Network-based and statistical analysis of the human gut microbiome in the context of inflammatory bowel disease

In recent years, scientific interest in the human gut microbiota has surged, with publications containing the term "human gut microbiota" doubling in the past five years to exceed 80,000 hits on Google Scholar. This growth reflects the increasing recognition of the microbiome’s profound impact on its host, shaping processes that influence both health and disease. However, it is still uncertain whether microbiome changes drive disease or if disease alters the microbiome. Insights gained from microbiome data have highlighted the critical importance of maintaining microbial balance in the gut. Disruptions to this balance, termed dysbiosis, are strongly associated with various diseases, including inflammatory bowel disease (IBD). IBD is a chronic inflammatory condition affecting over 7 million people worldwide, and its prevalence continues to rise. Together, IBD and the gut microbiome represent a complex system that is challenging to analyze and describe due to their intricate interdependencies. Network-based approaches have emerged as powerful tools to address these challenges, offering a unique perspective to explore the relationships between microbial taxa and their interactions. This dissertation employs network-based and statistical methods to characterize the human gut microbiome, with a particular focus on co-occurrence network properties and their differences between healthy individuals and those with IBD. Through three interrelated studies, the research investigates how lifestyle factors and microbiome composition are associated with disease status and age-at-disease-onset, how network-based approaches can uncover dysbiosis patterns and characterize important taxa in IBD, and how variability in microbiome abundance data impact network properties, influencing the robustness and interpretation of microbiome studies.

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