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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/221219
Decoding Functional Genomics: Exploring Gene Expression Regulation and Unveiling the Drivers of Inter-Individual Variation
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[eng] Understanding gene expression and its regulation is crucial for uncovering the molecular mechanisms driving phenotypic diversity and disease susceptibility. Regulatory regions of the genome, including non-coding elements, play a pivotal role in controlling gene activity, yet their influence on transcriptomic variation remains incompletely understood. My work, conducted within the Transcriptomics and Functional Genomics group at the Barcelona Supercomputing Center under the guidance of Dr. Marta Melé, aims to unravel these complexities by leveraging high-throughput sequencing technologies and advanced computational analyses. This PhD thesis explores the regulatory mechanisms of the human transcriptome in health and disease, employing both large-scale transcriptomic analyses and functional genomics approaches. It consists of two main chapters, each addressing distinct aspects of gene regulation using complementary methodologies. The main goal is to elucidate how genetic, demographic, and environmental factors shape transcriptional and epigenetic regulation, ultimately contributing to phenotypic diversity and disease susceptibility. These insights pave the way for more effective and personalized medical approaches. The thesis is structured into two main chapters. In Chapter 1, we investigate how demographic factors (such as sex, age, ancestry, and BMI) collectively influence gene expression, alternative splicing, and DNA methylation across multiple human tissues. While previous transcriptomic studies have provided valuable insights into gene regulation, the full extent of inter-individual variability remains poorly characterized. Using large-scale publicly available datasets, we systematically quantify the contributions of these demographic traits, identify common and tissue-specific regulatory patterns, and explore the potential functional consequences of DNA methylation variations. Our findings reveal widespread hypermethylation in the female autosomal genome, particularly at Polycomb-repressed regions, across tissues. Additionally, we observe a systematic age-related hypermethylation pattern at Polycomb target regions and regulatory sites associated with developmental genes in both sexes across tissues, with the exception of the gonads. Furthermore, ancestry-related differences, particularly in ribosomal protein regulation and the epigenetic landscape, underscore the importance of population diversity in genomic studies. These insights highlight the need for inclusive research frameworks to better understand human biology and disease mechanisms across populations. In Chapter 2, we shift focus to the functional effects of non-coding genetic variants associated with cardiovascular traits. Many disease-associated variants identified through genome-wide association studies (GWAS) lie within regulatory regions, yet their precise roles remain unclear. To address this, we employ Massively Parallel Reporter Assays (MPRAs) to functionally assess 4,608 genetic variants linked to blood pressure regulation in vascular smooth muscle cells (VSMCs) and cardiomyocytes (CMs). By integrating MPRA results with publicly available epigenomic datasets, we refine a set of likely regulatory variants and identify their potential roles in cardiovascular disease. Our findings highlight the complexity of non-coding variant function, demonstrating that regulatory effects are often cell type-specific. This work not only advances our understanding of the genetic architecture of cardiovascular disease but also lays the foundation for future research aimed at developing targeted therapies. By combining large-scale transcriptomic analyses with high-throughput functional assays, this thesis provides a comprehensive view of gene regulation in both health and disease contexts. This research underscores the importance of considering demographic diversity in transcriptomic studies and highlights the power of functional genomics approaches in deciphering the roles of non-coding variants. These findings have important implications for personalized medicine, contributing to a deeper understanding of the molecular mechanisms underlying disease susceptibility.
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OLIVEROS DIEZ, Winona. Decoding Functional Genomics: Exploring Gene Expression Regulation and Unveiling the Drivers of Inter-Individual Variation. [consulta: 26 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/221219]