Beyond blood sugar: the potential of NMR-based metabonomics for type 2 human diabetes, and the horse as a possible model

The influence of sample collection, handling and low temperature storage upon NMR metabolic profiling analysis in human synovial fluid

The impact of metabolism upon the altered pathology of joint disease is rapidly becoming recognized as an important area of study. Synovial joint fluid is an attractive and representative biofluid of joint disease. A systemic review revealed little evidence of the metabolic stability of synovial joint fluid collection, handling or storage, despite recent reports characterizing the metabolic phenotype in joint disease. We aim to report the changes in small molecule detection within human synovial fluid (HSF) using nuclear magnetic resonance (NMR) spectroscopy at varying storage temperatures, durations and conditions. HSF was harvested by arthrocentesis from patients with isolated monoarthropathy or undergoing joint replacement (n = 30). Short-term storage (0-12 h, 4°C & 18°C) and the effect of repeated freeze-thaw cycles (-80°C to 18°C) was assessed. Long-term storage was evaluated by early (-80°C, <21days) and late analysis (-80°C, 10-12 months). 1D NMR spectroscopy experiments, NOESYGPPR1D and CPMG identified metabolites and semi-quantification was performed. Samples demonstrated broad stability to freeze-thaw cycling and refrigeration of <4 h. Short-term room temperature or refrigerated storage showed significant variation in 2-ketoisovalerate, valine, dimethylamine, succinate, 2-hydroxybutyrate, and acetaminophen glucuronide. Lipid and macromolecule detection was variable. Long-term storage demonstrated significant changes in: acetate, acetoacetate, creatine, N,N-dimethylglycine, dimethylsulfone, 3-hydroxybutyrate and succinate. Changeable metabolites during short-term storage appeared to be energy-synthesis intermediates. Most metabolites were stable for the first four hours at room temperature or refrigeration, with notable exceptions. We therefore recommend that HSF samples should be kept refrigerated for no more than 4 hours prior to freezing at -80°C. Furthermore, storage of HSF samples for 10-12 months before analysis can affect the detection of selected metabolites.

The equine mononuclear phagocyte system: The relevance of the horse as a model for understanding human innate immunity

The mononuclear phagocyte system (MPS) is a family of cells of related function that includes bone marrow progenitors, blood monocytes and resident tissue macrophages. Macrophages are effector cells in both innate and acquired immunity. They are a major resident cell population in every organ and their numbers increase in response to proinflammatory stimuli. Their function is highly regulated by a wide range of agonists, including lymphokines, cytokines and products of microorganisms. Macrophage biology has been studied most extensively in mice, yet direct comparisons of rodent and human macrophages have revealed many functional differences. In this review, we provide an overview of the equine MPS, describing the variation in the function and phenotype of macrophages depending on their location and the similarities and differences between the rodent, human and equine immune response. We discuss the use of the horse as a large animal model in which to study macrophage biology and pathological processes shared with humans. Finally, following the recent update to the horse genome, facilitating further comparative analysis of regulated gene expression between the species, we highlight the importance of future transcriptomic macrophage studies in the horse, the findings of which may also be applicable to human as well as veterinary research.

Can metabolic profiling provide a new description of osteoarthritis and enable a personalised medicine approach?

Osteoarthritis (OA) is a multifactorial disease contributing to significant disability and economic burden in Western populations. The aetiology of OA remains poorly understood, but is thought to involve genetic, mechanical and environmental factors. Currently, the diagnosis of OA relies predominantly on clinical assessment and plain radiographic changes long after the disease has been initiated. Recent advances suggest that there are changes in joint fluid metabolites that are associated with OA development. If this is the case, biochemical and metabolic biomarkers of OA could help determine prognosis, monitor disease progression and identify potential therapeutic targets. Moreover, for focussed management and personalised medicine, novel biomarkers could sub-stratify patients into OA phenotypes, differentiating metabolic OA from post-traumatic, age-related and genetic OA. To date, OA biomarkers have concentrated on cytokine action and protein signalling with some progress. However, these remain to be adopted into routine clinical practice. In this review, we outline the emerging metabolic links to OA pathogenesis and how an elucidation of the metabolic changes in this condition may provide future, more descriptive biomarkers to differentiate OA subtypes.

A First Step Toward Unraveling the Energy Metabolism in Endurance Horses: Comparison of Plasma Nuclear Magnetic Resonance Metabolomic Profiles Before and After Different Endurance Race Distances

Endurance racing places high demands on energy metabolism pathways. Metabolomics can be used to investigate biochemical responses to endurance exercise in humans, laboratory animals, and horses. Although endurance horses have previously been assessed in the field (i.e., during races) using broad-window Nuclear Magnetic Resonance metabolomics, these studies included several different race locations, race distances, age classes, and race statuses (finisher or elimination). The present NMR metabolomics study focused on 40 endurance horses racing in three race categories over 90, 120, or 160 km. The three races took place in the same location. Given that energy metabolism is closely related to exercise intensity and duration (and therefore distance covered), the study's objective was to determine whether the metabolic pathways recruited during the race varied as a function of the total ride distance. For each horse, a plasma sample was collected the day before the race, and another was collected at the end of the race. Sixteen, 15, and 9 horses raced over 90, 120, and 160 km, respectively. Proton NMR spectra (500 MHz) were acquired for these 80 plasma samples. After processing, the spectra were divided into bins representing the NMR variables and then classified using orthogonal projection on latent structure models supervised by the sampling time (pre- or post-race) or the distance covered. The models revealed that the post-race metabolomic profiles are associated to the total ride distance groups. By combining biochemical assay results and NMR data in multiblock models, we further showed that enzymatic activities and metabolites are significantly associated to the race category. In the highest race category (160 km), there appears to be a metabolic switch from carbohydrate consumption to lipid consumption in order to maintain glycaemia. Furthermore, signs of protein breakdown were more apparent in the longest race category. The metabolic shift seen in the different racing categories could be related to a mixture of three important factors that are the ride distance, the training status and the inherited endurance capacity of the various horses competing.

Protective Effects of Total Glycoside From Rehmannia glutinosa Leaves on Diabetic Nephropathy Rats via Regulating the Metabolic Profiling and Modulating the TGF-β1 and Wnt/β-Catenin Signaling Pathway

Rehmannia glutinosa Libosch (RG), is officially listed in the Chinese Pharmacopoeia and is widely used in China. The leaves of RG (LR) is an important vegetative organ of the plant. At present, the total glycosides of RG (TLR) were extracted from RG, and developed a national second class of new drugs to the Dihuangye total glycoside capsule (DTG). Additionally, DTG has the effect of nourishing yin and tonifying kidney, promoting blood circulation and blood cooling, and applicable to chronic glomerulonephritis mild to Qi and Yin Deficiency. Moreover, diabetic nephropathy (DN) rats model was induced by intraperitoneal injection of a small dose of streptozotocin (45 mg/kg) and high-fat diet and plus 5% glucose drinking water. Over 15 days, after oral administration TLR and DTG in DN rats, samples from serum, urine and kidney were collected for biochemical indicators measurements, pathological analysis, western blotting and metabolomics. Therefore, the analytical results of biochemical indicators, histopathological observations and western blotting showed that TLR and DTG exhibited a significant effect in renal protection. And 27 endogenous metabolites (12 in serum and 15 in urine) could be tentatively identified in the process of DN in rats using metabolomics method. Those endogenous metabolites were chiefly involved in sphingolipid metabolism; pentose, glucuronate interconversion; terpenoid backbone biosynthesis; purine metabolism and retinol metabolism. After drug intervention, these endogenous metabolites turned back to normal level some extent (P < 0.05). Furthermore, TLR and DTG prevent high glucose-induced glomerular mesangial cells (GMCs) by inhibiting TGF-β1 and Wnt/β-catenin signaling pathway, providing a powerful supports to develop a new therapeutic agent for DN. This study paved the way for further exploration of the pathogenesis of DN, early diagnosis and the evaluation of curative effect.

Metabolomic analysis based on 1H-nuclear magnetic resonance spectroscopy metabolic profiles in tuberculous, malignant and transudative pleural effusion

Pleural effusion is a common clinical manifestation with various causes. Current diagnostic and therapeutic methods have exhibited numerous limitations. By involving the analysis of dynamic changes in low molecular weight catabolites, metabolomics has been widely applied in various types of disease and have provided platforms to distinguish many novel biomarkers. However, to the best of our knowledge, there are few studies regarding the metabolic profiling for pleural effusion. In the current study, 58 pleural effusion samples were collected, among which 20 were malignant pleural effusions, 20 were tuberculous pleural effusions and 18 were transudative pleural effusions. The small molecule metabolite spectrums were obtained by adopting ¹H nuclear magnetic resonance technology, and pattern-recognition multi-variable statistical analysis was used to screen out different metabolites. One-way analysis of variance, and Student-Newman-Keuls and the Kruskal-Wallis test were adopted for statistical analysis. Over 400 metabolites were identified in the untargeted metabolomic analysis and 26 metabolites were identified as significantly different among tuberculous, malignant and transudative pleural effusions. These metabolites were predominantly involved in the metabolic pathways of amino acids metabolism, glycometabolism and lipid metabolism. Statistical analysis revealed that eight metabolites contributed to the distinction between the three groups: Tuberculous, malignant and transudative pleural effusion. In the current study, the feasibility of identifying small molecule biochemical profiles in different types of pleural effusion were investigated reveal novel biological insights into the underlying mechanisms. The results provide specific insights into the biology of tubercular, malignant and transudative pleural effusion and may offer novel strategies for the diagnosis and therapy of associated diseases, including tuberculosis, advanced lung cancer and congestive heart failure.

Utility of Novel Plasma Metabolic Markers in the Diagnosis of Pediatric Tuberculosis: A Classification and Regression Tree Analysis Approach

Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of sensitive biomarkers. Metabolomics is increasingly being applied in infectious diseases. But little is known regarding metabolic biomarkers in children with TB. A combination of a NMR-based plasma metabolic method and classification and regression tree (CART) analysis was used to provide a broader range of applications in TB diagnosis in our study. Plasma samples obtained from 28 active TB children and 37 non-TB controls (including 21 RTIs and 16 healthy children) were analyzed by an orthogonal partial least-squares discriminant analysis (OPLS-DA) model, and 17 metabolites were identified that can separate children with TB from non-TB controls. CART analysis was then used to choose 3 of the markers, l-valine, pyruvic acid, and betaine, with the least error. The sensitivity, specificity, and area under the curve (AUC) of the 3 metabolites is 85.7% (24/28, 95% CI, 66.4%, 95.3%), 94.6% (35/37, 95% CI, 80.5%, 99.1%), and 0.984(95% CI, 0.917, 1.000), respectively. The 3 metabolites demonstrated sensitivity of 82.4% (14/17, 95% CI, 55.8%, 95.3%) and specificity of 83.9% (26/31, 95% CI, 65.5%, 93.9%), respectively, in 48 blinded subjects in an independent cohort. Taken together, the novel plasma metabolites are potentially useful for diagnosis of pediatric TB and would provide insights into the disease mechanism.

Combining biochemical with (1)H NMR-based metabolomics approach unravels the antidiabetic activity of genipin and its possible mechanism

Diabetes mellitus is a typical heterogeneous metabolic disorder characterized by abnormal metabolism of carbohydrates, lipids and proteins. Genipin possesses a wide spectrum of biological activities including ameliorating effects on diabetes, but the definite mechanism of this effect remains unknown. To investigate the antidiabetic activities of genipin and explore the biochemical changes of serum endogenous metabolites on diabetic rats induced by alloxan, (1)H NMR spectroscopy coupled with multivariate data analysis was used to. All rats were randomly divided into six groups including negative control (NC) group, diabetic mellitus (DM) group, metformin hydrochloride group, high dose group of genipin, middle dose group of genipin and low dose group of genipin. Diabetes was induced by a single intraperitoneal injection of 120mg/kg body weight of alloxan. Serum samples were collected for the (1)H NMR-based metabolomics and clinical biochemical analysis. Daily oral administration of genipin (25, 50 and 100mg/kg body weight) and metformin hydrochloride (125mg/kg) for two weeks showed beneficial effects on blood glucose level (P<0.01). Significant differences in the metabolic profile as well as the result of biochemical parameters between the diabetic group and the control group were observed. The PLS-DA scores and corresponding loading plots demonstrated that genipin significantly restored the abnormal metabolic state. Detailed analysis of the altered metabolite levels indicated that genipin significantly ameliorated the disturbance in glucose metabolism, tricarboxylic acid cycle, lipid metabolism and amino acid metabolism. Genipin showed the best anti-diabetic effects at a dose of 100mg/kg in rats. This finding indicates that chemical and metabolomic approaches could be powerful tools for the investigation of the biochemical changes in pathological conditions or drug treatment.

Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics

Long-term endurance exercise severely affects metabolism in both human and animal athletes resulting in serious risk of metabolic disorders during or after competition. Young horses (up to 6 years old) can compete in races up to 90 km despite limited scientific knowledge of energetic metabolism responses to long distance exercise in these animals. The hypothesis of this study was that there would be a strong effect of endurance exercise on the metabolomic profiles of young horses and that the energetic metabolism response in young horses would be different from that of more experienced horses. Metabolomic profiling is a powerful method that combines Nuclear Magnetic Resonance (NMR) spectrometry with supervised Orthogonal Projection on Latent Structure (OPLS) statistical analysis. ¹H-NMR spectra were obtained from plasma samples drawn from young horses (before and after competition). The spectra obtained before and after the race from the same horse (92 samples) were compared using OPLS. The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001). For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1. The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism. An OPLS model was calculated to compare spectra obtained on plasma taken after the race from 6-year-old horses and from experienced horses (cross-validated ANOVA p < 0.001). The comparison of metabolomic profiles in young horses to those from experienced horses showed that experienced horses maintained their glycemia with higher levels of lactate and a decrease of plasma lipids after the race.

Dominant components of the Thoroughbred metabolome characterised by (1) H-nuclear magnetic resonance spectroscopy: A metabolite atlas of common biofluids

REASONS FOR PERFORMING STUDY

Metabonomics is emerging as a powerful tool for disease screening and investigating mammalian metabolism. This study aims to create a metabolic framework by producing a preliminary reference guide for the normal equine metabolic milieu.

OBJECTIVES

To metabolically profile plasma, urine and faecal water from healthy racehorses using high resolution (1) H-nuclear magnetic resonance (NMR) spectroscopy and to provide a list of dominant metabolites present in each biofluid for the benefit of future research in this area.

STUDY DESIGN

This study was performed using 7 Thoroughbreds in race training at a single time point. Urine and faecal samples were collected noninvasively and plasma was obtained from samples taken for routine clinical chemistry purposes.

METHODS

Biofluids were analysed using (1) H-NMR spectroscopy. Metabolite assignment was achieved via a range of one- and 2-dimensional experiments.

RESULTS

A total of 102 metabolites were assigned across the 3 biological matrices. A core metabonome of 14 metabolites was ubiquitous across all biofluids. All biological matrices provided a unique window on different aspects of systematic metabolism. Urine was the most populated metabolite matrix with 65 identified metabolites, 39 of which were unique to this biological compartment. A number of these were related to gut microbial host cometabolism. Faecal samples were the most metabolically variable between animals; acetate was responsible for the majority (28%) of this variation. Short-chain fatty acids were the predominant features identified within this biofluid by (1) H-NMR spectroscopy.

CONCLUSIONS

Metabonomics provides a platform for investigating complex and dynamic interactions between the host and its consortium of gut microbes and has the potential to uncover markers for health and disease in a variety of biofluids. Inherent variation in faecal extracts along with the relative abundance of microbial-mammalian metabolites in urine and invasive nature of plasma sampling, infers that urine is the most appropriate biofluid for the purposes of metabonomic analysis.

Protein catabolism and high lipid metabolism associated with long-distance exercise are revealed by plasma NMR metabolomics in endurance horses

During long distance endurance races, horses undergo high physiological and metabolic stresses. The adaptation processes involve the modulation of the energetic pathways in order to meet the energy demand. The aims were to evaluate the effects of long endurance exercise on the plasma metabolomic profiles and to investigate the relationships with the individual horse performances. The metabolomic profiles of the horses were analyzed using the non-dedicated methodology, NMR spectroscopy and statistical multivariate analysis. The advantage of this method is to investigate several metabolomic pathways at the same time in a single sample. The plasmas were obtained before exercise (BE) and post exercise (PE) from 69 horses competing in three endurance races at national level (130-160 km). Biochemical assays were also performed on the samples taken at PE. The proton NMR spectra were compared using the supervised orthogonal projection on latent structure method according to several factors. Among these factors, the race location was not significant whereas the effect of the race exercise (sample BE vs PE of same horse) was highly discriminating. This result was confirmed by the projection of unpaired samples (only BE or PE sample of different horses). The metabolomic profiles proved that protein, energetic and lipid metabolisms as well as glycoproteins content are highly affected by the long endurance exercise. The BE samples from finisher horses could be discriminated according to the racing speed based on their metabolomic lipid content. The PE samples could be discriminated according to the horse ranking position at the end of the race with lactate as unique correlated metabolite. As a conclusion, the metabolomic profiles of plasmas taken before and after the race provided a better understanding of the high energy demand and protein catabolism pathway that could expose the horses to metabolic disorders.

Metabonomics-based study of clinical urine samples in suboptimal health with different syndromes

Objective. To explore the urinary biochemistry features of syndromes of traditional Chinese medicine (TCM) such as syndrome of stagnation of liver Qi, spleen deficiency, liver Qi stagnation, and spleen deficiency (LSSDS) in sub-optimal health status (SHS). Methods. 12 cases for each syndrome group in SHS were selected, 12 subjects were used as a normal control group, and ¹H NMR detection was, respectively, carried out, and the data was corrected by the orthogonal signal correction (OSC) and then adopted a partial least squares (PLS) method for discriminate analysis. Results. The OSC-PLS (ctr) analysis results of the nuclear overhauser enhancement spectroscopy (NOESY) detection indicated that the syndromes in SHS could be differentiated, and there were significant differences in the levels of metabolites of the urine samples of the four groups; the biomarkers of LSSDS in SHS were found out. The contents of citric acid (2.54 and 2.66), trimethylamineoxide (3.26), and hippuric acid (3.98, 7.54, 7.58, 7.62, 7.66, 7.82, and 7.86) in the urine samples of LSSDS group were lower than that of the normal control group. Conclusion. There are differences in the ¹H-NMR metabolic spectrum of the urine samples of the four groups, and the specific metabolic products of the LSSDS in SHS can be identified from metabonomics analysis.

Rutgers Young Horse Teaching and Research Program: undergraduate student outcomes

Equine teaching and research programs are popular but expensive components of most land grant universities. External funding for equine research, however, is limited and restricts undergraduate research opportunities that enhance student learning. In 1999, a novel undergraduate teaching and research program was initiated at Rutgers University, New Brunswick, NJ. A unique aspect of this program was the use of young horses generally considered "at risk" and in need of rescue but of relatively low value. The media interest in such horses was utilized to advantage to obtain funding for the program. The use of horses from pregnant mare urine (PMU) ranches and Bureau of Land Management (BLM) mustangs held the risks of attracting negative publicity, potential of injury while training previously unhandled young horses, and uncertainty regarding re-sale value; however, none of these concerns were realized. For 12 years the Young Horse Teaching and Research Program received extensive positive press and provided invaluable learning opportunities for students. Over 500 students, at least 80 of which were minorities, participated in not only horse management and training but also research, event planning, public outreach, fund-raising, and website development. Public and industry support provided program sustainability with only basic University infrastructural support despite severe economic downturns. Student research projects generated 25 research abstracts presented at national and international meetings and 14 honors theses. Over 100 students went on to veterinary school or other higher education programs, and more than 100 others pursued equine- or science-related careers. Laudatory popular press articles were published in a wide variety of breed/discipline journals and in local and regional newspapers each year. Taking the risk of using "at risk" horses yielded positive outcomes for all, especially the undergraduate students.

Metabonomics reveals plasma metabolic changes and inflammatory marker in polycystic ovary syndrome patients

Polycystic ovary syndrome (PCOS) is a common, clinically heterogeneous endocrine disorder affecting women of reproductive age, associated with endocrinopathy and metabolic abnormalities. Although some metabolic parameters have been investigated, very little information has been reported on the changes of small metabolites in biofluids. The aim of this study was to establish the metabolic profile of PCOS and compare it with that of controls. In this cross-sectional study of 34 women with PCOS and 36 controls, contents of small metabolites and lipids in plasma samples were measured using nuclear magnetic resonance (NMR)-based techniques and analyzed using multivariate statistical methods. Significant decrease (P < 0.05) in the levels of amino acids (leucine, isoleucine, methionine, glutamine, and arginine), citrate, choline, and glycerophosphocholine/phosphocholine (GPC/PC), and increase (P < 0.05) in the levels of lactate, dimethylamine (DMA), creatine, and N-acetyl glycoproteins were observed in PCOS patients compared with the controls. Subgroups of patients with obesity, metabolic syndrome, or hyperandrogenism exhibited greater metabolic deviations than their corresponding subgroups without these factors. PCOS patients have perturbations in amino acid metabolism, the tricarboxylic acid (TCA) cycle, and gut microflora, as well as mild disturbances in glucose and lipid metabolism. The elevated level of N-acetyl glycoproteins demonstrates the existence of low-grade chronic inflammation in PCOS patients.

Metabolomics: a new era in cardiology?

The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end-products of cellular processes. Metabolomics is the systematic study of small-molecule metabolite profiles that specific cellular processes leave behind. RNA messenger gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell. Metabolic profiling, in turn, amplifies changes both in the proteome and the genome, and represents a more accurate approximation to the phenotype of an organism in health and disease. In this article, we have provided a description of metabolomics, in the presence of other, more familiar 'omics' disciplines, such as genomics and proteomics. In addition, we have reviewed the current rationale for metabolomics in cardiology, its basic methodology and the data actually available in human studies in this discipline. The discussed topics highlight the importance of being able to use the metabolomics information in order to understand disease mechanisms from a systems biology perspective as a noninvasive approach to diagnose, grade and treat cardiovascular diseases.

Evaluation of commercially available assays for the measurement of equine insulin

Determining circulating equine insulin concentrations is becoming increasingly important in equine clinical practice and research. Most available assays are optimized for human medicine, but there is strong equine cross-reactivity because of the highly conserved nature of insulin. To identify an accurate and reliable assay for equine insulin, 6 commercial immunoassays were evaluated for precision, accuracy, and specificity. Only 1 assay initially reached the requisite standard: Mercodia Equine Insulin Enzyme-linked Immunosorbent assay (ELISA). Plasma matrix interferences were identified when the provided assay buffer was used with the Siemens Count-a-Coat Insulin radioimmunoassay (RIA) but not when charcoal-stripped equine plasma was used as the diluent. This modified RIA and the Mercodia Equine Insulin ELISA were evaluated further by directly examining accuracy by comparing their results for 18 equine plasma samples with values obtained using liquid chromatography and high-resolution/high-accuracy mass spectrometry (LC-MS). Compared with LC-MS measurements, the modified Siemens Insulin RIA rendered a moderate Lin's concordance coefficient (ρ(c)) of 0.41, whereas the Mercodia Equine Insulin ELISA rendered a very poor ρ(c) of 0.06. This suggests that the Siemens Insulin RIA is appropriate to use for routine evaluations when LC-MS is not available.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

Contribution of animal models to the understanding of the metabolic syndrome: a systematic overview

The metabolic syndrome (MetS) is one of the most important challenges to public health and biomedical research. Animal models of MetS, such as leptin-deficient obese mice, obese spontaneously hypertensive rats, JCR: LA-cp rats and the Ossabaw and Göttingen minipigs, have contributed to our understanding of the pathophysiological basis and the development of novel therapies. For a complex disease syndrome, no animal model can be expected to serve all needs of research. Although each animal model has limitations and strengths, used together in a complementary fashion, they are essential for research on the MetS and for rapid progress in understanding the aetiology and pathogenesis towards a cure. The purpose of this review is to assess how current animal models contributed to our knowledge of the human MetS, and to systematically evaluate the strengths and weaknesses of the currently available 78 animal models from 11 species.

Equine clinical genomics: A clinician's primer

The objective of this review is to introduce equine clinicians to the rapidly evolving field of clinical genomics with a vision of improving the health and welfare of the domestic horse. For 15 years a consortium of veterinary geneticists and clinicians has worked together under the umbrella of The Horse Genome Project. This group, encompassing 22 laboratories in 12 countries, has made rapid progress, developing several iterations of linkage, physical and comparative gene maps of the horse with increasing levels of detail. In early 2006, the research was greatly facilitated when the US National Human Genome Research Institute of the National Institutes of Health added the horse to the list of mammalian species scheduled for whole genome sequencing. The genome of the domestic horse has now been sequenced and is available to researchers worldwide in publicly accessible databases. This achievement creates the potential for transformative change within the horse industry, particularly in the fields of internal medicine, sports medicine and reproduction. The genome sequence has enabled the development of new genome-wide tools and resources for studying inherited diseases of the horse. To date, researchers have identified 11 mutations causing 10 clinical syndromes in the horse. Testing is commercially available for all but one of these diseases. Future research will probably identify the genetic bases for other equine diseases, produce new diagnostic tests and generate novel therapeutics for some of these conditions. This will enable equine clinicians to play a critical role in ensuring the thoughtful and appropriate application of this knowledge as they assist clients with breeding and clinical decision-making.

Web server suite for complex mixture analysis by covariance NMR

Elucidation of the chemical composition of biological samples is a main focus of systems biology and metabolomics. Their comprehensive study requires reliable, efficient, and automatable methods to identify and quantify the underlying metabolites. Because nuclear magnetic resonance (NMR) spectroscopy is a rich source of molecular information, it has a unique potential for this task. Here we present a suite of public web servers (http://spinportal.magnet.fsu.edu), termed COLMAR, which facilitates complex mixture analysis by NMR. The COLMAR web portal presently consists of three servers: COLMAR covariance calculates the covariance NMR spectrum from an NMR input dataset, such as a TOCSY spectrum; COLMAR DemixC method decomposes the 2D covariance TOCSY spectrum into a reduced set of nonredundant 1D cross sections or traces, which belong to individual mixture components; and COLMAR query screens the traces against a NMR spectral database to identify individual compounds. Examples are presented that illustrate the utility of this web server suite for complex mixture analysis.

Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling

Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in (1)H NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.

1H NMR-based metabonomics for investigating diabetes

Diabetes is characterized by hyperglycemia due to dysfunction of insulin secretion or action. The two most common forms are Type 1 diabetes, in which pancreatic β-cells are destroyed, and Type 2 diabetes, in which a combination of disordered insulin action and secretion results in abnormal carbohydrate, lipid and protein metabolism. Metabonomics employs analytical technologies to measure ‘global’ metabolic responses to a disease state. With the aid of statistical pattern recognition, this can reveal novel insights into the biochemical consequences of diabetes. The metabonomic method can be divided into four stages: sample collection; preparation; data acquisition and processing; and statistical analyses. In this review, we describe the most recent developments at each experimental stage in detail, and comment on specific precautions or improvements that should be taken into account when studying diabetes. Finally, we end with speculations as to where and how the field will develop in the future. Metabonomics provides a logical framework for understanding the global metabolic effects of diabetes. Continuing technological improvements will expand our knowledge of the causes and progression of this disease, and enhance treatment options for individuals.

[Recent development of metabonomics and its applications in clinical research]

In the post-genomic era, systems biology is central to the biological sciences. Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or other intervention. However, these changes can't be coupled directly to changes in biological function. As a result, metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past several years. Metabonomics is a rapidly growing research area and a system approach for comprehensive and quantitative analysis of the global metabolites in a biological matrix. Analytical chemistry approach is necessary for the development of comprehensive metabonomics investigations. Fundamentally, there are two types of metabonomics approaches: mass-spectrometry (MS) based and nuclear magnetic resonance (NMR) methodologies. Metabonomics measurements provide a wealth of data information and interpretation of these data relies mainly on chemometrics approaches to perform large-scale data analysis and data visualization, such as principal and independent component analysis, multidimensional scaling, a variety of clustering techniques, and discriminant function analysis, among many others. In this review, the recent development of analytical and statistical techniques used in metabonomics is summarized. Major applications of metabonomics relevant to clinical and preclinical study are then reviewed. The applications of metabonomics in study of liver diseases, cancers and other diseases have proved useful both as an experimental tool for pathogenesis mechanism re-search and ultimately a tool for diagnosis and monitoring treatment response of these diseases. Next, the applications of metabonomics in preclinical toxicology are discussed and the role that metabonomics might do in pharmaceutical research and development is explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology (COMET), and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Finally, the role of metabonomics in elucidating the function of the unknown or novel enzyme is mentioned.

Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system

Ultra performance liquid chromatography (UPLC) coupled with orthogonal acceleration time-of-flight (oaTOF) mass spectrometry has showed great potential in diabetes research. In this paper, a UPLC-oaTOF-MS system was employed to distinguish the global serum profiles of 8 diabetic nephropathy (DN) patients, 33 type 2 diabetes mellitus (T2DM) patients and 25 healthy volunteers, and tried to find potential biomarkers. The UPLC system produced information-rich chromatograms with typical measured peak widths of 4 s, generating peak capacities of 225 in 15 min. Furthermore, principal component analysis (PCA) was used for group differentiation and marker selection. As shown in the scores plot, the distinct clustering between the patients and controls was observed, and DN and T2DM patients were also separated into two individual groups. Several compounds were tentatively identified based on accurate mass, isotopic pattern and MS/MS information. In addition, significant changes in the serum level of leucine, dihydrosphingosine and phytosphingosine were noted, indicating the perturbations of amino acid metabolism and phospholipid metabolism in diabetic diseases, which having implications in clinical diagnosis and treatment.

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Correlative and quantitative 1H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats

Type 1 diabetes was induced in Sprague-Dawley rats using streptozotocin. Rat urine samples (8 diabetic and 10 control) were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy. The derived metabolites using univariate and multivariate statistical analysis were subjected to correlative analysis. Plasma metabolites were measured by a series of bioassays. A total of 17 urinary metabolites were identified in the 1H NMR spectra and the loadings plots after principal components analysis. Diabetic rats showed significantly increased levels of glucose (P < 0.00001), alanine (P < 0.0002), lactate (P < 0.05), ethanol (P < 0.05), acetate (P < 0.05), and fumarate (P < 0.05) compared with controls. Plasma assays showed higher amounts of glucose, urea, triglycerides, and thiobarbituric acid-reacting substances in diabetic rats. Striking differences in the Pearson's correlation of the 17 NMR-detected metabolites were observed between control and diabetic rats. Detailed analysis of the altered metabolite levels and their correlations indicate a significant disturbance in the glucose metabolism and tricarboxylic acid (TCA) cycle and a contribution from gut microbial metabolism. Specific perturbed metabolic pathways include the glucose-alanine and Cori cycles, the acetate switch, and choline metabolism. Detection of the altered metabolic pathways and bacterial metabolites using this correlative and quantitative NMR-based metabolomics approach should help to further the understanding of diabetes-related mechanisms.

Metabonomics and diabetes mellitus

Diabetes mellitus is one of the most serious heterogeneous endocrinologic diseases; more than 90% of those given this diagnosis have type 2 diabetes mellitus, usually accompanied by macrovascular and microvascular complications. Because most cases include a 7- to 10-y latency period before disease onset, however, it is of vital importance that more powerful and practical approaches be developed for early diagnosis and improved prevention and treatment. Metabonomics is a powerful new approach that is especially useful for the treatment of patients with metabolic diseases because it can evaluate the holistic responses of the body to any subtle perturbation with the use of advanced analytical apparatus and chemometric methods that have been applied in the diagnosis and evaluation of type 2 diabetes mellitus. It is a promising approach to the study of complex diseases that should prove useful in the future, along with other -omics techniques performed in the context of systems biology.