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Proefschrift Universiteit van Amsterdam. / Auteursnaam op omslag: Amâde M'charek. Lit. opg.: p. 205-219. - Met een samenvatting in het Nederlands.

Thesis (M.A.). / Written for the Dept. of Philosophy. Title from title page of PDF (viewed 2008/07/28). Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 150-166). Also available online.

Masters of Science / The viral Integrase (IN) protein is an essential enzyme of all known retroviruses, including HIV-1. It is responsible for the insertion of viral DNA into the human genome. It is known that HIV-1 is highly diverse with a high mutation rate as evidenced by the presence of a large number of subtypes and even strains that have become resistant to antiretroviral drugs. It remains inconclusive what effect this diversity in the form of naturally occurring polymorphisms/variants exert on IN in terms of its function, structure and susceptibility to IN inhibitory antiretroviral drugs. South Africa is home to the largest HIV-1 infected population, with (group M) subtype C being the most prevalent subtype. An investigation into IN is therefore pertinent, even more so with the introduction of the IN strand-transfer inhibitor (INSTI) Dolutegravir (DTG). This study makes use of computational methods to determine any structural and DTG drug binding differences between the South African subtype C IN protein and the subtype B IN protein. The methods employed included homology modelling to predict a three-dimensional model for HIV-1C IN, calculating the change in protein stability after variant introduction and molecular dynamics simulation analysis to understand protein dynamics. Here we compared subtype C and B IN complexes without DTG and with DTG.

Genome-wide neutral diversity levels are shaped by both positive and purifying selection on linked sites. In humans like most species, the relative importance of these types of selection in shaping patterns of neutral diversity remains an open question. We can infer their relative contribution from observed patterns of neutral diversity by using information about recombination rates and targets of natural selection. To this end, I fit a joint model of the effects of positive selection (selective sweeps) and purifying selection (background selection) to genetic polymorphism data from the 1000 Genomes Project. I show that a model of the effects of background selection provides a good fit to patterns in diversity data and that incorporating the effects of selective sweeps does not improve the fit. Using my approach, the effects of background selection explain up to 60% of the variation in neutral diversity levels on the 1Mb scale and account for patterns in the data for which positive selection via selective sweeps had been invoked as explanations. I find that over 80% of the selected regions affecting neutral diversity levels are located outside of exons and that phylogenetic conservation is the best predictor of the source of selection in these regions. My results show that the genome-wide effects of background selection are pervasive, with measurable reductions in neutral diversity throughout almost the entirety of the autosomes. I provide maps of the effects of background selection and software for making similar inferences, which should provide important tools for future research that relies on interpreting patterns in neutral diversity levels.

Recent research in human genetics has sparked popular interest in genetic explanations for all human phenomena. In turn, bioethicists have been busy responding to their own call for stringent guidelines for the use of genetic information. But bioethicists in general fail to attend to deeper considerations of the nature of scientific knowledge and its role in the transformation of human subjectivity. For this reason, bioethicists are accessories after the fact to that transformation, and hence in order to study that change we must displace bioethical analyses of the Human Genome Project --that is, displace virtually all of the literature on the HGP. In this thesis, I offer a different and more radical interpretation of the role of scientific knowledge in altering our conception of what it is to be a human being. Physicians, genetic counsellors, and other experts in our gene culture offer fundamentally questionable and yet practically unquestioned genetic explanations of who and what we really are. These genetic experts, by virtue of their prestigious position in our economy of knowledge, impute needs only they can satisfy, impart a vocabulary only they are invited (and certified) to understand, and draw us into new networks of administration and control at the subcellular level. Drawing on the work of Duden, Foucault, Illich, and Poerksen, I argue that our attraction to technoscientific understandings of our "essence" is dangerous and disabling, and I sketch a strategy of resistance. / Thesis / Master of Arts (MA)

The Epidermal Differentiation Complex (EDC) is a co-ordinately regulated locus that is evolutionarily conserved within mammals. It consists of a large number of genes, organised into clusters of gene families, which mainly encode structural constituents of the cornified envelope which replaces the plasma membrane of fully differentiated keratinocytes. It is thought that the developmental program of gene expression at the locus is regulated by specific changes in chromatin structure (Williams et al., 2002). To investigate this, I have characterised the chromatin structure of the EDC in cultured cell lines. These include a keratinocyte cell line, HaCaT cells, in which the locus is active and control cell lines where the locus is inactive. Chromatin is structured on a number of different levels, by the covalent modification of nucleosomes, the arrangement of nucleosomes into chromatin fibres and the arrangement of chromatin fibres into higher order structures within the interphase nucleus. I have assayed chromatin structure on all these levels using Chromatin Immunoprecipitation and Sucrose Gradient Sedimentation Analysis of Chromatin Fibre structure, partnered with oligonucleotide microarrays and Fluorescent In-Situ Hybridisation. By doing so I have examined the role each level of chromatin structure plays in regulating the human EDC and, characterised the relationships between the different levels across a large co-ordinately regulated locus in the human genome.

Advances in microarray and sequencing technology enable the era of personalized medicine. With increasing availability of genomic assays, clinicians have started to utilize genetics and gene expression of patients to guide clinical care. Signatures of gene expression and genetic variation in genes have been associated with disease risks and response to clinical treatment. It is therefore not difficult to envision a future where each patient will have clinical care that is optimized based on his or her genetic background and genomic profiles. However, many challenges exist towards the full realization of the potential personalized medicine. The human genome is complex and we have yet to gain a better understanding of how to associate genomic data with phenotype. First, the human genome is very complex: more than 50 million sequence variants and more than 20,000 genes have been reported. Many efforts have been devoted to genome-wide association studies (GWAS) in the last decade, associating common genetic variants with common complex traits and diseases. While many associations have been identified by genome-wide association studies, most of our phenotypic variation remains unexplained, both at the level of the variants involved and the underlying mechanism. Finally, interaction between genetics and environment presents additional layer of complexity governing phenotypic variation. Currently, there is much research developing computational methods to help associate genomic features with phenotypic variation. Modeling techniques such as machine learning have been very useful in uncovering the intricate relationships between genomics and phenotype. Despite some early successes, the performance of most models is disappointing. Many models lack robustness and predictions do not replicate. In addition, many successful models work as a black box, giving good predictions of phenotypic variation but unable to reveal the underlying mechanism. In this thesis I propose two methods addressing this challenge. First, I describe an algorithm that focuses on identifying causal genomic features of phenotype. My approach assumes genomic features predictive of phenotype are more likely to be causal. The algorithm builds models that not only accurately predict the traits, but also uncover molecular mechanisms that are responsible for these traits. . The algorithm gains its power by combining regularized linear regression, causality testing and Bayesian statistics. I demonstrate the application of the algorithm on a yeast dataset, where genotype and gene expression are used to predict drug sensitivity and elucidate the underlying mechanisms. The accuracy and robustness of the algorithm are both evaluated statistically and experimentally validated. The second part of the thesis takes on a much more complicated system: cancer. The availability of genomic and drug sensitivity data of cancer cell lines has recently been made available. The challenge here is not only the increasing complexity of the system (e.g. size of genome), but also the fundamental differences between cancers and tissues. Different cancers or tissues provide different contexts influencing regulatory networks and signaling pathways. In order to account for this, I propose a method to associate contextual genomic features with drug sensitivity. The algorithm is based on information theory, Bayesian statistics, and transfer learning. The algorithm demonstrates the importance of context specificity in predictive modeling of cancer pharmacogenomics. The two complementary algorithms highlight the challenges faced in personalized medicine and the potential solutions. This thesis detailed the results and analysis that demonstrate the importance of causality and context specificity in predictive modeling of drug response, which will be crucial for us towards bringing personalized medicine in practice.

MicroRNAs (miRNAs) are studied as key genetic elements that regulate the gene expression involved in different human diseases. Clinical sequence based variations like copy number variations (CNVs) affect miRNA biogenesis, dosage and target recognition that may represent potentially functional variants and relevant target bindings. To systematically analyze miRNA-related CNVs and their effects on related genes, a user-friendly free online database was developed to provide further analysis of co-localization of miRNA loci with human genome CNV regions. Further analysis pipelines such as miRNA-target to estimate the levels or locations of variations for genetic duplications, insertions or deletions were also offered. Such information could support the simulation of miRNA-target interactions.