Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P<0.05) were identified across the three lines. With GS, 70 selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167–0.198) and BLUP (0.105–0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.     

Sampling methods for NMR-based metabolomics of Staphylococcus aureus

To select an appropriate sampling method for comparison of metabolite profiles between planktonic and biofilm Staphylococcus aureus using NMR techniques, we evaluated three methods: quenching-centrifugation (QC), filtration-quenching (FQ) and filtration-quenching-lyophilization (FQL). We found differences in metabolite loss, yield, reproducibility and metabolite profile. QC caused severe metabolite leakage and possible decomposition of nucleotides. FQ achieved high yields and reproducibility, although it had the disadvantages of long filtration and rinse times before quenching. FQL resulted in a loss of a few metabolites and a lower yield due to lyophilization. Although the biomarkers discovered by each method were nearly the same and seemed insensitive to technical variances, we conclude that FQ is the most appropriate sampling method because of its high yield and reproducibility.     

Genetical metabolomics of flavonoid biosynthesis in Populus: a case study

Genetical metabolomics [metabolite profiling combined with quantitative trait locus (QTL) analysis] has been proposed as a new tool to identify loci that control metabolite abundances. This concept was evaluated in a case study with the model tree Populus. Using HPLC, the peak abundances were analyzed of 15 closely related flavonoids present in apical tissues of two full-sib poplar families, Populus deltoides cv. S9-2 x P. nigra cv. Ghoy and P. deltoides cv. S9-2 x P. trichocarpa cv. V24, and correlation and QTL analysis were used to detect flux control points in flavonoid biosynthesis. Four robust metabolite quantitative trait loci (mQTL), associated with rate-limiting steps in flavonoid biosynthesis, were mapped. Each mQTL was involved in the flux control to one or two flavonoids. Based on the identities of the affected metabolites and the flavonoid pathway structure, a tentative function was assigned to three of these mQTL, and the corresponding candidate genes were mapped. The data indicate that the combination of metabolite profiling with QTL analysis is a valuable tool to identify control points in a complex metabolic pathway of closely related compounds.     

PTR-ToF-MS and data mining methods: a new tool for fruit metabolomics

Proton Transfer Reaction-Mass Spectrometry (PTR-MS) in its recently developed implementation based on a time-of-flight mass spectrometer (PTR-ToF-MS) has been evaluated as a possible tool for rapid non-destructive investigation of the volatile compounds present in the metabolome of apple cultivars and clones. Clone characterization is a cutting-edge problem in technical management and royalty application, not only for apple, aiming at unveiling real properties which differentiate the mutated individuals. We show that PTR-ToF-MS coupled with multivariate and data mining methods may successfully be employed to obtain accurate varietal and clonal apple fingerprint. In particular, we studied the VOC emission profile of five different clones belonging to three well known apple cultivars, such as ??Fuji??, ??Golden Delicious?? and ??Gala??. In all three cases it was possible to set classification models which can distinguish all cultivars and some of the clones considered in this study. Furthermore, in the case of ??Gala?? we also identified estragole and hexyl 2-methyl butanoate contributing to such clone characterization. Beside its applied relevance, no data on the volatile profiling of apple clones are available so far, our study indicates the general viability of a metabolomic approach for volatile compounds in fruit based on rapid PTR-ToF-MS fingerprinting.     

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

Introduction

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput ‘omics’ technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

Objectives

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

Methods

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC–TOF–MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

Results

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

Conclusion

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

     

Predation-dependent oviposition habitat selection by the mosquito Culiseta longiareolata: a test of competing hypotheses

We investigated the mechanism underlying oviposition habitat selection (OHS) in the mosquito Culiseta longiareolata. The putative outcome of a trade‐off between the risk of predation and detrimental density dependence, OHS in this species presents an opportunity to test two competing alternatives: (1) a polymorphic scenario, in which a fixed proportion of females constantly avoid ‘predator pools’, while the remainder oviposits at random; and (2) a monomorphic scenario, in which all females oviposit in predator pools with a certain probability. We present a conceptual framework that demonstrates how a simple experimental design – whereby predator incidence in artificial pools is alternated between 0.25 and 0.75 – can distinguish between, or refute, the two scenarios. Given the proportional use, by ovipositing females, of predator‐free pools observed under each treatment, and a bootstrap estimate of the ratio of daily oviposition rates, we find the monomorphic scenario twice as likely as the polymorphic.     

High-dimensional feature selection in competing risks modeling: A stable approach using a split-and-merge ensemble algorithm

Variable selection is critical in competing risks regression with high-dimensional data. Although penalized variable selection methods and other machine learning-based approaches have been developed, many of these methods often suffer from instability in practice. This paper proposes a novel method named Random Approximate Elastic Net (RAEN). Under the proportional subdistribution hazards model, RAEN provides a stable and generalizable solution to the large-p-small-n variable selection problem for competing risks data. Our general framework allows the proposed algorithm to be applicable to other time-to-event regression models, including competing risks quantile regression and accelerated failure time models. We show that variable selection and parameter estimation improved markedly using the new computationally intensive algorithm through extensive simulations. A user-friendly R package RAEN is developed for public use. We also apply our method to a cancer study to identify influential genes associated with the death or progression from bladder cancer.     

Habitat selection rules in breeding mallards (Anas platyrhynchos): a test of two competing hypotheses

Ideal preemption and conspecific attraction are alternative hypotheses of the habitat selection rules used by individuals. According to the former an occupied site is assumed to be preempted and therefore not available for later arriving individuals, whereas according to the latter individuals are assumed to be attracted by conspecifics to occupied sites, rather than avoiding them. We studied these competing hypotheses in breeding mallards (Anas platyrhynchos) by a cross-over experiment in 2 years, introducing birds onto lakes before migratory wild mallards arrived. If mallards use the ideal preemptive rule, breeding density of wild mallards in experimental lakes should be lower and they should be occupied less frequently than control lakes, but if mallards use the conspecific attraction rule the reverse should be true. Our results allowed us to reject the ideal preemptive rule whereas the conspecific attraction rule was to some extent supported. We discuss these findings in relation to population limitation. The results suggest that the local breeding population studied is not limited by spacing behaviour related to habitat selection. Received: 14 July 1997 / Accepted: 17 November 1997     

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting

Background

Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.

Methodology/Principal Findings

40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).

Conclusions/Significance

Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.     

NMR-based metabolomics study of canine bladder cancer

Bladder cancer is one of the leading lethal cancers worldwide. With the high risk of recurrence for bladder cancer following the initial diagnoses, lifelong monitoring of patients is necessary. The lack of adequate sensitivity and specificity of current noninvasive monitoring approaches including urine cytology, other urine tests, and imaging, underlines the importance of studies that focus on the detection of more reliable biomarkers for this cancer. The emerging area of metabolomics, which deals with the analysis of a large number of small molecules in a single step, promises immense potential for discovering metabolite markers for screening and monitoring treatment response and recurrence in patients with bladder cancer. Since naturally-occurring canine transitional cell carcinoma of the urinary bladder is very similar to human invasive bladder cancer, spontaneous canine transitional cell carcinoma has been applied as a relevant animal model of human invasive transitional cell carcinoma. In this study, we have focused on profiling the metabolites in urine from dogs with transitional cell carcinoma and healthy control dogs combining nuclear magnetic resonance spectroscopy and statistical analysis methods. ¹H NMR-based metabolite profiling analysis was shown to be an effective approach for differentiating samples from dogs with transitional cell carcinoma and healthy controls based on a partial least square-discriminant analysis of the NMR spectra. In addition, there were significant differences in the levels of six individual metabolites between samples from dogs with transitional cell carcinoma and the control group based on the Student's t-test. These metabolites were selected to build a separate partial least square‐discriminant analysis model that was then used to test the classification accuracy. The result showed good classification between transitional cell carcinoma and control groups with the area under the receiver operating characteristic curve of 0.85. The sensitivity and specificity of the model were 86% and 78%, respectively. These results suggest that urine metabolic profiling may have potential for early detection of bladder cancer and of bladder cancer recurrence following treatment, and may enhance our understanding of the mechanisms involved.     

Empirical comparison study of approximate methods for structure selection in binary graphical models

Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine, and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. In the binary case, however, exact inference is generally very slow or even intractable because of the form of the so‐called log‐partition function. In this paper, we review various approximate methods for structure selection in binary graphical models that have recently been proposed in the literature and compare them through an extensive simulation study. We also propose a modification of one existing method, that is shown to achieve good performance and to be generally very fast. We conclude with an application in which we search for associations among causes of death recorded on French death certificates.     

Normalization method for metabolomics data using optimal selection of multiple internal standards

Background  

Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.     

Quantifying sexual selection: a comparison of competing indices with mating system data from a terrestrially breeding salamander

Calculations for quantifying the potential for sexual selection remain controversial. Many indices have been promoted in the literature, but each has unique sets of advantages and disadvantages. Using marbled salamanders, I evaluated the performance of several measures by manipulating intensity of sexual selection in experimental breeding replicates of varying operational sex ratio. Theory predicts that sexual selection among males will be higher when sex ratio is male‐biased and lower when female‐biased. I used microsatellite data to assign hatchling parentage, estimate adult fitness, and calculate several indices of inequality for quantifying sexual selection. Opportunity for selection and Morisita index always conformed to theoretical expectations, which was not the case for index of resource monopolization, standardized Morisita index, or binomial skew index. Although I conclude that opportunity for selection is advantageous in sexual selection studies because of its link to formal theory, this should be tested against the null hypothesis of random variation in ambiguous cases. In the present study, although variation in both reproductive and mating success was high when quantified using opportunities for selection, it was only significantly greater than random expectations for reproductive success. This study provides further empirical support for the continued use of opportunity for selection in sexual selection studies. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 99 , 73–83.     

Case-control methods in the presence of multiple failure times and competing risks

The link between cohort and incident case-control studies has been considered by many authors. In particular, under the Cox proportional hazards model, follow-up data (implicit or explicit) can be analyzed as a case-control study by randomly selecting controls from the risk sets of each incident case, thereby obviating the necessity of working with the entire cohort when interest is primarily on exposure effects. This paper extends this linkage to competing risks and to diseases with multiple incidence or recurrence times by matching to each event (case) a sample of controls from the appropriate risk set. Illustrations are given.     

Systematic considerations for a multicomponent pharmacokinetic study of Epimedii wushanensis herba: From method establishment to pharmacokinetic marker selection

BackgroundPrenylflavonoids are major active components of Epimedii wushanensis herba (EWH). The global pharmacokinetics of prenylflavonoids are unclear, as these compounds yield multiple, often unidentified metabolites.PurposeThis study successfully elucidated the pharmacokinetic profiles of EWH extract and five EWH-derived prenylflavonoid monomers in rats.Study designThe study was a comprehensive analysis of metabolic pathways and pharmacokinetic markers.MethodsMajor plasma compounds identified after oral administration of EWH-derived prototypes or extract included: (1) prenylflavonoid prototypes, (2) deglycosylated products, and (3) glucuronide conjugates. To select appropriate EWH-derived pharmacokinetic markers, a high performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) method was established to simultaneously monitor 14 major compounds in unhydrolyzed plasma and 10 potential pharmacokinetic markers in hydrolyzed plasma.ResultsThe pharmacokinetic profiles indicated that the glucuronide conjugates of icaritin were the principle circulating metabolites and that total icaritin accounted for ∼99% of prenylflavonoid exposure after administration of EWH-derived materials to rats. To further investigate icaritin as a prospective pharmacokinetic marker, correlation analysis was performed between total icaritin and its glucuronide conjugates, and a strong correlation (r > 0.5) was found, indicating that total icaritin content accurately reflected changes in the exposure levels of the glucuronide conjugates over time. Therefore, icaritin is a sufficient pharmacokinetic marker for evaluating dynamic prenylflavonoid exposure levels. Next, a mathematical model was developed based on the prenylflavonoid content of EWH and the exposure levels in rats, using icaritin as the pharmacokinetic marker. This model accurately predicted exposure levels in vivo, with similar predicted vs. experimental area under the curve (AUC)_0–96 h values for total icaritin (24.1 vs. 32.0 mg/L h).ConclusionIcaritin in hydrolyzed plasma can be used as a pharmacokinetic marker to reflect prenylflavonoid exposure levels, as well as the changes over time of its glucuronide conjugates.     

Application of metabolomics to understanding biofilms in water distribution systems: a pilot study

Biofilms formed in pipes are known to contribute to waterborne diseases, accelerate corrosion and cause aesthetic taste and odour issues within the potable water supply network. This paper describes a pilot study, undertaken to assess the potential of using metabolomics to monitor bacterial activity in biofilms of an urban water network. Using samples from a water mains flushing programme, it was found that a profile of intracellular and extracellular metabolites associated with microbial activity could be obtained by analysing samples using gas chromatography mass spectrometry. Chemometric analysis of the chromatograms in conjunction with data from the mass spectrometer showed that it is possible to differentiate between biofilms from different pipe materials and planktonic bacteria. This research demonstrates that metabolomics has the potential for investigating biofilms and other microbial activity within water networks, and could provide a means for enhancing monitoring programmes, understanding the source of water quality complaints, and optimising water network management strategies.