Current Metabolomics (v.2, #1)

Editorial by Robert Powers (1-1).

Applications of Circadian Metabolomics by Joshua J. Gooley (2-14).
Behavioral and physiologic rhythms are temporally coordinated with daily changes in the environment. This isachieved by the circadian timing system, which synchronizes the body's rhythms with the 24-hour solar day and feedingcycles. To better understand the role of circadian rhythms in metabolism and disease processes, an increasing number ofstudies 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 inmammals. 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 responseto 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 regulatingsteroid hormone metabolism and lipid homeostasis. A method for estimating internal body time has also been developedusing plasma metabolomics, which could potentially be used to optimize the timing of drug delivery to improve patientoutcomes and reduce unwanted side effects. Looking forward, metabolomics approaches can be used to evaluate theimpact of genetic and environmental factors on circadian-regulated metabolic pathways, while providing valuable insightinto the role of the circadian clock in regulating complex biomolecular networks.

KEMREP: A New Qualitative Method for the Assessment of an Analystµs Ability to Generate a Metabolomics Data Matrix by Gas Chromatography- Mass Spectrometry by Shayne Mason, Gontse P. Moutloatse, A. Marceline van Furth, Regan Solomons, Mari van Reenen, Carolus Reinecke, Gerhard Koekemoer (15-26).
The analytical procedures required to generate a quantified metabolomics data matrix include many and widelydifferent potential sources of error, complicating the generation of reliable data. The methods generally used to assess precisionof such data all have distinct merits but some clear limitations as well. In this paper we describe KEMREP (kernelmethod for the assessment of repeatability and reproducibility), a new method with the advantage and focus aimed specificallyat 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 andacross laboratories, using cerebrospinal fluid (CSF) and urine as biological samples for analysis. KEMREP provides avisual overlay of the smoothed and scaled versions of the data from repeated samples for a direct and easy qualitative assessmentof repeatability or reproducibility of a distinct chromatographic region (univariate) or for the experiment as awhole (multivariate). The KEMREP method can also be extended by the imposition of confidence bounds which providelower and upper limits that indicate quantitatively whether the experiment was repeatable or reproducible at a predefinedinput coefficient of variation (CV). KEMREP is thus a novel approach which supplements existing methods of assessmentof 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 protocolfor metabolomics.

Associations of Body Mass Index and Obesity-Related Genetic Variants with Serum Metabolites by Jitender Kumar, Robert Karlsson, Corey D. Broeckling, Mun-Gwan Hong, Jonathan A. Prince, Jessica E. Prenni, Erik Ingelsson, Fredrik Wiklund (27-36).
Objectives: Body mass index (BMI) is one of the most important risk factors for different metabolic and cardiovasculardisorders. Previously, both genetic and environmental agents associated with BMI have been described. Themain focus of this exploratory study was to find the circulating metabolites associated with BMI utilizing an untargetedmetabolomics approach. Additionally, significant metabolites identified were studied for their relation with BMIassociatedsingle 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. Bloodsamples were collected and serum metabolic profiling was obtained using ultra-performance liquid chromatography followedby mass spectrometry. Genotyping data was available for 26 out of 32 SNPs (21 genotyped and 5 proxies) previouslyrobustly associated with BMI in individuals of European descent. Weighted genetic risk score was generated usingthese 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 werefound to be significantly associated with BMI (p<2.49*10-4). Seven of these significant features were isotopes for four ofthe 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 withthese four metabolites.;Conclusion: Four identifiable metabolites (monoacylglyceride, diacyclglyrcerol and two phosphatidylcholines) werefound to be significantly associated with BMI in both discovery and replication samples. Common variants associatedwith BMI did not show association with these four metabolites.

UPLC-MS Metabonomics Reveals Perturbed Metabolites in HIV-Infected Sera by Aurelia Williams, Khanyisile Kgoadi, Francois Steffens, Paul Steenkamp, Debra Meyer (37-52).
Immune responses to infection by the human immunodeficiency virus (HIV) and the use of highly activeantiretroviral therapy (HAART) to treat HIV infection, contribute to metabolic irregularities in the host. Current methodsfor 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, reproducibleextraction of metabolites from multiple serum samples which were then analyzed by ultra-performance liquidchromatography mass spectrometry (UPLC-MS). The easy to use solid phase extraction (SPE) protocol eliminated numerouspotential contaminants while the UPLC-MS detection of metabolites produced visibly different chromatogramsfor 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 greatestvariances between the groups. The 21 metabolites altered by HIV and/or HAART primarily represented those linked tolipid and energy pathways where known metabolic changes associated with HIV infection occur. This work demonstratedfor the first time that OstroTM plates and UPLC-MS metabonomics were able to successfully identify distinct differencesbetween the experimental groups and detected metabolites related to HAART and other drugs used in the treatment ofHIV-associated conditions. The findings of this approach suggest a possible role for this methodology in disease prognosisas 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, oxidativephosphorylation, lipid metabolism, and amino acid metabolism. Metabolites like peptides, nucleotides, products ofglycolysis, the TCA cycle, fatty acids, and steroids can be an important read out of disease when characterized in biologicalsamples such as tissues and body fluids like urine, serum, etc. The cancer metabolome has been studied since the1960s 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 stratifyhigh-risk patients and distinguish between benign and advanced metastatic forms of the disease. In this review, we discussthe current state of prostate cancer metabolomics, the biomarkers that show promise in distinguishing indolent from aggressiveforms of the disease, the strengths and limitations of the analytical techniques being employed, and future applicationsof 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 Amishusing a 1H nuclear magnetic resonance (1H NMR)-based metabolomic method. Subjects received a high and low sodiumdiet for 6 days each, separated by a 6-14 day washout period. Urine samples were collected on the fourth to sixth days ofeach diet and evaluated by NMR. Over 30 metabolites were identified and the whole 1H NMR spectra of 37 samples fromtwo 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 differencesbetween 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 metabolomictechnology is of value for the analysis of human dietary data.