Chemometric analysis of metabolism disorders in blood plasma of S180 and H22 tumor‐bearing mice by high performance liquid chromatography‐diode array detection

The aim of this paper is to characterize metabolism disorders in Kunming mice induced by S180 and H22 tumor cells. Metabolic fingerprint based on high performance liquid chromatography‐diode array detector (HPLC‐DAD) was developed to map the disturbed metabolic responses. In vivo testing of the antitumor activity of paclitaxel (Taxol) was carried out by inhibiting the growth of S180 and H22 tumor cells. Based on 27 common peaks, principal component analysis (PCA) and partial least squares‐discriminant analysis (PLS‐DA) were used to distinguish the abnormal from control and to find significant endogenous compounds (SECs) which have significant contributions to classification. The tumor growth inhibition ratios (TIRs) of Taxol groups were used to validate the predictive accuracies of the PLS‐DA models. The predictive accuracies of PLS‐DA models for S180 and H22 tumor model groups were 97.6 and 100%, respectively. Nine (S180) and seven (H22) SECs were discovered, including uric acid and cytidine. In addition, the correlations between relative tumor weights (RTWs) and chromatographic data for the SECs were significant (p < 0.05). Investigations on the stability and precision of the established metabolic fingerprints demonstrate that the experiment is well controlled and reliable. This work shows that the platform of HPLC‐DAD coupled with chemometric methods provides a promising method for the study of metabolism disorders induced by tumor cells. Copyright © 2011 John Wiley & Sons, Ltd.     

Gas chromatography/time‐of‐flight mass spectrometry‐based metabonomics of hepatocarcinoma in rats with lung metastasis: elucidation of the metabolic characteristics of hepatocarcinoma at formation and metastasis

Hepatocarcinoma (HCC) has a very high mortality rate and the high recurrence and metastasis rates contribute to the poor prognosis of HCC patients. To understand HCC formation and metastasis, we assessed the metabonomics of rat HCC and HCC with lung metastasis (HLM). The HLM rat model was established by exposure to diethylnitrosamine (DEN). Levels of serum and urine metabolites were quantified with gas chromatography/time‐of‐flight mass spectrometry (GC/TOFMS), and data were analyzed with partial least‐squares discrimination analysis (PLS‐DA). Serum and urine levels of some metabolites differed significantly between the control, HCC, and HLM groups. The products and intermediates from glycolysis and glutamate metabolism were elevated, while the tricarboxylic acid (TCA) cycle was inhibited, in both HCC and HLM. HLM samples revealed enhanced metabolism of nucleic acids, amino acids and glucuronic acid. PLS‐DA indicated that principal component weighting was greatest for serum serine, phenylalanine, lactic acid, tyrosine and glucuronic acid, and urine glycine, serine, 5‐oxyproline, malate, hippuric acid and uric acid. These data provide novel information that will improve understanding of the pathophysiological processes involved in HCC and HLM, and revealed potential metabolic markers for HCC invasion and metastasis. Copyright © 2010 John Wiley & Sons, Ltd.     

Morphine-induced conditioned place preference in mice: Metabolomic profiling of brain tissue to find “molecular switch” of drug abuse by gas chromatography/mass spectrometry

Conditioned place preference (CPP) is a widely used model to explore the mechanism of context-dependent learning. In this work, we developed a GC–MS method to investigate the metabolites in mice brain which was used to study the mechanism of context-dependent learning associated with rewarding effect of morphine. Metabolites were extracted from brain tissues and derivatized followed by analysis by gas chromatography/mass spectrometry (GC–MS). In total, 69 peaks were identified as known compounds. By a Wilcoxon ran sum test with p value ≤0.05, 21 metabolites were selected and considered as the potential biomarkers of morphine in mice brain. Using principal component analysis (PCA) and receiver-operator characteristic (ROC) curves, a model was constructed with a combination of these 21 metabolic markers. Multivariate statistics of the model yielded separation between the two groups with an area under the curve value of 0.947. Some metabolites were further discussed in detail about their pathway. Results showed that our technique can be successfully applied to profile for biomarkers and in understanding molecular mechanisms of drug abuse.     

Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra‐high performance liquid chromatography fingerprints and chemical pattern recognition

In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra‐high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS‐DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter‐propagation artificial neural network (CP‐ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS‐DA, SVMDA and CP‐ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS‐DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.     

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz ¹H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India

Water quality data set from the alluvial region in the Gangetic plain in northern India, which is known for high fluoride levels in soil and groundwater, has been analysed by chemometric techniques, such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares (PLS) in order to investigate the compositional differences between surface and groundwater samples, spatial variations in groundwater composition and influence of natural and anthropogenic factors. Trilinear plots of major ions showed that the groundwater in this region is mainly of Na/K-bicarbonate type. PCA performed on complete data matrix yielded six significant PCs explaining 65% of the data variance. Although, PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types (dug well, hand-pump and surface water). However, a visible differentiation between the water samples pertaining to two watersheds (Khar and Loni) was obtained. DA identified six discriminating variables between surface and groundwater and also between different types of samples (dug well, hand pump and surface water). Distinct grouping of the surface and groundwater samples was achieved using the PLS technique. It further showed that the groundwater samples are dominated by variables having origin both in natural and anthropogenic sources in the region, whereas, variables of industrial origin dominate the surface water samples. It also suggested that the groundwater sources are contaminated with various industrial contaminants in the region.     

Therapeutic Effects of Selaginella tamariscina on the Model of Acute Gout with Hyperuricemia in Rats Based on Metabolomics Analysis

Gout is a disease of purine metabolic disorders which results from long‐term hyperuricemia and the sodium urate deposition in and around the joints. Selaginella tamariscina (ST ) is an important traditional Chinese herbal medicine and is used for the treatment of gout and hyperuricemia. In this study, the rat model of acute gout with hyperuricemia was established by intraperitoneal injection of xanthine and oxonic acid potassium salt and articular injection monosodium urate (MSU ). The effect of ST in the treatment of gout was investigated by measuring joint swelling, the expression of IL ‐1β in serum and histological changes of joint by haematoxylin eosin (H&E) staining. Subsequently, urine metabolomics analysis for biomarkers discovery in acute gout with hyperuricemia rats was performed by the ultra‐performance liquid chromatography‐electrospray ionization quadruple time‐of‐flight mass spectrometry (UPLC‐ESI‐QTOF /MS ) combined with chemometric approach. Principal component analysis (PCA ) and orthogonal partial least squares‐discriminant analysis (OPLS‐DA ) were used to detect potential biomarkers. A total of 18 potential biomarkers were identified mainly including tryptophan metabolism; tyrosine metabolism; lysine methylation; pyrimidine metabolism; purine metabolism; TCA cycle and fatty acid metabolisms. This study indicates that ST could efficiently ameliorate the disease of acute gout with hyperuricemia in rats. The related metabolic biomarkers could provide useful information and the metabolic mechanism could be used for further study about the model of acute gout with hyperuricemia in rats.     

壬基酚和辛基酚联合染毒的尿液代谢组学研究

运用代谢组学方法研究壬基酚和辛基酚联合染毒对大鼠尿液代谢的影响.在高效液相色谱-飞行时间质谱技术检测的基础上,通过主成分分析观察了联合染毒的时间-毒效应和剂量-毒效应.根据主成分分析和判别分析,结合t检验,筛选出染毒组和对照组中具有明显差异的化合物,并在Metlin Scripps Center for Mass Spectrometry代谢物数据库中查询,推断其可能的代谢标志物.结果表明,壬基酚和辛基酚联合染毒后,尿液中可能出现的生物标志物有5种,分别为4,8-二羟基喹啉-2-甲酸(黄尿酸)、色氨酸及N-乙酰-5-羟色胺、RG- 13022和十六碳烯酸.由这些物质涉及的代谢途径,推测壬基酚和辛基酚联合染毒可能对生物体的蛋白质代谢、神经系统和心血管系统、生物节律、细胞抗氧化、性激素的平衡等方面产生毒效应,另外还可能影响细胞的信号传递和脂类代谢.     

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd.     

Partial least‐squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with ¹H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright © 2015 John Wiley & Sons, Ltd.     

Dynamic Metabolic Profiling of Urine From Type 2 Diabetic KK-Ay Mice Treated with Repaglinide by GC-MS

Repaglinide is a short-acting insulin secretagogue, commonly used for the treatment of type 2 diabetes. In this paper, metabolomics were first applied to research of dynamic urine metabolic profiling and biomarkers of type 2 diabetic KK-A^y mice treated with repaglinide based on GC-MS. Twenty diabetic KK-A^y mice were randomly assigned to four groups and fed with repaglinide for 6, 9, 12, and 14 weeks, respectively. Five C57BL/6 J mice were used as the healthy control group and fed with water as contrast. The PCA scores plot of the identified 41 metabolites showed that as treating time went on, the diabetic groups got closer to the healthy group. Furthermore, five marker metabolites, d-Glucose, d-Galactose, 1,5-Anhydro-d-glucitol, myo-inositol and tartaric acid were screened out, which have similar change footprints of the whole metabolic profiles. The results demonstrated that repaglinide not only regulates the sugars and polyalcohol but also the organic acid in the organism. This work has illustrated the potential of metabolomics to disease diagnosis, pharmacology, and pharmacodynamics research.     

Metabolomic profiling of serum in the progression of Alzheimer's disease by capillary electrophoresis–mass spectrometry

There is high interest in the discovery of early diagnostic biomarkers of Alzheimer's disease, for which metabolomics exhibits a great potential. In this work, a metabolomic approach based on ultrafiltration and analysis by CE‐MS has been used to obtain representative fingerprints of polar metabolites from serum samples in order to distinguish between patients with Alzheimer's disease, mild cognitive impairment, and healthy controls. By the use of partial least squares discriminant analysis it was possible to classify patients according to the disease stage and then identify potential markers. Significant increase was observed with progression of disease in levels of choline, creatinine, asymmetric dimethyl‐arginine, homocysteine‐cysteine disulfide, phenylalanyl‐phenylalanine, and different medium chain acylcarnitines. On the other hand, asparagine, methionine, histidine, carnitine, acetyl‐spermidine, and C5‐carnitine were reduced in these serum samples. In this way, multiple essential pathways were found implicated in the underlying pathology, such as oxidative stress or defects in energy metabolism. However, the most interesting results are related to the association of several vascular risk factors with Alzheimer's disease.     

Higher detectability method for the analysis of nucleosides,putative tumor biomarkers,in blood serum samples by CE‐UV with reversed EOF

The development and validation of methodologies for the analysis of biological samples is of outcome importance in order to obtain trustworthy results. This work reports a novel CE‐UV method for the assessment of nucleosides, putative tumor biomarkers, in blood serum. The separation of seven nucleosides within c.a. 20 min has been achieved with: BGE 30 mmol/L borate at pH 9.90, 50 mmol/L CTAB, and 10% methanol; V = –10 kV; T = 20°C; and capillary dimensions of 56 cm × 50 μm. The sample plug was concentrated by a modified large volume sample stacking strategy that provided better detectability. Validation showed that the method is suitable for bioanalytical purposes and initial applications in serum samples from healthy subjects are also presented. Finally, statistical methods were applied to verify the effect of characteristics such as age, smoking habits, and alcohol consumption on nucleoside concentrations in blood serum. Univariate statistical analysis tests emphasized the need for age matching, which was confirmed by PCA‐DA and PLS‐DA. Cancer history in the nearby family may also interfere in nucleoside levels in blood serum, since adenosine concentrations were statistically higher for volunteers who declared having diseased relatives.     

Modified Jiu Wei Qiang Huo decoction improves dysfunctional metabolomics in influenza A pneumonia‐infected mice

In order to study the effective mechanism of a traditional Chinese medicine (TCM), modified Jiu Wei Qiang Huo decoction (MJWQH), against H1N1‐induced pneumonia in mice, we chose a holistic approach. A reverse‐phase liquid chromatography with quadruple time‐of‐flight mass spectrometry (LC‐Q‐TOF‐MS) was developed to determine metabolomic biomarkers in mouse serum for the MJWQH effects. Thirteen biomarkers of H1N1‐induced pneumonia in mice serum were identified, which comprised l ‐valine, lauroylcarnitine, palmitoyl‐l ‐carnitine, l ‐ornithine, uric acid, taurine, O‐succinyl‐l ‐homoserine, l ‐leucine, l ‐phenylalanine, PGF2α, 20‐ethyl‐PGE2, arachidonic acid, and glycerophospho‐N‐arachidonoyl ethanolamine. Among them, metabolites of amino acids, fatty acids and arachidonic acid had the most relevant changes in mice with H1N1‐induced pneumonia. MJWQH effectively improved weight loss, lung index, biomarkers and inflammatory mediators such as prostaglandin E2 and phospholipase A2 in the infected mice. Importantly, MJWQH reversed the elevated biomarkers to the control levels from the infection, which provided a systematic view and a theoretical basis for its prevention or treatment. The results suggest that the protective effect of MJWQH against H1N1‐induced pneumonia is possibly through regulation of pathways for amino acid, fatty acid and arachidonic acid metabolism. They also suggest that the LC‐MS‐based metabolomic strategy is a powerful tool for elucidation of the mechanisms of TCM. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates

Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

基于超高效液相色谱-四极杆飞行时间质谱联用技术的血瘀模型大鼠血浆代谢组学分析

采用盐酸肾上腺素加冰水浴建立急性血瘀大鼠模型，使用超高效液相色谱-四极杆飞行时间质谱（UPLC-Q-TOF/MS）检测空白对照组与血瘀模型组中血浆代谢物，用主成分分析（PCA）、有监督偏最小二乘法判别分析（PLS-DA）及正交偏最小二乘法判别分析（OPLS-DA）对代谢组学数据进行多维统计分析，筛选潜在生物标志物。与对照组相比，在血瘀模型组大鼠血浆中检测出46个差异代谢物，血瘀模型组中乙酰胆碱、N6，N6，N6-三甲基-L-赖氨酸、胞嘧啶、乙酰肉碱等21个代谢物显著上调，吲哚丙酸、LysoPC（14：0）等25个代谢物显著下调，可能与脂质代谢、半乳糖代谢、亚油酸代谢、不饱和脂肪酸生物合成、糖酵解、花生四烯酸代谢等通路有关。代谢产物可作为血瘀证研究中的重要标记物，该研究结果有助于揭示血瘀证的发病机制，可为临床血瘀疾病的诊断及选用药物治疗提供思路，为后续治疗手段提供参考依据。     

基于超高效液相色谱-静电场轨道阱高分辨质谱的深静脉血栓模型大鼠血浆代谢组学分析

深静脉血栓(DVT)是一种血栓栓塞性疾病,具有高发病率、高死亡率和高后遗症3大特点。采用左股静脉不完全结扎加高渗盐水刺激建立DVT大鼠模型,使用超高效液相色谱-静电场轨道阱高分辨质谱(UHPLC-Orbitrap HRMS)检测假手术组与DVT模型组的血浆代谢谱,用主成分分析(PCA)及正交偏最小二乘-判别分析(OPLS-DA)对代谢组数据进行多元统计分析,观察两组间的代谢表型差异,将多变量模型分析中的变量重要性值(VIP>1)以及代谢物在模型组中的变化倍数(FC≤0.5或FC≥2,且P<0.05)作为差异代谢物筛选条件。最终在DVT模型组与假手术组间筛选得到27种差异代谢物,这些代谢物反映了DVT大鼠的代谢紊乱情况。具体表现为与假手术组相比,DVT模型组中三甲基胺氮氧化物(TMAO)、维生素K、鹅去氧胆酸、牛磺酸、1-甲基烟酰胺、7-酮胆固醇、反式十六烷基-2-烯醇肉碱、乙烯基乙酰甘氨酸、丙酰脯氨酸、咪唑乙酸、咪唑乙酸核糖苷、1,3,7-三甲基尿酸、1-丁胺、2-羟基异丙酸、吡哆醛、5α-四氢皮质酮、苯乳酸的水平显著升高;而3-脱氢肉碱、磷脂酰胆碱22∶6/20∶2(PC...     

阿卡波糖对Ⅱ型糖尿病大鼠尿液代谢轮廓的影响

采用超高效液相色谱-质谱联用(UPLC-MS/MS)方法研究了阿卡波糖对Ⅱ型糖尿病大鼠代谢轮廓的影响, 分析了健康组、 糖尿病模型组和糖尿病给予阿卡波糖组的大鼠尿样, 采用主成分分析法(PCA)和偏最小二乘法-判别分析(PLS-DA)对数据进行分析. PCA得分图表明, 健康组、 糖尿病组和阿卡波糖组的代谢轮廓有显著差别, 根据PLS-DA载荷图筛选, 将对各组分离贡献大的化合物的串联质谱分析数据经Human Metabolome Database(HMDB)和Mass Bank.jp等数据库检索, 进行质谱信息匹配, 鉴定出苯乙酰甘氨酸、 肌酐及葡萄糖酸等8种内源性代谢物为潜在生物标记物.