Origin, seasonality and chemical characteristics of rainwater in Aveiro

Este trabalho teve como objectivo caracterizar quimicamente a água da chuva recolhida na cidade de Aveiro, localizada a sudoeste da Europa, no período de Setembro de 2008 a Setembro de 2009. Para matrizes diluídas como a da água da chuva, as metodologias analíticas a utilizar para se conseguir uma rigorosa caracterização química são de grande importância e ainda não estão uniformizadas. Assim, para caracterizar a fracção orgânica, primeiramente foram comparadas duas metodologias de filtração (0.22 e 0.45 μm) e foram estudados dois procedimentos de preservação da água da chuva (refrigeração e congelação), utilizando a espectroscopia de fluorescência molecular. Além disso, foram comparados dois procedimentos de isolamento e extracção da matéria orgânica dissolvida (DOM) da água da chuva, baseados na sorção nos sorbentes DAX-8 e C-18, utilizando as espectroscopias de ultravioleta-visível e fluorescência molecular. Relativamente aos resultados das metodologias de filtração e preservação, é recomendada a filtração por 0.45 μm, assim como, as amostras de água da chuva deverão ser mantidas no escuro a 4ºC, no máximo até 4 dias, até às análises espectroscópicas. Relativamente à metodologia de isolamento e extracção da DOM, os resultados mostraram que o procedimento de isolamento baseado na C-18 extraiu a DOM que é representativa da matriz global, enquanto que o procedimento da DAX-8 extraiu preferencialmente a fracção do tipo húmico. Como no presente trabalho pretendíamos caracterizar a fracção do tipo húmico da DOM da água da chuva, foi escolhida a metodologia de isolamento e extracção baseada na sorção no sorvente DAX-8. Previamente ao isolamento e extracção da DOM da água da chuva, toda a fracção orgânica das amostras de água da chuva foi caracterizada pelas técnicas de ultravioleta-visível e de fluorescência molecular. As amostras mostraram características semelhantes às de outras águas naturais, e a água da chuva do Verão e Outono apresentou maior conteúdo da matéria orgânica dissolvida cromofórica que a do Inverno e Primavera. Posteriormente, a fracção do tipo húmico de algumas amostras de água da chuva, isolada e extraída pelo procedimento baseado na DAX-8, foi caracterizada utilizando as técnicas espectroscópicas de ultravioleta-visível, fluorescência molecular e ressonância magnética nuclear de protão. Todos os extractos continham uma mistura complexa de compostos hidroxilados e ácidos carboxílicos, com uma predominância da componente alifática e um baixo conteúdo da componente aromática. A fracção inorgânica da água da chuva foi caracterizada determinando a concentração das seguintes espécies iónicas: H+, NH4 +, Cl-, NO3 -, SO4 2-. Os resultados foram comparados com os obtidos na chuva colectada no mesmo local entre 1986-1989 e mostraram que de todos os iões determinados a concentração de NO3 - foi a única que aumentou (cerca do dobro) em 20 anos, tendo sido atribuído ao aumento de veículos e emissões industriais na área de amostragem. Durante o período de amostragem cerca de 80% da precipitação esteve associada a massas de ar oceânicas, enquanto a restante esteve relacionada com massas que tiveram uma influência antropogénica e terrestre. De um modo geral, para as fracções orgânica e inorgânica da água da chuva analisadas, o conteúdo químico foi menor para as amostras que estiveram associadas a massas de ar marítimas do que para as amostras que tiveram contribuições terrestres e antropogénicas.

Pregnancy and newborns disorders followed by urine metabolomics

Chapter 1 introduces the scope of the work by identifying the clinically relevant prenatal disorders and presently available diagnostic methods. The methodology followed in this work is presented, along with a brief account of the principles of the analytical and statistical tools employed. A thorough description of the state of the art of metabolomics in prenatal research concludes the chapter, highlighting the merit of this novel strategy to identify robust disease biomarkers. The scarce use of maternal and newborn urine in previous reports enlightens the relevance of this work. Chapter 2 presents a description of all the experimental details involved in the work performed, comprising sampling, sample collection and preparation issues, data acquisition protocols and data analysis procedures. The proton Nuclear Magnetic Resonance (NMR) characterization of maternal urine composition in healthy pregnancies is presented in Chapter 3. The urinary metabolic profile characteristic of each pregnancy trimester was defined and a 21-metabolite signature found descriptive of the metabolic adaptations occurring throughout pregnancy. 8 metabolites were found, for the first time to our knowledge, to vary in connection to pregnancy, while known metabolic effects were confirmed. This chapter includes a study of the effects of non-fasting (used in this work) as a possible confounder. Chapter 4 describes the metabolomic study of 2nd trimester maternal urine for the diagnosis of fetal disorders and prediction of later-developing complications. This was achieved by applying a novel variable selection method developed in the context of this work. It was found that fetal malformations (FM) (and, specifically those of the central nervous system, CNS) and chromosomal disorders (CD) (and, specifically, trisomy 21, T21) are accompanied by changes in energy, amino acids, lipids and nucleotides metabolic pathways, with CD causing a further deregulation in sugars metabolism, urea cycle and/or creatinine biosynthesis. Multivariate analysis models´ validation revealed classification rates (CR) of 84% for FM (87%, CNS) and 85% for CD (94%, T21). For later-diagnosed preterm delivery (PTD), preeclampsia (PE) and intrauterine growth restriction (IUGR), it is found that urinary NMR profiles have early predictive value, with CRs ranging from 84% for PTD (11-20 gestational weeks, g.w., prior to diagnosis), 94% for PE (18-24 g.w. pre-diagnosis) and 94% for IUGR (2-22 g.w. pre-diagnosis). This chapter includes results obtained for an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) study of pre-PTD samples and correlation with NMR data. One possible marker was detected, although its identification was not possible. Chapter 5 relates to the NMR metabolomic study of gestational diabetes mellitus (GDM), establishing a potentially predictive urinary metabolic profile for GDM, 2-21 g.w. prior to diagnosis (CR 83%). Furthermore, the NMR spectrum was shown to carry information on individual phenotypes, able to predict future insulin treatment requirement (CR 94%). Chapter 6 describes results that demonstrate the impact of delivery mode (CR 88%) and gender (CR 76%) on newborn urinary profile. It was also found that newborn prematurity, respiratory depression, large for gestational age growth and malformations induce relevant metabolic perturbations (CR 82-92%), as well as maternal conditions, namely GDM (CR 82%) and maternal psychiatric disorders (CR 91%). Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the value of maternal or newborn urine metabolomics for pregnancy monitoring and disease prediction, towards the development of new early and non-invasive diagnostic methods.

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.