DNA damage, mutations and genomic instability are established driving forces of cancer and other age-related diseases. Mutations in tumor suppressor genes and oncogenes are very frequently found in... Show moreDNA damage, mutations and genomic instability are established driving forces of cancer and other age-related diseases. Mutations in tumor suppressor genes and oncogenes are very frequently found in tumors and genomic instability is the most common enabling characteristic of cancer. Aging is also believed to be enabled, amongst others, by genomic instability. DNA repair pathways, like the nucleotide excision repair (NER) pathway and cell cycle control (e.g. p53-dependent) processes are therefore vital to organisms, since these processes counteract or prevent genomic instability, and are thought to underlie, when affected, aging and age-related diseases like cancer. To unravel the functions, mechanisms and pathways involved in the onset of aging and age-related diseases we have investigated several mouse models deficient in either DNA repair (NER) capacity (Chapter 3, 4), cell cycle control (p53) (Chapter 6) or both (Chapter 5), and compared this to a wild type situation (Chapter 2). The use of mouse models enabled us to investigate cancer and aging in a controlled environment, minimizing possible confounding factors. Additionally, the mouse models can be useful as an alternative tool to identify genotoxic and non-genotoxic carcinogens that can be harmful to the society and the environment (Chapter 5). Show less
This dissertation mainly focuses on interdisciplinary approaches for biomedical knowledge discovery. This required special efforts in developing systematic strategies to integrate various data... Show moreThis dissertation mainly focuses on interdisciplinary approaches for biomedical knowledge discovery. This required special efforts in developing systematic strategies to integrate various data sources and techniques, leading to improved discovery of mechanistic insights on human diseases. Chapter one looks at the possibility in which combining various bioinformatics-based strategies can significantly improve the characterization of the OPMD mouse model. We discuss that this approach in knowledge discovery, on the basis of our extensive analysis, helped us to shed some light on how this model system relates to OPMD pathophysiology in human. In Chapter two, we expand on this combinatory approach by conducting a cross-species data analysis. In this study, we have looked for common patterns that emerge by assessing the transcriptome data from three OPMD model systems and patients. This strategy led to unravelling the most prominent molecular pathway involved in OPMD pathology. The third chapter achieves a similar goal to identify similar molecular and pathophysiological features between OPMD and the common process of skeletal muscle ageing. Engaging in a study in which the focus was made on the universality of biological processes, in the light of evolutionary mechanisms and common functional features, led to novel discoveries. This work helped us uncover remarkable insights on molecular mechanisms of ageing muscles and protein aggregation. Chapters four and five take a different route by tackling the field of computational biology. These chapters aim to extend network inference by providing novel strategies for the exploitation and integration of multiple data sources. We show that these developments allow us to infer more robust regulatory mechanisms to be identified while translations and predictions are made across very different datasets, platforms, and organisms. Finally, the dissertation is concluded by providing an outlook on ways the field of systems biology can evolve in order to offer enhanced, diversified and robust strategies for knowledge discovery. Show less
