Chemometric variance analysis of <Superscript>1</Superscript>H NMR metabolomics data on the effects of oral rinse on saliva

In order to investigate exposure end effect markers of fruit and fruit fibre intake we investigated how fresh apple or apple-pectin affects the urinary metabolome of rats. Twenty-four Fisher 344 male rats were randomized into three groups and fed a standard diet with different supplementations added in two of the groups (7% apple-pectin or 10 g raw apple). After 24 days of feeding, 24 h urine was collected and analyzed by UPLC-QTOF-MS in positive and negative ionization mode. Metabolites that responded to the apple or pectin diets were selected and classified as either potential exposure or effect markers based on the magnitude and pattern of their response. An initial principal component analysis (PCA) of all detected metabolites showed a clear separation between the groups and during marker identification several new apple and/or pectin markers were found. Quinic acid, m-coumaric acid and (-)epicatechin were identified as exposure markers of apple intake whereas hippuric acid behaved as an effect marker. Pyrrole-2-carboxylic acid and 2-furoylglycine behaved as pectin exposure markers while 2-piperidinone was recognized as a potential pectin effect marker. None of them has earlier been related to intake of pectin or other fibre products. We discuss these new potential exposure and effect markers and their interpretation.     

1H NMR metabolomics analysis of glioblastoma subtypes: correlation between metabolomics and gene expression characteristics

Glioblastoma multiforme (GBM) is the most common form of malignant glioma, characterized by unpredictable clinical behaviors that suggest distinct molecular subtypes. With the tumor metabolic phenotype being one of the hallmarks of cancer, we have set upon to investigate whether GBMs show differences in their metabolic profiles. (1)H NMR analysis was performed on metabolite extracts from a selection of nine glioblastoma cell lines. Analysis was performed directly on spectral data and on relative concentrations of metabolites obtained from spectra using a multivariate regression method developed in this work. Both qualitative and quantitative sample clustering have shown that cell lines can be divided into four groups for which the most significantly different metabolites have been determined. Analysis shows that some of the major cancer metabolic markers (such as choline, lactate, and glutamine) have significantly dissimilar concentrations in different GBM groups. The obtained lists of metabolic markers for subgroups were correlated with gene expression data for the same cell lines. Metabolic analysis generally agrees with gene expression measurements, and in several cases, we have shown in detail how the metabolic results can be correlated with the analysis of gene expression. Combined gene expression and metabolomics analysis have shown differential expression of transporters of metabolic markers in these cells as well as some of the major metabolic pathways leading to accumulation of metabolites. Obtained lists of marker metabolites can be leveraged for subtype determination in glioblastomas.     

Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

Background  

Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) ¹H, projections of 2D ¹H, ¹H J-resolved (pJRES), and intact 2D J-resolved (JRES).     

Phenotyping COPD by 1H NMR metabolomics of exhaled breath condensate

Spirometry is used to establish the diagnosis of chronic obstructive pulmonary disease (COPD) and to assess disease progression, but it seems inadequate to characterize COPD phenotypes. Metabolomics has been introduced for molecular fingerprinting of biosamples in a variety of clinical disorders. The aim of the study was to establish whether exhaled breath condensate (EBC) in COPD features a distinct metabolic fingerprint, and to identify the metabolites that characterize the EBC profile in COPD. EBC was collected using a home-made glass condenser in 37 stable COPD patients, and 25 non-obstructed controls. Samples were analyzed using proton nuclear magnetic resonance spectroscopy (¹H NMR). Random forest was applied for both supervised and unsupervised learning, using spectral buckets as input variables. Metabolomics of EBC discriminated COPD patients from controls with an overall accuracy of 86 %. As compared to controls, EBC from COPD featured significantly lower (p < 0.05) levels of acetone, valine and lysine, and significantly higher (p < 0.05) levels of lactate, acetate, propionate, serine, proline, and tyrosine. Based on unsupervised analysis of NMR spectra, the COPD sample was split in three clusters, one of which had the highest prevalence of radiologic emphysema. NMR spectroscopy of EBC holds promise in COPD fingerprinting. It may prove valuable in outcome studies, and in assessing the efficacy of therapeutic interventions.     

Identifying metabolic alterations associated with coral growth anomalies using 1H NMR metabolomics

Coral growth anomalies (GAs) are tumor-like protrusions that are detrimental to coral health, affecting both the coral skeleton and soft tissues. These lesions are increasingly found throughout the tropics and are commonly associated with high human population density, yet little is known about the molecular pathology of the disease. Here, we investigate the metabolic impacts of GAs through ¹H nuclear magnetic resonance (NMR) metabolomics in Porites compressa tissues from a site of high disease prevalence (Coconut Island, Hawaii). We putatively identified 18 metabolites (8.1% of total annotated features) through complementary ¹H and ¹H–¹³C heteronuclear single quantum correlation NMR data that increase confidence in pathway analyses and may bolster future coral metabolite annotation efforts. Extract yield was elevated in both GA and unaffected (normal tissue from a diseased colony) compared to reference (normal tissue from GA-free colony) samples, potentially indicating elevated metabolic activity in GA-impacted colonies. Relatively high variation in metabolomic profiles among coral samples of the same treatment (i.e., inter-colony variation) confounded data interpretation, however, analyses of paired GA and unaffected samples identified 73 features that differed between these respective metabolome types. These features were largely annotated as unknowns, but 1-methylnicotinamide and trigonelline were found to be elevated in GA samples, while betaine, glycine, and histamine were lower in GA samples. Pathway analyses indicate decreased choline oxidation in GA samples, making this a pathway of interest for future targeted studies. Collectively, our results provide unique insights into GA pathophysiology by showing these lesions alter both the absolute and relative metabolism of affected colonies and by identifying features (metabolites and unknowns) and metabolic pathways of interest in GA pathophysiology going forward.

     

Determination of the illicit drug gamma-hydroxybutyrate (GHB) in human saliva and beverages by 1H NMR analysis

High resolution 1H NMR spectroscopy has been employed to investigate the detection and quantification of the illicit "date-rape" drug gamma-hydroxybutyrate (GHB) in both human saliva and a commonly-consumed low-alcohol beer product. Data acquired revealed that this multicomponent analytical technique provided unequivocal evidence for the detection of this agent by this technique in both of these matrices, i.e., all three of its resonances [those ascribable to the alpha-CH2 (t, delta=2.25 ppm), beta-CH2 (tt, delta=1.81 ppm) and gamma-CH2 (t, delta=3.61 ppm) group protons] were present in spectra acquired on human saliva, and two of these (the alpha- and beta-CH2 group signals) in the beverage product examined, the latter observation attributable to overlap of the gamma-CH2 1H resonance with those of carbohydrates. Since good linear calibration relationships between the intensities of each of the NMR-visible signals and added GHB concentration (the former normalised to that of an external 3-trimethylsilyl [2,2,3,3-2H4]- propionate standard present in a coaxial NMR tube insert) were observed, this illicit drug is also readily quantifiable in such multicomponent samples. Our data demonstrate the advantages offered by this technique when applied to the analysis of illicit drugs in multicomponent sample matrices such as human biofluids and beverage products.     

Strategies for data analyses in a high resolution 1H NMR based metabolomics study of a mouse model of Batten disease

Using an NMR based approach, employing both solution state and high resolution magic angle spinning (HR MAS) ¹H NMR spectroscopy, in conjunction with an array of statistical methods, we report cerebral metabolic deficits in a mouse model of Batten disease (Cln3 null mutant mice). Batten disease is the most common progressive neurodegenerative disorder of childhood and is caused by mutations in the Cln3 gene. In particular, brain tissue from Cln3 mice was characterised by increased concentrations of glutamine, myo-inositol, scyllo-inositol, aspartate and lactate, alongside decreased concentrations of N-acetyl-l-aspartate (NAA), N-acetyl-l-glutamate (NAG), γ-amino butyric acid (GABA), glutamate and creatine. Accompanying changes in lipid deposition were also detected in intact cortical tissue by HR MAS ¹H NMR spectroscopy. To realise the true potential of metabolomic datasets necessitates a comprehensive analysis of the data, such that useful biological information can be extracted and used to generate hypotheses which can be further tested and refined. We found that using a combination of univariate and multivariate analyses, a maximal number of metabolic deficits were successfully identified. In particular the complementary nature of the statistical approaches allowed the definition of changes which were relative, absolute or simply a change in variance, allowing a greater understanding of the disease processes detected.     

Cross-platform analysis of longitudinal data in metabolomics

Metabolic profiling is considered to be a very promising tool for diagnostic purposes, for assessing nutritional status and response to drugs. However, it is also evident that human metabolic profiles have a complex nature, influenced by many external factors. This, together with the understanding of the difficulty to assign people to distinct groups and a general move in clinical science towards personalized medicine, raises the interest to explore individual and variable metabolic features for each individual separately in longitudinal study design. In the current paper we have analyzed a set of metabolic profiles of a selection of six urine samples per person from a set of healthy individuals by (1)H NMR and reversed-phase UPLC-MS. We have demonstrated that the method for recovery of individual metabolic phenotypes can give complementary information to another established method for analysis of longitudinal data--multilevel component analysis. We also show that individual metabolic signatures can be found not only in (1)H NMR data, as has been demonstrated before, but also even more strongly in LC-MS data.     

Reflections on univariate and multivariate analysis of metabolomics data

Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.     

1H NMR investigations of the molecular nature of cobalt(II) ions in human saliva

High-resolution (1)H NMR spectroscopy demonstrated that addition of Co(II) ions to isolated human salivary supernatants (HSSs) gave rise to its complexation by a variety of biomolecules. The relative efficacies of these complexants/chelators in this context were classifiable by the influence of added Co(II) on their line-widths and chemical shift values, and also the added Co(II) concentration-dependence of these spectral modifications. Those which were most affected by the addition of this metal ion were lactate > formate ≈histidinate > succinate, this order reflecting the ability of these complexants to compete for the available Co(II) in terms of (1) thermodynamic equilibrium constants for the formation of their complexes and (2) their HSS concentrations. Since many of these HSS Co(II) complexants (particularly lactate, formate and histidine) serve as powerful ()OH scavengers, the results acquired indicate that any of this radical generated from the Co(II) source in such complexes via pseudo-Fenton reactions may be 'site-specifically' scavenged. The significance of these observations regarding the in vivo corrosion of cobalt-containing metal alloy dental prostheses (e.g., Co-Cr alloys), the availability of trace levels of this metal ion in human saliva, and cobalt toxicity, is discussed.     

1H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system

Hypoxia can promote invasive behavior in cancer cells and alters the response to therapeutic intervention as a result of changes in the expression many genes, including genes involved in intermediary metabolism. Although metabolomics technologies are capable of simultaneously measuring a wide range of metabolites in an untargeted manner, these methods have been relatively under utilized in the study of cancer cell responses to hypoxia. Thus, (1)H NMR metabolomics was used to examine the effects of hypoxia in the MDA-MB-231 human breast cancer cell line, both in vitro and in vivo. Cell cultures were compared with respect to their metabolic responses during growth under either hypoxic (1% O(2)) or normoxic conditions. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify a set of metabolites that were responsive to hypoxia. Via intracardiac administration, MDA-MB-231 cells were also used to generate widespread metastatic disease in immuno-compromised mice. Serum metabolite analysis was conducted to compare animals with and without a large tumor burden. Intriguingly, using a cross-plot of the OPLS loadings, both the in vitro and in vivo samples yielded a subset of metabolites that were significantly altered by hypoxia. These included primarily energy metabolites and amino acids, indicative of known alterations in energy metabolism, and possibly protein synthesis or catabolism. The results suggest that the metabolite pattern identified might prove useful as a marker for intra-tumoral hypoxia.     

1H NMR metabolomics study of metastatic melanoma in C57BL/6J mouse spleen

Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. In this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal component analysis, an unsupervised multivariate data analysis method, is used to detect possible outliers, while orthogonal projection to latent structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e. spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-phosphoethanolamine, niacinamide, ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2′-deoxyguanosine are statistically and significantly elevated. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.     

1H NMR and GC/MS metabolomics of earthworm responses to sub-lethal DDT and endosulfan exposure

The metabolic response of the earthworm Eisenia fetida to two pesticides, dichlorodiphenyltrichloroethane (DDT) and endosulfan, was characterized in contact tests using proton nuclear magnetic resonance (¹H NMR) and principal component analysis (PCA). PCA loading plots suggested that maltose, leucine and alanine were important metabolites contributing to the differences in dosed and control earthworms for both compounds at doses of 0.5, 1.0 and 2.0 μg/cm². Gas chromatography/mass spectrometry (GC/MS) was used to quantify the metabolites identified in E. fetida and determine if the changes in maltose, leucine and alanine following exposure to DDT and endosulfan (at 0.5 and 1.0 μg/cm²) were reproducible and greater than the natural variability. Quantification by GC/MS suggested that maltose was not a reliable biomarker since it both increased and decreased in earthworms exposed to DDT and increased by just 3% with exposure to endosulfan. Leucine was not stable with the GC/MS derivitization method used in this study and could not be confirmed as a reliable biomarker. However, alanine consistently increased for both DDT and endosulfan exposed E. fetida. Alanine showed considerable variability in control earthworms (±41.6%), yet the variability in alanine to glycine ratios was just ±10.5%. Increases in the alanine to glycine ratio were statistically significant at the P = 0.05 level for the 1.0 μg/cm² DDT dose and both the 0.5 and 1.0 μg/cm² endosulfan doses, suggesting that deviations from the normal homeostatic ratio of 1.5 for alanine to glycine is a potential biomarker of DDT and endosulfan exposure warranting further study. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Environmental Metabolomics Special Issue of Metabolomics.     

1H NMR spectral analysis of the malylated anthocyanins from Dianthus

The structures of malylated anthocyanins from carnation Dianthus caryophyllus flowers were confirmed as the 3-O-(6-O-malyl-β-D-glucopyranosides) of pelargonidin and cyanidin by 400 MHz FT-NMR.     

High-throughput,multi-platform metabolomics on very small volumes: 1H NMR metabolite identification in an unadulterated tube-in-tube system

As small animal models of disease become more widely used, there is increasing importance and potential for characterizing their metabolomes. However, as the animal becomes smaller, the amounts of biofluids such as urine and cerebral spinal fluid available for metabolomic studies are more limited. Further, in multi-platform systems biology when the same small sample must be used for several analyses, it is a frequent requirement that no additions are made to the sample (even as simple as D₂O or an NMR chemical shift reference) to maintain sample integrity. Herein we describe a method for high-throughput ¹H-NMR studies using ~30 µl volumes, suitable for biofluid matrices. The compartmentalization of the sample and NMR standards, however, requires chemical shift corrections due to bulk magnetic susceptibility and ionic strength changes for metabolite profiling using a reference library or data-binning of the chemical shift axis. This set-up minimizes the cost of individual data collection per small animal and is suitable for high-throughput, longitudinal, multimodal metabolomic studies of biofluids available in limited quantities.     

Biochemical characterization of rat intestine development using high-resolution magic-angle-spinning 1H NMR spectroscopy and multivariate data analysis

We report details of metabolic profiles for small intestinal samples obtained using high-resolution magic-angle-spinning (HRMAS) (1)H NMR spectroscopy. Intact samples of jejunum and ileum from male Long Evans rats were analyzed on a 600 MHz spectrometer using standard one and two-dimensional (1)H NMR spectroscopic pulse sequences. The metabolic profiles of ileum and jejunum predominantly comprised a number of amino acids, lipids, glycerophosphocholine (GPC), choline, creatine, and ethanol, a number of carboxylic acids including acetate and lactate, and nucleoside bases including cytosine, isocytosine, and uracil. Principal component analysis (PCA) was applied to these NMR data to characterize the biochemical differences between jejunum and ileum tissues. Compared with ileum, jejunum contained higher levels of lipids, GPC, choline, lactate and creatinine, but lower levels of amino acids and acetate. In addition, the age dependence of the biochemical composition of intestinal tissues from young rats (15, 36 days and 3-4 months old) was studied. In general, levels of lipids, lactate, taurine and creatinine were positively correlated with age while amino acids and GPC decreased in the older age group. This study will provide a metabolic reference for further studies assessing the metabolic consequences of nutrition, stress and gut microbiota on intestinal composition.     

Two data pre-processing workflows to facilitate the discovery of biomarkers by 2D NMR metabolomics

Metabolomics - Sleep is increasingly being viewed as an issue of public health concern, yet few epidemiologic studies have explored associations between sleep habits and metabolomic profile. To...