MarVis: a tool for clustering and visualization of metabolic biomarkers

Background  

A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.     

Effects of heat-treatment on the stability and composition of metabolomic extracts from the earthworm <Emphasis Type="Italic">Eisenia fetida</Emphasis>

Environmental metabolomics studies employing earthworms as sentinels for soil contamination are numerous, but the instability of the metabolite extracts from these organisms has been minimally addressed. This study evaluated the efficacy of adding a heat-treatment step in two commonly used extraction protocols (Bligh and Dyer and D₂O phosphate buffer) as a pre-analytical stabilization method. The resulting metabolic profiles of Eisenia fetida were assessed using principal component analysis and NMR spectral evaluations. The heated Bligh and Dyer extractions produced stabilized profiles with minimal variation of the extracted metabolomic profiles over time, providing a more suitable method for metabolomic analysis of earthworm extracts.     

Metabolomic strategies for the identification of new enzyme functions and metabolic pathways

Recent technological advances in accurate mass spectrometry and data analysis have revolutionized metabolomics experimentation. Activity-based and global metabolomic profiling methods allow simultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes, making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. By using the metabolome of the relevant organism or close species, these methods capitalize on biological relevance, avoiding the assignment of artificial and non-physiological functions. This review discusses state-of-the-art metabolomic approaches and highlights recent examples of their use for enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolic activities across diverse biological sources.     

Effects of age,sex, and genotype on high‐sensitivity metabolomic profiles in the fruit fly,Drosophila melanogaster

Researchers have used whole‐genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high‐sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one‐quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high‐sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females.     

Reliable profile detection in comparative metabolomics

A strategy for processing of metabolomic GC/MS data is presented. By considering the relationship between quantity and quality of detected profiles, representative data suitable for multiple sample comparisons and metabolite identification was generated. Design of experiments (DOE) and multivariate analysis was used to relate the changes in settings of the hierarchical multivariate curve resolution (H-MCR) method to quantitative and qualitative characteristics of the output data. These characteristics included number of resolved profiles, chromatographic quality in terms of reproducibility between analytical replicates, and spectral quality defined by purity and number of spectra containing structural information. The strategy was exemplified in two datasets: one containing 119 common metabolites, 18 of which were varied according to a DOE protocol; and one consisting of rat urine samples from control rats and rats exposed to a liver toxin. It was shown that the performance of the data processing could be optimized to produce metabolite data of high quality that allowed reliable sample comparisons and metabolite identification. This is a general approach applicable to any type of data processing where the important processing parameters are known and relevant output data characteristics can be defined. The results imply that this type of data quality optimization should be carried out as an integral step of data processing to ensure high quality data for further modeling and biological evaluation. Within metabolomics, this degree of optimization will be of high importance to generate models and extract biomarkers or biomarker patterns of biological or clinical relevance.     

Recommended strategies for spectral processing and post-processing of 1D <Superscript>1</Superscript>H-NMR data of biofluids with a particular focus on urine

¹H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.     

A genomics and multi-platform metabolomics approach to identify new traits of rice quality in traditional and improved varieties

Using a novel approach combining four complementary metabolomic and mineral platforms with genome-wide genotyping at 1536 single nucleotide polymorphism (SNP) loci, we have investigated the extent of biochemical and genetic diversity in three commercially-relevant waxy rice cultivars important to food production in the Lao People??s Democratic Republic (PDR). Following cultivation with different nitrogen fertiliser regimes, multiple metabolomic data sets, including minerals, were produced and analysed using multivariate statistical methods to reveal the degree of similarity between the genotypes and to identify discriminatory compounds supported by multiple technology platforms. Results revealed little effect of nitrogen supply on metabolites related to quality, despite known yield differences. All platforms revealed unique metabolic signatures for each variety and many discriminatory compounds could be identified as being relevant to consumers in terms of nutritional value and taste or flavour. For each platform, metabolomic diversity was highly associated with genetic distance between the varieties. This study demonstrates that multiple metabolomic platforms have potential as phenotyping tools to assist breeders in their quest to combine key yield and quality characteristics. This better enables rice improvement programs to meet different consumer and farmer needs, and to address food security in rice-consuming countries.     

Use of the Local False Discovery Rate for Identification of Metabolic Biomarkers in Rat Urine Following Genkwa Flos-Induced Hepatotoxicity

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.     

Validation of Metabolic Alterations in Microscale Cell Culture Lysates Using Hydrophilic Interaction Liquid Chromatography (HILIC)-Tandem Mass Spectrometry-Based Metabolomics

By standard convention, in order to increase the efficacy of metabolite detection from cell culture lysates, metabolite extracts from a large quantity of cells are utilized for multiple reaction monitoring-based metabolomic studies. Metabolomics from a small number of cell extracts offers a potential economical alternative to increased cell numbers, in turn increasing the utility of cell culture-based metabolomics. However, the effect of reduced cell numbers on targeted metabolomic profiling is relatively unstudied. Considering the limited knowledge available of the feasibility and accuracy of microscale cell culture metabolomics, the present study analyzes differences in metabolomic profiles of different cell numbers of three pancreatic cancer cell lines. Specifically, it examines the effects of reduced cell numbers on metabolite profiles by obtaining extracts either directly from microscale culture plates or through serial dilution of increased numbers of cellular metabolite extracts. Our results indicate reduced cell numbers only modestly affect the number of metabolites detected (93% of metabolites detected in cell numbers as low as 10⁴ cells and 97% for 10⁵ cells), independent of the method used to obtain the cells. However, metabolite peak intensities were differentially affected by the reduced cell numbers, with some peak intensities inversely proportional to the cell numbers. To help eliminate such potential inverse relationships, peak intensities for increased cell numbers were excluded from the comparative analysis. Overall, metabolite profiles from microscale culture plates were observed to differ from the serial dilution samples, which may be attributable to the medium-to-cell-number ratios. Finally, findings identify perturbations in metabolomic profiling for cellular extracts from reduced cell numbers, which offer future applications in microscale metabolomic evaluations.     

Prediction of Glycated Hemoglobin Levels at 3 Months after Metabolic Surgery Based on the 7-Day Plasma Metabolic Profile

Metabolic surgery has been shown to provide better glycemic control for type 2 diabetes than conventional therapies. Still, the outcomes of the surgery are variable, and prognostic markers reflecting the metabolic changes by the surgery are yet to be established. NMR-based plasma metabolomics followed by multivariate regression was used to test the correlation between the metabolomic profile at 7-days after surgery and glycated hemoglobin (HbA1c) levels at 3-months (and up to 12 months with less patients), and to identify the relevant markers. Metabolomic profiles at 7-days could differentiate the patients according to the HbA1c improvement status at 3-months. The HbA1c values were predicted based on the metabolomics profile with partial least square regression, and found to be correlated with the observed values. Metabolite analysis suggested that 3-Hydroxybutyrate (3-HB) and glucose contributes to this prediction, and the [3-HB]/[glucose] exhibited a modest to good correlation with the HbA1c level at 3-months. The prediction of 3-month HbA1c using 7-day metabolomic profile and the suggested new criterion [3-HB]/[glucose] could augment current prognostic modalities and help clinicians decide if drug therapy is necessary.     

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.     

Improving human embryos selection in IVF: non-invasive metabolomic and chemometric approach

We present here a new metabolomic methodology to predict embryo implantation ability in in vitro fertilization (IVF). In the present study we have included a total of 23 patients scheduled for IVF. Embryos were selected to be transferred by using morphological criteria on day 3 of in vitro culture. The relative amino acid concentrations in the embryo culture media were analyzed by HPLC–MS and HPLC–MS/MS. ¹H NMR metabolomic profiles were also obtained for the embryo culture media. Chemometric models were performed with SIMCA (soft independent modeling of class analogy) for samples from both, non-pregnancy and pregnancy cycles. The metabolic differences between the embryos, with pregnancy and non-pregnancy outcome, can be correlated with the relative amino acid concentrations and with ¹H NMR profiles. We used interval partial least square (iPLS) in order to identify the higher correlation between regions in the ¹H NMR spectra and the embryo implantation capability. The ¹H NMR regions with higher correlation are between 1.2 and 0.5 ppm, that included the signals for cholesterol backbone –C(18)H₃, –CH₃ and CH₂ groups of triglycerides, cholesterol compounds and phospholipids. Our results can allow building a quick, non invasive, useful and feasible chemometric models in order to identify embryos with a high pregnancy rate and embryos unable to achieve successful pregnancies.     

Metabolomic assays of the concentration and mass isotopomer distribution of gluconeogenic and citric acid cycle intermediates

We developed gas chromatography-mass spectrometry assays for the relative concentration and for the mass isotopomer distribution of gluconeogenic and citric acid cycle intermediates in tissues. The assay involves (i) spiking the sample with one or more internal standards, (ii) chloroform–methanol extraction at −25 °C, (iii) Folch wash of the extract, (iv) treatment of the water-methanol phase with methoxylamine, (v) evaporation and trimethylsilyl derivatization, and (vi) ammonia positive chemical ionization gas chromatography-mass spectrometry. For metabolomic computations, indices of concentrations for all compounds assayed are calculated as (Area of analyte)/(Area of reference compound). The assay was applied to a study of the effect of mercaptopicolinate, an inhibitor of phosphoenolpyruvate carboxykinase, on the profile of gluconeogenic intermediates in rat livers perfused with pyruvate. Crossover analysis of concentrations indices, compared to a control group, yielded very similar profiles as previous enzymatic assays, and correctly identified the site of action of mercaptopicolinate. Principal component analysis distinguished between control and drug treated samples. A loadings plot was used to identify the site of action of the drug in the metabolic pathway. Since metabolite concentrations do not address the flux through a pathway, perfusions with [1,4-¹³C₂] succinate dimethylester were conducted to assess fluxes around PEPCK. This allowed a dynamic metabolomics analysis which indicated that considerable flux through the pathway remained in the presence of mercaptopicolinate. This study illustrates the power of dynamic metabolomics to complement concentration based metabolomic studies.

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Metabolomic profile in hyperthyroid patients before and after antithyroid drug treatment: Correlation with thyroid hormone and TSH concentration

Hyperthyroidism (HT) is characterized by an intense metabolic impact which affects the lipid, carbohydrate and amino acids metabolism, with increased resting energy expenditure and thermogenesis. Metabolomics is a new comprehensive technique that allows to capture an instant metabolic picture of an organism, reflecting peculiar molecular and pathophysiological states. The aim of the present prospective study was to identify a distinct metabolomic profile in HT patients using ¹H NMR spectroscopy before and after antithyroid drug treatment. This prospective study included 15 patients (10 female, 5 male) who were newly diagnosed hyperthyroidism. A nuclear magnetic resonance (¹H NMR) based analysis was performed on plasma samples from the same patients at diagnosis (HypT₀) and when they achieved euthyroidism (HypT₁). The case groups were compared with a control group of 26 healthy volunteers (C). Multivariate statistical analysis was performed with Partial Least Squares-Discriminant Analysis (PLS-DA). PLS-DA identified a distinct metabolic profile between C and untreated hyperthyroid patients (R²X 0.638, R²Y 0.932, Q² 0.783). Interestingly, a significant difference was also found between C and euthyroid patients after treatment (R²X 0.510, R²Y 0.838, Q² 0.607), while similar cluster emerged comparing HypT₀ vs HypT₁ patients. This study shows that metabolomic profile is deeply influenced by hyperthyroidism and this alteration persists after normalization of thyrotropin (TSH) and free thyroid hormone (FT3, FT4) concentration. This suggests that TSH, FT3 and FT4 assays may not be insufficient to detect long lasting peripheral effects of the thyroid hormones action. Further studies are needed to clarify whether and to what extent the evaluation of metabolomics profile may provide relevant information in the clinical management of hyperthyroidism.     

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Introduction

Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis.

Objectives

The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles.

Methods

Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates.

Results

Baroni-Urbani–Buser (BUB) and Hawkins–Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis.

Conclusion

Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.

     

Application of 1H-NMR Metabolomic Profiling for Reef-Building Corals

In light of global reef decline new methods to accurately, cheaply, and quickly evaluate coral metabolic states are needed to assess reef health. Metabolomic profiling can describe the response of individuals to disturbance (i.e., shifts in environmental conditions) across biological models and is a powerful approach for characterizing and comparing coral metabolism. For the first time, we assess the utility of a proton-nuclear magnetic resonance spectroscopy (¹H-NMR)-based metabolomics approach in characterizing coral metabolite profiles by 1) investigating technical, intra-, and inter-sample variation, 2) evaluating the ability to recover targeted metabolite spikes, and 3) assessing the potential for this method to differentiate among coral species. Our results indicate ¹H-NMR profiling of Porites compressa corals is highly reproducible and exhibits low levels of variability within and among colonies. The spiking experiments validate the sensitivity of our methods and showcase the capacity of orthogonal partial least squares discriminate analysis (OPLS-DA) to distinguish between profiles spiked with varying metabolite concentrations (0 mM, 0.1 mM, and 10 mM). Finally, ¹H-NMR metabolomics coupled with OPLS-DA, revealed species-specific patterns in metabolite profiles among four reef-building corals (Pocillopora damicornis, Porites lobata, Montipora aequituberculata, and Seriatopora hystrix). Collectively, these data indicate that ¹H-NMR metabolomic techniques can profile reef-building coral metabolomes and have the potential to provide an integrated picture of the coral phenotype in response to environmental change.     

Dietary salecan reverts partially the metabolic gene expressions and NMR-based metabolomic profiles from high-fat-diet-induced obese rats

Previous studies suggest that dietary salecan (a water-soluble β-glucan) effectively reduces high-fat-diet-induced adiposity through disturbing bile-acid-promoted emulsification in mice. However, the effects of salecan on metabolic genes and metabolites involved in lipid accumulation are mostly unknown. Here, we confirmed that dietary 3% and 6% salecan for 4 weeks markedly decreased fat accumulation in liver and adipose tissue in high-fat-diet rats, displaying a decrease in mRNA levels of SREBP1-C, FAS, SCD1 and ACC1 involved in de novo lipogenesis and a reduction of levels of GPAT1, DGAT1 and DGAT2 related to triglyceride synthesis. Dietary salecan also increased the mRNA levels of PPARα and CYP7A1, which are related to fatty acid oxidation and cholesterol decomposition, respectively. In the ¹H nuclear magnetic resonance metabolomic analysis, both the serum and liver metabolite profiles differed among the control groups, and the metabolic profiles of the salecan groups were shifted toward that of the low-fat-diet group. Metabolites analysis showed that salecan significantly increased hepatic glutathione and betaine levels which are related to regulation of cellular reactive oxygen species. These data demonstrate that dietary salecan not only disturbed fat digestion and absorption but also influenced lipid accumulation and metabolism in diet-induced obesity.     

Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics

A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely; 1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were subjected to further statistical analysis following correction to account for the control variation over time. These dose related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach, named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods the detected metabolites were subjected to identification by means of data base comparison as well as verification in the raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations in metabolomic studies.     

Chemometric variance analysis of 1H NMR metabolomics data on the effects of oral rinse on saliva

Saliva is an easy to obtain bodily fluid that is specific to the oral environment. It can be used for metabolomic studies as it is representative of the overall wellbeing of an organism, as well as mouth health and bacterial flora. The metabolomic structure of saliva varies greatly depending on the bacteria present in the mouth as they produce a range of metabolites. In this study we have investigated the metabolomic profiles of human saliva that were obtained using ¹H NMR (nuclear magnetic resonance) analysis. 48 samples of saliva were collected from 16 healthy subjects over 3 days. Each sample was split in two and the first half treated with an oral rinse, while the second was left untreated as a control sample. The 96 ¹H NMR metabolomic profiles obtained in the dataset are affected by three factors, namely 16 subjects, 3 sampling days and 2 treatments. These three factors contribute to the total variation in the dataset. When analysing datasets from saliva using traditional methods such as PCA (principal component analysis), the overall variance is dominated by subjects’ contributions, and we cannot see trends that would highlight the effect of specific factors such as oral rinse. In order to identify these trends, we used methods such as MSCA (multilevel simultaneous component analysis) and ASCA (ANOVA simultaneous component analysis), that provide variance splits according to the experimental factors, so that we could look at the particular effect of treatment on saliva. The analysis of the treatment effect was enhanced, as it was isolated from the overall variance and assessed without confounding factors.