Maria Vinaixa

Metabolomic assessment of the effect of dietary cholesterol in the progressive development of fatty liver disease.

Nonalcoholic fatty liver disease is considered to be the hepatic manifestation of metabolic syndrome and is usually related to high-fat, high-cholesterol diets. With the rationale that the identification and quantification of metabolites in different metabolic pathways may facilitate the discovery of clinically accessible biomarkers, we report the use of (1)H NMR metabolomics for quantitative profiling of liver extracts from LDLr(-/-) mice, a well-documented mouse model of fatty liver disease. A total of 55 metabolites were identified, and multivariate analyses in a diet- and time-comparative strategy were performed. Dietary cholesterol increased the hepatic concentrations of cholesterol, triglycerides, and oleic acid but also decreased the [PUFA/MUFA] ratio as well as the relative amount of long-chain polyunsaturated fatty acids in the liver. This was also accompanied by variations of the hepatic concentration of taurine, glutathione, methionine, and carnitine. Heat-map correlation analyses demonstrated that hepatic inflammation and development of steatosis correlated with cholesterol and triglyceride NMR derived signals, respectively. We conclude that dietary cholesterol is a causal factor in the development of both liver steatosis and hepatic inflammation.

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.

Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics.

Because of the wide range of chemically and structurally diverse metabolites, efforts to survey the complete metabolome rely on the implementation of multiplatform approaches based on nuclear magnetic resonance (NMR) and mass spectrometry (MS). Sample preparation disparities between NMR and MS, however, may limit the analysis of the same samples by both platforms. Specifically, deuterated solvents used in NMR strategies can complicate LC/MS analysis as a result of potential mass shifts, whereas acidic solutions typically used in LC/MS methods to enhance ionization of metabolites can severely affect reproducibility of NMR measurements. These intrinsically different sample preparation requirements result in the application of different procedures for metabolite extraction, which involve additional sample and unwanted variability. To address this issue, we investigated 12 extraction protocols in liver tissue involving different aqueous/organic solvents and temperatures that may satisfy the requirements for both NMR and LC/MS simultaneously. We found that deuterium exchange did not affect LC/MS results, enabling the measurement of metabolites by NMR and, subsequently, the direct analysis of the same samples by using LC/MS with no need for solvent exchange. Moreover, our results show that the choice of solvents rather than the temperature determined the extraction efficiencies of metabolites, a combination of methanol/chloroform/water and methanol/water being the extraction methods that best complement NMR and LC/MS analysis for metabolomic studies.

Metabolic Heterogeneity in Polycystic Ovary Syndrome Is Determined by Obesity: Plasma Metabolomic Approach Using GC-MS

BACKGROUND Abdominal adiposity and obesity influence the association of polycystic ovary syndrome (PCOS) with insulin resistance and diabetes. We aimed to characterize the intermediate metabolism phenotypes associated with PCOS and obesity. METHODS We applied a nontargeted GC-MS metabolomic approach to plasma samples from 36 patients with PCOS and 39 control women without androgen excess, matched for age, body mass index, and frequency of obesity. RESULTS Patients with PCOS were hyperinsulinemic and insulin resistant compared with the controls. The increase in plasma long-chain fatty acids, such as linoleic and oleic acid, and glycerol in the obese patients with PCOS suggests increased lipolysis, possibly secondary to impaired insulin action at adipose tissue. Conversely, nonobese patients with PCOS showed a metabolic profile consisting of suppression of lipolysis and increased glucose utilization (increased lactic acid concentrations) in peripheral tissues, and PCOS patients as a whole showed decreased 2-ketoisocaproic and alanine concentrations, suggesting utilization of branched-chain amino acids for protein synthesis and not for gluconeogenesis. These metabolic processes required effective insulin signaling; therefore, insulin resistance was not universal in all tissues of these women, and different mechanisms possibly contributed to their hyperinsulinemia. PCOS was also associated with decreased α-tocopherol and cholesterol concentrations irrespective of obesity. CONCLUSIONS Substantial metabolic heterogeneity, strongly influenced by obesity, underlies PCOS. The possibility that hyperinsulinemia may occur in the absence of universal insulin resistance in nonobese women with PCOS should be considered when designing diagnostic and therapeutic strategies for the management of this prevalent disorder.

Metabolomics approach for analyzing the effects of exercise in subjects with type 1 diabetes mellitus.

The beneficial effects of exercise in patients with type 1 diabetes (T1D) are not fully proven, given that it may occasionally induce acute metabolic disturbances. Indeed, the metabolic disturbances associated with sustained exercise may lead to worsening control unless great care is taken to adjust carbohydrate intake and insulin dosage. In this work, pre- and post-exercise metabolites were analyzed using a 1H-NMR and GC-MS untargeted metabolomics approach assayed in serum. We studied ten men with T1D and eleven controls matched for age, body mass index, body fat composition, and cardiorespiratory capacity, participated in the study. The participants performed 30 minutes of exercise on a cycle-ergometer at 80% VO2max. In response to exercise, both groups had increased concentrations of gluconeogenic precursors (alanine and lactate) and tricarboxylic acid cycle intermediates (citrate, malate, fumarate and succinate). The T1D group, however, showed attenuation in the response of these metabolites to exercise. Conversely to T1D, the control group also presented increases in α-ketoglutarate, alpha-ketoisocaproic acid, and lipolysis products (glycerol and oleic and linoleic acids), as well as a reduction in branched chain amino acids (valine and leucine) determinations. The T1D patients presented a blunted metabolic response to acute exercise as compared to controls. This attenuated response may interfere in the healthy performance or fitness of T1D patients, something that further studies should elucidate.

1 H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors

Moderate wine consumption is associated with health‐promoting activities. An H‐NMR‐based metabolomic approach was used to identify urinary metabolomic differences of moderate wine intake in the setting of a prospective, randomized, crossover, and controlled trial. Sixty‐one male volunteers with high cardiovascular risk factors followed three dietary interventions (28 days): dealcoholized red wine (RWD) (272mL/day, polyphenol control), alcoholized red wine (RWA) (272mL/day) and gin (GIN) (100mL/day, alcohol control). After each period, 24‐h urine samples were collected and analyzed by 1H‐NMR. According to the results of a one‐way ANOVA, significant markers were grouped in four categories: alcohol‐related markers (ethanol); gin‐related markers; wine‐related markers; and gut microbiota markers (hippurate and 4‐hydroxphenylacetic acid). Wine metabolites were classified into two groups; first, metabolites of food metabolome: tartrate (RWA and RWD), ethanol, and mannitol (RWA); and second, biomarkers that relates to endogenous modifications after wine consumption, comprising branched‐chain amino acid (BCAA) metabolite (3‐methyl‐oxovalerate). Additionally, a possible interaction between alcohol and gut‐related biomarkers has been identified. To our knowledge, this is the first time that this approach has been applied in a nutritional intervention with red wine. The results show the capacity of this approach to obtain a comprehensive metabolome picture including food metabolome and endogenous biomarkers of moderate wine intake.

AStream: an R package for annotating LC/MS metabolomic data

UNLABELLED AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results. AVAILABILITY The AStream R package and a study example can be freely accessed at http://www.urr.cat/AStream/AStream.html.

Liposcale: a novel advanced lipoprotein test based on 2D diffusion-ordered 1H NMR spectroscopy

Determination of lipoprotein particle size and number using advanced lipoprotein tests (ALTs) is of particular importance to improve cardiovascular risk prediction. Here we present the Liposcale test, a novel ALT based on 2D diffusion-ordered 1H NMR spectroscopy. Our method uses diffusion coefficients to provide a direct measure of the mean particle sizes and numbers. Using 177 plasma samples from healthy individuals and the concentration of ApoB and ApoA from isolated lipoprotein fractions, our test showed a stronger correlation between the NMR-derived lipoprotein particle numbers and apolipoprotein concentrations than the LipoProfile® test commercialized by Liposcience. We also converted LDL particle numbers to ApoB equivalents (milligrams per deciliter) and our test yielded similar values of LDL-ApoB to the LipoProfile® test (absolute mean bias of 8.5 and 7.4 mg/dl, respectively). In addition, our HDL particle number values were more concordant with the calibrated values determined recently using ion mobility. Finally, principal component analysis distinguished type 2 diabetic patients with and without atherogenic dyslipidemia (AD) on a second cohort of 307 subjects characterized using the Liposcale test (area under the curve = 0.88) and showed concordant relationships between variables explaining AD. Altogether, our method provides reproducible and reliable characterization of lipoprotein particles and it is applicable to pathological states such as AD.

Metabolomics Reveals Reduction of Metabolic Oxidation in Women with Polycystic Ovary Syndrome after Pioglitazone-Flutamide-Metformin Polytherapy

Polycystic ovary syndrome (PCOS) is a variable disorder characterized by a broad spectrum of anomalies, including hyperandrogenemia, insulin resistance, dyslipidemia, body adiposity, low-grade inflammation and increased cardiovascular disease risks. Recently, a new polytherapy consisting of low-dose flutamide, metformin and pioglitazone in combination with an estro-progestagen resulted in the regulation of endocrine clinical markers in young and non-obese PCOS women. However, the metabolic processes involved in this phenotypic amelioration remain unidentified. In this work, we used NMR and MS-based untargeted metabolomics to study serum samples of young non-obese PCOS women prior to and at the end of a 30 months polytherapy receiving low-dose flutamide, metformin and pioglitazone in combination with an estro-progestagen. Our results reveal that the treatment decreased the levels of oxidized LDL particles in serum, as well as downstream metabolic oxidation products of LDL particles such as 9- and 13-HODE, azelaic acid and glutaric acid. In contrast, the radiuses of small dense LDL and large HDL particles were substantially increased after the treatment. Clinical and endocrine-metabolic markers were also monitored, showing that the level of HDL cholesterol was increased after the treatment, whereas the level of androgens and the carotid intima-media thickness were reduced. Significantly, the abundance of azelaic acid and the carotid intima-media thickness resulted in a high degree of correlation. Altogether, our results reveal that this new polytherapy markedly reverts the oxidant status of untreated PCOS women, and potentially improves the pro-atherosclerosis condition in these patients.

Metabolic phenotyping of genetically modified mice: An NMR metabonomic approach☆

Monocyte chemoattractant protein-1 (MCP-1) plays a relevant role in macrophage migration but recent findings suggest an additional role in lipid and glucose metabolism. We report the use of (1)H NMR spectroscopy as a useful complementary method to assess the metabolic function of this gene in a comparative strategy. This metabonomic analysis was rapid, simple, quantitative and reproducible, and revealed a suggestive relationship between the expression of the MCP-1 gene and hepatic glucose and taurine concentrations. This approach should be considered in genetically modified mice when a metabolic alteration is suspected, or in routine assessment of metabolic phenotype.