António S. Barros

Metabolic Signatures of Lung Cancer in Biofluids: NMR-Based Metabonomics of Urine

In this study, ¹H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution ¹H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.

Metabolic profiling of human lung cancer tissue by 1H high resolution magic angle spinning (HRMAS) NMR spectroscopy.

This work aims at characterizing the metabolic profile of human lung cancer, to gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic value in the future. Paired samples of tumor and noninvolved adjacent tissues from 12 lung tumors have been directly analyzed by (1)H HRMAS NMR (500/600 MHz) enabling, for the first time to our knowledge, the identification of over 50 compounds. The effect of temperature on tissue stability during acquisition time has also been investigated, demonstrating that analysis should be performed within less than two hours at low temperature (277 K), to minimize glycerophosphocholine (GPC) and phosphocholine (PC) conversion to choline and reduce variations in some amino acids. The application of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to the standard 1D (1)H spectra resulted in good separation between tumor and control samples, showing that inherently different metabolic signatures characterize the two tissue types. On the basis of spectral integration measurements, lactate, PC, and GPC were found to be elevated in tumors, while glucose, myo-inositol, inosine/adenosine, and acetate were reduced. These results show the valuable potential of HRMAS NMR-metabonomics for investigating the metabolic phenotype of lung cancer.

Metabolic Biomarkers of Prenatal Disorders: An Exploratory NMR Metabonomics Study of Second Trimester Maternal Urine and Blood Plasma

This work describes an exploratory NMR metabonomic study of second trimester maternal urine and plasma, in an attempt to characterize the metabolic changes underlying prenatal disorders and identify possible early biomarkers. Fetal malformations have the strongest metabolic impact in both biofluids, suggesting effects due to hypoxia (leading to hypoxanthine increased excretion) and a need for enhanced gluconeogenesis, with higher ketone bodies (acetone and 3-hydroxybutyric acid) production and TCA cycle demand (suggested by glucogenic amino acids and cis-aconitate overproduction). Choline and nucleotide metabolisms also seem affected and a distinct plasma lipids profile is observed for mothers with fetuses affected by central nervous system malformations. Urine from women who subsequently develop gestational diabetes mellitus exhibits higher 3-hydroxyisovalerate and 2-hydroxyisobutyrate levels, probably due to altered biotin status and amino acid and/or gut metabolisms (the latter possibly related to higher BMI values). Other urinary changes suggest choline and nucleotide metabolic alterations, whereas lower plasma betaine and TMAO levels are found. Chromosomal disorders and pre-preterm delivery groups show urinary changes in choline and, in the latter case, in 2-hydroxyisobutyrate. These results show that NMR metabonomics of maternal biofluids enables the noninvasive detection of metabolic changes associated to prenatal disorders, thus unveiling potential disorder biomarkers.

FTIR SPECTROSCOPY AS A TOOL FOR THE ANALYSIS OF OLIVE PULP CELL-WALL POLYSACCHARIDE EXTRACTS

Abstract The sequential extraction of olive pulp cell-wall material (CWM) and its subsequent fractionation by ethanol precipitation and anion-exchange chromatography gave a wide range of cell-wall polysaccharide fractions, characterised by sugar analysis. Several multivariate procedures, such as principal component analysis (PCA), trimmed object projections (TOP), canonical correlation analysis (CCA), and partial least squares regression (PLS), were applied to their FTIR spectra, in the region between 1200 and 850 cm−1. The combination of these chemometric techniques along with the chemical information allowed the type of polymers present to be distinguished: pectic polysaccharides rich in uronic acid, pectic polysaccharides rich in arabinose, arabinose-rich glycoproteins, xyloglucans, and glucuronoxylans. It was also possible to highlight the most important and characteristic wavenumbers for each type of polymer present. A calibration model for the quantification of xylose residues of the hemicellulosic polysaccharides was proposed. The high diversity of samples used and their characteristic features allowed models to be obtained, using a very expeditious methodology, that represent a realistic description of the olive pulp cell-wall polymers.

Genetic algorithm applied to the selection of principal components

Abstract The application of a genetic algorithm (GA) to the selection of principal components (PCs) is proposed as an efficient method to determine the optimal multivariate regression model. This stochastic method was compared with other deterministic methods such as: exhaustive search (here taken as a validation procedure), forward and backward-stepwise variable selection and correlation principal components regression (CPCR). It is shown that for the range of data sets used, the GA gives the same result as the those obtained by an exhaustive search and by CPCR whereas the stepwise procedures do not. These results also show that in order to build optimal predictive models using principal components regression (PCR) one needs to select the best subset of PCs rather than simply use those with the highest eigenvalues.

Impact of prenatal disorders on the metabolic profile of second trimester amniotic fluid: a nuclear magnetic resonance metabonomic study.

This paper describes a metabonomic study of prenatal disorders using nuclear magnetic resonance (NMR) spectroscopy of amniotic fluid (AF) collected in the second trimester of pregnancy, to search for metabolite markers of fetal malformations, prediagnostic gestational diabetes (GD), preterm delivery (PTD), early rupture of membranes (PROM), and chromossomopathies. Fetal malformations were found to have the highest impact on AF metabolite composition, enabling statistical validation to be achieved by several multivariate analytical tools. Results confirmed previous indications that malformed fetuses seem to suffer altered energy metabolism and kidney underdevelopment. Newly found changes (namely in α-oxoisovalerate, ascorbate, creatinine, isoleucine, serine, threonine) suggest possible additional effects on protein and nucleotide sugar biosynthesis. Prediagnostic GD subjects showed an average increase in glucose and small decreases in several amino acids along with acetate, formate, creatinine, and glycerophosphocholine. Small metabolite changes were also observed in the AF of subjects eventually undergoing PTD and PROM, whereas no relevant changes were found for chromossomopathies (for which a low number of samples was considered). The potential value of these results for biochemical insight and prediction of prenatal disorders is discussed, as well as their limitations regarding number of samples and overlap of different disorders.

1H NMR Based Metabonomics of Human Amniotic Fluid for the Metabolic Characterization of Fetus Malformations

An NMR-metabonomic study of malformed fetuses was carried out through human amniotic fluid (HAF) analysis. Over 70 compounds were detected in control HAF by NMR. Possible confounding variables (fetus gender and gestational and maternal ages) were shown not to induce detectable compositional trends in the control group considered. Malformed fetuses showed variations in glucose, some amino acids and organic acids and proteins. In tandem with enzymatic assays, these NMR results suggest that changes in gycolysis and gluconeogenesis as well as kidney underdevelopment occur in the malformed fetuses studied here.

Allergic asthma exhaled breath metabolome: a challenge for comprehensive two-dimensional gas chromatography.

Allergic asthma represents an important public health issue, most common in the paediatric population, characterized by airway inflammation that may lead to changes in volatiles secreted via the lungs. Thus, exhaled breath has potential to be a matrix with relevant metabolomic information to characterize this disease. Progress in biochemistry, health sciences and related areas depends on instrumental advances, and a high throughput and sensitive equipment such as comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC×GC-ToFMS) was considered. GC×GC-ToFMS application in the analysis of the exhaled breath of 32 children with allergic asthma, from which 10 had also allergic rhinitis, and 27 control children allowed the identification of several hundreds of compounds belonging to different chemical families. Multivariate analysis, using Partial Least Squares-Discriminant Analysis in tandem with Monte Carlo Cross Validation was performed to assess the predictive power and to help the interpretation of recovered compounds possibly linked to oxidative stress, inflammation processes or other cellular processes that may characterize asthma. The results suggest that the model is robust, considering the high classification rate, sensitivity, and specificity. A pattern of six compounds belonging to the alkanes characterized the asthmatic population: nonane, 2,2,4,6,6-pentamethylheptane, decane, 3,6-dimethyldecane, dodecane, and tetradecane. To explore future clinical applications, and considering the future role of molecular-based methodologies, a compound set was established to rapid access of information from exhaled breath, reducing the time of data processing, and thus, becoming more expedite method for the clinical purposes.

NMR metabolomics of esca disease-affected Vitis vinifera cv. Alvarinho leaves

Esca is a destructive disease that affects vineyards leading to important losses in wine production. Information about the response of Vitis vinifera plants to this disease is scarce, particularly concerning changes in plant metabolism. In order to study the metabolic changes in Vitis plants affected by esca, leaves from both infected and non-affected cordons of V. vinifera cv. Alvarinho (collected in the Vinho Verde region, Portugal) were analysed. The metabolite composition of leaves from infected cordons with visible symptoms [diseased leaves (dl)] and from asymptomatic cordons [healthy leaves (hl)] was evaluated by 1D and 2D (1)H-nuclear magnetic resonance (NMR) spectroscopy. Principal component analysis (PCA) of the NMR spectra showed a clear separation between dl and hl leaves, indicating differential compound production due to the esca disease. NMR/PCA analysis allowed the identification of specific compounds characterizing each group, and the corresponding metabolic pathways are discussed. Altogether, the study revealed a significant increase of phenolic compounds in dl, compared with hl, accompanied by a decrease in carbohydrates, suggesting that dl are rerouting carbon and energy from primary to secondary metabolism. Other metabolic alterations detected comprised increased levels of methanol, alanine, and gamma-aminobutyric acid in dl, which might be the result of the activation of other defence mechanisms.

Second Trimester Maternal Urine for the Diagnosis of Trisomy 21 and Prediction of Poor Pregnancy Outcomes

Given the recognized lack of prenatal clinical methods for the early diagnosis of preterm delivery, intrauterine growth restriction, preeclampsia and gestational diabetes mellitus, and the continuing need for optimized diagnosis methods for specific chromosomal disorders (e.g., trisomy 21) and fetal malformations, this work sought specific metabolic signatures of these conditions in second trimester maternal urine, using (1)H Nuclear Magnetic Resonance ((1)H NMR) metabolomics. Several variable importance to the projection (VIP)- and b-coefficient-based variable selection methods were tested, both individually and through their intersection, and the resulting data sets were analyzed by partial least-squares discriminant analysis (PLS-DA) and submitted to Monte Carlo cross validation (MCCV) and permutation tests to evaluate model predictive power. The NMR data subsets produced significantly improved PLS-DA models for all conditions except for pre-premature rupture of membranes. Specific urinary metabolic signatures were unveiled for central nervous system malformations, trisomy 21, preterm delivery, gestational diabetes, intrauterine growth restriction and preeclampsia, and biochemical interpretations were proposed. This work demonstrated, for the first time, the value of maternal urine profiling as a complementary means of prenatal diagnostics and early prediction of several poor pregnancy outcomes.