Andrew W. Nicholls

Proposed minimum reporting standards for chemical analysis

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography–mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography–MS (GC-MS) and ultraperformance liquid chromatography–MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control–based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans

The metabolic investigation of the human population is becoming increasingly important in the study of health and disease. The phenotypic variation can be investigated through the application of metabolomics; to provide a statistically robust investigation, the study of hundreds to thousands of individuals is required. In untargeted and MS-focused metabolomic studies this once provided significant hurdles. However, recent innovations have enabled the application of MS platforms in large-scale, untargeted studies of humans. Herein we describe the importance of experimental design, the separation of the biological study into multiple analytical experiments and the incorporation of QC samples to provide the ability to perform signal correction in order to reduce analytical variation and to quantitatively determine analytical precision. In addition, we describe how to apply this in quality assurance processes. These innovations have opened up the capabilities to perform routine, large-scale, untargeted, MS-focused studies.

Metabolic phenotyping of a model of adipocyte differentiation

The 3T3-L1 murine cell line is a robust and widely used model for the study of adipogenesis and processes occurring in mature adipocytes. The fibroblastic like cells can be induced by hormones to differentiate into mature adipocytes. In this study, the metabolic phenotype associated with differentiation of the 3T3-L1 cell line has been studied using gas chromatography-mass spectrometry, (1)H nuclear magnetic resonance spectroscopy, liquid chromatography-mass spectrometry, direct infusion-mass spectrometry, and 13C substrate labeling in conjunction with multivariate statistics. The changes in metabolite concentrations at distinct periods during differentiation have been defined including alterations in the TCA cycle, glycolysis, the production of odd chain fatty acids by alpha-oxidation, fatty acid synthesis, fatty acid desaturation, polyamine biosynthesis, and trans-esterification to produce complex lipids. The metabolic changes induced during differentiation of the 3T3-L1 cell line were then compared with the metabolic differences between pre- and postdifferentiation primary adipocytes. These metabolic alterations reflect the changing role of the 3T3-L1 cells during differentiation, as well as possibly providing metabolic triggers to stimulate the processes which occur during differentiation.

Standard reporting requirements for biological samples in metabolomics experiments: mammalian/in vivo experiments

With the increasing production of metabolomic data there is an awareness of a need for a standardised description of this data to aid assessment, exchange, storage and curation of information from metabolomic studies. In this manuscript the first draft of a minimum requirement for the description of the biological context of a metabolomic study involving mammalian subjects is described. This recommendation has been produced by the Metabolomics Standards Initiative–Mammalian Context Working Sub-Group (MSI-MCWSG) as part of the wider standardisation initiative led by the Metabolomics society. The experiments considered include functional genomic studies, drug toxicology, nutrigenomics, clinical trials, and other human studies. Two reporting requirements are described for pre-clinical (e.g. functional genomics, toxicology) and clinical (e.g. clinical trials, nutrigenomics) studies. It is planned that this will lead to the development of a tool for the description of metabolomic experiments that enables storage, retrieval and manipulation of large amounts of data. This will benefit the assessment and dissemination of metabolomic data from mammalian studies.

Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links.

Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10−8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10−44) and lysine (rs8101881, P = 1.2×10−33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohns disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.

Flow Injection Proton Nuclear Magnetic Resonance Spectroscopy Combined With Pattern Recognition Methods: Implications for Rapid Structural Studies and High Throughput Biochemical Screening

The applicability of novel NMR flow probe technology has been tested by the measurement of 300 MHz 1H NMR spectra of a series of rat urine samples. Compared with conventional automatic operation, the method resulted in a significantly increased rate of sample throughput, required minimal spectrometer optimisation before each measurement and avoided the need for expensive and fragile NMR sample tubes. The NMR approach has been coupled with computer methods for spectral data reduction and classification using, in this case, principal components analysis. The flow probe NMR approach offers distinct advantages in situations where large numbers of samples require NMR analysis in a short period of time. These could include routine samples from high throughput chemical synthesis, biofluid samples for drug toxicity monitoring as shown here, samples for clinical diagnosis or real-time analysis in chemical production facilities.

Increased hepatic oxidative metabolism distinguishes the action of Peroxisome proliferator-activated receptor δ from Peroxisome proliferator-activated receptor γ in the ob/ob mouse

BackgroundThe peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors and members of the nuclear receptor superfamily. The PPAR family consists of three members: PPARα, PPARγ, and PPARδ. PPARδ controls the transcription of genes involved in multiple physiological pathways, including cellular differentiation, lipid metabolism and energy homeostasis. The receptor is expressed almost ubiquitously, with high expression in liver and skeletal muscle. Although the physiological ligands of PPARδ remain undefined, a number of high affinity synthetic ligands have been developed for the receptor as a therapeutic target for type 2 diabetes mellitus, dyslipidemia and the metabolic syndrome.MethodsIn this study, the metabolic role of PPARδ activation has been investigated in liver, skeletal muscle, blood serum and white adipose tissue from ob/ob mice using a high affinity synthetic ligand and contrasted with PPARγ activation. To maximize the analytical coverage of the metabolome, 1H-nuclear magnetic resonance (1H-NMR) spectroscopy, gas chromatography-mass spectrometry (GC-MS) and ultra performance liquid chromatography-mass spectrometry (UPLC-MS) were used to examine metabolites from tissue extracts.ResultsAnalysis by multivariate statistics demonstrated that PPARδ activation profoundly affected glycolysis, gluconeogenesis, the TCA cycle and linoleic acid and α-linolenic acid essential fatty acid pathways.ConclusionsAlthough activation of both PPARδ and PPARγ lead to increased insulin sensitivity and glucose tolerance, PPARδ activation was functionally distinct from PPARγ activation, and was characterized by increased hepatic and peripheral fatty acid oxidative metabolism, demonstrating the distinctive catabolic role of this receptor compared with PPARγ.

Personalized medicine progresses

A new approach to personalized drug treatment emerges in a study examining the metabolic profile of rats. The profile, which is a measurement of small molecules such as sugars and amino acids, is used to predict the response to drugs that are toxic to the liver. This study proposes the extension of this concept into humans as a way of predicting the outcome of a therapy for a given profile.

1H NMR Spectroscopy-Based Metabolomic Assessment of Uremic Toxicity, with Toxicological Outcomes, in Male Rats Following an Acute, Mid-Life Insult from Ochratoxin A

Overt response to a single 6.25 mg dose of ochratoxin A (OTA) by oral gavage to 15 months male rats was progressive loss of weight during the following four days. Lost weight was restored within one month and animals had a normal life-span without OTA-related terminal disease. Decline in plasma OTA concentration only commenced four days after dosing, while urinary excretion of OTA and ochratoxin alpha was ongoing. During a temporary period of acute polyuria, a linear relationship between urine output and creatinine concentration persisted. Elimination of other common urinary solutes relative to creatinine was generally maintained during the polyuria phase, except that phosphate excretion increased temporarily. 1H NMR metabolomic analysis of urine revealed a progressive cyclic shift in the group principal components data cluster from before dosing, throughout the acute insult phase, and returning almost completely to normality when tested six months later. Renal insult by OTA was detected by 1H NMR within a day of dosing, as the most sensitive early indicator. Notable biomarkers were trimethylamine N-oxide and an aromatic urinary profile dominated by phenylacetylglycine. Tolerance of such a large acute insult by OTA, assessed by rat natural lifetime outcomes, adds a new dimension to toxicology of this xenobiotic.