小鼠血清中内源性代谢物的GC/TOF-MS分析

采用气相色谱/飞行时间质谱(GC/TOF-MS)联用仪建立了小鼠血清的代谢组学分析方法. 通过对硅烷化试剂的优化和去卷积分析, 共检测到269个峰, 其中相似度在800以上的代谢产物有46个; 以核糖醇为内标, 任意选取18种内源性代谢产物考察此方法的精密度和稳定性, 并通过14种标准氨基酸的混标溶液进行定量分析考察本方法的线性关系. 结果表明, 14种氨基酸在2.78~113.20 ng/ SymbolmA@ L浓度范围内线性关系良好; 18种内源性代谢产物的变异系数均在15%以内, 具有较好的稳定性; 并利用8种标准单糖, 通过肟化反应解决了糖在多个位置出峰的难点. 该方法可用于代谢组学研究, 并通过相关数据处理找出生物标志物, 为疾病诊断提供了新的思路.     

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.     

Liquid chromatography–tandem quadrupole mass spectrometry and comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry measurement of targeted metabolites of Methylobacterium extorquens AM1 grown on two different carbon sources

Complementary methods using liquid chromatography–tandem quadrupole mass spectrometry (LC–MS/MS) and comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOF-MS) were developed and applied to determine targeted metabolites involved in central carbon metabolism [including tricarboxylic acid cycle, serine cycle, ethylmalonyl-coenzyme A (ethylmalonyl-CoA) pathway and poly-β-hydroxybutyrate cycle] of the bacterium Methylobacterium extorquens AM1 grown on two carbon sources, ethylamine (C2) and succinate (C4). Nucleotides, acyl-CoAs and a few volatile metabolites in cell extracts of M. extorquens AM1 were readily separated using either hydrophilic interaction liquid chromatography or reversed-phase liquid chromatography, and detected with good sensitivity by MS/MS. However, volatile intermediates within a low mass range (<300 m/z), especially at low abundance (such as glyoxylic acid and others <500 nM), were more effectively analyzed by GC × GC–TOF-MS which often provided better sensitivity, resolution and reproducibility. The complementary nature of the LC-based and GC-based methods allowed the comparison of 39 metabolite concentrations (the lowest level was at 139.3 nM). The overlap between the LC-based and GC-based methods of seven metabolites provided a basis to check for consistency between the two methods, and thus provided some validation of the quantification accuracy. The abundance change of 20 intermediates further suggested differences in pathways linked to C2 and C4 metabolism.     

Metabolomic profiling of rat serum associated with isoproterenol-induced myocardial infarction using ultra-performance liquid chromatography/time-of-flight mass spectrometry and multivariate analysis

An ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC/TOF-MS)-based metabolomic approach was developed to characterize the metabolic profile associated with isoproterenol (ISO)-induced myocardial infarction (MI). Analysis of the serum samples revealed distinct changes in the biochemical patterns of ISO-induced rats. A multivariate statistical method, supervised partial least squares-discriminant analysis (PLS-DA), was then used for screening of potential biomarkers. As a result, 13 lipid biomarkers, including lysophosphatidylcholines (Lyso-PCs) and fatty acids were identified by the accurate mass measurement of TOF-MS. The relationship between abnormal lipid metabolism and the formation of MI were also studied. This work demonstrates the utility of UPLC/TOF-MS-based metabolic profiling combined with multivariate analysis as a powerful tool to further investigate the pathogenesis of cardiovascular diseases.     

Assessing the suitability of capillary electrophoresis‐mass spectrometry for biomarker discovery in plasma‐based metabolomics

The actual utility of capillary electrophoresis‐mass spectrometry (CE‐MS) for biomarker discovery using metabolomics still needs to be assessed. Therefore, a simulated comparative metabolic profiling study for biomarker discovery by CE‐MS was performed, using pooled human plasma samples with spiked biomarkers. Two studies have been carried out in this work. Focus of study I was on comparing two sets of plasma samples, in which one set (class I) was spiked with five isotope‐labeled compounds, whereas another set (class II) was spiked with six different isotope‐labeled compounds. In study II, focus was also on comparing two sets of plasma samples, however, the isotope‐labeled compounds were spiked to both class I and class II samples but with concentrations which differ by a factor two between both classes (with one compound absent in each class). The aim was to determine whether CEMS‐based metabolomics could reveal the spiked biomarkers as the main classifiers, applying two different data analysis software tools (MetaboAnalyst and Matlab). Unsupervised analysis of the recorded metabolic profiles revealed a clear distinction between class I and class II plasma samples in both studies. This classification was mainly attributed to the spiked isotope‐labeled compounds, thereby emphasizing the utility of CE‐MS for biomarker discovery.     

Evaluation of the chronic intoxication of fluoride on human serum metabolome using untargeted metabolomics

Drinking water is the main source of fluoride intake for the human body and its regulated consumption helps in decreasing dental caries. However, excessive fluoride consumption over a prolonged time period causes fluorosis disease which adversely affects many tissues and organs of the body. This paper describes the evaluation of chronic intoxication of fluoride on human serum metabolome. The untargeted metabolomics approach using UPLC-QTOF-MS/MS is applied for metabolomic profiling, whereas the estimation of fluoride in serum samples was carried out using the ion-selective electrode (ISE). Fluoride concentration was found to be 0.16–1.25 mg/L in serum samples of 39 fluorosis patients and 0.008–0.045 mg/L in 20 healthy samples. A total of 47 metabolites were identified based on the high-resolution mass spectrometry analysis. A volcano plot was generated to discriminate features that are significantly different between the fluorosis and healthy groups at the probability of 0.05 and fold change ≥ 2. Among all identified metabolites, intensities of ten differential identified metabolites including inosine, α-linolenic acid, guanosine, octanoyl-L-carnitine, His-Trp, phytosphingosine, lauroyl-L-carnitine, hydrocortisone, deoxyinosine and dodecanedioic acid have been found altered in disease samples compared to healthy controls. Major pathways identified based on these metabolites include energy metabolism, fatty acid oxidation, purine degradation pathway, elevated protein degradation, and increased ω-6 fatty acid linoleate signatures were observed.     

Untargeted GC-MS Metabolomics applied to wild leaves and callus produced by plant tissue culture of Hibiscus sabdariffa L.

Hibiscus sabdariffa L. is a naturalized medicinal species in Brazil commonly called a “vinagreira” and is a member of the Malvaceae Juss. family, which has a rich potential of bioactive compounds presenting extracts with antioxidant, antibacterial, anti-inflammatory, hepatoprotective, antiviral, antidiabetic, and antiobesity, among others. The production of secondary metabolites of medicinal plants using biotechnological tools such as the culture of callus of plant tissues is increasingly being used to produce high-quality compounds under in vitro conditions. From this perspective, the objective of this work was to analyze the chemical compounds of the leaves and callus culture of H. sabdariffa using techniques of Gas Chromatography Coupled to the Mass Spectrum (GC-MS),. The analysis methodology used consisted of removal of liposoluble compounds, acid hydrolysis, and derivatization, all stages were submitted to ultrasonic-assisted agitation, using a reduced amount of biomass. Based on the results obtained in the study, a total of 38 metabolites identified by GC-MS analysis can be observed. Among the identified substances, protocatechuic acid (26A) stands out as the main constituent, with a relative abundance of 26.86% and 16.68% for leaves and callus of H. sabdariffa, respectively. The principal component analysis (PCA) allowed the discrimination of the chemical composition of each sample, being useful for the observation and detection of the compounds trends patterns. The analysis of the hierarchical group combined with the heat map represented the visual relationship between the samples of the data set indicating the values of higher and lower concentrations of chemical compounds respectively, confirming that protocatechuic acid is the most abundant, for the leaves and callus of H. sabdariffa, followed by eicosanoid and isocitric acid, produced only in callus. It was concluded that the GC-MS technique combined with chemometric tools, helped identify the diversity of the compounds present in the leaves and callus of H. sabdariffa and that callus culture enables the production of bioactive compounds continuously and uniformly in a controlled environment and free of contamination.     

Integrated metabolomics analysis of the effect of PPARδ agonist GW501516 on catabolism of BCAAs and carboxylic acids in diabetic mice

The peroxisome proliferator-activated receptor(PPARδ) agonists are reported to improve insulin sensitivity,reduce glucose levels,and alleviate dysfunctional lipid metabolism in animal models of type 2 diabetes mellitus.However,the underlying mechanisms remain incompletely understood.Metabolism plays an essential role in the biological system.Monitoring of metabolic changes in response to disease conditions or drug treatment is critical for better understanding of the pathophysiological mechanisms.In this study,metabolic profiling analysis by gas chromatography-mass spectrometry integrated with targeted analysis by liquid chro matography-mass spectrometry was carried out in plasma samples of db/db diabetic mice after six-week treatment of PPARδ agonist GW501516.GW501516 treatment significantly altered levels of metabolites,such as branched-chain amino acids(BCAAs),BCAA metabolites(3-hydroxyisobutyric acid and 3-hydroxyisovaleric acid),long-chain fatty acids,uric acid and ketone bodies(3-hydroxybutyric acid and 2-hydroxybutyric acid) which are all associated with the impaired systemic insulin sensitivity.The pre sent results indicate the beneficial effect of PPARδ agonist in alleviating insulin resistance of diabetic mice by favorably modulating metabolic profile,thus providing valuable information in understanding the therapeutic potential of PPARδ agonists in correcting metabolic dysfunction in diabetes.     

A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.     

Development of a method for metabolomic analysis of human exhaled breath condensate by gas chromatography–mass spectrometry in high resolution mode

Exhaled breath condensate (EBC) is a promising biofluid scarcely used in clinical analysis despite its non-invasive sampling. The main limitation in the analysis of EBC is the lack of standardized protocols to support validation studies. The aim of the present study was to develop an analytical method for analysis of human EBC by GC–TOF/MS in high resolution mode. Thus, sample preparation strategies as liquid–liquid extraction and solid-phase extraction were compared in terms of extraction coverage. Liquid–liquid extraction resulted to be the most suited sample preparation approach providing an average extraction efficiency of 77% for all compounds in a single extraction. Different normalization approaches were also compared to determine which strategy could be successfully used to obtain a normalized profile with the least variability among replicates of the same sample. Normalization to the total useful mass spectrometry signal (MSTUS) proved to be the most suited strategy for the analysis of EBC from healthy individuals (n = 50) reporting a within-day variability below 7% for the 51 identified compounds and a suited data distribution in terms of percentage of metabolites passing the Skewness and Kurtosis test for normality distribution. The composition of EBC was clearly dominated by the presence of fatty acids and derivatives such as methyl esters and amides, and volatile prenol lipids. Therefore, EBC offers the profile of both volatile and non-volatile components as compared to other similar biofluids such as exhaled breath vapor, which only provides the volatile profile. This human biofluid could be an alternative to others such as serum/plasma, urine or sputum to find potential markers with high value for subsequent development of screening models.