Metabonomics of human amniotic fluid for prenatal diagnostics

O trabalho apresentado nesta tese teve como principais objectivos contribuir para o conhecimento da composição do líquido amniótico humano (LA), colhido no 2º trimestre de gravidez, assim como investigar possíveis alterações na sua composição devido à ocorrência de patologias pré-natais, recorrendo à metabonómica e procurando, assim, definir novos biomarcadores de doenças da grávida e do feto. Após uma introdução descrevendo o estado da arte relacionado com este trabalho (Capítulo 1) e os princípios das metodologias analíticas usadas (Capítulo 2), seguida de uma descrição dos aspectos experimentais associados a esta tese (Capítulo 3), apresentam-se os resultados da caracterização da composição química do LA (gravidez saudável) por espectroscopia de ressonância magnética nuclear (RMN), assim como da monitorização da sua estabilidade durante o armazenamento e após ciclos de congelamento-descongelamento (Capítulo 4). Amostras de LA armazenadas a -20°C registaram alterações significativas, tornando-se estas menos pronunciadas (mas ainda mensuráveis) a -70°C, temperatura recomendada para o armazenamento de LA. Foram também observadas alterações de composição após 1-2 ciclos de congelamento-descongelamento (a ter em conta aquando da reutilização de amostras), assim como à temperatura ambiente (indicando um período máximo de 4h para a manipulação e análise de LA). A aquisição de espectros de RMN de 1H de alta resolução e RMN acoplado (LC-NMR/MS) permitiu a detecção de 75 compostos no LA do 2º trimestre, 6 dos quais detectados pela primeira vez no LA. Experiências de difusão (DOSY) permitiram ainda a caracterização das velocidades de difusão e massas moleculares médias das proteínas mais abundantes. O Capítulo 5 descreve o estudo dos efeitos de malformações fetais (FM) e de cromossomopatias (CD) na composição do LA do 2º trimestre de gravidez. A extensão deste trabalho ao estudo dos efeitos de patologias no LA que ocorrem no 3º trimestre de gravidez é descrita no Capítulo 6, nomeadamente no que se refere ao parto pré-termo (PTD), pré-eclampsia (PE), restrição do crescimento intra-uterino (IUGR), ruptura prematura de membranas (PROM) e diabetes mellitus gestacional (GDM). Como complemento a estes estudos, realizou-se uma análise preliminar da urina materna do 2º trimestre para o estudo de FM e GDM, descrita no Capítulo 7. Para interpretação dos dados analíticos, obtidos por espectroscopia RMN de 1H, cromatografia líquida de ultra eficiência acoplada a espectrometria de massa (UPLC-MS) e espectroscopia do infravermelho médio (MIR), recorreu-se à análise discriminante pelos métodos dos mínimos quadrados parciais e o método dos mínimos quadrados parciais ortogonal (PLS-DA e OPLS-DA) e à correlação espectral. Após análise por validação cruzada de Monte-Carlo (MCCV), os modelos PLS-DA de LA permitiram distinguir as FM dos controlos (sensibilidades 69-85%, especificidades 80-95%, taxas de classificação 80-90%), revelando variações metabólicas ao nível do metabolismo energético, dos metabolismos dos aminoácidos e glícidos assim como possíveis alterações ao nível do funcionamento renal. Observou-se também um grande impacto das FM no perfil metabólico da urina materna (medido por UPLC-MS), tendo no entanto sido registados modelos PLS-DA com menor sensibilidade (40-60%), provavelmente devido ao baixo número de amostras e maior variabilidade da composição da urina (relativamente ao LA). Foram sugeridos possíveis marcadores relacionados com a ocorrência de FM, incluindo lactato, glucose, leucina, valina, glutamina, glutamato, glicoproteínas e conjugados de ácido glucurónico e/ou sulfato e compostos endógenos e/ou exógenos (<1 M) (os últimos visíveis apenas na urina). No LA foram também observadas variações metabólicas devido à ocorrência de vários tipos de cromossomopatias (CD), mas de menor magnitude. Os perfis metabólicos de LA associado a pré- PTD produziram modelos que, apesar do baixo poder de previsão, sugeriram alterações precoces no funcionamento da unidade fetoplacentária, hiperglicémia e stress oxidativo. Os modelos obtidos para os grupos pré- IUGR pré- PE, pré- PROM e pré-diagnóstico GDM (LA e urina materna) registaram baixo poder de previsão, indicando o pouco impacto destas condições na composição do LA e/ou urina do 2º trimestre. Os resultados obtidos demonstram as potencialidades da análise dos perfis metabólicos do LA (e, embora com base em menos estudos, da urina materna) do 2º trimestre para o desenvolvimento de novos e complementares métodos de diagnóstico, nomeadamente para FM e PTD.

Development of infrared spectroscopy for assessing bacterial quality in foods

Rapid and specific detection of foodborne bacteria that can cause food spoilage or illness associated to its consumption is an increasingly important task in food industry. Bacterial detection, identification, and classification are generally performed using traditional methods based on biochemical or serological tests and the molecular methods based on DNA or RNA fingerprints. However, these methodologies are expensive, time consuming and laborious. Infrared spectroscopy is a reliable, rapid, and economic technique which could be explored as a tool for bacterial analysis in the food industry. In this thesis it was evaluated the potential of IR spectroscopy to study the bacterial quality of foods. In Chapter 2, it was developed a calibration model that successfully allowed to predict the bacterial concentration of naturally contaminated cooked ham samples kept at refrigeration temperature during 8 days. In this part, it was developed the methodology that allowed the best reproducibility of spectra from bacteria colonies with minimal sample preparation, which was used in the subsequent work. Several attempts trying different resolutions and number of scans in the IR were made. A spectral resolution of 4 cm-1, with 32 scans were the settings that allowed the best results. Subsequently, in Chapter 3, it was made an attempt to identify 22 different foodborne bacterial genera/species using IR spectroscopy coupled with multivariate analysis. The principal component analysis, used as an exploratory technique, allowed to form distinct groups, each one corresponding to a different genus, in most of the cases. Then, a hierarchical cluster analysis was performed to further analyse the group formation and the possibility of distinction between species of the same bacterial genus. It was observed that IR spectroscopy not only is suitable to the distinction of the different genera, but also to differentiate species of the same genus, with the simultaneous use of principal component analysis and cluster analysis techniques. The utilization of IR spectroscopy and multivariate statistical analysis were also investigated in Chapter 4, in order to confirm the presence of Listeria monocytogenes and Salmonella spp. isolated from contaminated foods, after growth in selective medium. This would allow to substitute the traditional biochemical and serological methods that are used to confirm these pathogens and that delay the obtainment of the results up to 2 days. The obtained results allowed the distinction of 3 different Listeria species and the distinction of Salmonella spp. from other bacteria that can be mistaken with them. Finally, in chapter 5, high pressure processing, an emerging methodology that permits to produce microbiologically safe foods and extend their shelf-life, was applied to 12 foodborne bacteria to determine their resistance and the effects of pressure in cells. A treatment of 300 MPa, during 15 minutes at room temperature was applied. Gram-negative bacteria were inactivated to undetectable levels and Gram-positive showed different resistances. Bacillus cereus and Staphylococcus aureus decreased only 2 logs and Listeria innocua decreased about 5 logs. IR spectroscopy was performed in bacterial colonies before and after HPP in order to investigate the alterations of the cellular compounds. It was found that high pressure alters bands assigned to some cellular components as proteins, lipids, oligopolysaccharides, phosphate groups from the cell wall and nucleic acids, suggesting disruption of the cell envelopes. In this work, bacterial quantification and classification, as well as assessment of cellular compounds modification with high pressure processing were successfully performed. Taking this into account, it was showed that IR spectroscopy is a very promising technique to analyse bacteria in a simple and inexpensive manner.

Metabonomic studies to assess beer quality and stability

The work reported in this thesis aimed at applying the methodology known as metabonomics to the detailed study of a particular type of beer and its quality control, with basis on the use of multivariate analysis (MVA) to extract meaningful information from given analytical data sets. In Chapter 1, a detailed description of beer is given considering the brewing process, main characteristics and typical composition of beer, beer stability and the commonly used analytical techniques for beer analysis. The fundamentals of the analytical methods employed here, namely nuclear magnetic resonance (NMR) spectroscopy, gas-chromatography-mass spectrometry (GC-MS) and mid-infrared (MIR) spectroscopy, together with the description of the metabonomics methodology are described shortly in Chapter 2. In Chapter 3, the application of high resolution NMR to characterize the chemical composition of a lager beer is described. The 1H NMR spectrum obtained by direct analysis of beer show a high degree of complexity, confirming the great potential of NMR spectroscopy for the detection of a wide variety of families of compounds, in a single run. Spectral assignment was carried out by 2D NMR, resulting in the identification of about 40 compounds, including alcohols, amino acids, organic acids, nucleosides and sugars. In a second part of Chapter 3, the compositional variability of beer was assessed. For that purpose, metabonomics was applied to 1H NMR data (NMR/MVA) to evaluate beer variability between beers from the same brand (lager), produced nationally but differing in brewing site and date of production. Differences between brewing sites and/or dates were observed, reflecting compositional differences related to particular processing steps, including mashing, fermentation and maturation. Chapter 4 describes the quantification of organic acids in beer by NMR, using different quantitative methods: direct integration of NMR signals (vs. internal reference or vs. an external electronic reference, ERETIC method) and by quantitative statistical methods (using the partial least squares (PLS) regression) were developed and compared. PLS1 regression models were built using different quantitative methods as reference: capillary electrophoresis with direct and indirect detection and enzymatic essays. It was found that NMR integration results generally agree with those obtained by the best performance PLS models, although some overestimation for malic and pyruvic acids and an apparent underestimation for citric acid were observed. Finally, Chapter 5 describes metabonomic studies performed to better understand the forced aging (18 days, at 45 ºC) beer process. The aging process of lager beer was followed by i) NMR, ii) GC-MS, and iii) MIR spectroscopy. MVA methods of each analytical data set revealed clear separation between different aging days for both NMR and GC-MS data, enabling the identification of compounds closely related with the aging process: 5-hydroxymethylfurfural (5-HMF), organic acids, γ-amino butyric acid (GABA), proline and the ratio linear/branched dextrins (NMR domain) and 5-HMF, furfural, diethyl succinate and phenylacetaldehyde (known aging markers) and, for the first time, 2,3-dihydro-3,5-dihydroxy-6-methyl-4(H)-pyran-4-one xii (DDMP) and maltoxazine (by GC-MS domain). For MIR/MVA, no aging trend could be measured, the results reflecting the need of further experimental optimizations. Data correlation between NMR and GC-MS data was performed by outer product analysis (OPA) and statistical heterospectroscopy (SHY) methodologies, enabling the identification of further compounds (11 compounds, 5 of each are still unassigned) highly related with the aging process. Data correlation between sensory characteristics and NMR and GC-MS was also assessed through PLS1 regression models using the sensory response as reference. The results obtained showed good relationships between analytical data response and sensory response, particularly for the aromatic region of the NMR spectra and for GC-MS data (r > 0.89). However, the prediction power of all built PLS1 regression models was relatively low, possibly reflecting the low number of samples/tasters employed, an aspect to improve in future studies.

NMR in the characterization of heavy residual procedural streams

The main objective of this work was to monitor a set of physical-chemical properties of heavy oil procedural streams through nuclear magnetic resonance spectroscopy, in order to propose an analysis procedure and online data processing for process control. Different statistical methods which allow to relate the results obtained by nuclear magnetic resonance spectroscopy with the results obtained by the conventional standard methods during the characterization of the different streams, have been implemented in order to develop models for predicting these same properties. The real-time knowledge of these physical-chemical properties of petroleum fractions is very important for enhancing refinery operations, ensuring technically, economically and environmentally proper refinery operations. The first part of this work involved the determination of many physical-chemical properties, at Matosinhos refinery, by following some standard methods important to evaluate and characterize light vacuum gas oil, heavy vacuum gas oil and fuel oil fractions. Kinematic viscosity, density, sulfur content, flash point, carbon residue, P-value and atmospheric and vacuum distillations were the properties analysed. Besides the analysis by using the standard methods, the same samples were analysed by nuclear magnetic resonance spectroscopy. The second part of this work was related to the application of multivariate statistical methods, which correlate the physical-chemical properties with the quantitative information acquired by nuclear magnetic resonance spectroscopy. Several methods were applied, including principal component analysis, principal component regression, partial least squares and artificial neural networks. Principal component analysis was used to reduce the number of predictive variables and to transform them into new variables, the principal components. These principal components were used as inputs of the principal component regression and artificial neural networks models. For the partial least squares model, the original data was used as input. Taking into account the performance of the develop models, by analysing selected statistical performance indexes, it was possible to conclude that principal component regression lead to worse performances. When applying the partial least squares and artificial neural networks models better results were achieved. However, it was with the artificial neural networks model that better predictions were obtained for almost of the properties analysed. With reference to the results obtained, it was possible to conclude that nuclear magnetic resonance spectroscopy combined with multivariate statistical methods can be used to predict physical-chemical properties of petroleum fractions. It has been shown that this technique can be considered a potential alternative to the conventional standard methods having obtained very promising results.

Evaluation of the matabolic response of osteosarcoma cells to conventional and new anticancer drugs by NMR metabolomics

The main scope of this work was to evaluate the metabolic effects of anticancer agents (three conventional and one new) in osteosarcoma (OS) cells and osteoblasts, by measuring alterations in the metabolic profile of cells by nuclear magnetic resonance (NMR) spectroscopy metabolomics. Chapter 1 gives a theoretical framework of this work, beginning with the main metabolic characteristics that globally describe cancer as well as the families and mechanisms of action of drugs used in chemotherapy. The drugs used nowadays to treat OS are also presented, together with the Palladium(II) complex with spermine, Pd2Spm, potentially active against cancer. Then, the global strategy for cell metabolomics is explained and the state of the art of metabolomic studies that analyze the effect of anticancer agents in cells is presented. In Chapter 2, the fundamentals of the analytical techniques used in this work, namely for biological assays, NMR spectroscopy and multivariate and statistical analysis of the results are described. A detailed description of the experimental procedures adopted throughout this work is given in Chapter 3. The biological and analytical reproducibility of the metabolic profile of MG-63 cells by high resolution magic angle spinning (HRMAS) NMR is evaluated in Chapter 4. The metabolic impact of several factors (cellular integrity, spinning rate, temperature, time and acquisition parameters) on the 1H HRMAS NMR spectral profile and quality is analysed, enabling the definition of the best acquisition parameters for further experiments. The metabolic consequences of increasing number of passages in MG-63 cells as well as the duration of storage are also investigated. Chapter 5 describes the metabolic impact of drugs conventionally used in OS chemotherapy, through NMR metabolomics studies of lysed cells and aqueous extracts analysis. The results show that MG-63 cells treated with cisplatin (cDDP) undergo a strong up-regulation of lipid contents, alterations in phospholipid constituents (choline compounds) and biomarkers of DNA degradation, all associated with cell death by apoptosis. Cells exposed to doxorubicin (DOX) or methotrexate (MTX) showed much slighter metabolic changes, without any relevant alteration in lipid contents. However, metabolic changes associated with altered Krebs cycle, oxidative stress and nucleotides metabolism were detected and were tentatively interpreted at the light of the known mechanisms of action of these drugs. The metabolic impact of the exposure of MG-63 cells and osteoblasts to cDDP and the Pd2Spm complex is described in Chapter 6. Results show that, despite the ability of the two agents to bind DNA, the metabolic consequences that arise from exposure to them are distinct, namely in what concerns to variation in lipid contents (absent for Pd2Spm). Apoptosis detection assays showed that, differently from what was seen for MG-63 cells treated with cDDP, the decreased number of living cells upon exposure to Pd2Spm was not due to cell death by apoptosis or necrosis. Moreover, the latter agent induces more marked alterations in osteoblasts than in cancer cells, while the opposite seemed to occur upon cDDP exposure. Nevertheless, the results from MG-63 cells exposure to combination regimens with cDDP- or Pd2Spm-based cocktails, described in Chapter 7, revealed that, in combination, the two agents induce similar metabolic responses, arising from synergy mechanisms between the tested drugs. Finally, the main conclusions of this thesis are summarized in Chapter 8, and future perspectives in the light of this work are presented.