Development of knowledge base and methodology for the rational microbiome modulation in irritable bowel syndrome

Thumbnail Image
Das, Anubhav
Journal Title
Journal ISSN
Volume Title
University College Cork
Published Version
Research Projects
Organizational Units
Journal Issue
The gut microbiota typically comprises a steady-state community whose composition and functions are governed in part by factors including diet, disease, medications, ethnicity, geographic location, host genotype, and other lifestyle and clinical factors. There is a large body of research that supports the role of the microbiota in disease, host gene regulation and maintenance of host physiology. My thesis focuses on Irritable Bowel Syndrome (IBS), a chronic functional gastrointestinal disorder, associated with alterations in microbiota composition and function. IBS is marked by symptoms like pain and distension of the abdomen, abnormal bowel habits, leading to social disablement. Patients with IBS are mainly classified into four clinical sub-types based on symptoms, viz., IBS-C (Constipation predominant), IBS-D (diarrhoea predominant), IBS-M (mixed), and IBS-U (unclassified), but there is heterogeneity within IBS at various levels including symptoms as well as microbiome composition. Attributed by some investigators as a disorder of the gut-brain axis, the aetiology of IBS remains unclear. There has been growing interest in the development of efficient microbiome based diagnostic tools and therapeutic products that rely on robust and biologically relevant biomarkers which can be identified through a deeper understanding of the disorder. Considering this, the goals of my thesis include investigation of alterations in microbiome composition, function, and structure in IBS, which could be applied to diagnose or stratify IBS patients. My research also focused on understanding inter-microbial interaction patterns, and development of statistical models which can aid in screening of potential live bio-therapeutic products (LBPs) and augment transformative therapeutics for microbiome related disorders like IBS. To work toward the goals of my thesis, I analyzed a cohort of 80 IBS patients and 65 matched Control subjects. Using state of the art bioinformatics methods, along with statistical, and systems biology-based approaches, I explored the various microbiome data types searching for insights into the underlying biology of IBS pathophysiology. Firstly, I took a multi-omics analysis approach to investigate alterations in microbiome community composition and structure, predicted functionality, and faecal and urine metabolome, along with dietary habits in IBS. Subsequently, I analyzed the non-bacterial components of the microbiome (mycobiome and virome) to further understand their potential roles in IBS progression. An integrative, inter-kingdom analysis of the microbiome was also conducted to explore the differences in inter-kingdom interactions in IBS as well as comparison of the ability of these components to predict IBS. Finally, I performed a systems biology-based metabolic modelling of microbiomes of the samples to evaluate changes in metabolic events, and inter-microbial metabolic interactions in IBS, followed by identification of species predicted as being capable of modulating the overall metabolic output of the microbiome community of IBS and Controls. The findings of my analysis not only corroborated previously reported observations, but also provided novel insights which have clinical implications. I observed significant differences in faecal microbiome and metabolome of patients with IBS and Bile Acid Malabsorption (BAM) and developed predictive models to stratify IBS patients. I also observed reduced agreement within microbiome, and between omics datasets, in datasets from subjects with IBS. Inter-kingdom analyses also indicated a lack of concordance between bacteriome and non-bacterial components in IBS. Metabolic modelling analysis showed differences in predicted reaction rate profiles between IBS and Controls, along with lack of co-operation within the bacterial community, and provided a distinct taxonomic-metabolomic signature of dysbiosis in IBS. Based on perturbation analysis of the metabolic models, I could identify species acting as potent modulators of the community metabolic output. Given the stochasticity associated with the microbiota of IBS, there is scope for these modulating species to directionally perturb the microbial community towards a more favourable structure (i.e., one promoting gut health), thus representing a therapeutic target.
Bioinformatics , Gut microbiome , Irritable bowel syndrome (IBS) , Metagenomics , Metabolomics , Bile acid malabsorption , Computational biology , Multi-omics , Systems biology , Predictive biology , Machine learning
Das, A. 2023. Development of knowledge base and methodology for the rational microbiome modulation in irritable bowel syndrome. PhD Thesis, University College Cork.