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.     

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.     

Metabolic analysis of the antidepressive effects of Yangxinshi Tablet in a vascular depression model in mice

In recent years, vascular depression has become the focus of international attention. Yangxinshi Tablet (YXST) is usually used in cthe linic for the treatment of arrhythmia and heart failure, but we found that it also has antidepressive effects. The objective of the study was to identify biomarkers related to vascular depression in hippocampus and explore the antidepressive effects of YXST on the mouse model. Untargeted metabolomics based on UHPLC‐Q‐TOF/MS was applied to identify significantly differential biomarkers between the model group and control group. Unsupervised principal component analysis (PCA) was used to scan the tendency of groups and partial least squares‐discriminant analysis (PLS‐DA) to distinguish between the vascular depressive mice and the sham. PCA stores showed clear differences in metabolism between the vascular depressive mice and sham groups. The PLS‐DA model exhibited 38 metabolites as the biomarkers to distinguish the vascular depressive mice and the sham. Further, YXST significantly regulated 22 metabolites to normal levels. The results suggested that YXST has a comprehensive antidepressive effect on vascular depression via regulation of multiple metabolic pathways including amino acid, the tricarboxylic acid cycle and phosphoglyceride metabolisms. These findings provide insight into the pathophysiological mechanism underlying vascular depression and the mechanism of YXST.     

Powered partial least squares discriminant analysis

From the fundamental parts of PLS‐DA, Fisher's canonical discriminant analysis (FCDA) and Powered PLS (PPLS), we develop the concept of powered PLS for classification problems (PPLS‐DA). By taking advantage of a sequence of data reducing linear transformations (consistent with the computation of ordinary PLS‐DA components), PPLS‐DA computes each component from the transformed data by maximization of a parameterized Rayleigh quotient associated with FCDA. Models found by the powered PLS methodology can contribute to reveal the relevance of particular predictors and often requires fewer and simpler components than their ordinary PLS counterparts. From the possibility of imposing restrictions on the powers available for optimization we obtain an explorative approach to predictive modeling not available to the traditional PLS methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.     

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.     

Metabolomics‐based approach for ranking the candidate structures of unidentified peaks in capillary electrophoresis time‐of‐flight mass spectrometry

One of the technical challenges encountered during metabolomics research is determining the chemical structures of unidentified peaks. We have developed a metabolomics‐based chemoinformatics approach for ranking the candidate structures of unidentified peaks. Our approach uses information about the known metabolites detected in samples containing unidentified peaks and involves three discrete steps. The first step involves identifying “precursor/product metabolites” as potential reactants or products derived from the unidentified peaks. In the second step, candidate structures for the unidentified peak are searched against the PubChem database using a molecular formula. These structures are then ranked by structural similarity against precursor/product metabolites and candidate structures. In the third step, the migration time is predicted to refine the candidate structures. Two simulation studies were conducted to highlight the efficacy of our approach, including the use of 20 proteinogenic amino acids as pseudo‐unidentified peaks, and leave‐one‐out experiments for all of the annotated metabolites with and without filtering against the Human Metabolome Database. We also applied our approach to two unidentified peaks in a urine sample, which were identified as glycocyamidine and N ‐acetylglycine. These results suggest that our approach could be used to identify unidentified peaks during metabolomics analysis.     

CRAFT (complete reduction to amplitude frequency table) – robust and time‐efficient Bayesian approach for quantitative mixture analysis by NMR

The intrinsic quantitative nature of NMR is increasingly exploited in areas ranging from complex mixture analysis (as in metabolomics and reaction monitoring) to quality assurance/control. Complex NMR spectra are more common than not, and therefore, extraction of quantitative information generally involves significant prior knowledge and/or operator interaction to characterize resonances of interest. Moreover, in most NMR‐based metabolomic experiments, the signals from metabolites are normally present as a mixture of overlapping resonances, making quantification difficult. Time‐domain Bayesian approaches have been reported to be better than conventional frequency‐domain analysis at identifying subtle changes in signal amplitude. We discuss an approach that exploits Bayesian analysis to achieve a complete reduction to amplitude frequency table (CRAFT) in an automated and time‐efficient fashion – thus converting the time‐domain FID to a frequency‐amplitude table. CRAFT uses a two‐step approach to FID analysis. First, the FID is digitally filtered and downsampled to several sub FIDs, and secondly, these sub FIDs are then modeled as sums of decaying sinusoids using the Bayesian approach. CRAFT tables can be used for further data mining of quantitative information using fingerprint chemical shifts of compounds of interest and/or statistical analysis of modulation of chemical quantity in a biological study (metabolomics) or process study (reaction monitoring) or quality assurance/control. The basic principles behind this approach as well as results to evaluate the effectiveness of this approach in mixture analysis are presented. Copyright © 2013 John Wiley & Sons, Ltd.     

CE‐MS for metabolomics: Developments and applications in the period 2016–2018

In the field of metabolomics, CE‐MS is now recognized as a strong analytical technique for the analysis of (highly) polar and charged metabolites in a wide range of biological samples. Over the past few years, significant attention has been paid to the design and improvement of CE‐MS approaches for (large‐scale) metabolic profiling studies and for establishing protocols in order to further expand the role of CE‐MS in metabolomics. In this paper, which is a follow‐up of a previous review paper covering the years 2014–2016 (Electrophoresis 2017, 38, 190–202), main advances in CE‐MS approaches for metabolomics studies are outlined covering the literature from July 2016 to June 2018. Aspects like developments in interfacing designs and data analysis tools for improving the performance of CE‐MS for metabolomics are discussed. Representative examples highlight the utility of CE‐MS in the fields of biomedical, clinical, microbial, and plant metabolomics. A complete overview of recent CE‐MS‐based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings and MS detection mode. Finally, some general conclusions and perspectives are given.     

Orthogonal signal correction‐based prediction of total antioxidant activity using partial least squares regression from chromatograms

The multivariate calibration methods—partial least squares (PLS), orthogonal signal correction and partial least squares (OSC‐PLS)—were employed for the prediction of total antioxidant activities of four Prunella L. species. High‐performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total antioxidant activity of the Prunella L. samples. Several preprocessing techniques such as smoothing and normalization were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping. The importance of the preprocessing was investigated by calculating the root mean square error for the calibration set for the total antioxidant activity of Prunella L. samples. The models developed on the basis of the preprocessed data were able to predict the total antioxidant activity with a precision comparable to that of the reference 2,2‐azino‐di‐(3‐ethylbenzothialozine‐sulfonic acid) and 2,2‐diphenyl‐1‐picrylhydrazyl methods. The OSC‐PLS model seems preferable because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total antioxidant activity. The contribution of individual phenolic compounds to the total antioxidant activity was identified by HPLC. Copyright © 2012 John Wiley & Sons, Ltd.     

Positional Enrichment by Proton Analysis (PEPA): A One‐Dimensional 1H‐NMR Approach for 13C Stable Isotope Tracer Studies in Metabolomics

A novel metabolomics approach for NMR‐based stable isotope tracer studies called PEPA is presented, and its performance validated using human cancer cells. PEPA detects the position of carbon label in isotopically enriched metabolites and quantifies fractional enrichment by indirect determination of ¹³C‐satellite peaks using 1D‐¹H‐NMR spectra. In comparison with ¹³C‐NMR, TOCSY and HSQC, PEPA improves sensitivity, accelerates the elucidation of ¹³C positions in labeled metabolites and the quantification of the percentage of stable isotope enrichment. Altogether, PEPA provides a novel framework for extending the high‐throughput of ¹H‐NMR metabolic profiling to stable isotope tracing in metabolomics, facilitating and complementing the information derived from 2D‐NMR experiments and expanding the range of isotopically enriched metabolites detected in cellular extracts.     

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.     

HILIC‐MS‐based shotgun metabolomic profiling of maternal urine at 9–23 weeks of gestation – establishing the baseline changes in the maternal metabolome

In this data‐rich age it is no longer necessary to methodically isolate, characterize and measure specific molecules. What is important is to identify which of the hundreds or thousands of resolved and measured ‘unknown’ molecules are potentially associated with the pathophysiology of interest. We have taken LC‐MS data from pregnancy urine and applied SIMCA P+ data analysis software in shotgun metabolomics to search the large amount of data for significant metabolite changes that occur in the transition from the first to early second trimester of pregnancy. Seventy‐two individual urine samples were examined spanning 9–23 weeks of gestation. Three‐hundred and eighty‐three ions were identified and variations were mapped between profiles of different gestational age and the significance quantified. In urine collected during pregnancy, the transition from first to early second trimester revealed a relatively steady pattern of metabolites except for four that showed a dramatic fall in abundance as pregnancy progressed from the first to second trimester. The pattern of changes in urinary metabolites identified by Zwitterionic Hydrophilic Liquid Interaction Chromatography (ZIC‐HILIC) coupled to mass spectrometry was evaluated and we established a baseline of changes from which a search for metabolomic markers associated with clinical pathologies of pregnancy can be made as a part of wider ultraomics study. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

From dummy regression to prior probabilities in PLS‐DA

Different published versions of partial least squares discriminant analysis (PLS‐DA) are shown as special cases of an approach exploiting prior probabilities in the estimated between groups covariance matrix used for calculation of loading weights. With prior probabilities included in the calculation of both PLS components and canonical variates, a complete strategy for extracting appropriate decision spaces with multicollinear data is obtained. This idea easily extends to weighted linear dummy regression so that the corresponding fitted values also span the canonical space. Two different choices of prior probabilities are applied with a real dataset to illustrate the effect for the obtained decision spaces. Copyright © 2007 John Wiley & Sons, Ltd.     

Metabolomics approach for determining growth-specific metabolites based on Fourier transform ion cyclotron resonance mass spectrometry

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) is the best MS technology for obtaining exact mass measurements owing to its great resolution and accuracy, and several outstanding FT-ICR/MS-based metabolomics approaches have been reported. A reliable annotation scheme is needed to deal with direct-infusion FT-ICR/MS metabolic profiling. Correlation analyses can help us not only uncover relations between the ions but also annotate the ions originated from identical metabolites (metabolite derivative ions). In the present study, we propose a procedure for metabolite annotation on direct-infusion FT-ICR/MS by taking into consideration the classification of metabolite-derived ions using correlation analyses. Integrated analysis based on information of isotope relations, fragmentation patterns by MS/MS analysis, co-occurring metabolites, and database searches (KNApSAcK and KEGG) can make it possible to annotate ions as metabolites and estimate cellular conditions based on metabolite composition. A total of 220 detected ions were classified into 174 metabolite derivative groups and 72 ions were assigned to candidate metabolites in the present work. Finally, metabolic profiling has been able to distinguish between the growth stages with the aid of PCA. The constructed model using PLS regression for OD₆₀₀ values as a function of metabolic profiles is very useful for identifying to what degree the ions contribute to the growth stages. Ten phospholipids which largely influence the constructed model are highly abundant in the cells. Our analyses reveal that global modification of those phospholipids occurs as E. coli enters the stationary phase. Thus, the integrated approach involving correlation analyses, metabolic profiling, and database searching is efficient for high-throughput metabolomics. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Phosphonium labeling for increasing metabolomic coverage of neutral lipids using electrospray ionization mass spectrometry

Mass spectrometry has become an indispensable tool for the global study of metabolites (metabolomics), primarily using electrospray ionization mass spectrometry (ESI‐MS). However, many important classes of molecules such as neutral lipids do not ionize well by ESI and go undetected. Chemical derivatization of metabolites can enhance ionization for increased sensitivity and metabolomic coverage. Here we describe the use of tris(2,4,6,‐trimethoxyphenyl)phosphonium acetic acid (TMPP‐AA) to improve liquid chromatography (LC)/ESI‐MS detection of hydroxylated metabolites (i.e. lipids) from serum extracts. Cholesterol which is not normally detected from serum using ESI is observed with attomole sensitivity. This approach was applied to identify four endogenous lipids (hexadecanoyl‐sn‐glycerol, dihydrotachysterol, octadecanol, and alpha‐tocopherol) from human serum. Overall, this approach extends the types of metabolites which can be detected using standard ESI‐MS instrumentation and demonstrates the potential for targeted metabolomics analysis. Published in 2009 by John Wiley & Sons, Ltd.     

Metabolism of TM‐2, a potential antitumor drug,in rats by using LC–MS

TM‐2 (13‐(N‐Boc‐3‐i‐butylisoserinoyl‐4,10‐β‐diacetoxy‐2‐α‐benzoyloxy‐5‐β‐20‐epoxy‐1,13‐α‐dihydroxy‐9‐oxo‐19‐norcyclopropa[g]tax‐11‐ene) is a novel semisynthetic taxane derivative. Our previous study suggested that TM‐2 is a promising antitumor analogue. In this paper, the metabolism of TM‐2 was investigated in rats following intravenous administration. Two different types of mass spectrometry—hybrid linear trap quadrupole orbitrap (LTQ‐Orbitrap) mass spectrometry and triple‐quadrupole tandem (QQQ) mass spectrometry—were employed to acquire structural information of TM‐2 metabolites. A total of 17 components were identified as the metabolites of TM‐2 in bile, feces, and urine samples. Accurate mass measurement using LC–LTQ‐Orbitrap‐MS was used to determine the accurate mass data and elemental composition of metabolites thereby confirming the proposed structures of the metabolites. The metabolites proposed were mainly oxidates of TM‐2, including methoxy, hydroxyl, dihydroxy, and trihydroxyl analogues. The major metabolic pathway of TM‐2 was the hydroxylation of the taxane ring or the lateral chain. These important metabolic data serve as a useful resource to support further research of TM‐2.