Cláudia Rocha

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.

Analytical Approaches toward Successful Human Cell Metabolome Studies by NMR Spectroscopy

The aim of this work was to investigate the effects of cell handling and storage on cell integrity and (1)H high resolution magic angle spinning (HRMAS) NMR spectra. Three different cell types have been considered (lung tumoral, amniocytes, and MG-63 osteosarcoma cells) in order for sample-dependent effects to be identified. Cell integrity of fresh cells and cells frozen in cryopreservative solution was approximately 70-80%, with the former showing higher membrane degradation, probably enzymatic, as indicated by increased phosphocholine (PC) and/or glycerophosphocholine (GPC). Unprotected freezing (either gradual or snap-freezing) was found to lyse cells completely, similar to mechanical cell lysis. Besides enhanced metabolites visibility, lysed cells showed a different lipid profile compared to intact cells, with increased choline, PC, and GPC and decreased phosphatidylcholine (PTC). Cell lysis has, therefore, a significant effect on cell lipid composition, making handling reproducibility an important issue in lipid analysis. Sample spinning was found to disrupt 5-25% of cells, depending on cell type, and HRMAS was shown to be preferable to solution-state NMR of suspensions or supernatant, giving enhanced information on lipids and comparable resolution for smaller metabolites. Relaxation- and diffusion-edited NMR experiments gave limited information on intact cells, compared to lysed cells. The (1)H HRMAS spectra of the three cell types are compared and discussed.

NMR metabolomics of human lung tumours reveals distinct metabolic signatures for adenocarcinoma and squamous cell carcinoma

Lung tumour subtyping, particularly the distinction between adenocarcinoma (AdC) and squamous cell carcinoma (SqCC), is a critical diagnostic requirement. In this work, the metabolic signatures of lung carcinomas were investigated through (1)H NMR metabolomics, with a view to provide additional criteria for improved diagnosis and treatment planning. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (NMR) spectroscopy was used to analyse matched tumour and adjacent control tissues from 56 patients undergoing surgical excision of primary lung carcinomas. Multivariate modeling allowed tumour and control tissues to be discriminated with high accuracy (97% classification rate), mainly due to significant differences in the levels of 13 metabolites. Notably, the magnitude of those differences were clearly distinct for AdC and SqCC: major alterations in AdC were related to phospholipid metabolism (increased phosphocholine, glycerophosphocholine and phosphoethanolamine, together with decreased acetate) and protein catabolism (increased peptide moieties), whereas SqCC had stronger glycolytic and glutaminolytic profiles (negatively correlated variations in glucose and lactate and positively correlated increases in glutamate and alanine). Other tumour metabolic features were increased creatine, glutathione, taurine and uridine nucleotides, the first two being especially prominent in SqCC and the latter in AdC. Furthermore, multivariate analysis of AdC and SqCC profiles allowed their discrimination with a 94% classification rate, thus showing great potential for aiding lung tumours subtyping. Overall, this study has provided new, clear evidence of distinct metabolic signatures for lung AdC and SqCC, which can potentially impact on diagnosis and provide important leads for future research on novel therapeutic targets or imaging tracers.

Metabolic profiling of biofluids: potential in lung cancer screening and diagnosis

The knowledge that the organism’s metabolome is a potentially informative mirror of the impact of disease and its dynamics has led to promising developments in cancer research, strongly geared toward the discovery of new biomarkers of disease onset and progression. The present text reviews the advances made in the last 10 years in lung cancer research making use of the metabolomics strategies, particularly concerning metabolite profiling of human biofluids (blood serum and plasma, urine and others), expected to reflect the deviant metabolic behavior of lung tumors. The main goal of this article is to provide the reader with an up-to-date summary of the main metabolic variations taking place in biofluids, in relation to lung cancer, as well as of the analytical strategies employed to unveil them. Furthermore, particular needs and challenges are identified and possible developments envisaged.

NMR metabonomics for mammalian cell metabolism studies

The detailed knowledge of mammalian cell metabolism and its adjustments to different cell properties and perturbations, such as disease and drug exposure, is of enormous value in the deeper understanding of pathological processes and drug mechanisms, as well as in the development of new and improved methods for diagnosis, follow-up of disease progression and treatment response. This review covers recent developments in the use of NMR-based metabonomics to characterize cellular metabolomes and interpret them in terms of metabolic changes taking place in a wide range of situations. The analytical methodology available is briefly presented and the applications developed so far are reviewed. These include differences in cell properties (e.g., drug resistance, cell cycle stage, specific growth conditions and genetic characteristics) and changes induced in response to different perturbations (e.g., disease, drug exposure and irradiation).

Remodeling of liver phospholipidomic profile in streptozotocin-induced diabetic rats.

Lipid homeostasis in liver is known to be altered with diabetes mellitus, ultimately leading to liver damage and related complications. The present work aimed to evaluate changes in the liver phospholipid profile after 4 months of uncontrolled hyperglycemia. Twenty Wistar rats were divided into two groups: control and streptozotocin-treated (T1DM). After 4 months, animals were sacrificed and morphological characterization of liver was performed and related with serum markers of hepatic damage. Lipid extracts were obtained from liver and phospholipid (PL) classes were quantified. Lipid molecular species were determined by LC-MS and LC-MS/MS, and fatty acids by GC-MS. Concomitantly with signs of hepatic damage we found variations in the relative amount of phospholipid classes in T1DM, characterized by a decrease in PLs with choline head group, and by an increase in the relative content of other PL classes. A remodeling in PL fatty acyl chains was observed in T1DM liver, with a similar pattern to all the PL classes, and consisting in the reduction of 16:0 and an increase of 18:0 and 18:2 acyl chains. The observed changes in T1DM lipid profile may contribute to the altered membrane properties underlying hepatic damage, worsening the metabolic alterations that characterize T1DM.

Metabolic responses of the isopod Porcellionides pruinosus to nickel exposure assessed by 1H NMR metabolomics

UNLABELLED This work aimed at characterizing the metabolome of the isopod Porcellionides pruinosus and at assessing its variations over 14 days under laboratory culture conditions and upon exposure to the contaminant metal Nickel (Ni). The spectral profiles obtained by (1)H NMR spectroscopy were thoroughly assigned and subjected to multivariate analysis in order to highlight consistent changes. Over 50 metabolites could be identified, providing considerable new knowledge on the metabolome of these model organisms. Several metabolites changed non-linearly with Ni dose and exposure time, showing distinct variation patterns for initial (4 days) and later time points (7 and 14 days). In particular, at day 4, several amino acids were increased and sugars were decreased (compared to controls), whereas these variations were inverted for longer exposure, possibly reflecting earlier and more intensive moulting. Other variations, namely in betaines and choline-containing compounds, were suggested to relate with osmoregulation and detoxification mechanisms. Ni also had a marked effect on several nucleotides (increased upon exposure) and a moderate impact on lipids (decreased upon exposure). Overall, this study has provided new information on the Ni-induced metabolic adaptations of the P. pruinosus isopod, paving the way for improved mechanistic understanding of how these model organisms handle soil contamination. SIGNIFICANCE This study provided, for the first time to our knowledge, a detailed picture of the NMR-detectable metabolome of terrestrial isopods and of its fluctuations in time and upon exposure to the contaminant metal Nickel. Several time- and dose-dependent changes were highlighted, providing mechanistic insight into how these important model organisms handle Ni contamination.

NMR metabonomic study of lung cancer: metabolic profiling of tissues

This work aims to evaluate the potential of 1H Nuclear Magnetic Resonance (NMR) spectroscopy combined with multivariate statistics (metabonomics) for finding malignancy biomarkers in lung tissue and discriminating between different tumour histological types. Paired tissue samples from 32 patients with primary lung cancer were directly analysed by 1H High Resolution Magic Angle Spinning (HRMAS) NMR (500 MHz) and the spectral profiles subjected to Principal Component Analysis (PCA) and Partial Least Squares Regression Discriminant Analysis (PLS-DA). Tumor and control tissues were clearly discriminated in the PLS-DA model with 95% sensitivity and 100% specificity. In agreement with previous work [1], the metabolites giving rise to this separation were mainly lactate, glycerophosphocholine, phosphocholine, taurine, glutathione and uridine di/tri-phosphate (elevated in tumours), and glucose, phosphoethanolamine, acetate, lysine, methionine, glycine, myo- and scyllo-inositol (reduced in tumours compared to control tissues). Regarding differentiation of histological types, carcinoid tumors showed a distinct profile characterized by very low lipid levels, the presence of broad signals possibly arising from oligopeptides, decreased PC/GPC ratio and increased taurine and ascorbate levels. Epidermoid tumors could also be differentiated from adenocarcinomas due to increased lipids, lactate, creatine and GSH and decreased taurine, choline and PC. The biochemical information retrieved, not available by conventional histopathology, may provide additional criteria for improving clinical decisions and potentially aid in the differential diagnosis of lung tumors.

NMR metabonomic study of lung cancer: metabolic profiling of urine and blood plasma

Lung cancer is the leading cause of cancer death, its poor prognosis being related to asymptomatic development and late detection. Hence, there is great need for novel biomarkers that can aid in the early detection of lung cancer. In this study, NMR-metabonomics is applied for investigating lung cancer metabolic signatures in blood plasma and urine. Biofluid samples from lung cancer patients (n = 73) and a control healthy group (n = 56) were analysed 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. Multivariate modelling of urinary spectral profiles allowed cancer and control groups to be clearly discriminated with sensitivity and specificity levels of 93 and 94%, respectively. The metabolites giving rise to this separation were mainly creatinine, phenylacetylglycine and N-acetylglutamine/glutamate (elevated in patients), and hippurate and trigonelline (reduced in patients relatively to controls). In the case of blood plasma, good discrimination between the two groups was also achieved, mainly due to increased levels of lactate and LDL+VLDL, and lower levels of HDL, glucose, acetate, histidine, glutamine and valine in cancer compared to healthy subjects. These results show the promising potential of NMR metabonomics for finding putative biomarkers of lung cancer in biofluids, collected in a minimally invasive way, which may have important diagnostic/prognostic impact.