The Portuguese continental shelf soft-bottom benthic habitats

As comunidades de macrofauna bentónica são ecológica e economicamente relevantes, sendo fonte de diversos bens e serviços. A sua identificação, caracterização e mapeamento são importantes para identificar áreas marinhas protegidas e para uma melhor utilização do ambiente marinho. Este trabalho apresenta um estudo holístico da diversidade e distribuição espacial das comunidades de macrofauna bentónica ao longo da plataforma continental Portuguesa. Cerca de 145 locais posicionados ao longo da plataforma ocidental e setentrional foram amostrados com uma draga Smith-McIntyre de área 0,1 m2, a profundidades que variaram entre os 13 e 195 metros. Os sedimentos foram caracterizados em termos de granulometria, de matéria orgânica e geoquímica. São propostos seis habitats bentónicos principais para a plataforma continental Portuguesa, analisada a relação entre os dados biológicos e ambientais e discutidas questões biogeográficas relacionadas com a distribuição espacial de espécies e das comunidades. A distribuição da granulometria e assinatura geoquímica dos sedimentos da plataforma continental revelou-se bastante complexa, refletindo importantes diferenças nas fontes (naturais e antropogénicas), origem fluvial, geomorfologia da plataforma, hidrodinamismo e atividade biológica. Relativamente à macrofauna, entre os mais de 30 mil indivíduos recolhidos, foram identificados cerca de 737 taxa, dos quais quatro são novas espécies e aproximadamente 40 correspondem a primeiras ocorrências para a costa Portuguesa. As espécies mais frequentes foram a Ampharete finmarchica, Ampelisca sp. e Lumbrineris lusitanica sp. nov. enquanto as mais abundantes foram Mediomastus fragilis, Polygordius appendiculatus e Ampharete finmarchica. A abundância por local de amostragem variou entre 7 e 1.307 espécimens e a diversidade alfa atingiu um máximo de 96 taxa. Os sedimentos mais grosseiros apresentaram maior diversidade e abundância comparativamente com os sedimentos envasados. Foram identificados seis habitats bentónicos na plataforma continental Portuguesa: (a) sedimentos grosseiros com Protodorvillea kefersteini, Pisione remota, Angulus pygmaeus e várias espécies intersticiais; (b) areias finas hidrodinamicamente expostas e próximas da linha de costa com Magelona johnstoni, Urothoe pulchella e Angulus fabula; (c) comunidade de Abra alba em areia envasadas da plataforma profunda do noroeste; (d) Galathowenia oculata, Lumbrinerides amoureuxi e outros poliquetas escavadores e tubícolas em areais envasadas muito profundas na plataforma sudoeste; (e) Euchone rubrocincta, Nematonereis unicornis e várias espécies setentrionais nas areias envasadas da plataforma sul; (f) vasas com Sternaspis scutata, Heteromastus filiformis e Psammogammarus caecus. A granulometria do sedimento (particularmente teor em finos), matéria orgânica, profundidade e hidrodinamismo foram as variáveis ambientais com a maior relação com os padrões de distribuição da macrofauna. As espécies cosmopolitas e de latitudes superiores (clima Boreal ou Temperado Frio) dominaram o setor noroeste, sendo substituídas por espécies mais quentes na área de transição entre os canhões da Nazaré e S. Vicente, que dominaram por conseguinte a plataforma sul. O presente estudo evidenciou a abundância e diversidade da macrofauna bentónica ao longo da área costeira de Portugal, na qual coexistem faunas das províncias biogeográficas do norte da Europa, bem como subtropicais. Integrado com outro estudos, este poderá ser a base para uma melhor gestão da plataforma continental Portuguesa.

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.