Thesis (M. Med. Sc.)--University of Hong Kong, 2006.

abstract: Telomerase is a unique reverse transcriptase that has evolved specifically to extend the single stranded DNA at the 3' ends of chromosomes. To achieve this, telomerase uses a small section of its integral RNA subunit (TR) to reiteratively copy a short, canonically 6-nt, sequence repeatedly in a processive manner using a complex and currently poorly understood mechanism of template translocation to stop nucleotide addition, regenerate its template, and then synthesize a new repeat. In this study, several novel interactions between the telomerase protein and RNA components along with the DNA substrate are identified and characterized which come together to allow active telomerase repeat addition. First, this study shows that the sequence of the RNA/DNA duplex holds a unique, single nucleotide signal which pauses DNA synthesis at the end of the canonical template sequence. Further characterization of this sequence dependent pause signal reveals that the template sequence alone can produce telomerase products with the characteristic 6-nt pattern, but also works cooperatively with another RNA structural element for proper template boundary definition. Finally, mutational analysis is used on several regions of the protein and RNA components of telomerase to identify crucial determinates of telomerase assembly and processive repeat synthesis. Together, these results shed new light on how telomerase coordinates its complex catalytic cycle. / Dissertation/Thesis / Ph.D. Chemistry 2014

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Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 5 uppsatser.

Thesis (Ph. D.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains ix, 172 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references.

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published_or_final_version / abstract / Anatomy / Master / Master of Philosophy

<p>Nevirapine (NVP) is a cheap anti-retroviral drug used in poor countries worldwide, administered to pregnant women at the onset of labour to inhibit HIV enzyme reverse transcriptase. Viruses which may get transmitted to newborns are deficient in this enzyme, and HIV-1 infection cannot be established, thereby preventing mother to child transmission (MTCT). In some cases, babies get infected and positive selection for viruses resistant to nevirapine may be inferred. Positive selection can be inferred from sequence data, when the rate of nonsynonymous substitutions is significantly greater than the rate of synonymous substitutions.</p> <p>Unfortunately, it is found that available positive selection methods should not be used to analyse before- and after- NVP treatment sequence pairs associated with MTCT. Methods which use phylogenetic trees to infer positive selection trace synonymous and nonsynonymous substitutions further back in time than the short time duration during which selection for NVP occurred. The other group of methods for inferring positive selection, the pairwise methods, do not have appreciable power, because they average susbtituion rates over all codons in a sequence pair and not just at single codons. We introduce a simple counting method which we call the Pairwise Homologous Codons (PHoCs) method with which we have inferred positive selection resulting from NVP treatment in longitudinal HIV-1 reverse transcriptase sequences. The PHoCs method estimates rates of substitutions between before- and after- NVP treatment codons, using a simple pairwise method.</p>

Um grande número de medicamentos utilizados no tratamento contra o HIV agem procurando inibir a ação das proteínas transcriptase reversa e protease. Mutações existentes nas sequências dessas proteínas podem estar relacionadas à resistência aos medicamentos e podem prejudicar o desempenho de um tratamento. O estudo do genótipo dos vírus pode ajudar na tomada de escolhas específicas em tratamentos para cada indivíduo, tornando maiores a chance de sucesso. Com a maior acessibilidade a exames de genotipagem, uma grande quantidade de sequências do vírus está disponível, contendo um grande volume de informação. Padrões de ocorrência de mutações são exemplos de informações contidas nessas sequências e são importantes por estarem relacionados à resistência aos medicamentos. Um dos caminhos que pode ser capaz de nos levar ao entendimento desses padrões de mutações é a aplicação de técnicas de agrupamento e biclustering. Essas técnicas visam a geração de grupos ou biclusters que possuam dados com propriedades em comum. São empregadas em casos em que não há grande quantidade de informação prévia e existem poucas hipóteses sobre os dados. Assim, pode-se encontrar os padrões de mutações que ocorrem nessas sequências e tentar relacioná-los com a resistência aos medicamentos, utilizando métodos de agrupamento e bicluster em sequências de protease e transcriptase reversa. Existem alguns sistemas que tentam predizer a resistência ou susceptibilidade das sequências, porém, devido à grande complexidade dessa relação, ainda é necessário esclarecer o vínculo entre combinações de mutações e níveis de resistência fenotípica. Desta forma, a principal contribuição deste trabalho é o desenvolvimento de um framework baseado na aplicação dos algoritmos KMédias e Bimax às sequências de transcriptase reversa e protease de pacientes infectados com HIV, em uma codificação binária. O presente trabalho também introduz uma representação visual dos grupos e biclusters baseada em dados de microarranjos para casos em que se tem grandes volumes de dados, de forma a facilitar a visualização da informação extraída e a caracterização dos grupos e biclusters no domínio da doença. / Drugs used in HIV treatment intend to inhibit protease and reverse transcriptase. Mutations in the sequences of these proteins can be related to drug resistance and can reduce treatment efficacy. Studying virus genotype may help choosing specific treatments for each patient, increasing success probability. As genotyping tests become available, a great amount of virus sequences, which comprehend lots of information, are more accessible. Patterns of mutation are examples of information comprised in the sequences and are important since are related to drug resistance. One way that can lead to the understanding of these mutation patterns is the use of clustering and biclustering techniques. These techniques search for clusters or biclusters comprising data with similar attributes. They are used when there is not a lot of previous information and there are few hypothesis about the data. Therefore, it may be possible to find patterns of mutations in the sequences and to relate them to drug resistance using clustering and biclustering techniques with protease and reverse transcriptase sequences. There are a few systems that predict drug resistance according to the sequence of the virus, however, due to the complexity of the relationship, it is still necessary to elucidate the connection between mutation combinations and the level of phenotypic resistance. Accordingly, this work main contribution is the development of a framework based on Kmeans and Bimax algorithms with protease and reverse transcriptase sequences from HIV patients in a binary form. This work also presents a visual representation of the clusters and biclusters based on microarray data suitable for large data volumes, helping the visualization of information extracted from data and cluster and bicluster characterization in the disease domain.