Antonia García

Method validation strategies involved in non-targeted metabolomics.

Non-targeted metabolomics is the hypothesis generating, global unbiased analysis of all the small-molecule metabolites present within a biological system, under a given set of conditions. It includes several common steps such as selection of biological samples, sample pre-treatment, analytical conditions set-up, acquiring data, data analysis by chemometrics, database search and biological interpretation. Non-targeted metabolomics offers the potential for a holistic approach in the area of biomedical research in order to improve disease diagnosis and to understand its pathological mechanisms. Various analytical methods have been developed based on nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) coupled with different separation techniques. The key points in any analytical method development are the validation of every step to get a reliable and reproducible result and non-targeted metabolomics is not beyond this criteria, although analytical challenges are completely new and different to target methods. This review paper will describe the available validation strategies that are being used and as well will recommend some steps to consider during a non-targeted metabolomics analytical method development.

Validation of a HPLC quantification of acetaminophen, phenylephrine and chlorpheniramine in pharmaceutical formulations: capsules and sachets

Acetaminophen, phenylephrine and chlorpheniramine are frequently associated in pharmaceutical formulations against the common cold. Their quantification presents several problems. A HPLC method for the simultaneous determination of these compounds in pharmaceutical formulations such as capsules and sachets, including the separation of impurities and excipients has been developed and validated. The selectivity of the method was also tested to be used if phenylpropanolamine hydrochloride were employed instead of phenylephrine. Final chromatographic conditions were a gradient elution, being solvent A: phosphate buffer 40 mM at pH 6.0 and solvent B: acetonitrile. At t=0, the mobile phase consisted of 92% A and 8% B and it changed with a linear gradient during 8 min to 75% A and 25% B. At min 8, it changed to 30% A and 70% B for 5 min and at t=15 min, it returns to the initial conditions (92% A and 8% B) during 1 min remaining at this composition until t=20 min. UV detection was performed at 215 nm for phenylephrine and chlorpheniramine, because at this wavelength sensitivity was higher than in other more characteristic wavelengths and it was necessary for the detection of minor compounds. For acetaminophen 280 nm was employed. Validation parameters permit to consider the method adequate.

Development and validation of a capillary electrophoresis method for direct measurement of isocitric, citric, tartaric and malic acids as adulteration markers in orange juice.

Fruit juices each have very distinct organic acids profiles that can be used as fingerprints for establishing authenticity. A method has been developed, optimised and validated for measuring by capillary electrophoresis citric, isocitric, malic and tartaric acids as authenticity markers in orange juices, without any sample treatment other than dilution and filtration. Final conditions were phosphate buffer 200 mM, pH 7.50, -14 kV as applied potential, and 57 cm length neutral capillary. Detection was direct UV at 200 nm. Different kinds and marks of orange juice, chosen from the great variety existent in the market, were analysed and clear differences could be found between them and just pressed orange juice.

Plasma fingerprinting with GC-MS in acute coronary syndrome

New biomarkers of cardiovascular disease are needed to augment the information obtained from traditional indicators and to illuminate disease mechanisms. One of the approaches used in metabolomics/metabonomics for that purpose is metabolic fingerprinting aiming to profile large numbers of chemically diverse metabolites in an essentially nonselective way. In this study, gas chromatography-mass spectrometry was employed to evaluate the major metabolic changes in low molecular weight plasma metabolites of patients with acute coronary syndrome (n = 9) and with stable atherosclerosis (n = 10) vs healthy subjects without significant differences in age and sex (n = 10). Reproducible differences between cases and controls were obtained with pattern recognition techniques, and metabolites accounting for higher weight in the classification have been identified through their mass spectra. On this basis, it seems inherently plausible that even a simple metabolite profile might be able to offer improved clinical diagnosis and prognosis, but in addition, specific markers are being identified.

Gas Chromatography-Mass Spectrometry (GC-MS)-Based Metabolomics

Metabolic fingerprinting, the main tool in metabolomics, is a non-targeted methodology where all detectable peaks (or signals), including those from unknown analytes, are considered to establish sample classification. After pattern comparison, those signals changing in response to a specific situation under investigation are identified to gain biological insight. For this purpose, gas chromatographymass spectrometry (GC-MS) has a drawback in that only volatile compounds or compounds that can be made volatile after derivatization can be analysed, and derivatization often requires extensive sample treatment. However, once the analysis is focused on low molecular weight metabolites, GC-MS is highly efficient, sensitive, and reproducible. Moreover, it is quantitative, and its compound identification capabilities are superior to other separation techniques because GC-MS instruments obtain mass spectra with reproducible fragmentation patterns, which allow for the creation of public databases. This chapter describes well-established protocols for metabolic fingerprinting (i.e. the comprehensive analysis of small molecules) in plasma and urine using GC-MS. Guidelines will also be provided regarding subsequent data pre-treatment, pattern recognition, and marker identification.

High-fat diets induce changes in hippocampal glutamate metabolism and neurotransmission.

Obesity and high-fat (HF) diets have a deleterious impact on hippocampal function and lead to impaired synaptic plasticity and learning deficits. Because all of these processes need an adequate glutamatergic transmission, we have hypothesized that nutritional imbalance triggered by these diets might eventually concern glutamate (Glu) neural pathways within the hippocampus. Glu is withdrawn from excitatory synapses by specific uptake mechanisms involving neuronal (EAAT-3) and glial (GLT-1, GLAST) transporters, which regulate the time that synaptically released Glu remains in the extracellular space and, consequently, the duration and location of postsynaptic receptor activation. The goal of the present study was to evaluate in mouse hippocampus the effect of a short-term high-fat dietary treatment on 1) Glu uptake kinetics, 2) the density of Glu carriers and Glu-degrading enzymes, 3) the density of Glu receptor subunits, and 4) synaptic transmission and plasticity. Here, we show that HF diet triggers a 50% decrease of the Michaelis-Menten constant together with a 300% increase of the maximal velocity of the uptake process. Glial Glu carriers GLT-1 and GLAST were upregulated in HF mice (32 and 27%, respectively), whereas Glu-degrading enzymes glutamine synthase and GABA-decarboxilase appeared to be downregulated in these animals. In addition, HF diet hippocampus displayed diminished basal synaptic transmission and hindered NMDA-induced long-term depression (NMDA-LTD). This was coincident with a reduced density of the NR2B subunit of NMDA receptors. All of these results are compatible with the development of leptin resistance within the hippocampus. Our data show that HF diets upregulate mechanisms involved in Glu clearance and simultaneously impair Glu metabolism. Neurochemical changes occur concomitantly with impaired basal synaptic transmission and reduced NMDA-LTD. Taken together, our results suggest that HF diets trigger neurochemical changes, leading to a desensitization of NMDA receptors within the hippocampus, which might account for cognitive deficits.

Improving Metabolite Knowledge in Stable Atherosclerosis Patients by Association and Correlation of GC-MS and 1H NMR Fingerprints

The plasma of patients with stable carotid atherosclerosis (n = 9), and healthy subjects (n = 10) have been fingerprinted with both GC-MS and (1)H NMR. Principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) have been applied to the profiles from each technique both separately and in combination. These techniques complement each other and enable a clearer picture of the biological samples to be interpreted not only for classification purposes, but also more importantly to define the metabolic state of patients with carotid atherosclerosis. The results showed at least 24 metabolites that were significantly modified in the group of atherosclerotic patients by this nontargeted procedure. Most of the changes can be associated to alterations of the metabolism characteristics of insulin resistance that can be strongly related to the metabolic syndrome. In addition, correlations among variables accounting for the classification show amino acids as variables whose changes showed a high degree of correlation. GC-MS and (1)H NMR fingerprints can provide complementary information in the identification of altered metabolic pathways in patients with carotid atherosclerosis. Moreover, correlations among the results with both techniques, instead of a single study, can provide a deeper insight into the patient state.

Metabolic fingerprint of Gestational Diabetes Mellitus.

UNLABELLED Gestational Diabetes (GDM) is causing severe short- and long-term complications for mother, fetus or neonate. As yet, the metabolic alterations that are specific for the development of GDM have not been fully determined, which also precludes the early diagnosis and prognosis of this pathology. In this pilot study, we determine the metabolic fingerprint, using a multiplatform LC-QTOF/MS, GC-Q/MS and CE-TOF/MS system, of plasma and urine samples of 20 women with GDM and 20 with normal glucose tolerance in the second trimester of pregnancy. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls. In particular, lysoglycerophospholipids showed a close association with the glycemic state of the women. In addition, we identified some metabolites with a strong discriminative power, such as LPE(20:1), (20:2), (22:4); LPC(18:2), (20:4), (20:5); LPI(18:2), (20:4); LPS(20:0) and LPA(18:2), as well as taurine-bile acids and long-chain polyunsaturated fatty acid derivatives. Finally, we provide evidence for the implication of these compounds in metabolic routes, indicative of low-grade inflammation and altered redox-balance, that may be related with the specific pathophysiological context of the genesis of GDM. This highlights their potential use as prognostic markers for the identification of women at risk to develop severe glucose intolerance during pregnancy. BIOLOGICAL SIGNIFICANCE Gestational Diabetes Mellitus (GDM) is increasing worldwide and, although diabetes usually remits after pregnancy, women with GDM have a high risk of developing postpartum type 2-diabetes, particularly when accompanied by obesity. Therefore, understanding the pathophysiology of GDM, as well as the identification of potentially modifiable risk factors and early diagnostic markers for GDM are relevant issues. In the present study, we devised a multiplatform metabolic fingerprinting approach to obtain a comprehensive picture of the early metabolic alternations that occur in GDM, and may reflect on the specific pathophysiological context of the disease. Future studies at later stages of gestation will allow us to validate the discriminant power of the identified metabolites.

Metabolomic profiling of serum in the progression of Alzheimer's disease by capillary electrophoresis–mass spectrometry

There is high interest in the discovery of early diagnostic biomarkers of Alzheimers disease, for which metabolomics exhibits a great potential. In this work, a metabolomic approach based on ultrafiltration and analysis by CE‐MS has been used to obtain representative fingerprints of polar metabolites from serum samples in order to distinguish between patients with Alzheimers disease, mild cognitive impairment, and healthy controls. By the use of partial least squares discriminant analysis it was possible to classify patients according to the disease stage and then identify potential markers. Significant increase was observed with progression of disease in levels of choline, creatinine, asymmetric dimethyl‐arginine, homocysteine‐cysteine disulfide, phenylalanyl‐phenylalanine, and different medium chain acylcarnitines. On the other hand, asparagine, methionine, histidine, carnitine, acetyl‐spermidine, and C5‐carnitine were reduced in these serum samples. In this way, multiple essential pathways were found implicated in the underlying pathology, such as oxidative stress or defects in energy metabolism. However, the most interesting results are related to the association of several vascular risk factors with Alzheimers disease.

Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography–mass spectrometry and capillary electrophoresis–mass spectrometry metabolomics approach

The incidence and rate of recurrence of bladder cancer is high, particularly in developed countries, however current methods for diagnosis are limited to detecting high-grade tumours using often invasive methods. A panel of biomarkers to characterise tumours of different grades that could also distinguish between patients exhibiting the disease with first incidence or recurrence could be useful for bladder cancer diagnostics. In this study, potential metabolic biomarkers have been discovered through mass spectrometry based metabolomics of urine. Pre-treatment urine samples were collected from 48 patients diagnosed of urothelial bladder cancer. Patients were followed-up through the hospital pathological charts to identify whether and when the disease recurred or progressed. Subsequently, they were classified according to whether or not they suffered a tumour recurrence (recurrent or stable) as well as their risk group according to tumour grade and stage. Identified metabolites have been analysed in terms of disease characteristics (tumour stage and recurrence) and have provided an insight into bladder cancer progression. Using both liquid chromatography and capillary electrophoresis-mass spectrometry, a total of 27 metabolite features were highlighted as significantly different between patient groups. Some, for example histidine, phenylalanine, tyrosine and tryptophan have been previously linked with bladder cancer, however until now their connection with bladder cancer progression has not been previously reported. The candidate biomarkers revealed in this study could be useful in the clinic for diagnosis of bladder cancer and, through characterising the stage of the disease, could also be useful in prognostics.