A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis

Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches.     

A new strategy of exploring metabolomics data using Monte Carlo tree

Large amounts of data from high-throughput metabolomics experiments have become commonly more and more complex, which brings a number of challenges to existing statistical modeling. Thus there is a need to develop a statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In this work, we provide a new strategy based on Monte Carlo cross validation coupled with the classification tree algorithm, which is termed as the MCTree approach. The MCTree approach inherently provides a feasible way to uncover the predictive structure of metabolomics data by the establishment of many cross-predictive models. With the help of the sample proximity matrix such obtained, it seems to be able to give some interesting insights into metabolomics data. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by means of variable importance ranking in the MCTree approach. Two real metabolomics datasets are finally used to demonstrate the performance of the proposed approach.     

Recent advances in metabolomics in neurological disease, and future perspectives

Discovery of clinically relevant biomarkers for diseases has revealed metabolomics has potential advantages that classical diagnostic approaches do not. The great asset of metabolomics is that it enables assessment of global metabolic profiles of biofluids and discovery of biomarkers distinguishing disease status, with the possibility of enhancing clinical diagnostics. Most current clinical chemistry tests rely on old technology, and are neither sensitive nor specific for a particular disease. Clinical diagnosis of major neurological disorders, for example Alzheimer’s disease and Parkinson’s disease, on the basis of current clinical criteria is unsatisfactory. Emerging metabolomics is a powerful technique for discovering novel biomarkers and biochemical pathways to improve diagnosis, and for determination of prognosis and therapy. Identifying multiple novel biomarkers for neurological diseases has been greatly enhanced with recent advances in metabolomics that are more accurate than routine clinical practice. Cerebrospinal fluid (CSF), which is known to be a rich source of small-molecule biomarkers for neurological and neurodegenerative diseases, and is in close contact with diseased areas in neurological disorders, could potentially be used for disease diagnosis. Metabolomics will drive CSF analysis, facilitate and improve the development of disease treatment, and result in great benefits to public health in the long-term. This review covers different aspects of CSF metabolomics and discusses their significance in the postgenomic era, emphasizing the potential importance of endogenous small-molecule metabolites in this emerging field.     

An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Pseudotargeted metabolomics is a novel strategy integrating the advantages of both untargeted and targeted methods. The conventional pseudotargeted metabolomics required two MS instruments, i.e., ultra-high performance liquid chromatography/quadrupole-time- of-flight mass spectrometry (UHPLC/Q-TOF MS) and UHPLC/triple quadrupole mass spectrometry (UHPLC/QQQ-MS), which makes method transformation inevitable. Furthermore, the picking of ion pairs from thousands of candidates and the swapping of the data between two instruments are the most labor-intensive steps, which greatly limit its application in metabolomic analysis. In the present study, we proposed an improved pseudotargeted metabolomics method that could be achieved on an UHPLC/Q-TOF/MS instrument operated in the multiple ion monitoring (MIM) mode with time-staggered ion lists (tsMIM). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment and ion fusion, a stepwise ion picking procedure was used to generate the ion lists for subsequent single MIM and tsMIM. The UHPLC/Q-TOF tsMIM MS-based pseudotargeted approach exhibited better repeatability and a wider linear range than the UHPLC/Q-TOF MS-based untargeted metabolomics method. Compared to the single MIM mode, the tsMIM significantly increased the coverage of the metabolites detected. The newly developed method was successfully applied to discover plasma biomarkers for alcohol-induced liver injury in mice, which indicated its practicability and great potential in future metabolomics studies.     

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).     

MS-IAS:集成的质谱代谢组学数据分析系统

针对代谢组学研究中的数据处理问题,本研究建立了基于质谱的数据分析系统MS-IAS(Mass spectrometry based integrated analysis system).此系统集成了特征选择、聚类、分类等多种方法,用以处理质谱数据,具有多种统计分析方法能对所选的特征变量进行比较,以发现与所研究问题相关的潜在生物标志物.MS-IAS支持数据与多种算法结果可图形化显示,有助于对数据的解释与分析.以肝病患者的质谱代谢组数据为例,展示MS-IAS的功能,两种特征选择算法从数据集中筛选出了40个对肝病具有区分能力的特征变量,展示了MS-IAS成为代谢组学研究中的通用质谱数据分析系统的潜力.     

慢性心功能衰竭患者尿液的代谢组学研究

采用基于液相色谱-质谱联用的方法对慢性心力衰竭(Chronic heart failure, CHF)患者和正常对照(Control)人群的尿液进行分析, 筛选慢性心力衰竭患者尿液中的差异代谢物, 研究其发病机制, 并为临床治疗提供科学依据.选择15个慢性心力衰竭患者(年龄(62.27±3.14)岁)及15个正常人(年龄(65.41±4.63)岁), 采用高分辨度快速液相色谱-四极杆-飞行时间串联质谱(RRLC-QTOF/MS)技术对尿液代谢物进行分析, 采用主成分分析(PCA)对两组代谢物进行分类, 并筛选潜在生物标记物;运用偏最小二乘判别分析法(PLS-DA)建模, 考察生物标记物对疾病筛选的预测能力.研究结果表明, CHF组和Control组尿液代谢物谱能得到很好的区分, 发现并鉴定了2种潜在生物标记物尿苷及丙氨酰色氨酸, 提示嘧啶代谢和色氨酸代谢可能在心力衰竭发生发展中有重要作用.     

Metabolic fingerprinting of Schistosoma mansoni infection in mice urine with capillary electrophoresis

Schistosoma mansoni infection in mice has been fingerprinted using CE to study the capabilities of this technique as a diagnostic tool for this parasitic disease. Two modes of separation were used in generating the electrophoretic data, with each untreated urine sample the following methods were applied: (i) a fused-silica capillary, operating with an applied potential of 18 kV, in micellar EKC (MEKC) and (ii) a polyacrylamide-coated capillary, operating with an applied potential of -20 kV under zonal CZE conditions. By combining normal and reverse polarities in the data treatment we have extracted more information from the samples, which is a better approach for CE metabolomics. The traditional problems associated with variability in electrophoretic peak migration times for analytes were countered by using a dynamic programming algorithm for the electropherograms alignment. Principal component analyses of these aligned electropherograms and partial least square discriminant analysis (PLS-DA) data are shown to provide a valuable means of rapid and sample classification. This approach may become an important tool for the identification of biomarkers, diagnosis and disease surveillance.     

Metabolomics: a revolution for novel cancer marker identification

The repertoire of small-molecular-weight substances present in cells, tissue and body fluids are known as the metabolites. The global analysis of metabolites, such as by high-resolution 1H nuclear magnetic resonance spectroscopy and mass spectrometry, is integral to the rapidly expanding field of metabolomics, which is making progress in various diseases. In the area of cancer and metabolic phenotype, the integrated analysis of metabolites may provide a powerful platform for detecting changes related to cancer diagnosis and discovering novel biomarkers. In this review, metabolomics including the technologies in metabolomics research and extracting information from metabolomics datasets are described. Then we discuss the challenges and opportunities in metabolomics for finding metabolic processes in cancer and discovering novel cancer biomarkers. Finally, we assess the clinical applicability of metabolomics.     

A Hydrogel Microneedle Assay Combined with Nucleic Acid Probes for On-Site Detection of Small Molecules and Proteins

Point-of-care testing (POCT) of clinical biomarkers is critical to health monitoring and timely treatment, yet biosensing assays capable of detecting biomarkers without the need for costly external equipment and reagents are limited. Blood-based assays are, specifically, challenging as blood collection is invasive and follow-upprocessing is required. Here, we report a versatile assay that employs hydrogel microneedles (HMNs) to extract interstitial fluid (ISF), in a minimally invasive manner integrated with graphene oxide-nucleic acid (GO.NA)-based fluorescence biosensor to sense the biomarkers of interest in situ. The HMN-GO.NA assay is supplemented with a portable detector, enabling a complete POCT procedure. Our system could successfully measure four clinically important biomarkers (glucose, uric acid (UA), insulin, and serotonin) ex vivo, in addition, to accurately detecting glucose and UA in vivo.     

Serum metabolomics study of patients with allergic rhinitis

Allergic rhinitis (AR) negatively affects the healthy lives of many individuals. Most previous studies on AR focused on the expression of cytokines, with only a few analyzing cytokine expression from a metabolomics viewpoint. Therefore, it is worthwhile to study AR at the metabolic level. Consequently, we aimed to identify differential serum biomarkers by metabolomics. In this study, the orthogonal partial least squares discriminant analysis (OPLS-DA) model was applied to characterize the differences in serum samples collected from patients with AR and healthy volunteers. Ten metabolites (except hexadecanoic acid) were found to be altered significantly (p < .05) in the former group, according to results of principal component analysis and OPLS-DA, indicating that these metabolites could be potential biomarkers. MetaboAnalyst 4.0 and pathway enrichment analysis showed that these changes in metabolites mainly involved three pathways, namely, porphyrin and chlorophyll metabolism, arachidonic acid metabolism, and purine metabolism. Our findings may contribute to a better understanding of the potential pathogenesis mechanisms and provide a metabolic evidence for in-depth studies of AR.     

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.     

核磁共振代谢组学数据的尺度归一化新方法

在核磁共振代谢组学数据预处理中,尺度归一化主要目的是提高特征代谢物信息的权重,减小噪声及无关代谢物信息的影响,从而降低后续模式识别分析的难度. 本文提出一种新的尺度归一化方法,该方法不强调各变量在尺度上的归一,而是在原始数据的基础上,通过提高那些稳定性高、且在不同类别样本中具有显著差异性的变量的权重,以增强与特征代谢物相关的信息. 文中分别采用模拟数据和真实代谢组学数据对新归一化方法的性能进行评估,并与单位方差法(Unit Variance)、变量稳定性(Variable Stability)和尺度缩放法(Level Scaling)等常用的尺度归一化方法做比较. 研究结果表明：新归一化方法能够提高多变量统计模型的预测能力,较好地保留核磁共振谱的分子信息,有助于特征代谢物的识别,并使后续的数据分析结果具有更好的可解释性.     

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 different liquid chromatography stationary phases in LC-HRMS metabolomics for the detection of recombinant growth hormone doping control

Growth hormone (GH) is a polypeptide suspected of being used in horse racing to speed up physical performances. Despite scientific advances in the recent years, the control of its administration remains difficult. In order to improve it, a metabolomics study through LC-high resolution mass spectrometry measurements was recently initiated to assess the metabolic perturbations caused by recombinant equine growth hormone administration. Few tens of ions not identified structurally were highlighted as compounds responsible for the modification of metabolic profiling observed in treated animals. This previous work was based on the use of Uptisphere Strategy NEC as the chromatographic column. In parallel, more and more metabolomics studies showed the interest of the use of new chromatographic supports such as hydrophilic interaction chromatography for the analysis of polar compounds. It is in this context that an investigation was conducted on Uptisphere HDO and Luna hydrophilic interaction chromatography stationary phases to generate and process urinary metabolomics fingerprints, which could allow to establish a comparison with Uptisphere Strategy NEC. The chromatographic column the most adapted for the detection of new biomarkers of GH administration has been used to set up a relevant statistical model based on the analysis of more than hundred biological samples.     

(Xeno)metabolomics for the evaluation of aquatic organism’s exposure to field contaminated water

Environmental (xeno)metabolomics offers a major advantage compared to other approaches for the evaluation of aquatic organism’s exposure to contaminated water because its allows the simultaneous profiling of the xenometabolome (chemical xenobiotics and their metabolites accumulated in an organism exposed to environmental contaminants) and the metabolome (endogenous metabolites whose levels are altered due to an external stressor). This approach has been widely explored in lab exposure experiments, however in field studies environmental (xeno)metabolomics has only started in the last years. In this review, the papers published so far that have performed different (xeno)metabolomics approaches for the evaluation of aquatic organisms exposed to contaminated water are presented, together with their main achievements, current limitations, and future perspectives. The different analytical methods applied including sample pre-treatment (considering matrix type), platforms used (Nuclear Magnetic Resonance (NMR) and low- or high-resolution Mass Spectrometry (MS or HRMS)), and the analytical strategy (target vs non-target analysis) are discussed. The application of (xeno)metabolomics to provide information of xenobiotics mixtures accumulated in exposed organisms, either in lab or field studies, as well as biomarkers of exposure and biomarkers of effect are debated, and finally, the most commonly metabolic pathways disrupted by chemical contamination are highlighted.     

Bioanalytical methods for metabolomic profiling: Detection of head and neck cancer,including oral cancer

Metabolomics is an emerging field dealing with the measurement and interpretation of small molecular byproducts of biochemical processes, or metabolites, which can be used to generate profiles from biological samples. Promising for use in pathophysiology, metabolomic profiles give the immediate biological state of a sample. These profiles are altered in diseases and are detectable in biological samples, such as tissue, blood, urine, saliva, and others. Most remarkably, metabolic profiles usually are altered before symptoms appear in a patient. For this reason, metabolomics has potential as a reliable method for an early diagnosis of diseases through disease biomarker identification. This application is most prevalent in cancer, such as head and neck cancer (HNC). Metabolomic studies offer avenues to improve on current medical techniques through the application of mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and statistical analysis to determine better biomarkers than those currently known. In this review, we discuss the use of MS and NMR tools for detecting biomarkers in tissue and fluid samples, and the appropriateness of metabolomics in analyzing cancer. Advantages, disadvantages, and recent studies on metabolomic profiling techniques in HNC analysis are also discussed herein.     

基于液相色谱-质谱联用的代谢组学研究中代谢物的结构鉴定进展

基于液相色谱-质谱联用的代谢组学技术因其高效分离能力和高灵敏检测能力已成为生命科学研究的重要手段,但由于缺乏有效的通用标准谱图库,检测到的大量代谢物的结构难以鉴定。这制约了代谢组学覆盖度的提高和生物标志物的发现,造成化学和生物信息的严重丢失,成为代谢组学发展的主要技术瓶颈。随着质谱仪器及计算机技术的进步,基于大气压电离质谱（API-MS）的代谢物结构鉴定技术飞速发展,本文从质谱仪器、代谢物分子结构式判别、数据库及谱图检索以及计算机辅助谱图解析等方面,对代谢物结构鉴定的最新进展进行了综述。     

Synergistic therapeutic effects of Duzhong Jiangya Tablets and amlodipine besylate combination in spontaneously hypertensive rats using 1H-NMR- and MS-based metabolomics

Duzhong Jiangya Tablet (DJT) composed of Eucommia ulmoides Oliv. and several other traditional Chinese medicines is a Chinese herbal compound, which is clinically used to treat hypertension. The aim of this study was to evaluate the antihypertensive effect of DJT and amlodipine besylate (AB) on the synergistic treatment of spontaneously hypertensive rats (SHRs), and to explore its antihypertensive mechanism. The synergistic therapeutic effect of DJT in combination with AB on SHR was studied using two metabolomics methods based on mass spectrum (MS) and nuclear magnetic resonance. Metabolomics analysis of plasma, urine, liver, and kidney and the combination of orthogonal partial least squares discriminant analysis was performed to expose potential biomarkers. Then, the overall metabolic characteristics and related abnormal metabolic pathways in hypertensive rats were constructed. Blood pressure measurements showed that DJT combined with AB has better effects in treating hypertension than it being alone. A total of 30 biomarkers were identified, indicating that hypertension disrupted the balance of multiple metabolic pathways in the body, and that combined administration restored metabolite levels better than their administration alone. The changes of biomarkers revealed the synergistic therapeutic mechanism of DJT combined with AB, which provided a reference for the combination of Chinese and Western medicines.     

Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery

Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single “super”-list reflective of the overall preference or importance within the population. This “super”-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable.