Human reproductive success relies on the intricate interplay between the developing embryo and the maternal endometrium. These highly-coordinated interactions facilitate implantation, setting in... Show moreHuman reproductive success relies on the intricate interplay between the developing embryo and the maternal endometrium. These highly-coordinated interactions facilitate implantation, setting in motion a series of developmental programs to establish a sustained fetal-maternal interface. Understanding endometrial function and the early human embryo-maternal dialogue is thus an important prerequisite for refining clinical approaches to alleviate implantation failure, early pregnancy loss and other obstetric complications. Yet, many mediators of implantation remain elusive. Driven by endocrine factors, interactions at the embryo-maternal interface are tightly regulated and highly complex. Coupled to the inaccessibility of the in vivo environment and scarcity of research material, studying human implantation remains exceptionally challenging. Nevertheless, the field continues to gain momentum. Cutting-edge omics technologies and high-resolution imaging have revealed important structural and functional insights into endometrial biology, while emerging bioengineering tools are enhancing our ability to model the synergies and individual features of the embryo-maternal environment. Novel in vitro platforms using human cells and embryos are considerably more accessible and easier to manipulate compared to in vivo approaches, enhancing our ability to capture specific stages of implantation. This review aims to showcase current and emerging technologies used to study human endometrial biology and the early embryo-maternal interface, including single cell omics approaches, bioengineered endometrial models and embryo-endometrium co-culture platforms. We highlight the value of these approaches and provide our perspective on the current challenges faced by the field. Recognizing the physiological scope of these emerging technologies will be key for utilizing their full potential and driving future innovation. Show less
In this thesis, I study 1) metabolic alterations in tuberculosis related to wasting syndrome in human patients as well as in rodent and fish animal models. 2) effects of the mutation of the leptin... Show moreIn this thesis, I study 1) metabolic alterations in tuberculosis related to wasting syndrome in human patients as well as in rodent and fish animal models. 2) effects of the mutation of the leptin gene on cachexia and diabetes in rodent and zebrafish animal models. 3) how tuberculosis infection and resulting metabolic reprogramming are dependent on leptin signaling in mice and zebrafish larvae. Show less
Throughout this thesis, human aging and its relation to health are studied in the context of two parallel though complementary lines of research: biomarkers and genetics. The search for informative... Show moreThroughout this thesis, human aging and its relation to health are studied in the context of two parallel though complementary lines of research: biomarkers and genetics. The search for informative biomarkers of aging focuses on easy accessible and quantifiable substances of the body that can be used to predict the early signs of deteriorating health, prior to the development of overt age-related disease. The challenge in this field is to translate the molecular changes captured by omics platforms to the age-associated deterioration observed at the whole body-level. In this thesis, new integrative methodology was developed that lead to the identification of gene expression networks that serve as biomarkers for aging and mortality. The second part of this thesis is aimed at identifying genetic determinants that predispose to a decelerated rate of aging and an extension of life span. Using whole genome sequencing data created in 218 nonagenarians of the Leiden Longevity Study we observed that a long life is not necessarily hampered by potentially premalignant somatic mutations in either TET2 or DNMT3A. In addition, genetic variation at chr13q34 attenuating the thyroid function, may be beneficial at middle age, but seems to contribute causally to increased mortality above 90 years. 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
