This thesis pioneers diatom molecular identification and quantification through genome-scale methods, with four key aims: (i) reviewing DNA/RNA sequencing methods in aquatic biomonitoring to... Show moreThis thesis pioneers diatom molecular identification and quantification through genome-scale methods, with four key aims: (i) reviewing DNA/RNA sequencing methods in aquatic biomonitoring to highlight their strengths and limitations; (ii) unraveling the evolutionary history of Nitzschia palea and investigating species delimitation within the species complex; (iii) identifying silica genes in N. palea for insights into ecology and evolution; and (iv) assessing a genome-scale quantification method for diatom biomonitoring to improve accuracy and scalability in estimating abundances. The review (Chapter 2) emphasizes disparities between molecular and morphology-based approaches and introduces the challenges in accurately estimating species abundances. Chapter 3 explores N. palea's evolutionary history using transcriptome data and reveals reticulate evolutionary patterns resulting in a putative hybrid between populations with different morphological characteristics. Chapter 4 pinpoints silica genes in N. palea and reveals variations among different populations that may lead to differences in silica metabolism. Chapter 5 introduces a genome-scale quantification approach that provides a promising alternative for molecular diatom biomonitoring due to its improved taxonomic resolution and quantification accuracy. In summary, this thesis underscores that genome-scale methods' have a critical role in diatom identification and quantification, and in advancing our understanding of microalgal taxonomy, ecology, and evolution. Show less
The study of orchid flowers, fruits, and inflorescences is crucial due to the remarkable diversity of orchid species and their unique adaptations to pollinators and seed dispersers. However, our... Show moreThe study of orchid flowers, fruits, and inflorescences is crucial due to the remarkable diversity of orchid species and their unique adaptations to pollinators and seed dispersers. However, our understanding of the evolution and development of these organs within the orchid family remains limited. This research aims to fill this knowledge gap by investigating the genetic mechanisms underlying the evolution and development of floral structures, fruits and resupination in orchids, and the relationship between inflorescence stalk lignification and orientation. The research also includes a methodological chapter on the application of transcriptomics for plant species identification. Using advanced techniques such as microscopy imaging, 3D CT scanning, and anatomical analysis, the study provides detailed insights into the processes of root and fruit resupination and shows that inflorescence lignification is a heritable trait, with closely related orchid species displaying similar levels of lignification compared to distantly related species. The findings significantly advance our understanding of orchid biology by filling gaps in our knowledge of the evolutionary and developmental processes involved in flower and fruit development, resupination, and inflorescence lignification. By identifying specific genes and pathways associated with these traits, the study offers valuable insights into the genetic mechanisms that drive orchid diversity and adaptation. From a practical perspective, these findings hold great promise for the development of new orchid varieties with more robust and visually appealing varieties. The research also highlights the importance of conservation efforts to protect orchid diversity and their ecological relationships with pollinators and seed dispersal vectors. Show less
The aim of the research described in this thesis entitled ‘The use of transcriptomics data in detecting non-genotoxic carcinogens’ was to develop in vitro tests to improve testing strategies for... Show moreThe aim of the research described in this thesis entitled ‘The use of transcriptomics data in detecting non-genotoxic carcinogens’ was to develop in vitro tests to improve testing strategies for cancer hazard assessment of chemicals, to reduce the use of in vivo experiments. The scope of this thesis was twofold. First, an improved in vitro approach to assess genotoxicity was developed, with the intention to reduce the number of misleading positive test results. The emphasis was on characterization of the cell system, primary hepatocytes derived from transgenic mice. Results showed that this cell system will be of added value in genotoxicity testing. In the second part of this thesis, the focus was on the development of a ‘trancriptomics’-based approach to detect modes of action of non-genotoxic carcinogens. It has been demonstrated that the described comparison approach is promising in recognizing gene expression patterns, which can be related to modes of action. In addition, the approach is also suitable to detect toxicity of chemicals in general. In conclusion, through the development of in vitro approaches, as described within this thesis, an important contribution in the improvement of testing strategies for cancer hazard assessment of chemicals has been delivered. Show less
In this thesis novel statistical methods that help scientists extract maximal information from high-dimensional data, in particular those derived by transcriptomics, are presented.
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
In the studies comprising this thesis we evaluated the potential usefulness of cDNA microarray based gene expression profiling and 1H-NMR based metabolomics platforms as tools for the evaluation of... Show moreIn the studies comprising this thesis we evaluated the potential usefulness of cDNA microarray based gene expression profiling and 1H-NMR based metabolomics platforms as tools for the evaluation of novel PPAR_ and -_ agonists in future clinical __proof of concept studies__. We investigated the effects of rosiglitazone, (prototype PPAR_ agonist ) and ciprofibrate (prototype PPAR_ agonist) on global (target) tissue gene expression profiles and endogenous urinary and plasma metabolites of type 2 Diabetes Mellitus (T2DM) patients and healthy volunteers (HVs).The results from the transcriptomic analyses indicated that none of the genes in any of the tissues in either study group displayed a significant treatment response with either rosiglitazone of ciprofibrate vs. placebo at Bonferroni adjusted values and _=0.05. The results of the metabolomic analyses revealed significant rosiglitazone and ciprofibrate induced changes in endogenous urinary and plasma metabolite profiles of T2DM patients but not in HVs. We conclude that from the two molecular profiling platforms evaluated in this thesis, metabolomics currently appears to be the most promising platform for future application in clinical __proof of concept__ studies with novel PPAR agonist compounds in T2DM patients.In the studies comprising this thesis we evaluated the potential usefulness of cDNA microarray based gene expression profiling and 1H-NMR based metabolomics platforms as tools for the evaluation of novel PPAR_ and -_ agonists in future clinical __proof of concept studies__. We investigated the effects of rosiglitazone, (prototype PPAR_ agonist ) and ciprofibrate (prototype PPAR_ agonist) on global (target) tissue gene expression profiles and endogenous urinary and plasma metabolites of type 2 Diabetes Mellitus (T2DM) patients and healthy volunteers (HVs).The results from the transcriptomic analyses indicated that none of the genes in any of the tissues in either study group displayed a significant treatment response with either rosiglitazone of ciprofibrate vs. placebo at Bonferroni adjusted values and _=0.05. The results of the metabolomic analyses revealed significant rosiglitazone and ciprofibrate induced changes in endogenous urinary and plasma metabolite profiles of T2DM patients but not in HVs. We conclude that from the two molecular profiling platforms evaluated in this thesis, metabolomics currently appears to be the most promising platform for future application in clinical __proof of concept__ studies with novel PPAR agonist compounds in T2DM patients. Show less
