Thesis (PhD)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

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Not available / text

Systems biology studies the complex interactions between components of biological systems. One major goal of systems biology is to reconstruct the network of interactions between genes in response to normal and perturbed conditions. In order to accomplish this goal, large-scale data are needed. Accordingly, diverse powerful and high-throughput methods must be developed for this purpose. We have developed novel high-throughput technologies focusing on cellular phenotype profiling and now provide additional genome-scale analysis of gene and protein function. Few high-throughput methods can perform large-scale and high-throughput cellular phenotype profiling. However, analyzing gene expression patterns and protein behaviors in their cellular context will provide insights into important aspects of gene function. To complement current genomic approaches, we developed two technologies, the spotted cell microarray (cell chip) and the yeast spheroplast microarray, which allow high-throughput and highly-parallel cellular phenotype profiling including cell morphology and protein localization. These methods are based on printing collections of cells, combined with automated high-throughput microscopy, allowing systematic cellular phenotypic characterization. We used spotted cell microarrays to identify 15 new genes involved in the response of yeast to mating pheromone, 80 proteins associated with shmoo-tip 'localizome' upon pheromone stimulation and 5 genes involved in regulating the localization pattern of a group II intron encoded reverse transcriptase, LtrA, in Escherichia coli. Furthermore, in addition to morphology assays, yeast spheroplast microarrays were built for high-throughput immunofluorescence microscopy, allowing large-scale protein and RNA localization studies. In order to identify additional cell cycle genes, especially those difficult to identify in loss-of-function studies, we performed a genome-scale screen to identify yeast genes with overexpression-induced defects in cell cycle progression. After measuring the fraction of cells in G1 and G2/M phases of the cell cycle via high-throughput flow cytometry for each of ~5,800 ORFs and performing the validation and secondary assays, we observed that overexpression of 108 genes leads to reproducible and significant delay in the G1 or G2/M phase. Of 108 genes, 82 are newly implicated in the cell cycle and are likely to affect cell cycle progression via a gain-of-function mechanism. The G2/M category consists of 87 genes that showed dramatic enrichment in the regulation of mitotic cell cycle and related biological processes. YPR015C and SHE1 in the G2/M category were further characterized for their roles in cell cycle progression. We found that the G2/M delay caused by the overexpression of YPR015C and SHE1 likely results from the malfunction of spindle and chromosome segregation, which was supported by the observations of highly elevated population of large-budded cells in the pre-M phase, super-sensitivity to nocodazole, and high chromosome loss rates in these two overexpression strains. While the genes in the G2/M category were strongly enriched for cell cycle associated functions, no pathway was significantly enriched in the G1 category that is composed of 21 genes. However, the strongest enrichment for the G1 category consists of the genes involved in negative regulation of transcription. For instance, the overexpression of SKO1, a transcription repressor, resulted in strong cell cycle delay at G1 phase. Moreover, we found that the overexpression of SKO1 results in cell morphology changes that resembles mating yeast cells (shmoos) and activates the mating pheromone response pathway, thus explaining the G1 cell cycle arrest phenotype of SKO1 ORF strains.

Infrared spectra of microbial cells are highly specific, fingerprint-like signatures which can be used to differentiate microbial species and strains from each other. In this study, the potential applicability of Fourier transform infrared (FTIR) spectroscopy for the classification of yeast strains in terms of their biological taxonomy, their use in the production of wine, beer, and bread, and their sensitivity to killer yeast strains was investigated. Sample preparation, spectral data preprocessing methods and spectral classification techniques were also investigated. All yeast strains were grown on a single growth medium. The FTIR spectra were baseline corrected and the second derivative spectra were computed and employed in spectral analysis. The classification accuracy was improved when the principal component spectra (calculated from the second derivative spectra) were employed rather than the second derivative spectra or raw spectra alone. Artificial neural network (ANN) with 10 units in the input layer and 12 units in the hidden layer produced a robust prediction model for the identification of yeasts. Cluster analysis was employed for the classification of yeast strains in terms of their use in the production of wine, beer, and bread and in terms of their sensitivity to killer yeast strains. The optimum region for the classification in the former case was found to be between 1300 and 800 cm-1 in the infrared spectrum whereas the optimum region for the classification of yeast strains in terms of their sensitivity was between 900 and 800 cm-1 . The results of this work demonstrated that FTIR spectroscopy could be successfully employed for the classification and identification of yeast strains with minimal sample preparation.

The yeast expression system Pichia pastoris was investigated as an encapsulation technology capable of serving as a rumen escape vehicle. Cellularly encapsulated protein is protected from the ruminal environment so long as the cell membrane, which surrounds and isolates the intracellular protein is physically intact. Intracellular expression of Green Fluorescent Protein (GFP) allows for the monitoring of cellular integrity as necessary for the protection of encapsulated protein from ruminal proteases. Upon cellular lysis GFP is exposed to extracellular proteases which result in both the proteolytic degradation of the protein-based GFP chromophore and its associated fluorescence. Visualization of rumen fluid under epifluorescent microscopy revealed a high level of background autofluorescence owing to the fluorescent plant particles, microbes, and fluorescent compounds therein. Visualization of GFP in rumen fluid can be optimized through GFP variant selection, filter set design, and light source selection based on bulb emission spectra. Incubation of intracellular GFP expressing P. pastoris in batch culture ruminal in vitro simulations demonstrated that 93%, 97%, and 25% of the P. pastoris inoculum maintained cellular integrity in clarified rumen fluid, bacterial fraction of rumen fluid, and whole rumen fluid, respectively, when incubated over 36 to 48 h. Continuous fermentation in vitro rumen simulations (Rusitec) demonstrated a P. pastoris escape rate of 19% when added daily to fully adapted Rusitec vessels having a dilution rate of 0.75d-1. Abomasal in vitro simulations demonstrated that 84% of the P. pastoris inoculum was lysed within 12 h, as necessary for the release of encapsulated protein. P.pastoris may be an effective post-fuminal delivery vehicle, provided that similar results are obtained in vivo. / xiv, 120 leaves : ill. ; 28 cm.

<i>Saccharomyces cerevisiae </i>is able to sense and respond to amino acid availability in the environment by reprogramming its gene expression.  Upon starvation for any amino acid, the yeast cell induces the biosynthesis of nearly all amino acids by upregulating the master transcription factor Gcn4, which is primarily controlled at the translational level.  Amino acid consumption is decreased by reducing general mRNA translation activity via <i>GCN2.  </i>The molecular mechanisms that mediate this response are known as the General Control Nonderepressible (GCN) response.  The aim of this thesis was to quantitatively understand the GCN response in <i>S. cerevisiae </i>by constructing a series of mathematical models, both deterministic and stochastic, based on published experimental results. Firstly, a deterministic model of general mRNA translation is described, which elaborates the translation initiation apparatus.  This model was used to predict the effects upon general translation activity of changing the abundance of specific initiation factors in isolation and collectively.  This model was also used to study the robustness of general mRNA translation and the trade-off between robustness and performance of this system. Secondly, a series of probabilistic and stochastic models of <i>GCN4 </i>mRNA translation based on Gillespie algorithm are described.  These probabilistic models successfully explained published results regarding the changes in <i>GCN4 </i>mRNA translation resulting from variation in uORF intercistronic distances.  The subsequent stochastic simulations suggested that histidine codon translation rates contribute significantly to the observed changes in ribosomal loading that occur on the <i>GCN4 </i>mRNA. Finally, a comprehensive deterministic model is described for the entire GCN system, integrating the above models.  This comprehensive model was able to predict the GCN responses to both natural and artificial amino acid starvation.  It successfully reproduced the phenotypes of some <i>GCN2 </i>and <i>GCN4 </i>mutants, and was also used to examine the fragility of GCN system when faced with severe artificial histidine starvation.  These predictions await experimental verification.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of New South Wales, 2000. / Also available online.

Thesis (M.Sc.)--University of Wollongong, 2004. / Typescript. Includes bibliographical references: leaf 115-122.