Effects of Tao‐Hong‐Si‐Wu decoction on acute blood stasis in rats based on a LC‐Q/TOF‐MS metabolomics and network approach

A novel approach using metabolomics coupled with a metabolic network was used to investigate the effects of Tao‐Hong‐Si‐Wu decoction (THSWD) on the rat model of acute blood stasis syndrome. Acute blood stasis syndrome was induced by placing the rats in ice‐cold water following two injections with epinephrine. The hemorheological indicators [whole blood viscosity (WBV) and plasma viscosity (PV)] and the blood coagulation indicators [thrombin time (TT), prothrombin time (PT), activated partial thromboplastin time (APTT) and fibrinogen (FIB)] were detected. The nonparametric univariate method and multivariate statistical analysis were performed for determining the potential biomarkers. A correlation map was structured between biochemical indicators and hub metabolites to explain the effects mechanism of THSWD. After the administration of THSWD, the levels of WBV, PV, TT, APTT and FIB returned to levels observed in the control group. According to metabolomics coupled with metabolic network analysis, the intervention of THSWD in rats with acute blood stasis syndrome induced substantial and characteristic changes in their metabolic profiles. Fifteen metabolites were screened, which mainly involved 10 pathways and five hub metabolites, namely, l ‐glutamate, l ‐phenylalanine, N‐acylsphingosine, arachidonic acid and phosphatidate. The biochemical indicators and hub metabolites could be adjusted to close to normal levels by THSWD. Therefore, combining metabolomics and metabolic network helped to evaluate the effects of THSWD on acute blood stasis.     

Enhanced pseudotargeted analysis using a segment data dependent acquisition strategy by liquid chromatography–tandem mass spectrometry for a metabolomics study of liquiritin in the treatment of depression

An enhanced pseudotargeted method using a segment data‐dependent acquisition mode based on ultra‐high performance liquid chromatography–tandem mass spectrometry was developed. This segment data dependent acquisition‐based pseudotargeted method could improve the detection of co‐eluted ions and extend the coverage of analytes. A set of 502 multiple reaction monitoring channels were obtained by this segment strategy, which was twice the number created by the traditional data‐dependent acquisition mode. Compared with the untargeted method, the pseudotargeted profiling demonstrated higher sensitivity and higher precision. More than 90% of the metabolites detected by the enhanced pseudotargeted method had relative standard deviations less than 15%. The segment data dependent acquisition‐based pseudotargeted method was successfully applied to the metabolomics study of the depressed rats with the treatment of liquiritin. Forty‐seven differential metabolites were screened and five metabolic pathways were found to be related to depression including retinol metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, terpenoid backbone biosynthesis, and lysine degradation. The segment data dependent acquisition‐based pseudotargeted method widened the coverage of metabolites with good sensitivity and precision, which exhibited great potential in the discovery of differential metabolites in metabolomics studies.     

Metabolic profiles revealed synergistically antidepressant effects of lilies and Rhizoma Anemarrhenae in a rat model of depression

Depression is a major cause of illness and disability. We applied untargeted metabolomics using mass spectrometry to identify metabolic signatures associated with depression in serum and explored the antidepressant effects of lilies and Rhizoma Anemarrhenae on an experimental model of chronic unpredictable mild stress (CUMS). Meanwhile metabolomics based on UHPLC‐Q‐TOF‐MS was used to study the change in metabolites in CUMS rat serum and to evaluate the effects of Rhizoma Anemarrhenae and lilies (alone and in combination). Partial least squares‐discriminant analysis identified 30 metabolites as decisive marker compounds that discriminated the CUMS rats and the control rats. The majority of these metabolites were involved in amino acid metabolism, the tricarboxylic acid cycle, and phosphoglyceride metabolism. The reliability of the metabolites was evaluated by the administration of lilies, Rhizoma Anemarrhenae, fluoxetine and the combination of lilies and Rhizoma Anemarrhenae to the CUMS rats. Behavior studies demonstrated that treatment with the combination of lilies and Rhizoma Anemarrhenae resulted in optimal antidepressant effects. The combination treatment was almost as effective as fluoxetine. Our results suggest that lilies and Rhizoma Anemarrhenae demonstrate synergistically antidepressant effects in CUMS via the regulation of multiple metabolic pathways. These findings provide insight into the pathophysiological mechanisms underlying CUMS and suggest innovative and effective treatments for this disorder.     

PLS‐DA for compositional data with application to metabolomics

When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS‐DA)—the most widely used method in chemometrics for multivariate classification—can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters (metabolites) using a small data set resulting from targeted metabolomics, where just a subset of potential markers is selected. The second example—the approach of untargeted metabolomics—was used for the analysis detecting almost 500 metabolites. The significance of the metabolites is investigated by applying PLS‐DA, accommodated according to a compositional approach. The significance of important metabolites (markers of diseases) is more clearly visible with the compositional method in both examples. Also, cross‐validation methods lead to better results in case of using the compositional approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism

Fumarase catalyzes the interconversion of fumarate and l ‐malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt‐induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC–MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l ‐malic acid, l ‐aspartic acid, glycine and l ‐glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level.     

A metabolomic study of the urine of rats with Alzheimer's disease and the efficacy of Ding‐Zhi‐Xiao‐Wan on the afflicted rats

As a well‐known traditional Chinese medicine formula, Ding‐Zhi‐Xiao‐Wan has long been used for the routine treatment of Alzheimer's disease. However, the mechanism of Ding‐Zhi‐Xiao‐Wan in treating Alzheimer's disease is unclear. Therefore, a nontargeted metabolomics method based on ultrahigh performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry has been established to explore the metabolic variations in the urine of Alzheimer's disease rats and investigate the therapeutic mechanism of Ding‐Zhi‐Xiao‐Wan on Alzheimer's disease. To develop a better rat model of Alzheimer's disease, amyloid β25‐35 was injected into the bilateral hippocampus of Sprague–Dawley rats. Multivariate analysis approaches were applied to differentiate the urine components between the four groups. Thereafter, a targeted metabolomics method was used to verify the identified endogenous metabolites and determine the mechanism of action of Ding‐Zhi‐Xiao‐Wan. Altogether, 26 potential biomarkers were found, of which 15 biomarkers (10 of which are potential biomarkers found in nontargeted metabolomics) were identified. The results show that Ding‐Zhi‐Xiao‐Wan mainly affects the pathways of taurine and hypotaurine metabolism, tryptophan metabolism, and phenylalanine metabolism. Ding‐Zhi‐Xiao‐Wan might play a role in the treatment of Alzheimer's disease by mediating antioxidative stress, regulation of energy metabolism, improvement of intestinal microbes, and protection of nerve cells.     

Comparison of Widely Targeted Metabolomics and Untargeted Metabolomics of Wild Ophiocordyceps sinensis

The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.     

基于HPLC/Q-TOF MS的4种农药联合暴露人群的代谢组学研究

采用基于高效液相色谱-飞行时间质谱联用(HPLC-TOF MS)的代谢组学方法,研究了啶虫脒、高效氯氟氰菊酯、联苯菊酯、甲氨基阿维菌素苯甲酸盐4种农药联合暴露所致的施药人群尿液中内源性代谢物的变化。采集30位农民喷洒4种复合农药前和喷洒农药期1,3,5,7 d的尿液进行检测。提取正常尿液中常见代谢物并通过质控样品评价手段进行分析,结果表明该方法具有良好的稳定性和精密度,可用于尿液中代谢物分析。多变量分析结果表明,暴露人群施药前后尿液的代谢物含量存在较大差异。对选取的36个差异离子进行鉴定,确定了8个生物标志物的结构。结果显示联合暴露组人群尿液中多巴胺、5-羟色胺、酪氨酸、色氨酸、牛磺酸和马尿酸的含量显著下降;犬尿素和肌酸的含量显著上升。4种农药联合暴露导致接触人群尿液中色氨酸代谢途径的中间产物含量降低,肝代谢和能量代谢相关的代谢物蓄积,可能与神经系统和肝脏功能的受损有关。     

Metabolomics study on promoting blood circulation and ameliorating blood stasis: Investigating the mechanism of Angelica sinensis and its processed products

Angelica sinensis (Danggui, DG) parched with alcohol (Jiu Danggui, JDG) and charred DG are the main processed products of DG, which are used to treat blood stasis syndrome (BSS). However, their therapeutic effect and mechanisms are still unclear. Based on an acute rat BSS model, the intervention effects of DG and its processed products (DGPPs) were evaluated by the hemorheology and coagulation function parameters. Meanwhile, plasma and urine metabolites were detected and analyzed by liquid chromatography coupled to quadrupole time‐of‐flight mass spectrometry and multivariate statistical analysis method. The results of hemorheology, coagulation function parameters and metabolomics all showed that the BSS model was successfully established, DGPPs intervention could significantly relieve rats BSS and the therapeutic effect of JDG was best. Moreover, 23 differential metabolites (14 in plasma and nine in urine) were identified that were closely related to the BSS, involving seven potential target metabolic pathways. DGPP intervention showed different degrees of reverse effect on these metabolites. JDG was the most effective owing to extensive regulation effect on differential metabolites. This study provides a reference for understanding the pathological mechanism of BSS and the mechanism of DGPPs, which lays a theoretical foundation for the rational use of DGPPs in clinical practice.     

Urinary metabolomic study of systemic lupus erythematosus based on gas chromatography/mass spectrometry

Systemic lupus erythematosus (SLE) is an autoimmune disease with heterogeneous organ and system manifestations. In this study, urinary metabolic alterations related to SLE were investigated by performing gas chromatography/mass spectrometry (GC/MS) based metabolomics and multivariate statistical analysis. Patients with SLE and healthy controls could be clearly differentiated in view of the metabolic abnormity in urine. Among 70 identified endogenous metabolites, 23 metabolites were dramatically increased in SLE patients, which involved in several key metabolic pathways including energy metabolism, nucleotide metabolism, oxidative stress and gut‐microbiome‐derived metabolism. This noninvasive and GC/MS‐based metabolomic technique is a promising and potent strategy for identifying novel biomarkers and understanding pathogenesis of SLE. Copyright © 2016 John Wiley & Sons, Ltd.     

Untargeted metabolomic study on the insomnia effect of Suan‐Zao‐Ren decoction in the rat serum and brain using ultra‐high‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry combined with data processing analysis

Insomnia is a common clinical disease that can seriously damage the normal lives of sufferers. Suan‐Zao‐Ren decoction has been used to treat insomnia for a long time. However, the underlying molecular mechanism of Suan‐Zao‐Ren decoction is still not clear. In this study, the nontargeted metabolomics based on high‐resolution mass spectrometry and multiple statistical approaches were initially used to investigate the changes of potential serum and brain biomarkers and metabolic pathways in the insomnia model rat. Principal component analysis‐discriminate analysis indicated that the Suan‐Zao‐Ren decoction treatment improved the metabolic phenotype insomnia. Moreover, the heatmap analysis identified the most important biomarkers involved in insomnia. According to the pathway analysis, phenylalanine metabolism, tryptophan metabolism, and so on were recognized as the most affected metabolic pathways associated with insomnia disease. These findings provided a comprehensive understanding of the regulative effects of Suan‐Zao‐Ren decoction on the host metabolic phenotype of the insomnia rats. Our work demonstrated that the metabolomics approach is a promising tool that could help us to conduct the exploration of the therapeutic effects and mechanism of traditional Chinese medicines.     

Metabolomics study of cured tobacco using liquid chromatography with mass spectrometry: Method development and its application in investigating the chemical differences of tobacco from three growing regions

Cured tobacco is an important plant material. Component studies are a big challenge for its significantly diverse chemical properties and vastly different concentrations. In this work, liquid chromatography with quadrupole time‐of‐flight mass spectrometry was used to perform a metabolomics study of cured tobacco owing to its efficient separation and detection of semipolar metabolites. A solvent of methanol/water (8:2, v/v) and 30 min of ultrasound time were found to be optimal to perform extraction. 95, 92, and 93% of metabolite features had within 20% of coefficient of variation for repeatability, intraday and interday precision analysis, respectively, indicating a good stability of the method developed. 113 metabolites were identified in cured tobacco based on accurate mass, retention time, and MS/MS fragments. The developed method was applied to a metabolomics study of cured tobacco from three growing regions. Forty three metabolites were found to be contributed to the classification. It is shown that the developed method can be applied to metabolomics analysis of plant materials.     

基于肝脏代谢组分析研究低聚硒化氨基多糖对黑鲷的免疫调节作用

采用基于液相色谱-飞行时间质谱联用（LC-TOF-MS）技术的代谢组学方法，分析黑鲷肝脏内源性代谢物的变化，研究硒化氨基多糖增强黑鲷的免疫调节机制。采用XCMS^plus软件非靶向分析质谱采集数据，筛选潜在生物标志物，并通过MetaboAnalyst3.0网站分析相关代谢通路。结果表明，饲喂硒化氨基多糖组中的代谢物明显区分于空白组，发现并鉴定了32个有差异的生物标志物。代谢通路分析结果表明，硒化氨基多糖可通过氨基酰基-转运脱氧核糖核苷酸（tRNA）生物合成、氨基酸代谢、核苷酸代谢、氮代谢等代谢通路增强黑鲷自身的免疫机能。该研究为阐明硒化氨基多糖的免疫增强机制提供了科学依据。     

Accelerated,untargeted metabolomics analysis of cutaneous T‐cell lymphoma reveals metabolic shifts in plasma and tumor adjacent skins of xenograft mice

Cutaneous T‐cell lymphoma (CTCL) is a heterogeneous group of skin‐homing T‐cell neoplasms. Clinical management is stage based but diagnosis and prognosis could be extremely challenging. The presented study aims to explore the metabolic profiling of CTCL by an accelerated untargeted metabolomics data analysis tool “Mummichog” to facilitate the discoveries of potential biomarkers for clinical early stage diagnosis, prognosis, and treatments in CTCL. Ultra high‐performance liquid chromatography–quadrupole time‐of‐flight–based untargeted metabolomics were conducted on the skin and plasma of CTCL mice. It showed that the metabolism of skin changed greatly versus control samples in the development of CTCL. Increased l ‐glutamate and decreased adenosine monophosphate were the most essential metabolic features of CTCL tumor and tumor adjacent skins. Unique metabolism changes in tumor adjacent non‐involved skin tissues (ANIT) occurred in the progress of carcinogenesis, including upregulated cytidine‐5′‐triphosphate, aberrant biosynthesis of prostaglandins, pyrimidine, mevalonate pathway, and tryptophan degradation. Sharply elevated 5‐phospho‐α‐d ‐ribose 1‐diphosphate (PRPP) marked the final state of tumor in CTCL. In the plasma, systematic shifts in corticosterone, sphingolipid, and ceramide metabolism were found. These uncovered aberrant metabolites and metabolic pathways suggested that the metabolic reprogramming of PRPP in tumor tissues may cause the disturbance of cytidine and uridine metabolic homeostasis in ANIT. Accumulative cytidine‐5′‐triphosphate in ANIT may exert positive feedback on the PRPP level and leads to CTCL further development. In addition, the accelerated data analysis tool “Mummichog” showed good practicability and can be widely used in high‐resolution liquid chromatography mass spectrometry–based untargeted metabolomics.     

Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for young hypertensive men

The awareness, treatment, and control rates of hypertension for young adults are much lower than average. It is urgently needed to explore the variances of metabolic profiles for early diagnosis and treatment of hypertension. In current study, we applied a GC–MS based metabolomics platform coupled with a network approach to analyze plasma samples from young hypertensive men and age-matched healthy controls. Our findings confirmed distinct metabolic footprints of young hypertensive men. The significantly altered metabolites between two groups were enriched for the biological module of amino acids biosynthesis. The correlations of GC–MS metabolomics data were then visualized as networks based on Pearson correlation coefficient (threshold = 0.6). The plasma metabolites identified by GC–MS and the significantly altered metabolites (P < 0.05) between patients and controls were respectively included as nodes of a network. Statistical and topological characteristics of the networks were studied in detail. A few amino acids, glycine, lysine, and cystine, were screened as hub metabolites with higher values of degree (k), and also obtained highest scores of three centrality indices. The short average path lengths and high clustering coefficients of the networks revealed a small-world property, indicating that variances of these amino acids have a major impact on the metabolic change in young hypertensive men. These results suggested that disorders of amino acid metabolism might play an important role in predisposing young men to developing hypertension. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism underlying complex diseases.     

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.     

Urinary biomarker and treatment mechanism of Rhizoma Alismatis on hyperlipidemia

Rhizoma Alismatis (RA), a diuretic in Asia and Europe, was found to possess anti‐hyperlipidemic activity. Since the biomarkers and mechanisms of RA in the treatment of hyperlipidemia are inadequate, ultra‐performance liquid chromatography coupled with quadrupole time‐of‐flight synapt high‐definition mass spectrometry and multivariate data analysis were employed to investigate the urinary metabolomics of RA on hyperlipidemic rats induced by high‐fat diet. The metabolic profile of RA‐treated hyperlipidemic group located between control and diet‐induced hyperlipidemic groups. Nineteen metabolites with significant fluctuations were identified as potential biomarkers related to the hyperlipidemia and anti‐hyperlipidemia of RA using partial least‐squares‐discriminate analysis, heatmap analysis and correlation coefficient analysis. The fluctuations of these biomarkers represented disturbances in amino acid metabolism, purine metabolism, pyrimidine metabolism and energy metabolism. After RA treatment, these perturbed metabolites were restored to normal or nearly normal levels. RA can alleviate high‐fat diet‐induced dysfunctions in these metabolic pathways.     

Database-assisted global metabolomics profiling of pleural effusion induced by tuberculosis and malignancy

Database-assisted global metabolomics has received growing attention due to its capability for unbiased identification of metabolites in various biological samples. Herein, we established a mass spectrometry (MS)-based database-assisted global metabolomics method and investigated metabolic distance between pleural effusion induced by tuberculosis and malignancy, which are difficult to be distinguished due to their similar clinical symptoms. The present method utilized a liquid chromatography (LC) system coupled with high resolution mass spectrometry (MS) working on full scan and data dependent mode for data acquisition. Unbiased identification of metabolites was performed through mass spectral searching and then confirmed by using authentic standards. As a result, a total of 194 endogenous metabolites were identified and 33 metabolites were found to be differentiated between tuberculous and malignant pleural effusions. These metabolites involved in tryptophan catabolism, bile acid biosynthesis, and β-oxidation of fatty acids, provided non-invasive biomarkers for differentiation of the pleural effusion samples with high sensitivity and specificity.