Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of (bio)chemicals is both time consuming and challenging, largely because of the large number of LC and MS instrument parameters that need to be optimised. This bottleneck significantly impedes our ability to establish new (bio)analytical methods in fields such as pharmacology, metabolomics and pesticide research. We report the development of a multi-platform, user-friendly software tool MUSCLE (Multi-platform Unbiased optimisation of Spectrometry via Closed Loop Experimentation) for the robust and fully-automated multiobjective optimisation of targeted LC-MS/MS analysis. MUSCLE shortened the analysis times and increased the analytical sensitivities of targeted metabolite analysis which was demonstrated on two different manufacturers LC-MS/MS instruments. Availability: Available at http://www.muscleproject.org CONTACT: email@example.com SUPPLEMENTARY INFORMATION: See Supplementary Data available at the journal's web site.
Blood-vessel dysfunction arises before overt hyperglycemia in type-2 diabetes (T2DM). We hypothesised that a metabolomic approach might identify metabolites/pathways perturbed in this pre-hyperglycemic phase. To test this hypothesis and for specific metabolite hypothesis generation, serum metabolic profiling was performed in young women at increased, intermediate and low risk of subsequent T2DM.
Recently, it has been reported that anti-viral drugs, such as indinavir and lopinavir (originally targeted for HIV), also inhibit E6-mediated proteasomal degradation of mutant p53 in E6-transfected C33A cells. In order to understand more about the mode-of-action(s) of these drugs the metabolome of HPV16 E6 expressing cervical carcinoma cell lines was investigated using mass spectrometry (MS)-based metabolic profiling. The metabolite profiling of C33A parent and E6-transfected cells exposed to these two anti-viral drugs was performed by ultra performance liquid chromatography (UPLC)-MS and gas chromatography (GC)-time of flight (TOF)-MS. Using a combination of univariate and multivariate analyses, these metabolic profiles were investigated for analytical and biological reproducibility and to discover key metabolite differences elicited during anti-viral drug challenge. This approach revealed both distinct and common effects of these two drugs on the metabolome of two different cell lines. Finally, intracellular drug levels were quantified, which suggested in the case of lopinavir that increased activity of membrane transporters may contribute to the drug sensitivity of HPV infected cells.
Experimental MS(n) mass spectral libraries currently do not adequately cover chemical space. This limits the robust annotation of metabolites in metabolomics studies of complex biological samples. In-silico fragmentation libraries would improve the identification of compounds from experimental multi-stage fragmentation data when experimental reference data is unavailable. Here we present a freely-available software package to automatically control Mass Frontier software to construct in-silico mass spectral libraries, and to perform spectral matching. Based on two case studies we have demonstrated that HAMMER allows researchers to generate in-silico mass spectral libraries in an automated and high-throughput fashion with little or no human intervention required.
The application of reporting standards in metabolomics allow data from different laboratories to be shared, integrated and interpreted. Although minimum reporting standards related to metabolite identification were published in 2007, it is clear that significant efforts are required to ensure their continuous update and appropriate use by the metabolomics community. These include their use in metabolomics data submission (e.g., MetaboLights) and as a requirement for publication in peer-reviewed journals (e.g., Metabolomics). The Data Standards and Metabolite Identification Task Groups of the international Metabolomics Society are actively working to develop and promote these standards and educate the community on their use.
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.
Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.
Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus metabolic reconstruction, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ?2× more reactions and ?1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
Using direct injection mass spectrometry (DIMS) we discovered that deoxyribose-1-phosphate (dRP) is released by platelets upon activation. Interestingly, the addition of exogenous dRP to human platelets significantly increased platelet aggregation and integrin ?IIb?3 activation in response to thrombin. In parallel, genetically modified platelets with double genetic deletion of thymidine phosphorylase and uridine phosphorylase were characterised by reduced release of dRP, impaired aggregation and decreased integrin ?IIb?3 activation in response to thrombin. In vitro platelet adhesion onto fibrinogen and collagen under physiological flow conditions was potentiated by treatment of human platelets with exogenous dRP and impaired in transgenic platelets with reduced dRP release. Human and mouse platelets responded to dRP treatment with a sizeable increase in reactive oxygen species (ROS) generation and the pre-treament with the antioxidant apocynin abolished the effect of dRP on aggregation and integrin activation. Experiments directly assessing the activation of the small G protein Rap1b and protein kinase C suggested that dRP increases the basal levels of activity of these two pivotal platelet-activating pathways in a redox-dependent manner. Taken together, we present evidence that dRP is a novel autocrine amplifier of platelet activity, which acts on platelet redox levels and modulates integrin ?IIb?3.
Metabolomics is an emerging and powerful discipline that provides an accurate and dynamic picture of the phenotype of mammalian systems through the study of endogenous and exogenous metabolites in cells, tissues, culture supernatants as well as biofluids. In the last 5 years an increase in the number of metabolomic investigations of cardiovascular diseases and diabetes has been observed. In this article the experimental strategies applied and recent examples of their application in disease and drug efficacy/toxicity biomarker detection and the employment for the discovery of new molecular pathophysiological processes related to disease onset and progression, as well as their usefulness in drug efficacy/toxicity, will be reviewed. An outlook of the requirements for future successes will also be discussed.
The determination of intracellular metabolite concentrations in Saccharomyces cerevisiae cell systems requires appropriate experimental methods to (a) collect cells and rapidly inhibit metabolism (quenching), (b) fracture cell walls and extract metabolites from within the cellular envelope(s), and (c) detect and quantify metabolites. A range of methods are applied for each of these processes, and no single method is appropriate for all metabolites. For example, the physicochemical diversity of metabolites, including solubility in water or organic solvents, is large. No single extraction solvent is appropriate for all metabolites reported in S. cerevisiae, and multiple solvent systems for extraction employing water, methanol, and chloroform at different pH are recommended for targeted extraction of metabolites. In this chapter, methods for the targeted study of organic acids present in the tricarboxylic acid cycle will be described. These include (a) the quenching of metabolism in batch cell cultures, (b) a single extraction method which provides the extraction of a wide diversity of metabolites, and (c) an analytical method applying gas chromatography-mass spectrometry for targeted analysis of six organic acids present in the tricarboxylic acid cycle metabolic pathway.
The qualitative detection, quantification, and structural characterization of analytes in biological systems are important requirements for objectives to be fulfilled in systems biology research. One analytical tool applied to a multitude of systems biology studies is mass spectrometry, particularly for the study of proteins and metabolites. Here, the role of mass spectrometry in systems biology will be assessed, the advantages and disadvantages discussed, and the instrument configurations available described. Finally, general applications will be briefly reviewed.
The availability of label-free data derived from yeast cells (based on the summed intensity of the three strongest, isoform-specific peptides) permitted a preliminary assessment of protein abundances for glycolytic proteins. Following this analysis, we demonstrate successful application of the QconCAT technology, which uses recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, to the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae. A QconCAT of 88 kDa (59 tryptic peptides) corresponding to 27 isoenzymes was designed and built to encode two or three analyte peptides per protein, and after stable isotope labeling of the standard in vivo, protein levels were determined by LC-MS, using ultra high performance liquid chromatography-coupled mass spectrometry. We were able to determine absolute protein concentrations between 14,000 and 10 million molecules/cell. Issues such as efficiency of extraction and completeness of proteolysis are addressed, as well as generic factors such as optimal quantotypic peptide selection and expression. In addition, the same proteins were quantified by intensity-based label-free analysis, and both sets of data were compared with other quantification methods.
Metabolomics involves the investigation of the intracellular (endometabolome) and extracellular (exometabolome) pools of metabolites in biological systems. Methods to sample the exometabolome and to quench metabolism and extract intracellular metabolites for the model eukaryote Saccharomyces cerevisiae are presented here. These methods have been developed and validated to provide a fit-for-purpose protocol for global analyses of the S. cerevisiae metabolome. The protocol allows the extraction of a wide variety of metabolite classes and provides reproducible results to allow relative and semi-quantitative comparisons between samples of different origin. For exometabolome studies, fast sampling and separation of cells by syringe filtration is recommended. For endometabolome studies, fast quenching of intracellular metabolism is performed using a 60:40 (v/v) methanol:aqueous ammonium hydrogen carbonate solution at -48 °C. Extraction of intracellular metabolites is performed using multiple freeze/thaw cycles in a 60:40 (v/v) methanol:water solution at temperatures lower than 0 °C.
In clinical analyses, the most appropriate biofluid should be analyzed for optimal assay performance. For biological fluids, the most readily accessible is blood, and metabolomic analyses can be performed either on plasma or serum. To determine the optimal agent for analysis, metabolic profiles of matched human serum and plasma were assessed by gas chromatography/time-of-flight mass spectrometry and ultrahigh-performance liquid chromatography mass spectrometry (in positive and negative electrospray ionization modes). Comparison of the two metabolomes, in terms of reproducibility, discriminative ability and coverage, indicated that they offered similar analytical opportunities. An analysis of the variation between 29 small-cell lung cancer (SCLC) patients revealed that the differences between individuals are markedly similar for the two biofluids. However, significant differences between the levels of some specific metabolites were identified, as were differences in the intersubject variability of some metabolite levels. Glycerophosphocholines, erythritol, creatinine, hexadecanoic acid, and glutamine in plasma, but not in serum, were shown to correlate with life expectancy for SCLC patients, indicating the utility of metabolomic analyses in clinical prognosis and the particular utility of plasma in relation to the clinical management of SCLC.
In Arabidopsis, resistance to the necrotrophic fungus Botrytis cinerea is conferred by ethylene via poorly understood mechanisms. Metabolomic approaches compared the responses of the wild-type, the ethylene-insensitive mutant etr1-1, which showed increased susceptibility, and the constitutively active ethylene mutants ctr1-1 and eto2 both exhibited decreased susceptibility to B. cinerea. Fourier transform-infrared (FT-IR) spectroscopy demonstrated reproducible biochemical differences between treatments and genotypes. To identify discriminatory mass-to-charge ratios (m/z) associated with resistance, discriminant function analysis was employed on spectra derived from direct injection electrospray ionisation-mass spectrometry on the derived principal components of these data. Ethylene-modulated m/z were mapped onto Arabidopsis biochemical pathways and many were associated with hydroxycinnamate and monolignol biosynthesis, both linked to cell wall modification. A high-resolution linear triple quadrupole-Orbitrap hybrid system confirmed the identity of key metabolites in these pathways. The contribution of these pathways to defence against B. cinerea was validated through the use of multiple Arabidopsis mutants. The FT-IR microspectroscopy indicated that spatial accumulation of hydroxycinnamates and monolignols at the cell wall to confine disease was linked ot ethylene. These data demonstrate the power of metabolomic approaches in elucidating novel biological phenomena, especially when coupled to validation steps exploiting relevant mutant genotypes.
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.
Being born small for gestational age (SGA) confers increased risks of perinatal morbidity and mortality and increases the risk of cardiovascular complications and diabetes in later life. Accumulating evidence suggests that the etiology of SGA is usually associated with poor placental vascular development in early pregnancy. We examined metabolomic profiles using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) in three independent studies: (a) venous cord plasma from normal and SGA babies, (b) plasma from a rat model of placental insufficiency and controls, and (c) early pregnancy peripheral plasma samples from women who subsequently delivered a SGA baby and controls. Multivariate analysis by cross-validated Partial Least Squares Discriminant Analysis (PLS-DA) of all 3 studies showed a comprehensive and similar disruption of plasma metabolism. A multivariate predictive model combining 19 metabolites produced by a Genetic Algorithm-based search program gave an Odds Ratio for developing SGA of 44, with an area under the Receiver Operator Characteristic curve of 0.9. Sphingolipids, phospholipids, carnitines, and fatty acids were among this panel of metabolites. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of SGA offers insight into disease pathogenesis and offers the promise of a robust presymptomatic screening test.
Metabolomics can play a particularly important role in elucidating novel anabolic and catabolic pathways in bacteria and fungi, and in understanding the dynamics of metabolism. In these approaches, an isotopically labelled substrate, with an artificially high abundance of isotopic label, is fed to the microorganism under study. The products become isotopically labelled, and can be measured using a combination of mass spectrometry and nuclear magnetic resonance spectroscopy. This mass isotopomer analysis is referred to as time and relative differences in systems (TARDIS)-based analysis, as it measures and quantifies the temporal sequential emergence of these labelled products. In this review, we cover this topic from an experimental point of view in relation to the study of metabolism, and summarise how the application of radioactive and stable isotopes is being used in pathway elucidation and metabolic flux determination (fluxomics).
The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets.
In vitro cultured mammalian cells respond to mild hypothermia (27-33 °C) by attenuating cellular processes and slowing and arresting the cell cycle. The slowing of the cell cycle at the upper range (31-33 °C) and its complete arrest at the lower range (27-28 °C) of mild hypothermia is effected by the activation of p53 and subsequent expression of p21. However, the mechanism by which cold is perceived in mammalian cells with the subsequent activation of p53 has remained undetermined. In the present paper, we report that the exposure of Chinese-hamster ovary-K1 cells to mildly hypothermic conditions activates the ATR (ataxia telangiectasia mutated- and Rad3-related kinase)-p53-p21 signalling pathway and is thus a key pathway involved in p53 activation upon mild hypothermia. In addition, we show that although p38MAPK (p38 mitogen-activated protein kinase) is also involved in activation of p53 upon mild hypothermia, this is probably the result of activation of p38MAPK by ATR. Furthermore, we show that cold-induced changes in cell membrane lipid composition are correlated with the activation of the ATR-p53-p21 pathway. Therefore we provide the first mechanistic detail of cell sensing and signalling upon mild hypothermia in mammalian cells leading to p53 and p21 activation, which is known to lead to cell cycle arrest.
• Variations in tissue development and spatial composition have a major impact on the nutritional and organoleptic qualities of ripe fleshy fruit, including melon (Cucumis melo). To gain a deeper insight into the mechanisms involved in these changes, we identified key metabolites for rational food quality design. • The metabolome, volatiles and mineral elements were profiled employing an unprecedented range of complementary analytical technologies. Fruits were followed at a number of time points during the final ripening process and tissues were collected across the fruit flesh from rind to seed cavity. Approximately 2000 metabolite signatures and 15 mineral elements were determined in an assessment of temporal and spatial melon fruit development. • This study design enabled the identification of: coregulated hubs (including aspartic acid, 2-isopropylmalic acid, ?-carotene, phytoene and dihydropseudoionone) in metabolic association networks; global patterns of coordinated compositional changes; and links of primary and secondary metabolism to key mineral and volatile fruit complements. • The results reveal the extent of metabolic interactions relevant to ripe fruit quality and thus have enabled the identification of essential candidate metabolites for the high-throughput screening of melon breeding populations for targeted breeding programmes aimed at nutrition and flavour improvement.
Spoilage in meat is the result of the action of microorganisms and results in changes of meat and microbial metabolism. This process may include pathogenic food poisoning bacteria such as Salmonella typhimurium, and it is important that these are differentiated from the natural spoilage process caused by non-pathogenic microorganisms. In this study we investigated the application of metabolic profiling using gas chromatography-mass spectrometry, to assess the microbial contamination of pork. Metabolite profiles were generated from microorganisms, originating from the natural spoilage process and from the artificial contamination with S. typhimurium. In an initial experiment, we investigated changes in the metabolic profiles over a 72 hour time course at 25 °C and established time points indicative of the spoilage process. A further experiment was performed to provide in-depth analysis of the metabolites characteristic of contamination by S. typhimurium. We applied a three-way PARAllel FACtor analysis 2 (PARAFAC2) multivariate algorithm to model the metabolic profiles. In addition, two univariate statistical tests, two-sample Wilcoxon signed rank test and Friedman test, were employed to identify metabolites which showed significant difference between natural spoiled and S. typhimurium contaminated samples. Consistent results from the two independent experiments were obtained showing the discrimination of the metabolic profiles of the natural spoiled pork chops and those contaminated with S. typhimurium. The analysis identified 17 metabolites of significant interest (including various types of amino acid and fatty acid) in the discrimination of pork contaminated with the pathogenic microorganism.
For decades, superoxic ex vivo dual perfusion of the human placental lobule has been used as a model to study the physiology and metabolism of the placenta. The aim of this study was to further develop the technique to enable perfusion at soluble oxygen concentrations similar to those in normal pregnancy (normoxia) and in pre-eclampsia (PE; hypoxia). Our design involved reducing the mean soluble oxygen tension in the maternal-side intervillous space (IVS) perfusate to 5-7% and <3% for normoxia and hypoxia, respectively, while providing a more ubiquitous delivery of perfusate into the IVS, using 22 maternal-side cannulae. We achieved quasi-steady states in [O?](fetal venous (soluble)), which were statistically different between the two adaptations at t=150 to t=240?min of dual perfusion (2.1, 1.2, 2.8 and 0.4, 0.0, 1.5%; median, 25th, 75th percentiles, n=20 and 24 readings in n=5 and n=6 lobules, normoxic and hypoxic perfusion, respectively; P<0.001, Mann-Whitney U-test). Lactate dehydrogenase (LDH) levels in fetal and maternal venous outflow perfusates were unaffected by the adaptations. There was also no difference in tissue lactate release between the two adaptations. Glucose consumption from the fetal circulation and maternal-side venous pyruvate release were higher under normoxic conditions, indicative of a greater metabolic flux through glycolysis. Furthermore, there was greater release of the hypoxic-sensitive marker, macrophage inflammatory protein-1?, into the maternal venous perfusate in the hypoxic model. Also, during hypoxic perfusion, we found that fetal-side venous placental growth factor (PlGF) levels were higher compared with normoxic perfusion. We conclude that these ex vivo adapted methods of placental perfusion provide a means of studying aspects of placental metabolism in relation to normal oxygenation and hypoxia-associated pregnancy disease.
Micromolar concentrations of the proangiogenic metabolite deoxyribose-1-phosphate (dRP) were detected in platelet supernatants by mass spectrometry. In this study, we assessed whether the release of dRP by platelets stimulates endothelial cell migration and angiogenesis.
Metabolomics allows the simultaneous and relative quantification of thousands of different metabolites within a given sample using sensitive and specific methodologies such as gas or liquid chromatography coupled to mass spectrometry, typically in discovery phases of studies. Biomarkers are biological characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes or pharmacologic responses to a therapeutic intervention. Biomarkers are widely used in clinical practice for the diagnosis, assessment of severity and response to therapy in a number of clinical disease states. In human studies, metabolomics has been applied to define biomarkers related to prognosis or diagnosis of a disease or drug toxicity/efficacy and in doing so hopes to provide greater pathophysiological understanding of disease or therapeutic toxicity/efficacy. This review discusses the application of metabolomics in the discovery and subsequent application of biomarkers in the diagnosis and management of inborn errors of metabolism, cardiovascular disease and cancer. We critically appraise how novel biomarkers discovered through metabolomic analysis may be utilized in future clinical practice by addressing the following three fundamental questions: (1) Can the clinician measure them? (2) Do they add new information? (3) Do they help the clinician to manage patients? Although a number of novel biomarkers have been discovered through metabolomic studies of human diseases in the last decade, none have currently made the transition to routine use in clinical practice. Metabolites identified from these early studies will need to form the basis of larger, prospective, externally validated studies in clinical cohorts for their future use as biomarkers. At this stage, the absolute quantification of these biomarkers will need to be assessed epidemiologically, as will the ultimate deployment in the clinic via routine biochemistry, dip stick or similar rapid at- or near-patient care technologies.
Preeclampsia is a pregnancy-specific syndrome that causes substantial maternal and fetal morbidity and mortality. The etiology is incompletely understood, and there is no clinically useful screening test. Current metabolomic technologies have allowed the establishment of metabolic signatures of preeclampsia in early pregnancy. Here, a 2-phase discovery/validation metabolic profiling study was performed. In the discovery phase, a nested case-control study was designed, using samples obtained at 15±1 weeks gestation from 60 women who subsequently developed preeclampsia and 60 controls taking part in the prospective Screening for Pregnancy Endpoints cohort study. Controls were proportionally population matched for age, ethnicity, and body mass index at booking. Plasma samples were analyzed using ultra performance liquid chromatography-mass spectrometry. A multivariate predictive model combining 14 metabolites gave an odds ratio for developing preeclampsia of 36 (95% CI: 12 to 108), with an area under the receiver operator characteristic curve of 0.94. These findings were then validated using an independent case-control study on plasma obtained at 15±1 weeks from 39 women who subsequently developed preeclampsia and 40 similarly matched controls from a participating center in a different country. The same 14 metabolites produced an odds ratio of 23 (95% CI: 7 to 73) with an area under receiver operator characteristic curve of 0.92. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of preeclampsia offers insight into disease pathogenesis and offers the tantalizing promise of a robust presymptomatic screening test.
The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).
The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.
To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.
We compared the gas chromatography-mass spectrometry (GC-MS) metabolite profiles of mouse tumour necrosis factor alpha (mTNF-alpha) secreting Streptomyces lividans TK24 to the non-secreting wild type and the wild type harbouring the empty pIJ486 plasmid by multi-block principal component analysis (PCA). The multi-block PCA model successfully identified peaks that were statistically different between the protein secreting and non-secreting strains, and at the same time also uncovered the efficiency of intracellular metabolite extraction by an ultrasonic adaptive focused acoustics (AFA) technique compared to a manual vortex/freeze-thaw method. Fifty-one metabolites were significantly different between the three biological strains and 17 of these were abundant in the mTNF-alpha secreting strain compared to the non-secreting strains. No significant differences in the number of detected metabolite peaks were observed between the two extraction techniques. However, from the loadings of the multi-block PCA model, as well as univariate statistical analysis, we observed that the relative peak response ratios to the internal standard of 10 metabolites were higher for the AFA extraction, suggesting a more efficient recovery of these metabolites than achieved with the manual vortex/freeze thaw method.
Systems Biology has a mission that puts it at odds with traditional paradigms of physics and molecular biology, such as the simplicity requested by Occams razor and minimum energy/maximal efficiency. By referring to biochemical experiments on control and regulation, and on flux balancing in yeast, we show that these paradigms are inapt. Systems Biology does not quite converge with biology either: Although it certainly requires accurate stamp collecting, it discovers quantitative laws. Systems Biology is a science of its own, discovering own fundamental principles, some of which we identify here.
Dietary energy restriction (DER) reduces risk of spontaneous mammary cancer in rodents. In humans, DER in premenopausal years seems to reduce risk of postmenopausal breast cancer. Markers of DER are required to develop acceptable DER regimens for breast cancer prevention. We therefore examined markers of DER in the breast, adipose tissue, and serum. Nineteen overweight or obese women at moderately increased risk of breast cancer (lifetime risk, 1 in 6 to 1 in 3) ages between 35 and 45 were randomly allocated to DER [liquid diet, 3,656 kJ/d (864 kcal/d); n = 10] or asked to continue their normal eating patterns (n = 9) for one menstrual cycle. Biopsies of the breast and abdominal fat were taken before and after the intervention. RNA was extracted from whole tissues and breast epithelium (by laser capture microdissection) and hybridized to Affymetrix GeneChips. Longitudinal plasma and urine samples were collected before and after intervention, and metabolic profiles were generated using gas chromatography-mass spectrometry. DER was associated with significant reductions in weight [-7.0 (+/-2.3) kg] and in alterations of serum biomarkers of breast cancer risk (insulin, leptin, total and low-density lipoprotein cholesterol, and triglycerides). In both abdominal and breast tissues, as well as isolated breast epithelial cells, genes involved in glycolytic and lipid synthesis pathways (including stearoyl-CoA desaturase, fatty acid desaturase, and aldolase C) were significantly down-regulated. We conclude that reduced expressions of genes in the lipid metabolism and glycolytic pathways are detectable in breast tissue following DER, and these may represent targets for DER mimetics as effective chemoprophylactic agents.
A method for the preparation and GC-TOF-MS analysis of human serum samples has been developed and evaluated for application in long-term metabolomic studies. Serum samples were deproteinized using 3:1 methanol/serum, dried in a vacuum concentrator, and chemically derivatized in a two-stage process. Samples were analyzed by GC-TOF-MS with a 25 min analysis time. In addition, quality control (QC) samples were used to quantify process variability. Optimization of chemical derivatization was performed. Products were found to be stable for 30 h after derivatization. An assessment of within-day repeatability and within-week reproducibility demonstrates that excellent performance is observed with our developed method. Analyses were consistent over a 5 month period. Additional method testing, using spiked serum samples, showed the ability to define metabolite differences between samples from a population and samples spiked with metabolites standards. This methodology allows the continuous acquisition and application of data acquired over many months in long-term metabolomic studies, including the HUSERMET project (http://www.husermet.org/).
A metabolomics approach combining (1)H NMR and gas chromatography-electrospray ionization time-of-flight mass spectrometry (GC-EI-TOFMS) profiling was employed to characterize melon (Cucumis melo L.) fruit. In a first step, quantitative (1)H NMR of polar extracts and principal component analyses (PCA) of the corresponding data highlighted the major metabolites in fruit flesh, including sugars, organic acids, and amino acids. In a second step, the spatial localization of metabolites was investigated using both analytical techniques. Direct (1)H NMR profiling of juice or GC-EI-TOFMS profiling of tissue extracts collected from different locations in the fruit flesh provided information on advantages and drawbacks of each technique for the analysis of a sugar-rich matrix such as fruit. (1)H NMR and GC-EI-TOFMS data sets were compared using independently performed PCA and multiblock hierarchical PCA (HPCA), respectively. In addition a correlation-based multiblock HPCA was used for direct comparison of both analytical data sets. These data analyses revealed several gradients of metabolites in fruit flesh which can be related with differences in metabolism and indicated the suitability of multiblock HPCA for correlation of data from two (or potentially more) metabolomics platforms.
The application of gas chromatography-mass spectrometry (GC-MS) to the global analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR projects (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC-MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GCxGC-TOF/MS was compared with 1 dimensional GC-TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise.
Global metabolite analysis approaches, coupled with sophisticated data analysis and modeling procedures (metabolomics), permit a dynamic read-out of how cellular proteins interact with cellular and environmental conditions to determine cell status. This type of approach has profound potential for understanding, and subsequently manipulating, the regulation of cell function. As part of our study to define the regulatory events that may be used to maximize production of commercially valuable recombinant proteins from cultured mammalian cells, we have optimized the quenching process to allow retention of physiologically relevant intracellular metabolite profiles in samples from recombinant Chinese hamster ovary (CHO) cells. In a comparison of a series of candidate quenching procedures, we have shown that quenching in 60% methanol supplemented with 0.85% ammonium bicarbonate (AMBIC) at -40 degrees C generates a profile of metabolites that is representative of a physiological status based upon examination of key labile cellular metabolites. This represents a key feature for any metabolomic study with suspension cultured mammalian cells and provides confidence in the validity of subsequent data analysis and modeling procedures.
A method for performing untargeted metabolomic analysis of human serum has been developed based on protein precipitation followed by Ultra Performance Liquid Chromatography and Time-of-Flight mass spectrometry (UPLC-TOF-MS). This method was specifically designed to fulfill the requirements of a long-term metabolomic study, spanning more than 3 years, and it was subsequently thoroughly evaluated for robustness and repeatability. We describe here the observed drift in instrumental performance over time and its improvement with adjustment of the length of analytical block. The optimal setup for our purpose was further validated against a set of serum samples from 30 healthy individuals. We also assessed the reproducibility of chromatographic columns with the same chemistry of stationary phase from the same manufacturer but from different production batches. The results have allowed the authors to prepare SOPs for "fit for purpose" long-term UPLC-MS metabolomic studies, such as are being employed in the HUSERMET project. This method allows the acquisition of data and subsequent comparison of data collected across many months or years.
Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography-mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearsons R = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (R = 0.94).
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.
Dupuytrens disease (DD) is an ill-defined fibroproliferative disorder affecting the palm of the hand, resulting in progressive and irreversible digital contracture. In view of the abnormal gene dysregulation found in DD, and its potential effect on metabolites at a functional level, we chose to examine the metabolic profile involved in DD. Using Fourier transform infrared (FT-IR) spectroscopy to generate metabolic fingerprints of cultured cells, we compared the profiles of DD cords and nodules (1) against the unaffected transverse palmar fascia (internal control), (2) against carpal ligamentous fascia (external control), and (3) against fibroblasts from fat surrounding the nodule and skin overlying the nodule (environmental control). We also determined the effects of serial passaging of the cells on DD fingerprints. Subsequently, gas chromatography-mass spectrometry (GC-MS) was employed for metabolic profiling in order to identify metabolites characteristic of the DD tissue phenotypes. We developed a robust metabolomic analysis procedure of DD using cultured fibroblasts derived from DD tissues. Our carefully controlled culture conditions, combined with assessment of metabolic phenotypes by FT-IR and GC-MS, enabled us to demonstrate metabolic differences between DD and unaffected transverse palmar fascia and between DD and healthy control tissue. In early passage (0-3) the metabolic differences were clear, but cells from subsequent passages (4-6) started to lose this distinction between diseased and non-diseased origin. The dysregulated metabolites we identified were leucine, phenylalanine, lysine, cysteine, aspartic acid, glycerol-3-phosphate and the vitamin precursor to coenzyme A. Early passage DD cells exhibit a clear metabolic profile, in which central metabolic pathways appear to be involved. Experimental conditions have been identified in which these DD data are reproducible. The experimental reproducibility will be useful in DD diagnostics and for DD systems biology.
Major food adulteration and contamination events seem to occur with some regularity, such as the widely publicised adulteration of milk products with melamine and the recent microbial contamination of vegetables across Europe for example. With globalisation and rapid distribution systems, these can have international impacts with far-reaching and sometimes lethal consequences. These events, though potentially global in the modern era, are in fact far from contemporary, and deliberate adulteration of food products is probably as old as the food processing and production systems themselves. This review first introduces some background into these practices, both historically and contemporary, before introducing a range of the technologies currently available for the detection of food adulteration and contamination. These methods include the vibrational spectroscopies: near-infrared, mid-infrared, Raman; NMR spectroscopy, as well as a range of mass spectrometry (MS) techniques, amongst others. This subject area is particularly relevant at this time, as it not only concerns the continuous engagement with food adulterers, but also more recent issues such as food security, bioterrorism and climate change. It is hoped that this introductory overview acts as a springboard for researchers in science, technology, engineering, and industry, in this era of systems-level thinking and interdisciplinary approaches to new and contemporary problems.
Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se.
The prevalence, and associated healthcare burden, of diabetes mellitus is increasing worldwide. Mortality and morbidity are associated with diabetic complications in multiple organs and tissues, including the eye, kidney and cardiovascular system, and new therapeutics to treat these complications are required urgently. Triethylenetetramine (TETA) is one such experimental therapeutic that acts to chelate excess copper (II) in diabetic tissues and reduce oxidative stress and cellular damage.
Metabolomics offers a powerful holistic approach to examine the metabolite composition of biofluids to identify disruptions present in disease. We used ultra performance liquid chromatography-mass spectroscopy on the maternal serum obtained in the third trimester to address the hypothesis that pregnancies ending in poor outcomes (small for gestational age infant, preterm birth, or neonatal intensive care admission, n = 40) would have a different maternal serum metabolic profiles to matched healthy pregnancies (n = 40). Ninety-eight identified metabolic features differed between normal and poor pregnancy outcomes. Classes of metabolites perturbed included free fatty acids, glycerolipids, progesterone metabolites, sterol lipids, vitamin D metabolites, and sphingolipids; these highlight potential molecular mechanisms associated with pregnancy complications in the third trimester linked by placental dysfunction. In this clinical setting, metabolomics has the potential to describe differences in fetoplacental and maternal metabolites in pregnancies with poor pregnancy outcomes compared with controls.
Quantitative data on the dynamic changes in the transcriptome and the metabolome of yeast in response to an impulse-like perturbation in nutrient availability was integrated with the metabolic pathway information in order to elucidate the long-term dynamic re-organization of the cells. This study revealed that, in addition to the dynamic re-organization of the de novo biosynthetic pathways, salvage pathways were also re-organized in a time-dependent manner upon catabolite repression. The transcriptional and the metabolic responses observed for nitrogen catabolite repression were not as severe as those observed for carbon catabolite repression. Selective up- or down regulation of a single member of a paralogous gene pair during the response to the relaxation from nutritional limitation was identified indicating a differentiation of functions among paralogs. Our study highlighted the role of inosine accumulation and recycling in energy homeostasis and indicated possible bottlenecks in the process.
Prolonged peritoneal dialysis (PD) therapy can result in the development of encapsulating peritoneal sclerosis (EPS), characterized by extensive sclerosis of the peritoneum with bowel adhesions often causing obstruction.
Multiple drug resistance (MDR) in yeast is effected by two major superfamilies of membrane transporters: the major facilitator superfamily (MFS) and the ATP-binding cassette (ABC) superfamily. In the present work, we investigated the cellular responses to disruptions in both MFS (by deleting the transporter gene, QDR3) and ABC (by deleting the gene for the Pdr3 transcription factor) transporter systems by growing diploid homozygous deletion yeast strains in glucose- or ammonium-limited continuous cultures. The transcriptome and the metabolome profiles of these strains, as well as the flux distributions in the optimal solution space, reveal novel insights into the underlying mechanisms of action of QDR3 and PDR3. Our results show how cells rearrange their metabolism to cope with the problems that arise from the loss of these drug-resistance genes, which likely evolved to combat chemical attack from bacterial or fungal competitors. This is achieved through the accumulation of intracellular glucose, glycerol, and inorganic phosphate, as well as by repurposing genes that are known to function in other parts of metabolism in order to minimise the effects of toxic compounds.
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