Health is a fine balance between genetic background, lifestyle and environmental pressure. Factors such as diet, gut microbiota composition and physical activity, will augment or diminish the provability of suffering a disease. There are around 2kg of microbes living inside human gut and have important roles in maintaining human health. Humans carry 100 times more microbial genes than human ones, coding for essential functions. An unbalanced gut microbiota has lost some of this functions, lacking some essential biochemical compounds that ultimately affect human health. Understanding better the biochemical relationship between host and gut microbiota, opens new opportunities on how to prevent and treat complex disease such as obesity, diabetes or even cancer.
We aim to describe and understand the molecular mechanisms by which gut microbiota influences human health. Our research focus on deciphering the biochemical cross-talk between gut microbiota and their human host. We are interested in both, host (human) and commensal (gut microbiota) biochemical compounds and their interactions. Monitoring relevant biochemical compounds will allow us to understand how host cells “senses” (integrates and responds) to those biochemical signals. Two “sensing” molecular mechanisms are the biochemical modifications of DNA and proteins. A key step is to measure host DNA and proteins biochemical modifications and the possible origin of the biochemical compounds that “facilitates” them.
To test the origin and interactions between host and microbial biochemical compounds we use a mice model. In this model, we compare two groups of mice, a control group and germ-free group. Germ-free mice have never been in contact with any microbe, and do not have microbes inside their gut. We use this extreme model to determine which biochemical compound depend on gut microbiota. We take relevant metabolic tissues from the two groups of mice and we compare the DNA, proteins modifications and biochemical compounds levels. With these data, we apply linear and non-linear statistical models to correlate presence and absence of microbiota with levels of DNA and proteins biochemical modifications.