A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection

Metabolic markers are the core of metabonomic surveys. Hence selection of differential metabolites is of great importance for either biological or clinical purpose. Here, a feature selection method was developed for complex metabonomic data set. As an effective tool for metabonomics data analysis, support vector machine (SVM) was employed as the basic classifier. To find out meaningful features effectively, support vector machine recursive feature elimination (SVM-RFE) was firstly applied. Then, genetic algorithm (GA) and random forest (RF) which consider the interaction among the metabolites and independent performance of each metabolite in all samples, respectively, were used to obtain more informative metabolic difference and avoid the risk of false positive. A data set from plasma metabonomics study of rat liver diseases developed from hepatitis, cirrhosis to hepatocellular carcinoma was applied for the validation of the method. Besides the good classification results for 3 kinds of liver diseases, 31 important metabolites including lysophosphatidylethanolamine (LPE) C16:0, palmitoylcarnitine, lysophosphatidylethanolamine (LPC) C18:0 were also selected for further studies. A better complementary effect of the three feature selection methods could be seen from the current results. The combinational method also represented more differential metabolites and provided more metabolic information for a “global” understanding of diseases than any single method. Further more, this method is also suitable for other complex biological data sets.     

A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography-mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ～12 h from metabolite extraction to peak integration for a data set containing 15 total samples (～6 h for a single sample).     

An 1H NMR and UPLC�CMS-based plasma metabonomic study to investigate the biochemical changes in chronic unpredictable mild stress model of depression

Depression is a common and highly debilitating psychiatric illness. However, the pathophysiology of depression is not fully understood. In this study Sprague-Dawley rats were exposed to chronic unpredictable mild stress (CUMS) to induce depression. A metabonomic study on plasma of CUMS-induced depressive rats was performed to research the pathologic mechanism of depression by using ¹H nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography coupled to mass spectrometry (UPLC–MS). Clear separations between depressive rats and control rats were observed by principal component analysis (PCA) based on the data obtained using both analytical techniques and 18 significantly changed metabolites were identified as potential biomarkers of depression. Depressive rats were characterized by altered levels of plasma lysophosphatidylcholines, amino acids, cholic acid, choline, lactate, glycoproteins, glucose, ketone bodies, nucleosides and gut microflora metabolites, which were related to multiple perturbed metabolic pathways and contributed to the elucidation of the complex mechanism of depression. To the best of our knowledge, this is the first plasma metabonomic study on CUMS-induced depressive rats by using two complementary analytical technologies. Our results showed that metabonomic approach offers a useful tool to identify depression-specific biomarkers and provide new insights into the pathophysiology of depression.     

A novel R-package graphic user interface for the analysis of metabonomic profiles

Background  

Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries.     

Application of faecal metabonomics on an experimental model of tubulointerstitial fibrosis by ultra performance liquid chromatography/high-sensitivity mass spectrometry with MSE data collection technique

Chronic renal failure (CRF) is a major challenge for the public healthcare problem. A novel UPLC Q-TOF/MS method with MS^E data collection mode was developed as a very effective biochemical analytical tool for precise identification of important biomarkers in the adenine-induced CRF rats. Nine endogenous metabolites were identified by using metabonomic method combined with multivariate data analysis, the accurate mass, isotopic pattern, MS^E fragments information and MassLynx i-FIT algorithm. The identified metabolites indicated the perturbations of bile acid and phospholipid metabolism are related to CRF rats. This work shows that metabonomics method is a valuable tool in CRF mechanism study.     

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were recorded by both ¹H NMR and accurate mass LC-quadrupole time-of-flight (QTOF) MS. Different analytical selectivities were found for these both profiling techniques. In fact, NMR and LCMS provided complementary data, as the metabolites detected belong to essentially different metabolic pathways. Yet, upon unsupervised multivariate analysis, both NMR and LCMS datasets revealed a clear segregation of, on the one hand, the cherry tomatoes and, on the other hand, the beef and round tomatoes. Intra-method (NMR–NMR, LCMS–LCMS) and inter-method (NMR–LCMS) correlation analyses were performed enabling the annotation of metabolites from highly correlating metabolite signals. Signals belonging to the same metabolite or to chemically related metabolites are among the highest correlations found. Inter-method correlation analysis produced highly informative and complementary information for the identification of metabolites, even in de case of low abundant NMR signals. The applied approach appears to be a promising strategy in extending the analytical capacities of these metabolomics techniques with regard to the discovery and identification of biomarkers and yet unknown metabolites. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

代谢组学技术及其在临床研究中的应用

在后基因组时代, 系统生物学研究成为人们关注的焦点。转录组学、蛋白质组学等功能基因组学研究方法可同时检测药物或其他因素影响下大量基因或蛋白质的表达变化情况, 但这些变化不能与生物学功能的变化建立直接联系。代谢组学方法则可为代谢物含量变化与生物表型变化建立直接相关性。代谢组学研究的目的是定量分析一个生物系统内所有代谢物的含量, 进行全面代谢物分析需要分析化学技术的支撑, 核磁共振和基于质谱的分析技术是代谢组学研究的两种主要技术手段。代谢组学研究可产生大量数据信息, 对这些数据进行分析离不开化学统计学的应用, 比如主成分分析、多维缩放、各种聚类分析技术以及功能差异分析等。文章综述了近年来代谢组学分析技术及数据分析技术的研究进展, 在此基础上, 对代谢组学在临床研究及临床前研究中的应用研究进展进行了综述。对疾病代谢表型图谱的研究有助于人们了解疾病发生、发展以及致死的机制; 在临床条件下, 这些代谢图谱可以作为疾病诊断、预后以及治疗的评判标准。代谢物组成的变化是毒物胁迫对机体造成的最终影响, 利用代谢组技术可以直接反映毒物对机体的影响。质谱技术、核磁共振技术的应用使得药物筛选过程可以快速完成, 并有助于实现个性化用药。此外, 利用代谢组学技术还可以进行已知酶的新活性研究, 也可以研究未知酶。     

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.     

植物应答非生物胁迫的代谢组学研究进展

代谢组学技术是研究植物代谢的理想平台, 通过现代检测分析技术对胁迫环境下植物中代谢产物进行定性和定量分析, 可以监测其随时间变化的规律。而各种组学平台包括基因组学、转录组学及代谢组学的整合, 更是一个强有力的工具箱, 将所获得的不同组学的信息联系起来, 有利于从整体研究生物系统对基因或环境变化的响应, 如可判断代谢物的变化是从哪一个层面开始发生的, 帮助人们揭开复杂的植物胁迫应答机制。该文对近期代谢组学技术及其与蛋白质组学、基因组学技术相结合探索植物应答非生物胁迫的研究进行了综述。代谢组学的应用, 拓展了对植物耐受非生物胁迫分子机制的认识, 开展更多这方面的研究, 再通过植物代谢组学、转录组学、蛋白质组学和基因组学整合, 有助于从整体水平上把握植物胁迫应答机制。     

Metabolomics and its role in understanding cellular responses in plants

A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics. This special review article is dedicated to the commemoration of the retirement of Dr. Oluf L. Gamborg after 25 years of service as Founding Managing Editor of Plant Cell Reports. RB and KN have contributed equally to this review.     

The ABRF Metabolomics Research Group 2013 Study: Investigation of Spiked Compound Differences in a Human Plasma Matrix

Metabolomics is an emerging field that involves qualitative and quantitative measurements of small molecule metabolites in a biological system. These measurements can be useful for developing biomarkers for diagnosis, prognosis, or predicting response to therapy. Currently, a wide variety of metabolomics approaches, including nontargeted and targeted profiling, are used across laboratories on a routine basis. A diverse set of analytical platforms, such as NMR, gas chromatography-mass spectrometry, Orbitrap mass spectrometry, and time-of-flight-mass spectrometry, which use various chromatographic and ionization techniques, are used for resolution, detection, identification, and quantitation of metabolites from various biological matrices. However, few attempts have been made to standardize experimental methodologies or comparative analyses across different laboratories. The Metabolomics Research Group of the Association of Biomolecular Resource Facilities organized a “round-robin” experiment type of interlaboratory study, wherein human plasma samples were spiked with different amounts of metabolite standards in 2 groups of biologic samples (A and B). The goal was a study that resembles a typical metabolomics analysis. Here, we report our efforts and discuss challenges that create bottlenecks for the field. Finally, we discuss benchmarks that could be used by laboratories to compare their methodologies.     

NMR-based metabonomics: a useful platform of oncology research

Cancer threatens human health, thus research focusing on oncology has great significance. Metabonomics is the global quantitative assessment of the dynamic metabolic response of a biological system to some exogenous or genetic pathophysiological perturbation. The metabolites are detected in tissues or fluids by various analytical methods, such as nuclear magnetic resonance (NMR) and mass spectroscopy. Metabonomics, as a tool, can provide a link between the laboratory and clinic. NMR-based metabonomics offers a useful tool to understand tumour biochemistry and may also has some potentials for tumour diagnosis and prognosis, even when some other disease processes are present. Here, we review NMR-based metabonomics principles and their applications in oncology research.     

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.     

A 1H NMR-Based Metabonomic Investigation of Time-Related Metabolic Trajectories of the Plasma,Urine and Liver Extracts of Hyperlipidemic Hamsters

The hamster has been previously found to be a suitable model to study the changes associated with diet-induced hyperlipidemia in humans. Traditionally, studies of hyperlipidemia utilize serum- or plasma-based biochemical assays and histopathological evaluation. However, unbiased metabonomic technologies have the potential to identify novel biomarkers of disease. Thus, to obtain a better understanding of the progression of hyperlipidemia and discover potential biomarkers, we have used a proton nuclear magnetic resonance spectroscopy (¹H-NMR)-based metabonomics approach to study the metabolic changes occurring in the plasma, urine and liver extracts of hamsters fed a high-fat/high-cholesterol diet. Samples were collected at different time points during the progression of hyperlipidemia, and individual proton NMR spectra were visually and statistically assessed using two multivariate analyses (MVA): principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Using the commercial software package Chenomx NMR suite, 40 endogenous metabolites in the plasma, 80 in the urine and 60 in the water-soluble fraction of liver extracts were quantified. NMR analysis of all samples showed a time-dependent transition from a physiological to a pathophysiological state during the progression of hyperlipidemia. Analysis of the identified biomarkers of hyperlipidemia suggests that significant perturbations of lipid and amino acid metabolism, as well as inflammation, oxidative stress and changes in gut microbiota metabolites, occurred following cholesterol overloading. The results of this study substantially broaden the metabonomic coverage of hyperlipidemia, enhance our understanding of the mechanism of hyperlipidemia and demonstrate the effectiveness of the NMR-based metabonomics approach to study a complex disease.     

Bladder cancer determination via two urinary metabolites: a biomarker pattern approach

The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for low-grade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer patients, especially low-grade BC patients, from healthy controls.     

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA)

Background  

The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".     

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.     

Bibliometric analysis of personalized humanized mouse and Drosophila models for effective combinational therapy in cancer patients

Research performed using model organisms such as mice and the fruit fly, Drosophila melanogaster has significantly enhanced our knowledge about cancer biology and the fundamental processes of cancer. This is because the major biological properties and genes associated with cancer including signaling pathways, oncogenes, tumor suppressors, and other regulators of cell growth and proliferation are evolutionary conserved. This review provides bibliometric analysis of research productivity, and performance of authors, institutions, countries, and journals associated with personalized animal cancer models, focussing on the role of Drosophila in cancer research, thus highlighting emerging trends in the field. A total of 1469 and 2672 original articles and reviews for Drosophila cancer model and patient-derived xenograft (PDX) respectively, were retrieved from the Scopus database and the most cited papers were thoroughly analyzed. Our analysis indicates a steadily increasing productivity of the animal models and especially of mouse models in cancer research. In addition to the many different systems that address almost all aspects of tumor research in humanized animal models, a trend towards using tailored screening platforms with Drosophila models in particular will become widespread in the future. Having Drosophila models that recapitulate major genetic aspects of a given tumor will enable the development and validation of novel therapeutic strategies for specific cancers, and provide a platform for screening small molecule inhibitors and other anti-tumor compounds. The combination of Drosophila cancer models and mouse PDX models particularly is highly promising and should be one of the major research strategies the future.     

NFκB signaling in carcinogenesis and as a potential molecular target for cancer therapy

It has become increasingly clear that deregulation of the NFκB signaling cascade is a common underlying feature of many human ailments including cancers. The past two decades of intensive research on NFκB has identified the basic mechanisms that govern the functioning of this pathway but uncovering the details of why this pathway works differently in different cellular contexts or how it interacts with other signaling pathways remains a challenge. A thorough understanding of these processes is needed to design better and more efficient therapeutic approaches to treat complex diseases like cancer. In this review, we summarize the literature documenting the involvement of NFκB in cancer, and then focus on the approaches that are being undertaken to develop NFκB inhibitors towards treatment of human cancers.