Metabolomics analysis reveals variation in Schisandra chinensis metabolites from different origins

Wu Wei Zi (Schisandra chinensis), an important herbal medicine, is mainly distributed in the northeast of China. Its phytochemical compositions, which depend on geographical origin, climatic conditions and cultural practices, may vary largely among Wu Wei Zi from different areas. In this study, we applied a comprehensive metabolite profiling approach using GC–TOF‐MS, ultra‐performance LC (UPLC) quadrupole TOF (QTOF) MS and inductively coupled plasma MS to systematically investigate the metabolite variations of S. chinensis from four different areas including Heilongjiang, Liaoning, Jilin, and Shanxi of China. A total of 65 primary metabolites, 35 secondary metabolites and 64 inorganic elements were identified. Several primary metabolites, including shikimic acid and tricarboxylic acid cycle intermediates, were abundant in those located in Heilongjiang, Jilin, and Liaoning. Besides, bioactive lignans are also highly abundant in those from northeastern China than those from northwestern China. Inorganic elements varied significantly among the different locations. Our results suggested that the metabolite profiling approach using GC–TOF‐MS, ultra‐performance LC quadrupole TOF MS, and inductively coupled plasma MS is a robust and reliable method that can be effectively used to explore subtle variations among plants from different geographical locations.     

Pressurized CEC coupled with QTOF‐MS for urinary metabolomics

Pressurized CEC (pCEC) coupled with ESI‐QTOF‐MS using a sheathless interface was applied for metabolomics to develop an alternative analytical method for metabolic profiling of complex biofluid samples such as urine. The hyphenated system was investigated with mixed standards and pooled urine samples to evaluate its precision, repeatability, linearity, sensitivity, and selectivity. The applied voltage, mobile phase, and gradient elution were optimized and applied for the analysis of urinary metabolites. Multivariate data analysis was subsequently performed and used to distinguish lung cancer patients from healthy controls successfully. High separation efficiency has been achieved in pCEC due to the EOF. For metabolite identification, the pCEC‐MS separation mechnism was helpful for discriminating the fragment ions of glutamine conjugates from co‐eluted metabolites. Three glutamine conjugates, including phenylacetylglutamine, acylglutamine C8:1, and acylglutamine C6:1 were identified among 16 differential urinary metabolites of lung cancer. Receiver‐operating‐characteristic analysis of acylglutamine C8:1 resulted in an area‐under‐curve value of 0.882. Overall, this work suggests that this pCEC‐ESI‐QTOF‐MS method may provide a novel and useful platform for metabolomic studies due to its superior separation and identification.     

Rapid identification of plant materials by wooden-tip electrospray ionization mass spectrometry and a strategy to differentiate the bulbs of Fritillaria

The counterfeit plant products, especially by using incorrect plant materials in pharmaceutical industry, have become a global problem. The plant materials belonging to closely related species but differing in medicinal properties are difficult to be identified. Here, a novel and generally applicable approach to identify the sources of plant materials was developed, which was based on the use of wooden-tip electrospray ionization mass spectrometry (wooden-tip ESI-MS) and multivariate statistical analysis of unidentified MS features (non-targeted). Using this approach, six officinal species of Fritillariae Cirrhosae Bulbus had been successfully differentiated. In addition, Fritillariae Pallidiflorae Bulbus, a common adulterant of Fritillariae Cirrhosae Bulbus, was also identified by using the strategy reported here. Compared with DNA phylogenetic trees, our approach provided finer resolution in distinguishing the closely related Fritillaria species. By combining wooden-tip ESI-MS and multivariate statistical analysis, a useful method was developed here for rapid identification of the sources of herbs, which showed promising perspectives in tracking the supply chain of pharmaceutical suppliers.     

Metabolomics data exploration guided by prior knowledge

In metabolomics research, it is often important to focus the data analysis to specific areas of interest within the metabolome. In this paper, we describe the application of consensus principal component analysis (CPCA) and canonical correlation analysis (CCA) as a means to explore the relation between metabolome data and (i) biochemically related metabolites and (ii) an amino acid biosynthesis pathway. CPCA searches for major trends in the behavior of metabolite concentrations that are in common for the metabolites of interest and the remainder of the metabolome. CCA identifies the strongest correlations between the metabolites of interest and the remainder of the metabolome.CPCA and CCA were applied to two different microbial metabolomics data sets. The first data set, derived from Pseudomonas putida S12, was relatively simple as it contained metabolomes obtained under four environmental conditions only. The second data set, obtained from Escherichia coli, was much more complex as it consisted of metabolomes obtained under 28 different environmental conditions. In case of the simple and coherent P. putida S12 data set, CCA and CPCA gave similar results as the variation in the subset of the selected metabolites and the remainder of the metabolome was similar.In contrast, CCA and CPCA yielded different results in case of the E. coli data set. With CPCA the trends in the selected subset - the phenylalanine biosynthesis pathway - dominated the results. The main trends were related to high and low phenylalanine productivity, and the metabolites showing a similar behavior in concentration were metabolites regulating the phenylalanine biosynthesis route in the subset and metabolites related to general amino acid metabolism in the remainder of the metabolome. With CCA, neither subset truly dominated the data analysis. CCA described the differences between the wild type and the overproducing strain and the differences between the succinate and glucose grown cells. For the difference between the wild type and the overproducing strain, metabolites from the beginning and the end of aromatic amino acid pathways like erythrose-4-phosphate, tryptophan, and phenylalanine were important for the selected metabolites.CCA and CPCA proved to be complementary data analysis tools that enable the focusing of the data analysis on groups of metabolites that are of specific interest in relation to the remainder of the metabolome. Compared to an ordinary PCA, focusing the data analysis on biologically relevant metabolites lead especially for the complex E. coli data to a better biological interpretation of the data.     

Ultrasound: A subexploited tool for sample preparation in metabolomics

Metabolomics, one of the most recently emerged “omics”, has taken advantage of ultrasound (US) to improve sample preparation (SP) steps. The metabolomics–US assisted SP step binomial has experienced a dissimilar development that has depended on the area (vegetal or animal) and the SP step. Thus, vegetal metabolomics and US assisted leaching has received the greater attention (encompassing subdisciplines such as metallomics, xenometabolomics and, mainly, lipidomics), but also liquid–liquid extraction and (bio)chemical reactions in metabolomics have taken advantage of US energy. Also clinical and animal samples have benefited from US assisted SP in metabolomics studies but in a lesser extension. The main effects of US have been shortening of the time required for the given step, and/or increase of its efficiency or availability for automation; nevertheless, attention paid to potential degradation caused by US has been scant or nil. Achievements and weak points of the metabolomics–US assisted SP step binomial are discussed and possible solutions to the present shortcomings are exposed.     

Data-processing strategies for metabolomics studies

Metabolomics studies aim at a better understanding of biochemical processes by studying relations between metabolites and between metabolites and other types of information (e.g., sensory and phenotypic features). The objectives of these studies are diverse, but the types of data generated and the methods for extracting information from the data and analysing the data are similar. Besides instrumental analysis tools, various data-analysis tools are needed to extract this relevant information. The entire data-processing workflow is complex and has many steps. For a comprehensive overview, we cover the entire workflow of metabolomics studies, starting from experimental design and sample-size determination to tools that can aid in biological interpretation. We include illustrative examples and discuss the problems that have to be dealt with in data analysis in metabolomics. We also discuss where the challenges are for developing new methods and tailor-made quantitative strategies.     

Beam search for peak alignment of NMR signals

Recent work by Forshed et al. [Anal. Chim. Acta 487 (2003) 189] resulted in an important tool for aligning two NMR spectra. The recognition that the problem posed by Forshed et al. is separable results in fast heuristics that give results that are at least as good as the in an order of magnitude less time. A beam search algorithm is described along with experiments using two different NMR spectrometers and sets of subjects.     

Pre-analytical method for NMR-based grape metabolic fingerprinting and chemometrics

Although metabolomics aims at profiling all the metabolites in organisms, data quality is quite dependent on the pre-analytical methods employed. In order to evaluate current methods, different pre-analytical methods were compared and used for the metabolic profiling of grapevine as a model plant. Five grape cultivars from Portugal in combination with chemometrics were analyzed in this study. A common extraction method with deuterated water and methanol was found effective in the case of amino acids, organic acids, and sugars. For secondary metabolites like phenolics, solid phase extraction with C-18 cartridges showed good results. Principal component analysis, in combination with NMR spectroscopy, was applied and showed clear distinction among the cultivars. Primary metabolites such as choline, sucrose, and leucine were found discriminating for ‘Alvarinho’, while elevated levels of alanine, valine, and acetate were found in ‘Arinto’ (white varieties). Among the red cultivars, higher signals for citrate and GABA in ‘Touriga Nacional’, succinate and fumarate in ‘Aragonês’, and malate, ascorbate, fructose and glucose in ‘Trincadeira’, were observed. Based on the phenolic profile, ‘Arinto’ was found with higher levels of phenolics as compared to ‘Alvarinho’. ‘Trincadeira’ showed lowest phenolics content while higher levels of flavonoids and phenylpropanoids were found in ‘Aragonês’ and ‘Touriga Nacional’, respectively. It is shown that the metabolite composition of the extract is highly affected by the extraction procedure and this consideration has to be taken in account for metabolomics studies.     

Improved enantiomer resolution and quantification of free D-amino acids in serum and urine by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

The potential of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) in the quantitative analysis of amino acid enantiomers (AAEs) as their methyl chloroformate (MCF) derivatives in physiological fluids was investigated. Of the two column sets tested, the combination of an Rt-γDEXsa chiral column with a polar ZB-AAA column provided superior selectivity. Twenty AAEs were baseline resolved including L-Leu and D-Ile, which had failed separation by one-dimensional chiral GC-quadrupole-MS (GC-qMS). Lower limits of quantification (LLOQ) were in the range of 0.03-2 μM. Reproducibility of the analysis of a serum specimen in octaplicate ranged from 1.3 to 16.6%. The GC×GC-TOFMS method was validated by analyzing AAEs in 48 urine and 43 serum specimens, respectively, and by comparing the results with data obtained by a previously validated GC-qMS method. Mean recoveries ranged from 78.4% for D-Leu to 116.4% for D-Pro in urine and 72.2% for L-Thr to 129.4% for L-Ile in serum. The method was applied to the comparison of AAE serum levels in patients suffering from liver cirrhosis to a control group. Significantly increased D-AA concentrations were found for the patient group, whereas L-AA levels were slightly decreased.     

Application of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method to identify potential biomarkers of perinatal asphyxia in a non-human primate model

Perinatal asphyxia is a leading cause of brain injury in infants, occurring in 2-4 per 1000 live births. The clinical response to asphyxia is variable and difficult to predict with current diagnostic tests. Reliable biomarkers are needed to help predict the timing and severity of asphyxia, as well as response to treatment. Two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC×GC-TOFMS) was used herein, in conjunction with chemometric data analysis approaches for metabolomic analysis in order to identify significant metabolites affected by birth asphyxia. Blood was drawn before and after 15 or 18 min of cord occlusion in a Macaca nemestrina model of perinatal asphyxia. Postnatal samples were drawn at 5 min of age (n=20 subjects). Metabolomic profiles of asphyxiated animals were compared to four controls delivered at comparable gestational age. Fifty metabolites with the greatest change pre- to post-asphyxia were identified and quantified. The metabolic profile of post-asphyxia samples showed marked variability compared to the pre-asphyxia samples. Fifteen of the 50 metabolites showed significant elevation in response to asphyxia, ten of which remained significant upon comparison to the control animals. This metabolomic analysis confirmed lactate and creatinine as markers of asphyxia and discovered new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers. GC×GC-TOFMS coupled with chemometric data analysis are useful tools to identify acute biomarkers of brain injury. Further study is needed to correlate these metabolites with severity of disease, and response to treatment.