Metabolomics is a new, rapidly expanding field dedicated to the global study of metabolites in biological systems. In this manuscript metabolomics is applied to find urinary biomarkers for breast and ovarian cancer.

In this new and exciting field called metabolomics, where infections by specific microbes produce unique urinary metabolite patterns in their hosts, the promise of enhanced clinical chemistry diagnostics will allow physicians to diagnose and monitor health in a more quantitative way.

In the fourth century B.C., the Greek physician Hippocrates correlated urine characteristics such as color and sediment with specific illnesses. Today, sensitive analytical techniques allow researchers to study how the profile of urinary metabolites changes in response to disease.

Pneumonia, an infection of the lower respiratory tract, is caused by any of a number of different microbial organisms including bacteria, viruses, fungi, and parasites. Community-acquired pneumonia (CAP) causes a significant number of deaths worldwide, and is the sixth leading cause of death in the United States. However, the pathogen(s) responsible for CAP can be difficult to identify, often leading to delays in appropriate antimicrobial therapies. In the present study, we use nuclear magnetic resonance spectroscopy to quantitatively measure the profile of metabolites excreted in the urine of patients with pneumonia caused by Streptococcus pneumoniae and other microbes. We found that the urinary metabolomic profile for pneumococcal pneumonia was significantly different from the profiles for viral and other bacterial forms of pneumonia. These data demonstrate that urinary metabolomic profiles may be useful for the effective diagnosis of CAP.

Extracting meaningful information from complex spectroscopic data of metabolite mixtures is an area of active research in the emerging field of “metabolomics”, which combines metabolism, spectroscopy, and multivariate statistical analysis (pattern recognition) methods. Chemometric analysis and comparison of 1H NMR1 spectra is commonly hampered by intersample peak position and line width variation due to matrix effects (pH, ionic strength, etc.). Here a novel method for mixture analysis is presented, defined as “targeted profiling”. Individual NMR resonances of interest are mathematically modeled from pure compound spectra. This database is then interrogated to identify and quantify metabolites in complex spectra of mixtures, such as biofluids. The technique is validated against a traditional “spectral binning” analysis on the basis of sensitivity to water suppression (presaturation, NOESY-presaturation, WET, and CPMG), relaxation effects, and NMR spectral acquisition times (3, 4, 5, and 6 s/scan) using PCA pattern recognition analysis. In addition, a quantitative validation is performed against various metabolites at physiological concentrations (9 μM−8 mM). “Targeted profiling” is highly stable in PCA-based pattern recognition, insensitive to water suppression, relaxation times (within the ranges examined), and scaling factors; hence, direct comparison of data acquired under varying conditions is made possible. In particular, analysis of metabolites at low concentration and overlapping regions are well suited to this analysis. We discuss how targeted profiling can be applied for mixture analysis and examine the effect of various acquisition parameters on the accuracy of quantification.

Inflammatory bowel disease (IBD) is a chronic debilitating disorder that is thought to have both genetic and environmental contributors. Commensal microflora have been shown to play a key part in the disease process. Metabolomics, the study of large numbers of small molecule metabolites, has demonstrated that disease and/or changes in gut microbial composition modulate mammalian urine metabolite fingerprints. The aim of this project was to associate the development of IBD with specific changes in a mouse urinary metabolic fingerprint. Interleukin-10 (IL-10) gene-deficient mice were raised alongside age-matched 129/SvEv controls in conventional housing. Urine samples (22 h) were collected at ages 4, 6, 8, 12, 16, and 20 weeks. Metabolite concentrations were derived from analysis of nuclear magnetic resonance spectra, and both multivariate and two-way analysis of variance (ANOVA) statistical techniques were applied to the resulting data. Principal component analysis and partial least-squares-discriminant analysis of urine derived from the control and IL-10 gene-deficient mice revealed that while both groups initially had similar metabolic profiles, they diverged substantially with the onset of IBD as assessed through external phenotypic changes. Several metabolites, including trimethylamine (TMA) and fucose, changed dramatically in the IL-10 gene-deficient mice following 8 weeks of age, concomitant with the known timeline for development of severe histological injury. This study illustrates that metabolomics is effective at distinguishing IBD using urinary metabolite profiles.

Saliva is a readily accessible biofluid that is important for the overall health, aiding in the chewing, swallowing, and tasting of food as well as the regulation mouth flora. As a first step to determining and understanding the human saliva metabolome, we have measured salivary metabolite concentrations under a variety of conditions in a healthy population with reasonably good oral hygiene. Using 1H NMR spectroscopy, metabolite concentrations were measured in resting (basal) and stimulated saliva from the same subject and compared in a cohort of healthy male non-smoking subjects (n = 62). Almost all metabolites were higher in the unstimulated saliva when compared to the stimulated saliva. Comparison of the salivary metabolite profile of male smokers and non-smokers (n = 46) revealed citrate, lactate, pyruvate, and sucrose to be higher and formate to be lower in concentration in smokers compared with non-smokers (p < 0.05). Gender differences were also investigated (n = 40), and acetate, formate, glycine, lactate, methanol, propionate, propylene glycol, pyruvate, succinate, and taurine were significantly higher in concentration in male saliva compared to female saliva (p < 0.05). These results show that differences between male and female, stimulated and unstimulated, as well as smoking status may be observed in the salivary metabolome.

Metabolomics is an emerging field providing insight into physiological processes. It is an effective tool to investigate disease diagnosis or conduct toxicological studies by observing changes in metabolite concentrations in various biofluids. Multivariate statistical analysis is generally employed with nuclear magnetic resonance (NMR) or mass spectrometry (MS) data to determine differences between groups (for instance diseased vs healthy). Characteristic predictive models may be built based on a set of training data, and these models are subsequently used to predict whether new test data falls under a specific class. In this study, metabolomic data is obtained by doing a 1H NMR spectroscopy on urine samples obtained from healthy subjects (male and female) and patients suffering from Streptococcus pneumoniae. We compare the performance of traditional PLS-DA multivariate analysis to support vector machines (SVMs), a technique widely used in genome studies on two case studies: (1) a case where nearly complete distinction may be seen (healthy versus pneumonia) and (2) a case where distinction is more ambiguous (male versus female). We show that SVMs are superior to PLS-DA in both cases in terms of predictive accuracy with the least number of features. With fewer number of features, SVMs are able to give better predictive model when compared to that of PLS-DA.

Metabolomics may have the capacity to revolutionize disease diagnosis through the identification of scores of metabolites that vary during environmental, pathogenic, or toxicological insult. NMR spectroscopy has become one of the main tools for measuring these changes since an NMR spectrum can accurately identify metabolites and their concentrations. The predominant approach in analyzing NMR data has been through the technique of spectral binning. However, identification of spectral areas in an NMR spectrum is insufficient for diagnostic evaluation, since it is unknown whether areas of interest are strictly caused by metabolic changes or are simply artifacts. In this paper, we explore differences in gender, diurnal variation, and age in a human population. We use the example of gender differences to compare traditional spectral binning techniques (NMR spectral areas) to novel targeted profiling techniques (metabolites and their concentrations). We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences in a healthy population. Metabolites relating to mitochondrial energy metabolism were found to differentiate gender and age. Dietary components and some metabolites related to circadian rhythms were found to differentiate time of day urine collection. The mechanisms by which these differences arise will be key to the discovery of new diagnostic tests and new understandings of the mechanism of disease.

The pathophysiological underpinnings of bipolar disorder are not fully understood. However, they may be due in part to changes in the phosphatidylinositol second messenger system (PI-cycle) generally, or changes in myo-inositol concentrations more specifically. Dextro-amphetamine has been used as a model for mania in several human studies as it causes similar subjective and physiological symptoms. We wanted to determine if dextro-amphetamine altered myo-inositol concentrations in vivo as it would clearly define a mechanism linking putative changes in the PI-cycle to the subjective psychological changes seen with dextro-amphetamine administration. Fifteen healthy human volunteers received a baseline scan, followed by second scan 75 min after receiving a 25 mg oral dose of dextro-amphetamine. Stimulated echo proton magnetic resonance spectroscopy (MRS) scans were preformed at 3.0 Tesla (T) in the dorsal medial prefrontal cortex (DMPFC). Metabolite data were adjusted for tissue composition and analyzed using LCModel. Twelve adult male rats were treated acutely with a 5-mg/kg intraperitoneal dose of dextro-amphetamine. After 1 h rats were decapitated and the brains were rapidly removed and frozen until dissection. Rat brains were dissected into frontal, temporal, and occipital cortical areas, as well as hippocampus. Tissue was analyzed using a Varian 18.8 T spectrometer. Metabolites were identified and quantified using Chenomx Profiler software. The main finding in the present study was that myo-inositol concentrations in the DMPFC of human volunteers and in the four rat brain regions were not altered by acute dextro-amphetamine. While it remains possible that the PI-cycle may be involved in the pathophysiology of bipolar disorder, it is not likely that the subjective and physiological of dextro-amphetamine are mediated, directly or indirectly, via alternations in myo-inositol concentrations.