Submitted by Anderson Roberto Guerra null (andersonrguerra@hotmail.com) on 2017-03-24T15:51:27Z No. of bitstreams: 1 Dissertação Anderson Roberto Guerra.pdf: 1591587 bytes, checksum: 988e12ad9846b8f241313fe2038e610c (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-03-24T18:04:27Z (GMT) No. of bitstreams: 1 guerra_ar_me_bot.pdf: 1591587 bytes, checksum: 988e12ad9846b8f241313fe2038e610c (MD5) / Made available in DSpace on 2017-03-24T18:04:27Z (GMT). No. of bitstreams: 1 guerra_ar_me_bot.pdf: 1591587 bytes, checksum: 988e12ad9846b8f241313fe2038e610c (MD5) Previous issue date: 2017-02-24 / A doença do refluxo gastroesofágico (DRGE) é uma das mais importantes afecções digestivas, tendo em vista as elevadas e crescentes incidências, a intensidade dos sintomas e a gravidade das complicações. Existem alguns grupos de riscos independentes para o desenvolvimento desta doença e a obesidade está entre os principais fatores de risco para a DRGE. A DRGE pode acarretar em algumas complicações como o desenvolvimento de esôfago de Barrett, estenose, úlcera e sangramento esofágico, além de perda da qualidade de vida. Síndrome metabólica (SM) é uma associação central da obesidade, definido por medidas de circunferência abdominal em relação ao IMC, seguido por outros parâmetros como triglicérides alto, colesterol HDL baixo, pressão sistólica e/ou diastólica elevadas e glicemia em jejum acima de 100 mg/dL ou ainda algum tratamento prévio para uma ou mais dessas doenças citadas. A observação da possível associação de alterações clínicas às disfunções celulares e moleculares em pacientes com DRGE e/ou SM, torna-se crucial para o melhor entendimento da doença, bem como fornecer subsídios para a busca de novas alternativas terapêuticas. O objetivo deste trabalho foi avaliar a relação dos aspectos clínicos e celulares da DRGE e SM. Os resultados encontrados sugerem que há uma relação entre DRGE e a presença de SM ou obesidade central e ainda sugere que pessoas com circunferência abdominal, triglicérides e HDL alterados, podem apresentar DRGE. Também foram encontrados marcadores moleculares diferentes nos grupos DRGE+SM e SM, o que pode levar a possível diagnóstico diferencial no futuro além de ser um alvo potencial para intervenções ou diagnósticos. / Gastroesophageal reflux disease (GERD) is one of the most important digestive disorders, due to the high and increasing incidence, the intensity of the symptoms and the severity of the complications. There are some groups of independent risks for the development of this disease and obesity is among the main risk factors for GERD. GERD can lead to complications such as the development of Barrett's esophagus, stenosis, ulcer and esophageal bleeding, and loss of quality of life. Metabolic syndrome (MS) is a central association of obesity, defined by measures of waist circumference in relation to BMI, followed by other parameters such as high triglycerides, low HDL cholesterol, elevated systolic and / or diastolic blood pressure and fasting glycemia above 100 mg/dL or some previous treatment for one or more of these diseases. The observation of the possible association of clinical alterations to cellular and molecular dysfunctions in patients with GERD and / or MS is crucial for a better understanding of the disease, as well as providing subsidies for the search for new therapeutic alternatives. The aim of this study was to evaluate the relationship between clinical and cellular aspects of GERD and MS. The results suggest that there is a relationship between GERD and the presence of MS or central obesity and also suggests that people with altered abdominal circumference, triglycerides and HDL may present GERD. Different molecular markers were also found in the GERD + MS and MS groups, which may lead to a possible differential diagnosis in the future besides being a potential target for interventions or diagnoses.

Diversos estudos relacionados ao consumo de café na literatura não apresentam um consenso do papel deste alimento na saúde. Esta divergência pode ser o reflexo da utilização do consumo habitual auto-relatado pelos indivíduos como o único fator de exposição avaliado, visto que não existem biomarcadores de consumo, além de diferenças na composição química e na metabolização interindividual dos compostos bioativos (CBAs) da bebida. Desta forma, o presente estudo tem como objetivo identificar as alterações no perfil metabolômico de indivíduos saudáveis após o consumo controlado de café, bem como em extratos de café do tipo tradicional e expresso de diferentes marcas. Participaram deste estudo 35 homens saudáveis divididos entre os grupos café (n = 30) e controle (n = 5). As amostras de soro foram coletadas em jejum e 6, 12 e 24 horas após consumo de café (grupo café) ou água (grupo controle) seguida da extração de metabólitos, derivação com reagentes químicos e avaliação pelo método CG-EM. Posteriormente, os metabólitos foram identificados, selecionados, caracterizados e estatisticamente analisados. A análise metabolômica não direcionada permitiu a identificação de três compostos específicos do café (Caffeine, Quinic acid, m-Hydrocoumaric acid) a partir de 6 horas no grupo café. Além destes compostos, o metabólito Methylmalonic acid também demonstrou ser um candidato à biomarcador sérico da ingestão de café. Por meio das comparações entre os grupos café e controle ao decorrer dos tempos no modelo misto observamos diferentes respostas do perfil metabolômico de aminoácidos, carboidratos e lipídeos, promovidas pelos fatores tempo e grupo como também sua interação. Os aminoácidos 4-Hydroxyproline 1, LAlanine 1, L-Cystine 3, L-Methionine 1 e L-Threonine 2 apresentaram diferentes perfis de comportamento no grupo café ao longo de 24 horas, em relação ao grupo controle. Em paralelo, foram realizadas às análises metabolômicas de dois tipos de café: tradicional (Pilão, Melitta, 3 Corações e Prima Qualitá) e expresso (Nespresso, Dolce Gusto e Café Do Ponto). Após a preparação das bebidas, os extratos foram submetidos aos mesmos procedimentos do estudo de intervenção nutricional. No grupo tradicional, nenhum metabólito exclusivo foi identificado nas 4 marcas, porém o perfil metabolômico das marcas Melitta e 3 Corações foram similares e diferentes das demais. No grupo expresso, os cafés Nespresso e Dolce Gusto mostraram perfis semelhantes e diferentes do Café do Ponto, o qual apresentou exclusivamente Cellobiose 2 e beta- Gentiobiose. A comparação entre os grupos tradicional e expresso demonstrou que seus perfis metabolômicos foram distintos, bem como cada um deles apresentou metabólitos exclusivos com algumas funções celulares e moleculares diferentes. A quantidade dos principais compostos bioativos do café foi diferente entre os dois tipos e suas marcas. A caracterização do metaboloma em um grupo homogêneo de indivíduos saudáveis permitiu identificar candidatos à biomarcadores e alterações no perfil de alguns aminoácidos após a ingestão controlada de café. Assim, a identificação de candidatos à biomarcadores de efeito agudo do café mostra a importância de validar um painel com biomarcadores de alta confiabilidade e que possam colaborar com futuros estudos de intervenção nutricional, não apenas baseado do consumo de café reportado. Além disso, diferentes métodos de preparo do café, tradicional e expresso, bem como as diversas marcas analisadas, apresentaram diferença nos seus perfis metabolômicos. Desta forma, este estudo também pode ressaltar a importância de se considerar não somente a quantidade, mas também o tipo e a marca de café consumido como fatores de exposição em estudos de intervenção nutricional e de associação entre o consumo de café e seus efeitos no organismo / There are several studies associating coffee consumption and diseases, however they are not concordant about the coffee role in health. This divergence may reflect a lack of standard in data acquisition since most of the time the subjects\' self-reported habitual consumption is the only exposure factor evaluated and there is no biomarker. Add, there are differences of coffee beverage bioactive compounds composition and subjects show difference in coffee metabolization resulting in many covaries to be considered in these studies. Our study aimed to identify changes in metabolomic profile of healthy individuals after controlled consumption of coffee, as well as metabolic profile of traditional and espresso coffee beverages from different brands. 35 healthy men were distributed into coffee (n = 30) and control (n = 5) groups. Serum was sampled at fasting and 6, 12 and 24 hours after coffee (coffee group) or water (control group) intake. Metabolites were extracted, derivatized with MSTFA and TMCS, and run in GC-MS. Subsequently, the metabolites were identified using Fiehn Metabolomics Library and NIST, filtered, characterized and statistically analyzed. Untargeted metabolomic analysis identified three specific compounds of coffee (Caffeine, Quinic acid, m-Hydrocoumaric acid) in serum of subjects six hours after coffee intake. In addition to these compounds, Methylmalonic acid showed as a potential biomarker candidate of coffee intake in serum. Comparing coffee and control groups over time in a mixed model, we observed difference in the metabolomic profile related to amino acids, carbohydrates and lipids. 4-Hydroxyproline 1, L-Alanine 1, L-Cystine 3, L-Methionine 1 and L-Threonine 2 showed different profiles in coffee group over 24 hours compared to control group. In parallel to serum samples, we performed metabolomic analysis of the beverage. Two types of coffee were used: traditional powder (Pilão, Melitta, 3 Corações and Prima Qualitá) and capsule espresso (Nespresso, Dolce Gusto and Café Do Ponto). After beverages preparation, extracts were submitted to same metabolites extraction and analysis procedures of serum samples. In traditional coffee group, no exclusive metabolites were identified in four brands, but metabolomic profile of Melitta and 3 Corações were similar between them and different from the others. In espresso coffee group, Nespresso and Dolce Gusto showed similarity and they were different from of Café do Ponto, which presented exclusively Cellobiose 2 and beta-Gentiobiose. The comparison between traditional coffee and espresso coffee groups showed a difference in their metabolomic profiles, as well as each of them presented exclusive metabolites with different cellular and molecular functions. The amount of major bioactive compounds in coffee was different between the two modes of preparation and their brands. The characterization of metabolome in a homogeneous group of healthy individuals allowed the identification of potential biomarkers and showed alterations in some amino acids profile after controlled coffee intake. The identification of candidates for biomarkers of acute coffee effect showed the importance of validating a panel with biomarkers of high reliability and that may collaborate with future studies of nutritional intervention, not only based on the reported coffee consumption. In addition, coffee brewing method and brand of the product resulted in differences in their metabolomic profiles. Thus, this study may also highlight the importance of considering not only quantity, but also the brewing method and brand of coffee consumed as exposure factors in studies of nutritional intervention

O processo fermentativo da Saccharomyces cerevisiae para a produção de etanol tem grande relevância para o Brasil por ser responsável por uma fonte de energia renovável que é amplamente usada na indústria automotiva. No entanto, em escalas industriais, a fermentação da levedura não ocorre em ambiente asséptico, sendo que diferentes micro-organismos contaminantes são capazes de crescer, competir por nutrientes e até mesmo interferir na fermentação da S. cerevisiae. A fim de melhor compreender quem são os micro-organismos contaminantes e o que fazem na dorna de fermentação, foi utilizado uma abordagem polifásica. O levantamento da microbiota bacteriana presente nas usinas do estado de São Paulo foi realizado através de técnicas independentes de cultivo. Posteriormente, foram realizados ensaios fermentativos com S. cerevisiae CAT-1 na presença do contaminante Lactobacillus fermentum (I-2) para a análise da interação destes na dorna de fermentação. A análise foi realizada por metabolômica acessada através da cromatografia gasosa acoplada a espectrometria de massas (GC-MS) e, para isso, inicialmente foi estabelecida uma metodologia eficiente para análise através de GC-MS. Nas usinas, foi reportada uma porcentagem de Lactobacillus maior do que a descrita e a população bacteriana pareceu ser característica de cada usina e persistente ao longo do tempo. Já o estabelecimento da metodologia de análise de metabólitos da fermentação por GC-MS possibilitou a identificação de 261 metabólitos, e as três classes mais abundantes foram Carbohydrates and carbohydrate conjugates (16%), Carboxylic acids and derivatives (12%) e Fatty Acyls (5%). E no ensaio de S. cerevisiae CAT-1 na presença do contaminante L. fermentum (I-2), possibilitou a identificação de 208 metabólitos, onde 50 foram diferencialmente abundantes. Além disso, a via glicolítica foi reforçada na fermentação de S. cerevisiae CAT-1, sendo que nas fermentações de S. cerevisiae (CAT-1) na presença de L. fermentum (I-2), a produção de aminoácidos a partir do glutamate pareceu ser importante. Desta forma, uma análise polifásica pode auxiliar no esclarecimento da relação dos micro-organismos contaminantes com a levedura dentro da dorna de fermentação. / The fermentation of Saccharomyces cerevisiae for ethanol production has great importance to Brazil since it is responsible for the production of a renewable energy source that is widely used in the automotive industry. However, in industrial scale, the yeast fermentation does not occur in an aseptic environment, where different contaminant microorganisms are capable of growing, competing for nutrients and even interfering with the S. cerevisiae fermentation. In order to better understand who the contaminating microorganisms are and what they do in the fermenter, it was used one polyphasic approach. The survey of the bacterial microflora present in two different São Paulo state distilleries was carried out by cultivation independent techniques. Subsequently, fermentation assays were performed with S. cerevisiae CAT-1 in the presence of the contaminant Lactobacillus fermentum (I-2) to analyze the interaction of these microorganisms in the fermenter. The analysis was performed through metabolomics accessed by gas chromatography-mass spectrometry (GC-MS) and, for that, initially was established an efficient methodology for fermentation metabolite analysis by GC-MS. In the distilleries, it was reported a higher percentage of Lactobacillus than ever described and bacterial population appeared to be characteristic of each distillery and persistent over time. After the establishment of fermentation metabolite analysis methodology by GC-MS, it was possible to identify 261 metabolites, and the three most abundant classes were Carbohydrates and carbohydrate conjugates (16%), Carboxylic acids and derivatives (12%) and Fatty acyls ( 5%). In the fermentation assay of S. cerevisiae CAT-1 in the presence of the contaminant L. fermentum (I-2), it was possible to identify 208 metabolites, where 50 were differentially abundant. In addition, the glycolytic pathway was enhanced in the fermentation of S. cerevisiae CAT-1, and in fermentation of S. cerevisiae (CAT-1) in the presence of L. fermentum (I-2), the production of amino acids from glutamate appeared to be important. Thus, a polyphasic analysis can help in the understanding of the relationship between contaminating microorganisms with the yeast in the fermenter.

O uso da metabolômica ambiental tem sido usada para avaliar a interação dos organismos com o ambiente. Apesar do alto impacto que os metais têm no ambiente, essa abordagem analítica ainda está em seu início, em especial para as macroalgas. Como membro do primeiro nível trófico da cadeia alimentar marinha, fornecendo nutrientes e microelementos para os níveis superiores, as macroalgas são um alvo apropriado tanto para o desenvolvimento de ensaios toxicológicos quanto como bioindicador de degradação do ambiente marinho. Também por causa da sua posição na cadeia alimentar, essas macrófitas são consideradas o principal vetor para a magnificação desses elementos tóxicos. Os efeitos tóxicos dos metais sobre o ambiente aquático são documentados e bem conhecidos, mas relacionam principalmente ao desbalanço do potencial redox intracelular e, assim, o estresse oxidativo em organismos vivos. Com o objetivo de compreender a relação das macroalgas com o metal essencial Cu2+ e o metal não essencial Cd2+, a macroalga vermelha Gracilaria domingensis foi selecionada. Após 48h de exposição aos metais em águas do mar sintética e natural, as amostras foram extraídas e analisadas em cromatografia gasosa acoplada à espectrometria de massas. Em seguida, os dados foram pré-processados e pré-tratados para serem utilizados nas análises estatísticas multivariadas (AEM) de PCA e OPLS-DA. A G. domingensis exposta aos metais em água do mar sintética não foram significativamente influenciadas, como indicado pela MVA e pela análise de vias. Apesar disso, alterações significativas foram observadas na exposição aos metais em água do mar natural. Os principais resultados para o Cu2+ foram a interação do metabolismo de glicina, serina e treonina com o metabolismo de glioxilato e dicarboxilato. Foi sugerido que a macroalga poderia estar alterando o modo de adquirir carbono para uma via não fotossintetizante, uma vez que essa via está prejudicada na exposição ao metal. Também, o metabolismo de fenilalanina foi impactado por essa exposição, uma vez que é uma via fundamental para sintetizar compostos fenólicos antioxidantes. Por outro lado, apesar de oito vias terem sido identificadas como significativamente alteradas na exposição ao Cd2+, somente o metabolismo de arginina e prolina parece ter sido significativamente influenciado com o objetivo de produzir prolina, um aminoácido reconhecido por suas propriedades antioxidantes e protetoras em organismos estressados por metais. Em conclusão, os metais essenciais e não essenciais parecem ter mecanismos diferentes na tentativa de promover o combate aos danos gerados pela exposição aos metais. / The environmental metabolomics approach has been used to evaluate the interaction of organisms with their environment. Besides the high impact metals have on the environment, this method of analysis is still in its infancy, in special for macroalgae. As members of the first trophic level in the marine food chain, providing nutrients and microelements to upper levels, macroalgae are appropriate target organisms both for the development of toxicological assays and as a bioindicator of marine degradation. Also, because of their marine food chain position, these macrophytes are considered the main vectors to magnify these toxic elements. The toxic effects of metals on the aquatic environment are documented and well known, but regards mainly to the unbalance of intracellular redox potential and, therefore, oxidative stress in living organisms. In order to understand the relationship of macroalgae with the essential metal Cu2+ and the non-essential metal Cd2+, the red macroalga Gracilaria domingensis was chosen. After 48h of metal exposure in synthetic and natural seawater, the samples were extracted and analysed in gas chromatograph coupled to mass spectrometry. Afterward, the data were preprocessed and pretreated for the multivariative analysis (MVA) with PCA and OPLS-DA statistics. G. domingensis exposed to metals in synthetic seawater were not significantly affected, as indicated by MVA and pathway analysis. Though, significant changes were observed on exposure to metals in natural seawater. The main results for Cu2+ were the interlay of glycine, serine and threonine metabolism with glyoxylate and dicarboxylate metabolism. It was suggested that the macroalga could be shifting the metabolism to acquire carbon from a non-photosynthetic pathway, since it is injured on metal exposure. Also, phenylalanine metabolism was impacted by this metal exposure, since it is a pivotal source of phenolic antioxidant compounds. On the other hand, besides eigth pathways were identified as significantly changed on Cd2+ exposure, only arginine and proline metabolism seemed to be significantly affected, in order to produce proline, known for its antioxidative and protective properties in metal stressed organims. In conclusion, essential and non essential metals seem to have distinct mechanism to mitigate the damaged caused by metal exposure.

An important cause of pulmonary arterial hypertension (PAH) in children with congenital heart disease (CHD) is increased pulmonary blood flow (PBF). To gain a better understanding of the disease process, the changes in biochemical pathways and metabolism of pulmonary arterial smooth muscle cells (PASMCs) were studied using a unique surgical ovine model of increased pulmonary blood flow. PASMCs isolated from 4-week-old lambs with increased PBF (shunt) showed lower oxygen consumption rates and lower extracellular acidification rates linked to glutamine metabolism when compared to controls. Shunt and control PASMCs both exhibited a switch into the reverse tricarboxylic acid (TCA) cycle, while only shunt cells showed a decrease of glucose being transformed into Acetyl CoA to enter the forward TCA cycle. Shunt PASMCs also demonstrated increased levels of yes-associated protein 1 (YAP1) expression in the nucleus. These results indicate changes in glutamine metabolism, glucose metabolism, and protein signaling cascades associated with increased mechanical forces in the setting of increased PBF, as seen in PAH in children with CHD.

Mass spectrometry (MS) is widely used for proteomic and metabolomic profiling of biological samples. Data obtained by MS are often zero-inflated. Those zero values are called point mass values (PMVs). Zero values can be further grouped into biological PMVs and technical PMVs. The former type is caused by the absence of components and the latter type is caused by detection limit. There is no simple solution to separate those two types of PMVs. Mixture models were developed to separate the two types of zeros apart and to perform the differential abundance analysis. However, we notice that the mixture model can be unstable when the number of non-zero values is small. In this dissertation, we propose a new differential abundance (DA) analysis method, DASEV, which applies an empirical Bayes shrinkage estimation on variance. We hypothesized that performance on variance estimation could be more robust and thus enhance the accuracy of differential abundance analysis. Disregarding the issue the mixture models have, the method has shown promising strategies to separate two types of PMVs. We adapted the mixture distribution proposed in the original mixture model design and assumed that the variances for all components follow a certain distribution. We proposed to calculate the estimated variances by borrowing information from other components via applying the assumed distribution of variance, and then re-estimate other parameters using the estimated variances. We obtained better and more stable estimations on variance, means abundances, and proportions of biological PMVs, especially where the proportion of zeros is large. Therefore, the proposed method achieved obvious improvements in DA analysis. We also propose to extend the method for clustering analysis. To our knowledge, commonly used cluster methods for MS omics data are only K-means and Hierarchical. Both methods have their own limitations while being applied to the zero-inflated data. Model-based clustering methods are widely used by researchers for various data types including zero-inflated data. We propose to use the extension (DASEV.C) as a model-based cluster method. We compared the clustering performance of DASEV.C with K-means and Hierarchical. Under certain scenarios, the proposed method returned more accurate clusters than the standard methods. We also develop an R package dasev for the proposed methods presented in this dissertation. The major functions DASEV.DA and DASEV.C in this R package aim to implement the Bayes shrinkage estimation on variance then conduct the differential abundance and cluster analysis. We designed the functions to allow the flexibility for researchers to specify certain input options.

Metabolomics is the study of metabolomes, the sets of metabolites observed in living systems. Metabolism interconverts these metabolites to provide the molecules and energy necessary for life processes. Many disease processes, including cancer, have a significant metabolic component that manifests as differences in what metabolites are present and in what quantities they are produced and utilized. Thus, using metabolomics, differences between metabolomes in disease and non-disease states can be detected and these differences improve our understanding of disease processes at the molecular level. Despite the potential benefits of metabolomics, the comprehensive investigation of metabolomes remains difficult. A popular analytical technique for metabolomics is mass spectrometry. Advances in Fourier transform mass spectrometry (FT-MS) instrumentation have yielded simultaneous improvements in mass resolution, mass accuracy, and detection sensitivity. In the metabolomics field, these advantages permit more complicated, but more informative experimental designs such as the use of multiple isotope-labeled precursors in stable isotope-resolved metabolomics (SIRM) experiments. However, despite these potential applications, several outstanding problems hamper the use of FT-MS for metabolomics studies. First, artifacts and data quality problems in FT-MS spectra can confound downstream data analyses, confuse machine learning models, and complicate the robust detection and assignment of metabolite features. Second, the assignment of observed spectral features to metabolites remains difficult. Existing targeted approaches for assignment often employ databases of known metabolites; however, metabolite databases are incomplete, thus limiting or biasing assignment results. Additionally, FT-MS provides limited structural information for observed metabolites, which complicates the determination of metabolite class (e.g. lipid, sugar, etc. ) for observed metabolite spectral features, a necessary step for many metabolomics experiments. To address these problems, a set of tools were developed. The first tool identifies artifacts with high peak density observed in many FT-MS spectra and removes them safely. Using this tool, two previously unreported types of high peak density artifact were identified in FT-MS spectra: fuzzy sites and partial ringing. Fuzzy sites were particularly problematic as they confused and reduced the accuracy of machine learning models trained on datasets containing these artifacts. Second, a tool called SMIRFE was developed to assign isotope-resolved molecular formulas to observed spectral features in an untargeted manner without a database of expected metabolites. This new untargeted method was validated on a gold-standard dataset containing both unlabeled and 15N-labeled compounds and was able to identify 18 of 18 expected spectral features. Third, a collection of machine learning models was constructed to predict if a molecular formula corresponds to one or more lipid categories. These models accurately predict the correct one of eight lipid categories on our training dataset of known lipid and non-lipid molecular formulas with precisions and accuracies over 90% for most categories. These models were used to predict lipid categories for untargeted SMIRFE-derived assignments in a non-small cell lung cancer dataset. Subsequent differential abundance analysis revealed a sub-population of non-small cell lung cancer samples with a significantly increased abundance in sterol lipids. This finding implies a possible therapeutic role of statins in the treatment and/or prevention of non-small cell lung cancer. Collectively these tools represent a pipeline for FT-MS metabolomics datasets that is compatible with isotope labeling experiments. With these tools, more robust and untargeted metabolic analyses of disease will be possible.

In biological systems, small changes can have significant impacts. It is, therefore, very important to be able to identify these changes in order to understand what is occurring in the organism. In many cases, this is not an easy task. Mass spectrometry has proven to be a very useful tool in elucidating biological changes even at a very small scale. Several different mass spectrometry based techniques have been developed to discover and investigate complex biological changes. Some of these techniques, such as proteomics, have been through years of development and have advanced to the point that anyone can complete complex analyses of global protein identification and measurement with relative ease. Other techniques are still developing and still have some ground to cover in terms of experimental outcome and ease of execution. Herein we show improvements we have made in high-throughput high-resolution mass spectrometry based techniques to identify and quantify small molecules that are involved in significant biological changes. To begin, we show that our improved high-resolution mass spectrometry based lipidomics techniques are capable of identifying small changes in diseased states that are associated with inflammation, mitochondrial shape and function, and cancer. With our techniques we have been able to extract, identify, and quantify several thousand unique lipid species from complex samples with confidence. Our initial studies looked at global lipidome profiles of differing tissue types from human and mouse biopsies. This was then adapted to compare the global lipidomes of diseased states against healthy states in asthmatic lung tissue, cigarette smoke treated cells, high fat high sugar (HFHS) stressed animals (with and without additional treatment), and in signaling lipids associated with cell death resistance and growth signaling in pancreatic cancer. As a result of our success with lipidomic method improvement we then adapted our techniques and knowledge for use in elucidating small molecule signaling peptides and oxidation changes in proteins. We were able to show that our improved liquid chromatography mass spectrometry based small molecule assays are capable of identifying and quantifying small peptides and protein modifications that would otherwise be undetectable using traditional techniques. This work resulted in the development of a scalable method to detect and quantify the small iron-regulatory hormone known as hepcidin from a variety of samples such as blood, urine, and cell-culture media. We were also instrumental in evaluating and revising a new ultra-high pressure liquid chromatography (UHPLC) system that allows for better separation of analytes from complex mixtures for identification and quantification. Through these advances we hope to aid researchers and clinicians to enable them to use mass spectrometry to further our knowledge about the small but significant changes that regulate complex biological systems.

In spite of impressive recent progress, the aetiopathogenesis of Alzheimer’s disease (AD) remains incompletely understood. The distinctive neuropathological features of AD, in particular the plaques and tangles, have been the particular focus of most aetiological theories. It is well accepted that AD is a multifactorial disease, with alterations to a variety of brain structures and cell types, including neurons, glia and the brain vasculature. Studies of risk factors have revealed a diversity of genetic variables that interact with health, diet and lifestyle-related factors in the causation of AD. These factors influence the structure, aggregation and function of a set of proteins that are increasingly the focus of research. The work in this thesis has focused on the pathophysiological aspects of some of these proteins in a number of cellular compartments and brain. Several assays have been established and techniques utilized in the completion of this work, including; differential detergent fractionation of brain tissue, 1D and 2D PAGE, western blotting with chemiluminescence detection, ELISA assays of Abeta 1-40 and 1-42, quantitative ECNI GCMS of o- and m-tyrosine as well as metabolites of the kynurenine pathway, quantitative MALDI-TOF assay of hemorphins and LCMSMS based proteomics, to identify proteins with altered expression levels in AD relative to control brain tissue. A variety of regional differences have been observed in the biochemistry of the AD cortex which are probably the outcome of local response variations to AD pathology. One of the most consistent threads throughout this work has been an apparent resilience of the occipital lobe relative to the other brain regions, as reflected in lower overall levels of oxidative stress and increased levels of proteins associated with metabolic processes, neuronal remodeling and stress reduction.

<p>During photosynthesis sunlight is absorbed by photosynthetic pigments and converted into organic compounds, such as carbohydrates. Photosynthesis needs to be highly regulated, since both too much and too little light are harmful to plant. If too little light is absorbed, a plant cannot store enough energy, which will have effects on growth and fitness of the plant. With too much light absorbed, a dangerous side reaction of photosynthesis, the production of reactive oxygen species can happen. These reactive oxygen species can damage the proteins in the chloroplast and the lipids of the chloroplast.</p><p>To avoid the production of reactive oxygen species, plants have evolved many mechanisms, which act on different time-scales and different levels of organization. As a first measure, when the absorbed light is exceeding the capacity for its utilization, is to switch the light-harvesting antenna from efficient light harvesting to energy dissipation. This process is called feedback de-excitation (FDE). The protein PsbS is essential for this process as well as a functioning xanthophylls cycle with the enzyme violaxanthin de-epoxidase (VDE).</p><p>I have investigated the effects of plants with changes in their ability to dissipate excess excitation energy in the model plants species Arabidopsis thaliana. Three genotypes with either increased or decreased capacity for FDE were used during my experiments. The first genotype over-expresses the PsbS gene, having approximately two-fold increased amounts of PsbS and FDE. The second is a PsbS deletion mutant with no PsbS protein and no FDE. The third genotype cannot perform the conversion of violaxanthin to zeaxanthin, because the enzyme VDE is missing. This mutant has some FDE left. </p><p><i>Arabidopsis thaliana</i> is an annual plant, which flowers only once in its lifetime. Therefore, when counting the seeds produced an estimation of fitness can be made from the amount of seeds produced. This was done during my experiments and shown that FDE is a trait and that plants with increased FDE have a higher fitness and vice versa. </p><p>This was also the case for a collection of plants lacking a single protein from the light harvesting antenna. All of these genotypes had a fitness reduction, proving that their function is not redundant. </p><p>In an attempt to explain why the fitness is reduced in plants with altered FDE, photosynthetic measurements, as well as a determination of the transcriptome and the metabolome was performed. Plants lacking FDE had higher levels of photoinhibition, leading both to lower rates of photosynthesis and to higher repair cost. This could in part explain the reduction in fitness. These plants also had major changes in their transcriptome and their metabolome. Primary metabolism was most effected, for example carbohydrate and amino acid metabolism. But there were also changes in secondary metabolism such as an up regulation of the biosynthesis of anthocyanins.</p>