La spectroscopie de Résonance Magnétique Nucléaire (RMN) Haute Résolution à l’angle magique (HRMAS) joue un rôle de plus en plus prépondérant pour le diagnostic médical. Cette technique permet d’établir les empreintes ex vivo des métabolites de tissus sains et pathologiques. Cependant, pour certains métabolites, les valeurs des déplacements chimiques des groupes de protons peuvent légèrement varier en fonction de l’environnement des tissus ou cellules, particulièrement de son acidité. Cet effet gêne l’estimation correcte des concentrations des métabolites lorsqu’on utilise des algorithmes fondés sur des bases de métabolites. Ce travail est dévolu aux méthodes d’optimisation des bases de métabolites, notamment aux algorithmes de correction des changements de déplacements chimiques. Deux méthodes de traitement du signal ont été développées pour l’optimisation simple et rapide des signaux / spectres : contraction/expansion du signal moyennant ré-échantillonnage et fractionnement du spectre. Une autre méthode, QM-QUEST, conjuguant la simulation par Mécanique Quantique et la quantification, a été mise en œuvre. Cette dernière permet l’ajustement plus robuste des spectres en limitant l’implication de l’utilisateur et préserve les empreintes correctes des métabolites. Son efficacité est démontrée pour la quantification de spectres RMN de biopsies cérébrales humaines d’oligodendroglioma, obtenues à 11.7 Tesla et de spectres de cellules acquis à 9.4 T par la technique RMN-HRMAS. Etant donné la nécessité de simulation rapide des signaux RMN basée sur la Mécanique Quantique, une partie du travail est vouée à une méthode approchée accélérant la simulation. L’algorithme fondé sur la fragmentation du système de spins pourrait devenir une partie importante de la méthode d’optimisation QM-QUEST et sema mis en œuvre en tant qu’option de simulation de la méthode NMR-SCOPE, module du logiciel jMRUI. / From day to day, the role of HRMAS (High-Resolution Magic Angle Sinning) Nuclear Magnetic Resonance Spectroscopy (NMRS) in medical diagnosis is increasing. This technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. Automatic spectrum quantitation enables monitoring of diseases. However for several metabolites, the values of chemical shifts of proton groups may slightly differ according to the micro-environment in the tissue or cells, in particular to its pH. This hampers accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis-set. The present word is devoted to the optimization of NMR metabolite basis set signals, particularly to the algorithms of chemical shift mismatch correction. Two sighal processing (“warping”) methods were developed for simple and fast spectrum optimization : signal stretching/shrinking (resampling) and spectrum splitting. Then, another optimization method, QM-QUEST, coupling Quantrum Mechanical simulation and quantitation algorithms was implemented. The latter provides more robust fitting while limiting user involvement and respects the correct fingerprints of metabolites. Its efficiency is demonstrated by accurately quantitating signals from tissue samples of human brains with oligodendroglioma, obtained at 11.7 Tesla and spectra of cells acquired at 9.4T by HRMAS-NMR. As the necessity of fast NMR signal simulation based on quantum Mechanics is raised in the thesis, a part of the word is dedicated to an approximate method speeding-up the calculations. The algorithm based on spin-system fragmentation could become an important part of the QM-QUEST optimization method and will be implemented as an option of simulation in NMR-SCOPE, module of the jMRUI software package.

Beta-N-methylamino-L-alanine (BMAA) is a non-protein amino acid produced by many cyanobacteria, and thought to induce neurotoxic effects through excitotoxicity, contributing to neurodegenerative diseases such as Amyotrophic Lateral Sclerosis/Parkinsonism-dementia complex (ALS-PDC) and Alzheimer’s. The ubiquitous nature of cyanobacteria, and evidence of biomagnification through our food web, creates a dire need for the development of an analytical platform that will provide accurate identification and quantification of BMAA amounts in our ecosystem and potential food supply. The present study evaluated the ability of a MALDI-ToF-MS method to detect and quantify BMAA in a variety of biological matrices. Through validation procedures, it was demonstrated that this MALDI-ToF-MS method provided comparable data to currently accepted analytical methods, specifically LC-MS/MS. Further, the development of said method reduced sample preparation and data acquisition time (1-2 seconds per sample), while providing high throughput analysis and eliminating the need for derivatization, chromatographic separation, and modification of amino acids.

One of the challenges in the structural biological community is processing the wealth of protein data being produced today; therefore, the use of computational tools has been incorporated to speed up and help understand the structures of proteins, hence the functions of proteins. In this thesis, protein structure investigations were made through the use of Multivariate Analysis (MVA), and Fourier Transformed Infrared (FTIR), a form of vibrational spectroscopy. FTIR has been shown to identify the chemical bonds in a protein in solution and it is rapid and easy to use; the spectra produced from FTIR are then analysed qualitatively and quantitatively by using MVA methods, and this produces non-redundant but important information from the FTIR spectra. High resolution techniques such as X-ray crystallography and NMR are not always applicable and Fourier Transform Infrared (FTIR) spectroscopy, a widely applicable analytical technique, has great potential to assist structure analysis for a wide range of proteins. FTIR spectral shape and band positions in the Amide I (which contains the most intense absorption region), Amide II, and Amide III regions, can be analysed computationally, using multivariate regression, to extract structural information. In this thesis Partial least squares (PLS), a form of MVA, was used to correlate a matrix of FTIR spectra and their known secondary structure motifs, in order to determine their structures (in terms of "helix", "sheet", “310-helix”, “turns” and "other" contents) for a selection of 84 non-redundant proteins. Analysis of the spectral wavelength range between 1480 and 1900 cm-1 (Amide I and Amide II regions) results in high accuracies of prediction, as high as R2 = 0.96 for α-helix, 0.95 for β-sheet, 0.92 for 310-helix, 0.94 for turns and 0.90 for other; their Root Mean Square Error for Calibration (RMSEC) values are between 0.01 to 0.05, and their Root Mean Square Error for Prediction (RMSEP) values are between 0.02 to 0.12. The Amide II region also gave results comparable to that of Amide I, especially for predictions of helix content. We also used Principal Component Analysis (PCA) to classify FTIR protein spectra into their natural groupings as proteins of mainly α-helical structure, or protein of mainly β-sheet structure or proteins of some mixed variations of α-helix and β-sheet. We have also been able to differentiate between parallel and anti-parallel β-sheet. The developed methods were applied to characterize the secondary structure conformational changes of an unfolding protein as a function of pH and also to determine the limit of Quantitation (LoQ).Our structural analyses compare highly favourably to those in the literature using machine learning techniques. Our work proves that FTIR spectra in combination with multivariate regression analysis like PCA and PLS, can accurately identify and quantify protein secondary structure. The developed models in this research are especially important in the pharmaceutical industry where the therapeutic effect of drugs strongly depends on the stability of the physical or chemical structure of their proteins targets; therefore, understanding the structure of proteins is very important in the biopharmaceutical world for drugs production and formulation. There is a new class of drugs that are proteins themselves used to treat infectious and autoimmune diseases. The use of spectroscopy and multivariate regression analysis in the medical industry to identify biomarkers in diseases has also brought new challenges to the bioinformatics field. These methods may be applicable in food science and academia in general, for the investigation and elucidation of protein structure.

Polyamines are naturally occurring cellular polycations essential for cell growth and differentiation. This investigation focused on the quantitative analysis of polyamines and metabolites in mouse erythroleukemia (MEL) cells by mass spectrometry. Hexamethylene bisacetamide (HMBA) is a synthetic polyamine derivative known to induce differentiation of a variety of transformed cells such as MEL cells. A fast and sensitive quantitative method for HMBA and metabolites NADAH, DAH and AcHA was developed using atmosphere pressure chemical ionization mass spectrometry (APCI/MS) by flow injection analysis. Selected ion monitoring (SIM) mode was employed for the mass spectrometric detection and d 4 -DAH was used as the internal standard for quantitation. The intracellular concentrations of HMBA and metabolites were obtained in MEL cells treated with 5mM HMBA in the presence or absence of 500μM APAH, a potent N 8 -acetylspermidine deacetylase inhibitor. A significant increase in intracellular NADAH and decrease in DAH levels in MEL cells were observed in HMBA treatment in the presence of APAH. This result indicates that APAH inhibits the second deacetylation step in HMBA metabolism, the conversion of NADAH to DAH, but not the first deacetylation of HMBA to NADAH. Two histone deacetylase (HDAC) inhibitors including sodium butyrate (NaB) and m -carboxycinnamic acid bis-hydroxamide (CBHA) were also used as inducing agents for MEL cell differentiation. Both agents caused accumulation of hyperacetylated histone H4 and H2B in MEL cells at concentrations optimal for inducing differentiation, while neither HMBA nor APAH had detectable effect on the acetylation level of histones. A fast and sensitive method for five important polyamines including putrescine (PU), spermidine (SPD), spermine (SPM), N 1 -acetylspermidine (N 1 -AcSPD) and N 8 -acetylspermidine (N 8 -AcSPD) was also developed using APCI/MS by flow injection analysis. Selected reaction monitoring (SRM) mode was employed for the mass spectrometric detection and 1,7-diaminoheptane was used as the internal standard for quantitation. The intracellular polyamine concentrations was obtained in MEL cells treated with 5mM HMBA, 2mM NaB, 3μM CBHA and 500μM APAH respectively. A significant increase in N 8 -acetylspermidine levels was observed during 3hr to 4 days treatment with APAH. There was no change in N 8 -acetylspermidine levels in MEL cells treated either with NaB or CBHA. The results from the present study suggest APAH has a selective inhibitory effect on N 8 -acetylspermidine but not histone deacetylation. While HDAC inhibitors inhibit histone deacetylase but have no effect on N 8 -acetylspermidine deacetylation. In conclusion, despite the known similarities they share, the enzymes involved in the deacetylation process of N 8 -acetylspermidine and histones in MEL cells are different.

Applications of Hot Melt Extrusion (HME) in pharmaceuticals have become increasingly popular over the years but nonetheless a few obstacles still remain before wide scale implementation. In many instances these improvements are related to both processing and product performance. It is observed that HME process optimisation is majorly focused on the active pharmaceutical ingredient's (API) properties. Characterising polymeric properties for their suitability in HME should be equally studied since the impact of excipients on both product and process performance is just as vital. In this work, two well-established cellulose ester derivatives: Hydroxy Propyl Methyl Cellulose Acetate Succinate (HPMCAS) and Hydroxy Propyl Methyl Cellulose Phthalate (HPMCP) are studied for their HME suitability. Their thermal, thermodynamic, rheological, thermo-chemical and degradation kinetic properties were evaluated with model plasticisers and APIs. It was found the thermal properties of HPMCP are severely compromised whereas HPMCAS is more stable in the processing zone of 150 to 200 °C. Thermodynamic properties revealed that both polymers share an important solubility parameter range (20-30 MPa P1/2P) where the majority of plasticisers and BCS class II APIs lie. Thus, greater miscibility/solubility can be expected. Further, the processability of these two polymers investigated by rheometric measurements showed HPMCAS possesses better flow properties than HPMCP because HPMCP forms a weak network of chain interactions at a molecular level. However, adding plasticisers such as PEG and TEC the flow properties of HPMCP can be tailored. The study also showed that plasticisers have a major influence on thermo-chemical and kinetic properties of polymers. For instance, PEG reduced polymer degradation with reversal in kinetic parameters whereas blends of CA produced detrimental effects and increased polymer degradation with reduction in onset degradation temperatures. Further, both polymers are observed to be chemically reactive with the APIs containing free -OH, -SOR2RN- and -NH2 groups. Finally, these properties prove that suitability of HPMCP is highly debated for HME and demands great care in use while that of HPMCAS is relatively better than HPMCP in many instances.

The aim of this research was the development of mass spectrometry-based methodologies for the high-throughput quantitation of peptides in biological matrices. Glucagon and GLP-1, which are of interest as biomarkers and in the development of therapeutics, were chosen as model peptides. Immunoassays that are traditionally used to quantify these often perform poorly; therefore, necessitating the development of alternative methodologies. Application of mass spectrometry-based methodologies to these analytes has, however, been limited, primarily due to sensitivity challenges, but also due to analytical challenges associated with their endogenous nature and instability in biological matrices. Chapter 2 describes the development and qualification of the first liquid-chromatography coupled tandem mass spectrometry (LC-MS/MS) method for the quantitation of endogenous glucagon from human plasma. A novel 2D extraction procedure was developed to ensure robustness and sensitivity, whilst a novel surrogate matrix quantitation strategy took into account the endogenous nature of the analyte. A lower limit of quantitation (LLOQ) of 25 pg/mL was qualified, which was a considerable improvement over that previously reported in the literature (250 pg/mL) for a LC-MS/MS method. Clinical samples were cross-validated against a conventional radioimmunoassay (RIA), and similar pharmacokinetic (PK) profiles resulted, demonstrating that the methods were complementary. In Chapter 2 glucagon instability in biological matrix was noted. To characterise this further, in Chapter 3 in vitro glucagon metabolites were identified using high-resolution mass spectrometry (HRMS). Metabolites observed by others (glucagon19-29, glucagon3 29 and [pGlu]3glucagon3 29) in alternative matrices were identified, alongside novel metabolites (glucagon20-29 and glucagon21-29). Cross-interference of these metabolites in immunoassays may help to explain their poor performance, whilst knowledge of metabolism may also aid the development of future stabilisation strategies. The method developed in Chapter 2 was refined in Chapter 4 to improve sensitivity, robustness and throughput, and to add GLP-1 as a secondary analyte. The sensitivity achieved (glucagon: 15 pg/mL LLOQ, GLP-1: 25 pg/mL LLOQ) is the highest reported for both peptides for an extraction avoiding immunoenrichment. Specificity of endogenous glucagon quantitation was assured using a novel approach with a supercharging mobile phase additive to access a sensitive qualifier transition. A cross-validation against established immunoassays using physiological study samples demonstrated some similarities between the methods. Differences between the immunoassay results exemplified the need to develop alternative methodologies. The resulting LC-MS/MS method is considered a viable alternative to immunoassays, for the quantitation of endogenous glucagon, dosed glucagon and/or dosed GLP-1 in human plasma.

No description available.

Metabolic heterogeneity (MH) occurs when isogenic microbial populations display cell-to-cell differences in metabolic traits, albeit exposed to homogeneous conditions. Despite the increasing focus on MH, its triggering factors remain largely unknown. In the present thesis, I used stable isotope probing and chemical imaging with nanoscale Secondary Ion Mass Spectrometry (nanoSIMS) to study MH at single-cell level, in model organisms, synthetic and natural communities, to understand i) how abiotic factors, biotic interactions and antibiotics exposure influence MH and ii) its potential ecological role. Moreover, I optimized sample preparation for chemical and high-resolution imaging and suggested two different indices as ‘unit measure’ of MH. As results, I have shown for the first time that MH is displayed by microorganisms under favorable growth conditions, although none of the tested abiotic factors prevailed as the main trigger of MH. I brought insights on how biotic interactions play a role in the functional heterogeneity using bacteria pseudo-fungi co-cultures. I found that antibiotics reduce Carbon and Nitrogen assimilation rates of targeted phylogenetic groups in river-water communities, while increasing their MH, pointing to its ecological importance in natural environments. To conclude, I provided novel insights on the phenomenon of MH and its dynamics at different ecological scales.:Abbreviation list Summary Introduction Knowledge gaps Results and discussion - Optimization of sample preparation - Validation of quantitation methods - Abiotic factors shaping metabolic heterogeneity in bacterial populations - Influence of biotic factors in shaping heterogeneity - Metabolic Heterogeneity and ecophysiology of natural microbial populations influenced by emerging contaminants Conclusions Outlook Bibliography Appendix Acknowledgments Curriculum Vitae List of publications

Applications of Hot Melt Extrusion (HME) in pharmaceuticals have become increasingly popular over the years but nonetheless a few obstacles still remain before wide scale implementation. In many instances these improvements are related to both processing and product performance. It is observed that HME process optimisation is majorly focused on the active pharmaceutical ingredient's (API) properties. Characterising polymeric properties for their suitability in HME should be equally studied since the impact of excipients on both product and process performance is just as vital. In this work, two well-established cellulose ester derivatives: Hydroxy Propyl Methyl Cellulose Acetate Succinate (HPMCAS) and Hydroxy Propyl Methyl Cellulose Phthalate (HPMCP) are studied for their HME suitability. Their thermal, thermodynamic, rheological, thermo-chemical and degradation kinetic properties were evaluated with model plasticisers and APIs. It was found the thermal properties of HPMCP are severely compromised whereas HPMCAS is more stable in the processing zone of 150 to 200 °C. Thermodynamic properties revealed that both polymers share an important solubility parameter range (20-30 MPa P1/2P) where the majority of plasticisers and BCS class II APIs lie. Thus, greater miscibility/solubility can be expected. Further, the processability of these two polymers investigated by rheometric measurements showed HPMCAS possesses better flow properties than HPMCP because HPMCP forms a weak network of chain interactions at a molecular level. However, adding plasticisers such as PEG and TEC the flow properties of HPMCP can be tailored. The study also showed that plasticisers have a major influence on thermo-chemical and kinetic properties of polymers. For instance, PEG reduced polymer degradation with reversal in kinetic parameters whereas blends of CA produced detrimental effects and increased polymer degradation with reduction in onset degradation temperatures. Further, both polymers are observed to be chemically reactive with the APIs containing free -OH, -SOR2RN- and -NH2 groups. Finally, these properties prove that suitability of HPMCP is highly debated for HME and demands great care in use while that of HPMCAS is relatively better than HPMCP in many instances.