The intestinal tract of humans, pigs and rodents is colonized by complex microbial communities. Understanding and predicting microbial functionality within complex ecosystems requires a systems biology approach that can take into account the enormous compositional and functional diversity and redundancy of microorganisms.
Understanding and predicting microbially-mediated processes
To this end, this project aims to develop approaches for the generation of microbiota systems models that are applicable not only to the gut, but should also be instrumental towards understanding and predicting microbially-mediated processes that determine cycling of nutrients in natural environments as well as mitigation of environmentally relevant emissions.As the microbiota have a central role in the triad of diet – immunity – intestinal functioning & health, this PhD project will be closely linked to other PhD projects within the Virtual Gut, more specifically regarding the role of microbiota in lipid & energy metabolism and immune signalling.
In the small intestine
We will focus on the small intestine, as this is the first important point of contact between microbiota, diet and host. For pigs, we will furthermore focus on processes related to digestive efficiency and the emission of greenhouse gasses, providing leads towards sustainable animal production with reduced environmental footprint.Microbiomics data will serve as input for modelling towards understanding and predicting key microbial processes in the intestine:
- Microbial composition (who is there?), using phylogenetic microarrays and next-generation technology (NGT) sequencing;
- Metagenomics (what can they do?), NGT sequencing as is being done in MetaHit and other TIFN and FP7 projects;
- Analysis of microbial activity by metatranscriptomics, metaproteomics and metabolomics (what are they doing?), using data generated from vertically compartmentalized pigs and mice, which can serve as benchmarking for the human system where we have ileum samples available as well as a metagenomic reference set of over 100,000 genes through TIFN.
H. Smidt, W.M. de Vos, V.A.P. Martins dos Santos, M.A. Smits and A. Jansman