The dysfunction of tau protein has been implicated in a number of neurodegenerative diseases, including Alzheimer’s disease (AD) and frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17). In these diseases, the tau protein is aberrantly hyperphosphorylated and aggregated to form neuropathological deposits in the cell body of neurons. Evidence from genetics studies has shown a linkage between tau mutations and autosomal dominantly inherited FTD. In Chapter one, our current understanding of the mechanisms of tauopathies is summarized. In addition, multiple animal models for mechanistic studies of tauopathies are reviewed. In this thesis, endogenous tau genes in zebrafish were identified and investigated in an attempt to establish zebrafish as an animal model for study of tauopathies. Paper 1 describes the identification of two genes, mapta and maptb in zebrafish that represent duplicates of an ancestral tau orthologue. It examines their complex alternative mRNA splicing patterns and their patterns of expression during embryogenesis. Paper 2 (thesis chapter in the form of a manuscript) describes how we might use zebrafish as an animal model to investigate tau function. Two antibodies that detect Mapta and Maptb specifically are described. In addition, we establish that inhibition of Maptb translation causes an impairment of axonogenesis during zebrafish embryogenesis. / Thesis (M.Sc.) -- University of Adelaide, School of Molecular and Biomedical Science, 2010

Recent advances in DNA sequencing technology have enabled entire genomes to be sequenced quickly and accurately, resulting in an exponential increase in the number of organisms whose genome sequences have been elucidated. While the genome sequence of a given organism represents an important starting point in understanding its physiology, the functions of the protein products of many genes are still unknown; as such, computational methods for studying protein function are becoming increasingly important. In addition, this wealth of genomic information has created an unprecedented opportunity to compare the protein content of different organisms; among other applications, this can enable us to improve taxonomic classifications, to develop more accurate diagnostic tests for identifying particular bacteria, and to better understand protein content relationships in both closely-related and distantly-related organisms.<p> This thesis describes the design, evaluation, and use of a program called Proteome Subtraction and Intersection (PSI) that uses an idea called genome subtraction for discovering protein-phenotype relationships and for characterizing differences in protein content in groups of organisms. PSI takes as input a set of proteomes, as well as a partitioning of that set into a subset of "included" proteomes and a subset of "excluded" proteomes. Using reciprocal BLAST hits, PSI finds orthologous relationships among all the proteins in the proteomes from the original set, and then finds groups of orthologous proteins containing at least one orthologue from each of the proteomes in the "included" subset, and none from any of the proteomes in the "excluded" subset.<p> PSI is first applied to finding protein-phenotype relationships. By identifying proteins that are present in all sequenced isolates of the genus <i>Lactobacillus</i>, but not in the related bacterium <i>Pediococcus pentosaceus</i>, proteins are discovered that are likely to be responsible for the difference in cell shape between the lactobacilli and <i>P. pentosaceus</i>. In addition, proteins are identified that may be responsible for resistance to the antibiotic gatifloxacin in some lactic acid bacteria.<p> This thesis also explores the use of PSI for comparing protein content in groups of organisms. Based on the idea of genome subtraction, a novel metric is proposed for comparing the difference in protein content between two organisms. This metric is then used to create a phylogenetic tree for a large set of bacteria, which to the author's knowledge represents the largest phylogenetic tree created to date using protein content. In addition, PSI is used to find the proteomic cohesiveness of isolates of several bacterial species in order to support or refute their current taxonomic classifications.<p> Overall, PSI is a versatile tool with many interesting applications, and should become more and more valuable as additional genomic information becomes available.

Recent advances in DNA sequencing technology have enabled entire genomes to be sequenced quickly and accurately, resulting in an exponential increase in the number of organisms whose genome sequences have been elucidated. While the genome sequence of a given organism represents an important starting point in understanding its physiology, the functions of the protein products of many genes are still unknown; as such, computational methods for studying protein function are becoming increasingly important. In addition, this wealth of genomic information has created an unprecedented opportunity to compare the protein content of different organisms; among other applications, this can enable us to improve taxonomic classifications, to develop more accurate diagnostic tests for identifying particular bacteria, and to better understand protein content relationships in both closely-related and distantly-related organisms.<p> This thesis describes the design, evaluation, and use of a program called Proteome Subtraction and Intersection (PSI) that uses an idea called genome subtraction for discovering protein-phenotype relationships and for characterizing differences in protein content in groups of organisms. PSI takes as input a set of proteomes, as well as a partitioning of that set into a subset of "included" proteomes and a subset of "excluded" proteomes. Using reciprocal BLAST hits, PSI finds orthologous relationships among all the proteins in the proteomes from the original set, and then finds groups of orthologous proteins containing at least one orthologue from each of the proteomes in the "included" subset, and none from any of the proteomes in the "excluded" subset.<p> PSI is first applied to finding protein-phenotype relationships. By identifying proteins that are present in all sequenced isolates of the genus <i>Lactobacillus</i>, but not in the related bacterium <i>Pediococcus pentosaceus</i>, proteins are discovered that are likely to be responsible for the difference in cell shape between the lactobacilli and <i>P. pentosaceus</i>. In addition, proteins are identified that may be responsible for resistance to the antibiotic gatifloxacin in some lactic acid bacteria.<p> This thesis also explores the use of PSI for comparing protein content in groups of organisms. Based on the idea of genome subtraction, a novel metric is proposed for comparing the difference in protein content between two organisms. This metric is then used to create a phylogenetic tree for a large set of bacteria, which to the author's knowledge represents the largest phylogenetic tree created to date using protein content. In addition, PSI is used to find the proteomic cohesiveness of isolates of several bacterial species in order to support or refute their current taxonomic classifications.<p> Overall, PSI is a versatile tool with many interesting applications, and should become more and more valuable as additional genomic information becomes available.

>Magister Scientiae - MSc / Human African trypanosomiasis is the world’s third most important parasitic disease affecting human health after malaria and schistosomiaisis. The world health organization estimates approximately 60 million people at risk in sub-Saharan Africa and up to 50,000 deaths per year caused by trypanosomiasis. Current management of human African trypanosomiasis relies on active surveillance and chemotherapy of infected patients. Efforts to develop a vaccine to immunize the human host have been hampered by antigenic variation of the parasites cell coat. The advent of the genome era has opened up opportunities for developing novel strategies for interrupting the transmission cycle of trypanosomes, specifically using any of the three players,the human host, the tsetse fly vector and/or the parasite. The human genome has been deciphered and the genomes of several trypanosome species have been sequenced. Sequencing of additional neglected trypanosome species is in progress. The tsetse fly genome is currently being sequenced as part of the genomic activities of the International Glossina genome initiative (IGGI). In an attempt to support the tsetse fly sequencing effort, expressed sequence tags (ESTs) from various tissues and developmental stages of Glossina morsitans have been generated.In this study, tsetse fly EST data was analyzed using bioinformatics approaches, focusing on transcripts encoding serpin genes implicated in the immune defenses of tsetse flies. Glossina morsitans homologues to Drosophila melanogaster serpin4, serpin5, and serpin27A and Anopheles gambiae serpin10 were identified in the tsetse fly EST contigs. Comparison of the reactive center loop of tsetse fly serpins with human α-1-antitrypsin suggests that these tsetse serpins are inhibitory. Preliminary EST clustering did not succeed in assembling 3564 Tsal encoded ESTs into one contig. In this study, these ESTs were assembled together with three published Tsal cDNAs. A total of 29 Tsal-encoded contigs were generated. An analysis of the sequence variation within the Tsal EST assembled contigs identified five single base mismatches namely A-T, T-A, G-T and T-G.Results from this study form a basis onto which genetic and biochemical experimental studies can be designed, a process that will be successfully carried out once we have a reference genome. Specifically, studies aimed at genetic modification of tsetse flies towards populations that are inhabitable to trypanosomes. Ultimately, this will supplement current vector control strategies towards elimination of human African trypanosomiasis.

Les gènes codant pour des protéines peuvent souvent être regroupés et intégrés en modules fonctionnels par rapport à un organelle. Ces modules peuvent avoir des composantes qui suivent une évolution corrélée pouvant être conditionnelle à un phénotype donné. Les gènes liés à la motilité possèdent cette caractéristique, car ils se suivent en cascade en réponse à des stimuli extérieurs. L’hyperthermophilie, d’autre part, est interreliée à la reverse gyrase, cependant aucun autre élément qui pourrait y être associé avec certitude n’est connu. Ceci peut être dû à un déplacement de gènes non orthologues encore non résolu. En utilisant une approche bio-informatique, une modélisation mathématique d’évolution conditionnelle corrélée pour trois gènes a été développée et appliquée sur des profils phylétiques d’archaea. Ceci a permis d’établir des théories quant à la fonction potentielle du gène du flagelle FlaD/E ainsi que l’histoire évolutive des gènes lui étant liés et ayant contribué à sa formation. De plus, une histoire évolutive théorique a été établie pour une ligase liée à l’hyperthermophilie. / Protein coding gene may often be grouped and integrated in functional modules with respect to an organelle. These modules may have constituents that follow a conditional correlated evolution to a given phenotype. Genes linked to motility posses this characteristic as they follow a cascade in response to external stimuli. Similarly, hyperthermophily is related to reverse gyrase, however no other element that could be associated with certainty is known. This may be caused by an unresolved case of non-orthologous gene displacement. Using a bioinformatic approach, a mathematical model for conditional correlated evolution for three genes has been developed and applied to the phyletic profiles of archaea. This has helped to develop theories about the potential functions of the flagellar gene FlaD/E and the evolutionary history of the genes that are linked to it and that may have contributed to its formation. In addition, a theoretical evolutionary history has been established for a ligase associated with hyperthermophily.

Les gènes codant pour des protéines peuvent souvent être regroupés et intégrés en modules fonctionnels par rapport à un organelle. Ces modules peuvent avoir des composantes qui suivent une évolution corrélée pouvant être conditionnelle à un phénotype donné. Les gènes liés à la motilité possèdent cette caractéristique, car ils se suivent en cascade en réponse à des stimuli extérieurs. L’hyperthermophilie, d’autre part, est interreliée à la reverse gyrase, cependant aucun autre élément qui pourrait y être associé avec certitude n’est connu. Ceci peut être dû à un déplacement de gènes non orthologues encore non résolu. En utilisant une approche bio-informatique, une modélisation mathématique d’évolution conditionnelle corrélée pour trois gènes a été développée et appliquée sur des profils phylétiques d’archaea. Ceci a permis d’établir des théories quant à la fonction potentielle du gène du flagelle FlaD/E ainsi que l’histoire évolutive des gènes lui étant liés et ayant contribué à sa formation. De plus, une histoire évolutive théorique a été établie pour une ligase liée à l’hyperthermophilie. / Protein coding gene may often be grouped and integrated in functional modules with respect to an organelle. These modules may have constituents that follow a conditional correlated evolution to a given phenotype. Genes linked to motility posses this characteristic as they follow a cascade in response to external stimuli. Similarly, hyperthermophily is related to reverse gyrase, however no other element that could be associated with certainty is known. This may be caused by an unresolved case of non-orthologous gene displacement. Using a bioinformatic approach, a mathematical model for conditional correlated evolution for three genes has been developed and applied to the phyletic profiles of archaea. This has helped to develop theories about the potential functions of the flagellar gene FlaD/E and the evolutionary history of the genes that are linked to it and that may have contributed to its formation. In addition, a theoretical evolutionary history has been established for a ligase associated with hyperthermophily.

The last few years have seen dramatic advances in genomics, including the discovery of a large number of non-coding and antisense transcripts. This has revolutionised our understanding of multifaceted transcript structures found within gene loci and their roles in the regulation of development, neurogenesis and other complex processes. The recent and continuing surge of knowledge has prompted researchers to reassess and further dissect gene loci. The ghrelin gene (GHRL) gives rise to preproghrelin, which in turn produces ghrelin, a 28 amino acid peptide hormone that acts via the ghrelin receptor (growth hormone secretagogue receptor/GHSR 1a). Ghrelin has many important physiological and pathophysiological roles, including the stimulation of growth hormone (GH) release, appetite regulation, and cancer development. A truncated receptor splice variant, GHSR 1b, does not bind ghrelin, but dimerises with GHSR 1a, and may act as a dominant negative receptor. The gene products of ghrelin and its receptor are frequently overexpressed in human cancer While it is well known that the ghrelin axis (ghrelin and its receptor) plays a range of important functional roles, little is known about the molecular structure and regulation of the ghrelin gene (GHRL) and ghrelin receptor gene (GHSR). This thesis reports the re-annotation of the ghrelin gene, discovery of alternative 5’ exons and transcription start sites, as well as the description of a number of novel splice variants, including isoforms with a putative signal peptide. We also describe the discovery and characterisation of a ghrelin antisense gene (GHRLOS), and the discovery and expression of a ghrelin receptor (growth hormone secretagogue receptor/GHSR) antisense gene (GHSR-OS). We have identified numerous ghrelin-derived transcripts, including variants with extended 5' untranslated regions and putative secreted obestatin and C-ghrelin transcripts. These transcripts initiate from novel first exons, exon -1, exon 0 and a 5' extended 1, with multiple transcription start sites. We used comparative genomics to identify, and RT-PCR to experimentally verify, that the proximal exon 0 and 5' extended exon 1 are transcribed in the mouse ghrelin gene, which suggests the mouse and human proximal first exon architecture is conserved. We have identified numerous novel antisense transcripts in the ghrelin locus. A candidate non-coding endogenous natural antisense gene (GHRLOS) was cloned and demonstrates very low expression levels in the stomach and high levels in the thymus, testis and brain - all major tissues of non-coding RNA expression. Next, we examined if transcription occurs in the antisense orientation to the ghrelin receptor gene, GHSR. A novel gene (GHSR-OS) on the opposite strand of intron 1 of the GHSR gene was identified and characterised using strand-specific RT-PCR and rapid amplification of cDNA ends (RACE). GHSR-OS is differentially expressed and a candidate non-coding RNA gene. In summary, this study has characterised the ghrelin and ghrelin receptor loci and demonstrated natural antisense transcripts to ghrelin and its receptor. Our preliminary work shows that the ghrelin axis generates a broad and complex transcriptional repertoire. This study provides the basis for detailed functional studies of the the ghrelin and GHSR loci and future studies will be needed to further unravel the function, diagnostic and therapeutic potential of the ghrelin axis.

No description available.

Target-based screening projects for bioactive (orphan) compounds have been shown in many cases to be insufficiently predictive for in vivo efficacy, leading to attrition in clinical trials. Phenotypic screening has hence undergone a renaissance in both academia and in the pharmaceutical industry, partly due to this reason. One key shortcoming of this paradigm shift is that the protein targets modulated need to be elucidated subsequently, which is often a costly and time-consuming procedure. In this work, we have explored both improved methods and real-world case studies of how computational methods can help in target elucidation of phenotypic screens. One limitation of previous methods has been the ability to assess the applicability domain of the models, that is, when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models. Hence, a major focus of this work was to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. Additionally, many current protocols only offer probabilities for affinity, thus another key area for development was to expand the target prediction models with functional prediction (activation or inhibition). This extra level of annotation is important since the activation or inhibition of a target may positively or negatively impact the phenotypic response in a biological system. Furthermore, many existing methods do not utilize the wealth of bioactivity information held for orthologue species. We therefore also focused on an in-depth analysis of orthologue bioactivity data and its relevance and applicability towards expanding compound and target bioactivity space for predictive studies. The realized protocol was trained with 13,918,879 compound-target pairs and comprises 1,651 targets, which has been made available for public use at GitHub. Consequently, the methodology was applied to aid with the target deconvolution of AstraZeneca phenotypic readouts, in particular for the rationalization of cytotoxicity and cytostaticity in the High-Throughput Screening (HTS) collection. Results from this work highlighted which targets are frequently linked to the cytotoxicity and cytostaticity of chemical structures, and provided insight into which compounds to select or remove from the collection for future screening projects. Overall, this project has furthered the field of in silico target deconvolution, by improving the performance and applicability of current protocols and by rationalizing cytotoxicity, which has been shown to influence attrition in clinical trials.