic model combining 1-methyladenosine with AFP exhibited significant improved sensitivity, which could identify those patients who missed the diagnosis of HCC by determining serum AFP alone. Overall, these results suggested that LC/MS-based metabonomic study is a potent and promising strategy for identifying novel biomarkers of HCC.

Database searching for structural identification of metabolites in complex biofluids for mass spectrometry-based metabonomics

MS and HPLC are commonly used for compound characterization and obtaining structural information; in the field of metabonomics, these two analytical techniques are often combined to characterize unknown endogenous or exogenous metabolites present in complex biological samples. Since the structures of a majority of these metabolites are not actually identified, the result of most metabonomic studies is a list of m/z values and retention times. However, without knowing actual structures, the biological significance of these 'features' cannot be determined. The process of identifying the structures of unknown compounds can be time intensive, costly and frequently requires the use of multiple orthogonal analytical techniques - this laborious procedure seems insurmountable for the long lists of unknowns that must be identified for each study. In addition, the limited sample volume and the extremely low concentration of most endogenous analytes frequently make purification and identification by other instrumentation nearly impossible. This review is intended to explore the problems and progress with current tools that are available for MS-based structure identification for both endogenous and exogenous metabolites.

The state-of-the-art determination of urinary nucleosides using chromatographic techniques "hyphenated" with advanced bioinformatic methods

Over the last decade metabolomics has gained increasing popularity and significance in life sciences. Together with genomics, transcriptomics and proteomics, metabolomics provides additional information on specific reactions occurring in humans, allowing us to understand some of the metabolic pathways in pathological processes. Abnormal levels of such metabolites as nucleosides in the urine of cancer patients (abnormal in relation to the levels observed in healthy volunteers) seem to be an original potential diagnostic marker of carcinogenesis. However, the expectations regarding the diagnostic value of nucleosides may only be justified once an appropriate analytical procedure has been applied for their determination. The achievement of good specificity, sensitivity and reproducibility of the analysis depends on the right choice of the phases (e.g. sample pretreatment procedure), the analytical technique and the bioinformatic approach. Improving the techniques and methods applied implies greater interest in exploration of reliable diagnostic markers. This review covers the last 11 years of determination of urinary nucleosides conducted with the use of high-performance liquid chromatography in conjunction with various types of detection, sample pretreatment methods as well as bioinformatic data processing procedures.

Metabonomics study of urine and plasma in depression and excess fatigue rats by ultra fast liquid chromatography coupled with ion trap-time of flight mass spectrometry

A novel metabonomic method based on fast liquid chromatography coupled with ion trap-time of flight mass spectrometry (UFLC/MS-IT-TOF) was applied to study the metabolic changes of plasma and urine in depression and excess fatigue rats. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied for classifying the depression, excess fatigue and the control rats. Metabolites which were important for the classification in the three groups of rats were selected as potential biomarkers and identified by MS(n) information achieved from UFLC/MS-IT-TOF analysis. Spermine, propionylcarnitine, butyrylcarnitine, phenylalanine, lysophosphatidylcholine (LPC) C14:0 and LPC C18:2 were down-regulated, methyl-hippuric acid and chenodeoxycholic acid (CDCA) were up-regulated significantly in plasma of the excess fatigue rats. Spermine, leucine, propionylcarnitine, and butyrylcarnitine decreased, hippuric acid, methyl-hippuric acid, cholic acid, CDCA and LPC C16:0 increased markedly in plasma of the depression rats. Ethyl N2-acetyl-L-argininate and N-methyl-2-pyridone-5-carboxamide (2-PY) (or N-methyl-4-pyridone-3-carboxamide (4-PY)) were down-regulated, leucylproline and pantothenic acid were up-regulated remarkably both in urine of depression and excess fatigue rats. The concentration of kynurenic acid and N2-succinyl-L-ornithine was low in urine of depression rats compared with control rats. Based on the data, correlation networks for depression and excess fatigue rats revealed the abnormality of nicotinate and nicotinamide metabolism, arginine metabolism, cholesterol metabolism, tryptophan metabolism and kynurenine metabolism in depression rats, and in excess fatigue rat alterations of energy metabolism, nicotinate and nicotinamide metabolism and lecithin metabolism. Our results provide novel insights in the complex metabolic mechanisms occurring in depression and excess fatigue rats.

Combined use of GC-TOF MS and UHPLC-(Q)TOF MS to investigate the presence of nontarget pollutants and their metabolites in a case of honeybee poisoning

The combined use of gas chromatography (GC) and ultrahigh-pressure liquid chromatography (UHPLC), both coupled to time-of-flight mass spectrometry (TOF MS), has been explored in this work for the investigation of several cases of honeybee poisoning. The procedure applied involves a previous extraction with acetone followed by liquid-liquid extraction with dichloromethane. Both techniques, GC-TOF MS and UHPLC-(Q)TOF MS, have been applied to discover the presence of compounds that might be responsible of honeybee deaths. The application of a nontarget methodological approach to a first case of poisoning allowed the detection of the insecticide coumaphos at high concentration levels in the samples. The presence of possible metabolites of this organophosphorus insecticide was investigated by using both techniques. UHPLC-(Q)TOF MS showed its higher applicability in this case, as most of the metabolites were more polar than the parent compound. Four metabolites were identified by UHPLC-(Q)TOF MS, whereas only two of them were found by GC-TOF MS. The developed methodology was applied to other subsequent poisoning cases in which insecticides such as coumaphos, thiamethoxam, pyriproxyfen, and chlorfenvinphos were identified by both techniques, whereas GC-TOF MS also allowed the detection of fenitrothion and methiocarb. In all positive cases, the confirmation of the presence of the compound detected was feasible by means of accurate mass measurements of up to five ions together with their ion ratio evaluation.

Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations

In this study, urinary metabolites from liver cancer patients and healthy volunteers were studied by a metabonomic method based on ultra performance liquid chromatography coupled to mass spectrometry. Both hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RPLC) were used to separate the urinary metabolites. Principle component analysis (PCA) and partial least squares to latent structure-discriminant analysis (PLS-DA) models were built to separate the healthy volunteers from the liver cancer patients and to find compounds that are expressed in significantly different amounts between the two populations. 21 metabolite ions were considered as potential biomarkers according to the Variable importance in the Project (VIP) value and S-plot. Compared with RPLC, a more sensitive and stable response can be recorded in HILIC mode due to the high content of organic solvent used. Moreover, the liver cancer group and the healthy volunteers can be better separated based on the data from the HILIC separation, which indicates that HILIC is suitable for urinary metabonomic analysis. In HILIC mode, several polar compounds related to arginine and proline metabolism, alanine and aspartate metabolism, lysine degradation, nicotinate and nicotinamide metabolism were found to be significantly changed in the concentrations of the two different populations: healthy and cancer. In contrast, in RPLC mode, these changed compounds are related to fatty acids oxidation.

Metabonomic study of biochemical changes in the serum of type 2 diabetes mellitus patients after the treatment of metformin hydrochloride

A metabonomic study on biochemical changes in the serum of type 2 diabetes mellitus patients after the treatment of metformin hydrochloride was performed. (1)H NMR and UPLC/MS were used to generate metabolic fingerprints for the metabonomic analysis of serum samples obtained from 20 type 2 diabetes mellitus patients without any drugs treatment and 15 type 2 diabetes mellitus patients treated with metformin hydrochloride for 3 months. The resulting data were subjected to chemometric analysis (principal component analysis and partial least squares discriminant analysis) to investigate the effect of metformin hydrochloride on serum metabolite profiles of type 2 diabetes mellitus patients. (1)H NMR spectroscopic analysis revealed increased trimethylamine-N-oxide (TMAO), 3-hydroxybutyrate (3-HB) and decreased glucose, N-acetyl glycoprotein (NAC), lipoprotein, lactate, acetoacetate and unsaturated lipids in serum from metformin treated patients compared to untreated ones. UPLC/MS in positive electrospray ionization detected increased tryptophan and decreased lysophosphatidylcholines (C16:0 LPC, C18:0 LPC and C18:2 LPC) as well as phenylalanine in treated group. Both analytical techniques used in this study were able to detect biochemical changes in the serum of type 2 diabetes mellitus patients after the treatment of metformin hydrochloride, which may be helpful to the understanding of action mechanism of metformin hydrochloride.

Advances in separation science applied to metabonomics

Metabonomics focuses on metabolite profile changes in diverse living systems caused by a biological perturbation. These metabolite signatures can be achieved with techniques such as gas chromatography, high-performance liquid chromatography (ultra-high-performance/pressure liquid chromatography and capHPLC), capillary electrophoresis, and capillary electrochromatography normally hyphenated with MS. In this review we present the latest developments of the abovementioned techniques applied in the field of metabonomics, with applications covering phytochemistry, toxicology and clinical research.

Cytokinin profiling in plant tissues using ultra-performance liquid chromatography-electrospray tandem mass spectrometry

We have developed a simple, high-throughput batch immunoextraction (IAE) micropurification procedure for extracting a wide range of naturally occurring cytokinins (bases, ribosides, O- and N-glucosides, and nucleotides) from plant tissues in solutions that are compatible with ultra-performance liquid chromatography (UPLC), thereby facilitating sensitive subsequent analysis. The UPLC system was coupled to a tandem quadrupole mass spectrometer (MS/MS) equipped with an electrospray interface (ESI). Small (mg) amounts of tissues were purified by solid-phase extraction (SPE) followed by an immunoaffinity clean-up step and two fast chromatographic separations of most cytokinin metabolites (bases, ribosides, and 9-glucosides in the first, O-glucosides and nucleotides in the second). Using UPLC, the runs were up to 4-fold faster than in standard cytokinin analyses, and both retention times and injection volumes were less variable (RSDs, 0.15-0.3% and 1.0-5.5%, respectively). In multiple reaction monitoring (MRM) mode, the detection limit for most of the cytokinins analyzed was close to 1 fmol (5-25 fmol for O-glucosides and nucleotides) and the linear range spanned at least five orders of magnitude. The extraction and purification method was optimized using poplar (Populusxcanadensis Moench, cv Robusta) leaf samples, and the analytical accuracy was further validated using IAE-purified 10-day-old Arabidopsis thaliana plants spiked with 1 and 10 pmol of cytokinin derivatives. This approach can be used for rapid, sensitive qualitative and/or quantitative analysis of more than 50 natural cytokinins in minute amounts of plant tissues with high performance, robustness, and accuracy.

LC-MS-based metabonomics analysis

Metabonomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. It has shown particular promise in the areas of toxicology and drug development, functional genomics, systems biology, and clinical diagnosis. Comprehensive metabonomics investigations are primarily a challenge for analytical chemistry. High-performance liquid chromatography-mass spectrometry (HPLC-MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its main advantages include wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing HPLC-MS for metabonomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, the strategy and application of HPLC-MS-based metabonomics are reviewed.