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dc.contributor.advisorLoots, Du Toit
dc.contributor.authorOlivier, Ilse
dc.date.accessioned2013-11-19T10:58:06Z
dc.date.available2013-11-19T10:58:06Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10394/9529
dc.descriptionThesis (PhD (Biochemistry))--North-West University, Potchefstroom Campus, 2012
dc.description.abstractIn 2001, the WHO declared tuberculosis (TB) a global emergency, as one third of the world's population suffered from latent M. tuberculosis infection. Today, a decade later, millions of people still die worldwide as a result of this disease. This growing TB incidence may be ascribed to a variety of reasons, including, amongst others, the inadequacies associated with the currently available diagnostic methods and TB treatment regimes, especially when considering the growing MDR-TB and HIV epidemics. This study investigated the potential of metabolomics as a tool for characterising TB and various TB-causing bacteria, allowing for a better understanding of TB disease mechanisms, which may ultimately lead to improved diagnostic and treatment regimens. Firstly, we investigated the potential of a fatty acid, metabolomics approach to characterise various cultured Mycobacterium species. For this exploration, three fatty acid extraction procedures, prior to GC-MS analyses, were compared based on their respective repeatability and extraction capacities. Using the data obtained from the analyses done with the most optimal extraction approach (the modified Bligh-Dyer method), multivariate statistical analyses were able to differentiate between the various Mycobacterium species at a detection limit of 1 x 103 bacterial mL-1, in 16 hours. Subsequently, the compounds best describing the variation between the sample groups were identified as potential metabolite markers and were discussed in the light of previous studies. The most optimal GC-MS, fatty acid metabolomics approach, mentioned above, was then applied to analyse and characterise a wild-type M. tuberculosis parent strain and two rifampicinresistant conferring rpoB mutants (S522L and S531L). Due to the variation in their fatty acid profiles, a clear differentiation was achieved between these M. tuberculosis sample groups, and those metabolites contributing most to this variation were identified as metabolite markers characteristic for rifampicin-resistance. The altered metabolite markers detected in the rpoB mutants propose a decreased synthesis of various 10-methyl branched-chain fatty acids and cell wall lipids, and an increased use of the shorter-chain fatty acids and alkanes as alternative carbon sources. Furthermore, the rpoB S531L mutant, previously reported to occur in well over 50% of all clinical rifampicin-resistant M. tuberculosis strains, showed a better capacity for using these alternative energy sources, in comparison to the less frequently detected rpoB S522L mutant. The developed fatty acid GC-MS metabolomics approach was then successfully adapted in order to improve its speed, cost and complexity. This improved fatty acid extraction method was furthermore compared to another, similar approach (total metabolome extraction method), developed for the extraction of a much wider variety of compounds, prior to GC-MS and statistical data analyses. Although both these methods show promise for bacterial characterisation using matabolomics, the total metabolome extraction method proved the better of the two methods because it is comparatively simpler, faster (taking less than 4 hours), more repeatable, better differentiates between sample groups due to less within group variation, has a lower detection limit, and isolates a wider variety of biologically relevant metabolites (as opposed to fatty acids alone). We, furthermore, identified and described the occurrence of those compounds, extracted by both methods, which contribute most to the variation between the bacterial groups, in order to validate these methods for metabolomic applications and the isolation of compounds with biological relevance. In order to evaluate the potential of this developed metabolomics approach for application to biological samples other than bacteriological cultures, it was adapted for the direct analyses of complex sputum samples. For this application, four sputum pre-extraction preparation methods, including three standard Mycobacterium cell isolation procedures (Sputolysin, NALC-NaOH, and NaOH) and a fourth, applying only a simple ethanol homogenisation step, prior to direct sputum extraction, were compared. Of these methods, the ethanol homogenisation method proved to have the best comparative extraction efficiency, repeatability and differentiation capacity, when used in combination with the previously developed metabolomics methods. Subsequently, when applying this approach to patient collected sputum samples, a set of metabolite markers, differentiating the TB-positive from the TB-negative samples, were identified. These markers could directly be linked to: 1) the physical presence of the M. tuberculosis in these samples; 2) changes in the bacterial metabolome due to in vivo growth conditions and; 3) changes in the human metabolome due to pulmonary M. tuberculosis infection. In addition to the proposal of a number of new hypotheses, explaining various mechanisms of TB and drug-resistant TB, the mapping of the newly identified metabolite markers to known metabolic pathways led to the confirmation of various previously suggested metabolic pathways and alterations thereof due to an assortment of perturbations. Therefore, this study significantly contributes to the characterisation of various TB causing bacteria, rifampicin-resistant M. tuberculosis strains and the TB disease state, which may in future lead to the development of innovative TB vaccination, diagnostic and treatment protocols.en_US
dc.language.isoenen_US
dc.publisherNorth-West University
dc.subjectMetabolomicsen_US
dc.subjectMycobacteriumen_US
dc.subjectRifampicin-resistanceen_US
dc.subjectTuberculosisen_US
dc.titleA metabolomics approach for characterising tuberculosisen
dc.typeThesisen_US
dc.description.thesistypeDoctoralen_US
dc.contributor.researchID10799508 - Loots, Du Toit (Supervisor)


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