The gut microbiome in inflammatory bowel disease and its confounders

Inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), is a chronic remittent-relapsing inflammatory disorder of the gastrointestinal tract that affects millions of people worldwide. Despite progress in disentangling the pathogenesis of this disease, the exact cause of IBD remains unknown. As with other chronic inflammatory disorders, the tissue damage is immune mediated and arises from an interaction of genetic susceptibility factors, environmental triggers and indigenous gut microbiota. The gut microbiota play a central role in IBD pathogenesis. However, despite consistent reports of alterations in gut microbial composition, a coherent microbial signature for IBD remains elusive. Therefore, this work investigated the influence of IBD on the gut microbiota with a particular focus on the multitude of factors, both external and internal, that can potentially confound the distinctions between healthy and diseased states. Our investigation uncovered significant compositional disparities, especially in the case of CD, when comparing individuals with diseases to the control group. Furthermore, longitudinal analyses revealed reduced temporal stability in the microbiota of IBD patients, especially those experiencing fluctuations in disease activity. Geographic location emerged as one of the strongest drivers of microbiota variance, only second to a diagnosis with CD, followed by a history of surgical resection and a diagnosis with UC. Other life style factors also exerted an influence, however, the majority of the compositional variance remained either unexplained or was stochastic in nature. In view of the increasing evidence that commonly prescribed, non-antibiotic drugs interact with the gut microbiome, we re-examined the microbiota variance in IBD to determine the degree to which medications might account for compositional differences between disease-subtypes and geographic location. Although there were variations in medication profiles among individuals from different countries, treatments accounted for a relatively small proportion of the geographic contribution to microbiome. With that said, the cumulative effects of multiple medications significantly contributed to the microbiome differences between patients with UC and CD. Cognizant of the crucial role that microbial metabolites play as molecular messengers facilitating communication between the gut microbiota and the host, we next conducted an investigation into the role of microbial metabolites in patients with CD within the context of the liver-bile acid-microbiota axis. Our findings indicate that the typical signalling from the gut to the liver is disrupted in patients with CD compared to healthy controls, which led to excessive hepatic bile acid (BA) synthesis in a subset of patients. Moreover, variations in hepatic BA synthesis and BA reabsorption within the CD patient group were associated with the resection status. As a result of this disruption, we observed specific microbial changes among CD patients marked by an increase in bile-resistant and a decrease in bile-sensitive genera. This suggests that changes in BA metabolites significantly contribute to the observed differences in microbial composition between health and disease as well as between patients with CD. Collectively, these findings underscore the dynamic nature of microbiomes, highlighting their capacity to adapt to changing environmental conditions while also being subject to host-driven regulation. This added complexity, as well as the increasing data volumes, underscores the need for innovative analytical methodologies that can effectively capture all available information, especially considering the unique characteristics of microbiome data. Hence, we assessed machine learning algorithms, including Support Vector Machines (SVM), Extreme Gradient Boosting (XGB), and Random Forest (RF), for their ability to classify IBD phenotypes using gut microbiome data. All of the tested models successfully differentiated between IBD and non-IBD controls and, to a lesser degree, between IBD subtypes across studies conducted in diverse geographic locations. Importantly, all three algorithms exhibited variations in the selection of taxonomic features considered significant for the classification task, underscoring the need for caution when applying machine learning to tasks aimed at understanding underlying biological aspects rather than solely achieving precise phenotype predictions. In conclusion, the multifaceted approaches undertaken in this body of work yielded valuable insights into the complex interplay of lifestyle, medication, and microbial metabolites in the context of IBD, emphasising the importance of personalized approaches for host heterogeneity and environmental factors in the pursuit of precision medicine.

Description

Partial Restriction

Keywords

Microbiome , Inflammatory bowel disease

Citation

Eckenberger, J. 2023. The gut microbiome in inflammatory bowel disease and its confounders. PhD Thesis, University College Cork.