Natural compounds have been largely excluded from characterization via high-throughput profiling strategies due to their limited abundance. Herein, I describe the modification of high-throughput yeast chemical genomic (CG) interaction profiling to permit identifying the modes of action of natural compounds. The previous assay proceeded by evaluating the genome-wide yeast deletion collection for drug-hypersensitivity in a volume of 0.7mL. Compound consumption was minimized with the adapted approach by reducing the assay volume 70% through simplifying the complexity of the yeast deletion pool screened. By recreating each yeast mutant in a drug-hypersensitive background, I created a novel resource that increases compound efficiency and further diminishes compound use. Evaluating a series of characterized compounds analyzed previously by the traditional CG approach validated the adaptations incorporated did not negatively affect the quality of data yielded. Ultimately, this modified strategy will be used to screen thousands of natural compounds contained within the RIKEN NPDepo library.

In the quest to understand cell behavior and cure genetic diseases such as cancer, the fundamental approach being taken is undergoing a gradual change. It is becoming more acceptable to view these diseases as an engineering problem, and systems engineering approaches are being deployed to tackle genetic diseases. In this light, we believe that logic synthesis techniques can play a very important role. Several techniques from the field of logic synthesis can be adapted to assist in the arguably huge effort of modeling cell behavior, inferring biological networks, and controlling genetic diseases. Genes interact with other genes in a Gene Regulatory Network (GRN) and can be modeled as a Boolean Network (BN) or equivalently as a Finite State Machine (FSM). As the expression of genes deter- mine cell behavior, important problems include (i) inferring the GRN from observed gene expression data from biological measurements, and (ii) using the inferred GRN to explain how genetic diseases occur and determine the ”best” therapy towards treatment of disease. We report results on the application of logic synthesis techniques that we have developed to address both these problems. In the first technique, we present Boolean Satisfiability (SAT) based approaches to infer the predictor (logical support) of each gene that regulates melanoma, using gene expression data from patients who are suffering from the disease. From the output of such a tool, biologists can construct targeted experiments to understand the logic functions that regulate a particular target gene. Our second technique builds upon the first, in which we use a logic synthesis technique; implemented using SAT, to determine gene regulating functions for predictors and gene expression data. This technique determines a BN (or family of BNs) to describe the GRN and is validated on a synthetic network and the p53 network. The first two techniques assume binary valued gene expression data. In the third technique, we utilize continuous (analog) expression data, and present an algorithm to infer and rank predictors using modified Zhegalkin polynomials. We demonstrate our method to rank predictors for genes in the mutated mammalian and melanoma networks. The final technique assumes that the GRN is known, and uses weighted partial Max-SAT (WPMS) towards cancer therapy. In this technique, the GRN is assumed to be known. Cancer is modeled using a stuck-at fault model, and ATPG techniques are used to characterize genes leading to cancer and select drugs to treat cancer. To steer the GRN state towards a desirable healthy state, the optimal selection of drugs is formulated using WPMS. Our techniques can be used to find a set of drugs with the least side-effects, and is demonstrated in the context of growth factor pathways for colon cancer.

<p>In this thesis, we have firstly cloned and expressed the domains that harbours the putative catalytic GC domain in these receptor molecules and demonstrate that these molecules can convert GTP to cGMP in vitro. Secondly, we show that exogenous application of both Phytosulfokine and Brassinosteroid increase changes of intracellular cGMP levels in Arabidopsis mesophyll protoplast demonstrating that these molecules have GC activity in vivo and therefore provide a link as second messenger between the hormones and down-stream responses. In order to elucidate a relationship between the kinase and GC domains of the PSK receptor, we have used the AtPSKR1 receptor as a model and show that it has Serine/Threonine kinase activity using the Ser/Thr peptide 1 as a substrate. In addition, we show that the receptor`s ability to phosphorylate a substrate is affected by the product (cGMP) of its co-domain (GC) and that the receptor autophosphorylates on serine residues and this step was also observed to be affected by cGMP. When Arabidopsis plants are treated with a cell permeable analogue of cGMP, we note that this can affect changes in the phosphoproteome in Arabidopsis and conclude therefore that the cGMP plays a role in kinase-dependent downstream signalling. The obtained results suggest that the receptor molecules investigated here belong to a novel class of GCs that contains both a cytosolic kinase and GC domains, and thus have a domain organisation that is not dissimilar to that of atrial natriuretic peptide receptors NPR1 and NPR2. The findings also strongly suggest that cGMP has a role as a second messenger in both Brassinosteroid and Phytosulfokine signalling. We speculate that other proteins with similar domain organisations may also have dual catalytic activities and that a significant number of GCs, both in plants and animals, remain to be discovered and characterised.</p>

Medulloblastoma is the most common malignant pediatric brain tumour. Although survival rates have improved in recent years, long-term survivors exhibit a significantly diminished quality of life complicated by neurological, endocrine, intellectual, and social sequelae as a result of conventional therapies. In order to improve the current outlook for patients with medulloblastoma, rational, targeted therapies that are more efficient and less toxic are required. Despite insight gained from the study of hereditary tumour syndromes and candidate gene approaches, the molecular basis of medulloblastoma remains poorly defined, with more than half of all cases remaining unaccounted for at the genetic level. The intent of my PhD research program was to use high-resolution genomics in an attempt to gain an improved understanding of the medulloblastoma genome and potentially uncover novel genes and pathways driving its pathogenesis. By applying a combination of single nucleotide polymorphism (SNP) arrays, exon arrays, and microRNA arrays to a large cohort of primary medulloblastoma samples, we have identified novel oncogenes and tumour suppressors, implicated deregulation of the histone code as an important event in the pathogenesis of medulloblastoma, and refined the definition of medulloblastoma subgroups. This thesis demonstrates the extent of heterogeneity that exists in the medulloblastoma genome, showing that relatively few genomic aberrations are common when studying medulloblastoma as a single disease. In spite of this heterogeneity, we have identified novel candidate genes and processes that may serve as potential targets for future therapies. Importantly, we have established an improved method of classifying medulloblastomas into distinct molecular variants, showing that certain genomic changes are enriched and occasionally restricted to a specific subgroup. Finally, in addition to genomic differences, we have confirmed that medulloblastoma subgroups differ in their demographics and clinical behavior, and propose that medulloblastoma subgroup affiliation should become an integral component of patient stratification in the future.

Medulloblastoma is the most common malignant pediatric brain tumour. Although survival rates have improved in recent years, long-term survivors exhibit a significantly diminished quality of life complicated by neurological, endocrine, intellectual, and social sequelae as a result of conventional therapies. In order to improve the current outlook for patients with medulloblastoma, rational, targeted therapies that are more efficient and less toxic are required. Despite insight gained from the study of hereditary tumour syndromes and candidate gene approaches, the molecular basis of medulloblastoma remains poorly defined, with more than half of all cases remaining unaccounted for at the genetic level. The intent of my PhD research program was to use high-resolution genomics in an attempt to gain an improved understanding of the medulloblastoma genome and potentially uncover novel genes and pathways driving its pathogenesis. By applying a combination of single nucleotide polymorphism (SNP) arrays, exon arrays, and microRNA arrays to a large cohort of primary medulloblastoma samples, we have identified novel oncogenes and tumour suppressors, implicated deregulation of the histone code as an important event in the pathogenesis of medulloblastoma, and refined the definition of medulloblastoma subgroups. This thesis demonstrates the extent of heterogeneity that exists in the medulloblastoma genome, showing that relatively few genomic aberrations are common when studying medulloblastoma as a single disease. In spite of this heterogeneity, we have identified novel candidate genes and processes that may serve as potential targets for future therapies. Importantly, we have established an improved method of classifying medulloblastomas into distinct molecular variants, showing that certain genomic changes are enriched and occasionally restricted to a specific subgroup. Finally, in addition to genomic differences, we have confirmed that medulloblastoma subgroups differ in their demographics and clinical behavior, and propose that medulloblastoma subgroup affiliation should become an integral component of patient stratification in the future.

The long-term success of transplantation is limited by the need for immunosuppression; thus, tolerance induction is an important therapeutic goal. A 16-day treatment with rapamycin in mice led to indefinite graft survival of fully mismatched cardiac allografts, whereas untreated hearts were rejected after 8-10 days. Specific tolerance was confirmed through subsequent skin grafts and in vitro lymphocyte assays that showed recipient mice remained immunocompetent towards 3rd party antigens but were impaired in responding to donor antigens. Mechanisms that account for this tolerant state were then investigated. Splenic CD8+CD44+ memory T-cells were reduced in tolerant mice but had increased frequencies of the CD62LLO population. CD4+CD25+Foxp3+ regulatory T-cells were increased in tolerant mice. Through multiplex PCR, 4 regulatory T-cell related genes were found up-regulated and 2 proinflammatory genes were down-regulated in accepted hearts. This expression pattern may serve as a putative biomarker of tolerance in patients undergoing transplantation.

The long-term success of transplantation is limited by the need for immunosuppression; thus, tolerance induction is an important therapeutic goal. A 16-day treatment with rapamycin in mice led to indefinite graft survival of fully mismatched cardiac allografts, whereas untreated hearts were rejected after 8-10 days. Specific tolerance was confirmed through subsequent skin grafts and in vitro lymphocyte assays that showed recipient mice remained immunocompetent towards 3rd party antigens but were impaired in responding to donor antigens. Mechanisms that account for this tolerant state were then investigated. Splenic CD8+CD44+ memory T-cells were reduced in tolerant mice but had increased frequencies of the CD62LLO population. CD4+CD25+Foxp3+ regulatory T-cells were increased in tolerant mice. Through multiplex PCR, 4 regulatory T-cell related genes were found up-regulated and 2 proinflammatory genes were down-regulated in accepted hearts. This expression pattern may serve as a putative biomarker of tolerance in patients undergoing transplantation.

Natural compounds have been largely excluded from characterization via high-throughput profiling strategies due to their limited abundance. Herein, I describe the modification of high-throughput yeast chemical genomic (CG) interaction profiling to permit identifying the modes of action of natural compounds. The previous assay proceeded by evaluating the genome-wide yeast deletion collection for drug-hypersensitivity in a volume of 0.7mL. Compound consumption was minimized with the adapted approach by reducing the assay volume 70% through simplifying the complexity of the yeast deletion pool screened. By recreating each yeast mutant in a drug-hypersensitive background, I created a novel resource that increases compound efficiency and further diminishes compound use. Evaluating a series of characterized compounds analyzed previously by the traditional CG approach validated the adaptations incorporated did not negatively affect the quality of data yielded. Ultimately, this modified strategy will be used to screen thousands of natural compounds contained within the RIKEN NPDepo library.

Unlike many amphibians, the paedomorphic axolotl (Ambystoma mexicanum) rarely undergoes external morphological changes indicative of metamorphosis. However, internally, some axolotl tissues undergo cryptic metamorphic changes. A previous study examined interspecific patterns of larval brain gene expression and found that these species exhibited unique temporal expression patterns that were hypothesized to be morph specific. This thesis tested this hypothesis by examining differences in brain gene expression between juvenile (JUV), paedomorphic (PAED), and metamorphic (MET) axolotls. I identified 828 genes that were expressed differently between JUV, PAED, and MET. Expression estimates from JUV were compared to estimates from PAED and MET brains to identify genes that changed significantly during development. Genes that showed statistically equivalent expression changes across MET and PAED brains provide a glimpse at aging and maturation in an amphibian. The genes that showed statistically different expression estimates between metamorphic and paedomorphic brains provide new functional insights into the maintenance and regulation of paedomorphosis. For genes that were not commonly regulated due to aging, paedomorphs exhibited greater transcriptional similarity to juvenile than metamorphs did to juvenile. Overall, gene expression differences between metamorphic and paedomorphic development exhibit a mosaic pattern of expression as a function of aging and metamorphosis in axolotls.

Streptococcus agalactiae is a significant pathogen causing invasive disease in neonates and thus an understanding of the molecular basis of the pathogenicity of this organism is of importance. N-terminal lipidation is a major mechanism by which bacteria can tether proteins to membranes. Lipidation is directed by the presence of a cysteine-containing lsquolipoboxrsquo within specific signal peptides and this feature has greatly facilitated the bioinformatic identification of putative lipoproteins. We have designed previously a taxon-specific pattern (G+LPP) for the identification of Gram-positive bacterial lipoproteins, based on the signal peptides of experimentally verified lipoproteins (Sutcliffe I.C. and Harrington D.J. Microbiology 148: 2065¿2077). Patterns searches with this pattern and other bioinformatic methods have been used to identify putative lipoproteins in the recently published genomes of S. agalactiae strains 2603/V and NEM316. A core of 39 common putative lipoproteins was identified, along with 5 putative lipoproteins unique to strain 2603/V and 2 putative lipoproteins unique to strain NEM316. Thus putative lipoproteins represent ca. 2% of the S. agalactiae proteome. As in other Gram-positive bacteria, the largest functional category of S. agalactiae lipoproteins is that predicted to comprise of substrate binding proteins of ABC transport systems. Other roles include lipoproteins that appear to participate in adhesion (including the previously characterised Lmb protein), protein export and folding, enzymes and several species-specific proteins of unknown function. These data suggest lipoproteins may have significant roles that influence the virulence of this important pathogen.