No description available.

Aar2p is an essential yeast protein involved in pre-mRNA splicing and component of a U5 snRNP precursor form. It has been suggested that the mature U5 snRNP and U4/U6.U5 tri-snRNP assemble from the Aar2p-U5 core particle that is formed in the cytoplasm. After nuclear import, Aar2p would be displaced from its interaction with Prp8p in the Aar2p-U5 particle and the mature U5 snRNP would be formed by interaction of Brr2p with Prp8p. In one model, Aar2p acts as a transport factor either for nuclear import of the Aar2p-U5 particle, or for nucleocytoplasmic shuttling of Prp8p. As a non-mutually exclusive alternative, Aar2p can function as a chaperone, regulating the Prp8p/Brr2p interaction. In this thesis I investigate the role of Aar2p in U5 snRNP biogenesis. I demonstrate by fluorescence microscopy that Aar2p is not required for nuclear localisation of the U5 snRNP components Snu114p and Prp8p. By a yeast two-hybrid (Y2H) assay I establish that Prp8p1649-2413 interacts with Aar2p1-170 but not with Aar2p150-355. I also show for the first time that Aar2p is phosphorylated in five aminoacids: S253, T274, Y328, S331 and T345. In my Y2H system S253 phosphorylation disrupts the Aar2p/Prp8p interaction. This suggests a mechanism whereby formation of the mature U5 snRNP and activation of Brr2p by Prp8p may be regulated through phosphorylation. Surprisingly, when the S253A and S253E mutations are inserted into genomic AAR2 there is no change in the amount of Prp8p and U5 snRNA immunoprecipitated by Aar2p. Finally, using Aar2p1-170 as bait for a Y2H screen, a variety of new Aar2p interactors are revealed. The obtained preys include the other half of Aar2p itself, proteins involved in DNA damage repair, chromatin-binding, ubiquitin-binding and membrane proteins. Overall these results suggest that besides modulating splicing, Aar2p is involved in several other important cellular processes.

With the rapid development of DNA synthesis technologies, synthetic biology has made tremendous progress in the past 15 years, in particular for synthetic genomics. Synthetic genomics is a nascent field of synthetic biology, which aims to design new biological systems/organisms to satisfy human needs. Conventional synthetic biology focuses on the redesign, construction and modeling of biological parts, pathways or genomes that do not exist in nature, while synthetic genomics encompasses technologies that allow the generation of chemically synthesized larger parts of genomes or whole genomes, with simultaneous redesign of an organism's genetic material. Synthetic genomics is painting a blueprint for a new era of biology and holds great potential for a multitude of applications, such as pharmaceuticals, biofuels and rapid generation of vaccines against emerging diseases. Chapter One gives an introduction of the current state of the art and challenges of synthetic genomics and the objectives of this study. Chapter Two demonstrates the design and construction strategy of two megabase-long synthetic yeast chromosomes, SynII and SynVII. Chapter Three describes the full characterization of SynII and SynVII. Chapter Four introduces the SCRaMbLE (Synthetic Chromosome Rearrangement and Modification by LoxPsym-mediated Evolution) system and its application in SynII and SynVII. Taken together, this work demonstrates the utility of synthetic yeast for understanding biological systems and its potential for industrial applications.

Upon the loss of glucose as the main carbon source cells have developed different mechanisms in order to adapt to this stress and promote survival. In Saccharomyces cerevisiae one such mechanism is acetylation, a post-translational modification performed by a lysine acetyltransferase (KAT) complex, such as NuA4, which has been previously shown to regulate different glucose metabolic pathways. Despite its known role upon glucose starvation, it is not currently understood how NuA4 itself is regulated in response to acute glucose deprivation (GD). I determine here that NuA4 complex protein levels (including catalytic protein Esa1), structure, activity, and localization are not impacted by acute GD. Despite GD showing no impact to NuA4 itself, it does result in the remodelling of both the interactome and acetylome of the complex where 160 proteins were identified to change interaction with Esa1-TAP and 93 acetylation sites were identified. As well GD results in a shift in localization of interacting proteins from nuclear upon standard growth to cytoplasmic. As well the changing interactome shows enrichment for proteins related to regulation of transcription and translation, metabolic pathways like glycolysis and gluconeogenesis, and others related to the cellular stress response. From the interactome three sets of proteins, Pab1, Eaf5/7/3, and Fas1 and Fas2, were studied further to greater characterize their interaction with NuA4 as they change interaction upon GD. Eaf7 protein levels were shown to decrease upon GD and both Fas1 and Fas2 levels were shown to increase in response to NuA4 deletion mutants. Together this work provides a greater understanding of the cellular response to acute GD stress, and how NuA4 plays a role in response to that stress in order to promote cell survival.

No description available.

Bibliography : leaves 74-92. / v, 92, [59] leaves : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / An investigation was undertaken to identify a yeast with broad spectrum killer activity towards indigenous non-Saccaromyces yeasts of the wine ferment. / Thesis (Ph.D.)--University of Adelaide, Dept. of Plant Science, 2000

No description available.

There are three main categories of algorithms for identifying small transcription regulatory sequences in the promoters of genes, phylogenetic comparison, expectation maximization and combinatorial. For convenience, the combinatorial methods typically define motifs in terms of a canonical sequence and a set of sequences that have a small number of differences compared to the canonical sequence. Such motifs are referred to as (l, d)-motifs where l is the length of the motif and d indicates how many mismatches are allowed between an instance of the motif and the canonical motif sequence. There are limits to the complexity of the patterns of motifs that can be found by combinatorial methods. For some values of l and d, there will exist many sets of random words in a cluster of gene promoters that appear to form an (l, d)-motif. For these motifs, it will be impossible to distinguish biological motifs from randomly generated motifs. A better formalization of motifs is the (l, f, d)-motif that is derived from a biological consideration of motifs. The motivation for (l, f, d)-motifs comes from an examination of known transcription factor binding sites where typically a few positions in the motif are invariant. It is shown that there exist (l, f, d)-motifs that can be found in the promoters of gene clusters that would not be recognizable from random sequences if they were described as (l, d)-motifs. The inclusion of the f-value in the definition of motifs suggests that the sequence space that is occupied by a motif will consist of a several clusters of closely related sequences. An algorithm, CM, has been developed that identifies small sets of overabundant sequences in the promoters from a cluster of genes and then combines these simple sets of sequences to form complex (l, f, d)-motif models. A dataset from a yeast gene expression experiment is analyzed with CM. Known biological motifs and novel motifs are identified by CM. The performance of CM is compared to that of a popular expectation maximization algorithm, AlginACE, and to that from a simple combinatorial motif finding program.

The Saccharomyces cerevisiae origin recognition complex (ORC) binds to replication origins at the ARS consensus sequence (ACS), serving as a scaffold for the assembly of replication complexes needed for the initiation of DNA synthesis. I generated a genome-wide map of nucleosome positions surrounding replication origins because the precise locations of nucleosomes may influence replication. My map revealed a nucleosome-free region surrounding the ACS that is bordered by two well-positioned nucleosomes. I was able to explain differences in origin properties by clustering nucleosome profiles. I found an association between the replication time and nucleosome profile for a given origin cluster. An ORC depletion mutant nucleosome map indicated a shift in nucleosomes towards the ACS. I present the first genome-wide view of origin nucleosome architecture, indicate a relationship between chromatin structure and replication timing, and suggest a model whereby the interplay between DNA sequence and ORC binding defines the nucleosome occupancy pattern.

The Saccharomyces cerevisiae origin recognition complex (ORC) binds to replication origins at the ARS consensus sequence (ACS), serving as a scaffold for the assembly of replication complexes needed for the initiation of DNA synthesis. I generated a genome-wide map of nucleosome positions surrounding replication origins because the precise locations of nucleosomes may influence replication. My map revealed a nucleosome-free region surrounding the ACS that is bordered by two well-positioned nucleosomes. I was able to explain differences in origin properties by clustering nucleosome profiles. I found an association between the replication time and nucleosome profile for a given origin cluster. An ORC depletion mutant nucleosome map indicated a shift in nucleosomes towards the ACS. I present the first genome-wide view of origin nucleosome architecture, indicate a relationship between chromatin structure and replication timing, and suggest a model whereby the interplay between DNA sequence and ORC binding defines the nucleosome occupancy pattern.