Prion diseases are fatal neurodegenerative disorders of humans and animals. The prion hypothesis states that PrPSc, a misfolded conformational isoform of the cellular prion protein (PrPC), is the sole component of the infectious particle. Many open questions exist in prion biology including the cellular role of PrPC, the potential involvement of auxiliary factors in prion replication, and the mechanism of PrPSc-induced toxicity in prion disease. The identification of novel prion-like proteins and authentic in vivo prion protein-interacting proteins would certainly assist the process of demystifying these unsolved mysteries. Accordingly, two newly-identified proteins with potential relevance to prion protein biology, Shadoo and DPPX, were selected for biochemical and functional characterization. Shadoo, a hypothetical prion-like protein, is revealed as being a glycoprotein which possesses many overlapping properties with PrPC including neuronal expression, C1-like endoproteolytic processing, and the ability to protect against apoptotic stimuli in cerebellar neurons. Shadoo loosely resembles the disordered N-terminal domain of PrPC and consistent with this notion, Shadoo appears to lack a well-defined structure. Remarkably, Shadoo levels in the brains of mice with clinical prion disease are significantly decreased suggesting that Shadoo may be inherently linked to prion replication or prion disease pathogenesis. These experiments define Shadoo as the third member of the prion protein family and, because of its functional similarities to PrPC, Shadoo may be a useful tool for deciphering the in vivo function of PrPC. DPPX, a neuronal type II transmembrane protein, is demonstrated to be the first protein capable of interacting with all three members of the prion protein family (PrPC, Doppel, and Shadoo) in vivo. Complex formation between prion proteins and DPPX appears to be mediated by multiple binding sites. When coupled with high levels of DPPX expression in cerebellar granular neurons, DPPX is a strong candidate for mediating phenotypic interactions between prion proteins in cerebellar cells. Thus, Shadoo and DPPX comprise two new entry points for studying prion proteins. Further investigation of the roles of Shadoo and DPPX in both the cell biology of prion proteins and prion disease may yield important clues to these enigmatic topics.

Src homology 3 (SH3) domains are one family of the peptide recognition modules (PRMs), which bind peptides rich in proline or positively charged residues in the target proteins, and play important assembly or regulatory functions in dynamic eukaryotic cellular processes, especially in signal transduction and endocytosis. SH3 domains are conserved from yeast to human, and improper SH3 domain mediated protein-protein interaction (PPI) leads to defects in cellular function and may even result in disease states. Since commonly used large-scale PPI mapping strategies employed full-length proteins or random protein fragments as screening probes and did not identify the particular PPIs mediated by the SH3 domains, I employed a combined experimental and computational strategy to address this problem. I used yeast two-hybrid (Y2H) as my major experimental tool, as well as individual SH3 domains as baits, to map SH3 domain mediated PPI networks, “SH3 domain interactomes”. One of my important contributions has been the improvement for Y2H technology. First, I generated a pair of Y2H host strains that improved the efficiency of high-throughput Y2H screening and validated their usage. These strains were employed in my own research and also were adopted by other researchers in their large-scale PPI network mapping projects. Second, in collaboration with Nicolas Thierry-Mieg, I developed a novel smart-pooling method, Shifted Transversal Design (STD) pooling, and validated its application in large-scale Y2H. STD pooling was proven to be superior among currently available methods for obtaining large-scale PPI maps with higher coverage, high sensitivity and high specificity. I mapped the SH3 domain interactomes for both budding yeast Saccharomyces cerevisiae and nematode worm Caenorhabditis elegans, which contain 27 and 84 SH3 domains, respectively. Comparison of these two SH3 interactomes revealed that the role of the SH3 domain is conserved at a functional but not a structural level, playing a major role in the assembly of an endocytosis network from yeast to worm. Moreover, the worm SH3 domains are additionally involved in metazoan-specific functions such as neurogenesis and vulval development. These results provide valuable insights for our understanding of two important evolutionary processes from single cellular eukaryotes to animals: the functional expansion of the SH3 domains into new cellular modules, as well as the conservation and evolution of some cellular modules at the molecular level, particularly the endocytosis module.

Babesia microti is a tickborne hemoprotozoan parasite that causes the disease babesiosis in humans. Babesia microti Apical Membrane Antigen-1 (AMA-1) is a micronemal protein suspected to play a role in erythrocyte invasion. To investigate interaction between AMA-1 and the host cell, the ectodomain region of the B. microti ama-1 gene was cloned into an expression vector, expressed as a histidine-tagged fusion protein, and used to probe red blood cell membrane proteins in far Western blot assays. The B. microti ama-1 ectodomain, which excludes the signal peptide and the transmembrane region of the open reading frame, was amplified from a cloned gene sequence. The AMA-1 ectodomain is a membrane bound polypeptide that extends into the extracellular space and is most likely to interact or initiate interaction with the host red blood cell surface receptor(s). The amplicon was ligated into a protein expression vector to produce a 58.1 kDa recombinant His-tagged fusion protein, which was confirmed by Western blot analysis. The recombinant B. microti AMA-1 fusion protein was enriched on nickel affinity columns and then used to probe mouse, human and horse red blood cell membrane proteins in far Western blot assays. Babesia microti AMA-1 consistently reacted strongly with a protein migrating at 49 kDa. A similar reaction occurred between the B. microti AMA-1 and horse red blood cell membrane proteins, suggesting that similar interacting proteins of this size are shared by red blood cells from the three species. The B. microti AMA-1 may bind to red blood cell membrane sialic-acid groups, as shown for other Babesia spp. This may explain the signal at the 49 kDa position observed between B. microti AMA-1 and red blood cell membrane proteins from three different species. Further studies may determine if the binding epitopes of the red blood cell binding partner at this position vary and contribute to the specificity of each parasite AMA-1 for their respective host cells.

Microarray technologies enable scientists to identify co-expressed genes at large scale. However, the gene expression analysis does not show functional relationships between co-expressed genes. There is a demand for effective approaches to analyse gene expression data to enable biological discoveries that can lead to identification of markers or therapeutic targets of many diseases. In cancer research, a number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue such as Pancreatic Ductal Adenocarcinoma (PDAC). PDAC carries very poor prognosis, it eludes early detection and is characterised by its aggressiveness and resistance to currently available therapies. To identify molecular markers and suitable targets, there exist a research effort that maps differentially expressed genes to protein interactions to gain an understanding at systems level. Such interaction networks have a complex interconnected structure, whose the understanding of which is not a trivial task. Several formal approaches use simulation to support the investigation of such networks. These approaches suffer from the missing knowledge concerning biological systems. Reasoning in the other hand has the advantage of dealing with incomplete and partial information of the network knowledge. The initial approach adopted was to provide an algorithm that utilises a network-centric approach to pancreatic cancer, by re-constructing networks from known interactions and predicting novel protein interactions from structural templates. This method was applied to a data set of co-expressed PDAC genes. To this end, structural domains for the gene products are identified by using threading which is a 3D structure prediction technique. Next, the Protein Structure Interaction Database (SCOPPI), a database that classifies and annotates domain interactions derived from all known protein structures, is used to find templates of structurally interacting domains. Moreover, a network of related biological pathways for the PDAC data was constructed. In order to reason over molecular networks that are affected by dysregulation of gene expression, BioRevise was implemented. It is a belief revision system where the inhibition behaviour of reactions is modelled using extended logic programming. The system computes a minimal set of enzymes whose malfunction explains the abnormal expression levels of observed metabolites or enzymes. As a result of this research, two complementary approaches for the analysis of pancreatic cancer gene expression data are presented. Using the first approach, the pathways found to be largely affected in pancreatic cancer are signal transduction, actin cytoskeleton regulation, cell growth and cell communication. The analysis indicates that the alteration of the calcium pathway plays an important role in pancreas specific tumorigenesis. Furthermore, the structural prediction method reveals ~ 700 potential protein-protein interactions from the PDAC microarray data, among them, 81 novel interactions such as: serine/threonine kinase CDC2L1 interacting with cyclin-dependent kinase inhibitor CDKN3 and the tissue factor pathway inhibitor 2 (TFPI2) interacting with the transmembrane protease serine 4 (TMPRSS4). These resulting genes were further investigated and some were found to be potential therapeutic markers for PDAC. Since TMPRSS4 is involved in metastasis formation, it is hypothesised that the upregulation of TMPRSS4 and the downregulation of its predicted inhibitor TFPI2 plays an important role in this process. The predicted protein-protein network inspired the analysis of the data from two other perspectives. The resulting protein-protein interaction network highlighted the importance of the co-expression of KLK6 and KLK10 as prognostic factors for survival in PDAC as well as the construction of a PDAC specific apoptosis pathway to study different effects of multiple gene silencing in order to reactivate apoptosis in PDAC. Using the second approach, the behaviour of biological interaction networks using computational logic formalism was modelled, reasoning over the networks is enabled and the abnormal behaviour of its components is explained. The usability of the BioRevise system is demonstrated through two examples, a metabolic disorder disease and a deficiency in a pancreatic cancer associated pathway. The system successfully identified the inhibition of the enzyme glucose-6-phosphatase as responsible for the Glycogen storage disease type I, which according to literature is known to be the main reason for this disease. Furthermore, BioRevise was used to model reaction inhibition in the Glycolysis pathway which is known to be affected by Pancreatic cancer.

As genes with similar expression pattern are very likely having the same biological function, cluster analysis becomes an important tool to understand and predict gene functions from gene expression profi les. In many situations, each gene expression profi le only contains a few data points. Directly applying traditional clustering algorithms to such short gene expression profi les does not yield satisfactory results. Developing clustering algorithms for short gene expression profi les is necessary. In this thesis, two novel methods are developed for clustering short gene expression pro files. The fi rst method, called the network-based clustering method, deals with the defect of short gene expression profi les by generating a gene co-expression network using conditional mutual information (CMI), which measures the non-linear relationship between two genes, as well as considering indirect gene relationships in the presence of other genes. The network-based clustering method consists of two steps. A gene co-expression network is firstly constructed from short gene expression profi les using a path consistency algorithm (PCA) based on the CMI between genes. Then, a gene functional module is identi ed in terms of cluster cohesiveness. The network-based clustering method is evaluated on 10 large scale Arabidopsis thaliana short time-course gene expression profi le datasets in terms of gene ontology (GO) enrichment analysis, and compared with an existing method called Clustering with Over-lapping Neighbourhood Expansion (ClusterONE). Gene functional modules identi ed by the network-based clustering method for 10 datasets returns target GO p-values as low as 10-24, whereas the original ClusterONE yields insigni cant results. In order to more speci cally cluster gene expression profi les, a second clustering method, namely the protein-protein interaction (PPI) integrated clustering method, is developed. It is designed for clustering short gene expression profi les by integrating gene expression profi le patterns and curated PPI data. The method consists of the three following steps: (1) generate a number of prede ned profi le patterns according to the number of data points in the profi les and assign each gene to the prede fined profi le to which its expression profi le is the most similar; (2) integrate curated PPI data to refi ne the initial clustering result from (1); (3) combine the similar clusters from (2) to gradually reduce cluster numbers by a hierarchical clustering method. The PPI-integrated clustering method is evaluated on 10 large scale A. thaliana datasets using GO enrichment analysis, and by comparison with an existing method called Short Time-series Expression Miner (STEM). Target gene functional clusters identi ed by the PPI-integrated clustering method for 10 datasets returns GO p-values as low as 10-62, whereas STEM returns GO p-values as low as 10-38. In addition to the method development, obtained clusters by two proposed methods are further analyzed to identify cross-talk genes under fi ve stress conditions in root and shoot tissues. A list of potential abiotic stress tolerant genes are found.

Decoding the genomes from organisms that encompass all taxonomies provides the foundation for extensive, large scale studies of biological molecules such as RNA, protein and carbohydrates. The high-throughput studies facilitated by the existence of these genome sequences necessitate the development of new analytic methods for the interpretation of large sets of results. The work herein focuses on the development of a novel clustering method for the analysis of protein array results and examines its utilization in the analysis of integrated interaction data sets. Sets of proteins that interact with a molecule of interest were clustered according to their functional similarity. The simUI distance metric in the statistical analysis package BioConductor was applied to measure the similarity of two proteins utilizing the assembly of their Gene Ontology annotation. Clusters were identified by partitioning around medoids and interpreted using the summary label provided by the Gene Ontology annotation of the medoid. The utility of the method was tested on two published yeast protein array data sets and shown to allow interpretation of the data to yield novel biological hypotheses. We performed a protein array screen using the E3 ubiquitin ligase and PDZ domain-containing protein LNX1. We combined these results with other published LNX1 interactors to produce a set of 220 proteins that was clustered according to Gene Ontology annotation. From the clustering results, 14 proteins were selected for subsequent examination by co-immunoprecipitation, of which 8 proteins were confirmed as LNX1 interactors. Recognition of 6 proteins by specific LNX1 PDZ domains was confirmed by fusion protein pull-downs. This work supports the role of LNX1 as a signalling scaffold. The interpretation of protein array results using our novel clustering method facilitated the identification of candidate molecules for subsequent experimental analysis. Thus our analytical method facilitates identification of biologically relevant molecules within a large data set, making this method an essential component of complex, high-throughput experimentation.

Signal transducer and activator of transcription 3 (STAT3) is a cancer-driving proto-oncoprotein that represents a novel target for the development of chemotherapeutics. In this study, the functional requirements to furnish a potent STAT3 inhibitor was investigated. First, a series of peptidomimetic inhibitors were rationally designed from lead parent peptides. Prepared peptidomimetics overcame the limitations normally associated with peptide agents and displayed improved activity in biophysical evaluations. Notably, lead peptidomimetic agents possessed micromolar cellular activity which was unobserved in both parent peptides. Peptidomimetic design relied on computational methods that were also employed in the design of purine based STAT3 inhibitory molecules. Docking studies with lead STAT3-SH2 domain inhibitory molecules identified key structural and chemical information required for the construction of a pharmacophore model. 2,6,9-heterotrisubstituted purines adequately fulfilled the pharmacophore model and a library of novel purine-based STAT3 inhibitory molecules was prepared utilizing Mitsunobu chemistry. Several agents from this new library displayed high affinity for the STAT3 protein and effectively disrupted the STAT3:STAT3-DNA complex. Furthermore, these agents displayed cancer-cell specific toxicity through a STAT3 dependant mechanism. While purine agents elicited cellular effects, the dose required for cellular efficacy was much higher than those observed for in vitro STAT3 dimer disruption. The diminished cellular activity could be attributed to the apparent poor cell permeability of the first generation purine library; thus, a second library of purine molecules was constructed to improve cell penetration. Unfortunately, iii 2nd generation purine inhibitors failed to disrupt phosphorylated STAT3 activity and suffered from poor cell permeability. However, a lead sulfamate agent was discovered that showed potent activity against multiple myeloma cancer cells. Investigations revealed potential kinase inhibitory activity as the source of the sulfamate purine’s biological effect. Explorations into the development of a potent STAT3 SH2 domain binder, including the creation of salicylic purine and constrained pyrimidine molecules, are ongoing. Finally, progress towards the creation of a macrocyclic purine combinatorial library has been pursued and is reported herein.

Decoding the genomes from organisms that encompass all taxonomies provides the foundation for extensive, large scale studies of biological molecules such as RNA, protein and carbohydrates. The high-throughput studies facilitated by the existence of these genome sequences necessitate the development of new analytic methods for the interpretation of large sets of results. The work herein focuses on the development of a novel clustering method for the analysis of protein array results and examines its utilization in the analysis of integrated interaction data sets. Sets of proteins that interact with a molecule of interest were clustered according to their functional similarity. The simUI distance metric in the statistical analysis package BioConductor was applied to measure the similarity of two proteins utilizing the assembly of their Gene Ontology annotation. Clusters were identified by partitioning around medoids and interpreted using the summary label provided by the Gene Ontology annotation of the medoid. The utility of the method was tested on two published yeast protein array data sets and shown to allow interpretation of the data to yield novel biological hypotheses. We performed a protein array screen using the E3 ubiquitin ligase and PDZ domain-containing protein LNX1. We combined these results with other published LNX1 interactors to produce a set of 220 proteins that was clustered according to Gene Ontology annotation. From the clustering results, 14 proteins were selected for subsequent examination by co-immunoprecipitation, of which 8 proteins were confirmed as LNX1 interactors. Recognition of 6 proteins by specific LNX1 PDZ domains was confirmed by fusion protein pull-downs. This work supports the role of LNX1 as a signalling scaffold. The interpretation of protein array results using our novel clustering method facilitated the identification of candidate molecules for subsequent experimental analysis. Thus our analytical method facilitates identification of biologically relevant molecules within a large data set, making this method an essential component of complex, high-throughput experimentation.

Signal transducer and activator of transcription 3 (STAT3) is a cancer-driving proto-oncoprotein that represents a novel target for the development of chemotherapeutics. In this study, the functional requirements to furnish a potent STAT3 inhibitor was investigated. First, a series of peptidomimetic inhibitors were rationally designed from lead parent peptides. Prepared peptidomimetics overcame the limitations normally associated with peptide agents and displayed improved activity in biophysical evaluations. Notably, lead peptidomimetic agents possessed micromolar cellular activity which was unobserved in both parent peptides. Peptidomimetic design relied on computational methods that were also employed in the design of purine based STAT3 inhibitory molecules. Docking studies with lead STAT3-SH2 domain inhibitory molecules identified key structural and chemical information required for the construction of a pharmacophore model. 2,6,9-heterotrisubstituted purines adequately fulfilled the pharmacophore model and a library of novel purine-based STAT3 inhibitory molecules was prepared utilizing Mitsunobu chemistry. Several agents from this new library displayed high affinity for the STAT3 protein and effectively disrupted the STAT3:STAT3-DNA complex. Furthermore, these agents displayed cancer-cell specific toxicity through a STAT3 dependant mechanism. While purine agents elicited cellular effects, the dose required for cellular efficacy was much higher than those observed for in vitro STAT3 dimer disruption. The diminished cellular activity could be attributed to the apparent poor cell permeability of the first generation purine library; thus, a second library of purine molecules was constructed to improve cell penetration. Unfortunately, iii 2nd generation purine inhibitors failed to disrupt phosphorylated STAT3 activity and suffered from poor cell permeability. However, a lead sulfamate agent was discovered that showed potent activity against multiple myeloma cancer cells. Investigations revealed potential kinase inhibitory activity as the source of the sulfamate purine’s biological effect. Explorations into the development of a potent STAT3 SH2 domain binder, including the creation of salicylic purine and constrained pyrimidine molecules, are ongoing. Finally, progress towards the creation of a macrocyclic purine combinatorial library has been pursued and is reported herein.

Amyotrophic lateral sclerosis (ALS) is a disease in which the motor neurons die in a progressive manner, leading to paralysis and muscle wasting. ALS is always fatal, usually through respiratory failure when the disease reaches muscles needed for breathing. Most cases are sporadic, but approximately 5–10% are familial. The first gene to be linked to familial ALS encodes the antioxidant enzyme superoxide dismutase-1 (SOD1). Today, more than 160 different mutations in SOD1 have been found in ALS patients.  The mutant SOD1 proteins cause ALS by gain of a toxic property that should be common to all. Aggregates of SOD1 in motor neurons are hallmarks of ALS patients and transgenic models carrying mutant SOD1s, suggesting that misfolding, oligomerization, and aggregation of the protein may be involved in the pathogenesis. SOD1 is normally a very stable enzyme, but the structure has several components that make SOD1 sensitive to misfolding. The aim of the work in this thesis was to study misfolded SOD1 in vivo. Small amounts of soluble misfolded SOD1 were identified as a common denominator in transgenic ALS models expressing widely different forms of mutant SOD1, as well as wild-type SOD1. The highest levels of misfolded SOD1 were found in the vulnerable spinal cord. The amounts of misfolded SOD1 were similar in all the different models and showed a broad correlation with the lifespan of the different mouse strains. The misfolded SOD1 lacked the C57-C146 intrasubunit disulfide bond and the stabilizing zinc and copper ions, and was prinsipally monomeric. Forms with higher apparent molecular weights were also found, some of which might be oligomers. Misfolding-prone monomeric SOD1 appeared to be the principal source of misfolded SOD1 in the CNS. Misfolded SOD1 in the spinal cord was found to interact mainly with chaperones, with Hsc70 being the most important. Only a minor proportion of the Hsc70 was sequestered by SOD1, however, suggesting that chaperone depletion is not involved in ALS.  SOD1 is normally found in the cytoplasm but can be secreted. Extracellular mutant SOD1 has been found to be toxic to motor neurons and glial cells. Misfolded SOD1 in the extracellular space could be involved in the spread of the disease between different areas of the CNS and activate glial cells known to be important in ALS. The best way to study the interstitium of the CNS is through the cerebrospinal fluid (CSF), 30% of which is derived from the interstitial fluid. Antibodies specific for misfolded SOD1 were used to probe CSF from ALS patients and controls for misfolded SOD1. We did find misfolded SOD1 in CSF, but at very low levels, and there was no difference between ALS patients and controls. This argues against there being a direct toxic effect of extracellular SOD1 in ALS pathogenesis. In conclusion, soluble misfolded SOD1 is a common denominator for transgenic ALS model mice expressing widely different mutant SOD1 proteins. The misfolded SOD1 is mainly monomeric, but also bound to chaperones, and possibly exists in oligomeric forms also. Misfolded SOD1 in the interstitium might promote spread of aggregation and activate glial cells, but it is too scarce to directly cause cytotoxicity.