Daniel Raftery

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Early Detection of Recurrent Breast Cancer Using Metabolite Profiling

We report on the development of a monitoring test for recurrent breast cancer, using metabolite-profiling methods. Using a combination of nuclear magnetic resonance (NMR) and two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) methods, we analyzed the metabolite profiles of 257 retrospective serial serum samples from 56 previously diagnosed and surgically treated breast cancer patients. One hundred sixteen of the serial samples were from 20 patients with recurrent breast cancer, and 141 samples were from 36 patients with no clinical evidence of the disease during ∼6 years of sample collection. NMR and GC×GC-MS data were analyzed by multivariate statistical methods to compare identified metabolite signals between the recurrence samples and those with no evidence of disease. Eleven metabolite markers (seven from NMR and four from GC×GC-MS) were shortlisted from an analysis of all patient samples by using logistic regression and 5-fold cross-validation. A partial least squares discriminant analysis model built using these markers with leave-one-out cross-validation provided a sensitivity of 86% and a specificity of 84% (area under the receiver operating characteristic curve = 0.88). Strikingly, 55% of the patients could be correctly predicted to have recurrence 13 months (on average) before the recurrence was clinically diagnosed, representing a large improvement over the current breast cancer-monitoring assay CA 27.29. To the best of our knowledge, this is the first study to develop and prevalidate a prediction model for early detection of recurrent breast cancer based on metabolic profiles. In particular, the combination of two advanced analytical methods, NMR and MS, provides a powerful approach for the early detection of recurrent breast cancer.

Quantitative Metabolomics by 1H-NMR and LC-MS/MS Confirms Altered Metabolic Pathways in Diabetes

Insulin is as a major postprandial hormone with profound effects on carbohydrate, fat, and protein metabolism. In the absence of exogenous insulin, patients with type 1 diabetes exhibit a variety of metabolic abnormalities including hyperglycemia, glycosurea, accelerated ketogenesis, and muscle wasting due to increased proteolysis. We analyzed plasma from type 1 diabetic (T1D) humans during insulin treatment (I+) and acute insulin deprivation (I-) and non-diabetic participants (ND) by 1H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. The aim was to determine if this combination of analytical methods could provide information on metabolic pathways known to be altered by insulin deficiency. Multivariate statistics differentiated proton spectra from I- and I+ based on several derived plasma metabolites that were elevated during insulin deprivation (lactate, acetate, allantoin, ketones). Mass spectrometry revealed significant perturbations in levels of plasma amino acids and amino acid metabolites during insulin deprivation. Further analysis of metabolite levels measured by the two analytical techniques indicates several known metabolic pathways that are perturbed in T1D (I-) (protein synthesis and breakdown, gluconeogenesis, ketogenesis, amino acid oxidation, mitochondrial bioenergetics, and oxidative stress). This work demonstrates the promise of combining multiple analytical methods with advanced statistical methods in quantitative metabolomics research, which we have applied to the clinical situation of acute insulin deprivation in T1D to reflect the numerous metabolic pathways known to be affected by insulin deficiency.

Correlative and quantitative 1H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats

Type 1 diabetes was induced in Sprague-Dawley rats using streptozotocin. Rat urine samples (8 diabetic and 10 control) were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy. The derived metabolites using univariate and multivariate statistical analysis were subjected to correlative analysis. Plasma metabolites were measured by a series of bioassays. A total of 17 urinary metabolites were identified in the 1H NMR spectra and the loadings plots after principal components analysis. Diabetic rats showed significantly increased levels of glucose (P < 0.00001), alanine (P < 0.0002), lactate (P < 0.05), ethanol (P < 0.05), acetate (P < 0.05), and fumarate (P < 0.05) compared with controls. Plasma assays showed higher amounts of glucose, urea, triglycerides, and thiobarbituric acid-reacting substances in diabetic rats. Striking differences in the Pearsons correlation of the 17 NMR-detected metabolites were observed between control and diabetic rats. Detailed analysis of the altered metabolite levels and their correlations indicate a significant disturbance in the glucose metabolism and tricarboxylic acid (TCA) cycle and a contribution from gut microbial metabolism. Specific perturbed metabolic pathways include the glucose-alanine and Cori cycles, the acetate switch, and choline metabolism. Detection of the altered metabolic pathways and bacterial metabolites using this correlative and quantitative NMR-based metabolomics approach should help to further the understanding of diabetes-related mechanisms.

Highly efficient methane biocatalysis revealed in a methanotrophic bacterium

Methane is an essential component of the global carbon cycle and one of the most powerful greenhouse gases, yet it is also a promising alternative source of carbon for the biological production of value-added chemicals. Aerobic methane-consuming bacteria (methanotrophs) represent a potential biological platform for methane-based biocatalysis. Here we use a multi-pronged systems-level approach to reassess the metabolic functions for methane utilization in a promising bacterial biocatalyst. We demonstrate that methane assimilation is coupled with a highly efficient pyrophosphate-mediated glycolytic pathway, which under oxygen limitation participates in a novel form of fermentation-based methanotrophy. This surprising discovery suggests a novel mode of methane utilization in oxygen-limited environments, and opens new opportunities for a modular approach towards producing a variety of excreted chemical products using methane as a feedstock.

Brain-Targeted Proanthocyanidin Metabolites for Alzheimer's Disease Treatment

While polyphenolic compounds have many health benefits, the potential development of polyphenols for the prevention/treatment of neurological disorders is largely hindered by their complexity as well as by limited knowledge regarding their bioavailability, metabolism, and bioactivity, especially in the brain. We recently demonstrated that dietary supplementation with a specific grape-derived polyphenolic preparation (GP) significantly improves cognitive function in a mouse model of Alzheimers disease (AD). GP is comprised of the proanthocyanidin (PAC) catechin and epicatechin in monomeric (Mo), oligomeric, and polymeric forms. In this study, we report that following oral administration of the independent GP forms, only Mo is able to improve cognitive function and only Mo metabolites can selectively reach and accumulate in the brain at a concentration of ∼400 nm. Most importantly, we report for the first time that a biosynthetic epicatechin metabolite, 3′-O-methyl-epicatechin-5-O-β-glucuronide (3′-O-Me-EC-Gluc), one of the PAC metabolites identified in the brain following Mo treatment, promotes basal synaptic transmission and long-term potentiation at physiologically relevant concentrations in hippocampus slices through mechanisms associated with cAMP response element binding protein (CREB) signaling. Our studies suggest that select brain-targeted PAC metabolites benefit cognition by improving synaptic plasticity in the brain, and provide impetus to develop 3′-O-Me-EC-Gluc and other brain-targeted PAC metabolites to promote learning and memory in AD and other forms of dementia.

The future of NMR-based metabolomics

The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications.

Altered proteome turnover and remodeling by short-term caloric restriction or rapamycin rejuvenate the aging heart

Chronic caloric restriction (CR) and rapamycin inhibit the mechanistic target of rapamycin (mTOR) signaling, thereby regulating metabolism and suppressing protein synthesis. Caloric restriction or rapamycin extends murine lifespan and ameliorates many aging‐associated disorders; however, the beneficial effects of shorter treatment on cardiac aging are not as well understood. Using a recently developed deuterated‐leucine labeling method, we investigated the effect of short‐term (10 weeks) CR or rapamycin on the proteomics turnover and remodeling of the aging mouse heart. Functionally, we observed that short‐term CR and rapamycin both reversed the pre‐existing age‐dependent cardiac hypertrophy and diastolic dysfunction. There was no significant change in the cardiac global proteome (823 proteins) turnover with age, with a median half‐life 9.1 days in the 5‐month‐old hearts and 8.8 days in the 27‐month‐old hearts. However, proteome half‐lives of old hearts significantly increased after short‐term CR (30%) or rapamycin (12%). This was accompanied by attenuation of age‐dependent protein oxidative damage and ubiquitination. Quantitative proteomics and pathway analysis revealed an age‐dependent decreased abundance of proteins involved in mitochondrial function, electron transport chain, citric acid cycle, and fatty acid metabolism as well as increased abundance of proteins involved in glycolysis and oxidative stress response. This age‐dependent cardiac proteome remodeling was significantly reversed by short‐term CR or rapamycin, demonstrating a concordance with the beneficial effect on cardiac physiology. The metabolic shift induced by rapamycin was confirmed by metabolomic analysis.

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: Application to the detection of breast cancer

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

AbstractThe metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.