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Student Number : 0201444H - MSc dissertation - School of Molecular and Cell Biology - Faculty of Science / Due to the paucity of sensitive mutation markers available for studying mycobacterial species it was decided to explore the suitability of mycobacteriophage L5 as an analogous mutation detection system to phage Lambda in E. coli. The system relies on the detection of an increased production of clear plaque mutants (CPM) arising from turbid plaques, in response to DNA damage. A number of L5 phage experimental tools were developed and optimized, including a lysogen-based CPM confirmation assay. The mutant induction system was applied to wild type M. smegmatis mc2155 and its recA mutant, dinP mutant as well as an M. smegmatis(L5) lysogen. The lysogen system proved to be insensitive with respect to mutant induction since elevated CPM frequencies could not be detected. Interestingly, the wild type M. smegmatis mc2155 system demonstrated slightly elevated CPM frequencies in response to transfection of untreated L5 on UV irradiated host cells. This result suggests that a host SOS mutagenic system is able to act on normal, undamaged DNA bases. The involvement of the SOS response in untargeted mutagenesis was confirmed by the abrogation of increased CPM frequency, in an M. smegmatis recA mutant. This data supports suggestions that RecA is responsible for the control of the SOS response. The M. smegmatis dinP mutant system showed a decrease in CPM frequency which supports evidence that this gene does have mutator polymerase activity, as is in seen E. coli dinP homologues.

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Mutations found in genes encoding human Elongator complex subunits have been linked to neurodevelopmental disorders such as familial dysautonomia (FD), rolandic epilepsy and amyotrophic lateral sclerosis. In addition, loss-of-function mutations in genes encoding Elongator complex subunits cause defects in neurodevelopment and reduced neuronal function in both mice and nematodes. The Elongator complex is a conserved protein complex comprising six subunits (Elp1p-Elp6p) found in eukaryotes. The primary function of this complex in yeast is formation of the 5-methoxycarbonylmethyl (mcm5) and 5-carbamoylmethyl (ncm5) side chains found on wobble uridines (U34) in tRNAs. The aim of this thesis is to investigate the physiological consequences of Elongator complex inactivation in humans and in the yeast Saccharomyces cerevisiae. Inactivation of the Elongator complex causes widespread defects in a multitude of different cellular processes in S. cerevisiae. Thus, we investigated metabolic alterations resulting from Elongator complex inactivation. We show that deletion of the S. cerevisiae ELP3 gene leads to widespread metabolic alterations. Moreover, all global metabolic alterations observed in the elp3Δ strain are not restored in the presence of elevated levels of hypomodified tRNAs that normally have the modified nucleoside mcm5s2U. Collectively, we show that modified wobble nucleosides in tRNAs are required for metabolic homeostasis. Elongator mutants display sensitivity to DNA damage agents, but the underlying mechanism explaining this sensitivity remains elusive. We demonstrate that deletion of the S. cerevisiae ELP3 gene results in post-transcriptional reduction of Ixr1p levels. Further, we show that the reduced Ixr1p levels prevent adequate Rnr1p levels upon treatment with DNA damage agents. These findings suggest that reduced Ixr1p levels could in part explain why Elongator mutants are sensitive to DNA damage agents. Depletion of Elongator complex subunits results in loss of wobble uridine modifications in plants, nematodes, mice and yeast. Therefore, we investigated whether patients with the neurodegenerative disease familial dysautonomia (FD), who have lower levels of the ELP1 protein, display reduced amounts of modified wobble uridine nucleosides. We show that tRNA isolated from brain tissue and fibroblast cell lines derived from FD patients have 64–71% of the mcm5s2U nucleoside levels observed in total tRNA from non-FD brain tissue and non-FD fibroblasts. Overall, these results suggest that the cause for the neurodegenerative nature of FD could be translation impairment caused by reduced levels of modified wobble uridine nucleosides in tRNAs. Thus, our results give new insight on the importance of modified wobble uridine nucleosides for neurodevelopment.

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.

L'assurance de la sécurité sanitaire des aliments vis-à-vis des contaminants chimiques est un enjeu en constante évolution en raison des sources multiples de contamination (pesticides, mycotoxines, néoformés indésirables, et migrants des matériaux au contact entre autres). Actuellement, l'évaluation complète de la qualité sanitaire d'un aliment nécessite la multiplication de méthodes analytiques dites « ciblées » ayant un coût important. De plus, malgré la multiplication des méthodes ciblées, tout contaminant non recherché ne sera pas détecté. Il apparaît nécessaire aujourd'hui de faire évoluer ces méthodes vers des approches analytiques « non-ciblées » susceptibles, via l'analyse d'empreintes chimiques, d'évaluer la présence d'une gamme aussi large que possible des contaminants dans une matrice alimentaire. Les travaux de thèse ont porté sur l'utilisation de la spectrométrie de masse haute résolution (LC-HRMS) et de la chimiométrie pour développer une méthode capable de caractériser la qualité sanitaire des aliments. La matrice de développement est le thé, choisi pour sa complexité d’analyse, sa large consommation et les alertes sanitaires récurrentes à son sujet. Une première preuve de concept de la méthode a été mise en place sur un thé vert de référence et un panel de 32 contaminants choisis pour leur diversité de sources et structures chimiques, puis des situations plus complexes ont été investiguées : application à d’autres types de thé, analyse simultanée d’échantillons de marques et d’origines géographiques distinctes, et enfin application en aveugle à des situations de contamination complexes avec la présence de plusieurs schémas de contamination au sein du même jeu d’échantillons. L’utilisation d’outils de traitement de données libres et ouverts a permis de développer un processus de traitement des données unifié pour deux plateformes analytiques LC-HRMS de technologies et de marques différentes (ToF et Orbitrap), ce qui n’a jamais été réalisé pour l’étude de la sécurité sanitaire chimique des aliments. Par ailleurs, le développement de ce processus a été l’occasion de réaliser une étude méthodologique du comportement de certains outils pour les approches non-ciblées de détection des contaminants de l’aliment / Ensuring food safety, especially toward chemical contaminants, is an issue in constant evolution due to multiple sources of contamination (pesticides, mycotoxins, neoformed contaminants, migrants from packaging among others). Currently, several targeted analyses are needed to fully assess the chemical safety of foods, generating high cost. Moreover, despite the number of analyses performed, a contaminant not targeted is not detected. Therefore, it is necessary to develop new methods based on non-targeted approaches able to assess, through analysis of chemical fingerprints, the occurrence of as many contaminants as possible in a food matrix. The main purpose of this work lies in the use of high resolution mass spectrometry (LC-HRMS) and chemometrics in order to develop a method capable of assessing food safety. Tea has been chosen as a development product for its analytical complexity, its broad consumption and its safety issues. A first proof-of-concept of the method has been set up on a reference green tea with a pool of 32 representative food contaminants, chosen for their diversity in terms of sources and chemical structures. More complex situations were further investigated with different types of tea, several brands considered at once and, last but not least, with the application to blind detection of contaminants in complex cases. Free and open-source data analysis tools were used to build a unified data treatment process to analyze data from two LC-HRMS analytical platforms of different technologies (ToF and Orbitrap), which is new for food safety studies. The development of this process also enabled a methodological study of the behavior of several tools used in untargeted approaches for food safety.

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Metabolomics is a powerful science that can be applied for the discovery of disease biomarkers, and investigation of altered metabolomes due to abiotic and biotic perturbations. This dissertation is focused on untargeted metabolomic applications to investigate fungal metabolite alterations associated with pathogenicity, fungal disease propagations, and symbiosis. This dissertation employs qualitative analysis of metabolite mixtures using HS-SPME coupled GC-MS and TMS derivatization followed by GC-MS analytical platforms. In the first study, we discovered a biomarker combination to diagnose fungal soft tissue disease in sweet potato at an early stage of disease propagation. We used an HS-SPME GC-MS untargeted metabolomics workflow to analyze the VOC associated with Rhizopus stolonifer infected and healthy sweet potatoes in situ and simulated warehouse environments. A single combination of 4 biomarkers was able to diagnose R. stolonifer fungal soft tissue disease (AUC = 0.980, 95% C.I. 0.937-1) and the early stage of the fungal soft rot disease (AUC = 0.999, 95% C.I. 0.978-1). We were able to detect the biomarkers: 1- propanol, ethyl alcohol, ethyl propionate and 3-methyl-3- buten-1-ol during disease progression in a simulated warehouse environment. Therefore, this study shows the feasibility of early diagnosis of fungal soft tissue disease by a real-time screening of volatile profiles of sweet potato in post-harvest storage. When considering the study of a particular species metabolome, it is crucial to develop a metabolite extraction protocol. In the second study, the performance of the six different metabolite extraction solvents mixtures was tested with the preferred mix being: butanol:methanol:water (2:1:1, v/v at -20 °C) which was used as a single solvent mix to extract both polar and relatively non-polar metabolites simultaneously in a single extraction step. The Macrophomina phaseolina fungal metabolome was investigated using the solvent mix. Finally, fungal mutualism was studied using untargeted metabolomics. Most often mycorrhizal metabolomics workflows are based on analyzing the Arbuscular Mycorrhizae colonized root metabolome. But here, we used hyphal materials to examine the mutualistic symbiotic association of the AM fungi. All untargeted metabolomic studies included chemometric data analysis and specific biomarkers and or metabolites were determined using multivariate statistics or prediction model building and validating.

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Understanding the complex metabolic networks present in organisms, through the use of high throughput liquid chromatography coupled mass spectrometry, will give insight into the physiological changes responding to stress. However the lack of a proper work flow and robust methodology hinders verifiable biological interpretation of mass profiling data. In this study a novel workflow has been developed. A novel Kernel based feature alignment algorithm, which outperformed Agilent’s Mass profiler and showed roughly a 20% increase in alignment accuracy, is presented for the alignment of mass profiling data. Prior to statistical analysis post processing of data is carried out in two stages, noise filtering is applied to consensus features which were aligned at a 50% or higher rate. Followed by missing value imputation a method was developed that outperforms both at model recovery and false positive detection. The use of parametric methods for statistical analysis is inefficient and produces a large number of false positives. In order to tackle this three non-parametric methods were considered. The histogram method for statistical analysis was found to yield the lowest false positive rate. Data is presented which was analysed using these methods to reveal metabolomic changes during plant pathogenesis. A high resolution time series dataset was produced to explore the infection of Arabidopsis thaliana by the (hemi) biotroph Pseudomonas syringe pv tomato DC3000 and its disarmed mutant DC3000hrpA, which is incapable of causing infection. Approximately 2000 features were found to be significant through the time series. It was also found that by 4h the plants basal defence mechanism caused the significant ‘up-regulation’ of roughly 400 features, of which 240 were found to be at a 4-fold change. The identification of these features role in pathogenesis is supported by the fact that of those features found to discriminate between treatments a number of pathways were identified which have previously been documented to be active due to pathogenesis