In the United Kingdom and Ireland, a major percentage of fertilized agricultural area is devoted to grasslands, which helps to support the associated milk and beef production industries. In temperate grasslands, perennial ryegrass (L. perenne) is the major forage grass and this species is particularly suitable as a forage grass due to its high yield and digestibility, when compared with other species. However, perennial ryegrass is not well adapted to abiotic stress conditions which are likely to occur in its natural environment. Some of the abiotic stress factors which have significant impacts on plant growth and development include water and nutrient availability. Therefore, this project set out to unravel some of the mechanisms involved in the adaptation of perennial ryegrass to limited water, phosphorous and nitrogen. In order to understand the metabolic mechanisms acting in response to these stresses, metabolite profiling was performed using GC-MS. Furthermore, for the water- and phosphorous-limitation studies this approach was complemented with transcript analysis.In order to study water-limitation a hydroponics system supplemented with polyethyleneglycol (PEG) was used to induce water-limitation for a period of one-week. A clear difference in the metabolic profiles of the leaves of plants grown under water stress was observed. Differences were principally due to a reduction in fatty acid levels in the more water stress-susceptible genotype Cashel and an increase in sugars and compatible solutes in the drought-tolerant PI 462336 genotype. Sugars exhibiting a significant increase included, raffinose, trehalose, glucose, fructose and maltose. Raffinose was identified as the metabolite exhibiting the largest accumulation under water-stress in the more tolerant genotype and may represent a target for engineering superior drought tolerance or form the basis of marker-assisted breeding in perennial ryegrass. The metabolomics approach was combined with a transcriptomics approach in the water stress tolerant genotype PI 462336 which identified genes in perennial ryegrass that were regulated by this stress.The characterization of the response to phosphorus-limitation was performed in a hydroponics system containing two solutions with different levels of phosphorus. Samples were collected from the roots and leaves of two genotypes 24 hours after being exposed to stress. Internal phosphate concentrations were reduced and significant alterations were detected in the metabolome and transcriptome of two perennial ryegrass genotypes. Results indicated a replacement of phospholipids with sulfolipids in response to P deficiency and that this occurs at the very early stages of P deficiency in perennial ryegrass. Additionally, the results suggested the role of glycolytic bypasses and the re-allocation of carbohydrates in response to P deficiency The characterization of the metabolic response of L. perenne leaves to different levels of nitrogen supply was performed for seven different genotypes with variability in the regrowth response rate to nitrogen supply in a hydroponics system. This facilitated the identification of common mechanisms of response between genotypes to nitrogen. The metabolic response observed included modifications of the lipid metabolism, as well as alterations of secondary aromatic metabolite precursors in plants exposed to nitrogendeficit. In contrast, plants grown in a nitrogen saturated media appeared to modify to some extent the metabolism of ascorbate. Additionally, it was found that amino acid levels increased with increasing concentrations of nitrogen supplied. This study suggested that the involvement of secondary metabolism, together with lipid and ascorbate metabolism, is of crucial importance in the early-adaptation of perennial ryegrass plants to different levels of nitrogen supply.

Employing a combination of biochemistry and chemometrics, the field of metabolomics has the potential to reveal some very significant insights into biological pathways related to drugs and diseases. This thesis explores this field in its depths; specifically focusing on Nuclear Magnetic Resonance (NMR) based methods. The thesis begins with an exploration of the quantum level relationships of molecules, and how these coupling patterns evolve into an NMR spectrum. The thesis will describe the development of a simplified spin simulation algorithm to predict NMR spin coupling patterns that are computed in fractions of a second and to build mathematically relevant basis functions. Later in the thesis, the issue of baseline distortions of real NMR experimental data is addressed by the development of an automated baseline correction algorithm. Data reduction techniques are further analyzed to understand the importance of the quality of the data used in advanced chemometric methods. For analysis of the data, the use of simple univariate techniques applied to NMR spectra of urine is explored to determine statistically significant biomarkers between disease states in asthma. More advanced statistics in the way of multivariate models, namely Partial Least Squares – Discriminant Analysis (PLS-DA), were used to build predictive models of Streptococcus pneumoniae pneumonia from NMR spectra of urine. Potential characteristics of the data that may invalidate assumptions required in our models were accounted for, such as ensuring the statistical normality of the S. pneumoniae pneumonia data by using log transformations. After the analysis, focus was given to the use of unique visualization techniques to further explore the complex relationships that exist between samples and variables, and relationships between variables. As will be made evident, this thesis deals with the basic physics of an NMR signal to building highly sophisticated models to help understand the NMR spectra from complex mixtures. All of these notions are important in the objective to garner the most information provided through an NMR experiment, as such to aid in the discovery of biochemical knowledge. / Process Control

Glioblastoma multiforme (GBM) is the most common primary brain tumor. Given the current standard of care, the prognosis for patients diagnosed with this disease is still poor. There consequently exists a need to improve current treatments, as well as to develop new ones. Many obstacles however need to be overcome to facilitate this effort and one of these involves the development of improved methods to monitor treatment effects. At present, the effects of treatment are typically assessed by radiological means several months after its initiation, which is unsatisfactory for a fast growing tumor like GBM. It is however likely that treatment effects can be detected on a molecular level long before radiological response, especially considering many of the targeted therapies that are currently being developed. Biomarkers for treatment efficacy may be of great importance in the future individualization of brain tumor treatment. The work presented herein was primarily focused on detecting early effects of GBM treatment. To this end, we designed experiments in the BT4C rat glioma model in which we studied effects of both conventional radiotherapy and an experimental angiogenesis inhibitor, vandetanib. Brain tissue samples were analyzed using a high throughput mass spectrometry (MS) based screening, known as Surface Enhanced Laser Desorption/Ionization - Time of Flight - Mass Spectrometry (SELDI-TOF-MS). The vast amounts of data generated were subsequently analyzed by established multivariate statistical methods, such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and Orthogonal Partial Least Squares (OPLS), developed for analysis of large and complex datasets. In the radiotherapy study we detected a protein spectrum pattern clearly related to tumor progression. We notably observed how this progression pattern was hampered by radiotherapy. The vandetanib study also revealed significant alterations of protein expression following treatment of different durations, both in tumor tissue and in normal brain contralateral to the tumor. In an effort to further elucidate the pathophysiology of GBM, particularly in relation to treatment, we collected extracellular fluid (ECF) samples from 11 patients diagnosed with inoperable GBM. The samples were collected by means of stereotactic microdialysis, both from within the contrast enhancing tumor and the brain adjacent to tumor (BAT). Samples were collected longitudinally from each patient in a time span of up to two weeks, during which the patient received the first five fractions of radiotherapy. The ECF samples were then analyzed by Gas Chromatography Mass Spectrometry (GC-MS) to screen them with respect to concentrations of low molecular weight compounds (metabolites). Suitable multivariate analysis strategies enabled us to extract patterns of varying metabolite concentrations distinguishing between samples collected at different locations in the brain as well as between samples collected at different time points in relation to treatment. In a separate study, we also applied SELDI-TOF-MS and multivariate statistical methods to unravel possible differences in protein spectra between invasive and non-invasive WHO grade I meningiomas. This type of tumor can usually be cured by surgical resection however sometimes it grows invasively into the bone, ultimately causing clinical problems. This study revealed the possibility to differentiate between invasive and non-invasive benign meningioma based on the expression pattern of a few proteins. Our approach, which includes sample analysis and data handling, is applicable to a wide range of screening studies. In this work we demonstrated that the combination of MS screening and multivariate analyses is a powerful tool in the search for patterns related to treatment effects and diagnostics in brain tumors.

Recent advancements in mass spectrometry have facilitated new analytical approaches capable of comprehensively characterizing metabolites in biological samples. Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) combines excellent mass accuracy (ppm<1) and ultra-high resolution, which enables the separation and identification of individual components within complex mixtures, and the determination of elemental composition for each detected mass. FTICR-MS is an ideal method for non-targeted metabolomics as the majority of small molecular compounds (100-1000 Da) in a biological sample can be detected. The objective of this research was to investigate metabolomic alterations associated with key cellular processes deemed fundamental to cancer development and progression. Differentiating U937 cells, fibroblasts synchronously progressing through the cell cycle and a transformed cell line containing a temperature sensitive oncogene were collected and subject to FTICR-MS analysis for non-targeted comprehensive metabolomics. Putative metabolite identifications were confirmed with targeted metabolite analysis using multiple reaction monitoring triple quadrupole mass spectrometry. Analysis of the resulting metabolic profiles revealed robust metabolic alterations associated with fundamental cellular processes. Changes in glycerolipid content were observed in all cellular processes studied. During cell cycle progression, elevated levels of triacylglycerols and vinyl acylglycerols were detected as cells approached mitosis; increased levels of plasmalogens were detected during the induced differentiation of human leukemic cells and activation of the oncogene p130gag-fps in fibroblasts resulted in increased levels of phospholipids, including plasmalogens. When de novo fatty acid synthesis was inhibited in the differentiation cell model, the cells were not able to complete the differentiation process. Removal of the inhibitor resulted in increased lipid content, particularly plasmalogens, and the continuation of differentiation, suggesting a requirement for the de novo synthesis of lipids during this cellular process. This work demonstrates the advantages of non-targeted metabolic profiling for identifying non-intuitive metabolic associations with specific cellular processes. Collectively, the results of this thesis have implicated glycerolipids, in particular phospholipids, in the processes of cell cycle progression, differentiation and tumourigenic transformation. A broadened understanding of the role of global lipid metabolism during fundamental cellular processes may one day lead to new approaches for their modulation, and potentially new therapeutic strategies.

Recent advancements in mass spectrometry have facilitated new analytical approaches capable of comprehensively characterizing metabolites in biological samples. Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) combines excellent mass accuracy (ppm<1) and ultra-high resolution, which enables the separation and identification of individual components within complex mixtures, and the determination of elemental composition for each detected mass. FTICR-MS is an ideal method for non-targeted metabolomics as the majority of small molecular compounds (100-1000 Da) in a biological sample can be detected. The objective of this research was to investigate metabolomic alterations associated with key cellular processes deemed fundamental to cancer development and progression. Differentiating U937 cells, fibroblasts synchronously progressing through the cell cycle and a transformed cell line containing a temperature sensitive oncogene were collected and subject to FTICR-MS analysis for non-targeted comprehensive metabolomics. Putative metabolite identifications were confirmed with targeted metabolite analysis using multiple reaction monitoring triple quadrupole mass spectrometry. Analysis of the resulting metabolic profiles revealed robust metabolic alterations associated with fundamental cellular processes. Changes in glycerolipid content were observed in all cellular processes studied. During cell cycle progression, elevated levels of triacylglycerols and vinyl acylglycerols were detected as cells approached mitosis; increased levels of plasmalogens were detected during the induced differentiation of human leukemic cells and activation of the oncogene p130gag-fps in fibroblasts resulted in increased levels of phospholipids, including plasmalogens. When de novo fatty acid synthesis was inhibited in the differentiation cell model, the cells were not able to complete the differentiation process. Removal of the inhibitor resulted in increased lipid content, particularly plasmalogens, and the continuation of differentiation, suggesting a requirement for the de novo synthesis of lipids during this cellular process. This work demonstrates the advantages of non-targeted metabolic profiling for identifying non-intuitive metabolic associations with specific cellular processes. Collectively, the results of this thesis have implicated glycerolipids, in particular phospholipids, in the processes of cell cycle progression, differentiation and tumourigenic transformation. A broadened understanding of the role of global lipid metabolism during fundamental cellular processes may one day lead to new approaches for their modulation, and potentially new therapeutic strategies.

The WHO classification of brain tumors is based on histological features and the aggressiveness of the tumor is classified from grade I to IV, where grade IV is the most aggressive. Today, the correlation between prognosis and tumor grade is the most important component in tumor classification. High grade gliomas, glioblastomas, are associated with poor prognosis and a median survival of 14 months including all available treatments. Low grade meningiomas, usually benign grade I tumors, are in most cases cured by surgical resection. However despite their benign appearance grade I meningiomas can, without any histopathological signs, in some cases develop bone invasive growth and become lethal. Thus, it is necessary to improve conventional treatment modalities, develop new treatment strategies and improve the knowledge regarding the basic pathophysiology in the classification and treatment of brain tumors. In this thesis, both proteomics and metabolomics have been applied in the search for biomarkers or biomarker patterns in two different types of brain tumors, gliomas and meningiomas. Proteomic studies were carried out mainly by surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS). In one of the studies, isobaric tags for relative and absolute quantitation (iTRAQ) labeling in combination with high-performance liquid chromatography (HPLC) was used for protein detection and identification. For metabolomics, gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS) has been the main platform used throughout this work for generation of robust global metabolite profiles in tissue, blood and cell cultures. To deal with the complexity of the generated data, and to be able to extract relevant biomarker patters or latent biomarkers, for interpretation, prediction and prognosis, bioinformatic strategies based on chemometrics were applied throughout the studies of the thesis. In summary, we detected differentiating protein profiles between invasive and non-invasive meningiomas, in both fibrous and meningothelial tumors. Furthermore, in a different study we discovered treatment induce protein pattern changes in a rat glioma model treated with an angiogenesis inhibitor. We identified a cluster of proteins linked to angiogenesis. One of those proteins, HSP90, was found elevated in relation to treatment in tumors, following ELISA validation. An interesting observation in a separate study was that it was possible to detect metabolite pattern changes in the serum metabolome, as an effect of treatment with radiotherapy, and that these pattern changes differed between different patients, highlighting a possibility for monitoring individual treatment response.  In the fourth study of this work, we investigated tissue and serum from glioma patients that revealed differences in the metabolome between glioblastoma and oligodendroglioma, as well as between oligodendroglioma grade II and grade III. In addition, we discovered metabolite patterns associated to survival in both glioblastoma and oligodendroglioma. In our final work, we identified metabolite pattern differences between cell lines from a subgroup of glioblastomas lacking argininosuccinate synthetase (ASS1) expression, (ASS1 negative glioblastomas), making them auxotrophic for arginine, a metabolite required for tumor growth and proliferation, as compared to glioblastomas with normal ASS1 expression (ASS1 positive). From the identified metabolite pattern differences we could verify the hypothesized alterations in the arginine biosynthetic pathway. We also identified additional interesting metabolites that may provide clues for future diagnostics and treatments. Finally, we were able to verify the specific treatment effect of ASS1 negative cells by means of arginine deprivation on a metabolic level.

Genetic perturbations and foreign chemicals can result in a multitude of changes across a wide range of biochemical processes in a biological system. These perturbations may affect the metabolome, the small molecule metabolites in an organism. Recently, liquid-chromatography coupled to mass spectrometry (LC-MS) technology has been used to quantify large proportions of the metabolome, however standardized protocols are not yet available for use with Drosophila melanogaster. Here, I developed an ion-pairing LC-MS protocol for the metabolomic characterization of D. melanogaster and demonstrated its implementation in establishing the metabolomic profile of flies under oxidative stress and in the metabolic profiles of four different Drosophila species. I demonstrated that this new method allows for the detection of otherwise difficult metabolites and that it is repeatable and sensitive with acceptable levels of ionsuppression, matrix effects, limits of detection and quantification. I then used this method to determine and quantify the metabolomic fingerprints of loss of Superoxide dismutase activity and paraquat-induced stress. Comparing and contrasting the effects of these two sources of oxidative stress, I document both similarities and stressor-specific effects.

Green algae have evolved several photo-protective responses to cope with high-light stress. The present study examines the metabolic changes during photoacclimation to high-light in Chlamydomonas reinhardtii using nuclear magnetic resonance and mass spectrometry. Using principal component analysis, a clear metabolic response to highlight intensity was observed on global metabolite pools in Chlamydomonas, with major changes in the levels of amino acids and related nitrogen metabolites. Amino acid biosynthesis was induced during short-term photoacclimation presumably to alleviate excess excitation pressure in the plastid. An increase in mitochondrial metabolism through downstream photorespiratory and glyoxylate metabolism, pathways thought to act in a photo-protective capacity, was also observed. Long-term light stress resulted in a significant increase in antioxidant metabolites, ascorbate and dehydroascorbate. These results suggest that metabolism plays a direct role in coping with the imbalance in the excess excitation pressure generated during high-light stress; however, this metabolomics survey has generated additional questions about the roles of nitrogen assimilation associated metabolites in photoacclimatory responses to high-light in Chlamydomonas.

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