INTEGRATED MULTI-OMICS APPROACHES TO INVESTIGATE THE ROLE OF THE GUT MICROBIOME IN HUMAN HEALTH

Abstract

The human gut microbiome is a complex ecosystem whose composition and functions play a crucial role in shaping our health and susceptibility to various diseases. In this study, we applied integrated multi-omics methods to explore how gut microbial communities relate to health outcomes, using publicly available data to better understand microbial diversity in both healthy individuals and those with disease. We analysed large scale 16S rRNA sequencing data drawn from resources such as the Human Microbiome Project, the American Gut Project, and disease-focused cohorts. Our investigation covered multiple conditions including inflammatory bowel disease, metabolic syndromes, and neurological disorders. By combining taxonomic profiling with functional predictions through tools, and considering important host factors like demographics, lifestyle, and clinical data, we gained a comprehensive view of the gut ecosystem. Our diversity analyses revealed clear differences in microbial richness and community composition when comparing healthy subjects to those with disease. Visualization with principal coordinate analysis showed distinct microbial signatures tied to specific diseases, with some bacterial groups consistently linked to disease states across various datasets. Particularly, inflammatory diseases were associated with reduced microbial diversity, a rise in potentially harmful bacteria, and a decrease in beneficial species. These results help pinpoint reliable microbial markers that could improve disease diagnosis and open doors for targeted microbiome therapies. Integrating multiple layers of data provides valuable insights into how the gut microbiome interacts with the human body, deepening our understanding of its role in health and disease. Ultimately, this thesis supports the move toward personalized medicine approaches that uses the microbiome, paving the way for new clinical strategies.