Current Metabolomics - Volume 2, Issue 1, 2014

Behavioral and physiologic rhythms are temporally coordinated with daily changes in the environment. This is achieved by the circadian timing system, which synchronizes the body’s rhythms with the 24-hour solar day and feeding cycles. To better understand the role of circadian rhythms in metabolism and disease processes, an increasing number of studies have used large-scale metabolite profiling (metabolomics) to analyze biological specimens collected over the diurnal/ circadian cycle. Here, we review recent progress in the application of metabolomics to circadian rhythms research in mammals. Based on studies of the liver metabolome in mice, the circadian clock plays a key role in regulating carbohydrate, lipid, and nucleotide metabolism. Circadian metabolomics has also revealed marked changes in liver function in response to the timing of food consumption, and has led to the discovery of novel signaling pathways underlying fat metabolism. In humans, circadian profiling of metabolites in plasma has confirmed an important role for the clock in regulating steroid hormone metabolism and lipid homeostasis. A method for estimating internal body time has also been developed using plasma metabolomics, which could potentially be used to optimize the timing of drug delivery to improve patient outcomes and reduce unwanted side effects. Looking forward, metabolomics approaches can be used to evaluate the impact of genetic and environmental factors on circadian-regulated metabolic pathways, while providing valuable insight into the role of the circadian clock in regulating complex biomolecular networks.

The analytical procedures required to generate a quantified metabolomics data matrix include many and widely different potential sources of error, complicating the generation of reliable data. The methods generally used to assess precision of such data all have distinct merits but some clear limitations as well. In this paper we describe KEMREP (kernel method for the assessment of repeatability and reproducibility), a new method with the advantage and focus aimed specifically at analysis of the reliability of metabolomics data. Repeatability and reproducibility were assessed on gas chromatography- mass spectrometry (GC-MS) generated metabolomics data matrices produced by and between analysts and across laboratories, using cerebrospinal fluid (CSF) and urine as biological samples for analysis. KEMREP provides a visual overlay of the smoothed and scaled versions of the data from repeated samples for a direct and easy qualitative assessment of repeatability or reproducibility of a distinct chromatographic region (univariate) or for the experiment as a whole (multivariate). The KEMREP method can also be extended by the imposition of confidence bounds which provide lower and upper limits that indicate quantitatively whether the experiment was repeatable or reproducible at a predefined input coefficient of variation (CV). KEMREP is thus a novel approach which supplements existing methods of assessment of reliability of metabolomics data; provides a benchmark for assessing the quality of practical work performed by analysts; monitors the sequence of data pre-treatment steps; and tests the robustness of an experimentally designed protocol for metabolomics.

Objectives: Body mass index (BMI) is one of the most important risk factors for different metabolic and cardiovascular disorders. Previously, both genetic and environmental agents associated with BMI have been described. The main focus of this exploratory study was to find the circulating metabolites associated with BMI utilizing an untargeted metabolomics approach. Additionally, significant metabolites identified were studied for their relation with BMIassociated single nucleotide polymorphisms (SNPs). Materials and Methods: A total of 971 individuals from the Cancer of the Prostate in Sweden study (discovery sample- 275 prostate cancers patients and 182 controls; replication sample- 514 prostate cancer patients) were utilized. Blood samples were collected and serum metabolic profiling was obtained using ultra-performance liquid chromatography followed by mass spectrometry. Genotyping data was available for 26 out of 32 SNPs (21 genotyped and 5 proxies) previously robustly associated with BMI in individuals of European descent. Weighted genetic risk score was generated using these SNPs and studied for its association with metabolites. Results: A total of 6138 and 5209 metabolite features were detected in discovery and replication samples, respectively. Out of 6138 metabolite features in discovery sample, 201 were found to be significantly associated with BMI (p<8.15*10-6) after multiple testing correction. These 201 features were further investigated in the replication samples and 16 were found to be significantly associated with BMI (p<2.49*10-4). Seven of these significant features were isotopes for four of the primary metabolites. Four metabolites were putatively identified: monoacylglyceride (18:1), diacylglyrcerol (32:1) and two phosphatidylcholines (34:0 and 36:0). Weighted genetic score of BMI-associated SNPs was not associated with these four metabolites. Conclusion: Four identifiable metabolites (monoacylglyceride, diacyclglyrcerol and two phosphatidylcholines) were found to be significantly associated with BMI in both discovery and replication samples. Common variants associated with BMI did not show association with these four metabolites.

Immune responses to infection by the human immunodeficiency virus (HIV) and the use of highly active antiretroviral therapy (HAART) to treat HIV infection, contribute to metabolic irregularities in the host. Current methods for the extraction and identification of metabolites in biofluids generally make use of laborious, time-consuming protocols. Here, 96-well Ostro™ plates and filtration under positive pressure were used to facilitate the simultaneous, reproducible extraction of metabolites from multiple serum samples which were then analyzed by ultra-performance liquid chromatography mass spectrometry (UPLC-MS). The easy to use solid phase extraction (SPE) protocol eliminated numerous potential contaminants while the UPLC-MS detection of metabolites produced visibly different chromatograms for HIV negative (n=16), HIV+ (n=13) and HIV+HAART+ (n=15) serum samples. Linear discriminant analysis (LDA) amplified these differences, classified the groups with 100% accuracy and identified biomarkers explaining the greatest variances between the groups. The 21 metabolites altered by HIV and/or HAART primarily represented those linked to lipid and energy pathways where known metabolic changes associated with HIV infection occur. This work demonstrated for the first time that OstroTM plates and UPLC-MS metabonomics were able to successfully identify distinct differences between the experimental groups and detected metabolites related to HAART and other drugs used in the treatment of HIV-associated conditions. The findings of this approach suggest a possible role for this methodology in disease prognosis as well as in the monitoring of treatment success or failure and linking treatment to metabolic complications.

Tumor progression and metastasis are linked to cellular metabolism. Cancer cells, being highly proliferative, show significant alterations in metabolic pathways such as glycolysis, respiration, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, lipid metabolism, and amino acid metabolism. Metabolites like peptides, nucleotides, products of glycolysis, the TCA cycle, fatty acids, and steroids can be an important read out of disease when characterized in biological samples such as tissues and body fluids like urine, serum, etc. The cancer metabolome has been studied since the 1960s by analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Current research is focused on the identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients and distinguish between benign and advanced metastatic forms of the disease. In this review, we discuss the current state of prostate cancer metabolomics, the biomarkers that show promise in distinguishing indolent from aggressive forms of the disease, the strengths and limitations of the analytical techniques being employed, and future applications of metabolomics in diagnostic imaging and personalized medicine of prostate cancer.

Studies have been performed to investigate the metabolic effects of high and low salt diets in Old Order Amish using a 1H nuclear magnetic resonance (1H NMR)-based metabolomic method. Subjects received a high and low sodium diet for 6 days each, separated by a 6-14 day washout period. Urine samples were collected on the fourth to sixth days of each diet and evaluated by NMR. Over 30 metabolites were identified and the whole 1H NMR spectra of 37 samples from two diet groups were analyzed by principle component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Osmolytes, such as trimethlamine N-oxide (TMAO), glycine, 1- and 3- methylhistidine, showed distinct differences between the low and high salt groups which may be associated with renal stress coming from the high salt diet. From this study, it can be established that the NMR spectrum provides a unique profile for the high salt diet and that metabolomic technology is of value for the analysis of human dietary data.