The use of biomarkers to diagnose the ecological impact of pollutants in Mediterranean rivers

Um assunto que requer atenção é a avaliação ecológica da qualidade da água de ecossistemas de água doce. Uma abordagem que surge como promissora é a biomonitorização baseada em biomarcadores, porque pode avaliar a saúde dos organismos e obter sinais de alerta precoce acerca dos riscos ambientais. Até agora, porém, o uso de biomarcadores em espécies de invertebrados, para diagnosticar danos ecológicos nos rios, é escasso. Por essa razão, existe uma necessidade urgente de desenvolver biomarcadores nas principais espécies de macroinvertebrados dos ecossistemas fluviais que são alvo de estudo. Esta tese tem como objectivo averiguar se as respostas in situ, aliadas aos biomarcadores, podem ser um método viável para avaliar os danos ecológicos de contaminantes em ecossistemas de água doce. Numa primeira fase, os biomarcadores foram usados para averiguar os mecanismos fisiológicos de adaptação genética de clones de Daphnia magna ao pesticida organofosforado fenitrothion. Numa segunda fase, os biomarcadores foram usados como ferramentas de diagnóstico de poluição em zonas ribeirinhas. Estes estudos foram realizados com três espécies-chave de macroinvertebrados: Daphnia magna, Corbicula fluminea e Hydropsyche exocellata, nos rios Besós e Llobregat e no Delta do rio Ebro (NE Espanha). Além disso, foram realizados com animais capturados nos rios, ou com ensaios de transplantes, e foram complementados com índices biológicos de macroinvertebrados e análises químicas da água e dos animais. Como os contaminantes químicos têm vários modos toxicológicos de acção e, portanto, afectam várias respostas bioquímicas dos organismos, foram analisados nas três espécies um conjunto de biomarcadores pertencentes a diferentes vias metabólicas. A abordagem experimental indica que o uso combinado de biomarcadores e outras medidas, tais como índices biológicos e testes in situ, contribui para diagnosticar os efeitos prejudiciais de contaminantes nas comunidades ribeirinhas.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.

Concentration of human pollution tracers with ionic liquids

The main objective of the present thesis consists on the development of an analytical preconcentration technology for the concomitant extraction and concentration of human pollution tracers from wastewater streams. Due to the outstanding tunable properties of ionic liquids (ILs), aqueous biphasic systems (ABS) composed of ILs can provide higher and more selective extraction efficiencies for a wide range of compounds, being thus a promising alternative to the volatile and hazardous organic solvents (VOCs) typically used. For that purpose, IL-based ABS were employed and adequately characterized as an one-step extraction and concentration technique. The applicability of IL-based ABS was verified by their potential to completely extract and concentrate two representative pharmaceutical pollution tracers, namely caffeine (CAF) and carbamazepine (CBZ), from wastewaters. The low concentration of these persistent pollutants (usually found in μg·dm-3 and ng·dm-3 levels, respectively) by conventional analytical equipment does not permit a proper detection and quantification without a previous concentration step. Preconcentration methods commonly applied are costly, timeconsuming, with irregular recoveries and make use of VOCs. In this work, the ABS composed of the IL tetrabutylammonium chloride ([N4444]Cl) and the salt potassium citrate (K3[C6H5O7]) was investigated while demonstrating to be able to completely extract and concentrate CAF and CBZ, in a single-step, overcoming thus the detection limit of the applied analytical equipment. Finally, the hydrotropic effect responsible for the ability of IL-based ABS to extract and concentrate a wide variety of compounds was also investigated. It was shown that the IL rules the hydrotropic mechanism in the solubility of CAF in aqueous solutions, with an increase in solubility up to 4-fold. Moreover, the proper selection of the IL enables the design of the system that leads to a more enhanced solubility of a given solute in the IL-rich phase, while allowing a better extraction and concentration. IL-based ABS are a promising and more versatile technique, and are straightforwardly envisaged as selective extraction and concentration routes of target micropollutants from wastewater matrices.