1. Background
The human colon is home to trillions of microorganisms, or the gut microbiota, which affect the health of the host through complex interactions with the host. Much research is focused on identifying host-microbiome interactions in both health and disease. Epigenetic regulation of gene function has emerged as a strong candidate mechanism of host response to microbial stimuli.
Epigenetic marks regulate gene function, are stable across cell division, but can also change in response to environmental stimuli. Previous work has shown that the gut microbiota can influence host biology through multiple epigenetic mechanisms, and results from the supervisor’s lab have characterised some of these effects in specific disease contexts (Ryan et al. 2000. Nature Communications). However, there are limited data characterising host-microbiota interactions in healthy individuals, and specifically in large-scale epigenomic datasets with matched gut microbiota profiles.
2.Novelty & Importance
The proposal will generate and explore novel host epigenomic data in healthy colon tissue from 200 genetically-identical twins, and integrate these data with multi-omic profiles to characterise host-microbiome. This will form the largest and most deeply molecularly and phenotypically characterised healthy colon epigenetic dataset to date. The results from the analyses will characterise molecular mechanisms underlying host-microbiome interactions in healthy subjects. The project will also carry out electronic health record linkage, to assess whether the putative host-microbial interactions are relevant to human health.
3.Aims & Objectives
The proposal will generate novel epigenetic data in a unique colon multi-omic cohort of 200 twins, set up by our team (Mora-Ortiz et al. 2024). We will apply machine learning to integrate epigenetic and multi-omic (gut microbiota, expression and metabolomic) data to characterise host-microbial interactions. The multi-modal biological data will be further integrated with electronic health records and deep clinical phenotyping to identify key predictors of gut health.

