Variation in the composition of gut microbiota is of relevance to human diseases though the factors that induce and/or maintain such variation are still poorly understood. Understanding whether, and if so how, the genetic make-up of the host affects the composition of the healthy human microbiota is now of great interest. This will be approached by comparing host genome with microbiome. Also relatively novel is the potential to correlate microbiota variation with human bowel behavior both in terms of stool frequency and consistency (as surrogates of gut function). Based on these, we aim to establish computational models to describe and predict how these three elements (host genome, gut microbiome and gut function) are functionally and causally related in gastrointestinal health and disease. For these objectives, our main focus is on the general population, and on patients suffering from irritable bowel syndrome (IBS) and/or inflammatory bowel disease (IBD).
- To understand how gut microbiota influence gastrointestinal function/symptoms in the general population
- To reveal how gut microbiota contribute to gastrointestinal disease (IBS, IBD)
- To identify host genes that affect the composition of microbiota in humans
- To model the interactions between host genes and gut microbiota in the modulation of gastrointestinal function and eventually GI disease (IBS, IBD)?
- Collect and analyze diary-based bowel symptom data, including number of bowel movements, Bristol Stool Form Scale scores as well as recordings of abdominal pain in a general population settings.
- To study gut microbiota composition based on 16S sequencing and different community analysis methods in healthy individuals, as well as IBS and IBD patients.
- To characterize human genome variation through genome-wide SNP genotyping.
- To establish and validate computational models based on the integrative analysis of genotype, microbiota composition and bowel function
- To delineate predictors of GI disease from the exploitation of computational models for multi-variate risk identification.