In this thesis I describe a functional genomics resource in which each yeast gene, with its native promoter and 3’UTR, is cloned on a uniquely barcoded low-copy vector. We refer to this resource as the Molecular Barcoded Yeast ORF (MoBY-ORF) library 1.0. Each gene carried by MoBY-ORF 1.0 should mimic its native expression and thus is best suited for complementation cloning. The vector backbone of MoBY-ORF 1.0 is compatible with the mating-assisted genetically integrated cloning (MAGIC) system for recombination cloning in bacterial cells, which allows the transfer of the ORF fragment and its barcoded cassette to other vector backbones. Taking advantage of the MAGIC system, we created a multi-copy version of the library, which we refer to as MoBY-ORF 2.0. I used MoBY-ORF 1.0 to map drug resistant mutants by complementation cloning with a barcode microarray readout. I investigated several drugs with known targets in my proof-of-principle experiments and showed the feasibility of this method. I identified a single mutation that causes resistance to two different natural products, theopalauamide and stichloroside. By doing so, I was able to link these two chemicals to their cellular target, ergosterol. In fact, theopalauamide represents a new class of sterol binding chemical. I also describe the use of MoBY-ORF 2.0 to clone dosage suppressors of conditional temperature-sensitive mutants. By doing so, and combing our own data with published literature, we showed that dosage suppression interactions often overlap with protein-protein interactions and negative genetic interactions but not positive interactions; however the majority of dosage suppression interactions are unique and thus they represent an unique edge on a global functional interaction map. We also describe the first genome-wide dosage suppressor interaction map of budding yeast.

In this thesis I describe a functional genomics resource in which each yeast gene, with its native promoter and 3’UTR, is cloned on a uniquely barcoded low-copy vector. We refer to this resource as the Molecular Barcoded Yeast ORF (MoBY-ORF) library 1.0. Each gene carried by MoBY-ORF 1.0 should mimic its native expression and thus is best suited for complementation cloning. The vector backbone of MoBY-ORF 1.0 is compatible with the mating-assisted genetically integrated cloning (MAGIC) system for recombination cloning in bacterial cells, which allows the transfer of the ORF fragment and its barcoded cassette to other vector backbones. Taking advantage of the MAGIC system, we created a multi-copy version of the library, which we refer to as MoBY-ORF 2.0. I used MoBY-ORF 1.0 to map drug resistant mutants by complementation cloning with a barcode microarray readout. I investigated several drugs with known targets in my proof-of-principle experiments and showed the feasibility of this method. I identified a single mutation that causes resistance to two different natural products, theopalauamide and stichloroside. By doing so, I was able to link these two chemicals to their cellular target, ergosterol. In fact, theopalauamide represents a new class of sterol binding chemical. I also describe the use of MoBY-ORF 2.0 to clone dosage suppressors of conditional temperature-sensitive mutants. By doing so, and combing our own data with published literature, we showed that dosage suppression interactions often overlap with protein-protein interactions and negative genetic interactions but not positive interactions; however the majority of dosage suppression interactions are unique and thus they represent an unique edge on a global functional interaction map. We also describe the first genome-wide dosage suppressor interaction map of budding yeast.

Characterization of DAP1/YPL170W: the Saccharomyces cerevisiae Membrane Associated Progesterone Receptor (MAPR) Homologue Christopher D. Banna 135 pages Directed by Dr. Jung H. Choi MAPRs (Membrane Associated Progesterone Receptors) from several sources have been isolated, studied and minimally characterized in mammalian systems, yet the specific role of this protein family has not been fully determined. Early worked characterized this protein family as a type of steroid binding protein, unrelated to the classical nuclear receptors, and linked this family to non-genomic cellular responses. The MAPR homologues as a group have been suggested to play widely varying roles from axon guidance and neuronal formation, to steroid hydroxylation, to influencing reproductive behavior. Their specific role has not yet been clearly demonstrated in any organism. There is some debate as to whether MAPRs do indeed bind steroid compounds, but there is clear evidence this family of proteins is involved in steroid perception. Recent work has begun to link a specific member of the MAPR family, IZAg from rat, to steroid metabolism/production, specifically, in the hydroxylation step of glucocorticoid production from progesterone. In the yeast Saccharomyces cerevisiae, the MAPR homologue is DAP1. Preliminary work on haploid strains demonstrated several phenotypes associated with the DAP1 deletion mutant, most notably an altered sterol profile. Previous characterization of diploid homozygous mutant strain has shown a differential sensitivity to alcohol, an altered sterol profile, and a strong yeast two-hybrid interaction with Ypr118wp; methylthioribose-1-phosphate isomerase. Work in this study link the localization of Dap1p to lipid particles and on the ER, both sites of sterol synthesis. The sterol profiles of the control strain and the dap1Ġdeletion mutant strain were examined in detail. The most notable difference was the presence of an additional sterol compound associated with the deletion mutant strain. The structure of this compound does not correspond to normal sterols in the ergosterol biosynthetic pathway, but does correspond to structure of sterols in so-called alternate aberrant sterol pathways. The data presented in this study demonstrates that Dap1p was involved in sterol processing, although its specific role is unknown. Two possible scenarios are proposed; one where Dap1p is involved in regulating the flux of sterols from one internal membrane to another, and another where Dap1p is involved in aberrant sterol pathways.

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.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (doctoral)--Helsinki University of Technology, 2001. / Includes bibliographical references. Also available on the World Wide Web.

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